<%BANNER%>

Hurricane Impacts on Coastal Dunes and Spatial Distribution of Santa Rosa Beach Mice (Peromyscus polionotus leucocephalu...

xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E20110218_AAAADU INGEST_TIME 2011-02-18T22:17:20Z PACKAGE UFE0013415_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES
FILE SIZE 24941 DFID F20110218_AACMFM ORIGIN DEPOSITOR PATH pries_a_Page_23.QC.jpg GLOBAL false PRESERVATION BIT MESSAGE_DIGEST ALGORITHM MD5
79b501a986f6a87e39228c9353651f85
SHA-1
f7c708abb60cd5a7a330c25d4164812b08774fe1
25913 F20110218_AACLOU pries_a_Page_09.pro
b4a43f15a6eaa07df705637a781976fb
dcfca3f1de0d3a91021fd66bfe3e8f412cf0a135
82849 F20110218_AACLYP pries_a_Page_21.jpg
c420f06f0b532d92f60f76d5a07f117a
897cfb27d82c44a4cf2529ebc3ae923bc8dbf536
1051969 F20110218_AACMAP pries_a_Page_14.jp2
518cfaab6aff5668f3580937abe8d44b
a161e5848bc5f493274da4d305e9ddbc173b6445
1784 F20110218_AACLTS pries_a_Page_03.txt
7ad939542712e9acb003a31f20236196
b52b3a770fb1f20025db0ea2ca1d5378e2c4a312
26059 F20110218_AACMFN pries_a_Page_26.QC.jpg
c861f6caaecf3ded81951b941e722dd3
2578b746bf62015a0ec7d8d8d8d3270681173d9f
8423998 F20110218_AACLOV pries_a_Page_60.tif
9415f7ac6023ae83f121e02e66cf787d
eed5d93eabb91aec5a53668ea22eb943bed1385c
81221 F20110218_AACLYQ pries_a_Page_22.jpg
d2265e58f3f5b83b532104af0bea18be
22c17b599a5481e575777a743dd3a2edfb294fda
878041 F20110218_AACMAQ pries_a_Page_15.jp2
f44ee0f4f3acd5ebe841bd78e9cb481d
93b042708b4ad408221b9766c9e3daf02b0dd265
984 F20110218_AACLTT pries_a_Page_04.txt
d4a0cab025c75551fd8e7ca511406d15
dc2605f244f4fd1a3ea1a0aa94cc2867e44f6696
9856 F20110218_AACMFO pries_a_Page_28.QC.jpg
2d4c2d4686a773e3f21fde503fe4a94f
b924330e98037130ace25b992b9a3a254f8155a4
26382 F20110218_AACLOW pries_a_Page_54.QC.jpg
d99c24a00b98c3d0539ea1345a8c5f52
5afbfedd470d7f3e093ae028d0d44341dbc5e230
78254 F20110218_AACLYR pries_a_Page_23.jpg
31611e2dad3d517de642893be40f13ba
b8f019598384630473b5afe7c7825c0d9ca01d6d
1051971 F20110218_AACMAR pries_a_Page_17.jp2
728344dbe377530fba0af0641532d77c
f99bf169659aff516ed62607329e546b76a8f472
2356 F20110218_AACLTU pries_a_Page_05.txt
99c76b9a83a82b3a66f4d0d92ee55690
42a51056c84ec5450a24799fb6334160313ce28c
9865 F20110218_AACMFP pries_a_Page_29.QC.jpg
e2e49348d7665c241f9fdc4ff6d7c935
805e9c0959f8d04a3a4564022c98dd17a8dfd27a
F20110218_AACLOX pries_a_Page_53.tif
de6fd889aaa34404125b3f078f19d3dd
eba42167e042c4c56ca9ae60e17b6e31b65a5b3b
83372 F20110218_AACLYS pries_a_Page_24.jpg
bc36d063bfd985f2d5b8b9511a964e9a
f300ba843f9a1b1895705c689fa38045bbe3eddd
1051964 F20110218_AACMAS pries_a_Page_18.jp2
170f5ee17b5fc661792758565b6d0c92
07ea5bcd0d5b3acf86ab0feb5810bd4a1bb62710
2042 F20110218_AACLTV pries_a_Page_06.txt
dfb2ee58d620a1a3ebc401e296f2ab57
36e043630d776ed7c2e7c8d7dffe021ec08f3278
23765 F20110218_AACMFQ pries_a_Page_35.QC.jpg
6281774bc715adffbe5048735bc26036
aed86c6fa4a6a12094a9118c1c5a1d2de684d4fe
79395 F20110218_AACLOY pries_a_Page_37.jpg
b1ac2b0172e5e5ec785b561860539004
d84e268bc55b79cbee147e8f3e5d9736994cd7c2
78955 F20110218_AACLYT pries_a_Page_25.jpg
dbf21b7f63417b562e92a5cfa2a2acc3
fe6b980fb93dd73f88f57ba42e4ad472f75cc9ab
1051974 F20110218_AACMAT pries_a_Page_20.jp2
a5e3a1a72ac85d2315e213f7f6c74462
ad18945af9a2f1e111f5cdc2a24784b3e9fe171a
194 F20110218_AACLTW pries_a_Page_08.txt
6ab230c0732c560d5f08a903d364f815
cf629e4d7189a631381e83f42f2a18f509aaa3d4
27685 F20110218_AACMFR pries_a_Page_40.QC.jpg
c27b5d418a140bdd1bc9989930b2e6b1
27611dfab3a5fa978b6917b7417949036ae07098
8368 F20110218_AACLOZ pries_a_Page_34.QC.jpg
20f053e31a5cc41addbd0c930346e963
d420d94bf0c83660c44f5b9f3aeb757f106a4313
81803 F20110218_AACLYU pries_a_Page_27.jpg
11fa620dfa33e5e89dbb9f21c8d7ca6e
badabb8ab1dac487d2a5ebea7106b07fae68314f
1051975 F20110218_AACMAU pries_a_Page_21.jp2
fdbf8561edb5300454ce9ea4e8ea38a2
f2f93156ea27f3e931702d61f401425ca0c258de
1083 F20110218_AACLTX pries_a_Page_09.txt
9cbd79d90f0b2990966d66e527ee47ea
31a4b42de9f69dc011d8ff010cab8e488eeb86d8
25097 F20110218_AACMFS pries_a_Page_47.QC.jpg
33ca38c91a5e16742b2a7d4961bac440
619e0f7a6471a4abad00576d8778d4d095fad987
30594 F20110218_AACLYV pries_a_Page_28.jpg
3684cd6807e38735fe6040ee41130360
3a3f57b8aa653b0cb49b8e7dd735725504177321
1051958 F20110218_AACMAV pries_a_Page_22.jp2
ff1bede4d01ddf2dcaac90facf310850
de09bf1d78426d5d2978a72d34808c908527c0f4
26919 F20110218_AACMFT pries_a_Page_55.QC.jpg
a97766b25824b6e1978831dd517feeab
f04feff1b654fa831bf3faf2717acb5193049140
37251 F20110218_AACLYW pries_a_Page_29.jpg
8ca0ffcdc8355e848153155a790219cd
7479a5398f1251d4cc6a8f04a74a8d18fba7b356
1038618 F20110218_AACMAW pries_a_Page_23.jp2
a4ef9cd82898babf8e771246bfc53845
77971ab2867fb721fc8ff3d1072cefd05d2cb9d0
17528 F20110218_AACLRA pries_a_Page_62.QC.jpg
9653dd4661fb8c3bbf702c132b705c02
8e0d6ecb44bde5efaec527ed7519b4df871092fa
1742 F20110218_AACLTY pries_a_Page_10.txt
1032ddb0db1e9dead97a0bfb45f77009
5145b568fb7202b94a4a7530180fe457a91bfff8
25439 F20110218_AACMFU pries_a_Page_56.QC.jpg
5f9ff1e49926dcaa3524a192224b83c5
d88d75f4c7746f69efc99dfb837ff2a56e81262e
48192 F20110218_AACLYX pries_a_Page_30.jpg
db87218f99d732e3c6817b29121723b0
dfa724da08cf4930f986d6ff7bf8a4b62d1f423e
F20110218_AACMAX pries_a_Page_24.jp2
1b568888efefd9193d2a20982b4a032d
0ddd184d7c023e20418c243bc3aa2456655c7d31
1925 F20110218_AACLRB pries_a_Page_23.txt
effc466d2f3149980f14798fbba74d56
11061cf425103a4e319bde29e323bf486b6d5c27
1824 F20110218_AACLTZ pries_a_Page_12.txt
09f4452f24e09fb39ed40a72b40f9892
4b461bde04130775c0c1b9cb5700e7c2a1abc1d0
9491 F20110218_AACMFV pries_a_Page_60.QC.jpg
c7d04e07b28facfecf45295c902b1b89
1b9a72adf23f1e0c7ab8124c25a899332e9cf9ad
29900 F20110218_AACLYY pries_a_Page_31.jpg
62ad68d9e37446378af1c4ecf4841f7f
678ea1693b970da6abb25a28a927137cf87b7fb4
1051441 F20110218_AACMAY pries_a_Page_25.jp2
66fda5f6a416c69af44f1adce5591d16
f61fa1c13b20da26ac96d72ce677ec9aec554a49
F20110218_AACLRC pries_a_Page_59.tif
4cd4659d55362a814e9b80e4724137e5
c0695e0ac6a02df7f1e499d2937a3eb7e2478a30
21111 F20110218_AACMFW pries_a_Page_61.QC.jpg
46fab5c3f824cb26a32878f60c0e2a44
fc5e3940a1b7edd8d568dcb8e399380c97532c4c
4664 F20110218_AACLWA pries_a_Page_08.pro
88c6d7c4e88a74b7a83836dbeb6a5a6d
78f0dbd8fc801f334045a8330f2e3025fca20b63
41998 F20110218_AACLYZ pries_a_Page_32.jpg
a4e921aceceac35f2cd22c6e1fc9703b
29ec13c325a5fd632d52af57881a3d9ebdd03b3a
385915 F20110218_AACMAZ pries_a_Page_28.jp2
be3f3d38a3eafee3e6f5994d7b4527e0
6897898073490455ef0113b35cfc67eda97a0d5c
47188 F20110218_AACLRD pries_a_Page_64.pro
f9002da3e41a7290c59c77ca63bfd4ce
544e5492a38451e724d68db012750920a8cb0ad8
22340 F20110218_AACMFX pries_a_Page_64.QC.jpg
17b82bfaf12660af6de0a218a7d3e9d8
021bbbe5a128ed0a4b82f72e2ccb8cb8f38863e1
F20110218_AACLRE pries_a_Page_69.tif
5720a9f6809d42889d9375877e290dba
41651154257bcb679778d24abd07946d392f945a
39287 F20110218_AACLWB pries_a_Page_10.pro
a243527046267ade50f8e1b9abee1757
51a320b7bde55bfa7a440fce18902cbcbbc49869
25225 F20110218_AACMFY pries_a_Page_65.QC.jpg
1fe1ea1af4e27ac6674cf3bec45e0c5b
6614433c57c8a0642d6b322dbca1db0d05877d6c
21909 F20110218_AACMDA pries_a_Page_52.QC.jpg
4642772cb7359367e75df737fe848f45
a8cb4f6a62710e0b0c2bb65034c17e66d7e66a48
1946 F20110218_AACLRF pries_a_Page_38.txt
e59df1b09f208b872a3ab3b51f7f2834
ea0a4d4d95b6294571426b340e80ac3346be5f51
50475 F20110218_AACLWC pries_a_Page_11.pro
7734eb7886e592d874a80cd5801f63cf
9f11bfdb9773f56163005b3e692df9165f771727
25448 F20110218_AACMFZ pries_a_Page_66.QC.jpg
fd3b56abb4c7f9e3fee874aaf8aa2938
648c16a01220a8f66340908549efccf1b9dad157
5043 F20110218_AACMDB pries_a_Page_07thm.jpg
8ea58381b0b752a56d17c7177bde9421
04d1d39dc48e2c76a09181d7b71eb15b7f5b72d1
6668 F20110218_AACLRG pries_a_Page_57.QC.jpg
78661cbbc2f4b8894bf40bcd75674c95
5ee5b9505c50b105cfde19c373075138772f1b5e
43044 F20110218_AACLWD pries_a_Page_12.pro
0ba91d0ccaf0e8cdb4360ef8111facb6
951a16ad20e4602d1c5fda7c4e43b0648511c934
25326 F20110218_AACMDC pries_a_Page_18.QC.jpg
893f20d92fe2a36ff34bccd424a684ec
10f5107c33aa6e9052a42dd1249b20a8a5fb3a9f
F20110218_AACLRH pries_a_Page_28.tif
13eb956f6eee2973542c4823e39e6937
cf500fe2a38ad9f25b8e91edc2938e5d884a060c
50082 F20110218_AACLWE pries_a_Page_13.pro
d1e2206244c72da99eefcf11ed2ca79e
4a47651bcf839ad01cd9e90d7df2aaa1adb21fc1
26154 F20110218_AACMDD pries_a_Page_41.QC.jpg
4f09841cb757b5322883841355606801
f6c15ff2305143cbce628c83896bf70628e84767
26078 F20110218_AACLRI pries_a_Page_24.QC.jpg
65b024b3c8aa89bfa9562a92cb8f51fa
e039d5c945fe52d03a84156808357ea673f9a303
50278 F20110218_AACLWF pries_a_Page_14.pro
a3444485abd7adb357e007798987d1b5
3c0b8caef8cd9cdb853110566ce2bdd523052dc6
24238 F20110218_AACMDE pries_a_Page_69.QC.jpg
dbc00de3edb153f452610b3b4f9373b9
21537d30d1f1686ebce9b01c63720b9ecdd6f40b
22974 F20110218_AACLRJ pries_a_Page_12.QC.jpg
07b5ac8f1acb6b09e443d49a0a754632
e9c0c9ad872f3375db7daefefa10caf9ccf10628
39109 F20110218_AACLWG pries_a_Page_15.pro
7ad15334757fc2bd3ffae86c055719ed
c2c4f86f6373791937e0eebbdb12cc8231ccf0ec
25309 F20110218_AACMDF pries_a_Page_67.QC.jpg
2a59f9dc7b550bfcf7ae222910895276
e5390c37a824ff6964f233255dc74702ab3fd901
17053 F20110218_AACLRK pries_a_Page_28.pro
6bb526a176ea95046b58b6bbb4396dc4
25851ae87206a1178de65a71e5072cc84fdddc6e
43434 F20110218_AACLWH pries_a_Page_16.pro
dabf59382625b42c6c1796e95745dc54
f6e9a1a1dcee9b7a9eefd9868343594fc89d21d6
2031 F20110218_AACMDG pries_a_Page_01thm.jpg
6c97c35bb4ad1f8feee27ee64719be0b
e517c07f6799e0fc9fcf6495d1004824a27e515b
25916 F20110218_AACLRL pries_a_Page_42.QC.jpg
2411b357ce4be8b9a8117bd066ea8548
d76b3426d587b57cc78a6be234d23c30112c9e27
48163 F20110218_AACLWI pries_a_Page_17.pro
ad0edebe20761f37a81039513e30e521
c48f376a69b4d125a218bd3c9437dc16c0ba3952
F20110218_AACLRM pries_a_Page_16.tif
8fe7790e72b7b797f79f760cf0672076
88f8cf56fb023449b2a595a545115c19ac1b1031
45327 F20110218_AACLWJ pries_a_Page_19.pro
4cde6d87b28c73e699d0251e5b7c6fa2
69ab32720ceecfe404715a591ad4d092e8dd7eb1
6259 F20110218_AACMDH pries_a_Page_36thm.jpg
642e0b240a37e9d62cbda8434434a785
478910c04bda9f08f690d8ae08777cc62ab9458a
84207 F20110218_AACLRN UFE0013415_00001.mets FULL
0dce752f97a090b7ff8638e57b28ff9a
ca3e3eb685f0176886589c10e4d26eae9215a4d3
52059 F20110218_AACLWK pries_a_Page_20.pro
f7a23b9bb241910a3184c0b68f50332f
8dc4dc9ad5739d5ebf064cf75769aa2c7c7655c1
6044 F20110218_AACMDI pries_a_Page_24thm.jpg
66b07e56863550b4187f94ab71859d5c
bbc14b827f38e314d6fe69d34e4645740a6400c5
50862 F20110218_AACLWL pries_a_Page_21.pro
5dd11a32a0e74a846bf9f1b3ca81867d
1b7a0b9e28ff952514a80ffa40170cda945dde42
10073 F20110218_AACMDJ pries_a_Page_09.QC.jpg
0897d056485e96eac6b2c7940d49c488
683b17b06342b847d926795c0098bfb40060d51a
49254 F20110218_AACLWM pries_a_Page_22.pro
b3d3c4b4fa3b58a49921afa17147f9e6
634320d0e60bf4f5b0192c427b8c77b4e977582a
25267 F20110218_AACMDK pries_a_Page_43.QC.jpg
b17b35c06be7c6f9d41cbfe4bd4a900c
76c7bed7e8568ce4ae5aa8c8ac31e75cd8bec86c
F20110218_AACLRQ pries_a_Page_01.tif
f75aeed38479546d538e5e222aaa8aa1
16564d05ea29d9aab7436118af65109521ff7854
47861 F20110218_AACLWN pries_a_Page_23.pro
463a5a8d1d007f42bb1f44d49352be16
1ec1309c3ec9d0a945f7bcefdf32c1575804cf7a
1987 F20110218_AACMDL pries_a_Page_57thm.jpg
b596016c3212f72e2d818b5a36a632d1
566e9dd132d37024c7c833cbeaadba14ccf0bcfe
F20110218_AACLRR pries_a_Page_02.tif
cea65da8de0de496e0b034a4677832eb
b056e28e9e723a619467c421205be67a968ab90d
50226 F20110218_AACLWO pries_a_Page_24.pro
bd3b192e09d52f44351bc51b137b22c9
1c67f44ceb0f727ba637533bd81a4dea44927f73
6050 F20110218_AACMDM pries_a_Page_65thm.jpg
0a9335abd0c3b7690ed4d56294bed790
8cdc7a7201bb2da3613f519ea51aea9764dbd41a
F20110218_AACLRS pries_a_Page_03.tif
ca2c7a7ee0da801828dcbeee7a5ce9ff
9336673364febd1e63ef35a0e1d4145f3aaa7858
47274 F20110218_AACLWP pries_a_Page_25.pro
baefd5c4c604e0b41f25cc1f454975ad
972e7a26e9bc1ac52845a41a7ca022d3b671bdf5
26057 F20110218_AACMDN pries_a_Page_11.QC.jpg
17273e0f3ec4a314b3967bf27c48e7f0
0cf91c279e5a9c95ac7aee4a05eb006df87910df
F20110218_AACLRT pries_a_Page_04.tif
66805eff8537e52a208ed5f827ba8673
7e7da074ad38a07e40edfe80b973ba3989b9a504
51126 F20110218_AACLWQ pries_a_Page_26.pro
20f114a78d77cb4e4d981d4233f2641d
3d3c2a9e5b3bcce78eb8fcb192bb45a5ae4d447e
4179 F20110218_AACMDO pries_a_Page_70thm.jpg
b5d9a65eb8c02e1b790d38ecfd609c90
464ac967a1e7004952ac85e434d7d12a81e8257f
F20110218_AACLRU pries_a_Page_05.tif
25e9ef43f70784905aed9fa120aad8b7
bf7b330138c427231cb0d7c5a9732ff40e58e76e
33283 F20110218_AACLWR pries_a_Page_30.pro
7d806a05653fba4028dc463f3d474eac
e51284ecbd360c354428eac317907fd6f0fffebd
6299 F20110218_AACMDP pries_a_Page_21thm.jpg
cc13a0a8a5a83b0e2ab9d39b18ac723b
b5ecd5de2f80d85a9014d5a5ff5c25c882188159
F20110218_AACLRV pries_a_Page_07.tif
36305f3f54f32705b4cc526af004bdd9
e8d2ae3a6a8dea9a26bb88d8cf78ee2e033676e3
30119 F20110218_AACLWS pries_a_Page_33.pro
3dfb3f4a2fe13d046c6e80f6f5220601
1d0e6b649c15435d7948c0efdf08598cf795fe56
26188 F20110218_AACMDQ pries_a_Page_13.QC.jpg
81a6cb1b1c6094f267513075c7bf5400
629444f903739145191bdb8ba2a32a4a586a960a
18550 F20110218_AACLWT pries_a_Page_34.pro
12d3e0757cc70788f90481aa066822ca
f13745c24db0eadb96833d6389fed80edc925971
6279 F20110218_AACMDR pries_a_Page_39thm.jpg
a09a240d500aec45512420999a2de88a
0c6606c8c4ea952b8176a891aa450a211286bf66
F20110218_AACLRW pries_a_Page_08.tif
72a5b9c27b9799970347adf5e44dd6dc
fa90121e5cc12d217753b3e467b078c3da405e3e
46228 F20110218_AACLWU pries_a_Page_35.pro
3439f6db97ea6df49145cdb7d30dfd48
47da432b534a5e249877829ac647416f4527e63f
26230 F20110218_AACMDS pries_a_Page_14.QC.jpg
487fa9846be7a2ac7712fbbd5c1651f1
bf5f5070829f96239fe0aef0e9cb6eb2c5e263bc
F20110218_AACLRX pries_a_Page_09.tif
d377d2d26cb8f1564acacd5ee13b29c4
0025becc45a9ee2d65dfca52fce88777afd075eb
50590 F20110218_AACLWV pries_a_Page_36.pro
b539634cad18bfa589317614aad48cbd
e6b8dc43eecae7baf4b8bbe364bb6918941034e9
7187 F20110218_AACMDT pries_a_Page_01.QC.jpg
d5eaec32cbdc95448b740e711868f577
4cec425b86379d52828bf16f26f313c453e6d054
F20110218_AACLRY pries_a_Page_10.tif
74516b74f52290c5ca4e2d2e1128d383
200861e7d9be5d5f72b4cc33ce031b440329f3a0
47306 F20110218_AACLWW pries_a_Page_37.pro
220799a868a93851f5d8580b8f7fc394
5c26cb70179649cc5f172575f3b31302f9c93d9e
F20110218_AACLPA pries_a_Page_24.txt
024880b01cd7fd818f1b0e9c491dcb99
3483fefc3724d4e771c6b81671f5a30c73f55122
2348 F20110218_AACMDU pries_a_Page_29thm.jpg
a78489631e0df4249d88f080c9b1c5ea
3f92f048c7279278137aa951671583a46c982d88
F20110218_AACLRZ pries_a_Page_11.tif
21314f8fc043c5ff11648758ca90a916
da58e9a2e30ee6925a4f84fc0c38a58d92d0828e
49330 F20110218_AACLWX pries_a_Page_38.pro
8ea1a03180f6af535b567effb5de1efa
11ca1cd6daec97111462436e09f71f78911a28ef
33563 F20110218_AACLPB pries_a_Page_60.jpg
3b6cfe8c3f3e33ed078875a8d36ae017
a4adb30a93046ef8914894af61e0ef4afb38bc2d
12109 F20110218_AACMDV pries_a_Page_32.QC.jpg
30f536a10cc1933775216d17a77eeec9
96aee5c45c0b405e3aebe24bb6296c28d2e4b6ac
48430 F20110218_AACLWY pries_a_Page_39.pro
33a0e50c0d957fc6a165381d418e60bb
bb3c862ff220662b2295e78edb87500cf8dae6f5
1037743 F20110218_AACLPC pries_a_Page_19.jp2
0f634025b6967c0b675b9e282aa85738
8c834cf40f03c57c4e2b047e7b4cb9c116acb56f
17436 F20110218_AACMDW pries_a_Page_70.QC.jpg
74a91e2f3a7a144313ac74f13d7d1d76
efa10a396a593ece3c2f1d93b37007b31e4a5257
2010 F20110218_AACLUA pries_a_Page_14.txt
6533d15f699f9441b91d13437d0a7a4b
17574a16067be19d4a41118e292139cda20e31c9
52657 F20110218_AACLWZ pries_a_Page_40.pro
7983b3ac542fa3e60358318cff886db0
f0c85d9ddcd1f7cd2516713ba304c97414e6c9e1
45033 F20110218_AACLPD pries_a_Page_53.pro
bbc1ee30323cd7ace849f1415d8a7060
e34c694d9bb158b4aaad51d0e2ebdb4e68a16d65
23302 F20110218_AACMDX pries_a_Page_19.QC.jpg
2dff777dd426d67dab0cd368d4ed2cb3
8791543acaa2f0408227a123891772fdd3b9a27b
1578 F20110218_AACLUB pries_a_Page_15.txt
5a8a61aed06dce5c132708c4feef425b
cc8aeb13e54feaf74fdfe5988f1c9c8f8ba6c709
2581 F20110218_AACLPE pries_a_Page_34thm.jpg
a19718a47a747dd9184061d7947141e2
8eca4eb09dcd4e1ea2f20cd94a3c12542e44c4a0
5285 F20110218_AACMDY pries_a_Page_52thm.jpg
fc74c4a248db142646292283ba0b3dee
93c93f1034a933d8b4b8461ff26ff00d2635d60e
623441 F20110218_AACMBA pries_a_Page_30.jp2
d0ca956d90a1d6aea633ab451fa1bf4b
5023c8149b1b79c8be9024b827d340b7f50a69ae
1793 F20110218_AACLUC pries_a_Page_16.txt
766456c818006bec5cbac722d552a6ac
8b3d70abe1809a9ea54fe1798e21eb1c28994a83
36975 F20110218_AACLPF pries_a_Page_63.pro
782072a234accba300d2439237a7b6d5
f0c21b03bbb8fb5a545b60361fb0f8d63e7f774b
4986 F20110218_AACMDZ pries_a_Page_15thm.jpg
46cfaeee0d0e8d5a26723091035a7de5
63b47173fd61c79a3df3c82e64fb516637a0990d
408500 F20110218_AACMBB pries_a_Page_31.jp2
9307c7b5d8d4bc520d7abe6a80f1a156
37f5572176ee61ebe252db8be0e00f31c548b291
1905 F20110218_AACLUD pries_a_Page_17.txt
dfcad64b4762f719f74ebc49c2cf5963
e0321f57a0ac2e35cfecb429050beb4c019b22b0
76231 F20110218_AACLPG pries_a_Page_61.jpg
ccdfb8be825326e888a489570c6fc6cd
8087cbaa231f5d68e9e674335858ced74a124650
29033 F20110218_AACLZA pries_a_Page_34.jpg
233e1e4d4be97d60616856cc166bfc12
130bbc21fe8c8b39816bed3c927aeb19b4f39854
489715 F20110218_AACMBC pries_a_Page_32.jp2
eff330948dcb0fe304bf7010abf15d12
25f7c40e2bb714d71664bdbcfdc9591e1de00048
1944 F20110218_AACLUE pries_a_Page_18.txt
1111d0f23f2eee58fe7baf4a94d61af3
5304d629154f8d9e4dc0fd01d2d1de66858283da
121 F20110218_AACLPH pries_a_Page_02.txt
0c9d5e631f6e72d0223f6a8a44c815ff
ce15d0402792f35f56bad8114d7127bfc0f440d5
78876 F20110218_AACLZB pries_a_Page_35.jpg
37d2257cef958f9f40a64f36e7590af4
f0432f7bafcbf6fd2b99f9c38538a3f99ad92f43
6278 F20110218_AACMGA pries_a_Page_11thm.jpg
8105d1189e31a5de0a6ab4c55c259b4b
a253756c1a05099d283f8c2fb975923f22880f7f
514517 F20110218_AACMBD pries_a_Page_33.jp2
b35510a3020a674551610f878acdb83e
9c3a956cc04c76a4161145a30b69927d5cc87c73
1800 F20110218_AACLUF pries_a_Page_19.txt
98aff825a4a8baa86e9dad5eee6f1634
35bbd52d47ee1f43ffbc135132559933f2c899e1
5909 F20110218_AACLPI pries_a_Page_17thm.jpg
31bfc6e270904a9401903b031c1bbe7d
5a8f321ae577a48c2ec6caed11534b89e8acab76
84744 F20110218_AACLZC pries_a_Page_36.jpg
b2b7e24c44b9c5d0ab8b248126e89f8e
25028058ed1ec666b182019eb683cd4f266a013f
5926 F20110218_AACMGB pries_a_Page_25thm.jpg
77c32fd8a263f5935185749bc32e08ed
45b3e6f9a3c79596a68328b66de36b85e27b70f8
337232 F20110218_AACMBE pries_a_Page_34.jp2
47e1c3c8866fe711ac0088fa144f0642
c23d7046aad7f66d10f169a7bac0dcf8b8c49eaf
2001 F20110218_AACLUG pries_a_Page_21.txt
75050e3a57c11baba91ff4bbd06aade2
478cc43f55fbe6aed402ba5af565b9581d443021
11969 F20110218_AACLPJ pries_a_Page_33.QC.jpg
8838e07df4b1bf1006843cb1f8b44d8f
5419a75d360a7a3c14e75db6204951d495316cf3
83411 F20110218_AACLZD pries_a_Page_38.jpg
450ff40d98b794ca4bc7a9c30848e79e
84c17e8cb7f26ec68e2748717533da0d28868ab4
6207 F20110218_AACMGC pries_a_Page_26thm.jpg
35ea6890470fe9c0b0d60855fc6b94ad
4f39ab85ead6d479a084a85d91d242225eccbdf6
1941 F20110218_AACLUH pries_a_Page_22.txt
57cff7068ca7f75ec5167e7efc75e919
14a26bbaae186b9cc8269d5c12bdeae7dd5b82a6
513 F20110218_AACLPK pries_a_Page_01.txt
6df74e2fa3bbbabbf62fe69ddb308a76
a65c0e07194528b258213daede911776175ee0ea
79114 F20110218_AACLZE pries_a_Page_39.jpg
fd8c80726aa6aa8aeabda1bca64bf188
1bd308667497952a0d7d9e29881052b1d6f46081
6395 F20110218_AACMGD pries_a_Page_27thm.jpg
9dcd49fdabf659bec7219400d8dbd126
eccaadfad428083448d3d97bc1d4bc0ae1037efb
679 F20110218_AACLUI pries_a_Page_28.txt
438fb3103ebffc461ab213984b5ef29a
b272b32ffdbcfd6e6fc69b8ac55c221dbbc4ed83
1044807 F20110218_AACLPL pries_a_Page_64.jp2
6b486e2eeb4a628c2225b31ed0a1ebe7
f0fee45b92dd220b03b93062f45991548e2a7881
87469 F20110218_AACLZF pries_a_Page_40.jpg
4f6a73ccf122b91c7e3f2bc37528b255
ee5163b4b8dbaccdcf9d09e90a5c1d40293b7ea5
1048709 F20110218_AACMBF pries_a_Page_35.jp2
2a94db961109b4d2716736e20ac6a06f
2e554c44c4d1b1f499c22d4ffe34f0919693c916
2392 F20110218_AACMGE pries_a_Page_28thm.jpg
2d76cf8f7d21a1148245b9451ed3a390
24c6099ffb13e1cdd5fa0c48894c73f300d70f6c
1157 F20110218_AACLUJ pries_a_Page_29.txt
3e9d999d813462e5b20ec1f3daaa507e
bb6e418f8816d9330be8ad2377d88a1ad71b7edd
3307 F20110218_AACLPM pries_a_Page_33thm.jpg
5b5f03790655fd84e3956e36b198be79
c878d3f211ad4fe2979f6cea6726d2d672ed2134
85019 F20110218_AACLZG pries_a_Page_41.jpg
d9a0cfe9219eeaec9fdec7cf612a18a2
d1aae1e6e126e819e34c131cef3b8e3daf3ebde4
1051910 F20110218_AACMBG pries_a_Page_36.jp2
609c429774a5451af4ec86c0c421b514
ae3c9d4f68adb84767680b8913c54c7bb11971d5
2809 F20110218_AACMGF pries_a_Page_31thm.jpg
140a62fbfaf7a2dc0680cbc9a8058bb0
d92c4a346c5f9cc7c4f18c0630e17957bcdcd4e6
1701 F20110218_AACLUK pries_a_Page_30.txt
99e8e12604948e324239e29eb53278c1
b0659a3294d95eb4a69a2fd9fb428d5d3b3b144c
2059 F20110218_AACLPN pries_a_Page_26.txt
1e90b8c3a23763da76a13584bb7bd5ad
8073ce97ea07d7701cd18cec18f1c1b49b5591ba
79708 F20110218_AACLZH pries_a_Page_43.jpg
c3ff2aad32883baae50bc4bcbe122d26
e4faa9237cedae35d95a8a4210aceec3bebed21b
1051963 F20110218_AACMBH pries_a_Page_37.jp2
e7ec0e3913f3a8bde6ffeaff79b4efbf
7ae6c3c2ede69914c45da20f7f4f12d318a89f61
6448 F20110218_AACMGG pries_a_Page_44thm.jpg
2032a49880465956aa9214fd9aed6934
75bc4fb1cd60d7f07d2a6d6aae88fff478ea7e3f
207 F20110218_AACLUL pries_a_Page_31.txt
0849ea90f4925be16cd95722a87336dc
9110294c557b74f9c6ded4156759cb8eecb4d56b
2632 F20110218_AACLPO pries_a_Page_07.txt
db6b2ae9e6290bd690ba036ffc2df44e
f67645a3cc0a762bf18f9020c95dee81b153804b
87671 F20110218_AACLZI pries_a_Page_44.jpg
8fec0de1ee86c9467e1103999ff48d5b
c81bf4a379395709da67c0285a8a7ea59c3b9e71
F20110218_AACMBI pries_a_Page_38.jp2
22c626eeb91d45e2273ab09448029345
5138528dd1a2f1ecce66dbe560bfa0bec865c292
5123 F20110218_AACMGH pries_a_Page_51thm.jpg
9a2523ab84b10979c359de8730ddbba1
4e4223fefc1aa444b2f4f2f91c8da70f614956a2
1974 F20110218_AACLUM pries_a_Page_33.txt
cb45aa8e778cbe5e2541c5076c474f78
9fbaf8d79081b0b90ad5c1eb4a606e16ade5ab15
18357 F20110218_AACLPP pries_a_Page_50.QC.jpg
42bdc32d9d0031c787538c865be4da5a
54417ac14da59aa233830a04de2f9be15a1ab9cb
87284 F20110218_AACLZJ pries_a_Page_45.jpg
1adfbc99399257b9dc6428311c1fba14
9e201b8c7fbed10b4136aabb175ce0bbf8efdc17
1051938 F20110218_AACMBJ pries_a_Page_39.jp2
e92ba3941b728f8b4cb43f7ec75d8cb5
f8f4c5f5a27083ed78e2b21624719cbc46da1733
3563 F20110218_AACMGI pries_a_Page_58thm.jpg
e46d094e3733bdec5ed686fe1c85f1ae
b6e08e575778ef9a020e9bfa28e9cf15901f27d0
925 F20110218_AACLUN pries_a_Page_34.txt
452f9a952d35e491b5d92c844e46c581
dcc3b6c7ecc277b1a6ce739662c3c2a90f29e558
20092 F20110218_AACLPQ pries_a_Page_51.QC.jpg
aed5e2b7741158711b881bb6d8193903
52915db3f46f73a3af2ada2e41adbfd836936646
86826 F20110218_AACLZK pries_a_Page_46.jpg
90f6b0cbd4b6c519b583eceb5cd5a685
23dff2d92087f116ab08e8cb830430f3bc825fb6
1051925 F20110218_AACMBK pries_a_Page_40.jp2
f59a6c1917fd9c5fc1d097b97201ddc7
b1646c9b9cb19207d8ea3f3583f7da227b244bff
2449 F20110218_AACMGJ pries_a_Page_60thm.jpg
3455c247ed01b0f1dab9b767eeed9d3f
cbf7b58b633b9f6d304bfcfbaa435a9c55a4bba5
F20110218_AACLUO pries_a_Page_35.txt
a8e8c5f664386aaf8ff1970b3e6be218
bc8b88dbb83608c7aab35a2b286006784c36fb21
25035 F20110218_AACLPR pries_a_Page_37.QC.jpg
b9c0b76a9be0dda622e31df857d79e54
598825d86ff33a334eafc13d5b53b369b4c73944
80373 F20110218_AACLZL pries_a_Page_47.jpg
bd644a0ee947519fb84309f2cba8393e
f8a8f0aca865685355942578d8ede22e93787def
1051955 F20110218_AACMBL pries_a_Page_43.jp2
c4f9b041c804b33fb787faff84b0005e
37c17945fdc4ebc8648731d112811044282b1295
1988 F20110218_AACLUP pries_a_Page_36.txt
47e91fd5f13092664a4e6a955d48d4c5
e30a5af85d99ca56d5061d0d47d30dce820139ae
474147 F20110218_AACLPS pries_a_Page_29.jp2
e1eee03558c9250f03b43f4b29a097a8
080932763094bbf7b40936b833f0d75aff9bf49e
81662 F20110218_AACLZM pries_a_Page_48.jpg
fc12fea3a02707a5d2500d2644ce54f1
1301477148868273d113de21c886654cc1eac008
1051933 F20110218_AACMBM pries_a_Page_44.jp2
93126faf75fe8b103c1c66a99a6e9109
be2441036e47e4b23e49e4dbe3729f0a198d4ac5
1914 F20110218_AACLUQ pries_a_Page_37.txt
47e613cf8af8b73b18d08dc8d57acd6e
0befe8076af938292a3b157c601aadc3b6c6d4e7
9620 F20110218_AACLPT pries_a_Page_01.pro
e6f4da64277a69bea2cbea34b633ed58
00441f541fbe6051808e3668c9d7e0c88aa5a290
53275 F20110218_AACLZN pries_a_Page_49.jpg
f66b4c7fc3ccd93e76ff9b05feb5603c
71b5d59a1993fc6598edc20caef8594f4aeed666
1051897 F20110218_AACMBN pries_a_Page_45.jp2
453de8d7695346155bf3ea05ab504d4e
8ccd71b3d715a5aa1740339b711645eee0a46be6
F20110218_AACLUR pries_a_Page_39.txt
5d6756b9bf55cea1e4615f42226d6ca4
d41a9aa7d4faa35cdee6088568eb20b670e6cac6
67951 F20110218_AACLZO pries_a_Page_50.jpg
cda7ca1b8c1e0a916cf06c6e4a6a5706
37ed82dbccffd171ebf7a8c0c192a898755e710c
1051966 F20110218_AACMBO pries_a_Page_46.jp2
cbfd0122dc3f070ddf1e951549dad1a4
154a16d014b83d3c5a977204125073ac71f6242b
2071 F20110218_AACLUS pries_a_Page_40.txt
b4eeb0a24c7f12dbd2f1221ee6134839
c6e08a460a3a6918def3eb4ebfe3b87dc3303f34
6078 F20110218_AACLPU pries_a_Page_56thm.jpg
62c7a07cf0b076447d12cf611f3688c3
ac3cdaee22d9d156bbf401824550b7777f6538ba
72269 F20110218_AACLZP pries_a_Page_51.jpg
04872f8f9175d775f8c2772d377d9116
860e3a4a9ef899815fdc98f88fbc65ae5a5ad35f
1051915 F20110218_AACMBP pries_a_Page_47.jp2
dc44aa6c74f2fbc7cb4190eced8a04e7
d2a4e482fb1237f21e7ac56f35d7fa5a09f9aceb
1969 F20110218_AACLUT pries_a_Page_41.txt
819650d96f899c52f45efeb904969670
e2f41d265da1d65c4c1e7e3de46d5fd6e059c1ed
1051973 F20110218_AACLPV pries_a_Page_68.jp2
7f7974ee0922033896719f8eafa2e97f
153a952a9eaba5775393463a87bb3122e31d19e9
84507 F20110218_AACLZQ pries_a_Page_52.jpg
f39bee0e96efdf3b7862f2396dd69066
f9babb499269f3103974139d32c3fd1309dc1b19
1051927 F20110218_AACMBQ pries_a_Page_48.jp2
ee861dc95047babd78717309d092d0d4
d2da977da58481ee454f451742feb9ebabe41da5
1964 F20110218_AACLUU pries_a_Page_42.txt
e4ead4923ae92591c4c07cf725584d10
a2184f170443a044087368762c45c4a6ba217367
55866 F20110218_AACLPW pries_a_Page_51.pro
cb931cb998239319da21f73420f7b3d1
ea4b8b40c350a74a94ea8f70d8907913629fafbb
84332 F20110218_AACLZR pries_a_Page_54.jpg
4651e0c2e6a0f6118b1226d31777217a
0cb1ffcf3b40aa8e81cbcbf7e3bb3262efc1c9bf
702285 F20110218_AACMBR pries_a_Page_49.jp2
ee6be43fbec4c03eb8a8829f59c9cc0c
6ebd69a3a3d86be73694a84895c0b7f1faf26473
F20110218_AACLUV pries_a_Page_43.txt
24157104b0002a9267204426c21b1d36
544ab68153bb92000945c765446c5493583b9a2e
6178 F20110218_AACLPX pries_a_Page_43thm.jpg
39e2baac8530e24cf911f287b49605c2
f303d50e4589178444044ad89cea0a194a88d8f7
86769 F20110218_AACLZS pries_a_Page_55.jpg
1d5c35e4ef7e8e27d38a5dedee768740
495df80ae7681bfa87dd16760fec53daba26d0de
905072 F20110218_AACMBS pries_a_Page_50.jp2
d08bac40e624847a484c83b81d0bb774
b04f48c2ffa584cfbb2981126b8cd86a7d7f2dbe
2074 F20110218_AACLUW pries_a_Page_44.txt
3bfa28bf5ff86417c5cbeaf5534dad37
b085df2b24f264ca0f1dc23d57adf0c9226ba32e
22196 F20110218_AACLPY pries_a_Page_07.QC.jpg
a3348bb55eea2f1c1cc8fd05e9d639cf
02a5b3cc3c2e2fd5603ba38fa90b9425b5f069de
83289 F20110218_AACLZT pries_a_Page_56.jpg
ed06bb6c3193c2ed0a178f359cd51311
28f5f6edb8cb84576da100903f70df3dab38e715
1006331 F20110218_AACMBT pries_a_Page_53.jp2
743f4ca7714da4ae453e273a57042a86
91f094623aa0182309bf407372feff3340d33807
2077 F20110218_AACLUX pries_a_Page_46.txt
32461c362b983403a61f844f3643c905
6e65cb3d53d38c965b46ce58071563c5dc7a6d99
998235 F20110218_AACLPZ pries_a_Page_16.jp2
b19b318d2a809f4adc3a2d6dddc15e22
7855659f01d53176941418fb469152d9f1718fe2
21979 F20110218_AACLZU pries_a_Page_57.jpg
732407dc0d6858c50fc43ed623ce565e
0b1692a1ba863f4a33e9b2686b2e9bdedc633cba
1051986 F20110218_AACMBU pries_a_Page_54.jp2
b5f5ab74f58b753b4896d6c50ef15570
2f1d3e20a4a120eba9c057c8126b9a1b2d35c7b9
1932 F20110218_AACLUY pries_a_Page_47.txt
0d69076ad2b1cdb16b8ce2d0fb3a72f0
d39fed20d79bec84ec8a4bc86bde4defdd3e9fba
47271 F20110218_AACLZV pries_a_Page_58.jpg
2ee0449da53cd49c7a8a96f88917fe7c
0fdcd4a5daf552c2c290e6c01eff8971ce3afc67
1051934 F20110218_AACMBV pries_a_Page_56.jp2
867ec2b72d3103741bdfa90867cbbfee
c95c31df8367f646027e00115253138fff950adb
85802 F20110218_AACLZW pries_a_Page_59.jpg
9a24140c6d6ffc0fd4f911c2d8d59d4c
4341067fd2edd3aab019dd3d2b160b7455ae526d
259487 F20110218_AACMBW pries_a_Page_57.jp2
af2b5e17ff01dc295aeaca31ea8eda19
2a36ef56d74518c6e3df04c54b1b4324e0cf0c5f
F20110218_AACLSA pries_a_Page_12.tif
8229db75c6c1f85b258c993e95b8ec67
2d05edb7c93b497058c436547d718e45055ac05e
58691 F20110218_AACLZX pries_a_Page_62.jpg
1e1c89e4338eca3856530fc37e591385
90beb1967b2153b5ce4a9b2475427fe7f8f1a923
555690 F20110218_AACMBX pries_a_Page_58.jp2
197a4be769b963beab222cf15b6d3f4d
ef0483024c94abbcda25f328e7b73d2a2c0dcb5e
F20110218_AACLSB pries_a_Page_14.tif
482475ba1f84ad1f8797d6b8f419ce42
5176aa8f46a85b9159b6e1295fe47a24cb88dc26
F20110218_AACLUZ pries_a_Page_48.txt
ec8f1ca7c2d5f50db17cee7f541277d9
2f228605210bf61d563afd9a76b1a3a916f38d2d
79858 F20110218_AACLZY pries_a_Page_64.jpg
820817740feed38ecf8777a38dcf6952
8d490d569a5c15c3b4bbe26aec41c3e1655b3ef9
1051611 F20110218_AACMBY pries_a_Page_59.jp2
41745f4c2e72433c92048b4f9a952393
81d62a708d9e19b4688d3ba45b1e38e02edbd1bf
F20110218_AACLSC pries_a_Page_17.tif
b22daf932f457892c5bfb55f3334e3c9
2a7f1e6616928de73305652ceb063df2b375ff2d
50121 F20110218_AACLXA pries_a_Page_41.pro
98e050a2d3f96c7de9b0d0abd0014b62
298c8de7ce71b77e327bf2536ceb6cf947817976
91003 F20110218_AACLZZ pries_a_Page_65.jpg
2bc78223fbbe67f1448f93f6f366b8a3
6831639994de5dd0a1acbfc150e11538725cecf3
376141 F20110218_AACMBZ pries_a_Page_60.jp2
5ad91384c72263450a63dacea55ab9b5
a89aa6df16919cdc669f8c95749fe1e843d527db
F20110218_AACLSD pries_a_Page_18.tif
93b3abab1412e8fdf7c5a9f4ffa24dd5
4a6fd001d02002d82d88f57ee8e6f2b5932e694a
49623 F20110218_AACLXB pries_a_Page_42.pro
d0bc9fdeb2260c2ddbbac4fb5038ed4f
6ee1b30301bd5374638195aa7d3f714534619140
F20110218_AACLSE pries_a_Page_19.tif
45e32b6a27b3d0cd76e1d2a808b19cf4
1c5a457d6006a3733a78720c7f013ae103b819a8
24695 F20110218_AACMEA pries_a_Page_25.QC.jpg
c802bd0d1a5c76c1083d5eeee720f2ef
55a2dd99f97634ab923a0242aa521d62d26f7c32
47682 F20110218_AACLXC pries_a_Page_43.pro
147a81e8a01c29bda5fae24ed14daa84
2d831963f87c109dbbb1890121e4759054830ee8
F20110218_AACLSF pries_a_Page_20.tif
efbd531ecf5a10154f67bc8e74be0edd
a1a4aa95d8d201d05c8d5ca65a1d2395ddef333d
25483 F20110218_AACMEB pries_a_Page_48.QC.jpg
7bc46a88bf89aea41dff8d869e1b7a71
c1fe173a71442948bb254de90288a00bcf3748ae
4895 F20110218_AACLNJ pries_a_Page_10thm.jpg
630f2ef7342e29394fb752f689cecf5a
7c6b5414d38338e8244ed2233743a2b22dfc04eb
52879 F20110218_AACLXD pries_a_Page_44.pro
622beaf5e5ff91f9aab08c37b395668c
671ced5ddfb82e1f4488ac7c6510ceadb323129f
F20110218_AACLSG pries_a_Page_21.tif
2f60b681a14ac3cb5d3112ed3f2887ce
6f92be942ba1eedb42ee5eff0e172ad3c7fa5604
3940 F20110218_AACMEC pries_a_Page_49thm.jpg
58ad38d0710b0f0e78b820b4b5aad61c
5b80c204447b0bc33c7de11bae2d99f849ae5b0e
F20110218_AACLNK pries_a_Page_62.tif
d34555a6718693d6081dff44c0e61f3c
dcaf9a99c470da9e91cea0d2a8db9c83c3fba20f
52728 F20110218_AACLXE pries_a_Page_45.pro
7b1d4e2c4fed7c00e10f1076fe727196
8d66b6f3bd8af84ad61eac65632823820210ea20
F20110218_AACLSH pries_a_Page_22.tif
d5d2ff39595eddc253738bc2dd27f118
a62b5a43e9a2aa7a64d155370d9cfd33d8511453
6128 F20110218_AACMED pries_a_Page_37thm.jpg
7f2130b6ee0af6bf2068052bb92f46a5
1bc3e544643e643c874028e4f1bedaa9d6f88301
F20110218_AACLNL pries_a_Page_13.tif
56af3b835f668171d0229d95a96fd805
6f8c0f62fe23fd71f222462a0947302637db5047
52988 F20110218_AACLXF pries_a_Page_46.pro
f6be8193236aa616e5f280ca68fd972c
80b141ad502c4dc071347fbfe3dfc6435de063cb
F20110218_AACLSI pries_a_Page_23.tif
f772d68498578bab01c2827c2a2c8fdd
cd5983c58f102059ce9f77767a0e402c1223ad79
19116 F20110218_AACMEE pries_a_Page_63.QC.jpg
26cae79f6f011840b39d009a2a99be04
c5230e587197784c39945d12a4319ec477ad0d02
9182 F20110218_AACLNM pries_a_Page_08.jpg
d9da7cedc9ee66e1364753c482e19823
c6f789de6bdaa267d1047637ab9e0ff6d8530219
48995 F20110218_AACLXG pries_a_Page_47.pro
1539e8d666c1602107bafc20638f985b
f86355ce25820484ca1b80eae4e65756d3402d48
F20110218_AACLSJ pries_a_Page_24.tif
89cf819917c12e03e0e3c040a51adb8c
266d27d0105b54593735407b916a2560e88c006f
6158 F20110218_AACMEF pries_a_Page_68thm.jpg
b170e3a7e89ca129fb11894f4903deab
d197b7ffd3cc8ee9b945f0ec7df1e4b8820192ee
49336 F20110218_AACLXH pries_a_Page_48.pro
05a4a9d23cd9e113b0ff75b4fe133d47
3e3dfb1938c54eabe52e5af9cdfe043d8a431040
F20110218_AACLSK pries_a_Page_25.tif
22ffbef46413003260b18d842766c552
0a74487c4afc19ce98c8e89d6e349f77e0186b0a
24628 F20110218_AACLNN pries_a_Page_29.pro
2bd1c267db0ceee46ece4e7b204d2546
4140829d5a6cf0ab415a96471906cc7d0a917993
6339 F20110218_AACMEG pries_a_Page_38thm.jpg
28e1593fef92e857b10452177f99485f
b207f95f47586efabd093554c6829ee106405a11
31023 F20110218_AACLXI pries_a_Page_49.pro
0a5177e8a9b8ef993cbef7d22e5b9a60
969b5a515fce00cf9f46ab2b5333136c74e28f57
F20110218_AACLSL pries_a_Page_26.tif
cd2904da802b09d9cc83e57e480640f3
7d9c4a37593c49e72d1701170adf4bb8f06aff13
5456 F20110218_AACLNO pries_a_Page_64thm.jpg
3128b65e3c96cc5f9ea559407706a9b2
2fefa2210304f1b55a36179f7b0f6e2c6833e9be
540 F20110218_AACMEH pries_a_Page_02thm.jpg
99919f048447ac30de921b37d54dd44f
61541e79d6ca18241456cc06f78145bd26f2aad8
49486 F20110218_AACLXJ pries_a_Page_50.pro
f7ccb488f540a31ad6e845a4f1be319f
6a0df8c6a4628c524d97b0047d21563d1183e825
F20110218_AACLSM pries_a_Page_27.tif
c26b82800c2ed02080187213a9f2faa5
107e5714b34aa64ea31271cdf6aca6b0599729bb
14891 F20110218_AACLNP pries_a_Page_05.QC.jpg
04a4c9abb206af02ac0c8f77073c7a4d
9d3022e18323b84a63fe627c89804d6b1addfc0d
81213 F20110218_AACLXK pries_a_Page_52.pro
bfd7b974e98f2a1a4c1e57b5f8b5b436
b3164d0a7467bae6f275eeb7cea1f38715ac79f7
F20110218_AACLSN pries_a_Page_29.tif
ca16e052e7212d7a58ab33b15c502915
7d8f0fc1f4588eee19019f3e35b30bc7d63d2102
F20110218_AACLNQ pries_a_Page_32.tif
5aa9e93268e17034d670183bf591aa11
15ecddbc21bafb7bf1118f7ecf4d348fc4c96ec4
5884 F20110218_AACMEI pries_a_Page_19thm.jpg
8f4bab69bf12e687c2ddf9de1a833803
69d8ba6e6826e3e85374ae285868b85a98307df1
51218 F20110218_AACLXL pries_a_Page_54.pro
5d091a508c6e9d5d591e2b6c6a0dac69
80932c8b8c37ced910d51bba497322d9bc7ae0cb
F20110218_AACLSO pries_a_Page_30.tif
3f55813e706995c020bb150353e46153
7b7e0322bdf1b8eee5cb68444b0910134a48c0ab
22849 F20110218_AACLNR pries_a_Page_16.QC.jpg
4c27a2b4201e6bf7f81cf326a4702d6e
05746e52c2a2a4c939711783825cf3fe36fdc1b0
20582 F20110218_AACMEJ pries_a_Page_10.QC.jpg
99b03d326ad4ca9e4c8af6a19cf0da62
a075cc34830461f0570d65e7352a1296be686d16
52006 F20110218_AACLXM pries_a_Page_55.pro
5403f464ee3f19262315302eb7ef1685
38c502d5f522ef943a02f3d058b32cafc85e4067
F20110218_AACLSP pries_a_Page_31.tif
a1e5d211154e6ae77a599c6b2164e57c
0c48a845a4fa545f118c4107324817bdc2db64e9
23988 F20110218_AACMEK pries_a_Page_59.QC.jpg
46b9494e22c19fca587a7528ba5bd75e
afe6725f26e9bc499372db89ef1a2317234bfb7b
49933 F20110218_AACLXN pries_a_Page_56.pro
beb1787565a312ac23fc79368e82840a
df7196d21ea9fa43aa515e45e875036414446f38
F20110218_AACLSQ pries_a_Page_33.tif
82fbd72dbb0d0794c4ae631c0b0b39f3
1da36512c222c56182c551bd32ece48506c44e24
1051981 F20110218_AACLNS pries_a_Page_42.jp2
1034a67d9923d711d6df77ba09f84be3
6531f1b0eb06c117531003878633d92f229d372a
3305 F20110218_AACMEL pries_a_Page_04thm.jpg
b8ebab86353df7784276c2f4795b1e86
b9d9be0495e9515127689870d972d6733a1d4cc2
11229 F20110218_AACLXO pries_a_Page_57.pro
6738deddf2e81d6b591f779a61142e25
d025b43de48a586a0cbf787097d8ab0041328dc5
F20110218_AACLSR pries_a_Page_34.tif
e1524e18bb1407f7a311f41453649797
950c804206c25c609272249c1b2e34f609896c5d
F20110218_AACLNT pries_a_Page_46.tif
1af628a8bfe33ad0df7b7301c84f0cd1
3c106059f4237ddaa2fb01f24f424352e057b546
9574 F20110218_AACMEM pries_a_Page_31.QC.jpg
df9e9f7bdd9c14ec049c21053d7710ad
e66e4673672850b2cac2865ca7ee085ac1d8e43b
36369 F20110218_AACLXP pries_a_Page_58.pro
a564582ceef37967acf06d03a9d1e12b
e94102e1a12af443d332fda62c7c2794522138ac
F20110218_AACLSS pries_a_Page_35.tif
9770528a4c5ffe783ea746bda44a295e
026cee4ded98e2c079f464c26f3ce1efe137b384
20374 F20110218_AACLNU pries_a_Page_32.pro
7425652e8164ee3ee1eb2fc93a82fdbc
753113276711bf14f7d37cda5b4767c87f82dac0
28159 F20110218_AACMEN pries_a_Page_44.QC.jpg
dee78f9b8c75f5b633b43aad5911227f
6a1c63d6350139f52c0b84ccebb5cde1610a3706
68078 F20110218_AACLXQ pries_a_Page_59.pro
1a591c3ef6c99d49398f582954a1c5d1
26cde48eb181f9a8a10a8184c67fc9a38d4400ba
F20110218_AACLST pries_a_Page_36.tif
043d5c618454776e38fc976654906785
bdb32cb62edb7b51db3c41be97dc61968e756ed0
76236 F20110218_AACLNV pries_a_Page_53.jpg
bab678818b62f265e8fa725b1b77e9bc
7af3c0652749bfcec1cbbe07d48a5f5e64bf667d
27509 F20110218_AACMEO pries_a_Page_45.QC.jpg
8b201f91ac6d778016287be34f50bca9
16b212eeaa6871a8590ee8f980ea63aa07bf70e4
16950 F20110218_AACLXR pries_a_Page_60.pro
87e5c9285f68e94a4b8c3aaa77bef6e4
32f3d3ff32310171ab09b65425b49adaa076a3e5
F20110218_AACLSU pries_a_Page_37.tif
75b47aa70618fef486232ea8ef00d8bb
5a1262ba43a16c82ee365410ad3718309ec033c1
4987 F20110218_AACLNW pries_a_Page_61thm.jpg
caff64677872b88d89d74592370b160a
5cedf39e47cabe78d9c1f09208a1409c788c0015
4725 F20110218_AACMEP pries_a_Page_63thm.jpg
7ef79d503879264516e9de436dcbc6cb
cd5f4feb14a849f1e0b440c9082654803968cc7a
52545 F20110218_AACLXS pries_a_Page_61.pro
2a6d74a6da7b82cdbfe6fedd1e294498
9339ca5b7a7731bb990ee8abbcd6f4df505668c7
F20110218_AACLSV pries_a_Page_38.tif
84de38cc2c2da8b044d56c32dbdebd27
3a2b9ce472e325c960c456ac71f1a83ca694b494
40604 F20110218_AACLNX pries_a_Page_33.jpg
26d15747d480220ce8fe61d155fd78c7
d954ea1cdebba7f54751049b74218fa9e38c5ee1
26126 F20110218_AACMEQ pries_a_Page_36.QC.jpg
8ec9b5e93a986ae0dc95a5d7df49d9c1
cb029387271dd3f78745d7df760dacc668d56791
56033 F20110218_AACLXT pries_a_Page_65.pro
372a6d9c194682703b783bc15e6d0fe6
b2822a741143da1845f9d6c57058440d7d62b9f7
F20110218_AACLSW pries_a_Page_39.tif
61f8c16c3b390061dc68b4569be552ce
ac8a72a82b6713ffa2459448a1f68d3e28b0b44c
F20110218_AACLNY pries_a_Page_15.tif
d56476e788d590a54d8967e2da5d3245
e4d2ed7ccc1cd1d0064a3f29c351d9249dbf39aa
6472 F20110218_AACMER pries_a_Page_66thm.jpg
7ed304eade666127986aa2e2a6ac6ac5
2f2398e78aa259206a2634a6a009fbb2ecde7887
57287 F20110218_AACLXU pries_a_Page_66.pro
269bee0dfdfd6f0edf2728f5b31868f4
ef57bbdef669eaf63cb816e8cfaa81eff74f8e30
F20110218_AACLNZ pries_a_Page_22thm.jpg
44d6609d8146d29547c97e96875b6af4
acd8658ccddc01baa11fb93211e629572db745b7
6398 F20110218_AACMES pries_a_Page_40thm.jpg
db5aa8011c86eab27f97641573749c4b
9681d5b76c019395f7c60b653ba2d5f6896c2380
56850 F20110218_AACLXV pries_a_Page_67.pro
1f493ee932992d7eaad931b9ac4917ed
f16d85424a616f85d874d93896d005b5802171d6
F20110218_AACLSX pries_a_Page_41.tif
96067940bf70d918e0bb883052549b58
28ebc8708805caa2e7534dff23defb0c91a61dae
4346 F20110218_AACMET pries_a_Page_62thm.jpg
176f137322fbc5474add386277eb7302
a099fb1375f0cb62fab10143b5c14312d6ff7f34
981948 F20110218_AACLQA pries_a_Page_51.jp2
8ccd5a661987124f1fefe4331e0ded10
c77a2662ed42903797dc2a146351a55426426387
F20110218_AACLSY pries_a_Page_42.tif
cd69ec591d2fb9df761d334829fb1061
07c63122cfcbba8ee93e7ba97318106aa7237079
55446 F20110218_AACLXW pries_a_Page_68.pro
a8b0d4c80bd52b59e5d645f393475ffd
4983813abdbae3ad6eee3458ddf8b4cf704f35bd
25711 F20110218_AACMEU pries_a_Page_27.QC.jpg
f77fcfb99e92440863ca95845571e1ea
eb0f7d15c0389b0eed198712638f53959fe5ad4f
26962 F20110218_AACLQB pries_a_Page_46.QC.jpg
2eee3d316de53199df10b985c89cb1bb
360dd711543a9035ed9a58e2bbbf6c11ffd3cd36
F20110218_AACLSZ pries_a_Page_45.tif
383d14ab0255b6726816922925ceff66
783985aa1a8a02f4349e6bb884f431b50572e74a
53860 F20110218_AACLXX pries_a_Page_69.pro
b19eac12052fbfc390dddebbed8b2997
64eb4c1f121669ef8f7ec66c5f38b92852caeed6
6385 F20110218_AACMEV pries_a_Page_48thm.jpg
0180a174c8489db8a1434d0d46be49de
041254db630930efe350697a886e10a1b740291b
1877 F20110218_AACLQC pries_a_Page_25.txt
7d333f709321e36869d351a6212ff9d0
9bfe2e3483b1e8bc803c9ed649b9a28ee19b64d3
30963 F20110218_AACLXY pries_a_Page_70.pro
0cf576b4c4754e5f76638ebb6b36032c
0a45aa6bcade85b2bd74e450129ac34e361dc6a5
4111 F20110218_AACMEW pries_a_Page_06thm.jpg
21fdb3b9d2c22a559d4dd2c2c03550ef
74e6b6d0825cfb79c86cb27dacbfc5f515972315
3231 F20110218_AACLQD pries_a_Page_32thm.jpg
ece57c36f1c73a561fb2f7776483f4af
4e62d1525acfd167f092aa64e460d2b09735ddc4
1240 F20110218_AACLVA pries_a_Page_49.txt
39bf2d3946b6bdd5aa104d2f998e793e
81cd0a5c99e0f14bc97502db3434feddd385e4c2
25568 F20110218_AACLXZ pries_a_Page_01.jpg
3b450b413204a88bd271f51543c259f8
e3454779e0db2a0834d0200d23414f2d9085a15d
26978 F20110218_AACMEX pries_a_Page_38.QC.jpg
4b29d3b4aab75c055c1101eabd4fbcea
eb6b47682f6bc0416a4aa0e07e30440a1a378600
31072 F20110218_AACLQE pries_a_Page_02.jp2
cdee9ba2b092a858a3cb218f693cd6dc
c03fa2463df77d1e39bc7e027de6b8c2dd3e11a7
2122 F20110218_AACLVB pries_a_Page_50.txt
9cde26a22c2eac0fc582d3c70bd4c477
3f6259eab54affb4be87263c7c1b7b470c7ebf4c
5723 F20110218_AACMEY pries_a_Page_03thm.jpg
7aaf70e25b41732ae4e0cafcb734dd2d
0ccac0c72b3b187ecace711fbfe71c8bdcdd17d4
F20110218_AACLQF pries_a_Page_40.tif
4b4fe0d6fa3c1e74d04584adb9f64883
84057da7f94bb7ce82ad1ef54345f8f2c4f39ebe
999653 F20110218_AACMCA pries_a_Page_61.jp2
627dc98aad4900b9695fe60ad3597f66
9e25028cf0e526669c941b7974895b9494f7584f
2535 F20110218_AACLVC pries_a_Page_51.txt
66d78d465a3071b5c04a87f552c59e40
17dae93f9bf5626e283ee0ac456522ce99e97105
5819 F20110218_AACMEZ pries_a_Page_23thm.jpg
549cfe5bffd8e47e257292a710cc10b9
fb622bb38560a89e9c6599708017263e8e6715e5
1961 F20110218_AACLQG pries_a_Page_27.txt
380bfe6505de7c2aa64119b5ee7228cb
694576cebf5ac53fdd81d3bc61848a4dd2c4cbb4
768854 F20110218_AACMCB pries_a_Page_62.jp2
d73f76ee2f457f9403cf70c0644fecc4
b1565c7fade0da743cb1d44658fc2470cc0591e0
3579 F20110218_AACLVD pries_a_Page_52.txt
04b15b16f17250c2f3a254a2d84201c8
2e395e04dbbafd70af5a351e73e268e484d4f66e
982263 F20110218_AACLQH pries_a_Page_12.jp2
86f1bef4d0ad069b0339dc4f9eb1689b
b4db4c155effaa1fd74b7bc9521de4ec1117e39e
867863 F20110218_AACMCC pries_a_Page_63.jp2
8541afc5de601fa086059c3aee9ddf22
58ad78b6dd06bfb4a7c1475d23ea8373bfb4144d
1850 F20110218_AACLVE pries_a_Page_53.txt
1476ad43814fb44048e0b36ea0c0ea74
ae65b7adbd48a20baee4a3284d4aa11646dc1fc2
82232 F20110218_AACLQI pries_a_Page_42.jpg
15b52a1b93d27b993dd2cd76a20deeab
af95cf770cc023cee62c2a5733679be96d13c73b
F20110218_AACMCD pries_a_Page_65.jp2
5ba1d709df34d5e23518b5548c6338c0
7adc3b2ee7922b91322e6514502bad4388109321
2012 F20110218_AACLVF pries_a_Page_54.txt
a374be9dd9f8bd104800957b47ebd362
e8568b1732552d96d10b51a6a51e495209590139
1051961 F20110218_AACMCE pries_a_Page_66.jp2
1b661d61259d4398f0c4b8169c5b7e70
de5689343496aa721f78f39c3d0bc5686e11ccbb
2065 F20110218_AACLVG pries_a_Page_55.txt
8d8ce5f71b8c950ed8d0079e8b56f30b
f60568860057153b2472a3099c89ccab8505ea99
1980 F20110218_AACLQJ pries_a_Page_11.txt
b674654d5e848ead1bfeab03700d82d5
e284b30220206afdc6fe01258866a177800b71e2
1051978 F20110218_AACMCF pries_a_Page_67.jp2
c3c2781e41446a8e58a14b81e88e1e64
32feb8f1d80ce85ff8ccc989ce9e6c66e15b0037
F20110218_AACLVH pries_a_Page_56.txt
06ff67771327b7c94a8774b783fb3a6d
eed938df6a5bf78bdf66b2cbb1f3336e30cea95a
915 F20110218_AACLQK pries_a_Page_32.txt
4d5fb1f74d7338695b1db50063bee0da
7c42dd91d884b5b0346ff9fa631439a13b325710
523 F20110218_AACLVI pries_a_Page_57.txt
87acc3269a0ec10def9d838752e71101
8b45b7f1dc895d3f3d76f65ffa7a3a6c710da2f9
F20110218_AACLQL pries_a_Page_58.tif
913c10d12b9ded87946b1031db852717
20b0a7c850eff71280ac7ed15f68ec836a5fae82
F20110218_AACMCG pries_a_Page_69.jp2
be303b7f4a3c7413a0a761201077e340
50749bead6e7e690b565fc0f7559f29810dca91a
2002 F20110218_AACLVJ pries_a_Page_58.txt
d95c03f506308839aacc06ef4d991e01
46ef8354ad99e30cfee4a29a560d9c4271bf59ea
49795 F20110218_AACLQM pries_a_Page_27.pro
70eb25a58a672182c39855f78010ec02
297c9ad22692689a9eb3095dc97e115adf28eb7a
704066 F20110218_AACMCH pries_a_Page_70.jp2
9e81cfc2ec57520e2d3cb8cc86378812
1e3401f63e15b1a37ca5d68e8a965b4b7d86e0f5
848 F20110218_AACLVK pries_a_Page_60.txt
f6302bff283258033be13145daa71109
eca2769c6001d461acb80373be746be9a96c05e9
6222 F20110218_AACLQN pries_a_Page_13thm.jpg
e0848136f9d131159bbf4983b21242dc
6865cf6c92fe28f66c2f8a692cceca5358bdab0a
1306871 F20110218_AACMCI pries_a.pdf
355fa00f6062465af7920e173fada4f3
5c6024d56a0ebab9d65945090209b16eed812034
2532 F20110218_AACLVL pries_a_Page_61.txt
b7effb116a698d2867fc653e37f8b641
57f28a11c6fe8600d74c37aa48ea7d8f4e1df832
2114 F20110218_AACLQO pries_a_Page_45.txt
07b496a84d3d36125366d25bf700f9ee
d2a7168d5577220b4c70573f3e9c634f126e711c
2750 F20110218_AACMCJ pries_a_Page_08.QC.jpg
b79e49f1fa0c733b37c7de5b2336dfdd
df32ce5fa6780e3945bb2b821f9aa7fec23c54b5
1788 F20110218_AACLVM pries_a_Page_62.txt
e5856a5f943ba12384cd39bdd5f705fb
8bb43f8e9f4f3b37f126d2bcd894c558729a378c
2047 F20110218_AACLQP pries_a_Page_20.txt
7d769068dd332105dd21e19b7abb9228
cf611131934ccddfd1fb497a9f3a04b9d3898a31
5865 F20110218_AACMCK pries_a_Page_35thm.jpg
8687dd40351e469d6ab0dda2786c5b62
bee4b658b2d90a02c5f56e07652fdd595b6a4298
1978 F20110218_AACLVN pries_a_Page_63.txt
95e6384f0823b00628a5563fbf1f976f
0e77890f1328315b832393e9fea9ed6d276cc6c1
F20110218_AACLQQ pries_a_Page_26.jp2
b1cf61f0460367b07b20ce1dcc86eeb1
5b541b926eb3c2b41f1c3cf2c0dfa715e998e63d
5447 F20110218_AACMCL pries_a_Page_12thm.jpg
3940c695d16ff0bb909a0789786dc569
d03c58569ece3b36a4eb706f75f1bfeb64cb3f00
1955 F20110218_AACLVO pries_a_Page_64.txt
0f62e5aee1d8034715e3067f6537909d
c71747edd9c89093264e86c83a0d1e759cdc7f1f
48742 F20110218_AACLQR pries_a_Page_06.pro
ae3f5dcfd7e4ce84ba6345ae8401fdbf
d9f9e997dacfa2c6eb8bae8d87910053d092b0be
4273 F20110218_AACMCM pries_a_Page_50thm.jpg
37d93bf891fdf77c5ca2d5943afcc3db
41dab48a416882a86a0d2eb17b3dc7f944b51f96
2315 F20110218_AACLVP pries_a_Page_65.txt
d2b983459e282088b5eb35888812abce
ccb015fc41b3e861206ffe782fe02101c61bd6d4
6301 F20110218_AACLQS pries_a_Page_67thm.jpg
80f76c2dd82bf2d4d9417c56669f95d2
124d1a2276a3aebc6139756f5cbed7e686fedfce
6309 F20110218_AACMCN pries_a_Page_45thm.jpg
6c8f2706790673831013a7ba6c6ea895
b7bbfff5677664709c88c6db96de6b7a90fdd7c3
2370 F20110218_AACLVQ pries_a_Page_66.txt
17aaf23612c071d97e1cb50609d739e1
9f83b5b782c67a749cc8b38740ce015e51310702
1051980 F20110218_AACLQT pries_a_Page_13.jp2
ba0f219c5b21bff329f5535020545b02
6d81e21dc4bdb2d5b7e1a855cde0da75bc4173c4
3153 F20110218_AACMCO pries_a_Page_30thm.jpg
8afa0237520d90a7485d2722561ea600
a194662852cfae556ffe9ae41e38ba21bb41d9a2
2351 F20110218_AACLVR pries_a_Page_67.txt
cc19eaf317068fdc1410e4c76c876fcd
a9c6400e7b1b99fd7d3ea27f31545f3fd72e7dbc
84631 F20110218_AACLQU pries_a_Page_26.jpg
f127a9f07ff82367f77f2bb62b637ba3
2249d59b6060251113abe35391c1be325c21370b
6384 F20110218_AACMCP pries_a_Page_55thm.jpg
23e5ea422ac4b67493ab1190f30bb4a2
040a15dd2323d88f65e866d2071193205d5e57fc
2285 F20110218_AACLVS pries_a_Page_68.txt
fcc3bfba746e5c683f5bbc8f7749be26
967ab1c65ada0baf353d5acc8411c6e55042e017
25581 F20110218_AACMCQ pries_a_Page_68.QC.jpg
86446afd8e9db51c6d2f0e0e5a817c9b
9831aeb3e8576b134e7744ed8ab58c4cf83ff173
2234 F20110218_AACLVT pries_a_Page_69.txt
2c6f08773ca4ef760b52470afcd87b39
ae6ddd64e8a3ecc243aab303a2bd5352f38fd2fa
F20110218_AACLQV pries_a_Page_06.tif
fb683fdacb25b58dd10f56d89a845088
841f7e2badb140f94d9c7929ea67a177c210c35e
6054 F20110218_AACMCR pries_a_Page_14thm.jpg
6fab32c668f649e24f5dd2047ff70067
5abdca1293725969b1aacf3d40f50f396a77c283
1279 F20110218_AACLVU pries_a_Page_70.txt
321ed52ff2215aca6e1a659166c96a52
b950a31f19231b8339dd5d3945b0ca326d34c459
65779 F20110218_AACLQW pries_a_Page_63.jpg
d93320b7152bec93bb56ef469bbb0810
ebf60afd6f309135bc54f2daa57d2e87a5318985
714 F20110218_AACMCS pries_a_Page_08thm.jpg
b52781645a1dbdc3efc55b5101f7660d
30606b2835f380439b69d3228620ee0c2752c214
1325 F20110218_AACLVV pries_a_Page_02.pro
6cfafbed49ed7e7d50258e48fa873829
4b1ca6142e0a69f65328bfdacf892638b91e3214
74048 F20110218_AACLQX pries_a_Page_12.jpg
87d954399143e6753e6ba1aafc1932a7
63795d8f5ea3ef03269f810f7dc59a972320d7f2
6317 F20110218_AACMCT pries_a_Page_41thm.jpg
be67a112974dacbf9c42735e7f83745f
c5996c8f7c5e11d1db78dbf7e441d4b9b07f87a3
44119 F20110218_AACLVW pries_a_Page_03.pro
84613dacc6be92c24f925ff90e3deae1
2d8db88cb65e505c7febdd812bc9c15f6fc07e6a
1051932 F20110218_AACLOA pries_a_Page_27.jp2
876a084f88a7ce2f6cf0c4499228d7ef
c7f159e09578e10df47a9158d57fff0b8848f9fd
66327 F20110218_AACLQY pries_a_Page_06.jpg
85bb982e4cd3a92d192b7108aa370cf0
e9afcdc1eb7087b4fb0d7013328cca7fb1b0170d
5613 F20110218_AACMCU pries_a_Page_53thm.jpg
89817bd48338d02a727a13243ace766e
2679d887276f7be9b4b3aef39469db1cb8a6adc1
24613 F20110218_AACLVX pries_a_Page_04.pro
ea975be6fe91104f098742f2ce45df8d
67b6a0a8de990c7aa0ec16177960eef984a71ada
F20110218_AACLOB pries_a_Page_43.tif
5c1988b600a79dc3d7d1cdb312a6ef30
11e050dc80a10650d973dfe961b485df57e90c24
23413 F20110218_AACLQZ pries_a_Page_53.QC.jpg
c66f0330f34e213d6c24136c8e86b9f8
4b9c5d36d50bd6438d960456dfd48cf838cf0d95
F20110218_AACMCV pries_a_Page_54thm.jpg
14d07fa211afa9bb38f88ac66b4fbec8
dbd29ff3c8392159f1e8a2880a355d54f7625526
53186 F20110218_AACLVY pries_a_Page_05.pro
97b2f4ba5d883ce0c3ed06a4df8e8a3a
9d8e54a438025644f87d78491804c48289206e45
4550 F20110218_AACLOC pries_a_Page_02.jpg
92769073d8a5d83f82e830cb3bd6ebb8
6002a5dc2a06662fed6521a6984240139bf841b2
5742 F20110218_AACMCW pries_a_Page_16thm.jpg
194e0219f3b9aa9b739304fcd15cd58c
58b64fbf63aec5be38aa3bc871c69cfcde3a6c23
F20110218_AACLTA pries_a_Page_47.tif
ee47ea932981ca7b3cd796431597b1ca
d0c4e12e6824f5aaf11d6c2d7667f20ea71c55e5
63295 F20110218_AACLVZ pries_a_Page_07.pro
74b98cd0b8f0435e68368cd7060cf828
d6cce98fe6a4e8f8295dd1ab5a0353b8f342eda6
6282 F20110218_AACLOD pries_a_Page_42thm.jpg
011ee3f44418cdf758284e2c7eaf5230
af693cce0770bec2ca3d1e8fa7ddf5b2c2fda722
24497 F20110218_AACMCX pries_a_Page_39.QC.jpg
5400f3d464e189c4f33918cd4a705d97
85b4be8c0ceee3dccb9d47fe3ca19bf3d8bb5f8e
F20110218_AACLTB pries_a_Page_48.tif
a949bff4b79fdc4c88052fa1564b4d8b
5e19b58a44a1cc232d265bf2bb583bfa21f3230c
F20110218_AACLOE pries_a_Page_44.tif
e03b5875e0c67cd3138fa8bf8cc3168a
8e54cf3a98d6198404ccd4e9610f0a2b47775153
12946 F20110218_AACMCY pries_a_Page_30.QC.jpg
d8bb0c0c1d020f74b82c40e9b2b6efd3
6b94cfaec91266c624700419fab8a205dfe3c00d
F20110218_AACLTC pries_a_Page_49.tif
f4ce6060ebf96270a8fea93a067b5e7b
eedff195cc5b4a6536a5d966ecfb9f7e1f3fa70b
3746 F20110218_AACLOF pries_a_Page_59.txt
8f293fae3e0b55134bc85d05fcd3656d
e89e2da33ca36ed7c45ba74ff9225d6d7d4e0f91
93798 F20110218_AACMAA pries_a_Page_66.jpg
c58f041f3101351ca8ebddc6c943970a
853cf4595a5b151ed06a6b64ced94a0c8c429658
14644 F20110218_AACMCZ pries_a_Page_58.QC.jpg
0d8500016ea5d920fb37486b98e42838
769283fcd15639ec1f85a9a806b177b0a3deffb6
F20110218_AACLTD pries_a_Page_50.tif
68705605046a9914f1ff35590e6ae1fe
c9c9bff703d19390b20013e6a663d272044e2a5a
33821 F20110218_AACLOG pries_a_Page_62.pro
1079aa1611a06cd5562a41b330a6cec5
bf19fbe3f8dbb34fe51854bf1d216eccd1c261c5
75652 F20110218_AACLYA pries_a_Page_03.jpg
bb70740c1342190bd0478f53532b506f
822d92a58b959e7556bff8e0e1777c0c68708ef6
93442 F20110218_AACMAB pries_a_Page_67.jpg
5dbe6c7b6dc9c494c210eb8886a2f5c8
15de3d8a704c9bd7387d8b3866f06db4918aac2c
F20110218_AACLTE pries_a_Page_51.tif
851d19ffc8328b74d4f4e06d0a7bcf06
3ef959562824f760f439be9f18e43b7ba9cceda4
5763 F20110218_AACLOH pries_a_Page_69thm.jpg
2763715bc713bf30b358a828c3df991a
2ccdc98ab58816642ba4c3229210bc5f2a67b04d
43900 F20110218_AACLYB pries_a_Page_04.jpg
e02efd63e56ed7aa705c8832a90560f0
c3e04176e2fac59efff8d3254d0d417a9d2692b0
92810 F20110218_AACMAC pries_a_Page_68.jpg
9e64d74db63e41b4127b1beb318ba93e
d193d4f692ba58b62872fb1ad2782b9222b20406
2468 F20110218_AACMFA pries_a_Page_09thm.jpg
c55cc01114759b17b05cc35722e74599
1057c4be56d316405f9d71f749f49ff91a13a9a9
26250 F20110218_AACLOI pries_a_Page_21.QC.jpg
5af5f7d14991d4f60f3b8599ac5c5337
eed7f176a8d91ed511cd35a870418fb166bddcab
63390 F20110218_AACLYC pries_a_Page_05.jpg
b951b62c348cf6b767299ae04bef2a56
f1de6b2c0cec957919b72940f70a13b0206e9f86
92153 F20110218_AACMAD pries_a_Page_69.jpg
d6da2aa4703695a3cc1bcb9124a04027
6a8aa75e4fba7f4c31ced05162fa8dfcb1966b75
F20110218_AACLTF pries_a_Page_52.tif
a0d925ef5c199bdbccf4314dd8b2760c
6cf2937db767cc0cf40cead5f616383ff99ca7a0
16519 F20110218_AACMFB pries_a_Page_49.QC.jpg
7ca61a30835a4a0ed0f4b43bc18eadb7
3b4a5c9cf964b43b95c396b5e7d03c0b6d132dea
48244 F20110218_AACLOJ pries_a_Page_18.pro
70b0b63bc54bd004688c382775e8c39c
0379459e0065bd980479a04e83d82fe75e2d9bb1
87840 F20110218_AACLYD pries_a_Page_07.jpg
d3eab3659686ba8613de299bff45d9d3
01629f0e152b2783ff659d7f82233e3ea301cdf2
F20110218_AACLTG pries_a_Page_54.tif
af53a38b7be4a84be2ddb133b74ddaf9
853148611f084145864be43437f308fe81177cb7
F20110218_AACMFC pries_a_Page_18thm.jpg
4d7b99eb25c33c86d7cbd985bbda4c17
69d2651ecfae858123d2023f5207f8986db23ed2
1051984 F20110218_AACLOK pries_a_Page_52.jp2
5e4720301499cea4537c95fd7894bcd1
54c8c31ed791b6b115b2f1a080215f843cb231fa
38737 F20110218_AACLYE pries_a_Page_09.jpg
a680d6b0bb3e0bb6ecf164bf33d0d250
2e77e82ab5b7222fb6d649f246b6ef51a085cbb7
54953 F20110218_AACMAE pries_a_Page_70.jpg
ac06faaf7ad48bb1e26f716d5a1fb346
3a713040a19b5cdc2c4731de2e076b5f7fda3325
F20110218_AACLTH pries_a_Page_55.tif
45ece0b4394eb7cfa1fab217b43469b9
11d44c7ca17359d1bb252ec8d292d6db91589c0d
6169 F20110218_AACMFD pries_a_Page_47thm.jpg
a0cfd656ada3f361ee393f30a405fff9
4cb592cf0096134b4137e6e79a27584939cc7972
F20110218_AACLOL pries_a_Page_13.txt
f413a3c99285aa1646500336a5a79574
a5ca26a438013d8c871bc8b4138e97de90ef4cb4
69405 F20110218_AACLYF pries_a_Page_10.jpg
25ba9ea9544a81e2017c411a2cdb5e23
3f1c3f3504d8901669ecb4ee57a91e492b485608
282588 F20110218_AACMAF pries_a_Page_01.jp2
bf1100280e2e4a0acef4f7758269fd37
ce965f13a3ada7e5b60eb02618a8c1b22f536b73
F20110218_AACLTI pries_a_Page_56.tif
a509506ac766b1ec20e872735a878148
94dd9a23a6bfdedf76a3a543e49fe5e4b5d86fbe
13999 F20110218_AACMFE pries_a_Page_04.QC.jpg
5a4d6872451a6b1d472798052e112fd7
8fe57bc4e805334935e7bcd6d72461f47849012f
3479 F20110218_AACLOM pries_a_Page_05thm.jpg
6c4a27229a1c2122e3c3a4f67edc39f1
9232ed1b9e7921557fd9b67933b535b14e7d7e67
83627 F20110218_AACLYG pries_a_Page_11.jpg
b5b018b0c28c8c33fb821c2d2a8a22f4
26b51799ecbf3e7adbff2dfe6522c4aa4bd19b89
993637 F20110218_AACMAG pries_a_Page_03.jp2
63af86f8f99e9c4118063f5728f69060
372aa2580dbd6a12c111e47489e3acd19bef7979
F20110218_AACLTJ pries_a_Page_57.tif
a21ea952d0aae307dff0e07ce825e8fd
5d8b80ac64e656f35ccfc8bde40a82a9ef7a6dac
5277 F20110218_AACMFF pries_a_Page_59thm.jpg
9c9f6e32ae5b878ef1884f531c4a31d6
f6b4ea60a96a886ac5409ffbc1287fdfcdad104a
6391 F20110218_AACLON pries_a_Page_20thm.jpg
9b75a07c26e19a0692045871373de8b3
b77dfa4f3cd6fc9ef9372887c541b9a9bfb92184
84026 F20110218_AACLYH pries_a_Page_13.jpg
dfe8482d45230ef12c2e5d8442f55bcc
54f11305bd2ffea393d5f37ebe6703e7023a12dc
558343 F20110218_AACMAH pries_a_Page_04.jp2
5c5c4af5ff46cc60eaab4b109d74af20
f993525a42293285da014e2908928d2a4cbd9d04
F20110218_AACLTK pries_a_Page_61.tif
fc13b8d0e60d04f7addf8ca5b2d80355
c5b874638336afd508e607a61a143a6b06acf624
16846 F20110218_AACMFG pries_a_Page_06.QC.jpg
2f42269ff844b690c3a36c2a2d80992d
f225bf5704101669975b46de189366ed19035ec5
1051957 F20110218_AACLOO pries_a_Page_55.jp2
dd3ddc386086fc9834ee31aabbcfaee5
bab227ff4b0a6daf4b997653249796749a328514
82972 F20110218_AACLYI pries_a_Page_14.jpg
a4052659629383e25e4784b5a9e53d41
d280dff5a17bdf5ccf6badcefe59f5279705b0d3
804596 F20110218_AACMAI pries_a_Page_05.jp2
a380fc94c94a3f08a5615ee7b7353e68
5aa0d2388a05f753e6fadbc4b306292214d35bfd
F20110218_AACLTL pries_a_Page_63.tif
22f2629f25d8e6650962b9562bcdb902
b11b964e707a14054b601446f6eed13cd9817834
114142 F20110218_AACMFH UFE0013415_00001.xml
7d7c493dca265501ccba81bf927677ab
a812960dc0d05e4e60f1c36430e9a8e0ab72cb82
6502 F20110218_AACLOP pries_a_Page_46thm.jpg
317fdfbc7f83de73d078f05ca4185e4f
a86ea3dc53a9d6ce74104376927f5ee0c4b39a47
65694 F20110218_AACLYJ pries_a_Page_15.jpg
b3f8d6e165ef8b9393cae20f6a04ab76
a971ef4d7c345b47fe20c591aea4bd39f7b3e29b
827418 F20110218_AACMAJ pries_a_Page_06.jp2
b4349383ba5f650c261394286202f9b4
9af3393571e9f88b972a8dedca94b3e7780049d9
F20110218_AACLTM pries_a_Page_64.tif
7269624d48acadaa9ff9e68407cc8867
fa1b38af0049d32178aa2ddb7319123c2a1a3e81
23339 F20110218_AACMFI pries_a_Page_03.QC.jpg
a4398fa4f79739f0ec3453d85cf3aa31
9b206c80a1d66008db8af6267b755120e6ab31b6
1508 F20110218_AACLOQ pries_a_Page_02.QC.jpg
543fe883c04fc3cbf8898635ec0ce4f4
1ffd34980e4e9b3862d5d858a7b898ac83c7778e
75196 F20110218_AACLYK pries_a_Page_16.jpg
70058a698f05e675403ff1ba522e3b66
edd6f309f0d9e9acc011c4b16ecaa77542aaa7a4
F20110218_AACMAK pries_a_Page_07.jp2
396dbe7bda9efb809b6006c8ea7b9faf
49bd419286a9ed4590b147d55d1dda6580809205
F20110218_AACLTN pries_a_Page_65.tif
218ac058dfe53b02bb543048e29abe9b
af4e6b56fd98955fdcbfe79f1666b209e6be9268
F20110218_AACLOR pries_a_Page_41.jp2
396a98a4c49f7b72d00d6f76cda0402c
c739e620f6ed89abc3949379204f238cc72df624
79864 F20110218_AACLYL pries_a_Page_17.jpg
341b3ce4fff133b674cebc53d5195ef5
b57ac2ddc11b4baebd0b79b393237b15d7dbe07f
89291 F20110218_AACMAL pries_a_Page_08.jp2
dd0ebb4c88c07d841acc95135c53462e
61716983b7d748e3b6f06489d04b477d8925492f
F20110218_AACLTO pries_a_Page_66.tif
dd6a9c2defccfc8cc864443ec9e7cd80
1b1a52531ed5849441a1f99f0f4ed0887d6d2254
24616 F20110218_AACMFJ pries_a_Page_17.QC.jpg
229962d2cbed2d7be8a3211ab75b21c6
f0a5c0fedee2dca25e507885767d4937c92efdd9
3339 F20110218_AACLOS pries_a_Page_31.pro
02946240f5d4665f8ad2b98ccad12cdf
2fbaf808dda0adcb4ea5afcac322a89f8c36f630
78989 F20110218_AACLYM pries_a_Page_18.jpg
e5e5dba1ff6d86d1a3f2c6d6bde6c70d
58a2376bbc2e892cbc59a0271b00a9684e622aca
489280 F20110218_AACMAM pries_a_Page_09.jp2
7ae7f4238fd864a3c1faa236d7d32ea0
f1d7984f837305accb56b6021b99ceca7e9ad550
F20110218_AACLTP pries_a_Page_67.tif
92587e92224e446072692712947fdfe7
90d481804404f8e845dcb8789fb1e38c2e9d87a6
27377 F20110218_AACMFK pries_a_Page_20.QC.jpg
2d0ea80250f43d2a063a870975d5ca6e
9f3a61b64f7a0c037a3a32ad689b6126387ffa95
75586 F20110218_AACLYN pries_a_Page_19.jpg
85e40e657536758bc29c5d72eb3a7725
e984fc0c4809c694b16dcbd0cae7fc53a4c7382b
899140 F20110218_AACMAN pries_a_Page_10.jp2
223078d911515db17e88dfd978494723
1916d5975bb1e4618f6630fda2c1b307229b6a6f
F20110218_AACLTQ pries_a_Page_68.tif
984ff33d94079114fa02a55156274c12
29f2c3f3bc5f0aba01ad679b8ee1e5b520d23837
25289 F20110218_AACMFL pries_a_Page_22.QC.jpg
814272b81e7026ff0ac43cdd01fc9377
fe18c4e3ca1c6b595a7215d7f48e9f69a777c417
20263 F20110218_AACLOT pries_a_Page_15.QC.jpg
a963ce056a09f5e5a68f9925568d1a8e
35bcd4f6bfd998b1dc939c7413750bf9dee87a27
87839 F20110218_AACLYO pries_a_Page_20.jpg
6d845c8db1f910b286a66c0ffedfdec5
14b4c4187c8e59019798055ab27d8bc906450983
1051968 F20110218_AACMAO pries_a_Page_11.jp2
dad08a2d518c8199a31ebc4359949880
1e4fa3e00afc376863818146c14b02336d07e19a
F20110218_AACLTR pries_a_Page_70.tif
685b19fd6cfc7dc791f4f302179a231f
8b18c97805f3395eed8e208e5cc1c60d19a7bfe9



PAGE 1

HURRICANE IMPACTS ON COASTAL DU NES AND SPATIAL DISTRIBUTION OF SANTA ROSA BEACH MICE ( Peromyscus polionot us leucocephalus ) IN DUNE HABITATS By ALEXANDER JAMES PRIES A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FUFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

PAGE 2

Copyright 2006 by Alexander James Pries

PAGE 3

iii ACKNOWLEDGMENTS I would like to acknowledge the support received from numerous organizations, groups, and people during the deve lopment of this project. Funding and logistic support was provided by the National Park Service and Eglin Air Force Base. The University of Florida, Milton campus and UF Department of Wildlife Ecology and Conservation provided equipment and additional funding. My committee members (Dr. Lyn C. Br anch, Dr. Debbie L. Miller, and Dr. George W. Tanner) went above and beyond the call of duty in helping with development and implementation of this research. Dr. M iller provided housing af ter my trailer broke and continued to graciously welcome me into her home during the endless process of data collection. I have enjoyed thoroughly our conversations on restoration ecology and ethics of being a scientist. Dr. Tanner was most insightful in steering me towards proper techniques for assessment of vegetation and ot her habitat features. Dr. Branch is a sound editor who masterfully checked and rechecked my thesis for clarity and scope. Thanks go to Riley Hoggard at Gulf Islands National Seashore and Bruce Hagedorn, Bob Miller, and Dennis Teague at Jackson Guard, Eglin AFB. These individuals served as important contacts and sources of support when materials were lacking or I had questions about the accessibility of certain sites. I am most thankful for their support. Many individuals assisted me during various phases of this project and I will attempt to name them all here. I apologize for those who I may miss but your deeds are not forgotten. Tanya Alva rez endured the hot conditions and was covered in black

PAGE 4

iv carbon powder for weeks. Mica Schneider and Li sa Yager created the initial cover layer of dunes (with over 750 polygons) on Santa Rosa Island that was massively important. Jonathan Shore and Cathy Hardin were fantas tic as temporary field technicians during the difficult conditions. I also offer apprecia tion to Bob Schooley and Arpat Ozgul for statistical assistance dur ing initial data analysis. Conve rsations with many graduate students including Jason Martin, Elizabet h Swiman, Ann George, Dan Thornton, and Traci Darnell helped to craft and refine appropriate research questions. Finally, I am thankful to my immediate fa mily and friends outside of the scientific or graduate school community. Your abi lity to listen and be a sounding board when things became difficult is a ma jor reason why I have been able to complete this research. Your support and patience are massively important to me and I dedicate this work to you.

PAGE 5

v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................ix ABSTRACT....................................................................................................................... ..x CHAPTER 1 INTRODUCTION........................................................................................................1 Beach Mice and Threats to Survival.............................................................................1 Habitat Use by Beach Mice..........................................................................................1 Coastal Dunes, Development, and Erosion..................................................................2 Dune Restoration and Protection for Beach Mice........................................................3 2 INFLUENCE OF DUNE STRUCTURE ON STORM-RELATED EROSION FOR FOREDUNES AND SECONDARY DUNES ON SANTA ROSA ISLAND, FLORIDA.....................................................................................................................5 Introduction...................................................................................................................5 Methods........................................................................................................................7 Study Area.............................................................................................................7 Characteristics of Hurricane Ivan..........................................................................8 Dune Mapping.......................................................................................................9 Statistical Analyses..............................................................................................10 Results........................................................................................................................ .12 Conditions before Hurricane Ivan.......................................................................12 Hurricane Ivans Impact on Fo redunes and Secondary Dunes............................12 Regression Trees.................................................................................................13 Discussion...................................................................................................................15 3 INFLUENCE OF HABITAT AND LANDSCAPE FEATURES ON SPATIAL DISTRIBUTION OF SANTA ROSA BEACH MICE IN TWO DUNE HABITATS BEFORE AND AFTER A HURRICANE.............................................24 Introduction.................................................................................................................24

PAGE 6

vi Methods......................................................................................................................26 Study Area and Habitat Mapping........................................................................26 Dune Occupancy.................................................................................................27 Predictor Variables: Vegetation Cover and Landscape Structure.......................29 Occupancy Models..............................................................................................30 Results........................................................................................................................ .32 Hurricane Impacts on Habitat Availability at EAFB..........................................32 Dune Occupancy.................................................................................................32 Habitat Models....................................................................................................33 Discussion...................................................................................................................34 4 CONCLUSIONS AND CONSERVATION IMPLICATIONS.................................42 Dune Erosion and Loss of Beach Mouse Habitat.......................................................42 Habitat Restoration for Beach Mice...........................................................................44 APPENDIX A DELINEATION OF DU NES IN THE FIELD...........................................................46 B CORRELATION MATRICES FOR VARIABLES BY HABITAT..........................47 C COMPARISON OF FRONTAL DUNES AT EGLIN AIR FORCE BASE AND GULF ISLANDS NATIONAL SEASHORE.............................................................49 D PREDICTORS OF CHANGE IN OCCUPANCY OF FRONTAL DUNES AFTER HURRICANE IVAN....................................................................................50 E CORRELATION MATRIX FOR STRUCTURAL FEATURES OF FRONTAL DUNES ON EGLIN AIR FORCE BASE..................................................................51 F CORRELATION MATRIX FOR STRUCTURAL FEATURES OF SECONDARY DUNES ON EGLIN AIR FORCE BASE.........................................52 LITERATURE CITED......................................................................................................53 BIOGRAPHICAL SKETCH.............................................................................................59

PAGE 7

vii LIST OF TABLES Table page 2-1 Means and standard errors for stru ctural variables measured to explain dune erosion in foredunes and secondary dunes on Santa Rosa Island............................17 2-2 Statistics for evaluation of dune char acteristics as predictors of dune loss as a result of Hurricane Ivan............................................................................................19 3-1 Means and standard errors for stru ctural and vegetation variables measured for modeling occupancy of frontal and scr ub habitat by Santa Rosa beach mice on Eglin Air Force Base (EAFB) and Gulf Islands National Seashore (GINS) on Santa Rosa Island, FL...............................................................................................38 3-2 AIC-based selection of site occupa ncy models of dune occupancy for Santa Rosa beach mice in frontal and scrub dune habitat...........................................................40 3-3 Relative importance (wsum), model-averaged parameter estimates, and unconditional standard errors for variab les used to model occupancy for beach mice in frontal and scrub habitat before and after Hurricane Ivan...........................41 B-1 Correlations for variables measured on 61 scrub dunes surveyed for beach mice before Hurricane Ivan (Jun. 2004 Sep. 2004).......................................................47 B-2 Correlations for variables measured on 61 scrub dunes surveyed for beach mice after Hurricane Ivan (Oct. 2004 Jan 2005)............................................................48 B-3 Correlations for variables measured on foredunes (Eglin Air Force Base, n = 11, and Gulf Islands National Seashore, n = 15) surveyed for beach mice after Hurricane Ivan. (Oct. 2004 Feb. 2005).................................................................48 C-1 Results of t-tests comparing vege tation, structure and landscape context for frontal dunes on Eglin Air Force Base and Gulf Islands National Seashore measured after Hurricane Ivan.................................................................................49 D-1 Mean values, standard errors, and ttest results for habita t variables on frontal dunes on EAFB that became unoccupied and for dunes that remained occupied after Hurricane Ivan.................................................................................................50 E-1 Correlations for structural and lands cape context variables measured on frontal dunes (N = 93) on Santa Rosa Isla nd prior to Hurricane Ivan.................................51

PAGE 8

viii F-1 Correlations for structural a nd landscape context variables measured on secondary dunes on Santa Rosa Island prior to Hurricane Ivan...............................52

PAGE 9

ix LIST OF FIGURES Figure page 2-1 Map of Santa Rosa Island, FL. The study area encompasses the section between Navarre and Fort Walton Beach.................................................................20 2-2 Cross validation rela tive error for regression tr ees for (a) foredunes and (b) secondary dunes to explain dune loss from Hurricane Ivan in relation to measured predictor variables....................................................................................21 2-3 Regression trees relating percentage of dune lost from Hurricane Ivan for (a) foredunes (N = 93) and (b) secondary dune s (N = 52) to physical features of dunes, spatial location of dunes with respect to where Hurricane Ivan made landfall, and width of island.....................................................................................22 2-4 Regression trees relating percentage of dune lost from Hurricane Ivan for (a) secondary dunes 0.25 ha (N = 61) and (b) seco ndary dunes <0.25 ha (N = 34) to dune features, spatial location, and island width..................................................23

PAGE 10

x Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science HURRICANE IMPACTS ON COASTAL DU NES AND SPATIAL DISTRIBUTION OF SANTA ROSA BEACH MICE ( Peromyscus polionot us leucocephalus ) IN DUNE HABITATS By Alexander James Pries May 2006 Chair: Lyn C. Branch Cochair: Deborah L. Miller Major Department: Wildlife Ecology and Conservation I examined the impact of Hurricane Ivan on dune erosion and changes in spatial distribution of beach mice ( Peromyscus polionotus leucocephalus ) in two dune habitats on Santa Rosa Island, FL. Foredunes (i.e., fr ontal dunes) and sec ondary (i.e., scrub) dunes were mapped and surveyed for the pres ence of beach mice before and after the hurricane using polyvinyl chlo ride (PVC) tracking tubes. I also collected data on physical structure, vegetation, and landscape context for each dune during these two time periods. Regression trees were used to evaluate structur al features of dunes that explained patterns in dune erosi on as a result of Hurricane Iv an for the two dune types. I used site-occupancy models and an informationtheoretic approach to evaluate predictors of occupancy for beach mice in frontal and s econdary dune habitats before and after the hurricane. Hurricane Ivan removed 68.2% of frontal dune area surveyed for beach mice (Chapter 3) and 76.8% of a ll frontal dune area mapped (Cha pter 2). Secondary dunes

PAGE 11

xi surveyed for beach mice only lost 14.8% of thei r total area (Chapter 3) and all secondary dunes surveyed lost 19.3% of their area (Chapter 2). Dune erosion for frontal dunes was related inversely to distance from where th e eye of Hurricane Ivan passed over the island, dune height, and dune width. Dune erosion for large sec ondary dunes was reduced when dunes were located behind foredunes. Erosi on of small secondary dunes increased with distance of the dune from where the eye of the hurricane passed over the island. The reason for this pattern is unknown, but it may be related to spatial distribution of storm surge in the Santa Rosa Sound. Dune erosi on decreased with incr easing length and area of small secondary dunes. Beach mice o ccupied 100% of frontal dunes before the hurricane and 59% of these dunes after the hur ricane. Occupancy of scrub habitat by beach mice was not statistically different be fore and after the hurricane, but was higher (~70% of sites) than frontal dune occupanc y after the storm. Frontal dune occupancy was influenced largely by percent cover of woody vegetation and distance to nearest occupied dune. Probability of occupancy by beach mice in scrub habitat increased with an increase in dune area and amount of dune habitat surrounding the dune within 200 m. This study indicates that scrub habitat, which currently is not protected for beach mice, is important for mice because of the stochastic and severe impacts of storms on frontal habitat. With further removal of frontal dune habitat, scrub could become essential for long-term persistence of beach mice. The study suggests that restoration programs for frontal dunes set targets for the construction of dunes that are tall and wide. Dunes with these two features likely will mitigate storm surge associated with hurricanes and protect coastal dunes, mouse habitat, or in frastructure located further inland.

PAGE 12

1 CHAPTER 1 INTRODUCTION Beach Mice and Threats to Survival Beach mice ( Peromyscus polionotus spp.) are a complex of eight subspecies of the oldfield mouse, which occupy the coastal dune s of Alabama and Flor ida (Holler 1992). Two subspecies along the Atlant ic coast of Florida are fede rally protected and one is extinct. Gulf Coast populations of beach mice comprise five subspecies, four of which are listed as threatened or endangered (Potter 1985; Milio 19 98). The remaining subspecies along the Gulf coast, the Santa Rosa beach mouse ( Peromyscus polionotus leucocephalus ), is not yet listed beca use its geographic range includes several federally managed lands (Gore and Schaffer 1993). All populations of beach mice suffer from severe habitat loss from coastal developmen t and habitat disturbance from hurricanes (Gore and Schaffer 1993; Swilling et al. 1998). Additional threats to beach mice include predation by feral cats ( Felis silvestris ), competition with house mice ( Mus musculus ) in dunes around coastal development, and low population levels in late summer when hurricane activity is most prevalent (Hum phrey and Barbour 1981; Oli et al. 2001). Habitat Use by Beach Mice Beach mice are considered habitat speci alists on dune habitats with burrow locations strongly correlated to these habita t types (Blair 1951; Humphrey and Barbour 1981). Foredunes or frontal dunes, located imme diately adjacent to th e Gulf of Mexico, are believed to be optimal ha bitat for beach mice (USFWS 198 7). Mice also occur in secondary dunes (also known as scrub), which are located farther from the shoreline and

PAGE 13

2 are characterized by increased dominance of woody vegetation. Although densities of beach mice generally are highest in frontal habitat (Swilling et al. 1998), abundance of beach mice in scrub can increase after hurrica nes. Prior to Hurricane Opal, population abundance of Alabama beach mice ( P. p. ammobates ) on trapping grids in scrub habitat was less than frontal dune habitat (Swilling et al. 1998). Four months after the storm, abundance in scrub habitat was almost twice that of frontal dune habita t. Despite use of scrub by beach mice, this habitat is not de signated as critical habitat under USWFS recovery plans for Gulf coast beach m ouse populations (USFWS 1987). Additionally, little is known about what feat ures define suitable scrub habitat for beach mice or how much of this habitat is utilized by mice. Although hurricanes are a natural feature of coastal disturbance, their impacts work in concert with coastal development and anthropogenic habitat loss to impact habitat availability for beach mice (Holler 1992). Population models suggest that hurricane impacts pose a significant threat to all s ubspecies of beach mice (Oli et al. 2001). Hurricanes, in addition to di rectly destroying dunes, fragment remaining dunes and potentially change features of dunes that make them suitable for beach mice. Fragmentation of dunes from hurricanes ma y require beach mice to travel more frequently between remaining dune patches, e xposing them to predators. Habitat loss and fragmentation also may reduce landscape connectivity for beach mice, limiting their ability to recolonize frontal dunes after stor m impacts or force them to utilize more marginal habitats. Coastal Dunes, Development, and Erosion The coastal dunes that beach mice occupy are valued for their fauna and flora, natural beauty, and ability to protect human-made infrastructu re (Nordstrom et al. 2000;

PAGE 14

3 Martinez et al. 2005). Despite this, many co astal dune ecosystems have been changed irreversibly as a result of exploitation of natural resources and anthropogenic development (Martinez et al. 2005). Increases in storm severity and frequency are predicted and storm impacts wi ll continue to alter dunes, re duce infrastructure protection and disturb habitat for wildlife. As a result interest has increase d in the creation or restoration of dunes that will withstand storm impacts. Coastal dunes are formed from aeolian processes with dune development occurring where sediment is trapped by existing ve getation (Hesp 2004; Psuty 2005). Foredunes, located closest to the shoreline, are dynamic structures th at are influenced greatly by the flow of water, wind, and sediment with norma l environmental fluctuations and periodic storm events (Psuty 2005). Secondary dunes are created by sediment flows from existing foredunes or they may be old foredunes. These dunes are no longe r maintained by the processes that drive foredune morphology. Hurricanes alter dune ecosystems by bur ying native vegetatio n under centimeters of deposited sand and also change the conf iguration or presence of dune structures (Ehrenfeld 1990). Dune erosion occurs as a result of storm surge and waves repeatedly narrowing the dune face to cause an eventual breach or when storm surge overwashes a dune and pushes sediment landward (Hesp 2002; Judge et al. 2003). Dune erosion is influenced by duration and intensity of a stor m event; however, structural features of the dune also alter the risk of erosion. Dune Restoration and Protection for Beach Mice Increasing the amount of protected beach m ouse habitat generally is not an option as most coastal dunes in Florida already are in public lands or have been developed (Bird 2002). Therefore, other approaches such as habitat restoration may be important for

PAGE 15

4 long-term maintenance of beach mouse populatio ns. Although habitat restoration is cited as critically important for the recovery of beach mouse populations (USFWS 1987; Oli et al. 2001), little work has been conducted to id entify habitat and lands cape features that influence use of frontal or secondary dune ha bitat by beach mice. Restoration techniques have been developed to promote regeneration of physical structure to dunes after storms (Miller et al. 2001; 2003). These techniques can be used to create dunes with particular structural features (e.g., tall and wide), but identification of structural features of dunes that confer resistance against st orm-related erosion is limited. Restoration techniques that promote crea tion of dunes and dunes with key habitat requirements of beach mice may aid in management of existing protected habitats for these mice. My study contributes to this effort in the following ways: Examining the relationship between dune er osion as a result of Hurricane Ivan and the physical structure of frontal and secondary dunes (Chapter 2) Assessing the impact of Hurricane Ivan on the overall occupancy of frontal and secondary dune habitat by beach mice (Chapter 3) Identifying habitat variables at the patch and landscape scale that influence occupancy of frontal and secondary dunes by beach mice (Chapter 3). Chapter 2 and 3 are written as stand-alone papers for publication. Therefore, some background material is repeated in each chapter.

PAGE 16

5 CHAPTER 2 INFLUENCE OF DUNE STRUCTURE ON STORM-RELATED EROSION FOR FOREDUNES AND SECONDARY DUNES ON SANTA ROSA ISLAND, FLORIDA Introduction Coastal dunes are valued for their aesthetic beauty and their ability to protect human-made structures during storms (Nords trom et al. 2000; Nordstrom and Mitteager 2001). Dunes absorb wave energy, block storm surge, and reduce damage to infrastructure. Coastal dunes also are importa nt wildlife habitat (Mar tinez et al. 2005). Hurricanes and tropical storms have altere d coastal dunes on barri er islands along the northern portion of the Gulf of Mexico in the last decade (Stone et al. 2004). Increases in the severity and frequency of tropical cyclones are predicted and will further modify dune configuration, reduce infrastr ucture protection, and distur b wildlife habitat (Emmanuel 2005). As a consequence, creation and rest oration of dunes has become an important issue in coastal management strategies (Nor dstrom et al. 2000). Strategies for dune protection and restoration coul d benefit from information on physical and spatial factors that influence storm impacts on dunes. Impacts of storms on dune erosion are a function of storm characteristics and structural features of dunes. Dune er osion occurs when storm surge and waves repeatedly narrow a dune face, causing irregula r slumping of sediment and an eventual breach, or when overtopping by storm surge co mpletely overwashes a dune and pushes sediment landward (Hesp 2002; Judge et al. 2003). Although severi ty and length of a storm influence dune erosion (Kriebel et al. 1997; Sallenger 2000), key structural features

PAGE 17

6 of dunes (e.g., height, width) also provide pr otection against dune er osion (Judge et al. 2003). Laboratory research and numerical models of dune erosion are extensive (Vellinga 1982), but few studies have evalua ted importance of dune structure in stormrelated erosion in the field (but see Judge et al. 2003). Additionall y, past evaluations of dune erosion often have been limited to fore dune structures (i.e., dunes nearest to the high tide line). Coastal foredunes are formed from aeolia n processes with dune development occurring where sediment is trapped by ve getation (Hesp 2004; Psuty 2005). Secondary dunes generally are found landward of foredunes and develop from sediment originating on foredunes or they may be relict foredunes that are no longer c ontrolled by aeolian processes (Hesp 2004). Foredunes are differentia ted as either incipi ent or established. Incipient foredunes are low-lying developi ng dunes associated with pioneer plant communities. Established foredunes evolve from incipient dunes and are distinguished by presence of an intermediate plant comm unity, including woody species. These dunes have greater height and widt h than incipient dunes (Hesp 2002). Although the location and development of incipient dunes may change annually, development of large established foredunes takes decades, and these dunes remain in a relatively fixed position unless removed by storms or anthropogenic dist urbance. Evolution and maintenance of established foredunes is not determined solely by sediment flows but rather by a suite of additional factors like vegetation density a nd the frequency of wave and wind forces (Hesp 2004). Established foredunes and secondary dunes, by way of their size, should provide greater resistance to increased tide levels and storm events than incipient dunes.

PAGE 18

7 However, storm surge and waves associated with hurricanes of cat egory 3 or above on the Saffir-Sampson scale can cause even large (> 3 m tall) established foredunes to return to a more erosional form or to be destroye d (Hesp 2002). Effects of strong hurricanes on secondary dunes are less well documented. Impact of storm surge on secondary dunes may be less severe as these structures ar e no longer governed by sand exchange, storm tides or wave activity associated with fo redune development (Hesp 2004). Additionally, as a result of their spatial location behind wave-absorbing foredunes, dune erosion from storm events may be lower for secondary dunes. I assessed dune erosion along a barrier island ecosystem in the Gulf of Mexico after Hurricane Ivan. The objectives of this study were to examin e impacts of Hurricane Ivan on established foredune and secondary dunes and to evaluate structural features of dunes as predictors of dune vulnerability for th ese two dune types. I also examined dune erosion as a function of the landscape cont ext of the dune, includi ng island width at the location of the dune, distance to neighboring dun es and distance of the dune from the position where Hurricane Ivan pass ed over the island. Identifica tion of structural features that allow dunes to resist storm-related erosi on and evaluation of lands cape attributes that influence erosion are important for future ma nipulation of coastal dunes in a restoration context. Methods Study Area The study was conducted on Santa Rosa Island, which is a barrier island approximately 60 km long and 0.5 km wide, in the Gulf of Mexico. The study site is located on property owned and managed by Eglin Air Force Base (30' N, 81' W). This portion of the island is approximatel y 20 km long and includes the islands entire

PAGE 19

8 width (Fig. 2-1). This area contains severa l military structures and a paved road for military traffic but otherwise is undeveloped. A thorough description of Santa Rosa Islands geomorphology can be found in Stone et al. 2004. Foredunes are found near the high tide line and, in the absence of hurricane activity, can run con tinuously the length of the isla nd. Prior to Hurricane Opal (1995), mean dune height was 3.8 m (Stone et al. 2004). These dunes are dominated by sea oats ( Uniola paniculata ), cakile ( Cakile spp.), beach morning glory ( Ipomoea imperati) and seashore elder ( Iva imbricata ) but various woody spec ies can be present on foredunes in the absence of frequent distur bance. Secondary dune s are located behind foredunes on the bayside of th e island. Woody species domin ate these dunes, including false rosemary ( Ceratiola ericodes ), woody goldenrod ( Chrysoma pauciflosculosa ), scrubby oaks ( Quercus geminata ) and sand pine (Pinus clausa ). Between these two types of dunes is grassland dominated by maritime bluestem ( Schizachrium maritimum ) and bitter panic grass ( Panicum amarum ), interspersed with de nsely vegetated ephemeral wetlands. Characteristics of Hurricane Ivan Hurricane Ivan made landfall as a category 3 hurricane on 16 September 2004, west of Gulf Shores, Alabama, and approximately 100 km west of our study site. Storm surge from the hurricane was estimated at 3 4.5 m from Mobile, AL to Destin, FL (Stewart 2005), which encompassed all of Santa Rosa Island. Ivan was the most destructive hurricane to make landfall along the Gulf coast in 100 years with a majority of damage resulting from wave action a ssociated with unusually high storm surge (Stewart 2005).

PAGE 20

9 Dune Mapping Established dunes (foredunes, N = 93, and secondary dunes, N = 484) were delineated in the field after Hurricane Opal (1995). Because established dunes change very slowly over time, except when they ar e impacted by storms, these data could be used as a baseline for dune stru cture prior to Hurricane Iva n. Dunes were mapped again after Hurricane Ivan (2004). Geographic location of dune peri meters were recorded with a TRIMBLE GPS unit in UTMs (Universal Tranverse Mercator) and differentially corrected for < 1 m accuracy. Dunes were included if they were greater than 1.0-m high with woody vegetation or greater than 1.5-m high with grasses or other herbaceous vegetation. Dunes were consider ed distinct if they were separated by more than 3.0 m of sand. Dune height (m) was measured ever y 15 m along the long axis of each dune using a telescoping pole. Dune perimeters we re incorporated into ArcView 3.2 (ESRI 1996) and the following variables were calculated: dune ar ea (ha), dune width (perpendicular to the shoreline), length (parallel to the shor eline), and distance of each dune from the position where Hurricane Ivan made landfa ll. Coordinates for the position where Hurricane Ivan made landfall were obtained from the National Oceanic and Atmospheric Association (Stewart 2005). Aerial photographs taken in 1995 were overlaid on dune location in ArcView 3.2 to calculate island widt h at each dune location. I also recorded presence or absence of foredunes located se award of secondary dunes before Hurricane Ivan. Gap distance for each dune was calculate d as the average of the distance between the closest dunes located immediately to the west and east of the target dune. After Hurricane Ivan all remaining foredune s (N = 26) were remapped or recorded as completely destroyed (100% loss) if not found during remapping (N = 67). A random subset of small secondary dunes (< 0.25 ha, N = 34) were remapped after Hurricane Ivan.

PAGE 21

10 All large secondary dunes ( 0.25 ha, N = 61) were remapped. The percentage of each foredune or secondary dune lost from Hurri cane Ivan was calculat ed by subtracting the dunes area after Hurric ane Ivan from the post-Opal dune area and by dividing this value by the post-Opal dune area. Statistical Analyses For statistical analysis, I used data fro m all foredunes and all secondary dunes prior to Hurricane Ivan, and I used all fore dunes and a subset of secondary dunes sampled after the hurricane. Because all sma ll secondary dunes were not remapped after Hurricane Ivan, I determined the proportion of the landscape occ upied by large dunes ( 0.25 ha) and small dunes (< 0.25 ha) prior to Hurr icane Ivan. I used these proportions to determine the sample size for large and sma ll dunes in analyses. The total area of scrub dunes prior to Hurricane Ivan was 131.55 ha with large dunes making up 109.99 ha (83.6%) of this total. To ma intain the proportional area of the two dune types, I used the 34 small dunes randomly chosen for remapping after Hurricane Ivan and I randomly selected 18 large dunes from the larger pool we mapped. I used Pearson correlation coefficients to examine relationships among structural variables for dunes, spatial location, and isla nd width for all frontal dunes and secondary dunes. Variables were examined for normality prior to examining correlations between variables. For frontal and secondary dune s, data on percentage of dune loss were transformed using arcsine transformation, and dune area and dune height prior to Hurricane Ivan and dune area after Hurricane Ivan were transformed using logtransformation (Zar 1998). I used t-tests to examine diff erences in dune area and dune height between dune types before Ivan. Univar iate linear regression initially was used to identify structural or spatial variables that were important pr edictors of the percentage of

PAGE 22

11 dune erosion after Hurricane Ivan for foredune s and secondary dunes, and I used logistic regression to evaluate the importance of presence of foredunes on dune erosion in secondary dunes. Changes in dune area of frontal and secondary dunes with the impacts of Hurricane Ivan were examined with paired t-tests. All univariate tests were conducted in SPSS version 13.0 (SPSS Inc., 2004) and I rejected null hypotheses of no influence on dune loss when p < 0.05. Traditional multiple regression technique s may not work well when variables do not meet parametric assumptions or when re lationships between variables are complex or non-linear (Bourg et al. 2005). I wanted to simultaneously assess the influence of all predictor variables on dune erosion from Hurricane Ivan and examine relationships between physical features of dunes and sp atial location on dune erosion. I used classification trees to assess how multiple pr edictor variables explained the impacts of Hurricane Ivan on dune structure. This non-para metric approach spl its the dataset into smaller groupings with relatively homogeneous values of response va riables (Breiman et al. 1984). An advantage of classification trees is that they are simp le to create, provide intuitive descriptions of complex relationshi ps, and explain variance in a dataset in a manner similar to multiple regression or an alysis of variance procedures (Death and Fabricius 2000). I used the RPART package in R (R De velopment Core Team, 2003) to build and evaluate classification trees. Trees for foredunes were construc ted with the percentage of dune lost as the response vari able and the following predictor variables: dune area (ha), dune width (m), dune height (m), island wi dth (km), gap distance (m), distance from where the eye of Hurricane Ivan made landfa ll (km). All data except distance from the

PAGE 23

12 eye of the hurricane were from measurements made prior to the hur ricane. Classification trees for secondary dunes used the same res ponse and predictor variables as trees for foredunes, but also included presence or ab sence of a foredune before Ivan. I used a cross-validation procedure to evaluate the rate of misclassification as a function of tree size (e.g., number of groupings) to select trees that were not over-fit (Breiman et al. 1984). Results Conditions before Hurricane Ivan Before Hurricane Ivan, small dunes (< 0.25 ha) were numerous comprising 80 of the 93 (86.1%) foredunes and 403 of the 484 (83. 3%) secondary dunes. Secondary dunes were larger in area but not ta ller than foredunes prior to Hu rricane Ivan (dune area t = 2.265, df = 575, p = 0.03; dune height t = 1.020, df = 575, p > 0.10; Table 2-1). Foredune area was correlated highly with dune height (r = 0.63, p < 0.01), but was not correlated with west-east location as might be expected because Hurricane Opal made landfall west of the study s ite (r = 0.06, p > 0.5). Correlations only considering foredunes 0.25 ha or larger also indicated no relati onship between dune area and position on the islands landscape (r = -0.05, p > 0.5). Similarly, dune area for secondary dunes was correlated with dune height (r = 0.65, p < 0.01) and not correlated w ith west-east location on the landscape (r = 0.02, p = 0.69). Hurricane Ivans Impact on Foredunes and Secondary Dunes Dune area for foredunes and secondar y dunes was reduced significantly by Hurricane Ivan (foredunes, t = 5.160, df = 92, p < 0.01; s econdary dunes, t = 3.267, df = 51, p < 0.01, Table 2-1). Hurricane Ivans storm surge physically removed 76.8% of the foredune area. Of the orig inal 93 foredunes measured, 67 were destroyed completely.

PAGE 24

13 The 34 small secondary dunes sampled lost 42.1% of their total area. The 61 large dunes lost 14.8% of total area. Based on the proporti on of the area occupi ed by small and large dunes on the pre-Ivan landscape, the total es timated loss of secondary dune area with Hurricane Ivan is 19.3%. Reduction in dune ar ea to these dunes was significantly less than to foredunes (t = -9.953, df = 143, p < 0.01). Univariate analyses of the relationshi p between dune structure, dune location, island width, and dune loss for foredunes indicated that all variables except gap distance were related significantly to dune loss (Table 2-2). However, many of these variables were highly correlated making th ese tests difficult to interpre t (Appendix E). In contrast, for secondary dunes only dune height, dune width, dune length, and the presence of a foredune were related to dune loss. Distan ce from the eye of Hurricane Ivan was an important predictor of dune loss in foredunes, but not in secondary dunes (Table 2-2). Regression Trees Cross validation indicated the smallest classification trees to fit data from foredunes and secondary dunes without an increa se in misclassificati on error rate each had 5 branches (Fig. 2-2). Regression tr ees for foredunes and secondary dunes indicated that a different set of structural features were linked to dune erosion for dunes on the oceanfront and bayside of the island (Fig. 2-3). Percent of dune lost in foredunes after Ivan was related to dune structure (e.g., height and width) and the dist ance from where the eye of Ivan made landfall (Fig. 2-3a). The amount of variance (R2) in dune erosion explained by the classification tree was 78.9%. The regression tree for foredunes indicat ed that structural features influencing dune erosion in foredunes changed with dist ance of the dune from the location where Ivan passed over the island (Fig. 2-3a).

PAGE 25

14 The regression tree for secondary dunes sa mpled to represent proportional area of small and large dunes on the landscape indi cated that dune erosi on of secondary dunes was related to structural features of th e dune, their position on the landscape, and the presence of foredunes (Fig. 2-3b). This tree explained 76.3% of the variance in dune erosion for secondary dunes. The tree fi rst divided dunes by the width of a dune. For wider dunes, the presence or absence of a foredune was important in determining dune erosion. Dune erosion was lowest where fore dunes were present. Where foredunes were absent, dune erosion increased as distance from where Hurricane Ivan made landfall increased. For narrow secondary dunes, is land width was the only important factor influencing dune erosion. Dune erosion was greater where the island was wide. Island width and distance from where Hurricane Iv an made landfall ar e correlated (r = 0.46) and, thus, may provide some of the same information (Appendix F). When large (N = 61) and small (N = 34) secondary dunes were analyzed separately with regression trees, results were easier to interpret. The presence of foredunes reduced dune erosion for large secondary dunes and th is was the only important variable (Fig. 24a). However, this tree explained only 19.7% of the variance in dune erosion for large secondary dunes. Erosion of small seconda ry dunes was lowest for dunes nearer to where Hurricane Ivan made landfall, and no othe r variable appeared to be important in predicting dune erosion for thes e dunes (Fig. 2-4b). Dune length and area were related negatively to dune erosion for dunes at grea ter distances from where Hurricane Ivan made landfall. This tree explained 76.6% of the varian ce in dune erosion for small secondary dunes.

PAGE 26

15 Discussion My field study and mechanistic rese arch in the laboratory (Vellinga 1982) indicate that dune structure plays an important role in resistance of dunes to storm damage. In addition, this study clearly demons trates the influence of landscape context of dunes on their vulnerability to dune erosion, including spat ial location relative to a hurricanes eye and presence of other dune stru ctures. Identificati on of features that promote resistance to storm-related erosion can aid agencies in the classification of coastal areas that are especially vulnerable to future storm events. This information also can assist in defining targ ets for coastal restoration. Larger dunes on Santa Rosa Island expe rienced less erosion than smaller dunes from Hurricane Ivan. However, the importan ce of location of the dune on the landscape, and the specific structural features of dune s important in describing amount of erosion were different for foredunes and secondary dune s, suggesting the processes that act upon dunes during storms are different depending on distance from the shoreline. Much of the erosion for foredunes probably is a result of st orm surge. Foredunes that remained after Hurricane Ivan showed signs of sediment slumping, dead or uprooted vegetation, and blowouts; all of which are common effects of storm surge, wave action, and overwash. Under these conditions height of a dune is like ly to play a key role in resistance of dunes to erosion, as demonstrated by the importance of this variable in our regression trees for foredunes. Along exposed oceanfront beaches, the magnitude of storm surge and wave action decreases with distance from the edge of the hurricanes eyewall and damage to foredunes follows a similar spatial pattern. Secondary dunes on the isla nd that experienced erosi on lost sediment along dune edges from passing storm surge and not from continual wave action. Dune erosion for

PAGE 27

16 secondary dunes likely is influenced by stor m surge from the Gulf of Mexico merging with rising water levels in the Santa Rosa Sound, located behind the island. The presence of foredunes substantially reduces erosi on of secondary dunes. This observation reinforces the importance of foredunes as bu ffers of storm surge for coastal features located further landward and for prot ection of human-made structures. One non-intuitive result of this research is that small secondary dunes that were father from the eye of the hurricane and that were on the widest part of the island were subject to more erosion that small secondary dunes nearer to the eye of Hurricane Ivan and on the narrower part of the island. Storm damage on small secondary dunes increased from west to east. The island wide ned from west to east, and the Santa Rosa Sound narrowed as the island widened. When th e flow of storm surge is confined and water is shallow, high penetration distances have been observed for washover (Morton and Sallenger 2003). I hypothesize that as th e sound became narrower, the magnitude of storm surge on the bayside of the island in creased and smaller secondary dunes were impacted more strongly, resu lting in an inverse relations hip between storm damage and distance from the hurricane and an inverse relationship between storm damage and island width. Previous research on coastal dune systems ha s suggested that dune systems exist in two opposing states: one where dune structur e and vegetation comm unities are arranged by environmental gradients generated from normal wind and wave activity and one dominated by periodic but high levels of di sturbance (e.g., hurricane s and tropical storms; Synder and Boss 2002; Stallins and Parker 2003). I believe that coastal dunes on Santa Rosa Island are beginning to shift towards the latter state, though assessment of dune

PAGE 28

17 erosion after additional storms is needed to evaluate this statement. Prior to hurricane Opal in 1995, the islands s horeline contained continuous fo redunes (Stone et al. 2004). Repeated hurricane activity has eroded or destroyed many foredune structures. My analysis indicates that foredunes are importa nt in protecting sec ondary dunes and thus, further storm impacts may begin to aff ect secondary dunes more severely. The consequences of this changing coastal la ndscape are large for maintenance of humandeveloped infrastructure, success of restorat ion projects, and conservation of wildlife species that depend on coastal dune habitat.

PAGE 29

18 Table 2-1. Means and standard errors for st ructural variables meas ured to explain dune erosion in foredunes and secondary dunes on Santa Rosa Island from Hurricane Ivan. Differences between means before and after Hurricane Ivan were compared using paired-t tests adju sted with Bonferr onis correction for multiple tests ** p < 0.05. Dunes were sub-sampled to represent proportional ar ea of large (83.6%) and small (16.4%) dunes on the landscape (N = 52 with 34 small dunes and 18 large dunes). Variable Foredunes Secondary Dunes1 Before Ivan (N = 93) After Ivan (N = 93) Before Ivan (N = 52) After Ivan (N = 52) Dune area (ha) 0.14 (0.03)** 0.05 (0.01) 0.37 (0.09)** 0.26 (0.07) Dune height (m) 2.84 (0.15)** 0.89 (0.16) 3.28 (0.18)** 2.81 (0.15) Dune length (m) 42.2 (5.8)** 12.1 (2.9) 80.6 (14.5)** 64.3 (11.3) Dune width (m) 29.9 (2.6)** 10.7 (2.2) 56.6 (6.2)** 44.3 (6.4) Gap distance (m) 95.6 (25.9) NA 50.8 (6.8) NA

PAGE 30

19 Table 2-2. Statistics for eval uation of dune character istics as predictors of dune loss as a result of Hurricane Ivan. Variables we re assessed along a 20-km stretch of Santa Rosa Island, FL prior to the hurri cane. The significance of a variables role as a predictor of dune loss wa s evaluated using univariate linear regression or a two sample t-test when examining the presence of absence of a foredune before a secondary dune. Hypothe ses of no association were rejected at p < 0.05. Variable t p-value R2 Foredunes (N = 93) Dune area (ha) 5.21 <0.01 0.21 Dune height (m) 5.43 <0.01 0.25 Dune length (m) -2.08 0.04 0.05 Dune width (m) -5.03 <0.01 0.22 Gap distance (m) 0.40 0.68 0.01 Island width (km) 3.08 <0.01 0.09 Distance from Ivans eye (km) 6.09 <0.01 0.25 Secondary Dunes1 (N = 52) Dune area (ha) -1.65 0.10 0.05 Dune height (m) -2.39 0.02 0.10 Dune length (m) -2.35 0.02 0.10 Dune width (m) -3.50 0.01 0.20 Gap distance (m) 0.78 0.40 0.02 Island width (km) 1.05 0.30 0.02 Distance from Ivans eye (km) -0.24 0.81 0.01 Presence / absence of foredune 2.47 0.02 NA Dunes sub-sampled to represent proportiona l area of large (83.6%) and small (16.4%) dunes on the landscape (N = 52 with 34 small dunes and 18 large dunes).

PAGE 31

20 Fig. 2-1. Map of Santa Rosa Island, FL The study area encompasses the section between Navarre and Fort Walton Beach.

PAGE 32

21 a) b) Fig 2-2. Cross validation relative error for regression trees for (a) foredunes and (b) secondary dunes to explain dune loss from Hurricane Ivan in relation to measured predictor variables. I used the 1-SE rule (Breiman et al. 1984) to identify regression trees that had the sm allest number of branches but were closest to the overall minimum misclassi fication error (dotted line). Arrows point to the best sized regression tr ee for each dune type. The complexity parameter, cp, represents a balance be tween the complexity of a tree (i.e., more branches) and the costs of utilizing a simpler tree.

PAGE 33

22 a) b) Fig. 2-3. Regression trees relating percenta ge of dune lost from Hurricane Ivan for (a) foredunes (N = 93) and (b) secondary dune s (N = 52) to physical features of dunes, spatial location of dunes with respect to where Hurricane Ivan made landfall, and width of island. Data for secondary dunes are based on sampling of small (<0.25 ha) and large dunes ( 0.25 ha) according to their proportional area on the landscape. Numbers at the e nds of terminal nodes are the average percentage of dune lost for all observati ons in that group. N is the number of observations within that group.

PAGE 34

23 a) b) Fig. 2-4. Regression trees relating percenta ge of dune lost from Hurricane Ivan for (a) secondary dunes 0.25 ha (N = 61) and (b) secondary dunes <0.25 ha (N = 34) to dune features, spatia l location, and island widt h. Numbers at the ends of terminal nodes are the av erage percentage of dune lost for all observations in that group. N is the number of observations within that group.

PAGE 35

24 CHAPTER 3 INFLUENCE OF HABITAT AND LANDSCAPE FEATURES ON SPATIAL DISTRIBUTION OF SANTA ROSA BE ACH MICE IN TWO DUNE HABITATS BEFORE AND AFTER A HURRICANE Introduction Identification and protection of habitat ar e critical for species conservation and an integral part of many conservation programs. GAP analysis, for example, utilizes information on land cover and predicted species di stributions to identify habitats that are poorly represented in reserves (Flather et al. 1997). Habitat su itability index (HSI) models establish relationships between a spec ies distribution and habitat variables to create an index of suitable habitat that can be used to evaluate suitability of other areas for habitat management or protection (UWF WS 1981). To aid in species recovery, the Endangered Species Act provides for designa tion of critical habitat, which is the geographic area that contains physical and biological featur es necessary for conservation of the species (16 U.S.C. 1531 et seq.). Ho wever, the effectiveness of critical habitat designation is controversial as critical habitat often is defi ned using limited data or only anecdotes, or found to be not determinable (Hoe kstra et al. 2002; Tayl or et al. 2005). Habitat models are a common tool used to examine the role of habitat features in explaining the spatial distribution, density, or diversity of species occurring on a landscape and these models aid in the develo pment of conservation strategies (Segurado and Araujo 2004; Guisan and Thuiller 2005). In corporation of spatial structure of habitat (e.g., size, shape and spatial distribution of habitat patches) along with traditional assessments of habitat quality has improve d the functionality of models (Cox and

PAGE 36

25 Engstrom 2001). However, one problem with application of habitat models to conservation problems is that their conc lusions are based upon a limited range of conditions because they generally are deve loped over short time periods (Pearce and Ferrier 2000). Habitat availability and quality can shift rapidly with stochastic events (VanHorne et al. 1997; Carlsson and Kindvall 2001), and key featur es that determine spatial distribution or population density could change. Designation of protected habitats often does not consid er impacts of environmental stochasticity on distribution and persistence of species even though dist urbance may influence habitat turnover and ultimately impact population persistence thr ough impacts on habitat (Oli et al. 2001; Jonzen et al. 2004; Frank 2005; Schrott et al. 2005). We demo nstrate this issue with an analysis of beach mouse habitat along the Gulf Coast of Florida before and after Hurricane Ivan which made landfall in September 2004. Coastal dunes are among the most dynamic and threatened habitats world-wide (Martinez et al. 2005). Gulf Coast populations of beach mice comprise 5 subspecies, all of which are subject to extreme stochastic events in the form of tropical storms and hurricanes (e.g., Hurricanes Opal, 1995; Iva n, 2004; Dennis, 2005). Four of these subspecies are listed as threatened or endangered (Potter 1985 ; Milio 1998). The remaining subspecies, the Santa Rosa beach mouse ( Peromyscus polionotus leucocephalus ), is not yet listed beca use its geographic range includes several federally managed lands (Gore and Schaffer 1993). All su bspecies suffer from severe habitat loss from destruction of coastal sand dunes by de velopment, and this habitat loss is exacerbated greatly by hurricanes (Swilling et al. 1998). Optimal habitat for beach mice is believed to be frontal dune habitat with sparse vege tative cover of sea oats ( Uniola

PAGE 37

26 paniculata ) adjacent to the high tide line (USF WS 1987). Mice also occur in scrub dunes, which are located farther from the beach and are characterized by increased dominance of woody vegetation. These dune s provide refugia for mice during and immediately after storms but are viewed as marginal habitat because of lower population density in this habitat (Swilling et al. 1998). Three subspecies of beach mice on the Gulf Coast are covered under the same federal recovery plan. This plan calls for protection of dune communities within 152 m (500ft) of the high tide line and includes all frontal dunes but excludes scrub habitat in most ar eas (Potter 1985; Swilling et al. 1998). The St. Andrews beach mouse ( P. p. peninsularis ) is protected under it s own recovery plan, which states designation of cr itical habitat is not necessary for conservation of this species (Milio 1998). I examined impacts of Hurricane Ivan on the structure of fron tal and scrub dunes, compared occupancy patterns of beach mice in these two habitats, and determined how these occupancy patterns changed after the hurri cane. I also developed habitat models for predicting dune occupancy by Santa Rosa b each mice and evaluated whether the factors that influenced patterns of habitat occupanc y were similar for frontal (optimal) and scrub (marginal) habitats. I examined whether pr edictors of habitat o ccupancy changed after the hurricane. My study demonstrates pr oblems associated with narrowly defining critical habitat as optimal habitat, part icularly in systems characterized by high stochasticity. Methods Study Area and Habitat Mapping The study was conducted on Santa Rosa Isla nd, a barrier island approximately 46km long and 0.5-km wide, located in the Gulf of Mexico near Fort Walton Beach, FL

PAGE 38

27 (30' N, 81' W). My study area incorpor ated a 15-km section of the island on Eglin Air Force Base (EAFB) and a 10-km secti on of the island on Gulf Island National Sea Shore (GINS). Dune habitat was similar in these two areas and bot h sections contain a single paved road and only a few structures. Frontal dunes were orient ed parallel to high tide line and were dominated by sea oats ( Uniola paniculata ), cakile ( Cakile spp.), beach morning glory ( Ipomoea imperati) and beach elder ( Iva imbricata) and various woody species in the absence of frequent disturba nce. Scrub dunes were located on the bayside of the island and woody species dominate sc rub habitat, including false rosemary ( Ceratiola ericodes ), woody goldenrod ( Chrysoma pauciflosculosa ), scrubby oaks ( Quercus geminata ) and sand pine (Pinus clausa ). The area between frontal and scrub dunes consisted of gently rolling grassla nds interspersed with densely vegetated wetlands. EAFB dunes were mapped in the field befo re and after Hurricane Ivan by recording their perimeters using a TRIMBLE GPS unit a nd then differentially corrected for < 1 m accuracy. GINS dunes were mapped only after the hurricane. Data were incorporated into a cover layer in ArcView 3.2 (ESRI 1996). Dune Occupancy I surveyed for presence of beach mice in all frontal (N = 15) and scrub (N = 61) dunes equal to or larger than 0.25 ha on EAFB before Hurricane Ivan (June September 2004) and after the hurricane (October 2004 December 2004). Frontal dunes (N = 15) on GINS also were surveyed for beac h mice after Hurricane Ivan (December 2004 February 2005). Presence of beach mice in each dune was determined with tracking tubes that register footprints of mice that enter the tube. Tracking with tubes is less dependent upon weather and less labor intens ive than live trapping and, therefore,

PAGE 39

28 particularly useful for large scale survey s of distribution (Mab ee 1998; Glennon et al. 2002). Tracking tubes were construc ted with PVC pipe (33-cm long x 5-cm diameter) and elevated 5-7 cm off the ground to prevent access by ghost crabs ( Ocypode quadrata ). Dowels placed at either end of the tube allowe d mice, but not crabs, to climb to the tube. A paper liner was inserted into the bottom of each tube, and the tube was baited in the middle with rolled oats. Felt inkpads located at each end of the pa per liner were coated with a 2:1 mineral oil and carbon power solution (Mabee 1998). Hispid cotton rats ( Sigmodon hispidus ) leave footprints that are substantia lly larger than footprints of Santa Rosa beach mice. No other small rodents occur on undeveloped portions of the island (Gore and Schaffer 1993). Dunes less than 0.50 ha received eight tubes; dunes > 0.50 ha < 2.00 ha received 16 tubes; and dunes greater than > 2.00 ha received 32 tubes. Tracking tubes were placed at 15-m intervals along transects that bega n and ended at the dunes boundary and ran parallel to the long axis of the dune. The star ting point for the first transect was selected randomly and, when more than one transect was needed, parallel transects were established 15 m apart. During each tracking session, tracking tubes remained in a dune for five nights and were checked after each night. For many species, probability of detection during presence/absen ce surveys is less than one resulting in underestimates of occupa ncy, biased parameter estimates for habitat models, and incorrect estimates of populat ion persistence (Gu and Swihart 2004; Kry 2004). Therefore, I used recent statistical appr oaches for analysis of site occupancy that build on traditional capture-recapture methods and used repeated cen suses to calculate

PAGE 40

29 detection probability ( p ) and to estimate the proportion of sites that are occupied ( ) after accounting for detectability (MacKenzie et al 2002). To estimate detection probability within each habitat type, I re-sampled a random subset of scrub dunes (N = 30) with tracking tubes three times after initial prestorm surveys, and I resurveyed another random subset of scrub dunes (N = 30) and all frontal dunes on EAFB three times after initial post-storm surveys. Each repeat survey was conduc ted over 5 nights following the sampling protocol described above. Predictor Variables: Vegetation Cover and Landscape Structure I measured vegetation cover and dune he ight on scrub dunes before and after Hurricane Ivan. Surveys for th ese variables were not completed on frontal dunes before the hurricane hit and, therefore, data on these variables were analyzed for frontal dunes only post-hurricane. Vegetation cover was qua ntified using the line-intercept method (Bonham 1989) along three 50-m transects place d 20 m apart and perpendicular to the long axis of each dune. I recorded distances (cm) that sea oats, other herbaceous vegetation, woody vegetation, and open sand oc cupied along each transect and divided the distance for each cover class by total length of the transect to obtain percent cover for each cover class. I averaged data for the thr ee transects prior to analysis. Sea oats and many herbaceous species are important food sources for beach mice (Moyers 1996). Amount of open sand may be important fo r burrow construction and woody vegetation may stabilize dunes during storms and provides food and cover for foraging. I recorded dune height (m) by measuri ng height every 15 m al ong the long axis of each dune using a telescoping pole and then averag ed all values for each dune. Height of a dune may influence perception of dune habitat by beach mice moving through the landscape or influence the imp act of storm surge on dunes. I calculated dune area and

PAGE 41

30 amount of dune habitat surrounding each dune in ArcView 3.2 from the GIS database created from field mapping of dunes. I also calculated the dist ance to the nearest occupied dune as a measure of isolation. Habitat area may influence size of local populations (Hanski 1994). We used the BU FFER function in ArcView 3.2 to estimate the total area of dune habitat surrounding each dune at the foraging (200 m) and dispersal (1 km) scales of beach mice (Bird 2002; Swilling & Wooten 2002). The east-west coordinate (UTM) at the center of each dune was included in habitat models to examine how spatial location relative to the eye of Hurricane Ivan influenced dune occupancy by mice. The eye of the hurricane passed approxima tely 75 km west of the western end of GINS and 100 km west of the western end of EAFB. Occupancy Models I created and ranked a series of models with the program PRESENCE to identify variables that influenced distribution of beach mice in frontal and scrub habitats (MacKenzie et al. 2003). Correlations among variables were examined and correlated variables ( r > 0.60) were not included in the same model (Welch and MacMahon 2005), or if correlated variables were used in the same model, a regression was conducted for the two variables and residua ls were included in the model as an independent measure of one of the variables (C ooper & Walters 2002). Correlate d variables requiring this approach were pre-hurricane dune habitat within 1 km and pre-hurricane east-west coordinate for scrub dunes (r = 0.69), posthurricane dune habitat within1 km and posthurricane east-west coordinate for scrub dunes (r = 0.70), post-hurricane dune habitat within 200 m and post-hurricane distance to ne arest occupied dune for frontal dunes (r = 0.62), and post-hurricane dune habitat with in 1 km and post-hurricane east-west coordinate for frontal dunes (r = 0.69).

PAGE 42

31 Fifty-six candidate models were evaluate d for scrub dunes using a combination of variables measured before Hurricane Ivan and similar models were created with posthurricane data. The first eight base models included a combin ation of patch-level features (e.g., dune area, % cover of woody vegetati on, % cover of herbaceous vegetation, dune height). An additional 24 models were cr eated by adding distance to nearest occupied dune, the 200-m habitat buffer or 1-km habita t buffer to the original base models. Finally, I created another 24 models by includ ing the east-west coordinate in the base model + landscape context models. I developed 40 candidate models for frontal dunes on EAFB and GINS after Hurricane Ivan. All frontal dunes were occupi ed before Hurricane Ivan, so no model was created for this period. To reduce risk of an over-parameterized model, I restricted the total number of variables in a model to three. The first eight base models were the same as in scrub habitat (i.e., patchlevel features). An additiona l 32 models were created by including distance to nearest occupied dune, 200-m habitat buffe r, 1-km habitat buffer, or spatial coordinate to base models. I also modeled post-hurricane occupancy of frontal and scrub dunes on EAFB with pre-hurricane conditions to assess the role of prehurricane conditions on post-hurricane occupanc y. Models were created using the same procedure as described above. I used an Akaike Information Criterion (AICc) corrected for small sample bias to select the best model and rank the rema inder. I present AIC differences ( i = AICci minimum AICc), so that the best model has i = 0 (Burnham & Anderson 2002). Models with i 2 are considered competitive models. I also include Akaike weight (wi), which indicates relative lik elihood that model i is the best model. The relative importance of

PAGE 43

32 each habitat variable ( wsum) was obtained by summing wi for all models that contained this variable (Burnham & A nderson 2002). I performed model averaging to obtain parameter estimates and unconditional standard e rrors for each habitat variable of interest to reduce the bias of estimating paramete r effects from a single model (Burnham and Anderson 2002). When the confidence inte rval around a model-averaged parameter estimate is > 0, an increase in the variable significantly increases the probability of occupancy, and a value < 0 indicates that an increase in the variable decreases the probability of occupancy (Buskirk 2005; Mazero lle et al. 2005). Estimated probability of detection ( p) and overall occupancy rate ( ) also were obtained us ing this approach. Results Hurricane Impacts on Habi tat Availability at EAFB Hurricane Ivan significantly re duced mean area of both types of dunes (Table 3-1), but frontal dunes lost a much greater proportion of area. Stor m damage resulted in a loss of 68.2% of the total area of frontal dunes surveyed for beach mice, including complete destruction of four dunes. No scrub dunes we re destroyed entirely but the total area of scrub dunes surveyed for beach mice was reduced by 14.8%. Dune height also was reduced significantly for both dune types (Table 3-1). The amount of habitat within 200 m and 1km of a dune was reduced significantly for scrub dunes but not frontal dunes. However, frontal dunes alrea dy had little habitat within 200 m prior to the hurricane (Table 3-1). Dune Occupancy Beach mice were detected in 100% of frontal dunes and 72.1% of scrub dunes prior to the hurricane, and in 51.8% of front al dunes and 73.8% of scrub dunes after the hurricane. Probability of detection was high in all surveys. Site-occupancy models

PAGE 44

33 suggest that before the hurricane 75.1 5.5% (model-averaged estimate unconditional SE) of scrub dunes were occupied, with a de tection rate of 88.6 5.6%, and after the hurricane 78.6 4.9% of sites were occupi ed, with a detection rate of 90.1 3.1%. Differences in occupancy of scrub dunes be fore and after the hurricane were not significant (t = 0.5, df = 60, p > 0.10). Occ upancy in frontal dunes dropped to 59.7 5.1%, with a detection rate of 89.8 5.5% afte r the hurricane, and occupancy in frontal dunes was significantly lower than occupanc y of scrub dunes (t = 1.8, df = 42, p < 0.05). Habitat Models A combination of patch-level and landscapelevel features ranked high in models of occupancy of scrub dunes before and after the hurricane (Table 3-2 and 3-3). The strongest model for scrub dunes before the hurricane included dune area, percent woody vegetation cover, and amount of dune habita t within 200 m. No other models were competitive. After the hurricane, the same model was the strongest; however, the Akaike weight was much lower and several additional models were competitive (Table 3-3). All models with i 2 contained some combination of the variables in the best model except dune height and total herbaceous cover were included in several models. Ranking of variables based on the sum of their Akakie we ights revealed that amount of dune habitat within 200 m of scrub dunes was the most importa nt variable in expl aining probability of occupancy of scrub dunes by mice before and after the hurricane (T able 3-3), followed closely by dune area, and pe rcent woody vegetation cover. Before and after the hurricane, probability of beach mice occ upying a scrub dune increased as amount of habitat surrounding the dune w ithin 200 m increased and dune area increased (Table 3-3). Occupancy of scrub habitat by beach mice al so appeared to increase with increasing cover of woody vegetation before and after the hurricane, but this relationship was not

PAGE 45

34 statistically significant. T op models of post-hurricane occupancy in scrub dunes using pre-hurricane conditions retained the same suite of predic tor variables (Table 3-2). The strongest model for occupancy of fr ontal dunes after Hurricane Ivan included percent woody vegetation cover a nd distance to nearest occupi ed dune (Table 3-2). In contrast to scrub habitat, the amount of habitat surrounding a dune within 200 m was not a factor in any competitive models. The likelihood of occupancy increased with increasing cover of woody vegetation and this variable was the top ranked variable in models of occupancy (Table 3-3). Increasi ng distance to the nearest occupied dune also appeared to reduce the probability of occupanc y after the hurricane but this relationship was not statistically significant (Table 3-3). Dune height was the third ranked variable and an increase in dune height appears to increase occupancy by beach mice in frontal habitats but also was not st atistically significant (Table 3-3). When post-hurricane occupancy of frontal dunes on EAFB was modeled with variables related to the structural and landscape context of dunes prior to the hurricane, dune height and distance to the nearest occupied dune were the most important predictors of occupancy (Tables 3-2 and 3-3). The likelihood of occupancy of fr ontal dunes by beach mice after the hurricane increased with a greater dune height prior to the hurricane, and a gr eater distance to the nearest occupied dune prior to the hurricane decreased the likelihood of occupancy after the hurricane (Table 3-3). Models for occ upancy of frontal dunes, with pre and posthurricane habitat data, had a better fit than models of scrub dunes (Table 3-2). Discussion Frontal dunes near the hi gh tide line are subjected to major impacts during hurricanes. Prior to Hurricane Opal ( 1995), frontal dunes ran relatively continuously along the entire length of Sant a Rosa Island (Stone et al. 2004). This hurricane and

PAGE 46

35 subsequent tropical storms fr agmented frontal dunes. St orm surge from Hurricane Ivan removed close to 70% of the remaining frontal dunes. In contrast, no scrub dunes, which are located on the bay side of the island, were completely lost with Hurricane Ivan and reduction in area of scrub dunes occurred al ong dune edges from passing storm surge. Distance from the eye of the hurricane influe nced dune lost for fr ontal and scrub dunes along this portion of Santa Rosa Island (Chapt er 2). Tropical storms and hurricanes are predicted to be increasing in number and se verity (Emanuel 2005). Frontal habitat for beach mice will continue to be fragmented and removed if the interval between hurricanes and other tropical storms remain s shorter than the time required for dunes to develop. In contrast, my results suggest th at the amount and configuration of scrub dunes on this barrier island may remain relatively consistent. However, as buffering capacity provided by frontal dunes is lost, scrub dunes ma y suffer more impacts. Also, Hurricane Ivan was a category 3 hurricane; stronger hur ricanes could have greater impacts. Predictors of occupancy for beach mice in frontal habitat after the hurricane were closely tied to local habitat features (e .g., percent cover of woody vegetation and dune height) and proximity to other occupied dunes. Optimal beach mouse habitat generally is described as tall frontal dune s vegetated by sea oats and ot her herbaceous plants (Holler 1992). My habitat model indicates that woody ve getation cover also is important to mice, at least during hurricane cycles. Foraging experiments demonstrate that mice consume more seeds under vegetation cover than in the open (Bird 2002). Woody plants provide cover for foraging, serve as a food source for mice, and also may promote dune stability during storms (Moyers 1996; Musila et al. 2001 ). Similarly, dune height may be an important factor in dune stabil ity, particularly in preventing overwash by storm surge.

PAGE 47

36 Beach mice also are semi-fossorial and an increase in dune height may facilitate conditions appropriate for burrow construc tion. For frontal dunes on EAFB where the impact was severe, dune height prior to th e hurricane was a significant predictor of posthurricane occupancy, but after the hurricane th e importance of this variable was not as clear. Isolation explained post-hur ricane occupancy of frontal habitat, whether modeled with preor post-hurricane habi tat conditions. This observation likely reflects the history of disturbance and loss of fr ontal habitat on this island. Beach mice occupying frontal dunes prior to Hurricane Opal experienced a fairly continuous ha bitat where habitat quality might have been determined largel y by resource availabil ity or appropriate burrow conditions. The current fragmented frontal dunes are too small to support separate populations of beach mice, but ra ther may serve as resource patches for mice moving among dunes. The most important predictors of occ upancy before and after the hurricane for beach mice in scrub habitat were landscape f eatures related to habitat amount (i.e., dune area and amount of surrounding hab itat). Predictors of occupancy in scrub habitat were similar before and after the hurricane, presum ably because the impact of the hurricane on the structure of these dunes was minimal. Woody vegetation also may play a role in occupancy of scrub dunes by beach mice, but this relationship is not as clear as in frontal dunes. The amount of dune habitat surroundi ng scrub dunes is greater than for frontal dunes, which may indicate less isolation for these dunes. The importance of surrounding dune habitat for occupancy of scrub may reflect reduced habitat quality in scrub dunes. Alabama beach mice travel further distances to forage in scrub habitat than in frontal

PAGE 48

37 habitat during the winter and spring (Sn eckenberger 2002). If habitat quality and population density are lower in scrub dunes, la rger areas may be required to maintain mouse populations. Dune restoration after hurricanes primarily has focused on re-establishment of sea oats, which produces a lattice of rhizomes that accumulate sand and also is an important food plant for beach mice. My results suggest that restoration programs for frontal dunes also should include re-establishment of woody plants and promote increases in dune height. Beach mice also should benefit from re storation programs that reduce isolation of frontal dunes. The results of my study sugge st that optimal habitat fo r beach mice differs under different environmental condi tions. Lower occupancy of scrub habitat than frontal habitat by beach mice prior to the hurricane, and documentation of lower density in scrub habitat from other studies, sugge st that scrub habitat could be lower quality than frontal dunes under pre-hurricane conditions, though dens ity is not always a good indicator of habitat quality (Van Horne 1983). However, pers istence of scrub habi tat and maintenance of occupancy levels by beach mice through the hurricane in this habitat versus the severe loss of habitat and significant reduction in occ upancy of frontal habi tat suggest scrub is an essential habitat. Scr ub was an important refugia habitat for Alabama beach mouse populations during Hurricane Opal and a sour ce of dispersing i ndividuals after the hurricane (Swilling et al. 1998). Re-colonization of frontal habitats by beach mice after Hurricane Opal occurred within nine months (Swilling et al. 1998). I observed beach mouse tracks on previously unoccupied front al dunes in March 2005, approximately six months after Hurricane Ivan. These mice may have dispersed from scrub or neighboring

PAGE 49

38 frontal dunes. Given the inevitable loss of frontal dune s with hurricanes, incorporation of scrub habitat into conservation efforts for Gu lf Coast beach mice is warranted to ensure long-term population persistence. Scrub habita t, even as marginal habitat, will improve population persistence and lessen extinction risk as frontal habitat is further removed. The role of stochasticity and uncerta inty in management outcomes has been explored extensively with respect to impact s on population size and persistence of species of economic or conservation concern (Ellner and Fieberg 2003). Our study demonstrates the need to incorporate these factors in habitat planning and protection. Habitat availability for species in dynamic landscapes can change quickly and additional habitats may become critically important after st ochastic events (Car lsson and Kindvall 2001; Biedermann 2004). When the dynamics of la ndscape and population are not understood, protection of habitat should follow a conser vative approach. Failure to consider and protect habitats required under different environmental conditions may exacerbate the impacts of habitat loss and change on extinction risk.

PAGE 50

39 Table 3-1. Means and standard errors for st ructural and vegetation variables measured for modeling occupancy of frontal a nd scrub habitat by Santa Rosa beach mice on Eglin Air Force Base (EAFB) and Gulf Islands National Seashore (GINS) on Santa Rosa Island, FL. Variab les were assessed before and after Hurricane Ivan made landfall on 18 September 2005. Differences between means before and after Ivan were compared for dunes on EAFB using paired t-tests adjusted with Bonfferonis co rrection for multiple tests. ** p < 0.05. Scrub Dunes Mean ( SE) Frontal Dunes Mean ( SE) Variable1 EAFB before hurricane (N = 61) EAFB after hurricane (N=61) EAFB before hurricane (N=15) EAFB after hurricane (N=11) GINS after hurricane (N=15) Dune area (ha) 1.82 (0.38) 1.55 (0.37) ** 0.59 (0.09) 0.26 (0.06) ** 0.15 (0.03) Dune height (m) 4.64 (0.3) 3.32 (0.17 ) ** 4.01 (0.33) 3.13 (0.35) ** 3.24 (0.20) Dune habitat within 200 m (ha) 2.21 (0.28) 1.71 (0.19) ** 0.29 (.09) 0.22 (0.08) 0.24 (0.05) Dune habitat within 1 km (ha) 12.73 (1.19) 11.29 (1.03) ** 8.45 (1.48) 8.67 (1.44) 1.94 (0.29) Distance to nearest occupied dune (m) 176.1 (38.3) 174.2 (37.6) 219.2 (54.7) 161.9 (25.9)2 126.2 (39.4) Percent woody cover 19.6 (1.6) 19.9 (1.6) no data 6.5 (1.4) 3.3 (2.1) Percent total herbaceous cover 14.1 (1.4) 7.4 (0.8) ** no data 24.5 (2.9) 19.2 (2.3) East-West Coordinate (UTM) (m) 524519 (653) 524519 (653) 522959 (1337) 524208 (879) 506117 (1709) 1 Dune perimeter was correlated highly (p < 0.01) with dune area and was omitted from analyses. 2 Distance to nearest occupied dune dropped after the hurrica ne because the four dunes that were destroyed were very isolated (mean distance to nearest oc cupied dune for those dunes = 459.5 m). Pre-hurricane mean for distance to nearest occupied dune for the 11 dunes to survive Ivan = 131.7 m.

PAGE 51

40 Table 3-2. AIC-based selecti on of site occupancy models of dune occupancy for Santa Rosa beach mice in frontal and scrub dune habitat. K = the number of explanatory variables plus 1, i = AICci minimum AICci, wi = Akaike weights. Models with i 2 are presented. Habitat and conditions Period of occupancy Location Model K i wi R2a Scrub prehurricane Pre-hurricane EAFB / N =61 Dune area, habitat within 200 m, percent woody cover 4 0.00 0.42 0.242 Scrub posthurricane Posthurricane EAFB / N = 61 Dune area, habitat within 200 m, percent woody cover 4 0.00 0.18 0.264 Dune area, habitat within 200 m 3 0.25 0.16 Dune height, percent woody cover, habitat within 200 m 4 0.50 0.14 Percent woody cover, habitat within 200 m 3 0.94 0.12 Percent total herbaceous cover, habitat within 200 m 3 1.63 0.08 Dune height, habitat within 200 m 3 1.69 0.08 Dune area, percent total herbaceous cover, habitat within 200 m 4 1.98 0.07 Scrub prehurricane Posthurricane EAFB / N =61 Dune area, habitat within 200 m, percent woody cover 4 0.00 0.26 0.287 Dune habitat within 200 m 2 0.70 0.18 Percent woody cover, dune habitat within 200 m 3 1.53 0.12 Dune area, dune habitat within 200 m 3 1.60 0.12 Dune height, dune habitat within 200 m, percent woody cover 4 1.91 0.10 Dune area, percent total herbaceous cover, dune habitat within 200 m 4 1.91 0.10 Frontal posthurricane Posthurricane EAFB; GINS / N=27 Percent woody cover, distance to nearest occupied dune 3 0.00 0.58 0.467 Frontal prehurricaneb Posthurricane EAFB / N = 15 Dune height, distance to nearest occupied dune 3 0.00 0.73 0.489 a There is no R2 analogue for patch occupancy models, instead we used a max-rescaled R2 value as an approximate measure of strength of association for the top model in each candidate set (Nagelkerke 1991). All top models provided a significantly better fit than a base model w ith no environmental predictors (p < 0.05). b This model was developed with data on the structure and landscape context of dunes and does not include vegetation variables that are in other models.

PAGE 52

41 Table 3-3. Relative importance (wsum), model-averaged parameter estimates, and unconditional standard errors for variable s used to model occupancy for beach mice in frontal and scr ub habitat before and af ter Hurricane Ivan. Wsum was estimated by summing Akakie weights (wi) of all models with a variable of interest. **Confidence interv als do not contain 0 and indicate variable significantly influences occupancy. Habitat Wsum Parameter Estimate SE 90% C.I. Scrub pre-hurricane habitat Pre-hurricane occupancy Dune area (ha) ** 0.679 0.709 0.418 0.022 1.396 Dune height (m) 0.165 0.029 0.039 -0.035 0.093 Percent cover woody vegetation 0.642 1.717 1.221 -0.292 3.726 Percent cover herbaceous 0.128 0.003 0.182 -0.296 0.302 Distance to nearest occupi ed dune (m) 0.046 0.028 0.035 -0.03 0.086 Dune habitat within 200 m (ha) ** 0.888 0.719 0.351 0.142 1.296 Dune habitat within 1 km (ha) 0.017 0.003 0.007 -0.009 0.015 East coordinate (m) 0.000 Scrub post-hurricane habitat Post-hurricane occupancy Dune area (ha) ** 0.536 0.548 0.319 0.023 1.073 Dune height (m) 0.257 0.081 0.106 -0.093 0.255 Percent cover woody vegetation 0.498 1.024 0.943 -0.527 2.575 Percent cover herbaceous 0.197 -0.106 0.714 -1.281 1.069 Distance to nearest occupi ed dune (m) 0.023 0.009 0.012 -0.011 0.029 Dune habitat within 200 m (ha) ** 0.864 1.089 0.574 0.145 2.033 Dune habitat within 1 km (ha) 0.065 0.006 0.007 -0.006 0.018 East coordinate (m) 0.000 Scrub pre-hurricane habitat Post-hurricane occupancy Dune area (ha) ** 0.524 0.516 0.298 0.026 1.006 Dune height (m) 0.162 0.036 0.045 -0.038 0.11 Percent cover woody vegetation 0.498 1.218 1.048 -0.506 2.942 Percent cover herbaceous 0.163 -0.305 0.377 -0.925 0.315 Distance to nearest occupied dune (m) 0.000 Dune habitat within 200 m (ha) ** 0.936 1.022 0.461 0.264 1.780 Dune habitat within 1 km (ha) 0.010 0.129 0.093 -0.024 0.282 East coordinate (m) 0.000 Frontal post-hurricane habitat Post-hurricane occupancy Dune area (ha) 0.141 0.383 0.629 -0.652 1.418 Dune height (m) 0.652 1.027 0.893 -0.353 2.407 Percent cover woody vegetation** 0.969 16.355 9.854 0.146 32.564 Percent cover herbaceous 0.014 0.009 0.033 -0.045 0.063 Distance to nearest occupi ed dune (m) 0.769 -2.627 1.851 -5.672 0.418 Dune habitat within 200 m (ha) 0.032 0.013 0.068 -0.106 0.132 Dune habitat within 1 km (ha) 0.048 -0.055 0.089 -0.201 0.091 East coordinate (m) 0.071 0.006 0.009 -0.009 0.021 Frontal pre-hurricane habitat Post-hurricane occupancy Dune area (ha) 0.067 0.032 0.098 -0.129 0.193 Dune height (m) ** 0.730 0.485 0.151 0.237 0.733 Distance to nearest occupied dune (m) ** 0.828 -0.015 0.008 -0.028 -0.002 Dune habitat within 200 m (h a) 0.053 0.093 0.121 -0.106 0.292 Dune habitat within 1 km (ha) 0.025 0.050 0.060 -0.048 0.148 East coordinate (m) 0.000

PAGE 53

42 CHAPTER 4 CONCLUSIONS AND CONSERVATION IMPLICATIONS Habitat loss and fragmentation from coastal development and hurricanes are believed to be major threats to the long-term population pers istence of Gulf Coast beach mice (Holler 1992; Oli et al. 2001). Frontal dun es are protected as critical habitat for Gulf Coast beach mice, but they are disturbed greatly by hurricanes (Chapter 2). Scrub dunes (also known as secondary dunes), which are not currently protected under federal recovery plans, are impacted less by hurrica nes and have been proposed to serve as important refugia habitat for beach mice dur ing hurricanes (USFWS 1987; Swilling et al. 1998). Although restoration techniques exis t to promote regeneration of physical structure of coastal dunes after storms (M iller et al 2001; 2003) understanding of the features that confer resistance against storm er osion is limited. Also, prior to this study, little quantitative information was availabl e on: 1) how hurricanes impact habitat availability for beach mice, 2) utilization of scrub habitat by beach mice, 3) habitat features that predict occupa ncy of frontal and scrub dunes by beach mice, and 4) relative impacts of hurricanes on beach mouse occupancy of frontal versus scrub dunes. My study contributes to filling these gaps. Dune Erosion and Loss of Beach Mouse Habitat Frontal dunes received much greater impacts from Hurricane Ivan than scrub dunes, and larger dunes in both frontal and sc rub habitat experienced less erosion than small dunes Structural features that conferred re sistance against storm erosion differed for frontal and scrub dunes, suggesting that different processes act upon these two dune

PAGE 54

43 types. For frontal dunes, tall and wide dune s experienced the leas t amount of erosion from Hurricane Ivans high storm surge. Dune erosion for secondary dunes was influenced by storm surge from the Gulf a nd probably also by rising water levels in the Santa Rosa Sound, located behind the island. Secondary dunes experience less erosion when located behind a frontal dune. This observation highlights the importance of maintaining frontal dunes as buffers of storm surge Small secondary dunes located farther from the eye of Hurricane Ivan a nd located on the widest parts of the island experienced more erosion than small secondary dunes closer to the eye of Hurricane Ivan and on narrow parts of the island. The reas on for this pattern is unknown, but it may be related to storm surge in the narrow parts of the Santa Rosa Sound. When the flow of storm surge is confined and water is shallow, high penetration dist ances have been noted for washover (Morton and Sallenger 2003). Hurricanes will continue to fragment and reduce coastal dunes if the interval between hurricanes and other tr opical storms remains shorte r than the time required to redevelop dunes through natura l processes or restoration. Tall and wide frontal dunes are more resistant to storm erosion than smalle r frontal dunes and may continue to provide suitable habitat for beach mice if they maintain appropriate habitat conditions. However, Hurricane Ivan alone reduced the frontal dune habitat of beach mice by 76.8% in our study area As dunes become smaller with subsequent storms, erosion of frontal dunes may accelerate. In contrast, secondary dune habitat was reduced by only 19.3% by Hurricane Ivan, indicating that, in periods of high hurricane activity, scrub dunes provide more stable habitat for beach mice than frontal dunes However, removal of frontal dunes is likely to increase impacts of hurricanes on secondary dunes as the buffering

PAGE 55

44 capacity of frontal dunes is lost. Given the inevitable lo ss of frontal dunes from hurricanes, incorporation of scrub habitat in to conservation efforts for Gulf Coast beach mice is warranted Although scrub habitat has been cons idered marginal for beach mice, my data suggest that conservation of scr ub habitat will promote population persistence and lessen extinction risk as frontal dunes continue to be removed from the landscape. Landscape-scale research is needed to unders tand the interdependency of subpopulations of mice in frontal and scrub dunes, the conditi ons under which either of these habitats is optimal or marginal, and the relative contribut ions of each of these habitats to long-term persistence of beach mice populations. Habitat Restoration for Beach Mice Dune restoration for frontal dunes after storms typically has focused on the reestablishment of sea oats, which produces a la ttice of rhizomes that can quickly trap and accumulate sand. My results indicate that cover of woody vegetation is important for promoting occupancy of frontal dunes by beach mice and woody plants also may influence occupancy of scrub dunes. This observation has important implications for conservation and management of beach mouse habitat, as optimal habitat for beach mice generally is believed to cons ist of tall frontal dunes vege tated by sea oats and other herbaceous species (Holler 1992). My data suggest that restoration programs should incorporate the re-establishment of woody plants on frontal dunes Scrub dunes are dominated by woody vegetation, and habitat management strategies for beach mice should aim to maintain this vegetation. We do not know the exact mechanism by which woody vegetation influences occupancy of dunes by mice, but woody species provide cover and food for mice and may stabilize dunes during storms. More research will be required to understand these mechanisms and to identify key woody species for mice.

PAGE 56

45 Results of my study indicate that lands cape context is important for enhancing occupancy of dune habitats by beach mice rega rdless of dune type. Isolation restricts occupancy of frontal dunes and amount of dune habitat surrounding scrub dunes influences occupancy of these dunes. As dune systems are eroded by hurricanes, dune fragments become more widely separated by open sand that does not provide resources for beach mice (e.g., food and substrate for burrow construction) and mice are forced to move over large open areas to obtain resource s in different patches. Movement of mice also is critical for recolonizat ion of the landscape in areas where mice are extirpated during hurricanes and for recolonization of rest ored habitat. These movements are likely to entail considerable risk (e.g., increased risk of predation). Management efforts should aim to minimize isolation of dunes. Restor ation techniques that provide connectivity (i.e., facilitate movement) between fragmen ted frontal dunes or between frontal and secondary dunes also may benefit beach mice. Vegetation cover faci litates foraging of beach mice (Bird et al. 2004) and, presumably, would enhance movement by reducing risks associated with moving between fragment s of habitat that re main after hurricanes. Although my occupancy data provide genera l evidence that landscape connectivity is important for beach mice, factors limiting mouse movement (e.g., the degree to which large open sand gaps restrict movement) are unk nown and this would be a fruitful area of research for understanding the long-term persistence of beach mice in dynamic landscapes Finally, restoration techniques to pr omote increases in dune height for frontal dunes also would be beneficial for beach mice as taller dunes are more resistant to storm-related erosion and may facilitate conditions appropriate for burrow construction.

PAGE 57

46 APPENDIX A DELINEATION OF DUNES IN THE FIELD Critical definition of dunes fo r delineation in the field: Dunes were mapped if greater than 1 m high with woody vegetation or greater than 1.5 m with grasses and other herbaceous vegetation. Dune spurs were considered part of a dune if the cleft between dunes was less than 1.5 m in height. Dunes were considered to be separate if they were separated by more than 3 m.

PAGE 58

47 APPENDIX B CORRELATION MATRICES FOR VARIABLES BY HABITAT Table B-1. Correlations for va riables measured on 61 scr ub dunes surveyed for beach mice before Hurricane Ivan (Jun. 2004 Sep. 2004).

PAGE 59

48 Table B-2. Correlations for va riables measured on 61 scr ub dunes surveyed for beach mice after Hurricane Ivan (Oct. 2004 Jan 2005). Table B-3. Correlations for va riables measured on foredune s (Eglin Air Force Base, N = 11, and Gulf Islands National Seashore, N = 15) surveyed for beach mice after Hurricane Ivan. (Oct. 2004 Feb. 2005).

PAGE 60

49 APPENDIX C COMPARISON OF FRONTAL DUNES AT EGLIN AIR FORCE BASE AND GULF ISLANDS NATIONAL SEASHORE Table C-1. Results of t-tests comparing ve getation, structure and landscape context for frontal dunes on Eglin Air Force Base and Gulf Islands National Seashore measured after Hurricane Ivan. Variable t df p Dune area (ha) 1.915 24 0.06 Dune height (m) -0.287 24 0.77 Dune habitat within 200 m (ha) -0.209 24 0.84 Dune habitat within 1 km (ha) 5.305 24 <0.01 % woody vegetation cover -2.260 24 0.03 % total herbaceous cover -1.060 24 0.30 Distance to nearest neighbor (m) 0.697 24 0.49 Distance to nearest scrub dune (m) -1.975 24 0.06

PAGE 61

50 APPENDIX D PREDICTORS OF CHANGE IN OCCUPANCY OF FRONTAL DUNES AFTER HURRICANE IVAN Table D-1. Mean values, standard errors, and t-test results for habitat variables on frontal dunes on EAFB that became unoccupied and for dunes that remained occupied after Hurricane Ivan. Where variances were found to not be equal (p < 0.05), a student t-test with the assu mption of unequal variances was used. For all other variables, t-statistics and p-value are for student t-tests with the assumption of equal variances. Variable1 Unoccupied Mean (SE) Occupied Mean ( SE) t df p Before hurricane Dune Area (ha) 0.56 (0.12) 0.62 (0.16) -0.31 13 0.76 East-west coordinate (UTM) 522516 (1901) 523624 (1919) -0.39 13 0.70 Dune height (m) 3.44 (0.32) 4.87 (0.53) -2.53 13 0.03 Dune habitat within 200 m (ha) 0.25 (0.13) 0.37 (0.15) -0.61 13 0.55 Dune habitat within 1km (ha) 6.59 (1.34) 11.24 (2.89) -1.63 13 0.13 Distance to nearest occupied dune (m) 317.57 (87.41) 106.61 (28.51) 2.16 13 0.05 After hurricane2 Dune Area (ha) 0.11 (0.06) 0.28 (0.07) -1.96 13 0.07 East-west coordinate (UTM) 522410 (2160) 525033 (793) -1.14 13 0.28 Dune height (m) 1.52 (0.59) 3.18 (0.53) -2.06 13 0.06 Dune habitat within 200 m (ha) 0.08 (0.03) 0.30 (0.12) -0.91 9 0.39 Dune habitat within 1 km (ha) 7.56 (1.04) 9.29 (2.21) -0.44 9 0.67 Distance to nearest occupied dune (m) 210.12 (25.46) 134.34 (35.07) 1.49 9 0.17 Percent cover of woody vegetation 2.0 (1.4) 8.5 (4.8) -3.87 9 0.01 Percent cover of total herbaceous 23.0 (4.0) 25.0 (5.0) -0.33 9 0.75 1 Habitat data are presented for variables measured before and after the hu rricane. Occupancy data presented are da ta taken after the hurricane. 2 Analysis of dune area, dune height, and east-west coordinate for frontal dunes post-Ivan included the four dunes that were completely destroyed. These four dunes, however, were not included when assessing vegetation, distance to nearest occupied dune, or amount of surrounding habitat.

PAGE 62

51 APPENDIX E CORRELATION MATRIX FOR STRUCTURAL FEATURES OF FRONTAL DUNES ON EGLIN AIR FORCE BASE Table E-1. Correlations for structural a nd landscape context variables measured on frontal dunes (N = 93) on Santa Rosa Island prior to Hurricane Ivan.

PAGE 63

52 APPENDIX F CORRELATION MATRIX FOR STRUCTURAL FEATURES OF SECONDARY DUNES ON EGLIN AIR FORCE BASE Table F-1. Correlations for structural a nd landscape context variables measured on secondary dunes on Santa Rosa Island pr ior to Hurricane Ivan. Dunes were sub-sampled to represent proportiona l area of large (83.6%) and small (16.4%) dunes on the landscape (N = 52 with 34 small dunes and 18 large dunes).

PAGE 64

53 LITERATURE CITED Biedermann, R. 2004. Modeling the spatial dynamics and persistence of the leaf beetle ( Gonioctena olivacea ) in dynamic habitats. Oikos 107:645-653. Bird, B. L. 2002. Effects of predatory risk, ve getation structure, and artificial lighting on the foraging behavior of beach mice. MS Thesis. University of Florida, Gainesville, FL. 58 pp. Bird, B. L., L. C. Branch, and D. L. Mill er. 2004. Effects of coastal lighting on foraging behavior of beach mice. Conservation Biology 18(5):1435-1439. Blair, W. F. 1951. Population structure, soci al behavior and environmental relations in a natural population of the beach mouse ( Peromyscus polionotus leucocephalus ). Pages 1-47. Contributions from the Laboratory of Vertebrate Biology, University of Michigan. Ann Arbor, MI. Bonham, C. D. 1989. Measurements for terr estrial vegetation. Wiley. New York, NY. Bourg, N.A., W.J. McShea, and D.E. G ill. 2005. Putting a cart before the search: successful habitat prediction for a ra re forest herb. Ecology 86:2793-2804. Breiman, L., Friedman, J.H., Olshen, R. A., Stone, C.G. 1984. Classification and regression trees. Wadsworth In ternational Group. Belmont, CA. Burnham, K. P. and D. R. Anderson 2002. M odel selection and multimodel inference: a practical information-theoretic ap proach. Springer. New York, NY. Buskirk, J.V. 2005. Local and landscape influence on amphibian occurrence and abundance. Ecology 86:1936-1947. Carlsson, A. and O. Kindvall. 2001. Spatial dynamics in a metapopulation network: recovery of a rare grasshopper ( Stauroderus scalaris ) from population refuges. Ecography 24:452-460. Cooper, C. B. and J. R. Walters. 2002. Independent effects of woodland loss and fragmentation on Brown Treecreeper distribution. Biologi cal Conservation 105:1-10.

PAGE 65

54 Cox, J. and R. T. Engstrom. 2001. Influence of the spatial pattern of conserved lands on the persistence of a large populat ion of red-cockaded woodpeckers. Biological Conservation 100:137-150. Dahl, B.E. and Woodard, D.W. 1977. Constr uction of Texas coastal foredunes with sea oats ( Uniola paniculata ) and bitter panicum ( Panicum amarum ). International Journal of Biometeorology 21:267-275. Death, G. and K.E. Fabricius. 2000. Cla ssification and regre ssion trees: a powerful yet simple technique for ecologi cal data analysis. Ecology 81:3178-3192. Ehrenfeld, J.G.,1990. Dynamics and processes of barrier island vegetation. Critical Reviews in Aquatic Science 2:437-480. Ellner, S.P. and J. Fieberg. 2003. Using PVA for management despite uncertainty: effects of habitat, hatcheries, and harvest on salmon. Ecology 84:1359-1269. Emanuel, K. 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436: 686-688. Environmental Systems Research Ins ititue. 1996. ArcView GIS, version 3.2. Environmental Systems Research Institute, Redlands, California, USA. Flather, CH, K.R. Wilson, D.J. Dean, a nd W.C. McComb. 1997. Id entifying gaps in conservation networks: Of indicators and uncertainty in geographic-based analyses. Ecological Applications 7:531-542. Frank, K. 2005. Metapopulation persistence in heterogeneous landscapes: Lessons about the effect of stochastic ity. American Naturalist 165:374-388. Glennon, M. J., W. F. Porter, and C. L. De mers. 2002. An alternative field technique for estimating diversity of small-mammal populations. Journal of Mammalogy 83:734-742. Gore, J. A. and T. L. Schafer. 1993. Distri bution and conservation of the Santa Rosa beach mouse. Proceedings Annual Conf erence Southeast Association of Fish and Wildlife Agenices 47:378-385. Gu, W. D. and R. K. Swihart. 2004. Absent or undetected? Effect s of non-detection of species occurrence on wildlife-ha bitat models. Biological Conservation 116:195-203. Guisan, A. and W. Thuiller. 2005. Predicting sp ecies distribution: offering more than simple habitat models. Ecology Letters 8:993-1009.

PAGE 66

55 Hallermier, R.J. and P.E. Rhodes. 1988. Gene ric treatment of dune erosion for 100-year event. Proceedings of the 20th International Conferen ce on Coastal Engineering, ASCE, New York, 1107-1115. Hanski, I. 1994. A practical model of me tapopulation dynamics. Journal of Animal Ecology 63:151-162. Hesp, P. 2002. Foredunes and blowouts: in itiation, geomorphology, and dynamics. Geomorphology 48:245-268. Hesp, P. 2004. Coastal dunes in the tropi cs and temperate regions; Location, formation, morphology and vegetation processes. Pages 29-49 in M. L. Martinez, and N.P. Psuty, editor. Co astal Dunes, Ecology and Conservation. Springer-Verlag. Berlin. Hoekstra, J. M., W. F. Fagan, and J. E. Brad ley. 2002. A critical role for critical habitat in the recovery planning process? Not yet. Ecological Applications 12:701-707. Holler, N.R. 1992. Perdido Key Beach Mous e. In Rare and Endangered Biota of Florida, Volume 1. Mammals (S. R. Humphrey ed.). University Press of Florida, Gainesville, FL 52pp. Humphrey, S. R. and D. B. Barbour. 1981. St atus and habitat of 3 subspecies of Peromyscus polionotus in Florida. Journal of Mammalogy 62:840-844. Jonzen, N., C. Wilcox, and H. P. Possi ngham. 2004. Habitat selection and population regulation in temporally fluctuati ng environments. American Naturalist 164:E103-E114. Judge, E.K., M.F. Overton, and J.S. Fisher 2003. Vulnerability indicators for coastal dunes. Journal of Waterway, Port, Co astal, and Ocean Engineering 129:270-278. Kery, M. 2004. Extinction rate, estimates for plant populations in revisitation studies: importance of detectability. Conservation Biology 18:570-574. Kriebel, D.L., R. Dalrymple, A. Pratt, and V. Sakovich. 1997. A shoreline risk index for northeasters. Proceedings of the AS CE International Conference on Natural Disaster Reduction, AS CE, New York, 251-252. Mabee, T. J. 1998. A weather-resistant tr acking tube for small mammals. Wildlife Society Bulletin 26:571-574. MacKenzie, D. I., J. D. Nichols, G. B. L achman, S. Droege, J. A. Royle, and C. A. Langtimm. 2002. Estimating site occupancy rates when detecti on probabilities are less than one. Ecology 83:2248-2255.

PAGE 67

56 MacKenzie, D. I. J. D. Nichols, J. E. Hine s, M. G. Knutson, and A. B. Franklin. 2003. Estimating site occupancy, colonization, a nd local extinction when a species is detected imperfectly. Ecology 84:2200-2207. Martinez, M. L., N.P. Psuty., and R.A. Lubke. 2005. A perspective on coastal dunes. Pages 1-10 in M. L. Martinez, and N.P. Psuty, editor. Coastal Dunes, Ecology and Conservation. Springer-Verlag. Berlin. Mazerolle, M.J., A. Desrochers, L. Rochef ort. 2005. Landscape characteristics influence pond occupancy by frogs after accounting for detectability. Ecological Applications 15:824-834. Mendelssohn, I.A., M.W. Hester, F.J. Montef emante, and F. Talbot. 1991. Experimental dune building and vegetative st abilization in a sand-defic ient barrier island setting on the Louisiana Coast. Journal of Coastal Research 7 :137-149. Milio, J.F. 1998. Endangered and Threatened Wildlife and Plants; Determination of Endangered Status for the St. Andrew Beach Mouse. U.S. Fish and Wildlife Service. Federal Register 63:70053-70062. Miller, D.L. and M. Thetford. 2001. Evalua tion of sand fence and vegetation for dune building following overwash by Hurricane Opal on Santa Rosa Island, Florida. Journal of Coastal Research 17:936-948. Miller, D. L., L. Yager, M. Thetford, and M. Schneider. 2003. Potential use of Uniola paniculata rhizome fragments for dune rest oration. Restoration Ecology 11:359369. Morton, R.A. and A.H. Sallenger. 2003. Morpho logical impacts of extreme storms on sandy beaches and barriers. Journal of Coastal Research 19:560-573. Moyers, J. E. 1996. Food habits of the Gulf Coast subspecies of beach mice ( Peromyscus polionotus spp.). MS Thesis. Auburn University, Auburn, AL. 54pp. Musila, W.M., J.I. Kinyamario, and P.D. Jungerius. 2001. Vegetation dynamics of coastal sand dunes near Malindi, Ke nya. African Journal of Ecology 39:170177. Nagelkerke, N. J. D. 1991. A note on a ge neral definition of the coefficient of determination. Biometrika 78:691-692. Nordstrom, K.F., R. Lampe., and L.M. Vandemark. 2000. Reestablishing naturally functioning dunes on developed coasts. Environmental Management 25:37-51.

PAGE 68

57 Nordstrom, K.F. and W.A. Mitteager. 2001. Pe rceptions of natural and restored beach and dune characteristics by high school students in New Jersey, USA. Ocean and Coastal Management 44: 545-559. Oli, M. K., N. R. Holler, and M. C. W ooten. 2001. Viability analysis of endangered Gulf Coast beach mice ( Peromyscus polionotus ) populations. Biological Conservation 97:107-118. Pearce, J. and S. Ferrier. 2000. Evaluating the predictive performance of habitat models developed using logistic regres sion. Ecological Mode lling 133:225-245. Potter, J. C. 1985. Endangered and Threatened Wildlife and Plants; Determination of Endangered Status and Critical Habita t for Three Beach Mice. U.S. Fish and Wildlife Service. Fede ral Register. 50:23872-23889. Psuty, N.P. 2005. The coastal foredune: A morp hological basis for regional coastal dune development. Pages 11-27 in M. L. Ma rtinez, and N.P. Psuty, editor. Coastal Dunes, Ecology and Conservati on. Springer-Verlag. Berlin. R Development Core Team. 2003. R: A langua ge and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Sallenger, A.H., Jr. 2000. Storm impact scale for barrier islands. Journal of Coastal Research 16:890-895. Schrott, G. R., K. A. With, and A. T. W. King. 2005. On the importance of landscape history for assessing extinction ris k. Ecological Appli cations 15:493-506. Segurado, P. and M.B. Araujo. 2004. An eval uation of methods for modeling species distributions. Journa l of Biogeography 31:1555-1568. Sneckenberger, S. I. 2001. Factors influenci ng habitat use by the Alabama beach mouse ( Peromyscus polionotus ammobates ). MS Thesis. Auburn University, Auburn, AL. 101pp. SPSS Inc. 2004. SPSS Base 13.0 for Windows Users Guide. SPSS Inc., Chicago IL. Stallins, J. A. and A. J. Parker. 2003. The influence of complex systems interactions on barrier island dune vegetation patte rn and process. Annals of the Association of American Geographers 93:13-29. Stewart, S.R. 2005. Tropical Cyclone Re port: Hurricane Ivan 2-26 September 2004. NOAA National Weather Service, NHC/TPC.

PAGE 69

58 Stone, G.W., B. Liu, D.A. Pepper, and P. Wang. 2004. The importance of extratropical and tropical cyclones on th e short-term evolution of barrier islands along the northern Gulf of Mexico, USA. Marine Geology 210:63-78. Swilling, W. R. and M. C. Wooten. 2002. Subadult dispersal in a monogamous species: The Alabama beach mouse ( Peromyscus polionotus ammobates ). Journal of Mammalogy 83:252-259. Swilling, W. R., M. C. Wooten, N. R. Holler, and W. J. Lynn. 1998. Population dynamics of Alabama beach mice ( Peromyscus polionotus ammobates ) following Hurricane Opal. Ameri can Midland Naturalist 140:287-298. Snyder, R. A. and C. L. Boss. 2002. Recove ry and stability in barrier island plant communities. Journal of Coastal Research 18:530-536. Taylor, M. F. J., K. F. Suckling, and J. J. Rachlinski. 2005. The effectiveness of the endangered species act: A quantitati ve analysis. Bioscience 55:360-367. U.S. Fish and Wildlife Service. 1981. Standard s for the development of habitat suitability index models for use in the habitat eval uation procedure. U.S. Fish and Wildlife Service, Division of Ecological Se rvices. Manuscript 103. Washington, DC. 171pp. U.S. Fish and Wildlife Service. 1987. Recove ry plan for the Choctawhatchee, Perdido Key and Alabama beach mouse. U.S. Fish and Wildlife Service, Atlanta, Georgia. 45pp. Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of Wildlife Management 47:893-901. Van Horne, B., G. S. Olson, R. L. Schooley, J. G. Corn, and K. P. Burnham. 1997. Effects of drought and prolonged wi nter on Townsend's ground squirrel demography in shrubsteppe habi tats. Ecological Monographs 67:295-315. Vellinga, P. 1982. Beach and dune erosion during storm surges. Coastal Engineering 6:361-387. Welch, N. E., and J. A. MacMahon. 2005. Iden tifying habitat variables important to the rare Columbia spotted frog in Ut ah (USA): An information-theoretic approach. Conservation Biology 19:473-481. Zar, J.H. 1998. Biostatistical Anal ysis. Prentice Hall. New York, NY.

PAGE 70

59 BIOGRAPHICAL SKETCH Alexander James Pries was born in S pooner, Wisconsin on May 26, 1980. Son of James and Constance Pries, he grew up in St. Paul, MN, but often escaped to the northern forests of Minnesota and Wisconsin during th e summer months. It was there, during hours of playing in the forests, lakes, and streams that he began to first observe and appreciate natural ecosystems. In 1998, he en rolled at The College of Wooster in Ohio and received a B.A. in biol ogy in the spring of 2002. After graduation, he traveled to Costa Rica, where he served as a teaching assi stant in a course on tropical ecology for the Organization for Tropical Studies. After this experience, he moved to Avon Park, FL, where he worked as a research technician for the University of Florida on a project looking at features of habitat use by round-tailed muskrats ( Neofiber alleni ). In 2003, he was hired by Archbold Biological Station to serve as a research technician for a population assessment of Florida Scrub Jays. He began graduate school in August of 2003 at the University of Floridas Depart ment of Wildlife Ecology and Conservation, from which he received his M.S. in 2006.


Permanent Link: http://ufdc.ufl.edu/UFE0013415/00001

Material Information

Title: Hurricane Impacts on Coastal Dunes and Spatial Distribution of Santa Rosa Beach Mice (Peromyscus polionotus leucocephalus) in Dune Habitats
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0013415:00001

Permanent Link: http://ufdc.ufl.edu/UFE0013415/00001

Material Information

Title: Hurricane Impacts on Coastal Dunes and Spatial Distribution of Santa Rosa Beach Mice (Peromyscus polionotus leucocephalus) in Dune Habitats
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0013415:00001


This item has the following downloads:


Full Text











HURRICANE IMPACTS ON COASTAL DUNES AND SPATIAL DISTRIBUTION
OF SANTA ROSA BEACH MICE (Peromyscus polionotus leucocephalus) IN DUNE
HABITATS













By

ALEXANDER JAMES PRIES


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA INT PARTIAL FUFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Alexander James Pries
















ACKNOWLEDGMENTS

I would like to acknowledge the support received from numerous organizations,

groups, and people during the development of this project. Funding and logistic support

was provided by the National Park Service and Eglin Air Force Base. The University of

Florida, Milton campus and UF Department of Wildlife Ecology and Conservation

provided equipment and additional funding.

My committee members (Dr. Lyn C. Branch, Dr. Debbie L. Miller, and Dr.

George W. Tanner) went above and beyond the call of duty in helping with development

and implementation of this research. Dr. Miller provided housing after my trailer broke

and continued to graciously welcome me into her home during the endless process of data

collection. I have enjoyed thoroughly our conversations on restoration ecology and

ethics of being a scientist. Dr. Tanner was most insightful in steering me towards proper

techniques for assessment of vegetation and other habitat features. Dr. Branch is a sound

editor who masterfully checked and rechecked my thesis for clarity and scope.

Thanks go to Riley Hoggard at Gulf Islands National Seashore and Bruce

Hagedorn, Bob Miller, and Dennis Teague at Jackson Guard, Eglin AFB. These

individuals served as important contacts and sources of support when materials were

lacking or I had questions about the accessibility of certain sites. I am most thankful for

their support. Many individuals assisted me during various phases of this project and I

will attempt to name them all here. I apologize for those who I may miss but your deeds

are not forgotten. Tanya Alvarez endured the hot conditions and was covered in black









carbon powder for weeks. Mica Schneider and Lisa Yager created the initial cover layer

of dunes (with over 750 polygons) on Santa Rosa Island that was massively important.

Jonathan Shore and Cathy Hardin were fantastic as temporary field technicians during the

difficult conditions. I also offer appreciation to Bob Schooley and Arpat Ozgul for

statistical assistance during initial data analysis. Conversations with many graduate

students including Jason Martin, Elizabeth Swiman, Ann George, Dan Thornton, and

Traci Darnell helped to craft and refine appropriate research questions.

Finally, I am thankful to my immediate family and friends outside of the scientific

or graduate school community. Your ability to listen and be a sounding board when

things became difficult is a maj or reason why I have been able to complete this research.

Your support and patience are massively important to me and I dedicate this work to you.




















TABLE OF CONTENTS


IM Le

ACKNOWLEDGMENT S ............ ..... .__ .............. iii...


LIST OF TABLES ............ ..... ._ ..............vii...


LIST OF FIGURES .............. .................... ix


AB S TRAC T ..... ._ ................. ............_........x


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


Beach Mice and Threats to Survival ................. ...............1...............
Habitat Use by Beach M ice ................. .......... ...............1 .....
Coastal Dunes, Development, and Erosion .............. ...............2.....
Dune Restoration and Protection for Beach Mice .............. ...............3.....


2 INFLUENCE OF DUNE STRUCTURE ON STORM-RELATED EROSION
FOR FOREDUNES AND SECONDARY DUNES ON SANTA ROSA ISLAND,
FLORIDA. .............. ...............5.....


Introducti on ............ ..... ._ ...............5....
M ethods .............. ...............7.....

Study Area .............. ......__ ...............7....
Characteristics of Hurricane Ivan ....._ .....___ .........__ ...........8
Dune M apping ............ ...... ...............9...
Statistical Analyses............... ...............10
Re sults........................._ .....__ .............1
Conditions before Hurricane Ivan ............. .. ...._ .......__ ............1
Hurricane Ivan' s Impact on Foredunes and Secondary Dunes. ........................12
Regression Trees .............. ...............13....
Discussion ............ ..... .._ ...............15...


3 INFLUENCE OF HABITAT AND LANDSCAPE FEATURES ON SPATIAL
DISTRIBUTION OF SANTA ROSA BEACH MICE INT TWO DUNE
HABITATS BEFORE AND AFTER A HIURRICANE.............__ .........___.......24


Introducti on ................. ...............24........_ .....











M ethods ............ ............. .... ...............2
Study Area and Habitat Mapping ................. ...............26........... ...
Dune Occupancy .................. ........... ... .... ............2
Predictor Variables: Vegetation Cover and Landscape Structure .......................29
Occupancy M odels .............. ...............30....
R e sults.........._.... .. ...._.__ ....... .._._.. .. ...... ... .............3
Hurricane Impacts on Habitat Availability at EAFB .............. .....................3
Dune Occupancy .............. ...............32....
Habitat M odels .............. ...............33....
Discussion ................. ...............34.................


4 CONCLUSIONS AND CONSERVATION IMPLICATIONS ................ ...............42


Dune Erosion and Loss of Beach Mouse Habitat ................. ......... ................42
Habitat Restoration for Beach Mice .............. ...............44....


APPENDIX

A DELINEATION OF DUNES INT THE FIELD ................. .............................46


B CORRELATION MATRICES FOR VARIABLES BY HABITAT ................... .......47

C COMPARISON OF FRONTAL DUNES AT EGLIN AIR FORCE BASE AND
GULF ISLANDS NATIONAL SEASHORE ................. ..............................49

D PREDICTORS OF CHANGE INT OCCUPANCY OF FRONTAL DUNES
AFTER HIURRICANE IVAN .............. ...............50....

E CORRELATION MATRIX FOR STRUCTURAL FEATURES OF FRONTAL
DUNES ON EGLIN AIR FORCE BASE ................. ...............51...............


F CORRELATION MATRIX FOR STRUCTURAL FEATURES OF
SECONDARY DUNES ON EGLIN AIR FORCE BASE ................. ................ ...52

LITERATURE CITED .............. ...............53....


BIOGRAPHICAL SKETCH .............. ...............59....

















LIST OF TABLES


Table pg

2-1 Means and standard errors for structural variables measured to explain dune
erosion in foredunes and secondary dunes on Santa Rosa Island. ...........................17

2-2 Statistics for evaluation of dune characteristics as predictors of dune loss as a
result of Hurricane Ivan ................. ...............19................

3-1 Means and standard errors for structural and vegetation variables measured for
modeling occupancy of frontal and scrub habitat by Santa Rosa beach mice on
Eglin Air Force Base (EAFB) and Gulf Islands National Seashore (GINS) on
Santa Rosa Island, FL............... ...............38...

3-2 AIC-based selection of site occupancy models of dune occupancy for Santa Rosa
beach mice in frontal and scrub dune habitat ................. .............................40

3-3 Relative importance (wsum), model-averaged parameter estimates, and
unconditional standard errors for variables used to model occupancy for beach
mice in frontal and scrub habitat before and after Hurricane Ivan. ................... .......41

B-1 Correlations for variables measured on 61 scrub dunes surveyed for beach mice
before Hurricane Ivan (Jun. 2004 Sep. 2004). ............. ...............47.....

B-2 Correlations for variables measured on 61 scrub dunes surveyed for beach mice
after Hurricane Ivan (Oct. 2004 Jan 2005) ................. .............................48

B-3 Correlations for variables measured on foredunes (Eglin Air Force Base, n = 11,
and Gulf Islands National Seashore, n = 15) surveyed for beach mice after
Hurricane Ivan. (Oct. 2004 Feb. 2005). ............. ...............48.....

C-1 Results of t-tests comparing vegetation, structure and landscape context for
frontal dunes on Eglin Air Force Base and Gulf Islands National Seashore
measured after Hurricane Ivan. ............. ...............49.....

D-1 Mean values, standard errors, and t-test results for habitat variables on frontal
dunes on EAFB that became unoccupied and for dunes that remained occupied
after Hurricane Ivan. ............. ...............50.....

E-1 Correlations for structural and landscape context variables measured on frontal
dunes (N = 93) on Santa Rosa Island prior to Hurricane Ivan. ............... ...............51










F-1 Correlations for structural and landscape context variables measured on
secondary dunes on Santa Rosa Island prior to Hurricane Ivan............_.._ .............52

















LIST OF FIGURES


Finure pg

2-1 Map of Santa Rosa Island, FL. The study area encompasses the section
between Navarre and Fort Walton Beach............... ...............20.

2-2 Cross validation relative error for regression trees for (a) foredunes and (b)
secondary dunes to explain dune loss from Hurricane Ivan in relation to
measured predictor variables ................. ...............21........... ....

2-3 Regression trees relating percentage of dune lost from Hurricane Ivan for (a)
foredunes (N = 93) and (b) secondary dunes (N = 52) to physical features of
dunes, spatial location of dunes with respect to where Hurricane Ivan made
landfall, and width of island ......................._ ...............22. ..

2-4 Regression trees relating percentage of dune lost from Hurricane Ivan for (a)
secondary dunes > 0.25 ha (N = 61) and (b) secondary dunes <0.25 ha (N = 34)
to dune features, spatial location, and island width ................. .......................23
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

HURRICANE IMPACTS ON COASTAL DUNES AND SPATIAL DISTRIBUTION
OF SANTA ROSA BEACH MICE (Peromyscus polionotus leucocephahts) IN DUNE
HABITATS

By

Alexander James Pries

May 2006

Chair: Lyn C. Branch
Cochair: Deborah L. Miller
Maj or Department: Wildlife Ecology and Conservation

I examined the impact of Hurricane Ivan on dune erosion and changes in spatial

distribution of beach mice (Peromyscus polionotus leucocephahts) in two dune habitats

on Santa Rosa Island, FL. Foredunes (i.e., frontal dunes) and secondary (i.e., scrub)

dunes were mapped and surveyed for the presence of beach mice before and after the

hurricane using polyvinyl chloride (PVC) tracking tubes. I also collected data on

physical structure, vegetation, and landscape context for each dune during these two time

periods. Regression trees were used to evaluate structural features of dunes that

explained patterns in dune erosion as a result of Hurricane Ivan for the two dune types. I

used site-occupancy models and an information-theoretic approach to evaluate predictors

of occupancy for beach mice in frontal and secondary dune habitats before and after the

hurricane. Hurricane Ivan removed 68.2% of frontal dune area surveyed for beach mice

(Chapter 3) and 76.8% of all frontal dune area mapped (Chapter 2). Secondary dunes









surveyed for beach mice only lost 14.8% of their total area (Chapter 3) and all secondary

dunes surveyed lost 19.3% of their area (Chapter 2). Dune erosion for frontal dunes was

related inversely to distance from where the eye of Hurricane Ivan passed over the island,

dune height, and dune width. Dune erosion for large secondary dunes was reduced when

dunes were located behind foredunes. Erosion of small secondary dunes increased with

distance of the dune from where the eye of the hurricane passed over the island. The

reason for this pattern is unknown, but it may be related to spatial distribution of storm

surge in the Santa Rosa Sound. Dune erosion decreased with increasing length and area

of small secondary dunes. Beach mice occupied 100% of frontal dunes before the

hurricane and 59% of these dunes after the hurricane. Occupancy of scrub habitat by

beach mice was not statistically different before and after the hurricane, but was higher

(~70% of sites) than frontal dune occupancy after the storm. Frontal dune occupancy

was influenced largely by percent cover of woody vegetation and distance to nearest

occupied dune. Probability of occupancy by beach mice in scrub habitat increased with

an increase in dune area and amount of dune habitat surrounding the dune within 200 m.

This study indicates that scrub habitat, which currently is not protected for beach mice, is

important for mice because of the stochastic and severe impacts of storms on frontal

habitat. With further removal of frontal dune habitat, scrub could become essential for

long-term persistence of beach mice. The study suggests that restoration programs for

frontal dunes set targets for the construction of dunes that are tall and wide. Dunes with

these two features likely will mitigate storm surge associated with hurricanes and protect

coastal dunes, mouse habitat, or infrastructure located further inland.















CHAPTER 1
INTTRODUCTION

Beach Mice and Threats to Survival

Beach mice (Peromyscus polionotus spp.) are a complex of eight subspecies of the

oldfield mouse, which occupy the coastal dunes of Alabama and Florida (Holler 1992).

Two subspecies along the Atlantic coast of Florida are federally protected and one is

extinct. Gulf Coast populations of beach mice comprise five subspecies, four of which

are listed as threatened or endangered (Potter 1985; Milio 1998). The remaining

subspecies along the Gulf coast, the Santa Rosa beach mouse (Peromyscus polionotus

leucocephahts), is not yet listed because its geographic range includes several federally

managed lands (Gore and Schaffer 1993). All populations of beach mice suffer from

severe habitat loss from coastal development and habitat disturbance from hurricanes

(Gore and Schaffer 1993; Swilling et al. 1998). Additional threats to beach mice include

predation by feral cats (Felis silvestris), competition with house mice (M~us muscuhts) in

dunes around coastal development, and low population levels in late summer when

hurricane activity is most prevalent (Humphrey and Barbour 1981; Oli et al. 2001).

Habitat Use by Beach Mice

Beach mice are considered habitat specialists on dune habitats with burrow

locations strongly correlated to these habitat types (Blair 1951; Humphrey and Barbour

1981). Foredunes or frontal dunes, located immediately adj acent to the Gulf of Mexico,

are believed to be optimal habitat for beach mice (USFWS 1987). Mice also occur in

secondary dunes (also known as scrub), which are located farther from the shoreline and










are characterized by increased dominance of woody vegetation. Although densities of

beach mice generally are highest in frontal habitat (Swilling et al. 1998), abundance of

beach mice in scrub can increase after hurricanes. Prior to Hurricane Opal, population

abundance of Alabama beach mice (P. p. amnmobates) on trapping grids in scrub habitat

was less than frontal dune habitat (Swilling et al. 1998). Four months after the storm,

abundance in scrub habitat was almost twice that of frontal dune habitat. Despite use of

scrub by beach mice, this habitat is not designated as critical habitat under USWFS

recovery plans for Gulf coast beach mouse populations (USFWS 1987). Additionally,

little is known about what features define suitable scrub habitat for beach mice or how

much of this habitat is utilized by mice.

Although hurricanes are a natural feature of coastal disturbance, their impacts work

in concert with coastal development and anthropogenic habitat loss to impact habitat

availability for beach mice (Holler 1992). Population models suggest that hurricane

impacts pose a significant threat to all subspecies of beach mice (Oli et al. 2001).

Hurricanes, in addition to directly destroying dunes, fragment remaining dunes and

potentially change features of dunes that make them suitable for beach mice.

Fragmentation of dunes from hurricanes may require beach mice to travel more

frequently between remaining dune patches, exposing them to predators. Habitat loss and

fragmentation also may reduce landscape connectivity for beach mice, limiting their

ability to recolonize frontal dunes after storm impacts or force them to utilize more

marginal habitats.

Coastal Dunes, Development, and Erosion

The coastal dunes that beach mice occupy are valued for their fauna and flora,

natural beauty, and ability to protect human-made infrastructure (Nordstrom et al. 2000;










Martinez et al. 2005). Despite this, many coastal dune ecosystems have been changed

irreversibly as a result of exploitation of natural resources and anthropogenic

development (Martinez et al. 2005). Increases in storm severity and frequency are

predicted and storm impacts will continue to alter dunes, reduce infrastructure protection

and disturb habitat for wildlife. As a result, interest has increased in the creation or

restoration of dunes that will withstand storm impacts.

Coastal dunes are formed from aeolian processes with dune development occurring

where sediment is trapped by existing vegetation (Hesp 2004; Psuty 2005). Foredunes,

located closest to the shoreline, are dynamic structures that are influenced greatly by the

flow of water, wind, and sediment with normal environmental fluctuations and periodic

storm events (Psuty 2005). Secondary dunes are created by sediment flows from existing

foredunes or they may be old foredunes. These dunes are no longer maintained by the

processes that drive foredune morphology.

Hurricanes alter dune ecosystems by burying native vegetation under centimeters

of deposited sand and also change the configuration or presence of dune structures

(Ehrenfeld 1990). Dune erosion occurs as a result of storm surge and waves repeatedly

narrowing the dune face to cause an eventual breach or when storm surge overwashes a

dune and pushes sediment landward (Hesp 2002; Judge et al. 2003). Dune erosion is

influenced by duration and intensity of a storm event; however, structural features of the

dune also alter the risk of erosion.

Dune Restoration and Protection for Beach Mice

Increasing the amount of protected beach mouse habitat generally is not an option

as most coastal dunes in Florida already are in public lands or have been developed (Bird

2002). Therefore, other approaches such as habitat restoration may be important for









long-term maintenance of beach mouse populations. Although habitat restoration is cited

as critically important for the recovery of beach mouse populations (USFWS 1987; Oli et

al. 2001), little work has been conducted to identify habitat and landscape features that

influence use of frontal or secondary dune habitat by beach mice. Restoration techniques

have been developed to promote regeneration of physical structure to dunes after storms

(Miller et al. 2001; 2003). These techniques can be used to create dunes with particular

structural features (e.g., tall and wide), but identification of structural features of dunes

that confer resistance against storm-related erosion is limited.

Restoration techniques that promote creation of dunes and dunes with key habitat

requirements of beach mice may aid in management of existing protected habitats for

these mice. My study contributes to this effort in the following ways:

*Examining the relationship between dune erosion as a result of Hurricane Ivan and

the physical structure of frontal and secondary dunes (Chapter 2)

*Assessing the impact of Hurricane Ivan on the overall occupancy of frontal and

secondary dune habitat by beach mice (Chapter 3)

*Identifying habitat variables at the patch and landscape scale that influence

occupancy of frontal and secondary dunes by beach mice (Chapter 3).

Chapter 2 and 3 are written as stand-alone papers for publication. Therefore, some

background material is repeated in each chapter.















CHAPTER 2
INFLUENCE OF DUNE STRUCTURE ON STORM-RELATED EROSION FOR
FOREDUNES AND SECONDARY DUNES ON SANTA ROSA ISLAND, FLORIDA

Introduction

Coastal dunes are valued for their aesthetic beauty and their ability to protect

human-made structures during storms (Nordstrom et al. 2000; Nordstrom and Mitteager

2001). Dunes absorb wave energy, block storm surge, and reduce damage to

infrastructure. Coastal dunes also are important wildlife habitat (Martinez et al. 2005).

Hurricanes and tropical storms have altered coastal dunes on barrier islands along the

northern portion of the Gulf of Mexico in the last decade (Stone et al. 2004). Increases in

the severity and frequency of tropical cyclones are predicted and will further modify dune

configuration, reduce infrastructure protection, and disturb wildlife habitat (Emmanuel

2005). As a consequence, creation and restoration of dunes has become an important

issue in coastal management strategies (Nordstrom et al. 2000). Strategies for dune

protection and restoration could benefit from information on physical and spatial factors

that influence storm impacts on dunes.

Impacts of storms on dune erosion are a function of storm characteristics and

structural features of dunes. Dune erosion occurs when storm surge and waves

repeatedly narrow a dune face, causing irregular slumping of sediment and an eventual

breach, or when overtopping by storm surge completely overwashes a dune and pushes

sediment landward (Hesp 2002; Judge et al. 2003). Although severity and length of a

storm influence dune erosion (Kriebel et al. 1997; Sallenger 2000), key structural features









of dunes (e.g., height, width) also provide protection against dune erosion (Judge et al.

2003). Laboratory research and numerical models of dune erosion are extensive

(Vellinga 1982), but few studies have evaluated importance of dune structure in storm-

related erosion in the field (but see Judge et al. 2003). Additionally, past evaluations of

dune erosion often have been limited to foredune structures (i.e., dunes nearest to the

high tide line).

Coastal foredunes are formed from aeolian processes with dune development

occurring where sediment is trapped by vegetation (Hesp 2004; Psuty 2005). Secondary

dunes generally are found landward of foredunes and develop from sediment originating

on foredunes or they may be relict foredunes that are no longer controlled by aeolian

processes (Hesp 2004). Foredunes are differentiated as either incipient or established.

Incipient foredunes are low-lying developing dunes associated with pioneer plant

communities. Established foredunes evolve from incipient dunes and are distinguished

by presence of an intermediate plant community, including woody species. These dunes

have greater height and width than incipient dunes (Hesp 2002). Although the location

and development of incipient dunes may change annually, development of large

established foredunes takes decades, and these dunes remain in a relatively fixed position

unless removed by storms or anthropogenic disturbance. Evolution and maintenance of

established foredunes is not determined solely by sediment flows but rather by a suite of

additional factors like vegetation density and the frequency of wave and wind forces

(Hesp 2004).

Established foredunes and secondary dunes, by way of their size, should provide

greater resistance to increased tide levels and storm events than incipient dunes.










However, storm surge and waves associated with hurricanes of category 3 or above on

the Saffir-Sampson scale can cause even large (> 3 m tall) established foredunes to return

to a more erosional form or to be destroyed (Hesp 2002). Effects of strong hurricanes on

secondary dunes are less well documented. Impact of storm surge on secondary dunes

may be less severe as these structures are no longer governed by sand exchange, storm

tides or wave activity associated with foredune development (Hesp 2004). Additionally,

as a result of their spatial location behind wave-absorbing foredunes, dune erosion from

storm events may be lower for secondary dunes.

I assessed dune erosion along a barrier island ecosystem in the Gulf of Mexico after

Hurricane Ivan. The objectives of this study were to examine impacts of Hurricane Ivan

on established foredune and secondary dunes and to evaluate structural features of dunes

as predictors of dune vulnerability for these two dune types. I also examined dune

erosion as a function of the landscape context of the dune, including island width at the

location of the dune, distance to neighboring dunes and distance of the dune from the

position where Hurricane Ivan passed over the island. Identification of structural features

that allow dunes to resist storm-related erosion and evaluation of landscape attributes that

influence erosion are important for future manipulation of coastal dunes in a restoration

context.

Methods

Study Area

The study was conducted on Santa Rosa Island, which is a barrier island

approximately 60 km long and 0.5 km wide, in the Gulf of Mexico. The study site is

located on property owned and managed by Eglin Air Force Base (30024' N, 81037' W).

This portion of the island is approximately 20 km long and includes the island' s entire









width (Fig. 2-1). This area contains several military structures and a paved road for

military traffic but otherwise is undeveloped.

A thorough description of Santa Rosa Island's geomorphology can be found in

Stone et al. 2004. Foredunes are found near the high tide line and, in the absence of

hurricane activity, can run continuously the length of the island. Prior to Hurricane Opal

(1995), mean dune height was 3.8 m (Stone et al. 2004). These dunes are dominated by

sea oats (Uniola paniculata),) cakile (Calkile spp.), beach morning glory (Ipomoea

imperati), and seashore elder (Iva imbricata) but various woody species can be present on

foredunes in the absence of frequent disturbance. Secondary dunes are located behind

foredunes on the bayside of the island. Woody species dominate these dunes, including

fal se rosemary (Ceratiola ericodes), woody goldenrod (Chrysoma pauciflosculosa),

scrubby oaks (Quercus geminata) and sand pine (Pinus clausa). Between these two types

of dunes is grassland dominated by maritime bluestem (Schizachrium maritimum) and

bitter panic grass (Pan2icum ama~rum), interspersed with densely vegetated ephemeral

wetlands.

Characteristics of Hurricane Ivan

Hurricane Ivan made landfall as a category 3 hurricane on 16 September 2004,

west of Gulf Shores, Alabama, and approximately 100 km west of our study site. Storm

surge from the hurricane was estimated at 3 4.5 m from Mobile, AL to Destin, FL

(Stewart 2005), which encompassed all of Santa Rosa Island. Ivan was the most

destructive hurricane to make landfall along the Gulf coast in 100 years with a maj ority

of damage resulting from wave action associated with unusually high storm surge

(Stewart 2005).









Dune Mapping

Established dunes (foredunes, N = 93, and secondary dunes, N = 484) were

delineated in the field after Hurricane Opal (1995). Because established dunes change

very slowly over time, except when they are impacted by storms, these data could be

used as a baseline for dune structure prior to Hurricane Ivan. Dunes were mapped again

after Hurricane Ivan (2004). Geographic location of dune perimeters were recorded with

a TRIMBLE GPS unit in UTMs (Universal Tranverse Mercator) and differentially

corrected for < 1 m accuracy. Dunes were included if they were greater than 1.0-m high

with woody vegetation or greater than 1.5-m high with grasses or other herbaceous

vegetation. Dunes were considered distinct if they were separated by more than 3.0 m of

sand. Dune height (m) was measured every 15 m along the long axis of each dune using

a telescoping pole. Dune perimeters were incorporated into ArcView 3.2 (ESRI 1996)

and the following variables were calculated: dune area (ha), dune width (perpendicular to

the shoreline), length (parallel to the shoreline), and distance of each dune from the

position where Hurricane Ivan made landfall. Coordinates for the position where

Hurricane Ivan made landfall were obtained from the National Oceanic and Atmospheric

Association (Stewart 2005). Aerial photographs taken in 1995 were overlaid on dune

location in ArcView 3.2 to calculate island width at each dune location. I also recorded

presence or absence of foredunes located seaward of secondary dunes before Hurricane

Ivan. Gap distance for each dune was calculated as the average of the distance between

the closest dunes located immediately to the west and east of the target dune.

After Hurricane Ivan all remaining foredunes (N = 26) were remapped or recorded

as completely destroyed (100% loss) if not found during remapping (N = 67). A random

subset of small secondary dunes (< 0.25 ha, N = 34) were remapped after Hurricane Ivan.









All large secondary dunes (> 0.25 ha, N = 61) were remapped. The percentage of each

foredune or secondary dune lost from Hurricane Ivan was calculated by subtracting the

dune's area after Hurricane Ivan from the post-Opal dune area and by dividing this value

by the post-Opal dune area.

Statistical Analyses

For statistical analysis, I used data from all foredunes and all secondary dunes

prior to Hurricane Ivan, and I used all foredunes and a subset of secondary dunes sampled

after the hurricane. Because all small secondary dunes were not remapped after

Hurricane Ivan, I determined the proportion of the landscape occupied by large dunes (>

0.25 ha) and small dunes (< 0.25 ha) prior to Hurricane Ivan. I used these proportions to

determine the sample size for large and small dunes in analyses. The total area of scrub

dunes prior to Hurricane Ivan was 131.55 ha with large dunes making up 109.99 ha

(83.6%) of this total. To maintain the proportional area of the two dune types, I used the

34 small dunes randomly chosen for remapping after Hurricane Ivan and I randomly

selected 18 large dunes from the larger pool we mapped.

I used Pearson correlation coefficients to examine relationships among structural

variables for dunes, spatial location, and island width for all frontal dunes and secondary

dunes. Variables were examined for normality prior to examining correlations between

variables. For frontal and secondary dunes, data on percentage of dune loss were

transformed using arcsine transformation, and dune area and dune height prior to

Hurricane Ivan and dune area after Hurricane Ivan were transformed using log-

transformation (Zar 1998). I used t-tests to examine differences in dune area and dune

height between dune types before Ivan. Univariate linear regression initially was used to

identify structural or spatial variables that were important predictors of the percentage of









dune erosion after Hurricane Ivan for foredunes and secondary dunes, and I used logistic

regression to evaluate the importance of presence of foredunes on dune erosion in

secondary dunes. Changes in dune area of frontal and secondary dunes with the impacts

of Hurricane Ivan were examined with paired t-tests. All univariate tests were

conducted in SPSS version 13.0 (SPSS Inc., 2004) and I rej ected null hypotheses of no

influence on dune loss when p < 0.05.

Traditional multiple regression techniques may not work well when variables do

not meet parametric assumptions or when relationships between variables are complex or

non-linear (Bourg et al. 2005). I wanted to simultaneously assess the influence of all

predictor variables on dune erosion from Hurricane Ivan and examine relationships

between physical features of dunes and spatial location on dune erosion. I used

classification trees to assess how multiple predictor variables explained the impacts of

Hurricane Ivan on dune structure. This non-parametric approach splits the dataset into

smaller groupings with relatively homogeneous values of response variables (Breiman et

al. 1984). An advantage of classification trees is that they are simple to create, provide

intuitive descriptions of complex relationships, and explain variance in a dataset in a

manner similar to multiple regression or analysis of variance procedures (De'ath and

Fabricius 2000).

I used the RPART package in R (R Development Core Team, 2003) to build and

evaluate classification trees. Trees for foredunes were constructed with the percentage of

dune lost as the response variable and the following predictor variables: dune area (ha),

dune width (m), dune height (m), island width (km), gap distance (m), distance from

where the eye of Hurricane Ivan made landfall (km). All data except distance from the










eye of the hurricane were from measurements made prior to the hurricane. Classification

trees for secondary dunes used the same response and predictor variables as trees for

foredunes, but also included presence or absence of a foredune before Ivan. I used a

cross-validation procedure to evaluate the rate of misclassification as a function of tree

size (e.g., number of groupings) to select trees that were not over-fit (Breiman et al.

1984).

Results

Conditions before Hurricane Ivan

Before Hurricane Ivan, small dunes (< 0.25 ha) were numerous comprising 80 of

the 93 (86.1%) foredunes and 403 of the 484 (83.3%) secondary dunes. Secondary dunes

were larger in area but not taller than foredunes prior to Hurricane Ivan (dune area t = -

2.265, df = 575, p = 0.03; dune height t = 1.020, df = 575, p > 0. 10; Table 2-1).

Foredune area was correlated highly with dune height (r = 0.63, p < 0.01), but was not

correlated with west-east location as might be expected because Hurricane Opal made

landfall west of the study site (r = 0.06, p > 0.5). Correlations only considering foredunes

0.25 ha or larger also indicated no relationship between dune area and position on the

island's landscape (r = -0.05, p > 0.5). Similarly, dune area for secondary dunes was

correlated with dune height (r = 0.65, p < 0.01) and not correlated with west-east location

on the landscape (r = 0.02, p = 0.69).

Hurricane Ivan's Impact on Foredunes and Secondary Dunes

Dune area for foredunes and secondary dunes was reduced significantly by

Hurricane Ivan (foredunes, t = 5.160, df = 92, p < 0.01; secondary dunes, t = 3.267, df

= 51, p < 0.01, Table 2-1). Hurricane Ivan's storm surge physically removed 76.8% of

the foredune area. Of the original 93 foredunes measured, 67 were destroyed completely.









The 34 small secondary dunes sampled lost 42.1% of their total area. The 61 large dunes

lost 14.8% of total area. Based on the proportion of the area occupied by small and large

dunes on the pre-Ivan landscape, the total estimated loss of secondary dune area with

Hurricane Ivan is 19.3%. Reduction in dune area to these dunes was significantly less

than to foredunes (t = -9.953, df = 143, p < 0.01).

Univariate analyses of the relationship between dune structure, dune location,

island width, and dune loss for foredunes indicated that all variables except gap distance

were related significantly to dune loss (Table 2-2). However, many of these variables

were highly correlated making these tests difficult to interpret (Appendix E). In contrast,

for secondary dunes only dune height, dune width, dune length, and the presence of a

foredune were related to dune loss. Distance from the eye of Hurricane Ivan was an

important predictor of dune loss in foredunes, but not in secondary dunes (Table 2-2).

Regression Trees

Cross validation indicated the smallest classification trees to fit data from

foredunes and secondary dunes without an increase in misclassification error rate each

had 5 branches (Fig. 2-2). Regression trees for foredunes and secondary dunes indicated

that a different set of structural features were linked to dune erosion for dunes on the

oceanfront and bayside of the island (Fig. 2-3).

Percent of dune lost in foredunes after Ivan was related to dune structure (e.g.,

height and width) and the distance from where the eye of Ivan made landfall (Fig. 2-3a).

The amount of variance (R2) in dune erosion explained by the classification tree was

78.9%. The regression tree for foredunes indicated that structural features influencing

dune erosion in foredunes changed with distance of the dune from the location where

Ivan passed over the island (Fig. 2-3a).










The regression tree for secondary dunes sampled to represent proportional area of

small and large dunes on the landscape indicated that dune erosion of secondary dunes

was related to structural features of the dune, their position on the landscape, and the

presence of foredunes (Fig. 2-3b). This tree explained 76.3% of the variance in dune

erosion for secondary dunes. The tree first divided dunes by the width of a dune. For

wider dunes, the presence or absence of a foredune was important in determining dune

erosion. Dune erosion was lowest where foredunes were present. Where foredunes were

absent, dune erosion increased as distance from where Hurricane Ivan made landfall

increased. For narrow secondary dunes, island width was the only important factor

influencing dune erosion. Dune erosion was greater where the island was wide. Island

width and distance from where Hurricane Ivan made landfall are correlated (r = 0.46)

and, thus, may provide some of the same information (Appendix F).

When large (N = 61) and small (N = 34) secondary dunes were analyzed separately

with regression trees, results were easier to interpret. The presence of foredunes reduced

dune erosion for large secondary dunes and this was the only important variable (Fig. 2-

4a). However, this tree explained only 19.7% of the variance in dune erosion for large

secondary dunes. Erosion of small secondary dunes was lowest for dunes nearer to

where Hurricane Ivan made landfall, and no other variable appeared to be important in

predicting dune erosion for these dunes (Fig. 2-4b). Dune length and area were related

negatively to dune erosion for dunes at greater distances from where Hurricane Ivan

made landfall. This tree explained 76.6% of the variance in dune erosion for small

secondary dunes.









Discussion

My field study and mechanistic research in the laboratory (Vellinga 1982)

indicate that dune structure plays an important role in resistance of dunes to storm

damage. In addition, this study clearly demonstrates the influence of landscape context

of dunes on their vulnerability to dune erosion, including spatial location relative to a

hurricane's eye and presence of other dune structures. Identification of features that

promote resistance to storm-related erosion can aid agencies in the classification of

coastal areas that are especially vulnerable to future storm events. This information also

can assist in defining targets for coastal restoration.

Larger dunes on Santa Rosa Island experienced less erosion than smaller dunes

from Hurricane Ivan. However, the importance of location of the dune on the landscape,

and the specific structural features of dunes important in describing amount of erosion

were different for foredunes and secondary dunes, suggesting the processes that act upon

dunes during storms are different depending on distance from the shoreline. Much of the

erosion for foredunes probably is a result of storm surge. Foredunes that remained after

Hurricane Ivan showed signs of sediment slumping, dead or uprooted vegetation, and

blowouts; all of which are common effects of storm surge, wave action, and overwash.

Under these conditions height of a dune is likely to play a key role in resistance of dunes

to erosion, as demonstrated by the importance of this variable in our regression trees for

foredunes. Along exposed oceanfront beaches, the magnitude of storm surge and wave

action decreases with distance from the edge of the hurricane' s eyewall and damage to

foredunes follows a similar spatial pattern.

Secondary dunes on the island that experienced erosion lost sediment along dune

edges from passing storm surge and not from continual wave action. Dune erosion for









secondary dunes likely is influenced by storm surge from the Gulf of Mexico merging

with rising water levels in the Santa Rosa Sound, located behind the island. The presence

of foredunes substantially reduces erosion of secondary dunes. This observation

reinforces the importance of foredunes as buffers of storm surge for coastal features

located further landward and for protection of human-made structures.

One non-intuitive result of this research is that small secondary dunes that were

father from the eye of the hurricane and that were on the widest part of the island were

subject to more erosion that small secondary dunes nearer to the eye of Hurricane Ivan

and on the narrower part of the island. Storm damage on small secondary dunes

increased from west to east. The island widened from west to east, and the Santa Rosa

Sound narrowed as the island widened. When the flow of storm surge is confined and

water is shallow, high penetration distances have been observed for washover (Morton

and Sallenger 2003). I hypothesize that as the sound became narrower, the magnitude of

storm surge on the bayside of the island increased and smaller secondary dunes were

impacted more strongly, resulting in an inverse relationship between storm damage and

distance from the hurricane and an inverse relationship between storm damage and island

width.

Previous research on coastal dune systems has suggested that dune systems exist in

two opposing states: one where dune structure and vegetation communities are arranged

by environmental gradients generated from normal wind and wave activity and one

dominated by periodic but high levels of disturbance (e.g., hurricanes and tropical storms;

Synder and Boss 2002; Stallins and Parker 2003). I believe that coastal dunes on Santa

Rosa Island are beginning to shift towards the latter state, though assessment of dune









erosion after additional storms is needed to evaluate this statement. Prior to hurricane

Opal in 1995, the island's shoreline contained continuous foredunes (Stone et al. 2004).

Repeated hurricane activity has eroded or destroyed many foredune structures. My

analysis indicates that foredunes are important in protecting secondary dunes and thus,

further storm impacts may begin to affect secondary dunes more severely. The

consequences of this changing coastal landscape are large for maintenance of human-

developed infrastructure, success of restoration proj ects, and conservation of wildlife

species that depend on coastal dune habitat.







18


Table 2-1. Means and standard errors for structural variables measured to explain dune
erosion in foredunes and secondary dunes on Santa Rosa Island from
Hurricane Ivan. Differences between means before and after Hurricane Ivan
were compared using paired-t tests adjusted with Bonferroni's correction for
multiple tests ** p < 0.05.
Variable Foredunes Secondary Dunes'
Before Ivan After Ivan Before Ivan After Ivan
(N = 9 3) (N = 9 3) (N = 5 2) (N = 5 2)
Dune area (ha) 0.14 (0.03)** 0.05 (0.01) 0.37 (0.09)** 0.26 (0.07)
Dune height (m) 2.84 (0.15)** 0.89 (0.16) 3.28 (0.18)** 2.81 (0.15)
Dune length (m) 42.2 (5.8)** 12.1 (2.9) 80.6 (14.5)** 64.3 (11.3)
Dune width (m) 29.9 (2.6)** 10.7 (2.2) 56.6 (6.2)** 44.3 (6.4)
Gap distance (m) 95.6 (25.9) NA 50.8 (6.8) NA
Dunes were sub-sampled to represent proportional area of large (83.6%) and small (16.4%) dunes on the
landscape (N = 52 with 34 small dunes and 18 large dunes).










Table 2-2. Statistics for evaluation of dune characteristics as predictors of dune loss as a
result of Hurricane Ivan. Variables were assessed along a 20-km stretch of
Santa Rosa Island, FL prior to the hurricane. The significance of a variable's
role as a predictor of dune loss was evaluated using univariate linear
regression or a two sample t-test when examining the presence of absence of a
foredune before a secondary dune. Hypotheses of no association were rej ected
at p < 0.05.
Variable t p-value R2
Foredunes (N~ = 93)
Dune area (ha) 5.21 <0.01 0.21
Dune height (m) 5.43 <0.01 0.25
Dune length (m) -2.08 0.04 0.05
Dune width (m) -5.03 <0.01 0.22
Gap distance (m) 0.40 0.68 0.01
Island width (km) 3.08 <0.01 0.09
Distance from Ivan's eve (km) 6.09 <0.01 0.25

Secondary Dunesl (N = 52)
Dune area (ha) -1.65 0.10 0.05
Dune height (m) -2.39 0.02 0.10
Dune length (m) -2.35 0.02 0.10
Dune width (m) -3.50 0.01 0.20
Gap distance (m) 0.78 0.40 0.02
Island width (km) 1.05 0.30 0.02
Distance from Ivan's eve (km) -0.24 0.81 0.01
Presence / absence of foredune 2.47 0.02 NA
Dunes sub-sampled to represent proportional area of large (83.6%) and small (16.4%)
dunes on the landscape (N = 52 with 34 small dunes and 18 large dunes).








































Fig. 2-1. Map of Santa Rosa Island, FL. The study area encompasses the section
between Navarre and Fort Walton Beach.














1 2 3 4 5 7 9


size of tree


Ini 0.28 0.18 0.075 0.021 0.012 0.01
cp


size of tree
1234567891011


Ini 0.22 0.088 0.039 0.019 0.012



Fig 2-2. Cross validation relative error for regression trees for (a) foredunes and (b)
secondary dunes to explain dune loss from Hurricane Ivan in relation to
measured predictor variables. I used the 1-SE rule (Breiman et al. 1984) to
identify regression trees that had the smallest number of branches but were
closest to the overall minimum misclassification error (dotted line). Arrows
point to the best sized regression tree for each dune type. The complexity
parameter, cp, represents a balance between the complexity of a tree (i.e.,
more branches) and the costs of utilizing a simpler tree.












a)

Distance from Ivan Distance from Iv-an
> 114 km < 114 km




Dune height IDune height Dune width Dune width
123 m <23m 245S5m <45.5 m



Dune height Dune height
>41m < 41m
30.6% 91.4%m 1 98.9%
N= 11 N= 5 N=64




53.6%/ 801%
N= 6 N= 7


b)

Dune width Dune width
2 47.8 m <47.8 m





Presence of Absence of Island width Island width
foredmne foredmne < 062 kmn > 0.62 kmn





Distance from Distance from
Ian < 104.1n km Ivan 2 104.1 lan

113% 38.8%/ 64.6%
N=14 N=7 N=20





14.1% 46.99
N= 4 N= 7


Fig. 2-3. Regression trees relating percentage of dune lost from Hurricane Ivan for (a)
foredunes (N = 93) and (b) secondary dunes (N = 52) to physical features of
dunes, spatial location of dunes with respect to where Hurricane Ivan made
landfall, and width of island. Data for secondary dunes are based on sampling
of small (<0.25 ha) and large dunes (10.25 ha) according to their proportional
area on the landscape. Numbers at the ends of terminal nodes are the average
percentage of dune lost for all observations in that group. N is the number of
observations within that group.


























































I


Presence of
foredane


Absence of
foredine


19.4%
N= 44


36.6%
N=17


Distance from Ivan
< 104.8 lan


Distance from Ivan
2 104.8 lnn


Dune length
> 54.6 m


Dune length
< 54.6 m


21.9%/
N= 8


29.9%
N= 4


Dune area
10.07 ha










N= 9


Fig. 2-4. Regression trees relating percentage of dune lost from Hurricane Ivan for (a)
secondary dunes > 0.25 ha (N = 61) and (b) secondary dunes <0.25 ha (N =

34) to dune features, spatial location, and island width. Numbers at the ends
of terminal nodes are the average percentage of dune lost for all observations
in that group. N is the number of observations within that group.


Dune area
<0.07 ha









71.1%m
N= 13














CHAPTER 3
INFLUENCE OF HABITAT AND LANDSCAPE FEATURES ON SPATIAL
DISTRIBUTION OF SANTA ROSA BEACH MICE IN TWO DUNE HABITATS
BEFORE AND AFTER A HURRICANE

Introduction

Identification and protection of habitat are critical for species conservation and an

integral part of many conservation programs. GAP analysis, for example, utilizes

information on land cover and predicted species distributions to identify habitats that are

poorly represented in reserves (Flather et al. 1997). Habitat suitability index (HSI)

models establish relationships between a species' distribution and habitat variables to

create an index of suitable habitat that can be used to evaluate suitability of other areas

for habitat management or protection (UWFWS 1981). To aid in species recovery, the

Endangered Species Act provides for designation of critical habitat, which is the

geographic area that contains physical and biological features necessary for conservation

of the species (16 U.S.C. @ 153 1 et seq.). However, the effectiveness of critical habitat

designation is controversial as critical habitat often is defined using limited data or only

anecdotes, or found to be not determinable (Hoekstra et al. 2002; Taylor et al. 2005).

Habitat models are a common tool used to examine the role of habitat features in

explaining the spatial distribution, density, or diversity of species occurring on a

landscape and these models aid in the development of conservation strategies (Segurado

and Arauj o 2004; Guisan and Thuiller 2005). Incorporation of spatial structure of habitat

(e.g., size, shape and spatial distribution of habitat patches) along with traditional

assessments of habitat quality has improved the functionality of models (Cox and










Engstrom 2001). However, one problem with application of habitat models to

conservation problems is that their conclusions are based upon a limited range of

conditions because they generally are developed over short time periods (Pearce and

Ferrier 2000). Habitat availability and quality can shift rapidly with stochastic events

(VanHorne et al. 1997; Carlsson and Kindvall 2001), and key features that determine

spatial distribution or population density could change. Designation of protected

habitats often does not consider impacts of environmental stochasticity on distribution

and persistence of species even though disturbance may influence habitat turnover and

ultimately impact population persistence through impacts on habitat (Oli et al. 2001;

Jonzen et al. 2004; Frank 2005; Schrott et al. 2005). We demonstrate this issue with an

analysis of beach mouse habitat along the Gulf Coast of Florida before and after

Hurricane Ivan which made landfall in September 2004.

Coastal dunes are among the most dynamic and threatened habitats world-wide

(Martinez et al. 2005). Gulf Coast populations of beach mice comprise 5 subspecies, all

of which are subj ect to extreme stochastic events in the form of tropical storms and

hurricanes (e.g., Hurricanes Opal, 1995; Ivan, 2004; Dennis, 2005). Four of these

subspecies are listed as threatened or endangered (Potter 1985; Milio 1998). The

remaining subspecies, the Santa Rosa beach mouse (Peromyscus polionotus

leucocephalus), is not yet listed because its geographic range includes several federally

managed lands (Gore and Schaffer 1993). All subspecies suffer from severe habitat loss

from destruction of coastal sand dunes by development, and this habitat loss is

exacerbated greatly by hurricanes (Swilling et al. 1998). Optimal habitat for beach mice

is believed to be frontal dune habitat with sparse vegetative cover of sea oats (Uniola









paniculata) adjacent to the high tide line (USFWS 1987). Mice also occur in scrub

dunes, which are located farther from the beach and are characterized by increased

dominance of woody vegetation. These dunes provide refugia for mice during and

immediately after storms but are viewed as marginal habitat because of lower population

density in this habitat (Swilling et al. 1998). Three subspecies of beach mice on the Gulf

Coast are covered under the same federal recovery plan. This plan calls for protection of

dune communities within 152 m (500ft) of the high tide line and includes all frontal

dunes but excludes scrub habitat in most areas (Potter 1985; Swilling et al. 1998). The

St. Andrews beach mouse (P. p. peninsularis) is protected under its own recovery plan,

which states designation of critical habitat is not necessary for conservation of this

species (Milio 1998).

I examined impacts of Hurricane Ivan on the structure of frontal and scrub dunes,

compared occupancy patterns of beach mice in these two habitats, and determined how

these occupancy patterns changed after the hurricane. I also developed habitat models for

predicting dune occupancy by Santa Rosa beach mice and evaluated whether the factors

that influenced patterns of habitat occupancy were similar for frontal (optimal) and scrub

(marginal) habitats. I examined whether predictors of habitat occupancy changed after

the hurricane. My study demonstrates problems associated with narrowly defining

critical habitat as optimal habitat, particularly in systems characterized by high

stochasticity.

Methods

Study Area and Habitat Mapping

The study was conducted on Santa Rosa Island, a barrier island approximately 46-

km long and 0.5-km wide, located in the Gulf of Mexico near Fort Walton Beach, FL










(30024' N, 81037' W). My study area incorporated a 15-km section of the island on Eglin

Air Force Base (EAFB) and a 10-km section of the island on Gulf Island National Sea

Shore ginsS). Dune habitat was similar in these two areas and both sections contain a

single paved road and only a few structures. Frontal dunes were oriented parallel to high

tide line and were dominated by sea oats (Uniola paniculata),) cakile (Calkile spp.), beach

morning glory (Ipomoea imperati), and beach elder (Iva imbricate), and various woody

species in the absence of frequent disturbance. Scrub dunes were located on the bayside

of the island and woody species dominate scrub habitat, including false rosemary

(Ceratiola ericodes), woody goldenrod (Chrysoma pauciflosculosa), scrubby oaks

(Quercus geminate) and sand pine (Pinus clause). The area between frontal and scrub

dunes consisted of gently rolling grasslands interspersed with densely vegetated

wetlands.

EAFB dunes were mapped in the field before and after Hurricane Ivan by recording

their perimeters using a TRIMVBLE GPS unit and then differentially corrected for < 1 m

accuracy. GINS dunes were mapped only after the hurricane. Data were incorporated

into a cover layer in ArcView 3.2 (ESRI 1996).

Dune Occupancy

I surveyed for presence of beach mice in all frontal (N = 15) and scrub (N = 61)

dunes equal to or larger than 0.25 ha on EAFB before Hurricane Ivan (June September

2004) and after the hurricane (October 2004 December 2004). Frontal dunes (N = 15)

on GINS also were surveyed for beach mice after Hurricane Ivan (December 2004 -

February 2005). Presence of beach mice in each dune was determined with tracking

tubes that register footprints of mice that enter the tube. Tracking with tubes is less

dependent upon weather and less labor intensive than live trapping and, therefore,









particularly useful for large scale surveys of distribution (Mabee 1998; Glennon et al.

2002).

Tracking tubes were constructed with PVC pipe (33-cm long x 5-cm diameter) and

elevated 5-7 cm off the ground to prevent access by ghost crabs (Ocypode quadrata).

Dowels placed at either end of the tube allowed mice, but not crabs, to climb to the tube.

A paper liner was inserted into the bottom of each tube, and the tube was baited in the

middle with rolled oats. Felt inkpads located at each end of the paper liner were coated

with a 2: 1 mineral oil and carbon power solution (Mabee 1998). Hispid cotton rats

(Sigmodon hispidus) leave footprints that are substantially larger than footprints of Santa

Rosa beach mice. No other small rodents occur on undeveloped portions of the island

(Gore and Schaffer 1993).

Dunes less than 0.50 ha received eight tubes; dunes > 0.50 ha < 2.00 ha received

16 tubes; and dunes greater than > 2.00 ha received 32 tubes. Tracking tubes were placed

at 15-m intervals along transects that began and ended at the dune's boundary and ran

parallel to the long axis of the dune. The starting point for the first transect was selected

randomly and, when more than one transect was needed, parallel transects were

established 15 m apart. During each tracking session, tracking tubes remained in a dune

for five nights and were checked after each night.

For many species, probability of detection during presence/absence surveys is less

than one resulting in underestimates of occupancy, biased parameter estimates for habitat

models, and incorrect estimates of population persistence (Gu and Swihart 2004; Kery

2004). Therefore, I used recent statistical approaches for analysis of site occupancy that

build on traditional capture-recapture methods and used repeated censuses to calculate









detection probability (p) and to estimate the proportion of sites that are occupied ('P) after

accounting for detectability (MacKenzie et al. 2002). To estimate detection probability

within each habitat type, I re-sampled a random subset of scrub dunes (N = 30) with

tracking tubes three times after initial pre-storm surveys, and I resurveyed another

random subset of scrub dunes (N = 30) and all frontal dunes on EAFB three times after

initial post-storm surveys. Each repeat survey was conducted over 5 nights following the

sampling protocol described above.

Predictor Variables: Vegetation Cover and Landscape Structure

I measured vegetation cover and dune height on scrub dunes before and after

Hurricane Ivan. Surveys for these variables were not completed on frontal dunes before

the hurricane hit and, therefore, data on these variables were analyzed for frontal dunes

only post-hurricane. Vegetation cover was quantified using the line-intercept method

(Bonham 1989) along three 50-m transects placed 20 m apart and perpendicular to the

long axis of each dune. I recorded distances (cm) that sea oats, other herbaceous

vegetation, woody vegetation, and open sand occupied along each transect and divided

the distance for each cover class by total length of the transect to obtain percent cover for

each cover class. I averaged data for the three transects prior to analysis. Sea oats and

many herbaceous species are important food sources for beach mice (Moyers 1996).

Amount of open sand may be important for burrow construction and woody vegetation

may stabilize dunes during storms and provides food and cover for foraging.

I recorded dune height (m) by measuring height every 15 m along the long axis of

each dune using a telescoping pole and then averaged all values for each dune. Height of

a dune may influence perception of dune habitat by beach mice moving through the

landscape or influence the impact of storm surge on dunes. I calculated dune area and









amount of dune habitat surrounding each dune in ArcView 3.2 from the GIS database

created from field mapping of dunes. I also calculated the distance to the nearest

occupied dune as a measure of isolation. Habitat area may influence size of local

populations (Hanski 1994). We used the BUFFER function in ArcView 3.2 to estimate

the total area of dune habitat surrounding each dune at the foraging (200 m) and dispersal

(1 km) scales of beach mice (Bird 2002; Swilling & Wooten 2002). The east-west

coordinate (UTM) at the center of each dune was included in habitat models to examine

how spatial location relative to the eye of Hurricane Ivan influenced dune occupancy by

mice. The eye of the hurricane passed approximately 75 km west of the western end of

GINTS and 100 km west of the western end of EAFB.

Occupancy Models

I created and ranked a series of models with the program PRESENCE to identify

variables that influenced distribution of beach mice in frontal and scrub habitats

(MacKenzie et al. 2003). Correlations among variables were examined and correlated

variables ( r > 0.60) were not included in the same model (Welch and MacMahon

2005), or if correlated variables were used in the same model, a regression was conducted

for the two variables and residuals were included in the model as an independent measure

of one of the variables (Cooper & Walters 2002). Correlated variables requiring this

approach were pre-hurricane dune habitat within 1 km and pre-hurricane east-west

coordinate for scrub dunes (r = 0.69), post-hurricane dune habitat within km and post-

hurricane east-west coordinate for scrub dunes (r = 0.70), post-hurricane dune habitat

within 200 m and post-hurricane distance to nearest occupied dune for frontal dunes (r = -

0.62), and post-hurricane dune habitat within 1 km and post-hurricane east-west

coordinate for frontal dunes (r = 0.69).










Fifty-six candidate models were evaluated for scrub dunes using a combination of

variables measured before Hurricane Ivan and similar models were created with post-

hurricane data. The first eight base models included a combination of patch-level features

(e.g., dune area, % cover of woody vegetation, % cover of herbaceous vegetation, dune

height). An additional 24 models were created by adding distance to nearest occupied

dune, the 200-m habitat buffer or 1-km habitat buffer to the original base models.

Finally, I created another 24 models by including the east-west coordinate in the "base

model + landscape context" models.

I developed 40 candidate models for frontal dunes on EAFB and GINS after

Hurricane Ivan. All frontal dunes were occupied before Hurricane Ivan, so no model was

created for this period. To reduce risk of an over-parameterized model, I restricted the

total number of variables in a model to three. The first eight base models were the same

as in scrub habitat (i.e., patch-level features). An additional 32 models were created by

including distance to nearest occupied dune, 200-m habitat buffer, 1-km habitat buffer, or

spatial coordinate to base models. I also modeled post-hurricane occupancy of frontal

and scrub dunes on EAFB with pre-hurricane conditions to assess the role of pre-

hurricane conditions on post-hurricane occupancy. Models were created using the same

procedure as described above.

I used an Akaike Information Criterion (AICe) corrected for small sample bias to

select the best model and rank the remainder. I present AIC differences (Ai = AICci -

minimum AIC,), so that the best model has Ai = 0 (Burnham & Anderson 2002). Models

with Ai < 2 are considered competitive models. I also include Akaike weight (wi), which

indicates relative likelihood that model i is the best model. The relative importance of









each habitat variable (ws,;;,) was obtained by summing w, for all models that contained

this variable (Burnham & Anderson 2002). I performed model averaging to obtain

parameter estimates and unconditional standard errors for each habitat variable of interest

to reduce the bias of estimating parameter effects from a single model (Burnham and

Anderson 2002). When the confidence interval around a model-averaged parameter

estimate is > 0, an increase in the variable significantly increases the probability of

occupancy, and a value < 0 indicates that an increase in the variable decreases the

probability of occupancy (Buskirk 2005; Mazerolle et al. 2005). Estimated probability of

detection (p) and overall occupancy rate (WI) also were obtained using this approach.

Results

Hurricane Impacts on Habitat Availability at EAFB

Hurricane Ivan significantly reduced mean area of both types of dunes (Table 3-1),

but frontal dunes lost a much greater proportion of area. Storm damage resulted in a loss

of 68.2% of the total area of frontal dunes surveyed for beach mice, including complete

destruction of four dunes. No scrub dunes were destroyed entirely but the total area of

scrub dunes surveyed for beach mice was reduced by 14.8%. Dune height also was

reduced significantly for both dune types (Table 3-1). The amount of habitat within 200

m and 1km of a dune was reduced significantly for scrub dunes but not frontal dunes.

However, frontal dunes already had little habitat within 200 m prior to the hurricane

(Table 3-1).

Dune Occupancy

Beach mice were detected in 100% of frontal dunes and 72. 1% of scrub dunes prior

to the hurricane, and in 51.8% of frontal dunes and 73.8% of scrub dunes after the

hurricane. Probability of detection was high in all surveys. Site-occupancy models










suggest that before the hurricane 75.1 & 5.5% (model-averaged estimate & unconditional

SE) of scrub dunes were occupied, with a detection rate of 88.6 & 5.6%, and after the

hurricane 78.6 & 4.9% of sites were occupied, with a detection rate of 90. 1 & 3.1%.

Differences in occupancy of scrub dunes before and after the hurricane were not

significant (t = 0.5, df = 60, p > 0. 10). Occupancy in frontal dunes dropped to 59.7 &

5.1%, with a detection rate of 89.8 & 5.5% after the hurricane, and occupancy in frontal

dunes was significantly lower than occupancy of scrub dunes (t = 1.8, df = 42, p < 0.05).

Habitat Models

A combination of patch-level and landscape-level features ranked high in models of

occupancy of scrub dunes before and after the hurricane (Table 3-2 and 3-3). The

strongest model for scrub dunes before the hurricane included dune area, percent woody

vegetation cover, and amount of dune habitat within 200 m. No other models were

competitive. After the hurricane, the same model was the strongest; however, the Akaike

weight was much lower and several additional models were competitive (Table 3-3). All

models with Ai < 2 contained some combination of the variables in the best model except

dune height and total herbaceous cover were included in several models. Ranking of

variables based on the sum of their Akakie weights revealed that amount of dune habitat

within 200 m of scrub dunes was the most important variable in explaining probability of

occupancy of scrub dunes by mice before and after the hurricane (Table 3-3), followed

closely by dune area, and percent woody vegetation cover. Before and after the

hurricane, probability of beach mice occupying a scrub dune increased as amount of

habitat surrounding the dune within 200 m increased and dune area increased (Table 3-3).

Occupancy of scrub habitat by beach mice also appeared to increase with increasing

cover of woody vegetation before and after the hurricane, but this relationship was not









statistically significant. Top models of post-hurricane occupancy in scrub dunes using

pre-hurricane conditions retained the same suite of predictor variables (Table 3-2).

The strongest model for occupancy of frontal dunes after Hurricane Ivan included

percent woody vegetation cover and distance to nearest occupied dune (Table 3-2). In

contrast to scrub habitat, the amount of habitat surrounding a dune within 200 m was not

a factor in any competitive models. The likelihood of occupancy increased with

increasing cover of woody vegetation and this variable was the top ranked variable in

models of occupancy (Table 3-3). Increasing distance to the nearest occupied dune also

appeared to reduce the probability of occupancy after the hurricane but this relationship

was not statistically significant (Table 3-3). Dune height was the third ranked variable

and an increase in dune height appears to increase occupancy by beach mice in frontal

habitats but also was not statistically significant (Table 3-3). When post-hurricane

occupancy of frontal dunes on EAFB was modeled with variables related to the structural

and landscape context of dunes prior to the hurricane, dune height and distance to the

nearest occupied dune were the most important predictors of occupancy (Tables 3-2 and

3-3). The likelihood of occupancy of frontal dunes by beach mice after the hurricane

increased with a greater dune height prior to the hurricane, and a greater distance to the

nearest occupied dune prior to the hurricane decreased the likelihood of occupancy after

the hurricane (Table 3-3). Models for occupancy of frontal dunes, with pre and post-

hurricane habitat data, had a better fit than models of scrub dunes (Table 3-2).

Discussion

Frontal dunes near the high tide line are subj ected to major impacts during

hurricanes. Prior to Hurricane Opal (1995), frontal dunes ran relatively continuously

along the entire length of Santa Rosa Island (Stone et al. 2004). This hurricane and









subsequent tropical storms fragmented frontal dunes. Storm surge from Hurricane Ivan

removed close to 70% of the remaining frontal dunes. In contrast, no scrub dunes, which

are located on the bay side of the island, were completely lost with Hurricane Ivan and

reduction in area of scrub dunes occurred along dune edges from passing storm surge.

Distance from the eye of the hurricane influenced dune lost for frontal and scrub dunes

along this portion of Santa Rosa Island (Chapter 2). Tropical storms and hurricanes are

predicted to be increasing in number and severity (Emanuel 2005). Frontal habitat for

beach mice will continue to be fragmented and removed if the interval between

hurricanes and other tropical storms remains shorter than the time required for dunes to

develop. In contrast, my results suggest that the amount and configuration of scrub dunes

on this barrier island may remain relatively consistent. However, as buffering capacity

provided by frontal dunes is lost, scrub dunes may suffer more impacts. Also, Hurricane

Ivan was a category 3 hurricane; stronger hurricanes could have greater impacts.

Predictors of occupancy for beach mice in frontal habitat after the hurricane were

closely tied to local habitat features (e.g., percent cover of woody vegetation and dune

height) and proximity to other occupied dunes. Optimal beach mouse habitat generally is

described as tall frontal dunes vegetated by sea oats and other herbaceous plants (Holler

1992). My habitat model indicates that woody vegetation cover also is important to mice,

at least during hurricane cycles. Foraging experiments demonstrate that mice consume

more seeds under vegetation cover than in the open (Bird 2002). Woody plants provide

cover for foraging, serve as a food source for mice, and also may promote dune stability

during storms (Moyers 1996; Musila et al. 2001). Similarly, dune height may be an

important factor in dune stability, particularly in preventing overwash by storm surge.










Beach mice also are semi-fossorial and an increase in dune height may facilitate

conditions appropriate for burrow construction. For frontal dunes on EAFB where the

impact was severe, dune height prior to the hurricane was a significant predictor of post-

hurricane occupancy, but after the hurricane the importance of this variable was not as

clear.

Isolation explained post-hurricane occupancy of frontal habitat, whether modeled

with pre- or post-hurricane habitat conditions. This observation likely reflects the history

of disturbance and loss of frontal habitat on this island. Beach mice occupying frontal

dunes prior to Hurricane Opal experienced a fairly continuous habitat where habitat

quality might have been determined largely by resource availability or appropriate

burrow conditions. The current fragmented frontal dunes are too small to support

separate populations of beach mice, but rather may serve as resource patches for mice

moving among dunes.

The most important predictors of occupancy before and after the hurricane for

beach mice in scrub habitat were landscape features related to habitat amount (i.e., dune

area and amount of surrounding habitat). Predictors of occupancy in scrub habitat were

similar before and after the hurricane, presumably because the impact of the hurricane on

the structure of these dunes was minimal. Woody vegetation also may play a role in

occupancy of scrub dunes by beach mice, but this relationship is not as clear as in frontal

dunes. The amount of dune habitat surrounding scrub dunes is greater than for frontal

dunes, which may indicate less isolation for these dunes. The importance of surrounding

dune habitat for occupancy of scrub may reflect reduced habitat quality in scrub dunes.

Alabama beach mice travel further distances to forage in scrub habitat than in frontal









habitat during the winter and spring (Sneckenberger 2002). If habitat quality and

population density are lower in scrub dunes, larger areas may be required to maintain

mouse populations.

Dune restoration after hurricanes primarily has focused on re-establishment of sea

oats, which produces a lattice of rhizomes that accumulate sand and also is an important

food plant for beach mice. My results suggest that restoration programs for frontal dunes

also should include re-establishment of woody plants and promote increases in dune

height. Beach mice also should benefit from restoration programs that reduce isolation of

frontal dunes.

The results of my study suggest that optimal habitat for beach mice differs under

different environmental conditions. Lower occupancy of scrub habitat than frontal

habitat by beach mice prior to the hurricane, and documentation of lower density in scrub

habitat from other studies, suggest that scrub habitat could be lower quality than frontal

dunes under pre-hurricane conditions, though density is not always a good indicator of

habitat quality (Van Horne 1983). However, persistence of scrub habitat and maintenance

of occupancy levels by beach mice through the hurricane in this habitat versus the severe

loss of habitat and significant reduction in occupancy of frontal habitat suggest scrub is

an essential habitat. Scrub was an important refugia habitat for Alabama beach mouse

populations during Hurricane Opal and a source of dispersing individuals after the

hurricane (Swilling et al. 1998). Re-colonization of frontal habitats by beach mice after

Hurricane Opal occurred within nine months (Swilling et al. 1998). I observed beach

mouse tracks on previously unoccupied frontal dunes in March 2005, approximately six

months after Hurricane Ivan. These mice may have dispersed from scrub or neighboring










frontal dunes. Given the inevitable loss of frontal dunes with hurricanes, incorporation of

scrub habitat into conservation efforts for Gulf Coast beach mice is warranted to ensure

long-term population persistence. Scrub habitat, even as marginal habitat, will improve

population persistence and lessen extinction risk as frontal habitat is further removed.

The role of stochasticity and uncertainty in management outcomes has been

explored extensively with respect to impacts on population size and persistence of species

of economic or conservation concern (Ellner and Fieberg 2003). Our study demonstrates

the need to incorporate these factors in habitat planning and protection. Habitat

availability for species in dynamic landscapes can change quickly and additional habitats

may become critically important after stochastic events (Carlsson and Kindvall 2001;

Biedermann 2004). When the dynamics of landscape and population are not understood,

protection of habitat should follow a conservative approach. Failure to consider and

protect habitats required under different environmental conditions may exacerbate the

impacts of habitat loss and change on extinction risk.











Table 3-1. Means and standard errors for structural and vegetation variables measured
for modeling occupancy of frontal and scrub habitat by Santa Rosa beach
mice on Eglin Air Force Base (EAFB) and Gulf Islands National Seashore
(GINS) on Santa Rosa Island, FL. Variables were assessed before and after
Hurricane Ivan made landfall on 18 September 2005. Differences between
means before and after Ivan were compared for dunes on EAFB using paired
t-tests adjusted with Bonfferoni's correction for multiple tests. ** p < 0.05.


GINS after
hurricane
(N= 15)
0.15 (0.03)
3.24 (0.20)
0.24 (0.05)

1.94 (0.29)

126.2 (39.4)

3.3 (2.1)

19.2 (2.3)

506117 (1709)


Scrub Dunes
Mean (a SE)


Dune perimeter was correlated highly (p <0.01l)with dune area and was omitted from analyses.
2 Distance to nearest occupied dune dropped after the hurricane because the four dunes that were destroyed were very
isolated (mean distance to nearest occupied dune for those dunes = 459.5 m). Pre-hurricane mean for distance to
nearest occupied dune for the 11 dunes to survive Ivan = 131.7 m.


Frontal Dunes
Mean (a SE)
EAFB after
hurricane
(N= 1 )
0.26 (0.06) **
3.13 (0.35) **
0.22 (0.08)

8.67 (1.44)


Variables


EAFB before
hurricane
(N = 6 1)
1.82 (0.38)
4.64 (0.3)
2.21 (0.28)

12.73 (1.19)

176.1 (38.3)

19.6 (1.6)

14.1 (1.4)

524519 (653)


EAFB after
hurricane
(N= 61)
1.55 (0.37) **
3.32 (0.17 ) **
1.71 (0.19) **

11.29 (1.03) **


EAFB before
hurricane
(N= 15)
0.59 (0.09)
4.01 (0.33)
0.29 (.09)

8.45 (1.48)


Dune area (ha)
Dune height (m)
Dune habitat within
200 m (ha)
Dune habitat within
1 km (ha)
Distance to nearest
occupied dune (m)
Percent woody
cover
Percent total
herbaceous cover
East-West
Coordinate (UTM)
(m)


174.2 (37.6) 219.2 (54.7) 161.9 (25.9)2


19.9 (1.6)

7.4 (0.8) **

524519 (653)


no data

no data

522959 (1337)


6.5 (1.4)

24.5 (2.9)

524208 (879)











Table 3-2. AIC-based selection of site occupancy models of dune occupancy for Santa
Rosa beach mice in frontal and scrub dune habitat. K = the number of
explanatory variables plus 1, Ai = AICci -minimum AICci, w, = Akaike
weights. Models with Ai < 2 are presented.


Habitat and
conditions
Scmub pre-
hurricane


Period of
occupancy
Pre-hurricane


a, w, Rza
0.00 0.42 0.242


Location
EAFB /
N =61


Model
Dune area, habitat within 200
m, percent woody cover


Scmub post- Post-
hurricane hurricane


EAFB / Dune area, habitat within 200
N= 61 m, percent woody cover
Dune area, habitat within 200 m
Dune height, percent woody
cover, habitat within 200 m
Percent woody cover, habitat
within 200 m
Percent total herbaceous cover,
habitat within 200 m
Dune height, habitat within 200

Dune area, percent total
herbaceous cover, habitat within
200 m

EAFB / Dune area, habitat within 200
N =61 m, percent woody cover
Dune habitat within 200 m
Percent woody cover, dune
habitat within 200 m
Dune area, dune habitat within
200 m
Dune height, dune habitat
within 200 m, percent woody
cover
Dune area, percent total
herbaceous cover, dune habitat
within 200 m


0.18

0.16

0.14


0.264


3 0.94 0.12

3 1.63 0.08

3 1.69 0.08


1.98 0.07


Scmub pre-
hurricane


Post-
hurricane


0.26

0.18

0.12


0.287


3 1.60 0.12


4 1.91 0.10


4 1.91 0.10


EAFB;'
GINS /
N=27


Frontal post-
hurricane


Post-
hurricane


Percent woody cover, distance
to nearest occupied dune


3 0.00 0.58 0.467



3 0.00 0.73 0.489


Frontal pre- Post-
hurricaneb hurricane


EAFB / Dune height, distance to nearest
N = 15 occupied dune


" There is no RL analogue for patch occupancy models, instead we used a niax-rescaled RL value as an approximate
measure of strength of association for the top model in each candidate set (Nagelk~erke 1991). All top models provided
a significantly better fit than a base model with no environmental predictors (p < 0.05).
b This model was developed with data on the structure and landscape context of dunes and does not include vegetation
variables that are in other models.





Table 3-3. Relative importance (wstmi), model-averaged parameter estimates, and
unconditional standard errors for variables used to model occupancy for
beach mice in frontal and scrub habitat before and after Hurricane Ivan. Wsum
was estimated by summing Akakie weights (wi) of all models with a variable
of interest. **Confidence intervals do not contain 0 and indicate variable

significantly influences occupancy.
Habitat W Parameter Estimate SE 9()% C.I.


Scrub pre-hurricane habitat
Pre-inovicane occupancy
Dume area (ha) **
Dume height (nt)
Percent cover woody vegetation
Percent cover herbaceous
Distance to nearest occupied dune (nl)
Dume habitat within 2()( n (ha) **
Dume habitat within 1 kn (ha)
East coordinate (na)

Scrub post-hurricane habitat
Post-inovicane occupancy
Dume area (ha) **
Dume height (nt)
Percent cover woody vegetation
Percent cover herbaceous
Distance to nearest occupied dune (nl)
Dume habitat within 2()( n (ha) **
Dume habitat within 1 kn (ha)
East coordinate (na)


().679
().165
().642
().128
).()46
().888
).()17
().()()




().536
().257
().498
().197
).()23
().864
).()65
().()()




().524
().162
().498
().163
().()()
().936
).()1()
().()()




().141
().652
().969
).()14
().769
).()32
).()48
).()71




).()67
().73()
().828
).()53
).()25
().()()


().7)9
).()29
1.717
).()(3
).()28
().719
).()(3


().418
).()39
1.221
().182
).()35
().351
).()(7


).()22
-).()35
-().292
-().296
-().)3
().142
-).()(9


1.396
).()93
3.726
().3()2
).()86
1.296
).()15


().548
).()81
1.)24
-().1)6
).()(9
1.)89
).()(6





().516
).()36
1.218
-().3)5

1.)22
().129


().319
().1)6
().943
().714
).()12
().574
).()(7





().298
).()45
1.)48
().377

().461
).()93





().629
().893
9.854
).()33
1.851
).()68
).()89
).()(9




).()98
().151
).()(8
().121
).()6()


).()23
-).()93
-().527
-1.281
-().()ll
().145
-).()(6


1.()73
().255
2.575
1.()69
).()29
2.()33
).()18


Scrub pre-hurricane habitat
Post-hunricane occupancy
Dume area (ha) **
Dume height (nt)
Percent cover woody vegetation
Percent cover herbaceous
Distance to nearest occupied dune (nl)
Dume habitat within 2()( n (ha) **
Dume habitat within 1 km (ha)
East coordinate (na)

Frontal post-hurricane habitat
Post-inovicane occupancy
Dume area (ha)
Dume height (nt)
Percent cover woody vegetation**
Percent cover herbaceous
Distance to nearest occupied dune (nl)
Dume habitat within 2()( n (ha)
Dume habitat within 1 kn (ha)
East coordinate (nt)

Frontal pre-hurricane habitat
Post-hunricane occupancy
Dume area (ha)
Dume height (nt) **
Distance to nearest occupied dune (nl) **
Dune habitat within 2()( n (ha)
Dume habitat within 1 km (ha)
East coordinate nx)


).()26 1.()(6
-).()38 ().11
-().5()6 2.942
-().925 ().315

().264 1.78()
-).()24 ().282


().383
1.)27
16.355
).()(9
-2.627
).()13
-).()55
).()(6




).()32
().485
-).()15
).()93
).()5()


-().652
-().353
().146 -
-).()45
-5.672
-().1)6
-().2)1
-).()(9




-().129
().237
-).()28 -
-().1)6
-).()48


- 1.418
- 2.4()7
32.564
- (.()63
- (.418
- (.132
- (.()91
- (.()21




- (.193
- (.733
-().()(2
- (.292
- (.148















CHAPTER 4
CONCLUSIONS AND CONSERVATION IMPLICATIONS

Habitat loss and fragmentation from coastal development and hurricanes are

believed to be maj or threats to the long-term population persistence of Gulf Coast beach

mice (Holler 1992; Oli et al. 2001). Frontal dunes are protected as critical habitat for

Gulf Coast beach mice, but they are disturbed greatly by hurricanes (Chapter 2). Scrub

dunes (also known as secondary dunes), which are not currently protected under federal

recovery plans, are impacted less by hurricanes and have been proposed to serve as

important refugia habitat for beach mice during hurricanes (USFWS 1987; Swilling et al.

1998). Although restoration techniques exist to promote regeneration of physical

structure of coastal dunes after storms (Miller et al 2001; 2003), understanding of the

features that confer resistance against storm erosion is limited. Also, prior to this study,

little quantitative information was available on: 1) how hurricanes impact habitat

availability for beach mice, 2) utilization of scrub habitat by beach mice, 3) habitat

features that predict occupancy of frontal and scrub dunes by beach mice, and 4) relative

impacts of hurricanes on beach mouse occupancy of frontal versus scrub dunes. My

study contributes to filling these gaps.

Dune Erosion and Loss of Beach Mouse Habitat

Frontal dunes received much greater impacts from Hurricane Ivan than scrub

dunes, and larger dunes in both frontal and scrub habitat experienced less erosion than

small dunes. Structural features that conferred resistance against storm erosion differed

for frontal and scrub dunes, suggesting that different processes act upon these two dune










types. For frontal dunes, tall and wide dunes experienced the least amount of erosion

from Hurricane Ivan's high storm surge. Dune erosion for secondary dunes was

influenced by storm surge from the Gulf and probably also by rising water levels in the

Santa Rosa Sound, located behind the island. Secondary dunes experience less erosion

when located behind a frontal dune. This observation highlights the importance of

maintaining frontal dunes as buffers of storm surge. Small secondary dunes located

farther from the eye of Hurricane Ivan and located on the widest parts of the island

experienced more erosion than small secondary dunes closer to the eye of Hurricane Ivan

and on narrow parts of the island. The reason for this pattern is unknown, but it may be

related to storm surge in the narrow parts of the Santa Rosa Sound. When the flow of

storm surge is confined and water is shallow, high penetration distances have been noted

for washover (Morton and Sallenger 2003).

Hurricanes will continue to fragment and reduce coastal dunes if the interval

between hurricanes and other tropical storms remains shorter than the time required to

redevelop dunes through natural processes or restoration. Tall and wide frontal dunes are

more resistant to storm erosion than smaller frontal dunes and may continue to provide

suitable habitat for beach mice if they maintain appropriate habitat conditions. However,

Hurricane Ivan alone reduced the frontal dune habitat of beach mice by 76.8% in our

study area. As dunes become smaller with subsequent storms, erosion of frontal dunes

may accelerate. In contrast, secondary dune habitat was reduced by only 19.3% by

Hurricane Ivan, indicating that, in periods of high hurricane activity, scrub dunes provide

more stable habitat for beach mice than frontal dunes. However, removal of frontal

dunes is likely to increase impacts of hurricanes on secondary dunes as the buffering










capacity of frontal dunes is lost. Given the inevitable loss of frontal dunes from

hurricanes, incorporation of scrub habitat into conservation efforts for Gulf Coast beach

mice is warranted. Although scrub habitat has been considered marginal for beach mice,

my data suggest that conservation of scrub habitat will promote population persistence

and lessen extinction risk as frontal dunes continue to be removed from the landscape.

Landscape-scale research is needed to understand the interdependency of subpopulations

of mice in frontal and scrub dunes, the conditions under which either of these habitats is

optimal or marginal, and the relative contributions of each of these habitats to long-term

persistence of beach mice populations.

Habitat Restoration for Beach Mice

Dune restoration for frontal dunes after storms typically has focused on the re-

establishment of sea oats, which produces a lattice of rhizomes that can quickly trap and

accumulate sand. My results indicate that cover of woody vegetation is important for

promoting occupancy of frontal dunes by beach mice and woody plants also may

influence occupancy of scrub dunes. This observation has important implications for

conservation and management of beach mouse habitat, as optimal habitat for beach mice

generally is believed to consist of tall frontal dunes vegetated by sea oats and other

herbaceous species (Holler 1992). My data suggest that restoration programs should

incorporate the re-establishment of woody plants on frontal dunes. Scrub dunes are

dominated by woody vegetation, and habitat management strategies for beach mice

should aim to maintain this vegetation. We do not know the exact mechanism by which

woody vegetation influences occupancy of dunes by mice, but woody species provide

cover and food for mice and may stabilize dunes during storms. More research will be

required to understand these mechanisms and to identify key woody species for mice.









Results of my study indicate that landscape context is important for enhancing

occupancy of dune habitats by beach mice regardless of dune type. Isolation restricts

occupancy of frontal dunes and amount of dune habitat surrounding scrub dunes

influences occupancy of these dunes. As dune systems are eroded by hurricanes, dune

fragments become more widely separated by open sand that does not provide resources

for beach mice (e.g., food and substrate for burrow construction) and mice are forced to

move over large open areas to obtain resources in different patches. Movement of mice

also is critical for recolonization of the landscape in areas where mice are extirpated

during hurricanes and for recolonization of restored habitat. These movements are likely

to entail considerable risk (e.g., increased risk of predation). Management efforts should

aim to minimize isolation of dunes. Restoration techniques that provide connectivity

(i.e., facilitate movement) between fragmented frontal dunes or between frontal and

secondary dunes also may benefit beach mice. Vegetation cover facilitates foraging of

beach mice (Bird et al. 2004) and, presumably, would enhance movement by reducing

risks associated with moving between fragments of habitat that remain after hurricanes.

Although my occupancy data provide general evidence that landscape connectivity is

important for beach mice, factors limiting mouse movement (e.g., the degree to which

large open sand gaps restrict movement) are unknown and this would be a fruitful area of

research for understanding the long-term persistence of beach mice in dynamic

landscapes. Finally, restoration techniques to promote increases in dune height for

frontal dunes also would be beneficial for beach mice as taller dunes are more resistant to

storm-related erosion and may facilitate conditions appropriate for burrow construction.















APPENDIX A
DELINEATION OF DUNES IN THE FIELD


Critical definition of dunes for delineation in the field:

*Dunes were mapped if greater than 1 m high with woody vegetation or greater than
1.5 m with grasses and other herbaceous vegetation.

*Dune spurs were considered part of a dune if the cleft between dunes was less than
1.5 m in height.

*Dunes were considered to be separate if they were separated by more than 3 m.

























Table B-1. Correlations for variables measured on 61 scrub dunes surveyed for beach
mice before Hurricane Ivan (Jun. 2004 Sep. 2004).
Dume Dume Dume Dume % w~Poody %6 total East-west Distance
araheight hab~itat habitat vegetation hembaeous Coordinnate to nearest
(h)(m) wi~thmn wpithin I cover cover (UTMu) occupied
200 me km (ha) dume (m)
(ha)

Dume area (ha): r 1 0.559 0.457 0.2L0 0.242 -0.204 0.278 -0.16i5
p <0.01 Dume height (m) :r 0.559 I 0.458 0.301r -0.183 -0060.451; -0.141
Sp <0.01 <00 0.017 0.159 0.613 <01 0.279
Dume hab~itat within 200 m (hai) :r 0.457 0.458 1 0.742 -0.111 0.090 0.488 -0.391

ip <0.01 <0.01 0.01 0.393 0.490 <0.01 0.02
Dume hab~ilat within 1 km(h) /r 0.250n 0.306; 0.742 1 -0.086; 0.002 0.1;89 -0.409)

/p 0.043 0.017 <0.01 -0.508 0.989 <0.01 0.01
%w~oodyvegeation cover :r 0.242 0.183 -0.111 -0.086 I -0.388 0.043 0.099

:p 0.06;0 0.159 0.393 0.508 -0.002 0.742 0.448

%/ totalhbembaeous cover r, -0.204 -O.Odd 0.090 0.002 -0.388 1 -0.047 0.050

!p 0.115 0.6i13 0.490 0.989 0.002 -0.718 0.700
East-west coordinate (UTM)f Ir 0.278 0.456; 0.488 0.1r89 0.043 -0.047 1 -0.166C

Ip 0.030 <0.01 <0.01 <0.01 0.742 0.718 0.200

Distance in nearest occupied dxute (m) r -0.165 -01.141 -0.391 -0.409 01.099 0.050 -0.166L I

:p 0.228 0.279 0.02 0.01 0.448 0.700 0.200


APPENDIX B
CORRELATION MATRICES FOR VARIABLES BY HABITAT









48




Table B-2. Correlations for variables measured on 61 scrub dunes surveyed for beach
mice after Hurricane Ivan (Oct. 2004 -Jan 2005).


Dumo Dime hitait %wnoody % total East-wasut )inao
area height within whi1 vegetation harbacous Coordinate ocpe
(ha) (m) 200 m m ha cover cover ([JTIV) dm m
(ha) a)due m

DL111 aroa~ha) ir I 0551 0.265 0.194 0973 -0.162 0.254 -0.154
ip 0.01 0.039 0.134 0.574 0.211 0.048 0.236
Duni hoigpt(m) ir 0-551 1 0958 0.155 0976 -0933 0.227 -0.104
p <0.01 0.657 0.234 0.563 0.798 0.078 0.426
Dunm habitatwithin2000m ha) r 0265 0958 I 0380 -0277 0909 0A77 -0383
p 0.039 0.657 -<0.01 0.031 0.945 <0.01 0.002
DLuIG habitat within l km (ha) r 0.194 0.155 0.780 1 -0.172 -0.124 0.702 -0A51
p 0.134 0.234 <0.01 .182 0.341 <0.01 <0.01
%woody vegetation cover Ir 0973 0976i -0277 -0.172 I -0974 -0078 0.171
ip 0.574 0.563 0.031 01.86 -0.540 0.549 0.187
% total harbacous cover ir -0.162 -033 0009 -0.124 -074 I -0.131 0929
p 0.211 0.798 0.945 0.341 0.540 -0.313 0.825
East-vest coordinate (UTMv) r 0254 0227 0A77 0.702 -0078 -0.131 1 -0A64

p 0.048 0.078 <0.01 <0.01 0.549 0.313 -<0.01

Distance to nearest occupied domeo (m) ir -0.154 -0.104 -0383 -0A51 0.171 OD29 -0A64

p 0.23(6 0.426 0.002 <0.01 0.187 0.825 <0.01



Table B-3. Correlations for variables measured on foredunes (Eglin Air Force Base,

N = 11, and Gulf Islands National Seashore, N = 15) surveyed for beach

mice after Hurricane Ivan. (Oct. 2004 Feb. 2005).


Drule ture D tat tat %woody % total EalSt-est Disancet
area height within itn1 vagotation harbacous Coordinateoccpe
(ha) (m) 200 m m ha cover cover (UTMv) dur m
(ha) a)due m

Dume aRen(Im) r I 0A33 0.110 0A69 0304 0322 0A31 -0D35
ip- 0.027 0.592 0.016; 0.132 0.109 0.028 0.862z
Dtun heiglt(m) r 0A33 I -0321 OD47 -0935 -0.118 OD36 0014
ip 0.027 -0.109 0.818 0.866; 0.565 0.861 0.946
Dume habitat within 200 m (ha) r 0.110 -0321 I 0.237 -0D74 -0.127 -0050 -0 21
IP 0.592 0.109 -0.244 0.718 0.536 0.809 <0.01
Doui habitatwnithinl krm Pa) ir 0469 OD47 0.237 I 0382 0A88 0088 -0978
ip 0.016 0.818 0.244 -0.049 0.011 <0.01 0.700
% woody vegetationl cover r 0304 -0035 -0974 0382 1 0605 0.740 0.139
ip 0.132 0,866 0.718 0.049 .001 <0,01 0.488
% total harbacous cover i r 0322 -0.118 -0.127 0A88 OLOB I 0304 -0231
: p 0.109 0.565 0.536 0.011 0.001 -0.123 0.247
East-west coordinate (UTMv) r 0431 0936 -0950 088 <091 0304 I 0099

Ip 0.028 0.861 0.809 <0.01 0.740 0.123 -0.624

Ditanco to noarestoucupied dto(m) i r -0935 0914 -0621 -0978 0.139 -0.231 0999 I

i p 0.862 0.946 <0.01 0.700 0.488 0.247 0.624
















APPENDIX C
COMPARISON OF FRONTAL DUNES AT EGLIN AIR FORCE BASE AND GULF
ISLANDS NATIONAL SEASHORE

Table C-1. Results of t-tests comparing vegetation, structure and landscape context for
frontal dunes on Eglin Air Force Base and Gulf Islands National Seashore
measured after Hurricane Ivan.
Variable t df p
Dune area (ha) 1.915 24 0.06
Dune height (m) -0.287 24 0.77
Dune habitat within 200 m (ha) -0.209 24 0.84
Dune habitat within 1 km (ha) 5.305 24 <0.01
% woody vegetation cover -2.260 24 0.03
% total herbaceous cover -1.060 24 0.30
Distance to nearest neighbor (m) 0.697 24 0.49
Distance to nearest scrub dune (m) -1.975 24 0.06
















APPENDIX D
PREDICTORS OF CHANGE IN OCCUPANCY OF FRONTAL DUNES AFTER
HURRICANE IVAN

Table D-1. Mean values, standard errors, and t-test results for habitat variables on frontal
dunes on EAFB that became unoccupied and for dunes that remained
occupied after Hurricane Ivan. Where variances were found to not be equal (p
< 0.05), a student t-test with the assumption of unequal variances was used.
For all other variables, t-statistics and p-value are for student t-tests with the
assumption of eaual variances.
Variable' Unoccupied Occupied
Mean (+SE) Mean (a SE) t df p
Before hurricane
Dune Area (ha) 0.56 (0.12) 0.62 (0.16) -0.31 13 0.76
East-west 522516 (1901) 523624 (1919) -0.39 13 0.70
coordinate (UTM)
Dune height (m) 3.44 (0.32) 4.87 (0.53) -2.53 13 0.03
Dune habitat within 0.25 (0.13) 0.37 (0.15) -0.61 13 0.55
200 m (ha)
Dune habitat within 6.59 (1.34) 11.24 (2.89) -1.63 13 0.13
1km (ha)
Distance to nearest 317.57 (87.41) 106.61 (28.51) 2.16 13 0.05
occupied dune (m)
After hurricane
Dune Area (ha) 0.11 (0.06) 0.28 (0.07) -1.96 13 0.07
East-west 522410 (2160) 525033 (793) -1.14 13 0.28
coordinate (UTM)
Dune height (m) 1.52 (0.59) 3.18 (0.53) -2.06 13 0.06
Dune habitat within 0.08 (0.03) 0.30 (0.12) -0.91 9 0.39
200 m (ha)
Dune habitat within 7.56 (1.04) 9.29 (2.21) -0.44 9 0.67
1 km (ha)
Distance to nearest 210.12 (25.46) 134.34 (35.07) 1.49 9 0.17
occupied dune (m)
Percent cover of 2.0 (1.4) 8.5 (4.8) -3.87 9 0.01
woody vegetation
Percent cover of 23.0 (4.0) 25.0 (5.0) -0.33 9 0.75
total herbaceous
SHabitat data are presented for variables measured before and after the hurricane. Occupancy
data presented are data taken after the hurricane.
SAnalysis of dune area, dune height, and east-west coordinate for frontal dunes post-Ivan
included the four dunes that were completely destroyed. These four dunes, however, were not
included when assessing vegetation, distance to nearest occupied dune, or amount of
surrounding habitat.

















APPENDIX E
CORRELATION MATRIX FOR STRUCTURAL FEATURES OF FRONTAL DUNES
ON EGLIN AIR FORCE BASE

Table E-1. Correlations for structural and landscape context variables measured on
frontal dunes (N = 93) on Santa Rosa Island prior to Hurricane Ivan.

Width
Dunce Dtme Dime 3 Distlano Onp D~une Dune
of
l ostLfrain hei grt ilsnlnc aren from Ivan Di ~lance lenglh width
rvan (96) (m (n) (k3 mr) (m) (m) (m


Dneln lost liom Ivan (96 i r 1 -0.50 025 -028 -0,50 -001 -021 -04C7
pv 0.01 0.02 0.01 <00 0.94 0.04 <0.01
Dunc hoi ILl~ln) r -050 I -0.17 047 0.12 -092 052 049
py <0.01 0.11 <0.01 0.24 0.92 <0.01 <0.01
Width~ of is~lanld km) I r OSS 0.17 I OSI 0.17 031 091 -0.01
Ip 0.02 0.11 091 0.1 I 0.94 0,96 0.97
Dneln aren (hn) I r *0.28 047 OB1I 1 0401 023 094 0.72
p 0.01 <0.01 0,91 -0.97 0 02 <0.01 <0,01
Distancc from Ivan Okm) I r *050 0.12 0.17 -001 1 0.4 005 0.10
p <0,01 0,24 0.1 0,97 -0.1 9 0,61 0,33
OnP distance (m) I r -0901 -042 081 033 0.14 1 0.15 020
: p 0.94 0.92 0.94 0.02 0.1 9 -0.14 0.06i
Dne Inclgth (In) r *021 052 091 094 .095 05 I 0 1

!y p 04 <0.01 0.96 <0.01 001 0.4 0.01
D~unewidth~(m) Ir *OA7 04Q9 .0.01 0.72 0.10 030 061 I

ip < 0.01 <0.01 0.97 <0.01 0.33 0.06 <0.01-




























dunes).

Wi dth
D3unc D~uame Dmam Di stance Gap Datea Duna
lostfromn heig TL isulanrl aro from Ivan Disalnce lenglh width
1 van (%) (m) (hr a) k m) (m) (m) (m)
(k m)

Dune lost from Ivan (%6) r 1 -037 0.19 -024 0.17 0.19 -031 -0AS
: -i 0.01 0.18 0.09 0.23 0.18 0.02 0.01
Otmro hoighl(mn) ; r -037 I -0.20 048 Oa5 0117 040 052
i p 0.01 *0.16 <0.01 0.72 0.61 <0.01 <0.01
Wi dt of: i slanld(km) R 0.19 .020 I Oh? OA6 0A6 01)3 008
ip 0.18 0.16, 61 0.01 ODI 0,86 0.56
Dllme aren (ha) jr 034 0 8 0907 I 0d2 *0.17 082 0 7
~p 0.09 <0.01 0.61 -0.12 0.23 <0.01 <0,01
Distance from Ivan (km) ir 0.17 095 0c16 062 1 *093 010 0.10
p 0.23 0,72 .01l 0,12 OS 0,16 0.50
Gap dlistanc (ml) r 0.19 097 046 -0.17 -093 I -0.13 -042
p 0.18 0.61 0.1 .23 0 6 0.37 0.08
DamecIcngth Om) r 1031 040 003 082 020 *0.13 I 035

p 0.02 <0.01 0.86 <0.01 0.16 0.37 0.01

Damew\pidthU IIm) : r 045 052 098 087 0.10 *0A2 035 I

jp 0.01 <0.01 0.56 <0.01 0.50 0.08 0.01


APPENDIX F
CORRELATION MATRIX FOR STRUCTURAL FEATURES OF SECONDARY
DUNES ON EGLIN AIR FORCE BASE

Table F-1. Correlations for structural and landscape context variables measured on
secondary dunes on Santa Rosa Island prior to Hurricane Ivan. Dunes were
sub-sampled to represent proportional area of large (83.6%) and small


(16.4%) dunes on the landscape (N


52 with 34 small dunes and 18 large
















LITERATURE CITED


Biedermann, R. 2004. Modeling the spatial dynamics and persistence of the leaf beetle
(Gonioctena olivacea) in dynamic habitats. Oikos 107:645-653.

Bird, B. L. 2002. Effects of predatory risk, vegetation structure, and artificial lighting on
the foraging behavior of beach mice. MS Thesis. University of Florida,
Gainesville, FL. 58 pp.

Bird, B. L., L. C. Branch, and D. L. Miller. 2004. Effects of coastal lighting on foraging
behavior of beach mice. Conservation Biology 18(5):1435-1439.

Blair, W. F. 1951. Population structure, social behavior and environmental relations
in a natural population of the beach mouse (Peromyscus polionotus
leucocephalus). Pages 1-47. Contributions from the Laboratory of Vertebrate
Biology, University of Michigan. Ann Arbor, MI.

Bonham, C. D. 1989. Measurements for terrestrial vegetation. Wiley. New York, NY.

Bourg, N.A., W.J. McShea, and D.E. Gill. 2005. Putting a cart before the search:
successful habitat prediction for a rare forest herb. Ecology 86:2793-2804.

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.G. 1984. Classification and
regression trees. Wadsworth International Group. Belmont, CA.

Burnham, K. P. and D. R. Anderson 2002. Model selection and multimodel inference: a
practical information-theoretic approach. Springer. New York, NY.

Buskirk, J.V. 2005. Local and landscape influence on amphibian occurrence and
abundance. Ecology 86:1936-1947.

Carlsson, A. and O. Kindvall. 2001. Spatial dynamics in a metapopulation network:
recovery of a rare grasshopper (Stauroderus scalaris) from population
refuges. Ecography 24:452-460.

Cooper, C. B. and J. R. Walters. 2002. Independent effects of woodland loss and
fragmentation on Brown Treecreeper distribution. Biological Conservation
105:1-10.










Cox, J. and R. T. Engstrom. 2001. Influence of the spatial pattern of conserved lands
on the persistence of a large population of red-cockaded woodpeckers.
Biological Conservation 100:137-150.

Dahl, B.E. and Woodard, D.W. 1977. Construction of Texas coastal foredunes with
sea oats (Uniola paniculata) and bitter panicum (Panicum ama~rum).
International Joumnal of Biometeorology 21:267-275.

De'ath, G. and K.E. Fabricius. 2000. Classification and regression trees: a powerful
yet simple technique for ecological data analysis. Ecology 81:3178-3192.

Ehrenfeld, J.G.,1990. Dynamics and processes of barrier island vegetation. Critical
Reviews in Aquatic Science 2:437-480.

Ellner, S.P. and J. Fieberg. 2003. Using PVA for management despite uncertainty:
effects of habitat, hatcheries, and harvest on salmon. Ecology 84: 1359-1269.

Emanuel, K. 2005. Increasing destructiveness of tropical cyclones over the past 30 years.
Nature 436: 686-688.

Environmental Systems Research Insititue. 1996. ArcView GIS, version 3.2.
Environmental Systems Research Institute, Redlands, Califomnia, USA.

Flather, CH, K.R. Wilson, D.J. Dean, and W.C. McComb. 1997. Identifying gaps in
conservation networks: Of indicators and uncertainty in geographic-based
analyses. Ecological Applications 7:531-542.

Frank, K. 2005. Metapopulation persistence in heterogeneous landscapes: Lessons
about the effect of stochasticity. American Naturalist 165:3 74-3 88.

Glennon, M. J., W. F. Porter, and C. L. Demers. 2002. An alternative field technique for
estimating diversity of small-mammal populations. Journal of Mammalogy
83:734-742.

Gore, J. A. and T. L. Schafer. 1993. Distribution and conservation of the Santa Rosa
beach mouse. Proceedings Annual Conference Southeast Association of Fish
and Wildlife Agenices 47:378-385.

Gu, W. D. and R. K. Swihart. 2004. Absent or undetected? Effects of non-detection
of species occurrence on wildlife-habitat models. Biological Conservation
116:195-203.

Guisan, A. and W. Thuiller. 2005. Predicting species distribution: offering more than
simple habitat models. Ecology Letters 8:993-1009.










Hallermier, R.J. and P.E. Rhodes. 1988. Generic treatment of dune erosion for 100-year
event. Proceedings of the 20th International Conference on Coastal Engineering,
ASCE, New York, 1107-1115.

Hanski, I. 1994. A practical model of metapopulation dynamics. Journal of Animal
Ecology 63:151-162.

Hesp, P. 2002. Foredunes and blowouts: initiation, geomorphology, and dynamics.
Geomorphology 48:245-268.

Hesp, P. 2004. Coastal dunes in the tropics and temperate regions; Location,
formation, morphology and vegetation processes. Pages 29-49 in M. L.
Martinez, and N.P. Psuty, editor. Coastal Dunes, Ecology and Conservation.
Springer-Verlag. Berlin.

Hoekstra, J. M., W. F. Fagan, and J. E. Bradley. 2002. A critical role for critical habitat
in the recovery planning process? Not yet. Ecological Applications
12:701-707.


Holler, N.R. 1992. Perdido Key Beach Mouse. In Rare and Endangered Biota of
Florida, Volume 1. Mammals (S. R. Humphrey ed.). University Press of
Florida, Gainesville, FL 52pp.


Humphrey, S. R. and D. B. Barbour. 1981. Status and habitat of 3 subspecies of
Peromyscus polionotus in Florida. Journal of Mammalogy 62:840-844.

Jonzen, N., C. Wilcox, and H. P. Possingham. 2004. Habitat selection and population
regulation in temporally fluctuating environments. American Naturalist
164:E103-E114.

Judge, E.K., M.F. Overton, and J.S. Fisher. 2003. Vulnerability indicators for coastal
dunes. Journal of Waterway, Port, Coastal, and Ocean Engineering 129:270-278.

Kery, M. 2004. Extinction rate, estimates for plant populations in revisitation studies:
importance of detectability. Conservation Biology 18:570-574.

Kriebel, D.L., R. Dalrymple, A. Pratt, and V. Sakovich. 1997. A shoreline risk index
for northeasters. Proceedings of the ASCE International Conference on Natural
Disaster Reduction, ASCE, New York, 251-252.

Mabee, T. J. 1998. A weather-resistant tracking tube for small mammals. Wildlife
Society Bulletin 26:571-574.

MacKenzie, D. I., J. D. Nichols, G. B. Lachman, S. Droege, J. A. Royle, and C. A.
Langtimm. 2002. Estimating site occupancy rates when detection probabilities are
less than one. Ecology 83:2248-2255.











MacKenzie, D. I. J. D. Nichols, J. E. Hines, M. G. Knutson, and A. B. Franklin. 2003.
Estimating site occupancy, colonization, and local extinction when a species is
detected imperfectly. Ecology 84:2200-2207.

Martinez, M. L., N.P. Psuty., and R.A. Lubke. 2005. A perspective on coastal dunes.
Pages 1-10 in M. L. Martinez, and N.P. Psuty, editor. Coastal Dunes, Ecology and
Conservation. Springer-Verlag. Berlin.

Mazerolle, M.J., A. Desrochers, L. Rochefort. 2005. Landscape characteristics influence
pond occupancy by frogs after accounting for detectability. Ecological
Applications 15:824-834.

Mendelssohn, I.A., M.W. Hester, F.J. Montefemante, and F. Talbot. 1991. Experimental
dune building and vegetative stabilization in a sand-deficient barrier island setting
on the Louisiana Coast. Journal of Coastal Research 7: 137-149.

Milio, J.F. 1998. Endangered and Threatened Wildlife and Plants; Determination of
Endangered Status for the St. Andrew Beach Mouse. U.S. Fish and Wildlife
Service. Federal Register 63:70053-70062.

Miller, D.L. and M. Thetford. 2001. Evaluation of sand fence and vegetation for dune
building following overwash by Hurricane Opal on Santa Rosa Island, Florida.
Journal of Coastal Research 17:936-948.

Miller, D. L., L. Yager, M. Thetford, and M. Schneider. 2003. Potential use of Thriola
paniculata rhizome fragments for dune restoration. Restoration Ecology 11:359-
369.

Morton, R.A. and A.H. Sallenger. 2003. Morphological impacts of extreme storms on
sandy beaches and barriers. Journal of Coastal Research 19:560-573.

Moyers, J. E. 1996. Food habits of the Gulf Coast subspecies of beach mice
(Peromyscus polionotus spp.). MS Thesis. Auburn University, Auburn, AL. 54pp.

Musila, W.M., J.I. Kinyamario, and P.D. Jungerius. 2001. Vegetation dynamics of
coastal sand dunes near Malindi, Kenya. African Journal of Ecology 39: 170-
177.

Nagelkerke, N. J. D. 1991. A note on a general definition of the coefficient of
determination. Biometrika 78:691-692.

Nordstrom, K.F., R. Lampe., and L.M. Vandemark. 2000. Reestablishing naturally
functioning dunes on developed coasts. Environmental Management 25:37-51.










Nordstrom, K.F. and W.A. Mitteager. 2001. Perceptions of natural and restored beach
and dune characteristics by high school students in New Jersey, USA. Ocean
and Coastal Management 44: 545-559.

Oli, M. K., N. R. Holler, and M. C. Wooten. 2001. Viability analysis of endangered
Gulf Coast beach mice (Peromyscus polionotus) populations. Biological
Conservation 97: 107-118.

Pearce, J. and S. Ferrier. 2000. Evaluating the predictive performance of habitat models
developed using logistic regression. Ecological Modelling 133:225-245.

Potter, J. C. 1985. Endangered and Threatened Wildlife and Plants; Determination of
Endangered Status and Critical Habitat for Three Beach Mice. U.S. Fish and
Wildlife Service. Federal Register. 50:23872-23889.

Psuty, N.P. 2005. The coastal foredune: A morphological basis for regional coastal dune
development. Pages 11-27 in M. L. Martinez, and N.P. Psuty, editor. Coastal
Dunes, Ecology and Conservation. Springer-Verlag. Berlin.

R Development Core Team. 2003. R: A language and environment for statistical
computing. R Foundation for Statistical Computing. Vienna, Austria.

Sallenger, A.H., Jr. 2000. Storm impact scale for barrier islands. Journal of Coastal
Research 16:890-895.

Schrott, G. R., K. A. With, and A. T. W. King. 2005. On the importance of landscape
history for assessing extinction risk. Ecological Applications 15:493-506.

Segurado, P. and M.B. Arauj o. 2004. An evaluation of methods for modeling species
distributions. Journal of Biogeography 31:1555-1568.

Sneckenberger, S. I. 2001. Factors influencing habitat use by the Alabama beach mouse
(Peromyscus polionotus amnmobates). MS Thesis. Auburn University, Auburn,
AL. 101pp.

SPSS Inc. 2004. SPSS Base 13.0 for Windows User' s Guide. SPSS Inc., Chicago IL.

Stallins, J. A. and A. J. Parker. 2003. The influence of complex systems interactions
on barrier island dune vegetation pattern and process. Annals of the
Association of American Geographers 93:13-29.

Stewart, S.R. 2005. Tropical Cyclone Report: Hurricane Ivan 2-26 September 2004.
NOAA National Weather Service, NHC/TPC.










Stone, G.W., B. Liu, D.A. Pepper, and P. Wang. 2004. The importance of extratropical
and tropical cyclones on the short-term evolution of barrier islands along the
northern Gulf of Mexico, USA. Marine Geology 210:63-78.

Swilling, W. R. and M. C. Wooten. 2002. Subadult dispersal in a monogamous species:
The Alabama beach mouse (Peronzyscus polionotus a~nmnobates). Journal of
Mammalogy 83:252-259.

Swilling, W. R., M. C. Wooten, N. R. Holler, and W. J. Lynn. 1998. Population
dynamics of Alabama beach mice (Peronzyscus polionotus a~nmnobates)
following Hurricane Opal. American Midland Naturalist 140:287-298.

Snyder, R. A. and C. L. Boss. 2002. Recovery and stability in barrier island plant
communities. Journal of Coastal Research 18:530-536.

Taylor, M. F. J., K. F. Suckling, and J. J. Rachlinski. 2005. The effectiveness of the
endangered species act: A quantitative analysis. Bioscience 55:360-367.

U. S. Fish and Wildlife Service. 1981. Standards for the development of habitat suitability
index models for use in the habitat evaluation procedure. U.S. Fish and Wildlife
Service, Division of Ecological Services. Manuscript 103. Washington, DC.
171pp.

U.S. Fish and Wildlife Service. 1987. Recovery plan for the Choctawhatchee, Perdido
Key and Alabama beach mouse. U.S. Fish and Wildlife Service, Atlanta,
Georgia. 45pp.

Van Horne, B. 1983. Density as a misleading indicator of habitat quality. Journal of
Wildlife Management 47:893-901.

Van Horne, B., G. S. Olson, R. L. Schooley, J. G. Corn, and K. P. Bumnham. 1997.
Effects of drought and prolonged winter on Townsend's ground squirrel
demography in shrubsteppe habitats. Ecological Monographs 67:295-315.

Vellinga, P. 1982. Beach and dune erosion during storm surges. Coastal Engineering
6:361-387.

Welch, N. E., and J. A. MacMahon. 2005. Identifying habitat variables important to
the rare Columbia spotted frog in Utah (USA): An information-theoretic
approach. Conservation Biology 19:473-481.


Zar, J.H. 1998. Biostatistical Analysis. Prentice Hall. New York, NY.
















BIOGRAPHICAL SKETCH

Alexander James Pries was born in Spooner, Wisconsin on May 26, 1980. Son of

James and Constance Pries, he grew up in St. Paul, MN, but often escaped to the northern

forests of Minnesota and Wisconsin during the summer months. It was there, during

hours of playing in the forests, lakes, and streams that he began to first observe and

appreciate natural ecosystems. In 1998, he enrolled at The College of Wooster in Ohio

and received a B.A. in biology in the spring of 2002. After graduation, he traveled to

Costa Rica, where he served as a teaching assistant in a course on tropical ecology for the

Organization for Tropical Studies. After this experience, he moved to Avon Park, FL,

where he worked as a research technician for the University of Florida on a proj ect

looking at features of habitat use by round-tailed muskrats (Neofiber alleni). In 2003, he

was hired by Archbold Biological Station to serve as a research technician for a

population assessment of Florida Scrub Jays. He began graduate school in August of

2003 at the University of Florida' s Department of Wildlife Ecology and Conservation,

from which he received his M. S. in 2006.