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Use of landscape metrics to predict avian nest survival in a fragmented Midwestern forest landscape

University of Florida Institutional Repository

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USE OF LANDSCAPE METRICS TO PRED ICT AVIAN NEST SURVIVAL IN A FRAGMENTED MIDWESTERN FOREST LANDSCAPE By MICHAEL R COTTAM A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Michael R Cottam

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This document is dedicated to KCC and JMC for sparking my early interest in science and to EAGC for her patience and suppor t during the years of graduate school.

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ACKNOWLEDGMENTS I thank Scott Robinson for his advice, dire ction, and mentoring dur ing this degree. I also thank my committee members, Ben Bolker and Bob Holt, for informative discussions and suggestions dur ing the preparation of this manuscript. Jeff Brawn, Ed Heske, and Kevin Rowe of the University of Illinois at Urbana-Champaign made significant contributions to the developmen t and execution of this project. My development as a scientist is in large part a result of many fruitful discussions with the graduate students and post-docs associated with the Robinson laboratory. Foremost among these, and to whom I owe a particular de bt of gratitude, are Christine Stracey, Jeff Hoover, and Wendy Schelsky. This work coul d not have been completed with out the assistance of an enormous and dedicated field crew, whom I also thank deeply. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................iv LIST OF TABLES ............................................................................................................vii LIST OF FIGURES .........................................................................................................viii ABSTRACT .......................................................................................................................ix CHAPTER 1 INTRODUCTION AND REVI EW OF LITERATURE..............................................1 2 MATERIALS AND METHODS.................................................................................7 Study Sites ....................................................................................................................7 Nest Monitoring ............................................................................................................9 Index of Abundance for Predatory Mammals ............................................................11 Data Analyses .............................................................................................................12 3 RESULTS AND DISCUSSION.................................................................................18 Predatory Mammals ....................................................................................................18 Acadian Flycatcher .....................................................................................................18 Wood Thrush ..............................................................................................................21 Low Nesters................................................................................................................23 Discussion ...................................................................................................................24 4 SUMMARY AND CONCLUSIONS.........................................................................30 APPENDIX A COMMON AND LA TIN NAMES............................................................................33 B TRACK STATION DATA.........................................................................................34 C MAYFIELD SUMMARY STATISTICS...................................................................36 LIST OF REFERENCES ...................................................................................................44 v

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BIOGRAPHICAL SKETCH .............................................................................................54 vi

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LIST OF TABLES Table page 1 Stratified design used in site selection .......................................................................7 2 Specific landscape characteristics of each of the 12 sites in this study ......................9 3 Dates on which birds were censused at each of the 12 study sites ...........................11 4 Comparison of eight candidate models relating habitat and/or temporal parameters to the probability of fledging young from a nest ...................................19 5 Model averaged parameter estimates .......................................................................20 vii

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LIST OF FIGURES Figure page 1 Map of southern Illinois, USA ...................................................................................8 2 Estimated daily predation rates during each nesting stage .......................................21 3 Mean number of Brown-headed Cowbird eggs per nest ..........................................22 4 Model-based estimates of the daily predation rates .................................................25 viii

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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 USE OF LANDSCAPE METRICS TO PRED ICT AVIAN NEST SURVIVAL IN A FRAGMENTED MIDWESTERN FOREST LANDSCAPE By Michael R Cottam May 2006 Chair: Scott K. Robinson Major Department: Zoology Habitat fragmentation fundamentally aff ects trophic interac tions and community structure. Studies of breeding birds have pr ovided some of the clearest examples of the negative consequences of habitat fragmenta tion. An understanding of the ways in which an agricultural matrix does or does not modulate avian nesting success in forest fragments could greatly improve our understanding of fragmentation ecology. We used a stratified random process to select 12 study sites in the Shawnee National Forest in southern Illinois, USA. We used an info rmation theoretic appr oach and generalized linear models to investigate eight a priori models that predicted th e probability of a nest being successful. These models incorpor ated landscape composition (% grassland, % agriculture, fragmentation), te mporal factors, conspecific density, predator density, and combinations of these. We also investig ated whether the freque ncy or intensity of parasitism by the Brown-headed Cowbird was related to landscape composition. Temporal factors had the mo st effect on nesting success; landscape factors did not ix

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influence nesting success. Pa rasitism rates and intensity were significantly influenced by the amount of grassland for the Wood Thrus h, but not for the Acadian Flycatcher. We conclude that simple landscape metrics may not be good predictors of avian nesting success in complex landscapes that have dive rse predator communities. We propose that a reevaluation of the tradeoff between multi-site and locally focused studies may be useful in directing future rese arch and manageme nt initiatives. x

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CHAPTER 1 INTRODUCTION AND REVIEW OF LITERATURE Habitat fragmentation fundamentally a ffects community stru cture and trophic interactions ( Noss and Csuti 1997 Hedlund et al. 2004 Tscharntke and Brandl 2004 ). Numerous studies support the widely held notion that the fragmentation of breeding habitat significantly decreases annual reproductive success and species viability (reviewed in Robinson and Wilcove 1994 Faaborg et al. 1995 Lloyd et al. 2005 ). The mechanisms underlying the reduction of breed ing success may include changes in the communities of predators ( Robinson 1992 Porneluzi et al. 1993 Hoover et al. 1995 Brawn and Robinson 1996 Chalfoun et al. 2002 ), invasive species (reviewed in Knick et al. 2003 Cronin and Haynes 2004 ), and parasites (reviewed in Trine 1998 ) that are associated with edges and the matrix surr ounding the fragments. Studies of breeding forest birds have provided some of the clearest examples of the negative consequences of habitat fragmentation ( Gates and Gysel 1978 Wilcove 1985 reviewed in Faaborg et al. 1995 Winfree 2004 ). In addition to the absence of many birds from small habitat patches, populations of many species that ne st in fragmented la ndscapes suffer reduced viability in small habitat patches and when close to habitat boundaries (edges) (reviewed in Robinson and Wilcove 1994 Faaborg et al. 1995 Weldon and Haddad 2005 ). Nest predation rates, for example, are often show n to be higher in small forest tracts ( Robinson 1992 Porneluzi et al. 1993 Hoover et al. 1995 Linder and Bollinger 1995 Brawn and Robinson 1996 Aquilani and Brewer 2004 ) and close to edges (reviewed in Paton 1994 Rich et al. 1994 Gates and Evans 1998 Manolis et al. 2002 Batary and Baldi 2004 ). 1

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2 Levels of brood parasitism by cowbirds are also usually greater closer to edges than in the forest interior (reviewed in Trine et al. 1998 Phillips et al. 2005 ). As a result of differential nesting success, fragmented la ndscapes have often been hypothesized to consist of a mosaic of populat ion sources and sinks (sensu Pulliam 1988 Pulliam and Danielson 1991 ), either at local spatial scales (e.g. Urban and Shugart 1986 Temple and Cary 1988 ) or at very large, regional scales (e.g. Donovan et al. 1995a Donovan et al. 1995b Robinson et al. 1995 Trine 1998 Hochachka et al. 1999 Lloyd et al. 2005 ). These results have been wide ly incorporated into cons ervation and land management plans ( Finch and Stangel 1993 Marzluff and Sallabanks 1993 Thompson 1996 Petit and Petit 2000 ). The adverse effects of forest fragmentat ion, however, are not uniform within or among regions. Evidence for higher rates of nest predation in small fragments is decidedly mixed; some have even argued that the negative effects of fragmentation are an artifact of the use of artificial ne sts to measure nest predation rates ( Haskell 1995 see also Moore and Robinson 2004 ) or of inappropriate lumping of species ( Bielefeldt and Rosenfield 1997 ). Similarly, many studies show no edge effects or only show such effects very close (<50m) to edges (reviewed in Paton 1994 Hartley and Hunter 1998 ). Stephens et al. ( 2004 ) suggested that these differences in results are related to the length of a study and to the ways in which fragmenta tion is defined, with multi-year studies that use large-scale definitions of fragmentation being more likely to find effects attributable to fragmentation. Levels of cowbird parasitism also vary greatly among regions ( Hoover and Brittingham 1993 Smith and Myers-Smith 1998 Thompson et al. 2000 ); birds nesting in small fragmented forests in so me regions experience very low levels of

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3 parasitism, even near edges, whereas parasitism levels in other regions remain very high even in large (>500 ha) fo rest tracts more than 1 km from an edge ( Robinson and Wilcove 1994 Trine 1998 Sisk and Battin 2002 ). Several recent papers have proposed that landscape composition (percentage cover of forest and non-forest habitats or total co re habitat) may be just as important as landscape structure (patch size, shape, and isolation) in determining avian nesting success ( Andren 1994 1995 Donovan et al. 1995a Robinson et al. 1995 Donovan et al. 1997 Howell et al. 2000 Rodewald 2003 Driscoll et al. 2005 ). Edge effects, for example, are demonstrably highest in landscapes with inte rmediate forest cover in the agricultural Midwest ( Donovan et al. 1997 Thompson et al. 2000 ), a pattern replicated in the northeastern U.S. ( Driscoll and Donovan 2004 ). Several other studies also failed to detect negative edge effects in mostly forested landscapes ( Yahner and Wright 1985 Small and Hunter 1988 Yahner and Delong 1992 Rudnicky and Hunter 1993 Hanski et al. 1996 Hawrot and Niemi 1996 Bayne and Hobson 1997 Darveau et al. 1997 Keyser et al. 1998 ). Rates of nest predation have been s hown to be high even in the interior of forest patches in mainly agricultural (>60% cover) landscapes ( Robinson and Wilcove 1994 Heske 1995 Marini et al. 1995 Bayne and Hobson 1997 Hartley and Hunter 1998 ). Agricultural edges generally appear to exert stronger nega tive effects on birds than edges of regenerating forest patches ( Hanski et al. 1996 Hawrot and Niemi 1996 Darveau et al. 1997 Hartley and Hunter 1998 Morse and Robinson 1999 Rodewald and Yahner 2001b but see King et al. 1996 Suarez et al. 1997 ). The composition of the surrounding landscap e matrix may also have a strong mediating influence on the effects of fore st fragmentation through the movements of

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4 predators in and out of habitats ( Wiens et al. 1993 Freemark et al. 1995 Wiens 1995 Rodewald and Yahner 2001a Rodewald 2002 2003 ). Agricultural regions may support greater numbers of some important generalist predators ( Wegner and Merriam 1979 Angelstam 1986 Moller 1989 Wegner and Merriam 1990 Andren 1992 Warner 1994 Andren 1995 Haskell 1995 Dijak 1996 Oehler and Litvaitis 1996 Bayne and Hobson 1997 Pedlar et al. 1997 ) than regions where the matrix consists primarily of grasslands or pasture. Because fields used for row crop ag riculture are barren for long periods between harvest in the fall and the pl anting and emergence of new crops in spring or early summer, many predators concentrate their activit y in forested habitats during winter and spring ( Cummings and Vessey 1994 ). As crop fields provide increased cover, and eventually food later in the gr owing season, predators tend to increase their use of these areas (E. Heske, Illinois Natural History Surv ey, pers. comm.). In contrast, grasslands retain some cover throughout the year and some resources (small mammals, insects, fruit, and green plant material) are available during the winter and early sp ring. Thus, predator activity may be more dispersed throughout th e landscape (via both lo wer overall predator density and allowing some indivi dual predators to s ubsist on resources in locations where forest birds do not nest) during the time of songbird nesting. Taken together, these studies suggest th at negative effects of fragmentation on avian productivity could be mediated by landscape composition, but what exactly the effects would be of a given matrix type on avian nesting success are not immediately clear, especially if the matrix contains a mixture of both agricultural and grassland land uses. Furthermore, there may also be a role for effects that emerge only over time as communities respond to landscape variation. Su ch effects have been demonstrated in

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5 plants ( Holt et al. 1995 Cook et al. 2005 ), but their importance in more mobile species has not been documented. As agriculture is the principle matrix t ype for mid-continental North America, an understanding of the ways in which an agri cultural matrix does or does not modulate avian nesting success in forest fragments could greatly improve our understanding of fragmentation ecology. Whatev er the driving mechanisms, a clearer picture of the relationship, or lack thereof, between the matrix and fragme ntation would also constitute a useful management tool. However, most studies have only looked at percentage of forest and non-forest cover and none of th ese studies of which we are aware have distinguished among the effects of different ki nds of agriculturally influenced landscape matrices (e.g. row crops vs. rural grassland). Our goal in this study was to explore the extent to which landscape composition mediates the effects of forest fragmentation on songbird nesting success and the abundance of their major predators and parasite s. We predicted that incorporating data on matrix composition would improve our ability to predict the nesti ng success of forest birds in a study region in which traditional fragme ntation variables (tract size, distance to edge) appear to explain little va riation in songbird nesting success ( Robinson and Wilcove 1994 Trine et al. 1998 Morse and Robinson 1999 Peak et al. 2004 ChapaVargas and Robinson in press-a b ). Specifically, we studied whether landscape composition alters the magnitude of the negative effects of forest fragmentation by studying patterns of nest predation and parasitism in forest song birds in southern Illi nois, USA. We tested three predictions. (1) Nesting success will be correlated with matr ix composition (% of different kinds of non

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6 forest cover) when controlling for the extent of fragmentation. (2) Nesting success will be correlated with the extent of fragment ation when the matrix composition is held constant. (3) Landscape composition will affect the abundance of important nest predators and parasites that determine nesti ng success. We predicted higher depredation rates of avian nests in landscapes with high cover of row crops ( Andren 1995 ), and higher levels of brood parasitism in landscapes with higher cover of grasses where cowbirds feed ( Trine et al. 1998 ).

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CHAPTER 2 MATERIALS AND METHODS Study Sites The study area was the southern 11 counties of Illinois, an area that contains the 108,000-ha Shawnee National Forest (SNF) ( Fig. 1 ). The SNF consists of hundreds of small forest tracts dominated by oak-hickory forests on steep hillsides and narrow ridge tops. The western half of the SNF includes the easternmost extension of the Ozark Mountains and the eastern section of the SNF lies mostly in the Shawnee Hills region. We restricted our studies to areas of upland oak-hickory and avoided pine plantations and floodplain forest, which tend to have different communities of birds and potential nest predators (Robinson unpubl. data). We used a stratified random process for si te selection. We selected sites that fell into four categories of land cover ( Table 1 2 ). We used the program FRAGSTATS ( McGarigal and Marks 1995 ) in conjunction with U.S. Geol ogical Survey Gap Analysis Table 1. Stratified design used in site selec tion. One additional site, Cave Hill, had very low fragmentation and approximately equal amounts of area devoted to grassland and row crop (see Table 2 ) and is thus not shown here. Matrix Composition row crop > grassland grassland > row crop row crop > 20 % grassland > 20 % Big Brushy Lusk Creek Pine Hills Bald Knob Low (>60% forest cover, >40% interior) Burke Branch Saline Mines Hayes Creek Wildcat Bluff Lick Creek Fragmentation High (<30% forest cover, <10% interior) Tansill Kaskaskia Forest 7

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8 0102030 kmBald Knob Lick Creek Pine Hills Big Brushy Wildcat Bluff Cave Hill Saline Mines KaskaskiaForest Lusk Creek Hayes Creek Tansill Burke Branch 0102030 km 0102030 kmBald Knob Lick Creek Pine Hills Big Brushy Wildcat Bluff Cave Hill Saline Mines KaskaskiaForest Lusk Creek Hayes Creek Tansill Burke Branch Figure 1. Map of southern Illinois, USA. A rrows indicate the locations of the twelve study sites. The Shawnee National Fore st comprises approximately the dark gray area. program classifications ( Scott et al. 1993 ) and digital maps provided by the Illinois Department of Agriculture ( Luman et al. 1996 updated in 1998 by the Illinois Natural History Survey) to characterize the land cove r (% forest, % row crop, % rural grassland) within a 3-km radius of each forested pixel in southern Illinois. Each pixel represented a 30 m X 30 m square of actual land area. Adjacen t forested pixels were lumped together into forest tracts. Once we identified all of th e potential candidate tracts in each category of forest and matrix cover, we randomly selected 3 sites that were separated from other sites in the same category by at least 20 km and from other sites by at least 10 km ( Table

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9 1 Fig. 1 ). Additionally, we included one site, Cave Hill, that was very unfragmented and had low and approximately equal amounts of grassland and agriculture. To minimize local edge effects, we focused our attenti on on searching for nests more than 50 m from external (row crop or grassland) edges ( Paton 1994 ); in practice, however, we also included nests found up to the edge of forests and investigated the effect of distance to edge on nest success (see below). Table 2. Specific landscape characteristics of each of the 12 sites in this study. Edge density is the ratio of forest/non-fores t edge in meters to the number of hectares of forest within 1 km of the cen ter of each site. Percent grassland and percent row crop respectively represent the proportion of land area within 3 km of the center of each site that wa s composed of grassland (old field, pasture, mowed grass, etc.) and row crop agriculture. The presence of a permanent water source (lake, stream, et c.) is noted in the right-most column. Edge Density Percent Percent Water (m edge/ha forest) Grassland Row Crop Present? Bald Knob 31 23.9 9.0 No Big Brushy 11 12.6 29.5 No Burke Branch 29 20.9 11.7 Yes Cave Hill 5 12.2 11.7 No Hayes 61 42.1 17.2 No Kaskaskia Forest 23 21.7 3.4 No Lick Creek 65 38.7 10.1 Yes Lusk Creek 7 25.3 4.8 Yes Pine Hills 21 7.3 25.2 No Saline Mines 39 17.1 50.5 Yes Tansill 35 26.6 30.3 No Wildcat Bluff 13 12.3 41.9 No Nest Monitoring We searched for and monitored nests at all 12 sites from 15 April to 10 Aug in 2001, 2002, and 2003. Nest locations were marked >3 m from the nest using plastic flagging. We monitored the nest approximately every 3 days and recorded the date, time, and a description of nest contents and pa rental activity. These data were used to determine nest stage (laying, incubation, or nestling) and the intensity of cowbird

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10 parasitism (number of cowbird eggs in the ne st). We marked the location of each nest on USGS topographical maps of the area. We ceased monitoring a nest when all nestlings had fledged, all contents of the nest disappeared, or no parental activity and no cha nges in the nest contents were observed for 14 days. We confirmed fledging by listening for nestling be gging calls, sighting parents carrying food or scolding as we appro ached the nesting area, and/or observing appreciable amounts broken pin-feather sheaths in an empty nest. We considered nests to be successful if one or more nestlings (not including cowbirds) fledged from the nest. Nests from which the contents disappeared a nd adults were not observed in the area were considered unsuccessful, as were nests whic h had no parental activity and no changes in the nest contents for 14 days. In 2000 and 2001, we conducted at least two cen suses at each site to assess the density of avian predator ( Corvidae ), parasite ( Molothrus ater ), and prey/host species ( Table 3 ). The exceptions were Kaskaskia Forest, which was censused only once in 2000, and Cave Hill which was censused once in 2000 and was not censused in 2001. We conducted one census at each site in 2002. The censuses took pla ce at the height of the breeding season, between late May and ear ly July. In order to minimize observer effects, all censuses were conducted by a single observer who was very familiar with the songs, calls, and plumages of North American birds. Censuses began at dawn and were completed within four hours. We establishe d a series of 15-20 poi nts at each site, at which we conducted 5-minute infinite-radius point counts during the breeding season. Using the point count data, we estimated the density of each species at each site using the program Distance ( Thomas et al. 2004 ).

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11 Table 3. Dates on which birds were censused at each of the 12 study sites. Five-minute infinite-radius point counts were co nducted along a predetermined census route by a single observer who was familiar with the calls, songs, and plumage variations of the birds of southern Illinois. In 2000 the Pine Hills June 27 census was terminated early due to weather. The remaining points were censused on June 28. All other da tes indicate complete census routes. Site 2000 2001 2002 Bald Knob June 7 June 26 June 19 June 29 July 11 Bug Brushy June 6 June 27 June 20 June 20 July 12 Burke Branch June 13 June 13 May 27 June 29 June 26 July 16 Cave Hill June 15 June 15 Hayes June 10 May 30 June 13 July 6 July 8 Kaskaskia June 9 June 9 June 14 July 6 July 9 Lick Creek June 6 June 28 June 30 June 29 July 10 Lusk Creek June 15 May 30 May21 July 3 July 7 Pine Hills June 7 June 8 June 21 June 27, 28 July 14 Saline Mines June 16 June 29 May 30 July 7 July 13 Tansill June 13 June 27 June 19 June 30 July 15 Wildcat Bluff June 16 June 27 June 24 July 19 Index of Abundance for Predatory Mammals We employed track stations to determin e an index of the relative abundance of predatory mammals at each site. There were six track stations in each of the twelve intensive study sites. Stati ons were at least 500 m apart to decrease the probability of individual predators detecting more than one station simultaneously and trap-lining. Track stations consisted of two 1 by 0.5 m sheets of 0.32 gauge aluminum coated with soot from a kerosene torch ( Barret 1983 ). The aluminum sheets were set side-by-side on

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12 a cleared, level 1 by 2 m area and baited with cat food (in 2000) or fatty acid scent tablets (in 2001 and 2002). Track sta tions were covered by plyw ood shelters in 2000 and 2001 to preserve the tracks from ra in. In 2002 we removed the sh elters due to concerns that they were deterring some predators from visiting the stations. Each study site was surveyed bi-weekly during the three month period of peak songbird nesting (May-July). To ensure that each station was allowed the sa me amount of time to attract predators, we always surveyed stations in the same orde r. In 2000 and 2001, when the stations were protected from weather, we re-coated the al uminum with soot one week before each survey date and counted at each survey the tracks that had accumulated during the week. In 2002, we re-coated the aluminum with soot and returned 24 hours later to count and identify tracks. We assumed that movement of individual animals was independent of other conspecifics (e.g. that animals were not traveling in family groups) and of other species. Because our methodology was consistent within, but not across, years, our data can be used to draw conclusions about the differences in predator activity among sites, but not among years. Therefore, for each site, we combined the data from all three years and used as an index of relative abundance the log of the number of total individuals that could be identified from the track stations. Data Analyses We modeled the nest success of the two most common species, Acadian Flycatcher and Wood Thrush (Latin names are given in Appendix A ), using a set of generalized linear models closely related to logistic regression ( Agresti 1996 ). We combined the data for six other species (Indigo Bunting, Ke ntucky Warbler, Louisiana Waterthrush, Northern Cardinal, Worm-eating Warbler, and Ovenbird) because they have similar nesting ecologies (each nests near or on th e ground) and would be exposed to similar

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13 predation pressures. We refer to this group of species as Low Ne sters. We did not have sufficient data to model each of th ese species independently. We assumed a binomial distribution of the response variable (nest fate = 0 if failed, nest fate = 1 if successful) and estimated daily nest success as a function of a nu mber of predictive factors ( Dinsmore et al. 2002 Shaffer 2004 ). To account for the fact that the interval between nest checks was typically greater than one day and varied in length, we used a modified ( Shaffer 2004 ) form of the logit link function that allowed the probability of surviving a given interval to vary with interval length: n t txxxlRRRg ..., 1ln21 /1 /1 where R is the interval survival rate, t is the interval length in days, and l( x) is a linear function composed of various combinations of our predictor variables. The role of t in this equation is more obvious upon solving the link function for R: t xl xln neeR)( )(..1 ..11 in which the portion within square brackets represents the daily survival rate, R is the inte rval survival rate, and t is the length of the interval in days. Thus, this method allowed us to model interval a nd daily survival rates as functions of explanatory variables that changed among intervals (e.g. nest stage). Following Shaffer ( 2004 ), we assumed that these e xplanatory variables remained constant within intervals. We fitted the models using PROC GENMOD ( SAS Institute 2002 ). Preliminary analyses using PROC NLMIXED ( SAS Institute 2002 ) to include a random variable that accounted for the poten tial effect of study site suggested that including site did not improve the models. Many models also failed to converge when we included distance to edge in the model. We therefore presen t the models without either a random site variable or distance to edge.

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14 We follow Burnham and Anderson ( 2002 ) in using an information-theoretic approach to evaluate alternat ive models derived from our a priori hypotheses concerning the relationships between avian nest success and landscape composition. Although we were primarily interested in the effects of landscape composition and predator and parasite abundance, we also included covariates dealing with temporal and hydrologic factors because recent studies ( Peak et al. 2004 Hoover 2006 Chapa-Vargas and Robinson in press-b ) have suggested that these may be extremely important. Our set of eight candidate models consisted of: 1. a habitat effects model with landscape composition (percent rural grassland and percent agriculture), presence of a permanent water source (yes, no), edge density within 1 km (linear meters of edge/hectares of forest), and two-way interactions between the landscape composition variables and edge density 2. a temporal effects model with year of study (2000, 2001, or 2002), nest stage (laying, incubating, or nestling), and Julian date 3. a species density model with conspecific de nsity, the index of re lative abundance of mammalian predators, and avian predator density 4. combination of the habitat effect s and temporal effects models 5. combination of the habitat eff ects and density effects models 6. combination of the temporal eff ects and density effects models 7. a global model including all effects 8. a null model with only an intercept We used PROC REG ( SAS Institute 2002 ) to estimate the tolerance for variables in the global model to diagnose collinearity; although there were some indications of collinearity (where toleran ce < 0.2), these cases were due to the interaction terms included in our habitat model. We explor ed reducing the collinearity both by centering the variables on their means and by removing th e collinear variables from the model, but neither of these methods had significant effects on the model results. Therefore, to most

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15 accurately reflect our a priori hypotheses, we present the models without corrections for collinearity. We plotted the standardized deviance residuals from the global model against the explanatory values and found no pa tterns suggesting that transformations of the data were necessary. Neither did we find values ( 3) that were indicative of outliers. We evaluated goodness-of-fit of the global model for each species with HosmerLemeshow tests ( Hosmer and Lemeshow 2000 ). We used Akaikes Information Criterion to rank candidate models for each species from most to least suppor ted and drew conclusions about our hypotheses by comparing the degree to which our data supported each of the candidate models. We used AIC (the difference between the AIC value for a particular model and the lowest observed AIC for that species) as our measure of model support. We considered all models with AIC values of less than two to have equivalent support ( Burnham and Anderson 2002 ). To account for model uncertainty, we used Akaike weights (a measure of model support that is based on AIC and sums to one across all candidate models for a particular species) to calculate model-av eraged coefficients and 95% confidence intervals. To derive each model-averaged coefficient, we multiplied the coefficient by the Akaike weight of the model containing it and summed across all models ( Burnham and Anderson 2002 ). To facilitate interpretation of these coefficients, we converted them to odds ratios (the ratio of the odds of survival under one level of an explanatory variable to the odds of survival under a reference le vel of the explanator y variable). Each categorical variable, such as y ear or nest state, has n-1 odds ratios associated with it, where n is the number of levels of the variable. Each is a ratio of th e odds of survival at one level of the variable to the odds of survival at the reference level of that variable. For

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16 continuous variables, such as percent agriculture or conspecific density, the interpretation of the odds ratio is that a one-unit increase in the variable multiplies the odds of a nest surviving one day by the odds ratio. Confidence intervals that included an odds ratio of one were considered to be non-significant. We used the most supported model for each sp ecies to estimate th e probability of a nest surviving one day. We ma de separate estimates for each level of the categorical variables (i.e. nest stage) and assumed mean values of the conti nuous variables in the model. This estimate of daily nest success is comparable to estimates calculated via other methods ( Mayfield 1975 Johnson 1979 ). We used these daily survival probabilities to estimate the probability of a nest surviving the entire breeding cycle, assuming lengths of the laying, incubation, and nest ling periods that were appr opriate for each species ( Payne 1992 Van Horn and Donovan 1994 Robinson 1995 Roth et al. 1996 Hanners and Patton 1998 McDonald 1998 Halkin and Linville 1999 Whitehead and Taylor 2002 ). To investigate the extent of Brown-h eaded Cowbird parasitism in different landscapes, we used a generali zed linear modeling approach. We modeled the extent and intensity of parasitism of the two most common species in these landscapes, Acadian Flycatcher and Wood Thrush. To investigate the number of nests th at were parasitized and how intensely each was parasitized, we used the number of cowbird eggs in each nest as a response variable. In th is analysis, we used a log li nk function and treated the laying of cowbird eggs as a Poisson-distributed variable. However, since a preliminary comparison of the mean and va riance of the number of co wbird eggs in each nest indicated that eggs were more clumped in Wood Thrush nests and more rare in Acadian Flycatcher nests than the Poisson distribut ion predicted, the Poisson was not entirely

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17 appropriate and the resulting models were ei ther overor underdispersed. We adjusted for the overor underdispersion by employing the DSCALE option in the model statement of PROC GENMOD ( SAS Institute 2002 ). In addition to using percent grassland as a predictor variable, we included the covariates percent agriculture and year. We used likelihood ratio tests and a stepwise approach to eliminate non-significant explanatory variables from each model ( =0.05). We plotted the standardized deviance residuals from each model against the explanatory values and found no patterns suggesting that transformations of the data were necessary. Neither did we find values ( 3) that were indicative of outliers. We also calculated the Mayfie ld daily predation rates ( Mayfield 1975 ) for each species at each site and year. While our mode ling techniques allow us to investigate the influence of a number of variables on daily predation rate (DPR=1-daily success rate), Mayfield estimates provide a pur ely data-based measure of the DPR. This is useful for visualizing the data themselves.

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CHAPTER 3 RESULTS AND DISCUSSION Predatory Mammals Our index of mammalian predator abundan ce showed moderate to low correlation with the percentage of the landscape that wa s composed of either agriculture (Pearsons = 0.48) or grassland (Pearsons = 0.03) and varied littl e among sites (2.9 to 3.8). Acadian Flycatcher We located and monitored 337 nests for 1773 intervals (5438 exposure days, see Appendix C ). 209 of these nests fledged young. The Hosmer-Lemeshow test gave no evidence to suggest that the global model fitted poorly ( 2 =14.4, df=9, p=0.11). The model with the most support based on the AIC criterion was the temporal model. Other models that included the temporal predicto rs (physical habitat + temporal, density + temporal, and global) also received some support ( Table 4 ). Nesting stage had the most influence on the odds of nest survival. Based on model averaged coefficients, the daily odds of survival for the incubation period we re twice those for the nestling period. The daily odds of survival for nests during th e laying period were 60% of those for the nestling period, but the confidence interv al overlapped one by 0.06. The confidence intervals of all other parameters overlapped one, and most estimates were themselves very close to one ( Table 5 ). Based on a laying stage of th ree days, an incubation stage of 14 days, and a nestling stage of 14 days, the temporal model ( Fig. 2 ) estimated a 42% probability that a given nest would fledge young. None of the 3 explanatory variables 18

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Table 4. Comparison of eight candidate mode ls relating habitat and/or temporal para meters to the probability of fledging young from a nest. The model parameters are descri bed further in the text. n gives the number of observation intervals for each species. K = number of parameters in each model. AIC = the difference between the AIC values for the most supported model and the given model. w = Akaike weight for each model. Models with the lowest AIC values and highest w values have the most support. Acadian Flycatcher Wood Thrush Low Nesters n=1773 n=453 128 failures / 337 nests 45 failure s / 112 nests 147 failures / 418 nests Model K AIC w K AIC w K AIC w Global 15 4.79 0.069 15 9.65 0.007 14 7.32 0.014 Physical Habitat (PH) 7 25.53 0.000 7 36.26 0.000 7 74.38 0.000 Temporal 6 0.00 0.755 6 0.00 0.866 6 0.49 0.420 Density 4 25.45 0.000 4 33.38 0.000 3 66.04 0.000 PH + Temporal 12 3.86 0.110 12 7.04 0.026 12 5.81 0.029 PH + Density 10 25.92 0.000 10 38.97 0.000 9 74.72 0.000 Temporal + Density 9 4.87 0.066 9 4.29 0.101 8 0.00 0.537 Null 1 20.53 0.000 1 31.18 0.000 1 65.31 0.000 19

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Table 5. Model averaged parameter estimat es. CI = 95% confidence interval base d on unconditional (model averaged) standard errors. Acadian Flycatcher Wood Thrush Low Nesters Parameter Odds Ratio CI Odds Ratio CI Odds Ratio CI % Agriculture 0.993 0.967 1.019 1.001 0.996 1.005 1.001 0.997 1.004 % Grassland 1.000 0.982 1.018 0.997 0.983 1.011 0.999 0.994 1.004 Dry vs. Wet 1.071 0.827 1.389 1.000 0.966 1.035 0.987 0.932 1.045 Edge Density 0.990 0.953 1.028 1.000 0.995 1.005 0.999 0.995 1.004 Edge Density X %Ag 1.000 0.999 1.001 1.000 1.000 1.000 1.000 1.000 1.000 Edge Density X %Gr 1.000 0.999 1.001 1.000 1.000 1.000 1.000 1.000 1.000 2000 vs. 2002 0.795 0.504 1.256 0.245 0.107 0.563 0.658 0.422 1.027 2001 vs. 2002 1.401 0.868 2.261 0.963 0.425 2.184 0.865 0.589 1.269 Laying vs. Nestling 0.594 0.333 1.058 0.146 0.066 0.324 0.291 0.185 0.457 Incubation vs. Nestling 2.031 1.338 3.083 1.110 0.524 2.347 1.997 1.337 2.984 Julian Date 1.006 0.991 1.021 0.986 0.971 1.001 0.984 0.976 0.991 Specific Density 0.957 0.808 1.133 1.010 0.837 1.218 Mammalian Predator Density 1.004 0.829 1.216 1.131 0.653 1.959 0.968 0.649 1.444 Avian Predator Density 0.750 0.193 2.918 1.077 0.525 2.210 0.280 0.017 4.486 20

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21 0 0.05 0.1 0.15 0.2 0.25Acadian FlycatcherWood Thrush Low NestersEstimated Daily Predation Rate Laying Incubation Nestling Figure 2. Estimated daily predation rate s during each nesting stage for Acadian Flycatcher, Wood Thrush, and Low Nest ers in 2002. These estimates were generated using the best-fitting model for each species (the temporal model). Error bars represent 95% confidence limits. we included in the parasitism analysis (% gr assland cover, % agri cultural cover, and year) was significant ( Fig. 3A ). Wood Thrush We located and monitored 112 nests fo r 453 intervals (1432 exposure days, see Appendix C ). 67 of these nests fledged young. The Hosmer-Lemeshow test gave no evidence to suggest that the global model fitted poorly ( 2 =9.9, df=9, p=0.36). The model with the most support based on the AIC criterion was the temporal model. The temporal + density model also received some support ( Table 4 ). Year had the largest effect on the odds of nest survival. Based on model averaged coefficients, the daily odds of survival during 2000 were 25% of thos e during 2001 or 2002. Nesting stage also influenced nest survival: the daily odds of survival during the laying period were 15% of those during the nestling period. The confid ence intervals of a ll other parameters

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22 0 0.5 1 1.5 2 2.5 3 3.5 4 01 02 03 04 05 0 % Grassland CoverMean number of Cowbird eggs per nest 0 0.5 1 1.5 2 2.5 3 3.5 4 01 02 03 04 05 0 % Grassland CoverMean number of Cowbird eggs per nest B A Figure 3. Mean number of Brown-headed Co wbird eggs per nest in each of two host species, as a function of pe rcent grassland c over within 1 km of the center of the study site. A) Data for the Acadian Flycatcher. B) Data for the Wood Thrush. The trend line shown in B descri bes the data much better than does a model with only an intercept (likelihood ratio test, df=1, 2=23.44, p<0.001).

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23 overlapped one, and most estimates were themselves very close to one ( Table 5 ). Based on a laying stage of three days, an incubation stage of 13 days, and a nestling stage of 13 days, the temporal model estimated a 43% pr obability that a given nest would fledge young. Of the 3 explanatory variables we in cluded in the parasitism analysis (% grassland cover, % agricultural cover, and year), % grassland cover was highly significant (df=1, 2 =23.44, p<0.001, Fig. 3B ). Low Nesters We located and monitored 418 nests for 1480 intervals (101 Indigo Bunting nests for 389 intervals and 1173 exposure days, 87 Kentucky Warbler nests for 299 intervals and 926 exposure days, 89 Louisiana Wate rthrush nests for 339 intervals and 1374 exposure days, 72 Northern Cardinal nests for 254 intervals and 965 exposure days, 64 Worm-eating Warbler nests for 194 interval s and 656 exposure days, 5 Ovenbird nests for 5 intervals and 16 exposure days, see Appendix C ). 271 of these nests fledged young. The Hosmer-Lemeshow test gave no evidence to suggest that the global model fitted poorly ( 2 =6.79, df=9, p=0.66). Two models receiv ed equivalent support based on the AIC criterion: the temporal model and the temporal + density model ( Table 4 ). Nesting stage had the most influence on the odds of nest survival. Based on model averaged coefficients, the daily odds of survival during the laying period were 29% of those during the nestling period. The daily odds of surviv al during the incubati on period were twice those for the nestling period. Year also affected nest survival to some degree: the daily odds of survival for nests during 2000 were 66% of those for 2002, but the confidence interval overlapped one by 0.03. One hab itat parameter, the presence of water, significantly influenced the odds of nest surv ival even though the physical habitat model

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24 itself received no support. The da ily odds of survival for nests in dry habitat were 98% of those for nests in wet habitat. The conf idence intervals of all other parameters overlapped one, and most estimates were themselves very close to one ( Table 5 ). Based on a laying stage averaging four days, an incubation stage averaging 13 days, and a nestling stage averaging nine days, the tempor al model estimated a 58% probability that a given nest would fledge young. Discussion Our results showed little evidence that fragmentation and landscape composition were good predictors of nesti ng success or of the abundance of potential nest predators and parasites in the 12 study s ites chosen for this study. We did not find evidence that nesting success varied with the land use in the surrounding landscape. In fact, our data suggest that nesting success in forest sites surrounded by agriculture was surprisingly similar to nesting success in forest sites surrounded by grassland. Daily predation rates for Acadian Flycatchers, Wood Thrushes, a nd all other low nesters combined were relatively consistent ac ross landscape types ( Fig. 4 ), although it should be noted that the physical habitat model was ranked for all th ree species groups as one of the least supported models. Interestingly, our estimates of the pr obability of a given nest fledging young suggest that low nesting species nests are 36% more likely to escape predation than the nests of either the Wood Thrush or Acadian Flycatcher. Martin ( 1995 ) compared the nest success rate of ground nesting species to sh rub and canopy nesting species in forest habitats and also found that the ground ne sting species had approximately 36% higher rates of nest success than shr ub or canopy nesters. Our Low Nesters category includes four species that nest dir ectly on the ground (Kentucky Warbler, Ovenbird, Louisiana

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25 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14Acadian FlycatcherWood ThrushLow NestersEstimated Daily Predation Rate High Fragmentation / Row Crop High Fragmentation / Grassland Low Fragmentation / Row Crop Low Fragmentation / Grassland Figure 4. Model-based estimates of the daily predation rates of A cadian Flycatcher and Wood Thrush nests, as well as the nests of several low-nesting species combined. To parameterize the models, we used high and low fragmentation values that respectively reflected the 75th and 25th percentiles of fragmentation i ndices from our study sites. Estimates for high agriculture reflect a value from the 75th percentile for agricultural cover and a value from the 25th percentile for grassland cover. Estimates for high grass reflect a value from the 25th percentile for agricultural cover and a value from the 75th percentile for grassland cover. Erro r bars represent 95% confidence limits. Waterthrush, and Worm-eating Warbler) and tw o species that typically nest within a meter of the ground in our study area (Indigo Bu nting and Northern Cardinal, Cottam and S. K. Robinson, unpubl. data). Even though our species groupings are not exactly the same as Martins, we find it striking that the two datasets arrive at similar conclusions. Martin points out, and we agree, that because these rates reflect averages across several sites and are therefore less likely to be perturbed by local site effects, they may reflect evolutionary differences that are driven by predation pre ssures unique to each nesting substrate.

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26 We found no evidence that nesting success va ried with the level of fragmentation when the landscape composition was held constant ( Fig. 4 ). We see several scenarios that may explain this result. First, our site s may have been too similar to one another in the degree to which they were fragmented. To ensure that e ach site would produce sufficient nests to allow comparisons among sites, we had to restrict our work to sites of at least 200ha. Nevertheless, our study sites ranged in edge density indices from 5 m/ha to 65 m/ha and from 32% to 76% total forest cover, which is comparable to the range of fragmentation indices in othe r studies that have shown effects of fragmentation on nesting success ( Robinson et al. 1995 ). Therefore, it does not seem likely that the range of fragmentation among our sites was too small to observe the effects of fragmentation on nesting success. Second, we note from the Mayfield estim ates of the daily predation rate ( Appendix C ) that there was variation in nest success among sites, year s, and species. This is important because it distinguishes between the case where landscape variables do not adequately predict nesting success because there was no variation in nesting success across the landscape and the case where landscape variables do not adequately predict nesting success because any variation due to landscape factors is outweighed by the variation introduced by sites, y ears, and or species. The latt er appears to be the case, which may indicate that fragmentation as cu rrently measured is not as good a general predictor of avian nest success as has been previously thought. Considering that many other studies have demonstrated effects of fragmentation, it may be that fragmentation does have an effect on local nesting success, but only in certain locations. Across the study landscape, our models suggest that temporal proces ses (and likely other local

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27 process which we did not measure) were much more influential on th e nesting success of birds, a result also obtained by Peak, et al. ( 2004 ). The future challenge will be to identify patterns in the suite of potential processes to determine whether it is possible to predict which ones will be important and wh ich ones will be less important at a given site. Although the surrounding land uses showed low to m oderate correlation with mammalian predator abundance in this study, th e actual range of predator densities was extremely narrow among our sites. Other studies have shown correlations between predator abundance and landscape composition (see above) and such ch anges in predator communities could affect avian nesting success, as well as other community members and processes. Our index of relative predat or abundance is admittedly imprecise, which may have obscured any real si gnal. Nevertheless, our resu lts are consistent with the hypothesis that the effect of the matrix on mammalian predator communities is not expressed is the same way in all geographic lo cations. This is an issue that should be studied further using more precise meas ures of mammalian abundance and activity. We emphasize that because our sites were randomly selected, our results indicate that the non-forest land uses surrounding a fo rest tract may only be a good predictor of nest success and predator abundance in sp ecific cases and are not likely to function satisfactorily as generally applicable predic tors. Local processes may be much more important in determining nesting success at a given site than are landscape level processes. Our results support recent research that suggests that nest predator abundance is not necessarily correlated with proximity to or amount of edge ( Smith 2004 ) and that landscape composition/structure does not al ways correlate well with avian nesting

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28 success ( Knutson et al. 2004 ). Nests are taken by divers e predators, each of which no doubt responds differently to landscape structure. Infrared cameras in southern Illinois forests have documented nest predation by th ree species of snakes, three species of mammals, two species of hawks, Blue Jays, Common Grackles, and even small songbirds (Robinson unpubl. data). Some predators (e.g., Broad-winged Hawk and Chipmunk) may even prefer the interior of large patches (see also Tewksbury et al. 1998 ). Other predators such as snakes may vary their use of the forest interior seasonally ( BlouinDemers and Weatherhead 2001 ). Therefore, the influence of landscape structure on nest predation rates may be difficult to predict except when there are only a few dominant predators involved. Brood parasitism, however, should have been more readily explained by grassland cover given that only a single species, the Br own-headed Cowbird, is involved and it has an extremely well documented relationshi p to both edges and landscape matrix composition (e.g. Morrison et al. 1997 ). The percentage of grassland cover (which included grazing pasture) was significant in explaining the extent and intensity of cowbird parasitism of Wood Thrush nests, but not of Acadian Flycatcher nests. Our models suggest that flycatchers are parasitized at a relativel y constant rate of 0.3 cowbird eggs per nest throughout the Shawnee Nationa l Forest, but the parasitism rate and intensity of Wood Thrush nests varies from site to site. The Wood Thrush data support the implications of research on the abundance of nest parasites such as the Brown-headed Cowbird, Molothrus ater, (Robinson unpubl. data, Goguen and Mathews 2000 ), in which the frequency of nest parasitism has been shown to be highly correlated with the proximity to a cowbird feeding site (e.g. a cattle pasture), but unc orrelated with most

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29 other physical or landscape and nest-site characteristics. Only a small percentage of potential grassland areas ar e used by cowbirds as feed ing sites (Robinson unpubl. data, Thompson 1994 ), but we know little about why cowbirds select one cattle pasture as a feeding site as opposed to another. We also know little about how and why cowbirds choose a particular host nest in which to lay and egg ( Robinson and Robinson 2001 ). Our data show that some host species (e.g. Wood Thrush) are parasitized much more often and heavily than others (e.g. Acadian Flycatcher, Fig. 3 ). This suggests that Wood Thru shes may have some behavior that is not present in Acadian Flycatchers a nd allows cowbirds to more easily find Wood Thrush nests. Wood Thrush nests also had a much lower rate of survival in the laying period than in other periods of the nesti ng cycle, a phenomenon not seen in Adacian Flycatcher nests, which further suggests th at Wood Thrush behavior (e.g. nest site selection, nest defense mechanisms, genera l behavior around the nest, etc.) during the nesting period may be clueing bo th predators and parasites in to the presence of a nest. Interestingly, the extent of this heavy pa rasitization may be moderated somewhat in particular locations by landscape compos ition and use. For Wood Thrushes, the grassland site with the highest levels of cowbird parasitism was surrounded by actively grazed pasture whereas the site with the le ast frequent parasitization was surrounded by hayfields that were not grazed during the study.

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CHAPTER 4 SUMMARY AND CONCLUSIONS In complex landscapes with reasonably diverse communities of birds and nest predators, simple landscape metrics may not be good predictors of avia n nesting success. Ideally, fragmentation studies should include detailed studies of wh ich nest predators attack the nests of each species in the comm unity, as well as studies of the use of the landscape by these predators. Such studies may only be feasible in relatively simple communities such as those in grasslands, but even they have proven to have diverse communities of nest predators ( FenskeCrawford and Niemi 1997 Renfrew and Ribic 2003 ). Furthermore, we caution that models developed in a partic ular geographic region may not be broadly applicable to land scapes and forests in other regions. Much of the literature showing extreme fr agmentation effects, including our own studies in the American Midwest ( Robinson et al. 1995 Brawn and Robinson 1996 ) may depend upon the extremes of the fragmentati on continuum for statis tical significance. Small, isolated woodlots in a sea of agri culture usually (althou gh not always) have extraordinarily high levels of nest predation and brood parasitism, and some extensive forests typically have only moderate levels of nest predation and essentially no brood parasitism. Very small tracts may be overrun with predators and pa rasites that use the matrix but require forest habitat to breed (e.g., raccoons) or strongly prefer to breed in forests (cowbirds, see Hahn and Hatfield 1995 ). In contrast, large tracts may only sustain populations of nest predators th at can survive year-round in the forest interior, such as snakes and raccoons. Although such forest-b ased nest predators may occasionally be 30

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31 responsible for very high rates of nest predation ( Tewksbury et al. 1998 Schmidt and Ostfeld 2003 ), over the long term, nest predation ra tes may be lower than in fragmented landscape where the matrix prov ides a constant, overwhelming input of predators. At intermediate levels of forest cover, all potenti al nest predators are li kely to be present, including those that rely on the landscape ma trix for access to the forest interior (e.g., cowbirds) and those that rely mainly on resour ces present in the forest itself. In such situations, local details may be just as important as la ndscape effects in determining which predators are present or absent and whether or not any predators are overabundant; simple landscape metrics may not be good predictors of nesting success. While our study suggests that landscape me trics do not add appreciable predictive power to models of avian nest success in our study area, it does not suggest that the matrix has no effect on avian communities. It is known, for example, that habitat structure can influence the chances of finding a mate ( Van Horn et al. 1995 ) and territorial density ( Porneluzi and Faaborg 1999 ). We do not have the data to assess whether there were differences among our site s in the percentage of birds that were nesting or the density of their territories. Even if predation rates among sites were identical, large population leve l effects could be observed if matrix-induced habitat changes caused differences among sites in the nu mber (or percentage) of birds that find a mate and nest. It is also important to consider that th e landscape matrix can change rapidly, particularly when it is heavily influe nced by humans as it is in the Midwestern United States (S. Robinson, pers. comm.). C onsiderable changes in landscape use occur from year to year as fallow fields are plow ed, cattle are introduced or removed, or crop land is allowed to grow wild. Because we used took a static view of the landscape in this

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32 study, our data cannot assess the potential effects of rapid ch anges on nesting success. We are also mindful of the poten tial for fragmentati on or particular matrix uses to affect local communities in ways that do not manifest themselves immediately (see Cook et al. 2005 ). These transient effects have the potenti al to greatly influence nesting birds, as well as other species in the community. It may be appropriate to reevaluate the trade-off between studying multiple sites, which allows for variance estimation and poten tially for the broad application of results, and studying a single site, which allows a gr eater amount of effort to be placed in understanding local processes. We point out th at in spite of the intensive nature of the present study, we were able to collect data from only 12 sites across southern Illinois. For the present dataset, 12 sites were not suffi cient to distinguish the variance in nesting success caused by local processes from that caused by landscape processes. Studies conducted on more local scales with two or three sites might allow a more thorough investigation of population dynamics, rapi dly changing landscapes, and transient community effects. Perhaps a number of we ll-designed, locally focu sed studies that are able to examine in fine detail community responses and the possible dependence of those responses on the landscape w ould better serve the scientific community both in terms of directing future research que stions and in providing clea r management recommendations.

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APPENDIX A COMMON AND LATIN NAMES This appendix gives the common and Latin names of the birds and mammals in this study, as well as the four-lette r abbreviations for the birds that appear in Appendix B. Common Name Latin Name Bird Code Birds Acadian Flycatcher Empidonax virescens ACFL Brown-headed Cowbird Molothrus ater BHCB Indigo Bunting Passerina cyanea INBU Kentucky Warbler Oporornis formosus KEWA Louisiana Waterthrush Seiurus motacilla LOWA Northern Cardinal Cardinalis cardinalis NOCA Ovenbird Seiurus aurocapillus OVEN Wood Thrush Hylocichla mustelina WOTH Worm-eating Warbler Helmitheros vermivorum WEWA Mammals Bobcat Lynx rufus Common Gray Fox Urocyon cinereoargenteus Coyote Canis latrans Eastern Chipmunk Tamias striatus House Cat Felis sylvestris catus Long-tailed Weasel Mustela frenata Northern Raccoon Procyon lotor Striped skunk Mephitis mephitis Virginia Opossum Didelphis virginiana 33

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APPENDIX B TRACK STATION DATA This table shows the records from the track stations in each year at each site. Track station methodology was not consistent across years; see Materials and Methods. Latin names are given in Appendix A. Northern Raccoon Virginia Opossum Coyote Common Gray Fox Long-tailed Weasel Striped Skunk House Cat Bobcat Eastern Chipmunk Bald Knob 2000 3 2 2001 15 2002 4 2 3 5 Big Brushy 2000 10 1 2 1 1 2001 10 2 1 2002 3 2 1 Burke Branch 2000 7 2 2001 8 8 1 1 2002 4 5 Cave Hill 2000 6 4 1 2001 1 3 2002 5 4 1 Hayes Creek 2000 3 4 4 2001 2 6 1 4 1 2002 2 4 1 Kaskaskia Forest 2000 12 5 1 2001 6 4 1 2002 1 2 1 Lick Creek 2000 7 3 2001 6 2 1 1 2002 3 2 1 Lusk Creek 2000 4 2 1 2001 3 3 2002 2 1 1 Pine Hills 2000 11 4 2001 3 1 2002 3 4 1 1 34

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35 Northern Raccoon Virginia Opossum Coyote Common Gray Fox Long-tailed Weasel Striped Skunk House Cat Bobcat Eastern Chipmunk Saline Mines 2000 2 5 2 1 1 2001 6 6 2002 3 10 2 1 Tansill 2000 10 5 2001 12 1 3 2002 4 2 3 1 Wildcat Bluff 2000 3 1 2001 10 4 2002 3 6 1

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APPENDIX C MAYFIELD SUMMARY STATISTICS This appendix shows the exposure days, number of depredated nests, Mayfield daily predation rate (DPR), standard error of the DPR, and the tota l number of nests for each species, site, and year. - in the Speci es, Site, or Year column indicates a summary line. For example, the first line of the table gives Acadia n Flycatcher data from 2000, summed across all sites. See A ppendix A for full species names. Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests ACFL 2000 1890.5 55 0.029 0.004 117 ACFL 2001 2296 42 0.018 0.003 144 ACFL 2002 1251 31 0.025 0.004 76 ACFL 5437.5 128 0.024 0.002 337 ACFL Bald 2000 187 6 0.032 0.013 11 ACFL Bald 2001 222.5 13 0.058 0.016 16 ACFL Bald 2002 90.5 0 0.000 0.000 4 ACFL Bald 500 19 0.038 0.009 31 ACFL Brushy 2000 329.5 12 0.036 0.010 21 ACFL Brushy 2001 270.5 7 0.026 0.010 17 ACFL Brushy 2002 118.5 3 0.025 0.014 7 ACFL Brushy 718.5 22 0.031 0.006 45 ACFL Burke 2000 38 1 0.026 0.026 3 ACFL Burke 2001 101.5 1 0.010 0.010 7 ACFL Burke 2002 64.5 1 0.016 0.015 5 ACFL Burke 204 3 0.015 0.008 15 ACFL Cave 2000 103.5 2 0.019 0.014 4 ACFL Cave 2001 136 0 0.000 0.000 5 ACFL Cave 2002 129 3 0.023 0.013 6 ACFL Cave 368.5 5 0.014 0.006 15 ACFL Hayes 2000 65.5 1 0.015 0.015 4 ACFL Hayes 2001 84.5 1 0.012 0.012 5 ACFL Hayes 2002 49.5 0 0.000 0.000 3 ACFL Hayes 199.5 2 0.010 0.007 12 ACFL Kask 2000 114 4 0.035 0.017 7 ACFL Kask 2001 43 2 0.047 0.032 4 ACFL Kask 2002 53.5 0 0.000 0.000 5 ACFL Kask 210.5 6 0.029 0.011 16 ACFL Lick 2000 117.5 5 0.043 0.019 8 36

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37 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests ACFL Lick 2001 135.5 2 0.015 0.010 10 ACFL Lick 2002 59 6 0.102 0.039 5 ACFL Lick 312 13 0.042 0.011 23 ACFL Lusk 2000 301 3 0.010 0.006 16 ACFL Lusk 2001 244.5 3 0.012 0.007 15 ACFL Lusk 2002 317.5 11 0.035 0.010 19 ACFL Lusk 863 17 0.020 0.005 50 ACFL Pine 2000 117.5 2 0.017 0.012 8 ACFL Pine 2001 148.5 2 0.013 0.009 11 ACFL Pine 2002 117.5 2 0.017 0.012 6 ACFL Pine 383.5 6 0.016 0.006 25 ACFL Saline 2000 365 15 0.041 0.010 26 ACFL Saline 2001 517 9 0.017 0.006 34 ACFL Saline 2002 103 2 0.019 0.014 8 ACFL Saline 985 26 0.026 0.005 68 ACFL Tansill 2000 96 2 0.021 0.015 5 ACFL Tansill 2001 226 1 0.004 0.004 12 ACFL Tansill 2002 124.5 1 0.008 0.008 6 ACFL Tansill 446.5 4 0.009 0.004 23 ACFL Wildcat 2000 56 2 0.036 0.025 4 ACFL Wildcat 2001 166.5 1 0.006 0.006 8 ACFL Wildcat 2002 24 2 0.083 0.056 2 ACFL Wildcat 246.5 5 0.020 0.009 14 INBU 2000 286 11 0.038 0.011 23 INBU 2001 457.5 15 0.033 0.008 40 INBU 2002 429.5 12 0.028 0.008 38 INBU 1173 38 0.032 0.005 101 INBU Bald 2001 39 1 0.026 0.025 3 INBU Bald 39 1 0.026 0.025 3 INBU Brushy 2000 50 2 0.040 0.028 3 INBU Brushy 2001 23 0 0.000 0.000 2 INBU Brushy 2002 25 0 0.000 0.000 1 INBU Brushy 98 2 0.020 0.014 6 INBU Burke 2000 38.5 1 0.026 0.026 4 INBU Burke 2002 67.5 0 0.000 0.000 3 INBU Burke 106 1 0.009 0.009 7 INBU Cave 2001 22.5 0 0.000 0.000 1 INBU Cave 2002 12 1 0.083 0.080 1 INBU Cave 34.5 1 0.029 0.029 2 INBU Hayes 2000 7 1 0.143 0.132 1 INBU Hayes 2001 41.5 3 0.072 0.040 4 INBU Hayes 2002 18.5 0 0.000 0.000 3 INBU Hayes 67 4 0.060 0.029 8 INBU Kask 2001 17.5 0 0.000 0.000 1 INBU Kask 17.5 0 0.000 0.000 1 INBU Lick 2000 11 1 0.091 0.087 1 INBU Lick 2001 58.5 2 0.034 0.024 4 INBU Lick 2002 19 1 0.053 0.051 2

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38 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests INBU Lick 88.5 4 0.045 0.022 7 INBU Lusk 2000 64.5 2 0.031 0.022 4 INBU Lusk 2001 39 2 0.051 0.035 5 INBU Lusk 2002 78.5 6 0.076 0.030 9 INBU Lusk 182 10 0.055 0.017 18 INBU Pine 2000 62 0 0.000 0.000 4 INBU Pine 2001 48 0 0.000 0.000 4 INBU Pine 2002 138.5 3 0.022 0.012 9 INBU Pine 248.5 3 0.012 0.007 17 INBU Saline 2000 33 2 0.061 0.042 4 INBU Saline 2001 56 0 0.000 0.000 5 INBU Saline 2002 15 0 0.000 0.000 3 INBU Saline 104 2 0.019 0.013 12 INBU Tansill 2001 86.5 4 0.046 0.023 6 INBU Tansill 2002 3 0 0.000 0.000 1 INBU Tansill 89.5 4 0.045 0.022 7 INBU Wildcat 2000 20 2 0.100 0.067 2 INBU Wildcat 2001 26 3 0.115 0.063 5 INBU Wildcat 2002 52.5 1 0.019 0.019 6 INBU Wildcat 98.5 6 0.061 0.024 13 KEWA 2000 311.5 7 0.022 0.008 29 KEWA 2001 413.5 8 0.019 0.007 37 KEWA 2002 201 7 0.035 0.013 21 KEWA 926 22 0.024 0.005 87 KEWA Bald 2001 19 0 0.000 0.000 1 KEWA Bald 2002 4.5 0 0.000 0.000 1 KEWA Bald 23.5 0 0.000 0.000 2 KEWA Brushy 2000 112 1 0.009 0.009 6 KEWA Brushy 2001 86 1 0.012 0.012 6 KEWA Brushy 2002 6.5 0 0.000 0.000 2 KEWA Brushy 204.5 2 0.010 0.007 14 KEWA Burke 2000 71.5 2 0.028 0.020 10 KEWA Burke 2001 42.5 1 0.024 0.023 4 KEWA Burke 2002 34 1 0.029 0.029 3 KEWA Burke 148 4 0.027 0.013 17 KEWA Cave 2001 63.5 2 0.031 0.022 7 KEWA Cave 2002 12 1 0.083 0.080 1 KEWA Cave 75.5 3 0.040 0.022 8 KEWA Hayes 2000 5.5 0 0.000 0.000 1 KEWA Hayes 2001 18.5 0 0.000 0.000 1 KEWA Hayes 2002 18 0 0.000 0.000 2 KEWA Hayes 42 0 0.000 0.000 4 KEWA Kask 2001 13 1 0.077 0.074 1 KEWA Kask 13 1 0.077 0.074 1 KEWA Lick 2000 6.5 0 0.000 0.000 1 KEWA Lick 2001 31.5 0 0.000 0.000 4 KEWA Lick 2002 21 0 0.000 0.000 1 KEWA Lick 59 0 0.000 0.000 6

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39 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests KEWA Lusk 2000 12.5 1 0.080 0.077 2 KEWA Lusk 2001 37 1 0.027 0.027 3 KEWA Lusk 2002 2.5 0 0.000 0.000 2 KEWA Lusk 52 2 0.038 0.027 7 KEWA Pine 2000 45.5 1 0.022 0.022 4 KEWA Pine 2001 87.5 1 0.011 0.011 7 KEWA Pine 2002 78.5 5 0.064 0.028 6 KEWA Pine 211.5 7 0.033 0.012 17 KEWA Saline 2000 58 2 0.034 0.024 5 KEWA Saline 2001 10.5 0 0.000 0.000 1 KEWA Saline 2002 13.5 0 0.000 0.000 1 KEWA Saline 82 2 0.024 0.017 7 KEWA Tansill 2001 3 1 0.333 0.272 1 KEWA Tansill 2002 6 0 0.000 0.000 1 KEWA Tansill 9 1 0.111 0.105 2 KEWA Wildcat 2001 1.5 0 0.000 0.000 1 KEWA Wildcat 2002 4.5 0 0.000 0.000 1 KEWA Wildcat 6 0 0.000 0.000 2 LOWA 2000 40.5 1 0.025 0.024 6 LOWA 2001 602.5 16 0.027 0.007 39 LOWA 2002 731.5 13 0.018 0.005 44 LOWA 1374.5 30 0.022 0.004 89 LOWA Bald 2000 14 0 0.000 0.000 1 LOWA Bald 2001 77 5 0.065 0.028 5 LOWA Bald 91 5 0.055 0.024 6 LOWA Brushy 2000 2.5 0 0.000 0.000 1 LOWA Brushy 2002 59.5 1 0.017 0.017 4 LOWA Brushy 62 1 0.016 0.016 5 LOWA Burke 2000 10 1 0.100 0.095 2 LOWA Burke 2001 68.5 1 0.015 0.014 5 LOWA Burke 2002 134 0 0.000 0.000 6 LOWA Burke 212.5 2 0.009 0.007 13 LOWA Cave 2001 28.5 1 0.035 0.034 2 LOWA Cave 2002 39 1 0.026 0.025 3 LOWA Cave 67.5 2 0.030 0.021 5 LOWA Hayes 2000 10 0 0.000 0.000 1 LOWA Hayes 2001 16.5 0 0.000 0.000 1 LOWA Hayes 26.5 0 0.000 0.000 2 LOWA Lick 2001 63.5 0 0.000 0.000 3 LOWA Lick 2002 145 3 0.021 0.012 8 LOWA Lick 208.5 3 0.014 0.008 11 LOWA Lusk 2000 4 0 0.000 0.000 1 LOWA Lusk 2001 208.5 4 0.019 0.009 13 LOWA Lusk 2002 217 7 0.032 0.012 15 LOWA Lusk 429.5 11 0.026 0.008 29 LOWA Pine 2001 7 1 0.143 0.132 1 LOWA Pine 7 1 0.143 0.132 1 LOWA Saline 2001 17.5 0 0.000 0.000 1

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40 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests LOWA Saline 17.5 0 0.000 0.000 1 LOWA Tansill 2001 39 3 0.077 0.043 3 LOWA Tansill 2002 103 0 0.000 0.000 6 LOWA Tansill 142 3 0.021 0.012 9 LOWA Wildcat 2001 76.5 1 0.013 0.013 5 LOWA Wildcat 2002 34 1 0.029 0.029 2 LOWA Wildcat 110.5 2 0.018 0.013 7 NOCA 2000 210 11 0.052 0.015 16 NOCA 2001 353.5 13 0.037 0.010 24 NOCA 2002 401.5 15 0.037 0.009 32 NOCA 965 39 0.040 0.006 72 NOCA Bald 2000 22 1 0.045 0.044 1 NOCA Bald 2001 15 1 0.067 0.064 1 NOCA Bald 37 2 0.054 0.037 2 NOCA Brushy 2000 40 3 0.075 0.042 3 NOCA Brushy 2001 5 1 0.200 0.179 1 NOCA Brushy 2002 23 1 0.043 0.043 2 NOCA Brushy 68 5 0.074 0.032 6 NOCA Burke 2000 49 3 0.061 0.034 3 NOCA Burke 2001 74.5 4 0.054 0.026 5 NOCA Burke 2002 108 6 0.056 0.022 8 NOCA Burke 231.5 13 0.056 0.015 16 NOCA Cave 2001 21 0 0.000 0.000 1 NOCA Cave 2002 3 1 0.333 0.272 1 NOCA Cave 24 1 0.042 0.041 2 NOCA Hayes 2000 24 1 0.042 0.041 2 NOCA Hayes 2001 72.5 2 0.028 0.019 4 NOCA Hayes 2002 19 3 0.158 0.084 3 NOCA Hayes 115.5 6 0.052 0.021 9 NOCA Lick 2000 8 1 0.125 0.117 1 NOCA Lick 2001 58.5 3 0.051 0.029 5 NOCA Lick 2002 74.5 0 0.000 0.000 3 NOCA Lick 141 4 0.028 0.014 9 NOCA Lusk 2000 16 0 0.000 0.000 1 NOCA Lusk 2002 16.5 1 0.061 0.059 2 NOCA Lusk 32.5 1 0.031 0.030 3 NOCA Pine 2000 22.5 1 0.044 0.043 2 NOCA Pine 2002 13 1 0.077 0.074 1 NOCA Pine 35.5 2 0.056 0.039 3 NOCA Saline 2000 28.5 1 0.035 0.034 3 NOCA Saline 2001 21 2 0.095 0.064 2 NOCA Saline 2002 6 1 0.167 0.152 1 NOCA Saline 55.5 4 0.072 0.035 6 NOCA Tansill 2001 86 0 0.000 0.000 5 NOCA Tansill 2002 132.5 1 0.008 0.008 10 NOCA Tansill 218.5 1 0.005 0.005 15 NOCA Wildcat 2002 6 0 0.000 0.000 1 NOCA Wildcat 6 0 0.000 0.000 1

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41 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests OVEN 2000 6 1 0.167 0.152 2 OVEN 2002 9.5 2 0.211 0.132 3 OVEN 15.5 3 0.194 0.100 5 OVEN Burke 2002 8 2 0.250 0.153 2 OVEN Burke 8 2 0.250 0.153 2 OVEN Lusk 2000 6 1 0.167 0.152 2 OVEN Lusk 2002 1.5 0 0.000 0.000 1 OVEN Lusk 7.5 1 0.133 0.124 3 WEWA 2000 88 4 0.045 0.022 10 WEWA 2001 336.5 8 0.024 0.008 35 WEWA 2002 231.5 3 0.013 0.007 19 WEWA 656 15 0.023 0.006 64 WEWA Bald 2000 13 1 0.077 0.074 1 WEWA Bald 2001 20 1 0.050 0.049 1 WEWA Bald 2002 1 0 0.000 0.000 1 WEWA Bald 34 2 0.059 0.040 3 WEWA Brushy 2000 3 0 0.000 0.000 1 WEWA Brushy 3 0 0.000 0.000 1 WEWA Burke 2000 5 1 0.200 0.179 2 WEWA Burke 2001 22 1 0.045 0.044 2 WEWA Burke 2002 66.5 1 0.015 0.015 5 WEWA Burke 93.5 3 0.032 0.018 9 WEWA Cave 2000 1.5 0 0.000 0.000 1 WEWA Cave 2001 52.5 1 0.019 0.019 6 WEWA Cave 2002 58.5 0 0.000 0.000 5 WEWA Cave 112.5 1 0.009 0.009 12 WEWA Hayes 2001 5 0 0.000 0.000 1 WEWA Hayes 5 0 0.000 0.000 1 WEWA Kask 2000 8 2 0.250 0.153 2 WEWA Kask 2001 43 3 0.070 0.039 7 WEWA Kask 51 5 0.098 0.042 9 WEWA Lick 2001 9 0 0.000 0.000 2 WEWA Lick 9 0 0.000 0.000 2 WEWA Lusk 2000 57.5 0 0.000 0.000 3 WEWA Lusk 2001 88 1 0.011 0.011 9 WEWA Lusk 2002 74 2 0.027 0.019 5 WEWA Lusk 219.5 3 0.014 0.008 17 WEWA Saline 2002 1.5 0 0.000 0.000 1 WEWA Saline 1.5 0 0.000 0.000 1 WEWA Tansill 2001 55 1 0.018 0.018 5 WEWA Tansill 2002 30 0 0.000 0.000 2 WEWA Tansill 85 1 0.012 0.012 7 WEWA Wildcat 2001 42 0 0.000 0.000 2 WEWA Wildcat 42 0 0.000 0.000 2 WOTH 2000 300.5 19 0.063 0.014 28 WOTH 2001 663.5 14 0.021 0.006 46 WOTH 2002 468 12 0.026 0.007 38 WOTH 1432 45 0.031 0.005 112

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42 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests WOTH Bald 2002 25 0 0.000 0.000 2 WOTH Bald 25 0 0.000 0.000 2 WOTH Brushy 2000 51.5 2 0.039 0.027 5 WOTH Brushy 2001 37 1 0.027 0.027 2 WOTH Brushy 88.5 3 0.034 0.019 7 WOTH Burke 2000 80.5 5 0.062 0.027 9 WOTH Burke 2001 179.5 3 0.017 0.010 12 WOTH Burke 2002 172.5 2 0.012 0.008 10 WOTH Burke 432.5 10 0.023 0.007 31 WOTH Cave 2001 18.5 0 0.000 0.000 1 WOTH Cave 2002 9 0 0.000 0.000 2 WOTH Cave 27.5 0 0.000 0.000 3 WOTH Hayes 2000 52.5 0 0.000 0.000 2 WOTH Hayes 2001 133 3 0.023 0.013 10 WOTH Hayes 2002 48 2 0.042 0.029 3 WOTH Hayes 233.5 5 0.021 0.009 15 WOTH Kask 2000 22 3 0.136 0.073 2 WOTH Kask 2001 53 0 0.000 0.000 3 WOTH Kask 2002 38 4 0.105 0.050 5 WOTH Kask 113 7 0.062 0.023 10 WOTH Lick 2001 24 1 0.042 0.041 2 WOTH Lick 24 1 0.042 0.041 2 WOTH Lusk 2000 21 3 0.143 0.076 3 WOTH Lusk 2001 20.5 2 0.098 0.066 3 WOTH Lusk 2002 12 0 0.000 0.000 2 WOTH Lusk 53.5 5 0.093 0.040 8 WOTH Pine 2001 66.5 2 0.030 0.021 3 WOTH Pine 2002 36 0 0.000 0.000 5 WOTH Pine 102.5 2 0.020 0.014 8 WOTH Saline 2000 28 3 0.107 0.058 3 WOTH Saline 2001 6 1 0.167 0.152 3 WOTH Saline 2002 22 0 0.000 0.000 1 WOTH Saline 56 4 0.071 0.034 7 WOTH Tansill 2001 40 0 0.000 0.000 2 WOTH Tansill 2002 34.5 1 0.029 0.029 2 WOTH Tansill 74.5 1 0.013 0.013 4 WOTH Wildcat 2000 45 3 0.067 0.037 4 WOTH Wildcat 2001 85.5 1 0.012 0.012 5 WOTH Wildcat 2002 71 3 0.042 0.024 6 WOTH Wildcat 201.5 7 0.035 0.013 15 Bald 2000 236 8 0.034 0.012 14 Bald 2001 392.5 21 0.054 0.011 27 Bald 2002 121 0 0.000 0.000 8 Bald 749.5 29 0.039 0.007 49 Brushy 2000 588.5 20 0.034 0.007 40 Brushy 2001 421.5 10 0.024 0.007 28 Brushy 2002 232.5 5 0.022 0.010 16 Brushy 1242.5 35 0.028 0.005 84

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43 Exposure Depredated Standard Total Species Site Year Days Nests DPR Error Nests Burke 2000 292.5 14 0.048 0.012 33 Burke 2001 488.5 11 0.023 0.007 35 Burke 2002 655 13 0.020 0.005 42 Burke 1436 38 0.026 0.004 110 Cave 2000 105 2 0.019 0.013 5 Cave 2001 342.5 4 0.012 0.006 23 Cave 2002 262.5 7 0.027 0.010 19 Cave 710 13 0.018 0.005 47 Hayes 2000 164.5 3 0.018 0.010 11 Hayes 2001 371.5 9 0.024 0.008 26 Hayes 2002 153 5 0.033 0.014 14 Hayes 689 17 0.025 0.006 51 Kask 2000 144 9 0.063 0.020 11 Kask 2001 169.5 6 0.035 0.014 16 Kask 2002 91.5 4 0.044 0.021 10 Kask 405 19 0.047 0.011 37 Lick 2000 143 7 0.049 0.018 11 Lick 2001 380.5 8 0.021 0.007 30 Lick 2002 318.5 10 0.031 0.010 19 Lick 842 25 0.030 0.006 60 Lusk 2000 482.5 10 0.021 0.006 32 Lusk 2001 637.5 13 0.020 0.006 48 Lusk 2002 719.5 27 0.038 0.007 55 Lusk 1839.5 50 0.027 0.004 135 Pine 2000 247.5 4 0.016 0.008 18 Pine 2001 357.5 6 0.017 0.007 26 Pine 2002 383.5 11 0.029 0.009 27 Pine 988.5 21 0.021 0.005 71 Saline 2000 512.5 23 0.045 0.009 41 Saline 2001 628 12 0.019 0.005 46 Saline 2002 161 3 0.019 0.011 15 Saline 1301.5 38 0.029 0.005 102 Tansill 2000 96 2 0.021 0.015 5 Tansill 2001 535.5 10 0.019 0.006 34 Tansill 2002 433.5 3 0.007 0.004 28 Tansill 1065 15 0.014 0.004 67 Wildcat 2000 121 7 0.058 0.021 10 Wildcat 2001 398 6 0.015 0.006 26 Wildcat 2002 192 7 0.036 0.014 18 Wildcat 711 20 0.028 0.006 54 2000 3133 109 0.035 0.003 231 2001 5123 116 0.023 0.002 365 2002 3723.5 95 0.026 0.003 271 11979.5 320 0.027 0.001 867

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BIOGRAPHICAL SKETCH Mr. Cottam received a Bachelor of Arts in biology from the University of Utah in 1998, where he completed a senior honors th esis on canine respiratory mechanics. Taking a hiatus from academia, he spent two years as a humanitarian servant in South America, then returned to Utah and worked for both the State Department of Natural Resources and the Department of Biology at the University of Utah. In 2002, Mr. Cottam entered graduate school at the University of Illinois, Urbana-Champaign and affiliated with the Program in Ecology and Evolutionary Biology. He transferred to the University of Floridas Department of Zoology in 2003. 54


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Permanent Link: http://ufdc.ufl.edu/UFE0013644/00001

Material Information

Title: Use of landscape metrics to predict avian nest survival in a fragmented Midwestern forest landscape
Physical Description: Mixed Material
Language: English
Creator: Cottam, Michael R. ( Dissertant )
Robinson, Scott K. ( Thesis advisor )
Bolker, Ben ( Reviewer )
Holt, Bob ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Zoology thesis, M.S
Dissertations, Academic -- UF -- Zoology
Genre: bibliography   ( marcgt )
theses   ( marcgt )

Notes

Abstract: Habitat fragmentation fundamentally affects trophic interactions and community structure. Studies of breeding birds have provided some of the clearest examples of the negative consequences of habitat fragmentation. An understanding of the ways in which an agricultural matrix does or does not modulate avian nesting success in forest fragments could greatly improve our understanding of fragmentation ecology. We used a stratified random process to select 12 study sites in the Shawnee National Forest in southern Illinois, USA. We used an information theoretic approach and generalized linear models to investigate eight a priori models that predicted the probability of a nest being successful. These models incorporated landscape composition (% grassland, % agriculture, fragmentation), temporal factors, conspecific density, predator density, and combinations of these. We also investigated whether the frequency or intensity of parasitism by the Brown-headed Cowbird was related to landscape composition. Temporal factors had the most effect on nesting success; landscape factors did not influence nesting success. Parasitism rates and intensity were significantly influenced by the amount of grassland for the Wood Thrush, but not for the Acadian Flycatcher. We conclude that simple landscape metrics may not be good predictors of avian nesting success in complex landscapes that have diverse predator communities. We propose that a reevaluation of the tradeoff between multi-site and locally focused studies may be useful in directing future research and management initiatives.
Subject: Acadian, aic, bird, Brown headed Cowbird, flycatcher, fragmentation, generalized, Illinois, linear, models, nest, parasitism, predation, Shawnee, success, thrush, wood
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 64 pages.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003589360
System ID: UFE0013644:00001

Permanent Link: http://ufdc.ufl.edu/UFE0013644/00001

Material Information

Title: Use of landscape metrics to predict avian nest survival in a fragmented Midwestern forest landscape
Physical Description: Mixed Material
Language: English
Creator: Cottam, Michael R. ( Dissertant )
Robinson, Scott K. ( Thesis advisor )
Bolker, Ben ( Reviewer )
Holt, Bob ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Zoology thesis, M.S
Dissertations, Academic -- UF -- Zoology
Genre: bibliography   ( marcgt )
theses   ( marcgt )

Notes

Abstract: Habitat fragmentation fundamentally affects trophic interactions and community structure. Studies of breeding birds have provided some of the clearest examples of the negative consequences of habitat fragmentation. An understanding of the ways in which an agricultural matrix does or does not modulate avian nesting success in forest fragments could greatly improve our understanding of fragmentation ecology. We used a stratified random process to select 12 study sites in the Shawnee National Forest in southern Illinois, USA. We used an information theoretic approach and generalized linear models to investigate eight a priori models that predicted the probability of a nest being successful. These models incorporated landscape composition (% grassland, % agriculture, fragmentation), temporal factors, conspecific density, predator density, and combinations of these. We also investigated whether the frequency or intensity of parasitism by the Brown-headed Cowbird was related to landscape composition. Temporal factors had the most effect on nesting success; landscape factors did not influence nesting success. Parasitism rates and intensity were significantly influenced by the amount of grassland for the Wood Thrush, but not for the Acadian Flycatcher. We conclude that simple landscape metrics may not be good predictors of avian nesting success in complex landscapes that have diverse predator communities. We propose that a reevaluation of the tradeoff between multi-site and locally focused studies may be useful in directing future research and management initiatives.
Subject: Acadian, aic, bird, Brown headed Cowbird, flycatcher, fragmentation, generalized, Illinois, linear, models, nest, parasitism, predation, Shawnee, success, thrush, wood
General Note: Title from title page of source document.
General Note: Document formatted into pages; contains 64 pages.
General Note: Includes vita.
Thesis: Thesis (M.S.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Text (Electronic thesis) in PDF format.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 003589360
System ID: UFE0013644:00001


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Full Text












USE OF LANDSCAPE METRICS TO PREDICT AVIAN NEST SURVIVAL IN A
FRAGMENTED MIDWESTERN FOREST LANDSCAPE
















By

MICHAEL R COTTAM


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

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Michael R Cottam
































This document is dedicated to KCC and JMC for sparking my early interest in science
and to EAGC for her patience and support during the years of graduate school.















ACKNOWLEDGMENTS

I thank Scott Robinson for his advice, direction, and mentoring during this degree.

I also thank my committee members, Ben Bolker and Bob Holt, for informative

discussions and suggestions during the preparation of this manuscript. Jeff Brawn, Ed

Heske, and Kevin Rowe of the University of Illinois at Urbana-Champaign made

significant contributions to the development and execution of this project. My

development as a scientist is in large part a result of many fruitful discussions with the

graduate students and post-docs associated with the Robinson laboratory. Foremost

among these, and to whom I owe a particular debt of gratitude, are Christine Stracey, Jeff

Hoover, and Wendy Schelsky. This work could not have been completed with out the

assistance of an enormous and dedicated field crew, whom I also thank deeply.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

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

LIST OF FIGURES .................................................... ............ .............. .. viii

ABSTRACT ........ .............. ............. ..... .......... .......... ix

CHAPTER

1 INTRODUCTION AND REVIEW OF LITERATURE ..............................................1

2 M A TERIAL S AN D M ETH OD S ........................................................ ....................7

Stu dy Sites ......................................................................... . 7
N est M monitoring ................. ....... .......... .. ................................ ..................
Index of Abundance for Predatory Mammals ......................................................11
D ata Analyses ................................... ................................ ......... 12

3 RESULTS AND D ISCU SSION ........................................... ............................18

P red atory M am m als ........................................................................... ............ .. .. 18
A cadian F ly catch er .................................................................. .. ......... .... 18
W ood T hrush .................................................................. 2 1
L ow N esters .................................23.............................
D isc u ssio n .............................................................................................................. 2 4

4 SUMMARY AND CONCLUSIONS.................................................... ..............30

APPENDIX

A COMMON AND LATIN NAMES ........................................ ......................... 33

B TRACK STATION DA TA ............................................................. ............... 34

C MAYFIELD SUMMARY STATISTICS.......................................... ............... 36

L IST O F R E FE R E N C E S .................................... ..................................... ....................44



v









B IO G R A PH IC A L SK E T C H ...................................................................... ..................54
















LIST OF TABLES


Table page

1 Stratified design used in site selection ............................................ ............... 7

2 Specific landscape characteristics of each of the 12 sites in this study..................9

3 Dates on which birds were censused at each of the 12 study sites...........................11

4 Comparison of eight candidate models relating habitat and/or temporal
parameters to the probability of fledging young from a nest..............................19

5 M odel averaged param eter estim ates ............................................ ............... 20
















LIST OF FIGURES


Figure p

1 M ap of southern Illinois, U SA ............................................................................8

2 Estimated daily predation rates during each nesting stage.................................21

3 Mean number of Brown-headed Cowbird eggs per nest..........................................22

4 Model-based estimates of the daily predation rates ...............................................25















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

USE OF LANDSCAPE METRICS TO PREDICT AVIAN NEST SURVIVAL IN A
FRAGMENTED MIDWESTERN FOREST LANDSCAPE

By

Michael R Cottam

May 2006

Chair: Scott K. Robinson
Major Department: Zoology

Habitat fragmentation fundamentally affects trophic interactions and community

structure. Studies of breeding birds have provided some of the clearest examples of the

negative consequences of habitat fragmentation. An understanding of the ways in which

an agricultural matrix does or does not modulate avian nesting success in forest

fragments could greatly improve our understanding of fragmentation ecology. We used a

stratified random process to select 12 study sites in the Shawnee National Forest in

southern Illinois, USA. We used an information theoretic approach and generalized

linear models to investigate eight apriori models that predicted the probability of a nest

being successful. These models incorporated landscape composition (% grassland, %

agriculture, fragmentation), temporal factors, conspecific density, predator density, and

combinations of these. We also investigated whether the frequency or intensity of

parasitism by the Brown-headed Cowbird was related to landscape composition.

Temporal factors had the most effect on nesting success; landscape factors did not









influence nesting success. Parasitism rates and intensity were significantly influenced by

the amount of grassland for the Wood Thrush, but not for the Acadian Flycatcher. We

conclude that simple landscape metrics may not be good predictors of avian nesting

success in complex landscapes that have diverse predator communities. We propose that

a reevaluation of the tradeoff between multi-site and locally focused studies may be

useful in directing future research and management initiatives.














CHAPTER 1
INTRODUCTION AND REVIEW OF LITERATURE

Habitat fragmentation fundamentally affects community structure and trophic

interactions (Noss and Csuti 1997, Hedlund et al. 2004, Tscharntke and Brandl 2004).

Numerous studies support the widely held notion that the fragmentation of breeding

habitat significantly decreases annual reproductive success and species viability

(reviewed in Robinson and Wilcove 1994, Faaborg et al. 1995, Lloyd et al. 2005). The

mechanisms underlying the reduction of breeding success may include changes in the

communities of predators (Robinson 1992, Porneluzi et al. 1993, Hoover et al. 1995,

Brawn and Robinson 1996, Chalfoun et al. 2002), invasive species (reviewed in Knick et

al. 2003, Cronin and Haynes 2004), and parasites (reviewed in Trine 1998) that are

associated with edges and the matrix surrounding the fragments. Studies of breeding

forest birds have provided some of the clearest examples of the negative consequences of

habitat fragmentation (Gates and Gysel 1978, Wilcove 1985, reviewed in Faaborg et al.

1995, Winfree 2004). In addition to the absence of many birds from small habitat

patches, populations of many species that nest in fragmented landscapes suffer reduced

viability in small habitat patches and when close to habitat boundaries (edges) (reviewed

in Robinson and Wilcove 1994, Faaborg et al. 1995, Weldon and Haddad 2005). Nest

predation rates, for example, are often shown to be higher in small forest tracts (Robinson

1992, Porneluzi et al. 1993, Hoover et al. 1995, Linder and Bollinger 1995, Brawn and

Robinson 1996, Aquilani and Brewer 2004) and close to edges (reviewed in Paton 1994,

Rich et al. 1994, Gates and Evans 1998, Manolis et al. 2002, Batary and Baldi 2004).









Levels of brood parasitism by cowbirds are also usually greater closer to edges than in

the forest interior (reviewed in Trine et al. 1998, Phillips et al. 2005). As a result of

differential nesting success, fragmented landscapes have often been hypothesized to

consist of a mosaic of population sources and sinks (sensu Pulliam 1988, Pulliam and

Danielson 1991), either at local spatial scales (e.g. Urban and Shugart 1986, Temple and

Cary 1988) or at very large, regional scales (e.g. Donovan et al. 1995a, Donovan et al.

1995b, Robinson et al. 1995, Trine 1998, Hochachka et al. 1999, Lloyd et al. 2005).

These results have been widely incorporated into conservation and land management

plans (Finch and Stangel 1993, Marzluff and Sallabanks 1993, Thompson 1996, Petit and

Petit 2000).

The adverse effects of forest fragmentation, however, are not uniform within or

among regions. Evidence for higher rates of nest predation in small fragments is

decidedly mixed; some have even argued that the negative effects of fragmentation are an

artifact of the use of artificial nests to measure nest predation rates (Haskell 1995, see

also Moore and Robinson 2004) or of inappropriate lumping of species (Bielefeldt and

Rosenfield 1997). Similarly, many studies show no edge effects or only show such

effects very close (<50m) to edges (reviewed in Paton 1994, Hartley and Hunter 1998).

Stephens et al. (2004) suggested that these differences in results are related to the length

of a study and to the ways in which fragmentation is defined, with multi-year studies that

use large-scale definitions of fragmentation being more likely to find effects attributable

to fragmentation. Levels of cowbird parasitism also vary greatly among regions (Hoover

and Brittingham 1993, Smith and Myers-Smith 1998, Thompson et al. 2000); birds

nesting in small fragmented forests in some regions experience very low levels of









parasitism, even near edges, whereas parasitism levels in other regions remain very high

even in large (>500 ha) forest tracts more than 1 km from an edge (Robinson and

Wilcove 1994, Trine 1998, Sisk and Battin 2002).

Several recent papers have proposed that landscape composition (percentage cover

of forest and non-forest habitats or total core habitat) may be just as important as

landscape structure (patch size, shape, and isolation) in determining avian nesting success

(Andren 1994, 1995, Donovan et al. 1995a, Robinson et al. 1995, Donovan et al. 1997,

Howell et al. 2000, Rodewald 2003, Driscoll et al. 2005). Edge effects, for example, are

demonstrably highest in landscapes with intermediate forest cover in the agricultural

Midwest (Donovan et al. 1997, Thompson et al. 2000), a pattern replicated in the

northeastern U.S. (Driscoll and Donovan 2004). Several other studies also failed to

detect negative edge effects in mostly forested landscapes (Yahner and Wright 1985,

Small and Hunter 1988, Yahner and Delong 1992, Rudnicky and Hunter 1993, Hanski et

al. 1996, Hawrot and Niemi 1996, Bayne and Hobson 1997, Darveau et al. 1997, Keyser

et al. 1998). Rates of nest predation have been shown to be high even in the interior of

forest patches in mainly agricultural (>60% cover) landscapes (Robinson and Wilcove

1994, Heske 1995, Marini et al. 1995, Bayne and Hobson 1997, Hartley and Hunter

1998). Agricultural edges generally appear to exert stronger negative effects on birds

than edges of regenerating forest patches (Hanski et al. 1996, Hawrot and Niemi 1996,

Darveau et al. 1997, Hartley and Hunter 1998, Morse and Robinson 1999, Rodewald and

Yahner 2001b, but see King et al. 1996, Suarez et al. 1997).

The composition of the surrounding landscape matrix may also have a strong

mediating influence on the effects of forest fragmentation through the movements of









predators in and out of habitats (Wiens et al. 1993, Freemark et al. 1995, Wiens 1995,

Rodewald and Yahner 2001a, Rodewald 2002, 2003). Agricultural regions may support

greater numbers of some important generalist predators (Wegner and Merriam 1979,

Angelstam 1986, Moller 1989, Wegner and Merriam 1990, Andren 1992, Warner 1994,

Andren 1995, Haskell 1995, Dijak 1996, Oehler and Litvaitis 1996, Bayne and Hobson

1997, Pedlar et al. 1997) than regions where the matrix consists primarily of grasslands or

pasture. Because fields used for row crop agriculture are barren for long periods between

harvest in the fall and the planting and emergence of new crops in spring or early

summer, many predators concentrate their activity in forested habitats during winter and

spring (Cummings and Vessey 1994). As crop fields provide increased cover, and

eventually food later in the growing season, predators tend to increase their use of these

areas (E. Heske, Illinois Natural History Survey, pers. comm.). In contrast, grasslands

retain some cover throughout the year and some resources (small mammals, insects, fruit,

and green plant material) are available during the winter and early spring. Thus, predator

activity may be more dispersed throughout the landscape (via both lower overall predator

density and allowing some individual predators to subsist on resources in locations where

forest birds do not nest) during the time of songbird nesting.

Taken together, these studies suggest that negative effects of fragmentation on

avian productivity could be mediated by landscape composition, but what exactly the

effects would be of a given matrix type on avian nesting success are not immediately

clear, especially if the matrix contains a mixture of both agricultural and grassland land

uses. Furthermore, there may also be a role for effects that emerge only over time as

communities respond to landscape variation. Such effects have been demonstrated in









plants (Holt et al. 1995, Cook et al. 2005), but their importance in more mobile species

has not been documented.

As agriculture is the principle matrix type for mid-continental North America, an

understanding of the ways in which an agricultural matrix does or does not modulate

avian nesting success in forest fragments could greatly improve our understanding of

fragmentation ecology. Whatever the driving mechanisms, a clearer picture of the

relationship, or lack thereof, between the matrix and fragmentation would also constitute

a useful management tool. However, most studies have only looked at percentage of

forest and non-forest cover and none of these studies of which we are aware have

distinguished among the effects of different kinds of agriculturally influenced landscape

matrices (e.g. row crops vs. rural grassland).

Our goal in this study was to explore the extent to which landscape composition

mediates the effects of forest fragmentation on songbird nesting success and the

abundance of their major predators and parasites. We predicted that incorporating data

on matrix composition would improve our ability to predict the nesting success of forest

birds in a study region in which traditional fragmentation variables (tract size, distance to

edge) appear to explain little variation in songbird nesting success (Robinson and

Wilcove 1994, Trine et al. 1998, Morse and Robinson 1999, Peak et al. 2004, Chapa-

Vargas and Robinson in press-a, b).

Specifically, we studied whether landscape composition alters the magnitude of the

negative effects of forest fragmentation by studying patterns of nest predation and

parasitism in forest song birds in southern Illinois, USA. We tested three predictions. (1)

Nesting success will be correlated with matrix composition (% of different kinds of non-






6


forest cover) when controlling for the extent of fragmentation. (2) Nesting success will

be correlated with the extent of fragmentation when the matrix composition is held

constant. (3) Landscape composition will affect the abundance of important nest

predators and parasites that determine nesting success. We predicted higher depredation

rates of avian nests in landscapes with high cover of row crops (Andren 1995), and higher

levels of brood parasitism in landscapes with higher cover of grasses where cowbirds

feed (Trine et al. 1998).














CHAPTER 2
MATERIALS AND METHODS

Study Sites

The study area was the southern 11 counties of Illinois, an area that contains the

108,000-ha Shawnee National Forest (SNF) (Fig. 1). The SNF consists of hundreds of

small forest tracts dominated by oak-hickory forests on steep hillsides and narrow ridge

tops. The western half of the SNF includes the easternmost extension of the Ozark

Mountains and the eastern section of the SNF lies mostly in the Shawnee Hills region.

We restricted our studies to areas of upland oak-hickory and avoided pine plantations and

floodplain forest, which tend to have different communities of birds and potential nest

predators (Robinson unpubl. data).

We used a stratified random process for site selection. We selected sites that fell

into four categories of land cover (Table 1, 2). We used the program FRAGSTATS

(McGarigal and Marks 1995) in conjunction with U.S. Geological Survey Gap Analysis

Table 1. Stratified design used in site selection. One additional site, Cave Hill, had very
low fragmentation and approximately equal amounts of area devoted to
grassland and row crop (see Table 2) and is thus not shown here.

Matrix Composition

row crop > grassland grassland > row crop
row crop > 20 % grassland > 20 %
Low ( % fo Big Brushy Lusk Creek
Low (>60% forest
SPine Hills Bald Knob
S cover, >40% interior) Burke Branch
_Burke Branch
SSaline Mines Hayes Creek
SHigh (<30% forest
o High fot Wildcat Bluff Lick Creek
c cover, <10% interior)
( Tansill Kaskaskia Forest


















Bald Knob Lick Creek .





Wildcat Bluff













0 10 20 30km
Figure 1. Map of southern Illinois, USA. Arrows indicate the locations of the twelve
study sites. The Shawnee National Forest comprises approximately the dark
Kaskaskia Forest






gray area.ek
Big Brushy Ha 'es' reek













program classifications (Scott et al. 1993) and digital maps provided by the Illinois

Department of Agriculture (Luman et al. 1996, updated in 1998 by the Illinois Natural

History Survey) to characterize the land cover (% forest, % row crop, % rural grassland)

within a 3-km radius of each forested pixel in southern Illinois. Each pixel represented a

30 mX 30 m square of actual land area. Adjacent forested pixels were lumped together

into forest tracts. Once we identified all of the potential candidate tracts in each category

of forest and matrix cover, we randomly selected 3 sites that were separated from other

sites in the same category by at least 20 km and from other sites by at least 10 km (Table









1, Fig. 1). Additionally, we included one site, Cave Hill, that was very unfragmented and

had low and approximately equal amounts of grassland and agriculture. To minimize

local edge effects, we focused our attention on searching for nests more than 50 m from

external (row crop or grassland) edges (Paton 1994); in practice, however, we also

included nests found up to the edge of forests and investigated the effect of distance to

edge on nest success (see below).

Table 2. Specific landscape characteristics of each of the 12 sites in this study. Edge
density is the ratio of forest/non-forest edge in meters to the number of
hectares of forest within 1 km of the center of each site. Percent grassland and
percent row crop respectively represent the proportion of land area within 3
km of the center of each site that was composed of grassland (old field,
pasture, mowed grass, etc.) and row crop agriculture. The presence of a
permanent water source (lake, stream, etc.) is noted in the right-most column.
Edge Density Percent Percent Water
(m edge/ha forest) Grassland Row Crop Present?
Bald Knob 31 23.9 9.0 No
Big Brushy 11 12.6 29.5 No
Burke Branch 29 20.9 11.7 Yes
Cave Hill 5 12.2 11.7 No
Hayes 61 42.1 17.2 No
Kaskaskia Forest 23 21.7 3.4 No
Lick Creek 65 38.7 10.1 Yes
Lusk Creek 7 25.3 4.8 Yes
Pine Hills 21 7.3 25.2 No
Saline Mines 39 17.1 50.5 Yes
Tansill 35 26.6 30.3 No
Wildcat Bluff 13 12.3 41.9 No


Nest Monitoring

We searched for and monitored nests at all 12 sites from 15 April to 10 Aug in

2001, 2002, and 2003. Nest locations were marked >3 m from the nest using plastic

flagging. We monitored the nest approximately every 3 days and recorded the date, time,

and a description of nest contents and parental activity. These data were used to

determine nest stage (laying, incubation, or nestling) and the intensity of cowbird









parasitism (number of cowbird eggs in the nest). We marked the location of each nest on

USGS topographical maps of the area.

We ceased monitoring a nest when all nestlings had fledged, all contents of the nest

disappeared, or no parental activity and no changes in the nest contents were observed for

14 days. We confirmed fledging by listening for nestling begging calls, sighting parents

carrying food or scolding as we approached the nesting area, and/or observing

appreciable amounts broken pin-feather sheaths in an empty nest. We considered nests to

be successful if one or more nestlings (not including cowbirds) fledged from the nest.

Nests from which the contents disappeared and adults were not observed in the area were

considered unsuccessful, as were nests which had no parental activity and no changes in

the nest contents for 14 days.

In 2000 and 2001, we conducted at least two censuses at each site to assess the

density of avian predator (Corvidae), parasite (Molothrus ater), and prey/host species

(Table 3). The exceptions were Kaskaskia Forest, which was censused only once in

2000, and Cave Hill which was censused once in 2000 and was not censused in 2001.

We conducted one census at each site in 2002. The censuses took place at the height of

the breeding season, between late May and early July. In order to minimize observer

effects, all censuses were conducted by a single observer who was very familiar with the

songs, calls, and plumages of North American birds. Censuses began at dawn and were

completed within four hours. We established a series of 15-20 points at each site, at

which we conducted 5-minute infinite-radius point counts during the breeding season.

Using the point count data, we estimated the density of each species at each site using the

program Distance (Thomas et al. 2004).










Table 3. Dates on which birds were censused at each of the 12 study sites. Five-minute
infinite-radius point counts were conducted along a predetermined census
route by a single observer who was familiar with the calls, songs, and
plumage variations of the birds of southern Illinois. In 2000 the Pine Hills
June 27 census was terminated early due to weather. The remaining points
were censused on June 28. All other dates indicate complete census routes.


Bald Knob

Bug Brushy

Burke Branch


Cave Hill

Hayes

Kaskaskia


Lick Creek

Lusk Creek

Pine Hills

Saline Mines

Tansill

Wildcat Bluff


2000
June 7
June 29
June 6
June 20
June 13
June 29

June 15

June 10
July 6
June 9


June 6
June 29
June 15
July 3
June 7
June 27, 28
June 16
July 7
June 13
June 30
June 16


2001
June 26
July 11
June 27
July 12
June 13
June 26
July 16


May 30
July 8
June 9
July 6
July 9
June 28
July 10
May 30
July 7
June 8
July 14
June 29
July 13
June 27
July 15
June 27
July 19


2002
June 19

June 20

May 27


June 15

June 13

June 14


June 30

May21

June 21

May 30

June 19

June 24


Index of Abundance for Predatory Mammals

We employed track stations to determine an index of the relative abundance of

predatory mammals at each site. There were six track stations in each of the twelve

intensive study sites. Stations were at least 500 m apart to decrease the probability of

individual predators detecting more than one station simultaneously and "trap-lining".

Track stations consisted of two 1 by 0.5 m sheets of 0.32 gauge aluminum coated with

soot from a kerosene torch (Barret 1983). The aluminum sheets were set side-by-side on









a cleared, level 1 by 2 m area and baited with cat food (in 2000) or fatty acid scent tablets

(in 2001 and 2002). Track stations were covered by plywood shelters in 2000 and 2001

to preserve the tracks from rain. In 2002 we removed the shelters due to concerns that

they were deterring some predators from visiting the stations. Each study site was

surveyed bi-weekly during the three month period of peak songbird nesting (May-July).

To ensure that each station was allowed the same amount of time to attract predators, we

always surveyed stations in the same order. In 2000 and 2001, when the stations were

protected from weather, we re-coated the aluminum with soot one week before each

survey date and counted at each survey the tracks that had accumulated during the week.

In 2002, we re-coated the aluminum with soot and returned 24 hours later to count and

identify tracks. We assumed that movement of individual animals was independent of

other conspecifics (e.g. that animals were not traveling in family groups) and of other

species. Because our methodology was consistent within, but not across, years, our data

can be used to draw conclusions about the differences in predator activity among sites,

but not among years. Therefore, for each site, we combined the data from all three years

and used as an index of relative abundance the log of the number of total individuals that

could be identified from the track stations.

Data Analyses

We modeled the nest success of the two most common species, Acadian Flycatcher

and Wood Thrush (Latin names are given in Appendix A), using a set of generalized

linear models closely related to logistic regression (Agresti 1996). We combined the data

for six other species (Indigo Bunting, Kentucky Warbler, Louisiana Waterthrush,

Northern Cardinal, Worm-eating Warbler, and Ovenbird) because they have similar

nesting ecologies (each nests near or on the ground) and would be exposed to similar









predation pressures. We refer to this group of species as "Low Nesters". We did not

have sufficient data to model each of these species independently. We assumed a

binomial distribution of the response variable (nest fate = 0 if failed, nest fate = 1 if

successful) and estimated daily nest success as a function of a number of predictive

factors (Dinsmore et al. 2002, Shaffer 2004). To account for the fact that the interval

between nest checks was typically greater than one day and varied in length, we used a

modified (Shaffer 2004) form of the logit link function that allowed the probability of

surviving a given interval to vary with interval length:

g(R)= In(Rit/[1- R1t D = i(xl, x2...x), where R is the interval survival rate, t is the

interval length in days, and l(x) is a linear function composed of various combinations of

our predictor variables. The role of t in this equation is more obvious upon solving the

link function for R: R = [e"(x ")/(l + e(x1 ) ) in which the portion within square brackets

represents the daily survival rate, R is the interval survival rate, and t is the length of the

interval in days. Thus, this method allowed us to model interval and daily survival rates

as functions of explanatory variables that changed among intervals (e.g. nest stage).

Following Shaffer (2004), we assumed that these explanatory variables remained

constant within intervals. We fitted the models using PROC GENMOD (SAS Institute

2002). Preliminary analyses using PROC NLMIXED (SAS Institute 2002) to include a

random variable that accounted for the potential effect of study site suggested that

including site did not improve the models. Many models also failed to converge when

we included distance to edge in the model. We therefore present the models without

either a random site variable or distance to edge.









We follow Burnham and Anderson (2002) in using an information-theoretic

approach to evaluate alternative models derived from our apriori hypotheses concerning

the relationships between avian nest success and landscape composition. Although we

were primarily interested in the effects of landscape composition and predator and

parasite abundance, we also included covariates dealing with temporal and hydrologic

factors because recent studies (Peak et al. 2004, Hoover 2006, Chapa-Vargas and

Robinson in press-b) have suggested that these may be extremely important. Our set of

eight candidate models consisted of:

1. a habitat effects model with landscape composition (percent rural grassland and
percent agriculture), presence of a permanent water source (yes, no), edge density
within 1 km (linear meters of edge/hectares of forest), and two-way interactions
between the landscape composition variables and edge density

2. a temporal effects model with year of study (2000, 2001, or 2002), nest stage
(laying, incubating, or nestling), and Julian date

3. a species density model with conspecific density, the index of relative abundance of
mammalian predators, and avian predator density

4. combination of the habitat effects and temporal effects models

5. combination of the habitat effects and density effects models

6. combination of the temporal effects and density effects models

7. a global model including all effects

8. a null model with only an intercept

We used PROC REG (SAS Institute 2002) to estimate the tolerance for variables in

the global model to diagnose collinearity; although there were some indications of

collinearity (where tolerance < 0.2), these cases were due to the interaction terms

included in our habitat model. We explored reducing the collinearity both by centering

the variables on their means and by removing the collinear variables from the model, but

neither of these methods had significant effects on the model results. Therefore, to most









accurately reflect our apriori hypotheses, we present the models without corrections for

collinearity. We plotted the standardized deviance residuals from the global model

against the explanatory values and found no patterns suggesting that transformations of

the data were necessary. Neither did we find values (>3) that were indicative of outliers.

We evaluated goodness-of-fit of the global model for each species with Hosmer-

Lemeshow tests (Hosmer and Lemeshow 2000).

We used Akaike's Information Criterion to rank candidate models for each species

from most to least supported and drew conclusions about our hypotheses by comparing

the degree to which our data supported each of the candidate models. We used AAIC

(the difference between the AIC value for a particular model and the lowest observed

AIC for that species) as our measure of model support. We considered all models with

AAIC values of less than two to have equivalent support (Burnham and Anderson 2002).

To account for model uncertainty, we used Akaike weights (a measure of model

support that is based on AAIC and sums to one across all candidate models for a

particular species) to calculate model-averaged coefficients and 95% confidence

intervals. To derive each model-averaged coefficient, we multiplied the coefficient by

the Akaike weight of the model containing it and summed across all models (Burnham

and Anderson 2002). To facilitate interpretation of these coefficients, we converted them

to odds ratios (the ratio of the odds of survival under one level of an explanatory variable

to the odds of survival under a reference level of the explanatory variable). Each

categorical variable, such as year or nest state, has n-1 odds ratios associated with it,

where n is the number of levels of the variable. Each is a ratio of the odds of survival at

one level of the variable to the odds of survival at the reference level of that variable. For









continuous variables, such as percent agriculture or conspecific density, the interpretation

of the odds ratio is that a one-unit increase in the variable multiplies the odds of a nest

surviving one day by the odds ratio. Confidence intervals that included an odds ratio of

one were considered to be non-significant.

We used the most supported model for each species to estimate the probability of a

nest surviving one day. We made separate estimates for each level of the categorical

variables (i.e. nest stage) and assumed mean values of the continuous variables in the

model. This estimate of daily nest success is comparable to estimates calculated via other

methods (Mayfield 1975, Johnson 1979). We used these daily survival probabilities to

estimate the probability of a nest surviving the entire breeding cycle, assuming lengths of

the laying, incubation, and nestling periods that were appropriate for each species (Payne

1992, Van Horn and Donovan 1994, Robinson 1995, Roth et al. 1996, Hanners and

Patton 1998, McDonald 1998, Halkin and Linville 1999, Whitehead and Taylor 2002).

To investigate the extent of Brown-headed Cowbird parasitism in different

landscapes, we used a generalized linear modeling approach. We modeled the extent and

intensity of parasitism of the two most common species in these landscapes, Acadian

Flycatcher and Wood Thrush. To investigate the number of nests that were parasitized

and how intensely each was parasitized, we used the number of cowbird eggs in each nest

as a response variable. In this analysis, we used a log link function and treated the laying

of cowbird eggs as a Poisson-distributed variable. However, since a preliminary

comparison of the mean and variance of the number of cowbird eggs in each nest

indicated that eggs were more clumped in Wood Thrush nests and more rare in Acadian

Flycatcher nests than the Poisson distribution predicted, the Poisson was not entirely









appropriate and the resulting models were either over- or underdispersed. We adjusted

for the over- or underdispersion by employing the DSCALE option in the model

statement of PROC GENMOD (SAS Institute 2002). In addition to using percent

grassland as a predictor variable, we included the covariates percent agriculture and year.

We used likelihood ratio tests and a stepwise approach to eliminate non-significant

explanatory variables from each model (a=0.05). We plotted the standardized deviance

residuals from each model against the explanatory values and found no patterns

suggesting that transformations of the data were necessary. Neither did we find values

(>3) that were indicative of outliers.

We also calculated the Mayfield daily predation rates (Mayfield 1975) for each

species at each site and year. While our modeling techniques allow us to investigate the

influence of a number of variables on daily predation rate (DPR=l-daily success rate),

Mayfield estimates provide a purely data-based measure of the DPR. This is useful for

visualizing the data themselves.














CHAPTER 3
RESULTS AND DISCUSSION

Predatory Mammals

Our index of mammalian predator abundance showed moderate to low correlation

with the percentage of the landscape that was composed of either agriculture (Pearson's p

= 0.48) or grassland (Pearson's p = 0.03) and varied little among sites (2.9 to 3.8).

Acadian Flycatcher

We located and monitored 337 nests for 1773 intervals (5438 exposure days, see

Appendix C). 209 of these nests fledged young. The Hosmer-Lemeshow test gave no

evidence to suggest that the global model fitted poorly (x2=14.4, df=9, p=0.11). The

model with the most support based on the AAIC criterion was the temporal model. Other

models that included the temporal predictors (physical habitat + temporal, density +

temporal, and global) also received some support (Table 4). Nesting stage had the most

influence on the odds of nest survival. Based on model averaged coefficients, the daily

odds of survival for the incubation period were twice those for the nestling period. The

daily odds of survival for nests during the laying period were 60% of those for the

nestling period, but the confidence interval overlapped one by 0.06. The confidence

intervals of all other parameters overlapped one, and most estimates were themselves

very close to one (Table 5). Based on a laying stage of three days, an incubation stage of

14 days, and a nestling stage of 14 days, the temporal model (Fig. 2) estimated a 42%

probability that a given nest would fledge young. None of the 3 explanatory variables












Table 4. Comparison of eight candidate models relating habitat and/or temporal parameters to the probability of fledging young from
a nest. The model parameters are described further in the text. n gives the number of observation intervals for each
species. K = number of parameters in each model. AAIC = the difference between the AIC values for the most supported
model and the given model. w = Akaike weight for each model. Models with the lowest AAIC values and highest w
values have the most support.


Acadian Flycatcher Wood Thrush Low Nesters
n=1773 n=453
128 failures / 337 nests 45 failures / 112 nests 147 failures / 418 nests
Model K AAIC w K AAIC w K AAIC w
Global 15 4.79 0.069 15 9.65 0.007 14 7.32 0.014
Physical Habitat (PH) 7 25.53 0.000 7 36.26 0.000 7 74.38 0.000
Temporal 6 0.00 0.755 6 0.00 0.866 6 0.49 0.420
Density 4 25.45 0.000 4 33.38 0.000 3 66.04 0.000
PH+ Temporal 12 3.86 0.110 12 7.04 0.026 12 5.81 0.029
PH+ Density 10 25.92 0.000 10 38.97 0.000 9 74.72 0.000
Temporal + Density 9 4.87 0.066 9 4.29 0.101 8 0.00 0.537
Null 1 20.53 0.000 1 31.18 0.000 1 65.31 0.000













Table 5. Model averaged parameter estimates. CI = 95% confidence interval based on unconditional (model averaged) standard
errors.


Parameter
% Agriculture
% Grassland
Dry vs. Wet
Edge Density
Edge Density X %Ag
Edge Density X %Gr
2000 vs. 2002
2001 vs. 2002
Laying vs. Nestling
Incubation vs. Nestling
Julian Date
Specific Density
Mammalian Predator Density
Avian Predator Density


Acadian Flycatcher
Odds Ratio CI


0.993
1.000
1.071
0.990
1.000
1.000
0.795
1.401
0.594
2.031
1.006
0.957
1.004
0.750


0.967
0.982
0.827
0.953
0.999
0.999
0.504
0.868
0.333
1.338
0.991
0.808
0.829
0.193


Wood Thrush
Odds Ratio CI


,1.019
, 1.018
,1.389
,1.028
,1.001
,1.001
,1.256
,2.261
,1.058
,3.083
,1.021
, 1.133
,1.216
,2.918


1.001
0.997
1.000
1.000
1.000
1.000
0.245
0.963
0.146
1.110
0.986
1.010
1.131
1.077


0.996
0.983
0.966
0.995
1.000
1.000
0.107
0.425
0.066
0.524
0.971
0.837
0.653
0.525


Low Nesters
Odds Ratio CI


1.005
1.011
1.035
1.005
1.000
1.000
0.563
2.184
0.324
2.347
1.001
1.218
1.959
2.210


1.001
0.999
0.987
0.999
1.000
1.000
0.658
0.865
0.291
1.997
0.984

0.968
0.280


0.997
0.994
0.932
0.995
1.000
1.000
0.422
0.589
0.185
1.337
0.976


1.004
1.004
1.045
1.004
1.000
1.000
1.027
1.269
0.457
2.984
0.991


0.649 1.444
0.017 4.486










2 0.25


o 0.2


2 0.15
C-

i 0.1


S0.05

w, 0
Acadian Flycatcher Wood Thrush Low Nesters

O Laying u Incubation o Nestling

Figure 2. Estimated daily predation rates during each nesting stage for Acadian
Flycatcher, Wood Thrush, and Low Nesters in 2002. These estimates were
generated using the best-fitting model for each species (the temporal model).
Error bars represent 95% confidence limits.

we included in the parasitism analysis (% grassland cover, % agricultural cover, and

year) was significant (Fig. 3A).

Wood Thrush

We located and monitored 112 nests for 453 intervals (1432 exposure days, see

Appendix C). 67 of these nests fledged young. The Hosmer-Lemeshow test gave no

evidence to suggest that the global model fitted poorly (x2=9.9, df=9, p=0.36). The

model with the most support based on the AAIC criterion was the temporal model. The

temporal + density model also received some support (Table 4). Year had the largest

effect on the odds of nest survival. Based on model averaged coefficients, the daily odds

of survival during 2000 were 25% of those during 2001 or 2002. Nesting stage also

influenced nest survival: the daily odds of survival during the laying period were 15% of

those during the nestling period. The confidence intervals of all other parameters












4
Cu
3.5

3

-a 2.5

2
0
16 1.5

" 1
E
! 0.5

1 0







4
in
- 3.5

S3

o 2.5

S2
0
U
6b 1.5

I 1
E
- 0.5

n 0


B % Grassland Cover



Figure 3. Mean number of Brown-headed Cowbird eggs per nest in each of two host
species, as a function of percent grassland cover within 1 km of the center of
the study site. A) Data for the Acadian Flycatcher. B) Data for the Wood
Thrush. The trend line shown in B describes the data much better than does a
model with only an intercept (likelihood ratio test, df=l, X2=23.44, p<0.001).


* *
20 30
% Grassland Cover









overlapped one, and most estimates were themselves very close to one (Table 5). Based

on a laying stage of three days, an incubation stage of 13 days, and a nestling stage of 13

days, the temporal model estimated a 43% probability that a given nest would fledge

young. Of the 3 explanatory variables we included in the parasitism analysis (%

grassland cover, % agricultural cover, and year), % grassland cover was highly

significant (df=l, x2=23.44, p<0.001, Fig. 3B).

Low Nesters

We located and monitored 418 nests for 1480 intervals (101 Indigo Bunting nests

for 389 intervals and 1173 exposure days, 87 Kentucky Warbler nests for 299 intervals

and 926 exposure days, 89 Louisiana Waterthrush nests for 339 intervals and 1374

exposure days, 72 Northern Cardinal nests for 254 intervals and 965 exposure days, 64

Worm-eating Warbler nests for 194 intervals and 656 exposure days, 5 Ovenbird nests

for 5 intervals and 16 exposure days, see Appendix C). 271 of these nests fledged young.

The Hosmer-Lemeshow test gave no evidence to suggest that the global model fitted

poorly (X2=6.79, df=9, p=0.66). Two models received equivalent support based on the

AAIC criterion: the temporal model and the temporal + density model (Table 4). Nesting

stage had the most influence on the odds of nest survival. Based on model averaged

coefficients, the daily odds of survival during the laying period were 29% of those during

the nestling period. The daily odds of survival during the incubation period were twice

those for the nestling period. Year also affected nest survival to some degree: the daily

odds of survival for nests during 2000 were 66% of those for 2002, but the confidence

interval overlapped one by 0.03. One habitat parameter, the presence of water,

significantly influenced the odds of nest survival even though the physical habitat model









itself received no support. The daily odds of survival for nests in dry habitat were 98% of

those for nests in wet habitat. The confidence intervals of all other parameters

overlapped one, and most estimates were themselves very close to one (Table 5). Based

on a laying stage averaging four days, an incubation stage averaging 13 days, and a

nestling stage averaging nine days, the temporal model estimated a 58% probability that a

given nest would fledge young.

Discussion

Our results showed little evidence that fragmentation and landscape composition

were good predictors of nesting success or of the abundance of potential nest predators

and parasites in the 12 study sites chosen for this study. We did not find evidence that

nesting success varied with the land use in the surrounding landscape. In fact, our data

suggest that nesting success in forest sites surrounded by agriculture was surprisingly

similar to nesting success in forest sites surrounded by grassland. Daily predation rates

for Acadian Flycatchers, Wood Thrushes, and all other low nesters combined were

relatively consistent across landscape types (Fig. 4), although it should be noted that the

physical habitat model was ranked for all three species groups as one of the least

supported models.

Interestingly, our estimates of the probability of a given nest fledging young

suggest that low nesting species' nests are 36% more likely to escape predation than the

nests of either the Wood Thrush or Acadian Flycatcher. Martin (1995) compared the nest

success rate of ground nesting species to shrub and canopy nesting species in forest

habitats and also found that the ground nesting species had approximately 36% higher

rates of nest success than shrub or canopy nesters. Our "Low Nesters" category includes

four species that nest directly on the ground (Kentucky Warbler, Ovenbird, Louisiana










0.14

a 0.12
0
0.1

0.08

0.06





0
G 0.04

E 0.02 -


Acadian Flycatcher Wood Thrush Low Nesters

o High Fragmentation / Row Crop u High Fragmentation / Grassland
o Low Fragmentation / Row Crop o Low Fragmentation / Grassland


Figure 4. Model-based estimates of the daily predation rates of Acadian Flycatcher and
Wood Thrush nests, as well as the nests of several low-nesting species
combined. To parameterize the models, we used "high" and "low"
fragmentation values that respectively reflected the 75th and 25th percentiles
of fragmentation indices from our study sites. Estimates for high agriculture
reflect a value from the 75th percentile for agricultural cover and a value from
the 25th percentile for grassland cover. Estimates for high grass reflect a
value from the 25th percentile for agricultural cover and a value from the 75th
percentile for grassland cover. Error bars represent 95% confidence limits.

Waterthrush, and Worm-eating Warbler) and two species that typically nest within a

meter of the ground in our study area (Indigo Bunting and Northern Cardinal, Cottam and

S. K. Robinson, unpubl. data). Even though our species groupings are not exactly the

same as Martin's, we find it striking that the two datasets arrive at similar conclusions.

Martin points out, and we agree, that because these rates reflect averages across several

sites and are therefore less likely to be perturbed by local site effects, they may reflect

evolutionary differences that are driven by predation pressures unique to each nesting

substrate.









We found no evidence that nesting success varied with the level of fragmentation

when the landscape composition was held constant (Fig. 4). We see several scenarios

that may explain this result. First, our sites may have been too similar to one another in

the degree to which they were fragmented. To ensure that each site would produce

sufficient nests to allow comparisons among sites, we had to restrict our work to sites of

at least 200ha. Nevertheless, our study sites ranged in edge density indices from 5 m/ha

to 65 m/ha and from 32% to 76% total forest cover, which is comparable to the range of

fragmentation indices in other studies that have shown effects of fragmentation on

nesting success (Robinson et al. 1995). Therefore, it does not seem likely that the range

of fragmentation among our sites was too small to observe the effects of fragmentation on

nesting success.

Second, we note from the Mayfield estimates of the daily predation rate (Appendix

C) that there was variation in nest success among sites, years, and species. This is

important because it distinguishes between the case where landscape variables do not

adequately predict nesting success because there was no variation in nesting success

across the landscape and the case where landscape variables do not adequately predict

nesting success because any variation due to landscape factors is outweighed by the

variation introduced by sites, years, and or species. The latter appears to be the case,

which may indicate that fragmentation as currently measured is not as good a general

predictor of avian nest success as has been previously thought. Considering that many

other studies have demonstrated effects of fragmentation, it may be that fragmentation

does have an effect on local nesting success, but only in certain locations. Across the

study landscape, our models suggest that temporal processes (and likely other local









process which we did not measure) were much more influential on the nesting success of

birds, a result also obtained by Peak, et al. (2004). The future challenge will be to

identify patterns in the suite of potential processes to determine whether it is possible to

predict which ones will be important and which ones will be less important at a given

site.

Although the surrounding land uses showed low to moderate correlation with

mammalian predator abundance in this study, the actual range of predator densities was

extremely narrow among our sites. Other studies have shown correlations between

predator abundance and landscape composition (see above) and such changes in predator

communities could affect avian nesting success, as well as other community members

and processes. Our index of relative predator abundance is admittedly imprecise, which

may have obscured any real signal. Nevertheless, our results are consistent with the

hypothesis that the effect of the matrix on mammalian predator communities is not

expressed is the same way in all geographic locations. This is an issue that should be

studied further using more precise measures of mammalian abundance and activity.

We emphasize that because our sites were randomly selected, our results indicate

that the non-forest land uses surrounding a forest tract may only be a good predictor of

nest success and predator abundance in specific cases and are not likely to function

satisfactorily as generally applicable predictors. Local processes may be much more

important in determining nesting success at a given site than are landscape level

processes. Our results support recent research that suggests that nest predator abundance

is not necessarily correlated with proximity to or amount of edge (Smith 2004) and that

landscape composition/structure does not always correlate well with avian nesting









success (Knutson et al. 2004). Nests are taken by diverse predators, each of which no

doubt responds differently to landscape structure. Infrared cameras in southern Illinois

forests have documented nest predation by three species of snakes, three species of

mammals, two species of hawks, Blue Jays, Common Grackles, and even small songbirds

(Robinson unpubl. data). Some predators (e.g., Broad-winged Hawk and Chipmunk)

may even prefer the interior of large patches (see also Tewksbury et al. 1998). Other

predators such as snakes may vary their use of the forest interior seasonally (Blouin-

Demers and Weatherhead 2001). Therefore, the influence of landscape structure on nest

predation rates may be difficult to predict except when there are only a few dominant

predators involved.

Brood parasitism, however, should have been more readily explained by grassland

cover given that only a single species, the Brown-headed Cowbird, is involved and it has

an extremely well documented relationship to both edges and landscape matrix

composition (e.g. Morrison et al. 1997). The percentage of grassland cover (which

included grazing pasture) was significant in explaining the extent and intensity of

cowbird parasitism of Wood Thrush nests, but not of Acadian Flycatcher nests. Our

models suggest that flycatchers are parasitized at a relatively constant rate of 0.3 cowbird

eggs per nest throughout the Shawnee National Forest, but the parasitism rate and

intensity of Wood Thrush nests varies from site to site. The Wood Thrush data support

the implications of research on the abundance of nest parasites such as the Brown-headed

Cowbird, Molothrus ater, (Robinson unpubl. data, Goguen and Mathews 2000), in which

the frequency of nest parasitism has been shown to be highly correlated with the

proximity to a cowbird feeding site (e.g. a cattle pasture), but uncorrelated with most









other physical or landscape and nest-site characteristics. Only a small percentage of

potential grassland areas are used by cowbirds as feeding sites (Robinson unpubl. data,

Thompson 1994), but we know little about why cowbirds select one cattle pasture as a

feeding site as opposed to another.

We also know little about how and why cowbirds choose a particular host nest in

which to lay and egg (Robinson and Robinson 2001). Our data show that some host

species (e.g. Wood Thrush) are parasitized much more often and heavily than others (e.g.

Acadian Flycatcher, Fig. 3). This suggests that Wood Thrushes may have some behavior

that is not present in Acadian Flycatchers and allows cowbirds to more easily find Wood

Thrush nests. Wood Thrush nests also had a much lower rate of survival in the laying

period than in other periods of the nesting cycle, a phenomenon not seen in Adacian

Flycatcher nests, which further suggests that Wood Thrush behavior (e.g. nest site

selection, nest defense mechanisms, general behavior around the nest, etc.) during the

nesting period may be clueing both predators and parasites in to the presence of a nest.

Interestingly, the extent of this heavy parasitization may be moderated somewhat in

particular locations by landscape composition and use. For Wood Thrushes, the

grassland site with the highest levels of cowbird parasitism was surrounded by actively

grazed pasture whereas the site with the least frequent parasitization was surrounded by

hayfields that were not grazed during the study.














CHAPTER 4
SUMMARY AND CONCLUSIONS

In complex landscapes with reasonably diverse communities of birds and nest

predators, simple landscape metrics may not be good predictors of avian nesting success.

Ideally, fragmentation studies should include detailed studies of which nest predators

attack the nests of each species in the community, as well as studies of the use of the

landscape by these predators. Such studies may only be feasible in relatively simple

communities such as those in grasslands, but even they have proven to have diverse

communities of nest predators (FenskeCrawford and Niemi 1997, Renfrew and Ribic

2003). Furthermore, we caution that models developed in a particular geographic region

may not be broadly applicable to landscapes and forests in other regions.

Much of the literature showing extreme fragmentation effects, including our own

studies in the American Midwest (Robinson et al. 1995, Brawn and Robinson 1996) may

depend upon the extremes of the fragmentation continuum for statistical significance.

Small, isolated woodlots in a sea of agriculture usually (although not always) have

extraordinarily high levels of nest predation and brood parasitism, and some extensive

forests typically have only moderate levels of nest predation and essentially no brood

parasitism. Very small tracts may be overrun with predators and parasites that use the

matrix but require forest habitat to breed (e.g., raccoons) or strongly prefer to breed in

forests cowbirdss, see Hahn and Hatfield 1995). In contrast, large tracts may only sustain

populations of nest predators that can survive year-round in the forest interior, such as

snakes and raccoons. Although such forest-based nest predators may occasionally be









responsible for very high rates of nest predation (Tewksbury et al. 1998, Schmidt and

Ostfeld 2003), over the long term, nest predation rates may be lower than in fragmented

landscape where the matrix provides a constant, overwhelming input of predators. At

intermediate levels of forest cover, all potential nest predators are likely to be present,

including those that rely on the landscape matrix for access to the forest interior (e.g.,

cowbirds) and those that rely mainly on resources present in the forest itself. In such

situations, local details may be just as important as landscape effects in determining

which predators are present or absent and whether or not any predators are overabundant;

simple landscape metrics may not be good predictors of nesting success.

While our study suggests that landscape metrics do not add appreciable predictive

power to models of avian nest success in our study area, it does not suggest that the

matrix has no effect on avian communities. It is known, for example, that habitat

structure can influence the chances of finding a mate (Van Horn et al. 1995) and

territorial density (Pomeluzi and Faaborg 1999). We do not have the data to assess

whether there were differences among our sites in the percentage of birds that were

nesting or the density of their territories. Even if predation rates among sites were

identical, large population level effects could be observed if matrix-induced habitat

changes caused differences among sites in the number (or percentage) of birds that find a

mate and nest. It is also important to consider that the landscape matrix can change

rapidly, particularly when it is heavily influenced by humans as it is in the Midwestern

United States (S. Robinson, pers. comm.). Considerable changes in landscape use occur

from year to year as fallow fields are plowed, cattle are introduced or removed, or crop

land is allowed to grow wild. Because we used took a static view of the landscape in this









study, our data cannot assess the potential effects of rapid changes on nesting success.

We are also mindful of the potential for fragmentation or particular matrix uses to affect

local communities in ways that do not manifest themselves immediately (see Cook et al.

2005). These transient effects have the potential to greatly influence nesting birds, as

well as other species in the community.

It may be appropriate to reevaluate the trade-off between studying multiple sites,

which allows for variance estimation and potentially for the broad application of results,

and studying a single site, which allows a greater amount of effort to be placed in

understanding local processes. We point out that in spite of the intensive nature of the

present study, we were able to collect data from only 12 sites across southern Illinois.

For the present dataset, 12 sites were not sufficient to distinguish the variance in nesting

success caused by local processes from that caused by landscape processes. Studies

conducted on more local scales with two or three sites might allow a more thorough

investigation of population dynamics, rapidly changing landscapes, and transient

community effects. Perhaps a number of well-designed, locally focused studies that are

able to examine in fine detail community responses and the possible dependence of those

responses on the landscape would better serve the scientific community both in terms of

directing future research questions and in providing clear management recommendations.
















APPENDIX A
COMMON AND LATIN NAMES

This appendix gives the common and Latin names of the birds and mammals in this

study, as well as the four-letter abbreviations for the birds that appear in Appendix B.


Common Name
Birds Acadian Flycatcher
Brown-headed Cowbird
Indigo Bunting
Kentucky Warbler
Louisiana Waterthrush
Northern Cardinal
Ovenbird
Wood Thrush
Worm-eating Warbler

Mammals Bobcat
Common Gray Fox
Coyote
Eastern Chipmunk
House Cat
Long-tailed Weasel
Northern Raccoon
Striped skunk
Virginia Opossum


Latin Name
Empidonax virescens
Molothrus ater
Passerina cyanea
Oporornis formosus
Seiurus motacilla
Cardinalis cardinalis
Seiurus aurocapillus
Hylocichla mustelina
Helmitheros vermivorum

Lynx rufus
Urocyon cinereoargenteus
Canis latrans
Tamias striatus
Felis sylvestris catus
Mustela frenata
Procyon lotor
Mephitis mephitis
Didelphis virginiana


Bird Code
ACFL
BHCB
INBU
KEWA
LOWA
NOCA
OVEN
WOTH
WEWA

















APPENDIX B
TRACK STATION DATA

This table shows the records from the track stations in each year at each site. Track

station methodology was not consistent across years; see Materials and Methods. Latin

names are given in Appendix A.


Bald Knob


Big Brushy


Burke Branch


Cave Hill


Hayes Creek


Kaskaskia Forest


Lick Creek


Lusk Creek


Pine Hills


2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002
2000
2001
2002


X
o U



E -
E O
o o
0
9 -


C0

CU) 0
0 0
o m
I m


3 2- -
15 -
4 2 3 5
10 1 2 1 1
10 2 1 -
3 2 1

7 2 -
8 8 1 1
4 5 -
6 4 1 -
1 3 -
5 4 1- -
3 4 4
2 6 1 4 1
2 4 1 -
12 5 1
6 4 1 -
1 2 1 -
7 3 -
6 2 1 1
3 2 1- -
4 2 1
3 3 -
2 1 1
11 4 -
3 1 -
3 4 1 1













o E
c0
0~
S0


0 21
z >


x 75
o v,
LL

03

0
E
E 6
o 0
Co -j


0

Cl) 0
o o
I m


Saline Mines 2000 2 5 2 1 1
2001 6 6 -
2002 3 10 2 1
Tansill 2000 10 5 -
2001 12 1 3
2002 4 2 3 1
Wildcat Bluff 2000 3 1
2001 10 4 -
2002 3 6 1
















APPENDIX C
MAYFIELD SUMMARY STATISTICS

This appendix shows the exposure days, number of depredated nests, Mayfield

daily predation rate (DPR), standard error of the DPR, and the total number of nests for

each species, site, and year. "-" in the Species, Site, or Year column indicates a summary

line. For example, the first line of the table gives Acadian Flycatcher data from 2000,

summed across all sites. See Appendix A for full species names.

Exposure Depredated Standard Total
Species Site Year Days Nests DPR Error Nests
ACFL 2000 1890.5 55 0.029 0.004 117
ACFL 2001 2296 42 0.018 0.003 144
ACFL 2002 1251 31 0.025 0.004 76
ACFL 5437.5 128 0.024 0.002 337
ACFL Bald 2000 187 6 0.032 0.013 11
ACFL Bald 2001 222.5 13 0.058 0.016 16
ACFL Bald 2002 90.5 0 0.000 0.000 4
ACFL Bald 500 19 0.038 0.009 31
ACFL Brushy 2000 329.5 12 0.036 0.010 21
ACFL Brushy 2001 270.5 7 0.026 0.010 17
ACFL Brushy 2002 118.5 3 0.025 0.014 7
ACFL Brushy 718.5 22 0.031 0.006 45
ACFL Burke 2000 38 1 0.026 0.026 3
ACFL Burke 2001 101.5 1 0.010 0.010 7
ACFL Burke 2002 64.5 1 0.016 0.015 5
ACFL Burke 204 3 0.015 0.008 15
ACFL Cave 2000 103.5 2 0.019 0.014 4
ACFL Cave 2001 136 0 0.000 0.000 5
ACFL Cave 2002 129 3 0.023 0.013 6
ACFL Cave 368.5 5 0.014 0.006 15
ACFL Hayes 2000 65.5 1 0.015 0.015 4
ACFL Hayes 2001 84.5 1 0.012 0.012 5
ACFL Hayes 2002 49.5 0 0.000 0.000 3
ACFL Hayes 199.5 2 0.010 0.007 12
ACFL Kask 2000 114 4 0.035 0.017 7
ACFL Kask 2001 43 2 0.047 0.032 4
ACFL Kask 2002 53.5 0 0.000 0.000 5
ACFL Kask 210.5 6 0.029 0.011 16
ACFL Lick 2000 117.5 5 0.043 0.019 8











Exposure Depredated
Year Days Nests


Species
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
ACFL
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU


Site
Lick
Lick
Lick
Lusk
Lusk
Lusk
Lusk
Pine
Pine
Pine
Pine
Saline
Saline
Saline
Saline
Tansill
Tansill
Tansill
Tansill
Wildcat
Wildcat
Wildcat
Wildcat





Bald
Bald
Brushy
Brushy
Brushy
Brushy
Burke
Burke
Burke
Cave
Cave
Cave
Hayes
Hayes
Hayes
Hayes
Kask
Kask
Lick
Lick
Lick


2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2001

2000
2001
2002

2000
2002

2001
2002

2000
2001
2002

2001

2000
2001
2002


135.5
59
312
301
244.5
317.5
863
117.5
148.5
117.5
383.5
365
517
103
985
96
226
124.5
446.5
56
166.5
24
246.5
286
457.5
429.5
1173
39
39
50
23
25
98
38.5
67.5
106
22.5
12
34.5
7
41.5
18.5
67
17.5
17.5
11
58.5
19


DPR
0.015
0.102
0.042
0.010
0.012
0.035
0.020
0.017
0.013
0.017
0.016
0.041
0.017
0.019
0.026
0.021
0.004
0.008
0.009
0.036
0.006
0.083
0.020
0.038
0.033
0.028
0.032
0.026
0.026
0.040
0.000
0.000
0.020
0.026
0.000
0.009
0.000
0.083
0.029
0.143
0.072
0.000
0.060
0.000
0.000
0.091
0.034
0.053


Standard Total
Error Nests
0.010 1
0.039
0.011 2
0.006 1
0.007 1
0.010 1
0.005 5
0.012
0.009 1
0.012
0.006 2
0.010 2
0.006 3
0.014
0.005 6
0.015
0.004 1
0.008
0.004 2
0.025
0.006
0.056
0.009 1
0.011 2
0.008 4
0.008 3
0.005 10
0.025
0.025
0.028
0.000
0.000
0.014
0.026
0.000
0.009
0.000
0.080
0.029
0.132
0.040
0.000
0.029
0.000
0.000
0.087
0.024
0.051











Exposure Depredated
Year Days Nests


Species
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
INBU
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA


Site
Lick
Lusk
Lusk
Lusk
Lusk
Pine
Pine
Pine
Pine
Saline
Saline
Saline
Saline
Tansill
Tansill
Tansill
Wildcat
Wildcat
Wildcat
Wildcat





Bald
Bald
Bald
Brushy
Brushy
Brushy
Brushy
Burke
Burke
Burke
Burke
Cave
Cave
Cave
Hayes
Hayes
Hayes
Hayes
Kask
Kask
Lick
Lick
Lick
Lick


2000
2001
2002

2000
2001
2002

2000
2001
2002

2001
2002

2000
2001
2002

2000
2001
2002

2001
2002

2000
2001
2002

2000
2001
2002

2001
2002

2000
2001
2002

2001

2000
2001
2002


88.5
64.5
39
78.5
182
62
48
138.5
248.5
33
56
15
104
86.5
3
89.5
20
26
52.5
98.5
311.5
413.5
201
926
19
4.5
23.5
112
86
6.5
204.5
71.5
42.5
34
148
63.5
12
75.5
5.5
18.5
18
42
13
13
6.5
31.5
21
59


DPR
0.045
0.031
0.051
0.076
0.055
0.000
0.000
0.022
0.012
0.061
0.000
0.000
0.019
0.046
0.000
0.045
0.100
0.115
0.019
0.061
0.022
0.019
0.035
0.024
0.000
0.000
0.000
0.009
0.012
0.000
0.010
0.028
0.024
0.029
0.027
0.031
0.083
0.040
0.000
0.000
0.000
0.000
0.077
0.077
0.000
0.000
0.000
0.000


Standard Total
Error Nests
0.022
0.022
0.035
0.030
0.017 1
0.000
0.000
0.012
0.007 1
0.042
0.000
0.000
0.013 1
0.023
0.000
0.022
0.067
0.063
0.019
0.024 1
0.008 2
0.007 3
0.013 2
0.005 8
0.000
0.000
0.000
0.009
0.012
0.000
0.007 1
0.020 1
0.023
0.029
0.013 1
0.022
0.080
0.022
0.000
0.000
0.000
0.000
0.074
0.074
0.000
0.000
0.000
0.000











Exposure Depredated
Year Days Nests


Species
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
KEWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA


Site
Lusk
Lusk
Lusk
Lusk
Pine
Pine
Pine
Pine
Saline
Saline
Saline
Saline
Tansill
Tansill
Tansill
Wildcat
Wildcat
Wildcat





Bald
Bald
Bald
Brushy
Brushy
Brushy
Burke
Burke
Burke
Burke
Cave
Cave
Cave
Hayes
Hayes
Hayes
Lick
Lick
Lick
Lusk
Lusk
Lusk
Lusk
Pine
Pine
Saline


2000
2001
2002

2000
2001
2002

2000
2001
2002

2001
2002

2001
2002

2000
2001
2002

2000
2001

2000
2002

2000
2001
2002

2001
2002

2000
2001

2001
2002

2000
2001
2002

2001

2001


12.5
37
2.5
52
45.5
87.5
78.5
211.5
58
10.5
13.5
82
3
6
9
1.5
4.5
6
40.5
602.5
731.5
1374.5
14
77
91
2.5
59.5
62
10
68.5
134
212.5
28.5
39
67.5
10
16.5
26.5
63.5
145
208.5
4
208.5
217
429.5
7
7
17.5


DPR
0.080
0.027
0.000
0.038
0.022
0.011
0.064
0.033
0.034
0.000
0.000
0.024
0.333
0.000
0.111
0.000
0.000
0.000
0.025
0.027
0.018
0.022
0.000
0.065
0.055
0.000
0.017
0.016
0.100
0.015
0.000
0.009
0.035
0.026
0.030
0.000
0.000
0.000
0.000
0.021
0.014
0.000
0.019
0.032
0.026
0.143
0.143
0.000


Standard Total
Error Nests
0.077
0.027
0.000
0.027
0.022
0.011
0.028
0.012 1
0.024
0.000
0.000
0.017
0.272
0.000
0.105
0.000
0.000
0.000
0.024
0.007 3
0.005 4
0.004 8
0.000
0.028
0.024
0.000
0.017
0.016
0.095
0.014
0.000
0.007 1
0.034
0.025
0.021
0.000
0.000
0.000
0.000
0.012
0.008 1
0.000
0.009 1
0.012 1
0.008 2
0.132
0.132
0.000











Exposure Depredated
Year Days Nests


Species
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
LOWA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA
NOCA


Site
Saline
Tansill
Tansill
Tansill
Wildcat
Wildcat
Wildcat





Bald
Bald
Bald
Brushy
Brushy
Brushy
Brushy
Burke
Burke
Burke
Burke
Cave
Cave
Cave
Hayes
Hayes
Hayes
Hayes
Lick
Lick
Lick
Lick
Lusk
Lusk
Lusk
Pine
Pine
Pine
Saline
Saline
Saline
Saline
Tansill
Tansill
Tansill
Wildcat
Wildcat


2001
2002

2001
2002

2000
2001
2002

2000
2001

2000
2001
2002

2000
2001
2002

2001
2002

2000
2001
2002

2000
2001
2002

2000
2002

2000
2002

2000
2001
2002

2001
2002

2002


17.5
39
103
142
76.5
34
110.5
210
353.5
401.5
965
22
15
37
40
5
23
68
49
74.5
108
231.5
21
3
24
24
72.5
19
115.5
8
58.5
74.5
141
16
16.5
32.5
22.5
13
35.5
28.5
21
6
55.5
86
132.5
218.5
6
6


DPR
0.000
0.077
0.000
0.021
0.013
0.029
0.018
0.052
0.037
0.037
0.040
0.045
0.067
0.054
0.075
0.200
0.043
0.074
0.061
0.054
0.056
0.056
0.000
0.333
0.042
0.042
0.028
0.158
0.052
0.125
0.051
0.000
0.028
0.000
0.061
0.031
0.044
0.077
0.056
0.035
0.095
0.167
0.072
0.000
0.008
0.005
0.000
0.000


Standard Total
Error Nests
0.000
0.043
0.000
0.012
0.013
0.029
0.013
0.015 1
0.010 2
0.009 3
0.006 7
0.044
0.064
0.037
0.042
0.179
0.043
0.032
0.034
0.026
0.022
0.015 1
0.000
0.272
0.041
0.041
0.019
0.084
0.021
0.117
0.029
0.000
0.014
0.000
0.059
0.030
0.043
0.074
0.039
0.034
0.064
0.152
0.035
0.000
0.008 1
0.005 1
0.000
0.000











Exposure Depredated
Year Days Nests


Species
OVEN
OVEN
OVEN
OVEN
OVEN
OVEN
OVEN
OVEN
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WEWA
WOTH
WOTH
WOTH
WOTH


Burke
Burke
Lusk
Lusk
Lusk





Bald
Bald
Bald
Bald
Brushy
Brushy
Burke
Burke
Burke
Burke
Cave
Cave
Cave
Cave
Hayes
Hayes
Kask
Kask
Kask
Lick
Lick
Lusk
Lusk
Lusk
Lusk
Saline
Saline
Tansill
Tansill
Tansill
Wildcat
Wildcat


DPR


2000
2002

2002

2000
2002

2000
2001
2002

2000
2001
2002

2000

2000
2001
2002

2000
2001
2002

2001

2000
2001

2001

2000
2001
2002

2002

2001
2002

2001

2000
2001
2002


6
9.5
15.5
8
8
6
1.5
7.5
88
336.5
231.5
656
13
20
1
34
3
3
5
22
66.5
93.5
1.5
52.5
58.5
112.5
5
5
8
43
51
9
9
57.5
88
74
219.5
1.5
1.5
55
30
85
42
42
300.5
663.5
468
1432


1 0.167
2 0.211
3 0.194
2 0.250
2 0.250
1 0.167
0 0.000
1 0.133
4 0.045
8 0.024
3 0.013
15 0.023
1 0.077
1 0.050
0 0.000
2 0.059
0 0.000
0 0.000
1 0.200
1 0.045
1 0.015
3 0.032
0 0.000
1 0.019
0 0.000
1 0.009
0 0.000
0 0.000
2 0.250
3 0.070
5 0.098
0 0.000
0 0.000
0 0.000
1 0.011
2 0.027
3 0.014
0 0.000
0 0.000
1 0.018
0 0.000
1 0.012
0 0.000
0 0.000
19 0.063
14 0.021
12 0.026
45 0.031


Standard Total
Error Nests
0.152
0.132
0.100
0.153
0.153
0.152
0.000
0.124
0.022 1
0.008 3
0.007 1
0.006 6
0.074
0.049
0.000
0.040
0.000
0.000
0.179
0.044
0.015
0.018
0.000
0.019
0.000
0.009 1
0.000
0.000
0.153
0.039
0.042
0.000
0.000
0.000
0.011
0.019
0.008 1
0.000
0.000
0.018
0.000
0.012
0.000
0.000
0.014 2
0.006 4
0.007 3
0.005 11











Exposure Depredated
Year Days Nests


Species
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH
WOTH


Site
Bald
Bald
Brushy
Brushy
Brushy
Burke
Burke
Burke
Burke
Cave
Cave
Cave
Hayes
Hayes
Hayes
Hayes
Kask
Kask
Kask
Kask
Lick
Lick
Lusk
Lusk
Lusk
Lusk
Pine
Pine
Pine
Saline
Saline
Saline
Saline
Tansill
Tansill
Tansill
Wildcat
Wildcat
Wildcat
Wildcat
Bald
Bald
Bald
Bald
Brushy
Brushy
Brushy
Brushy


2002

2000
2001

2000
2001
2002

2001
2002

2000
2001
2002

2000
2001
2002

2001

2000
2001
2002

2001
2002

2000
2001
2002

2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002


25
25
51.5
37
88.5
80.5
179.5
172.5
432.5
18.5
9
27.5
52.5
133
48
233.5
22
53
38
113
24
24
21
20.5
12
53.5
66.5
36
102.5
28
6
22
56
40
34.5
74.5
45
85.5
71
201.5
236
392.5
121
749.5
588.5
421.5
232.5
1242.5


DPR
0.000
0.000
0.039
0.027
0.034
0.062
0.017
0.012
0.023
0.000
0.000
0.000
0.000
0.023
0.042
0.021
0.136
0.000
0.105
0.062
0.042
0.042
0.143
0.098
0.000
0.093
0.030
0.000
0.020
0.107
0.167
0.000
0.071
0.000
0.029
0.013
0.067
0.012
0.042
0.035
0.034
0.054
0.000
0.039
0.034
0.024
0.022
0.028


Standard Total
Error Nests
0.000
0.000
0.027
0.027
0.019
0.027
0.010 1
0.008 1
0.007 3
0.000
0.000
0.000
0.000
0.013 1
0.029
0.009 1
0.073
0.000
0.050
0.023 1
0.041
0.041
0.076
0.066
0.000
0.040
0.021
0.000
0.014
0.058
0.152
0.000
0.034
0.000
0.029
0.013
0.037
0.012
0.024
0.013 1
0.012 1
0.011 2
0.000
0.007 4
0.007 4
0.007 2
0.010 1
0.005 8












Species Site
Burke
Burke
Burke
Burke
Cave
Cave
Cave
Cave
Hayes
Hayes
Hayes
Hayes
Kask
Kask
Kask
Kask
Lick
Lick
Lick
Lick
Lusk
Lusk
Lusk
Lusk
Pine
Pine
Pine
Pine
Saline
Saline
Saline
Saline
Tansill
Tansill
Tansill
Tansill
Wildcat
Wildcat
Wildcat
Wildcat


Year
2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002

2000
2001
2002


Exposure Depredated
Days Nests
292.5 14
488.5 11
655 13
1436 38
105 2
342.5 4
262.5 7
710 13
164.5 3
371.5 9
153 5
689 17
144 9
169.5 6
91.5 4
405 19
143 7
380.5 8
318.5 10
842 25
482.5 10
637.5 13
719.5 27
1839.5 50
247.5 4
357.5 6
383.5 11
988.5 21
512.5 23
628 12
161 3
1301.5 38
96 2
535.5 10
433.5 3
1065 15
121 7
398 6
192 7
711 20
3133 109
5123 116
3723.5 95
11979.5 320


DPR
0.048
0.023
0.020
0.026
0.019
0.012
0.027
0.018
0.018
0.024
0.033
0.025
0.063
0.035
0.044
0.047
0.049
0.021
0.031
0.030
0.021
0.020
0.038
0.027
0.016
0.017
0.029
0.021
0.045
0.019
0.019
0.029
0.021
0.019
0.007
0.014
0.058
0.015
0.036
0.028
0.035
0.023
0.026
0.027


Standard
Error
0.012
0.007
0.005
0.004
0.013
0.006
0.010
0.005
0.010
0.008
0.014
0.006
0.020
0.014
0.021
0.011
0.018
0.007
0.010
0.006
0.006
0.006
0.007
0.004
0.008
0.007
0.009
0.005
0.009
0.005
0.011
0.005
0.015
0.006
0.004
0.004
0.021
0.006
0.014
0.006
0.003
0.002
0.003
0.001


Total
Nests
33
35
42
110
5
23
19
47
11
26
14
51
11
16
10
37
11
30
19
60
32
48
55
135
18
26
27
71
41
46
15
102
5
34
28
67
10
26
18
54
231
365
271
867















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BIOGRAPHICAL SKETCH

Mr. Cottam received a Bachelor of Arts in biology from the University of Utah in

1998, where he completed a senior honors thesis on canine respiratory mechanics.

Taking a hiatus from academia, he spent two years as a humanitarian servant in South

America, then returned to Utah and worked for both the State Department of Natural

Resources and the Department of Biology at the University of Utah. In 2002, Mr. Cottam

entered graduate school at the University of Illinois, Urbana-Champaign and affiliated

with the Program in Ecology and Evolutionary Biology. He transferred to the University

of Florida's Department of Zoology in 2003.