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BODY CONDITION INDEX ANALYSIS FOR THE AMERICAN ALLIGATOR
CHRISTA L. ZWEIG
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
Christa L. Zweig
I thank my family for all of the incredible support they gave me while still letting
me make my own mistakes. I am grateful to Zach Welch, who had the demanding duty
of keeping me in high spirits. I also thank the members of my committee: Frank
Mazzotti for offering his support and giving me the opportunity to pursue this degree,
Ken Rice for providing me with all sorts of guidance, Laura Brandt for her thoughtful
comments, and Franklin Percival for his excellent, practical opinions. There are many
people to thank, and I wish I could enumerate the countless reasons why they are special
to me, but then this section would be longer than my thesis. Everyone knows why they
are on this list: Ab Abercrombie, Lindsey Hord, Mike Cherkiss, Ashley Traut, Gayle
Martin, Becky Hylton, Diana Swan, Jamie Duberstein, Jocie Graham, Phil Wilkinson,
Woody Woodward, Chris Tucker, Jason Williams, Geoff Cook, Phil George, Matt
Chopp, Adam Finger, Stan Howarter, Cathy Ritchie, Cherie Cook, Sarah Kern, Monica
Lindberg, Caprice McRae, Debra Hughes, Barbara Fesler, Marion Bailey, Mary Hudson-
Kelley, Lauren J. Chapman, Peggy VanArman, the Palm Beach Atlantic College Science
Club, IFAS statistics, Marinella Capanu, and Brenny.
TABLE OF CONTENTS
A C K N O W L E D G M E N T S ................................................................................................. iii
LIST OF TABLES ................... ......... ......... .......... ............ vi
L IST O F FIG U R E S .... ....... ................................................ .... ..... .. ............. vii
A B STR A C T ...................... .................................. ........... ... ....... ....... viii
1 IN T R O D U C T IO N ................................................................................ ............ .. .. 1
2 EVALUATION OF FIELD MEASUREMENTS OF THE AMERICAN
ALLIGATOR FOR USE IN MORPHOMETRIC STUDIES .............. ..................3
Introduction ......... ...... ............ ........................................................... . 3
M eth o d s ........................................................................................................................... 3
R e su lts .............. ...... ........... ................. ................................................... . 6
D isc u ssio n .............. ...... ........... ................. .......................... .................. 6
3 CONDITION INDEX ANALYSIS OF THE AMERICAN ALLIGATOR ..................8
In tro d u ctio n ....................................................................... 8
S tu d y A re a ............................................................................ 12
M materials and M ethods................................... .............. 13
C capture M methods ....................................................................................... ........ 13
Condition Indices Analysis .................................. ...................... .... ...... 14
Sim ulated D ata ..................................................... 15
Everglades Condition A analysis .................................................................... 16
R esu lts .................. .................... ................................ 16
Condition Indices Analysis .............................. .................. .. .......... 16
ANOVA/ANCOVA Analysis by Capture Event ............................................ 17
Sim ulated D ata ....................................................... 18
Everglades Condition A analysis ..................................................................... 18
D iscu ssio n ..................................................... 19
4 CON CLU SION ............... ................ ...................................... 37
A DATA SHEETS AND MEASUREMENT INSTRUCTIONS GIVEN TO
EXPERIM ENT PARTICIPANTS ........................................ .......................... 38
B GENERAL PICTORIAL MORPHOMETRIC MEASUREMENT
IN STRU CTION S .................. ........................................ ................. 41
L IST O F R E FE R E N C E S ............................................................................. ............. 45
B IO G R A PH IC A L SK E TCH ..................................................................... ..................49
LIST OF TABLES
1: T-test results for coefficient of variation of experienced individuals versus
inexperienced groups of volunteers measuring American alligators at A.R.M.
Loxahatchee National W wildlife Refuge ....................................... ............... 7
2: Within alligator variance and covariance components for length and volumetric
m e a su re s ...................... .. .. ......... .. .. ......... ....................................... 7
3: Descriptive statistics of indices used in condition analysis for the American
allig ato r ...................... .. .. ......... .. .. ................................................ 2 4
4: Condition indices calculated for the American alligator whose Pearson r-values
are less than 30% ................................................................................2 5
5: Results of ANOVA/Least Significant Difference and ANCOVA/ Least
Significant Difference analyses of condition indices for the American alligator..26
6: ANOVA/ANCOVA analyses of condition indices by capture event from October
1999 to Spring 2002 (n = 395). Order of condition index is from highest to lowest
mean. There were no ENP-Est captures for March/April 00..............................27
7: Ability of ANOVA/LSD analysis to detect differences in condition from Relative
K sim ulated data .....................................................................28
8: Ability of ANOVA/LSD analysis to detect differences in condition from Fulton's
K sim ulated data .....................................................................28
LIST OF FIGURES
1: Flowchart of condition indices analysis for the American alligator in the
Everglades ..................................... ................................ ......... 29
2: Capture areas in south Florida within Everglades wetlands core ........................30
3: Linear regression of tail girth vs. snout-vent length of the American alligator of
captures in south Florida from October 1999 to March 2002.............................31
4: Range of Fulton's K index values by capture area in south Florida. Captures are
from October 1999 to March 2002 .............................................32
5: Hierarchy of Everglades alligator condition from October 1999 to March 2002
u sing F u lton 's K an aly sis............................................................ .....................33
6: Sample condition range of alligators for Fulton's K from Everglades data from
October 1999 to March 2002 .................. ......... ................. 34
7: SVL/Mass Fulton's K analysis for the American alligator in areas of South
Carolina, north Florida and the Everglades ................................. ............... 35
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
BODY CONDITION FACTOR ANALYSIS FOR THE AMERICAN ALLIGATOR
Chair: Frank Mazzotti
Major Department: Wildlife Ecology and Conservation
The American alligator (Alligator mississippiensis) is an integral part of the
Everglades ecosystem. They affect and are affected by the landscape and changes in
hydrology, which makes them an excellent organism to use in evaluating current
Everglades restoration efforts. Information on alligators-census data, capture or
morphometric data, blood chemistry, and reproduction statistics-has been collected in
the Everglades since the 1950's. Historical information provides a suite of useful life
history characteristics or population parameters (i.e., health and condition, nesting effort,
growth rate and survival, and density) that can be used for evaluating restoration.
However, some life history traits (e.g., absolute density and survival) are difficult to
accurately measure and may take decades of data to detect trends. Body condition can be
measured using indices and requires less data to begin an analysis. If used properly,
condition can be a useful monitoring tool to assess the health of a population and thus the
health of its ecosystem. This project evaluated morphometric measurements taken for the
American alligator, available condition indices, and, using a stepwise process,
recommends the appropriate index for use in ecological applications.
We analyzed morphometric measurements of captured animals to determine which
are measured most accurately and are appropriate for condition analyses. Condition
indices are functions of a body length indicator and a volumetric measurement and are
only as accurate as the measurements used. Head length, snout-vent length and total
length are suitable for body length indicators, and tail girth, neck girth, chest girth, and
mass can all be used as volumetric measurements. We then compared four condition
indices and two models of volume/length relationships for their ability to distinguish
between populations with known qualitative condition differences.
It was determined through ANOVA/LSD analysis of the condition indices that the
HL/Mass combination of Fulton's K and the SVL/Mass combination of ANCOVA were
best able to distinguish differences in condition between areas of the Everglades;
however ANCOVA can not be used to compare across populations, unless strict
assumptions about the two populations can be met. HL/Mass Fulton's K can be used to
spatially and temporally compare populations of the American alligator, and is suggested
by this study as the best condition factor to use for these purposes.
The primary objective of this study was to determine the appropriate condition
index for the American alligator with the intention that it will be used as a monitoring
tool of population health for the current restoration process. Because the alligator's life
history is so closely linked to hydropattern, body condition can reflect the impacts of
changes in hydrology.
The American alligator (Alligator mississippiensis) ranges from eastern Texas to
North Carolina and south to the tip of Florida. Alligators are vital components of wetland
ecosystems, serving as keystone predators (Mazzotti and Brandt 1994) and exhibiting the
qualities of ecosystem engineers (Jones et al. 1994), creating critical landscape features.
In the Everglades, they create topography in a system where small changes can be very
important. They construct alligator holes for thermal refuge, feeding, and reproduction
by making depressions in the muck soil and keeping them clear of surrounding
vegetation. These areas hold water during dry-down periods and provide refugia for
aquatic and amphibious animals during the dry season (Craighead 1968; Kushlan 1974;
Campbell 1999). Alligators also create dry land in the marsh by mounding vegetation to
build their nests. Habitat for less flood-tolerant plants is created. Further, it has been
documented that other reptiles use alligator nest mounds to deposit their eggs (Kushlan
and Kushlan 1980; Hall and Meier 1993; Enge et al. 2000).
The natural history and reproductive patterns of the American alligator are closely
tied to its physical environment. There is a relationship between spring water levels and
late summer water levels in a premanaged landscape (Kushlan and Jacobsen 1990;
Mazzotti and Brandt 1994). Based on spring water levels, alligators construct nests that
will be above historical average high water in the summer, wet season. However,
because water levels in the Everglades have been altered from historical seasonal
fluctuations, unnatural water regimes may cause nest flooding (Kushlan and Jacobsen
1990). Further, water levels can affect body condition of Everglades alligators
The American alligator is an integral part of the Everglades ecosystem. They effect
and are affected by the landscape and changes in hydrology, which makes them an
excellent organism to use in evaluating current Everglades restoration efforts.
Information on alligators-census data, capture or morphometric data, blood chemistry,
and reproduction statistics, has been collected in the Everglades since the 1950's (Rice et
al. 2002). Historical information provides a suite of useful life history characteristics or
population parameters (i.e., health and condition, nesting effort, growth rate and survival,
and density) that can be used for evaluating restoration. However, some life history traits
(e.g., absolute density and survival) are difficult to accurately measure and may take
decades of data to detect trends. Condition is the "relative fatness of [an animal]. ... a
measure of how well that animal is coping with its environment" (Taylor 1979, p. 349),
and can be measured using indices and requires less data to begin an analysis. If used
properly, condition can be a useful monitoring tool to assess the health of a population
and thus the health of its ecosystem. This project evaluates morphometric measurements
taken for the American alligator (Chapter 2) and available condition indices, and using a
stepwise process (Figure 1), recommends the appropriate index for use in ecological
applications (Chapter 3).
EVALUATION OF FIELD MEASUREMENTS OF THE AMERICAN ALLIGATOR
FOR USE IN MORPHOMETRIC STUDIES
Long-term data are typically collected by different people under varying field
conditions, resulting in data sets that are difficult to standardize. Several researchers
have examined this problem in varied taxa, such as birds (Grant 1979; Zink 1983; Arendt
and Faaborg 1989; Yezerinac et al. 1992), bats (Palmeirim 1998), and snails (Bailey and
Byrnes 1990). However, they have used museum specimens or shells. Live animals are
even more difficult to standardize. The most common data collected on crocodilians are
morphometric measurements (e.g., head length, snout-vent length, total length, and
weight). These measurements are used in a wide range of analyses, from ecological to
taxonomic to evolutionary (Chabreck and Joanen 1979; Hutton 1986; Hall 1991).
However, these analyses are only as accurate as the data from which they are derived.
We performed morphometric measurement trials on the American alligator to
quantify inter-measurer error and determine which morphometric measurement had the
least measure error associated with it. This is necessary to increase confidence levels in
Two trials were performed: 1) measurers were inexperienced volunteers; 2)
measurers were experienced alligator biologists. The inexperienced trial was performed
*Chapter reprinted with permission from the editors ofHerpetological Review (in press)
in the fall of 2000 using ten alligators captured at Arthur R. Marshall Loxahatchee
National Wildlife Refuge (Loxahatchee) located in Boynton Beach, FL, with six groups
of volunteers. Alligators were located by observing eye shines from an airboat in the
marsh interior and captured using a wire snare. They were secured in the boat and
brought back to a covered area for measurement. Alligator size varied from 108-248 cm
total length. The volunteers consisted of Loxahatchee staff, University of Florida
employees, U.S. Geological Survey employees, and students from Palm Beach Atlantic
Community College. Each group consisted of 4-6 people and they were given both
verbal and written measurement instructions (Appendix A). Each group measured every
alligator. Different individuals within the group took measurements of each alligator, but
measurements were agreed upon within the group before being recorded. Participants
were allowed to collaborate within but not between groups. The following were
measured by each group on every alligator: head length (HL), snout-vent length (SVL),
total length (TL), right hind foot length (HFL), neck girth (NG), tail girth (TG), chest
girth (CG), and mass. HL was measured dorsally, while SVL and TL were measured
ventrally. A measurement kit was provided with every alligator that contained the
following: a clipboard, pencil, string for measuring tail girth between scutes, a Pesola
scale, and a flexible centimeter sewing tape. These kits stayed with the alligator so that
the same equipment was used. Measurements were made with the flexible sewing tape to
the nearest 0.1 cm. Mass was measured with 10-50 kg Pesola scales to the nearest 0.1
kg. The scales were calibrated before use using a weight of known mass. Alligators also
were marked with individually numbered size 3 Monel tags, provided by the Florida Fish
and Wildlife Conservation Commission, in the webbing of the back left foot and scute
clipped for permanent identification.
The second trial was performed in fall 2001 with experienced alligator biologists.
Nine alligators ranging from 156 cm to 255 TL were captured at Loxahatchee and used
for the trial. Ten biologists were present and measured each alligator using the same
procedures and instructions as in the previous experiment with three exceptions: 1) each
individual measured every alligator and was not allowed to discuss their findings; 2) the
participants worked in groups of two to facilitate measuring; and 3) each person recorded
their own measurements to reduce bias in and among groups.
Data were analyzed to determine which measurements were most reliable, or
contained the least percent measurement error (%ME). Reliability for between
experience and inexperienced groups was evaluated using coefficient of variation.
Standard deviation for each measurement was calculated and divided by the mean of that
measurement for that alligator. A T-test was performed on the coefficients of variation to
determine significant differences in the reliability of the measurements between
experienced and inexperienced measurers. Six of the experienced measurers were
randomly chosen so that the N in the T-test would be equal to the six groups of
Percent measurement error was calculated for the experienced group to determine
which measurements are more reliable for morphometric studies. Bailey and Byrnes
(1990) pioneered the use of Model II ANOVA and ANCOVA to estimate within-
individual and among-individual components of covariance and variance to predict
percent measurement error. A Model II ANOVA and ANCOVA was run as a part of the
SAS NESTED procedure (SAS Institute, Inc. 1988), according to the Bailey and Byrnes
procedure. The among-individual variance was calculated by the following equation
(Yezerinac, et al. 1992):
2 among = (MSamong MSwithin) / m,
where MS is the mean squared deviation and m is the number of repeated
measurements. Covariances were calculated as follows:
rx.y among = (COVx.y among / Sx among Sy among).
%ME was then determined by:
%ME = 100% (S2within / S2within + S2among).
The results of the T-test (Table 1) suggest that experienced individuals measure
more accurately for HL, CG, TG, and mass than groups of inexperienced volunteers.
There is no statistical difference for SVL, TL, HFL, and NG.
Percent measurement error ranged from 0.50 to 49.53% (Table 2). HFL had the
highest %ME because the landmarks used to measure it are not easily located. TL was
also relatively high. It should be one of the most reliable measurements, as it is the
largest and allows for greater margin of error. It's interesting to note the high covariance
that NG and CG share with the other volumetric measurements.
This assessment can be used as a guide for future crocodilian studies that use
morphometric measurements, such as the analysis of growth rate and condition. If
performing morphometric analysis using data collected from inexperienced groups, it
would be more accurate to use TL, SVL, or NG. For experienced individuals, HL, SVL,
TG, or weight should be used. For example, condition factor analysis requires a skeletal
measurement and a volumetric measurement. SVL would be adequate for the skeletal
measurement regardless of who collected the data, but more care should be taken in
choosing the volumetric measurement. If these trials were to be replicated, it would be
useful to modify the design so that each individual would take three or more
measurements of the same measurement (e.g. three measurements of HL, three of SVL,
etc.). This would provide data to quantify intra-observer error.
Table 1: T-test results for coefficient of variation of experienced individuals versus
inexperienced groups of volunteers measuring American alligators at A.R.M.
Loxahatchee National Wildlife Refuge
Table 2: Within alligator variance and covariance components for length and volumetric
measures. Diagonal elements are variance components represented by percent
measurement error (%ME). The off-diagonal elements are covariance
components represented by within-gator correlations withinin.
HL SVL TL HFL NG CG TG Weight
SVL -0.07 1.52
TL 0.02 0.01 3.88
HFL 0.09 -0.15 0.01 49.53
NG -0.04 0.16 -0.15 0.14 7.64
CG -0.03 0.10 0.16 -0.22 0.31 4.89
TG -0.06 0.09 0.10 0.10 0.63 0.40 2.93
Weight -0.08 -0.08 0.09 0.04 -0.25 -0.10 -0.13 0.50
CONDITION INDEX ANALYSIS OF THE AMERICAN ALLIGATOR
Condition as defined in chapter one is the "relative fatness of [an animal]. ... a
measure of how well that animal is coping with its environment"(Taylor 1979, p. 349).
This relationship and its relative ease of measurement is the key to using the American
alligator (Alligator mississippiensis) as an indicator of the health of its environment.
Condition indices are often used to quantify body condition over space and time
(LeCren 1951, Taylor 1979, Springer 1990, Brandt 1991, Elsey 1992, Krebs and
Singleton 1993, Dalrymple 1996, Jakob 1996, Barr 1997, Leslie 1997, Delany 1999).
Biologists often note that an alligator is too skinny or a "healthy" size, but those
observations are qualitative. For example, this project was developed because of
observed condition differences between alligators within parts of the Everglades system.
The difference was apparent, but not quantified. Quantitative condition indices can
compare condition within and among populations, and if used carefully, "can provide
insights into the status of ecosystems" (Murphy et al. 1990, p. 86)
Condition indices can be calculated in several different ways, but is always a function
of skeletal length and a volumetric measurement. They have been used in fisheries
literature since the 1940's, where they have become deeply entrenched as a research
paradigm (Cone 1989). LeCren first reviewed this process in 1951. He described one
relationship on which the condition indices are based:
(1) W= aLn,
where W = weight, L = length, "a" is a constant and "n" is an exponent that has been
calculated in past literature (LeCren 1951) to be between 2.5 and 4.0 for fish body forms.
In most fisheries literature, "n" = 3.0, the number used when growth is isometric.
Isometric growth occurs when both factors, i.e. length and weight, grow at the same rate.
If it is accepted that growth is isometric and "n" equals 3.0, then a condition index
equation can be written as:
(2) K = W/L3 x 10n,
where "n" = 2,3,4, or 5 and is commonly chosen so that the mean ofK is larger than one
(Cone 1989) and K is known as "Fulton's condition factor". This equation is quite
limited by its assumption of isometric growth and that "n" is equal to 3.0.
To solve the problem of the strict assumptions of K, LeCren (1951) formulated the
relative condition factor, or Kn. Instead of using an isometric length/weight relationship
(3.0), he used empirical data and a least squares regression to formulate "n" from (1):
The primary difference between the two K's is that Fulton's condition factor, K,
measures the deviation of an individual from an ideal, theoretical organism while the
relative condition factor, Kn, measures the deviation of an individual from the average of
actual population data. Kn is often used outside of fisheries studies where growth is
usually allometric and "n" is estimated from field data.
Relative condition factor is also limited in its use. Because you must calculate the
ideal length/weight standard separately for each population, it is impossible to compare
the condition factor value across populations (Murphy et al. 1990).
A third and more recent condition index is relative weight, Wr. This was postulated
by Wege and Anderson (1978) and is the ratio of an individual's weight (W) divided by a
standard weight (Ws) for that length times 100, or
(4) Wr = (W/W) x 100.
Ws is an optimal standard which establishes the index value for a species. It is regularly
defined in fisheries literature as the 75th percentile (Murphy et al. 1990), which is
considered optimal growth. Relative weight avoids the drawbacks of Fulton's condition
factor and relative condition factor, because it doesn't vary with changing species or
mean sample size (Springer and Murphy 1990; Quist et al. 1998).
The residual index is another method of calculating condition. The residual index is
calculated by regressing the volumetric measurement on body length. The residual
distance of the individual points to the regression line functions as the condition index.
Some data has to be transformed so that it meets the assumptions of regression. A
condition index also must be independent of body length, which can limit morphometric
measurements that can be used (Green 2001). The residual index possesses the same
flaw as relative condition factor in that it is not comparable across populations, because
the regression line is only valid for the current population.
Researchers also have used ANCOVA to evaluate condition (LeCren 1951; Packard
and Boardman 1988; Garcia-Berthou and Moreno-Amich 1993; Garcia-Berthou 2001).
ANCOVA allows the length-weight relationship to be examined without the confounding
effects of the covariance of body length. Packard (1988) and Garcia-Berthou (2001) rule
out other ratio indices for condition and endorse ANCOVA as the best method for
calculating condition. However, it suffers from the same fault as relative condition and
the residual index in that it is not possible to compare the condition of two populations
with this method, unless the "n" in equation 1 is identical for the two populations being
While condition index analysis is best known in the fisheries literature, it also has
been used for crocodilians. Condition index analyses (using relative condition factor)
have been used during studies on the food habits of alligators in Everglades National Park
(Barr 1997); to compare condition and growth of juvenile alligators in Par Pond, South
Carolina (Brandt 1991); and to compare condition index for Everglades alligators in
Shark Valley (Dalrymple 1996). Elsey et al. (1992) used growth rates and body
condition factors to compare wild and farm-released juveniles in Louisiana; and Santos,
et al. (1994) compared different habitats of the Pantanal in Brazil using condition factor
of Caiman crocodilusyacare. Leslie (1997) used relative condition factor to determine
the condition of a population of Crocodylus niloticus in South Africa. Australian
researchers have also explored condition relating to crocodiles (Garnett and Murray
1986; Grigg et al. 1986).
Many condition index analyses include an actual measure of condition by
sacrificing animals and extracting total body fat (Krebs and Singleton 1993; Jakob et al.
1996; Quist et al. 1998; Sutton et al. 2000; Vila-Gispert and Moreno-Amich 2001). The
body fat percentage is then regressed on the condition index to obtain a relationship.
None have been done with as large an animal as an alligator. It was not feasible to digest
whole alligators for this study.
Alligators are qualitatively fatter or skinnier in different areas of the Everglades,
and some differences in time have been noted in the literature (Dalrymple 1996, Barr
1997). Therefore a goal of this study was to find a condition index that is able to
distinguish the greatest number of differences between these Everglades areas. The main
objective was to determine the appropriate condition index for the American alligator
with the intention that it will used as a tool to monitor population health. Because the
alligator's life history is so closely linked to the water levels of their environment,
condition is a way to determine how changes in their environment are affecting alligators.
With this monitoring tool, condition could be used as a performance measure for the
current Everglades restoration effort.
Data for this study were collected from six areas within the Greater Everglades
Ecosystem in south Florida (Figure 2). The northernmost area, Water Conservation Area
1, which was designated as part of the Arthur R. Marshall Loxahatchee National Wildlife
Refuge (LOX) in 1951, is located in Boynton Beach, FL. It is a 572 square kilometer
area bounded by the West Palm Beach Canal on the north and the Hillsboro Canal to the
Alligators also were captured in Water Conservation Areas 2 and 3 (WCA2 and
WCA3), which are operated by the Florida Fish and Wildlife Conservation Commission
(FWC) and the South Florida Water Management District (SFWMD). WCA2 is a 448
square kilometer pool used for excess water from LOX, supplies water to urban areas of
southeast Florida, and wildlife conservation. Two locations were sampled for alligators
in WCA3, one at the southern end and other in the middle. WCA3 is 1948 square
kilometers of storage for excess water from WCA2 and also is designated a water supply
area for urban areas in south Florida and for wildlife conservation.
Two sites within Everglades National Park were used: Shark Slough and the
estuarine areas located near Flamingo. Shark Slough extends from the northern border of
Everglades National Park to the headwaters of the Shark and Harney Rivers and is the
largest drainage in the Park (Olmstead and Armentano 1997). All estuarine alligators
were caught near Flamingo in Everglades National Park.
Materials and Methods
Alligators were captured during the period from October of 1999 to March/April
2002 by a multi-agency team that consisted of members from U.S. Fish and Wildlife
Service (USFWS), U.S. Geological Survey (USGS), University of Florida (UF), and the
FWC. Animals were captured from all study areas in marsh habitats only, excluding
canal alligators, as their condition may differ from marsh alligators because of the altered
habitat. Due to a concurrent aging study, only 1.22-1.83 meter alligators were caught
during the first two catches. However, the size range restriction was eliminated in
October of 2000 in include a larger size range for an improved analysis.
Fifteen alligators were captured from each area using airboats in marshes and using
motorboats in estuaries during each of six capture events. Alligators were located by
spotlight and captured with a noose or dart. Head length (HL), snout-vent length (SVL),
total length (TL), right hind foot length (HFL), neck girth (NG), tail girth (TG) and chest
girth (CG) were measured to the nearest 0.1 cm (Appendix B). Mass was measured with
a Pesola spring scale to the nearest 0.1 kg. Alligators were sexed and blood was drawn
for a concurrent contaminants study and for hematocrit analyses. The alligators were
then released at their original capture site.
Measurements from several animals were eliminated from this analysis due to
discrepancies. For example, captures with bobtails (an alligator missing part of its tail),
missing data, or known anomalies such as an alligator that is fed by people and is heavier
than a typical Everglades alligator.
Condition Indices Analysis
A flowchart of the process used to choose the most appropriate length and volume
and condition index for this analysis is provided in Figure 1. Several length and
volumetric measurements were eliminated from consideration due to measurement error
(Chapter 1). Condition indices were located in the literature (see Introduction for
discussion of each index) and calculated using the four combinations from Chapter 1:
HL/TG, HL/Mass, SVL/TG, SVL/Mass. The slash used in this notation represents the
combination of two morphometric variables, not the division of one by the other. The
data were pooled (n = 395, max TL = 264 cm, min TL = 100.4 cm) for all areas when
calculating all indices to provide the largest sample size, since they could be considered
members of the Greater Everglades population. The regression for Relative K was run
using PROC NLIN in SAS and the regression for the residual index was run using PROC
REG in SAS (SAS Institute 1988).
A Pearson's correlation was run on the results of the five indices (Fulton's K,
Relative K, Relative Mass, Residuals, and Ratio index), using the four body length and
volume combinations, to determine whether body length was correlated to condition.
Combinations with correlations greater than 30% were eliminated from further
consideration, as this suggested lack of independence. Condition index should be
independent of the body size indicator or straight measurements for the assumptions of
the ordinary least squares regression used in these analyses (Green 2001).
An ANVOA was used (SAS PROC GLM [SAS Institute, Inc. 1988]) to determine
whether condition differed (p < 0.05) by area for all catch events combined for the
indices. Least Significant Difference (LSD) and Tukey's post-hoc tests were run using
SAS to establish which areas differed significantly. Both were calculated to establish
which tests were sensitive enough for our analysis. Both tests calculate differences
between areas, but Tukey's is more conservative than LSD. The condition model using
ANCOVA was run using PROC GLM in SAS (SAS Institute 1988) and results were
analyzed with the LSD post-hoc test to determine differences among areas.
Area differences were counted if an area was significantly different than another
according to the LSD analysis. Among six areas there are fifteen possible differences
and they are written as follows: 112, 113, 114, 115, 116, 213, 214, 215, 216, 314, 315, 316, 415,
416, 516, where area one is different than two, area one is different than three, etc.
Condition indices combinations that delineated significantly fewer populations were
Body length and volumetric combinations of indices that displayed the maximum
amount of differences were used for temporal analysis. Those data were divided by
capture event and the analysis was rerun using the capture event as an added parameter
for the ANOVA analysis. The same was also performed with body length and volumetric
combinations of ANCOVA. Capture events involved were October 1999, March/April
2000, October 2000, March/April 2001, October 2001, and March/April 2002. There
were no ENP-Est alligators for the March/April 2000 capture event.
To address the question of whether the differences found in the ANOVA and
ANCOVA analysis were Type I or Type II errors, simulated data sets were constructed to
test the analysis. Simulated data sets consisted of 15 populations-five populations of
100 individuals with the same condition index mean and standard deviation as the
original data set, five populations of 100 individuals with a 5% increase in the mean and
the same standard deviation as the original data set, and five populations of 100
individuals with a 10% increase in the mean and the same standard deviation as the
original data set. The populations were created using the RV.NORMAL syntax in SPSS
(SPSS Inc. 2001). ANOVA and LSD were used to analyze the simulated data.
ANCOVA data were created in a similar manner. Random populations of HL and
SVL were also generated with the RV.NORMAL syntax in SPSS (SPSS Inc. 2001).
Mass was then regressed on HL and SVL using PROC REG in SAS (SAS Insitute 1988)
and the resulting equation was used to generate mass.
Everglades Condition Analysis
A Fulton's K analysis was performed using HL and Mass. K was calculated for all
captures and was analyzed using an ANOVA and LSD post-hoc test to detect differences
between the areas in the Everglades. Areas were then grouped by significant differences
in mean condition.
Condition Indices Analysis
The mean, minimum, and maximum values for the indices were variable, even
within indices (Table 3). The "b" value calculated for Relative K HL/Mass, HL/TG,
SVL/TG combinations is significantly different than 3.0 (2.83 0.05 S.E., 1.03 0.02
S.E., and 1.05 0.02, respectively). The SVL/Mass combination was not significantly
different than 3.0 (3.01 0.04).
For the ratio index, both mass combinations (HL/Mass and SVL/Mass) were
eliminated from the analysis because they were correlated with the body length by more
than 30% (Table 4). However, Fulton's K mass combinations were included while those
calculated from TG were excluded. All Relative K combinations were incorporated into
the analysis, as were all residual indices. HL/Mass was excluded from the analysis for
Relative Mass because of its high correlation value (30%).
ANOVA analyses were performed on the remaining condition indices to determine
if there were significant condition differences between areas (Table 5). All were
significant except the residual index HL combinations. Several indices; ratio, Relative K,
and relative mass HL/TG combinations, had no more than three differences between
areas, according to the LSD post-hoc test. Tukey's test was deemed too conservative for
the analysis, because it did not show differences for a length and volumetric combination
that had a highly significant ANOVA p-value (p < 0.01). Two condition indices;
Relative K and Fulton's K HL/Mass combination, display seven differences between
The ANCOVA analysis only includes two significant differences out of four
combinations (Table 5). HL/Mass and SVL/Mass found seven and eight LSD post-hoc
area differences, respectively. HL/TG and SVL/TG display higher p-values (by a factor
of 10) than any of the condition indices.
ANOVA/ANCOVA Analysis by Capture Event
Fulton's K, Relative K HL/Mass, and ANCOVA SVL/Mass and HL/Mass were
chosen for further analysis by capture event because of the high number of differences
they exhibited in the first ANOVA analysis. Sample size for capture events ranged from
44 to 83 and had no effect on the significant differences. There were no patterns of
significance due to sample sizes. Results for the capture event ANOVA and LSD tests
were similar for the Fulton's and Relative K HL/Mass combinations (Table 6). There
was no difference in the order of means (from highest to lowest). Fulton's K only found
one additional condition difference among LOX and WCA2 in the first capture event that
Relative K did not. They found significant condition differences in the same three
capture events and their p-values were similar.
The HL/Mass ANCOVA analysis showed differences for the first and last capture
event. No area differences were found in the other capture events. The SVL/Mass
combination was not able to differentiate between areas for any capture events (p> 0.10).
The simulated data analysis confirmed that the procedure of evaluating the
condition indices does not commit any Type I errors. For both Relative and Fulton's K,
the analysis never found differences that were not there. The populations with the exact
mean and standard deviation as the original data were always significantly different than
the populations with the 10% increase in the mean. However, this did not hold true for
the 5% difference. Percent differences ranged from 64 to 100 percent (Tables 7 and 8).
The method for creating simulated data for the ANCOVA analysis was not
successful. The random HL and SVL data were correct for mean and standard deviation,
however the method of creating mass for the HL and SVL did not provide results. The
simulated mass did not conform to the bounds of the original data and could not be used
in a simulated data analysis.
Everglades Condition Analysis
In this study, during the 1999-2002 sampling period, areas within the Everglades
do show significant differences in condition (Figure 4). ENP-SS exhibited the highest
mean condition and is significantly higher than any other area (Figure 5). The two areas
of WCA3 display lower condition than ENP-SS, but are still significantly higher than
WCA2, the lowest mean of the groups. The LOX and ENP-Est condition means are
significantly lower than ENP-SS and are not higher than any other group.
Condition indices are a controversial way of estimating condition for populations
(Packard and Boardman 1988; Cone 1989; Krebs and Singleton 1993; Jakob et al. 1996;
Kotiaho 1999; Garcia-Berthou 2001; Green 2001; Vila-Gispert and Moreno-Amich
2001). They aren't appropriate for every situation and there are strict assumptions that
must be met for them to be used correctly. The main assumptions are that condition is
independent of body length, that the measure used for body length is an accurate measure
of structural size, that body length measurements are not subject to error, and that the
relationship between body size and mass is linear (Green 2001). However, they can be a
good, non-destructive way to rapidly evaluate the health of populations of concern. This
analysis was designed to evaluate the use of condition indices for alligators.
The most accurate measurement of an alligator's volume is mass. However,
because large alligators often can not be weighed in the field, TG was proposed as an
easily measured alternative. It is highly correlated to SVL (Figure 3), and is where
alligators store fat, so it should be a good measure of fat stores. However, the "b" that
was calculated for TG combinations for Relative K were calculated to be approximately
1.0, instead of the 3.0 that Fulton's K supposes for isometric growth. SVL/Mass and
HL/Mass were approximately 3.0. The TG combinations for Fulton's K were too highly
correlated with their straight lengths to be included in the analysis, but it would have been
incorrect to use them because their "b" value is too far away from 3.0 for it to be a valid
use of Fulton's K.
The ANOVA/ANCOVA analysis provided a simple way to narrow the condition
indices down to a manageable number. Fifteen of 19 index combinations had four
significant condition differences or less, as shown by the LSD post-hoc test. They are
still valid ways of comparing condition, especially where HL was not measured, but were
deemed not sensitive enough for this analysis.
The simulated data set allayed concerns of artifacts in the data allowing differences
to be found by the LSD post-hoc tests that were not legitimate. It also allowed us to
determine the range of this analysis. It could accurately detect condition differences of
10% or more. It also can detect differences of 5%, but only at a range of 64-96% (Tables
7 and 8). One can be safe, however, in supposing that a difference found is truly a
difference in the data.
The condition indices included in the final analysis produced different results. The
HL/Mass combinations of Relative K and Fulton's K were very similar in the ordering of
means and number of differences in condition between areas. This is most likely because
they are equations that were derived from the same weight/length relationship and only
vary by the distance of"b" from 3.0, the number used when growth is isometric. They
also were similar in the pooled data analysis.
The ANCOVA analysis also provides adequate differentiation and is used most
recently in the literature because of its ability to remove the covariance of size with
condition. However, it falls victim to the same shortcoming as many of the other
indices-you can not use it to compare populations, but only groups within a population,
such as across capture events in this study. The SVL/Mass ANCOVA combination
detected the maximum number of area differences in this study, but it did not detect any
differences in the temporal analysis. It would have been useful to use an ANCOVA for
temporal analyses of the same population if the sample size was sufficient..
This analysis has shown Fulton's K and Relative K to be nearly equal in their
ability to distinguish condition differences among areas, even though the "b" value
calculated is significantly different than 3.0. If Fulton's K and Relative K are so similar
for alligator populations, it would be wise to use Fulton's K because condition scores
calculated using Fulton's K can be compared across populations. A downside to both of
these indices is that they do not provide one with an easily interpreted biological number.
The Fulton's condition score for this analysis ranged from 1.4 to 3.2, unlike Relative
Mass, where 1.0 is an alligator in normal condition and anything less than 1.0 means the
alligator is in poorer condition. However, Relative Mass does not have the ability to
distinguish as finely between areas as Fulton's or Relative K did for our alligator data or
between populations. To overcome the problem of a lack of biological meaning in Fulton
K numbers, one could identify quartiles of the data and label the bottom quartile as low
condition, the middle two as average condition and the top quartile as good condition
One must be careful when separating condition into high and low that instant
judgments are not attached to those categories. When using a condition index, results
must be taken in the context of nearby populations. Fatter alligators are not necessarily
living in a better environment. Like with humans, there are upper and lower limits to fat
stores as they relate to condition. It is not minor fluctuations that we are interested in, but
those extremes that could give clues to population and ecosystem health. As an example,
I offer an analysis of SVL/Mass Fulton's K for areas in South Carolina (P. Wilkinson,
unpublished data), north Florida (A. R. Woodward, Florida Fish and Wildlife
Conservation Commission, unpublished data), and the Everglades. South Carolina data is
from Santee captures in 1980 (n = 100). North Florida captures are from Lake Griffin in
1987, 1989, and 1990 (n = 30), Lochloosa Lake in 1985 (n = 57), Newnan's Lake in 1985
(n = 40), Orange Lake in 1985, 1987, 1989, and 1990 (n = 142), and Lake Woodruff in
1987, 1989, and 1990 (n = 33). SVL/Mass was used because HL data were not available.
Lake Griffin would be classified in the high condition quartile (Figure 7) and the
Everglades to be in the low condition quartile. It is not necessarily true that the alligators
in Lake Griffin are in better health than those in the Everglades. If you look at Lake
Griffin in context with other north Florida lakes, it looks like its condition is high and that
perhaps something is wrong with that ecosystem, such as the fact that Lake Griffin is a
highly eutrophic lake from agricultural run off. The recent die-off of adult and subadult
alligators in Lake Griffin (Schoeb et al., 2002) corroborates the fact that Griffin is not a
healthy ecosystem. Everglades population is in lower condition as compared to north
Florida and South Carolina, but if data is analyzed over time for only the Everglades, it
would reveal a pattern of consistent low condition and should not signal a crisis for
Everglades alligators, especially when compared with other long-term Everglades
Condition is a very fluid measurement. Water management practices and rainfall
can dramatically change condition of animals in a relatively short amount of time,
because so many aspects of their life history (feeding, courtship, and nesting) depend on
seasonally fluctuating water levels. In this study, we found that ENP-SS alligators had
the highest condition of Everglades alligators, but Dalrymple (1996) and Barr (1997)
observed Shark Slough alligators to be in very poor condition only five or six years
earlier. Water levels were high during Dalrymple and Barr's period of capture, possibly
affecting food availability.
Hyrdologic conditions are important to the American alligator, but are not the only
factor that affects condition. Disease, climatic change, nutrient input, and contaminants
all contribute to condition and should be considered when using a condition index to
analyze population health. While condition is a useful way to evaluate current
Everglades restoration, it should not be taken out of context of events in the greater
Table 3: Descriptive statistics of indices used in condition analysis for the American
alligator, south Florida (n = 395) with captures from October 1999 to Spring
Index Equation Factors MinimumMaximum Mean Std. Deviation
Ratio Volumetric/Length HL/TG 0.944 1.725 1.447 0.103
SVL/TG 0.288 0.455 0.393 0.025
Fulton's K Volumetric/(Length^3) HL/Mass 2.572 16.389 10.849 1.556
SVL/Mass 1.430 3.200 2.162 0.253
Relative K Volumetric/(Length^b) HL/TG 8.454 15.612 13.118 0.915
HL/Mass 0.470 2.842 1.848 0.281
SVL/TG 2.327 3.719 3.204 0.203
SVL/Mass 1.381 3.089 2.087 0.243
Relative MassObserved mass/Expected mass HL/TG 0.648 1.185 0.992 0.070
SVL/TG 0.727 1.156 0.997 0.064
SVL/Mass 0.656 1.473 0.989 0.115
Residuals Actual value Expected value HL/TG -18.833 6.967 8.314 2.623
HL/Mass -29.728 18.748 3.294 3.685
SVL/TG -11.633 6.506 -2.569 2.393
SVL/Mass -7.744 19.405 -1.476 3.466
HL = Head Length, SVL = Snout-Vent Length, TG = Tail Girth
Table 4: Condition indices calculated for the American alligator in south Florida from
October 1999 to Spring 2002 (n = 395) whose Pearson r-values are less than
Condition Method Factors
Table 5: Results of ANOVA/Least Significant Difference and ANCOVA/ Least Significant Difference analyses of condition indices
for the American alligator in south Florida from October 1999 to Spring 2002 (n = 395). Order of condition index is from
highest to lowest mean.
Condition Index Factors p-value
Ratio HL/TG 0.007
Fulton's K HL/Mass <0.001
Relative K HL/Mass <0.001
RelativeMass SVL/TG 0.027
ANCOVA HL/Mass 0.004
Residuals HL/Mass 0.0164
1 = ENP-Estuaries, 2 = ENP-Shark Slough, 3
# groups LSD
A.R.M Loxahatchee NWR, 4 = WCA2A, 5 = WCA3A-Holiday Park, 6 = WCA3A-Tamiami
Table 6: ANOVA/ANCOVA analyses of condition indices by capture event from October 1999 to Spring 2002 (n = 395). Order of
condition index is from highest to lowest mean. There were no ENP-Est captures for March/April 00.
Factors Capture Event
HL/Mass October 99
HL/Mass October 99
HL/Mass October 99
SVL/Mass October 99
# groups LSD
1 = ENP--Estuaries, 2 = ENP-Shark Slough, 3 = Loxahatchee NWR, 4
1/3,1/6,1/5, 1/4, 2/3, 2/6, 2/5, 2/4, 3/4
2/6, 2/1 2/5, 2/3, 2/4
1/3, 1/6, 1/5, 1/4, 2/3, 2/6, 2/5, 2/4
2/6, 2/1, 2/5, 2/3, 2/4
2/3, 2/6, 2/4, 2/1, 2/5, 3/5, 5/6, 4/5
2/3, 4/3, 6/3, 5/3, 1/2, 1/4, 1/6, 1/5, 2/4, 2/6, 2/5,
WCA2A, 5 = WCA3A-Holiday Park, 6 = WCA3A-Tamiami
Table 7: Ability of ANOVA/LSD analysis to detect differences in condition from
Relative K simulated data. Numbers are the percentage of total populations for
which the analysis found a significant difference. Type 1 is mean and
standard deviation from original data, Type 2 is 5% increase of mean, and Type
3 is 10% increase of mean.
1 2 3
Table 8: Ability of ANOVA/LSD analysis to detect differences in condition from
Fulton's K simulated data. Numbers are the percentage of total populations for
which the analysis found a significant difference. Type 1 is mean and standard
deviation from original data, Type 2 is 5% increase of mean, and Type 3 is 10%
increase of mean.
meas. to HL,
SVL, TG, Mass
total data set
Reduced set of ANOVAs
Ordering of indices
Simulated Choice of index
Figure 1: Flowchart of condition indices analysis for the American alligator in the Everglades.
-i \ ^ WCA2
Figure 2: Capture areas in south Florida within Everglades wetlands core. Black dots
represent alligator captures from October 1999 to March 2002 (n = 395). LOX
= A.R.M. Loxahatchee National Wildlife Refuge, WCA2 = Water Conservation
Area 2, WCA3 = Water Conservation Area 3, ENP = Everglades National Park.
0 20 40 60 80 100 120 140 160
Snout-Vent Length (cm)
Figure 3: Linear regression of tail girth vs. snout-vent length of the American alligator of captures in south Florida from October 1999
to March 2002 (n = 395).
0 1 2 3 4 5 6 7
Figure 4: Range of Fulton's K index values by capture area in south Florida. Captures are from October 1999 to March 2002 (n
395). 1 = ENP-Est 2 = ENP-SS 3 = LOX 4 = WCA2 5 = WCA3-HP 6 = WCA3-T
Mid to Low
Figure 5: Hierarchy of Everglades alligator condition from October 1999 to March 2002
using Fulton's K analysis (n = 395). Brackets signify that condition does not
differ significantly within that group.
1st quartile: 2.57 -
2nd quartile: 4.14 -
3rd quartile: 10.85
4th quartile: 13.62
4.13 low condition
10.84 low to average condition
- 13.61 -average to high condition
- 16.39 high condition
Figure 6: Sample condition range of alligators for Fulton's K from Everglades data from
October 1999 to March 2002 (n = 395). This shows division of condition by
2.4 I I I
N= 30 57 142 100 33 395 40
1 2 3 4 5 6 7
Figure 7: SVL/Mass Fulton's K analysis for the American alligator in areas of South
Carolina, north Florida and the Everglades. 1 = Lake Griffin, FL (1987, 1989,
1990) 2 = Lochloosa Lake, FL (1985) 3 = Orange Lake, FL (1985, 1987, 1989,
1990) 4 = Santee, SC (1980) 5 = Lake Woodruff, FL (1987, 1989, 1990) 7=
Everglades, FL (1999, 2000, 2001, 2002) 8 = Newnan's Lake, FL (1985)
Condition factor can be a useful tool for evaluating the health of a population if
used appropriately. This analysis examined the strict assumptions of properly calculating
condition indices, beginning with body measurements that reduced measurement error.
Head length (HL), snout-vent length (SVL), tail girth (TG), and mass exhibited the least
measurement error and were used in the subsequent condition analysis. Five condition
factors and one model of condition (ANCOVA) were calculated to establish which index
or model could differentiate between Everglades areas. It was determined through
ANOVA/LSD analysis of the condition indices that the HL/Mass combination of
Fulton's K and the SVL/Mass combination of ANCOVA were best able to distinguish
differences in condition between areas of the Everglades. The condition index used
depends on the question asked. For example, ANCOVA is best way to determine
temporal differences between populations. It can not be used to compare across
populations, unless strict assumptions about the two populations can be met. HL/Mass
Fulton's K can be used to spatially compare populations of the American alligator, and is
suggested by this study as the best condition factor to use for that purpose.
In the future, more data needs to be collected to develop a model so that condition
of the American alligator can be predicted by water levels. While other aspects of
condition are important to the overall model, hydrologic conditions are the focus of the
current restoration and a monitoring tool needs to be sensitive to that feature. Also,
before a condition index of the American alligator can used, management goals must be
made to choose the power of the analysis and then determine an appropriate sample size
from that power analysis.
DATA SHEETS AND MEASUREMENT INSTRUCTIONS GIVEN TO EXPERIMENT
Web Tag# Scute Clip # Area
(N/A for most)
Capture Date Capture Time
General Location / Describe Vegetation
Time Blood 1
Cap Method Capture Status (Gross)
Time Blood 2
Head Length SV Length Total Length Hind Foot L
cm cm cm cm
Tail Girth Neck Girth Chest Girth Weight x
cm cm cm kg
Release Status Notes
Description of Measures and Coding
Web Tag # -- Number engraved on tag (GFC 37201).
Scute Clip # -- Animal number derived from scute clipping (used only on LOX).
Area -- Study area Everglades Nat Park Shark Slough (ENP-SS), Estuarine (ENP-EST); Loxahatchee
NWR (LOX); Water Conservation Area 3A South (WCA3AS, also WCA3AN, WCA2); Big
Cypress National Preserve (BCY).
Crew -- First initial and surname of boat crew (K. Rice).
Capture Date -- Date in 1 Oct 99 format.
Capture Time All times are in 24 hr (military) format (0215, 1622).
GPS Location -- UTM coordinates of capture site (Northing 548515, Easting 2891857).
General Location/egetation -- Describe capture site and dominant vegetation (1 mile N of ..Sawgrass).
Water Temp / Air Temp -- Temperature (in C) of Air and Water (- 6" below surface).
Habitat Type -- Specific habitat type:
Open water = 1 Cattail Marsh = 7
Forested Wetlands = 2 Water Lily/Floating Leaved Veg = 8
Shrubs/Shrub Islands 3 Canal 9
Mixed Emergents = 4 Alligator Hole = 10
Sawgrass Marsh =5 Levee Break 11
Spikerush Marsh = 6
Water Depth -- Water depth at capture site in cm.
Muck Depth -- Measure from surface of water to rock and subtract water depth in cm.
Cap Method -- Capture Method (Snare, Hand, Harpoon).
Capture Status -- Animal status at capture (Vigorous).
Time Blood 1 -- Time (24 hr) blood kit 1, Scute? Scute collected (y or n).
Time Blood 2 -- Time (24 hr) blood kit 2 used.
Head Length -- Measure (dorsal) from tip of snout to center of posterior end of skull in cm.
SV Length -- Measure (ventral) from tip of snout to posterior end of vent in cm.
Total Length -- Measure (ventral) from tip of snout to tip of tail in cm. Note if tip of tail missing, etc.
Hind Foot L -- Measure (ventral) from 1st single extended scute posterior to heel to the anterior end of
middle toe, not including the nail in cm.
Tail Girth Measure circumference of tail at 3rd scute row posterior of rear legs in cm.
Neck Girth -- Measure circumference of neck between head and shoulders in cm.
Chest Girth -- Measure circumference of chest just posterior to front legs in cm.
Weight -- Total weight of animal in kg.
Sex -- M or F.
Deformities -- Note any physical deformities or prominent scars (missing LR foot).
Release Status -- Animal status at release (Vigorous).
Notes -- Any additional information (found 1 dead 6' gator).
GENERAL PICTORIAL MORPHOMETRIC MEASUREMENT INSTRUCTIONS
Measure dorsally from tip of snout to center of posterior end of skull. Dashed line
Measure ventrally from tip of snout to back of vent or dorsally to the back of the
second tail scute row. The zero tail scute is the last short tail scute-the last one from the
body that doesn't wrap all the way around the tail.
Silk I ^^--i I ^9 \ ~ Si Sam
Detail of vent for snout-vent length:
*Modified from King and Brazaitis (1971).
Measure straight distance dorsally or ventrally from tip of snout to tip of tail. Note
if tip of tail is missing. Make sure gator and tail are in a straight line, not like this one.
Measure the circumference of tail at 3rd scut ar legs.
; / Zorn tail -,riite
Fir-t tail ite
'Z Roe,_n and tnil -nllte]
Third tail nmit"
Measure circumference of neck between head and shoulders-between two giant
scutes. In this pictures, the scutes look a little farther back than they should.
to front legs.
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Christa Zweig was born in Lexington, Kentucky, on December 22, 1974. Her
family moved to Springfield, Missouri, in 1977 where she attended elementary through
high school. She originally wanted to be a brain surgeon, but soon decided that she did
not like people enough to deal with them on a day-to-day basis. That and her frequent
trips to Cumberland Island National Seashore combined to shape her current career path.
Christa received her undergraduate degree in biology from the University of Richmond,
Virginia. She then spent the next three years working temporary federal jobs for
Canaveral National Seashore, Padre Island National Seashore, and the Red Bluff U.S.
Fish and Wildlife Fisheries Office in northern California. In 2000, she began her work in
south Florida with the Florida Alligator Research Team. Her plans for the future are
vague and varied, but she would like to continue a career in the field of conservation.