Journal of Herpetology, Vol. 38, No. 4, pp. 562–568, 2004
Copyright 2004 Society for the Study of Amphibians and Reptiles
Survival and Recruitment in a Human-Impacted Population of
Ornate Box Turtles, Terrapene ornata, with Recommendations
for Conservation and Management
KENNETH D. BOWEN,1 PAUL L. COLBERT,
AND
FREDRIC J. JANZEN
Department of Ecology, Evolution, and Organismal Biology, 339 Science II,
Iowa State University, Ames, Iowa 50011 USA
ABSTRACT.—Alteration and loss of habitat is a major factor in the recent declines of many turtle populations.
However, there are few studies of turtle populations in areas that are used intensively by humans. We used
temporal symmetry modeling and an information-theoretic approach to model selection to estimate survival
and recruitment in a population of Ornate Box Turtles, Terrapene ornata, in fragmented, isolated habitat over
an eight-year period. Apparent annual survival was high during this period (0.97, SE 5 0.06), as was the
seniority probability (0.95 6 0.04). Recruitment into the adult population (k) was estimated at 1.02 (6 0.06). Our
results suggest a healthy population, but we note several reasons for a cautious management approach. These
include a vulnerability of k to the removal of adults, the need for increased recruitment to offset loss of genetic
diversity, and the uncertainty of our estimates resulting from the sampling and modeling processes.
Habitat loss and fragmentation increase risk of
extirpation for populations of a variety of taxa
(Burkey, 1995; Fahrig, 2002; Hokit and Branch,
2003a). Once a patch of habitat becomes small
and isolated, stochastic and deterministic factors
can lead to a decline in the vital rates of
populations inhabiting that patch (Lande, 1993;
Hokit and Branch, 2003a,b). Even when populations persist, severe declines in vital rates can
result in bottlenecks and a loss of genetic
diversity (Hoelzel, 1999; Kuo and Janzen, 2004).
From a conservation and management perspective, it is, therefore, crucial to assess the demographic and genetic health of populations in
areas where human activity has reduced the
amount of available habitat.
Habitat alteration is implicated as a critical
factor in the widespread decline of turtle populations (Mitchell and Klemens, 2000), but there
are few attempts to rigorously estimate parameters of turtle populations in areas that are used
intensively by humans (Doroff and Keith, 1990;
Kazmaier et al., 2001). Studies of this kind are
needed to guide management decisions given
current rates of habitat destruction and the
conservation status of many turtle species.
Furthermore, there are few studies that use data
from multiple sources (i.e., population ecology
and molecular genetics) to gauge the health of
turtle populations in degraded areas (Rubin et
al., 2001, 2004).
Assessing demographic health of turtle populations begins with estimating adult survival
1
edu
Corresponding Author. E-mail: kbowen@iastate.
and recruitment into adult populations. Growth
rate of turtle populations is most sensitive to
adult survival (Heppell, 1998), and loss of adults
can lead to population declines regardless of
reproductive rates (Congdon et al., 1993, 1994;
Heppell et al., 1996). Although many studies of
turtle demography use mark-recapture models to
estimate population parameters, few authors
consider the assumptions of these models (Lindeman, 1990), and variance (uncertainty) of population estimates is rarely reported (Langtimm et
al., 1996). Furthermore, models such as the
Cormack-Jolly-Seber (CJS) model for open population survival estimates (Cormack, 1964; Jolly,
1965; Seber, 1965) and information-theoretic
approaches to model selection such as Akaike’s
Information Criterion (AIC; Akaike, 1973) are
used infrequently in studies of turtle demography (but see Kazmaier et al., 2001; Fonnesbeck
and Dodd, 2003). These methods are useful
because they allow researchers to construct
models of population processes and estimate
population parameters based on knowledge of
the study organism and study methods. In turn,
researchers can determine what models are most
appropriate for the data, retain the ‘‘best’’ model
or models, and calculate reliable estimates of
uncertainty for parameter estimates (Anderson et
al., 2000; Burnham and Anderson, 2002; Fonnesbeck and Dodd, 2003). In this study, we used
a CJS framework and AIC to estimate annual
adult survivorship and recruitment in a fragmented and isolated population of Ornate Box
Turtles (Terrapene ornata) on the periphery of the
species’ geographic range.
DEMOGRAPHY AND CONSERVATION OF TERRAPENE ORNATA
MATERIALS AND METHODS
Study Area and Field Methods.—Surveys were
conducted on a portion of the Upper Mississippi
River National Fish and Wildlife Refuge in
Carroll and Whiteside Counties, Illinois. The
study site consists of approximately 2750 m of
shoreline that extends along a slough on the
eastern bank of the Mississippi River and spans
approximately 75–175 m inland from the slough
to a bike path and/or boundary fence. The area is
primarily sand-prairie dominated by needlegrass
(Stipa sp.) with interspersed patches of prickly
pear cactus (Opuntia humifusa), skunkbrush (Rhus
aromatica), and Ohio spiderwort (Tradescantia
ohiensis). Trees are continuous along the shoreline
of the slough and are scattered throughout the
disturbed southern portion of the area where
several houses are located. The site is fragmented
into three major sections of sand prairie by roads,
bike paths, fences, and houses. We treat the three
sections as one site in demographic analyses
because we have observed turtles moving among
all of them. Much of the adjoining land is tilled, in
cultivation, or is otherwise unsuitable habitat for
T. ornata (see Kolbe and Janzen, 2002 for further
description of the study area).
We hand-captured turtles through systematic
searches of the study site. Investigators performed surveys in the same manner upon each
visit, walking on parallel transects through the
habitat while scanning for turtles. Searches were
conducted periodically from late-May to earlyJuly during cooler morning periods when turtles
were most active (Legler, 1960; Converse and
Savidge, 2003; Plummer, 2003). Capture effort
varied substantially among years and could be
grouped into four categories based on person
hours spent searching. In 1997, 340 h were spent
searching; in 1996, 287 h, in 1998–1999 from 207–
247 h, and in 2000–2003 search effort ranged from
30–52 person hours in each year.
Upon capture, previously uncaptured turtles
were individually marked by notching the
marginal scutes with a triangular file (Cagle,
1939). Turtles were classified as adults or
juveniles based on the presence/absence of
secondary sex characteristics (Legler, 1960). Sex
of adults was determined according to criteria
used by Legler (1960).
Demographic Analysis.—The CJS model uses
two parameters to model captures and recaptures for open populations: capture probability
(pi ; where i denotes a given time period)
describes the probability that an animal will be
captured at sampling period i; apparent survival
(/i) is the probability that a marked animal
survives and remains in the population until time
i þ 1. We used the temporal symmetry approach
of Pradel (1996) available in Program MARK
563
(White and Burnham, 1999) to estimate pi and /i.
This method is similar to CJS modeling but
allows inference about recruitment through
estimation of an additional seniority parameter
(ci). This new parameter is estimated by considering the capture-history data in reverse time
order, conditioning on the final capture of an
animal and observing its captures in earlier
samples (Williams et al., 2002). Therefore, ci is
the probability that an animal present just before
sampling period i was already present just after
sampling period i 1 (i.e., that an animal is
a survivor of period i rather than a new recruit).
The population growth rate can then be estimated as follows (Williams et al., 2002): ki 5
/i/ciþ1. MARK performs this calculation along
with the associated estimates of uncertainty.
The four CJS model assumptions that must be
met to reduce bias in parameter estimates are that
(1) every animal in the population at the time of
the ith sample has the same capture probability
(pi); (2) every marked animal in the population
immediately after the ith sample survives to the
(i þ 1)th sample with equal probability (/i); (3)
marks are neither lost nor overlooked; and (4) all
samples are instantaneous and releases occur
immediately following the sample (Pollock et al.,
1990). These assumptions hold for reverse-time
modeling as well, but assumption (2) extends to
the seniority parameter as follows: every marked
animal in the population just before the ith
sample was present in the (i 1)th sample with
equal probability (ci) (Williams et al., 2002).
There is a risk of violating assumptions (1) and
(2) whenever capture histories from different
populations or from different segments of the
same population (e.g., across age classes or
sexes), are pooled (Pollock et al., 1990; Lebreton
et al., 1992). Heterogeneity in / and p is thought
to exist between juvenile and adult Terrapene
(Legler, 1960; Iverson, 1991), but no known
studies have had sufficient juvenile captures to
test this assumption. This study also had insufficient juvenile captures to allow separate
parameter estimation; thus, juveniles were excluded to eliminate age-based heterogeneity in /
and p. We tested for sex-based heterogeneity in /,
p, and c by comparing models that either pooled
or grouped sexes (see below).
The potential for violation of assumption (3) is
greatest when less permanent marks are used
(paint marks, tags, etc.), small or obscure marks
are applied, or capture methodology does not
allow close examination of individuals. This
source of bias is unlikely in this study because
of the marking and capture methodologies
employed. Assumption (4) is violated by degrees
in most field studies. Few samples are truly
instantaneous, but it is best if sampling takes
place in a period of negligible length relative to
564
K. D. BOWEN ET AL.
the interval over which survival is estimated
(Lebreton et al., 1992). Even more important is
that the probability of mortality is low over the
sampling interval (Williams et al., 2002). In our
study design, we captured turtles in June of each
year to estimate annual survival and seniority.
Although this ratio of sampling period to
survival period (month to year) is substantial,
T. ornata survival rates are generally high (Doroff
and Keith, 1990; S. J. Converse and J. B. Iverson,
unpubl. data), and the bias induced should be
minimal.
Model notation in the following sections is as
follows: group-specific parameters are denoted
(g), time-specific parameters (t), and a single,
constant parameter (.). Interactions between
effects are denoted by an asterisk, for example,
/(g*t) indicates an interaction between group
and time on survival. Modeling of capture
histories followed the procedure recommended
by Lebreton et al. (1992). First, a global model
allowing time and group-specific (in our case sex
was the group) variation in /, p, and c was
constructed. We then performed a goodness-offit test in Program RELEASE to generate an
estimate of overdispersion of the data (^c)
(Burnham et al., 1987). Overdispersion (extra
binomial variation) signifies that heterogeneity
exists among individuals, a violation of assumptions (1) and (2) that results in underestimation
of sampling variances and covariances (Williams
et al., 2002). TEST 2 and TEST 3 in RELEASE
yielded a combined v2 of 9.31 (df 5 22, P 5
0.992), indicating that overdispersion was not an
issue (^c 5 0.42).
We proceeded by applying constraints to /, p,
and c (i.e., removing time effects, sex effects, or
both) and comparing these reduced models to
their more complicated counterparts to identify
the most parsimonious model (the model with
the fewest parameters that best fit the data). Sex
effects were assessed by comparing models
allowing sex-specific /, p, and c parameters to
models constraining /, p, and c to be equal across
sexes. To determine the relative importance of
time variation in /, p, and c, we compared
models that held these parameters constant
across years to those allowing separate parameters for each year. Because p was expected to vary
largely with sampling effort, and sampling effort
was somewhat categorical, we also constructed
models that captured the nature of this variation
with as few parameters as possible (p(effort) and
p(g*effort) models). We did so by allowing four
parameters for p corresponding to our four
categories of search effort; capture probability
p1 corresponds to the year 1996, p2 corresponds to
the year 1997, p3 corresponds to years 1998 and
1999, and p4 corresponds to years 2000–2003. All
effort-based p models out-competed their year-
specific and constant-p counterparts, suggesting
that search effort was most responsible for
variation in p. Therefore, a reduced set of 32
p(effort) and p(g*effort) models (allowing all
combinations of group and time variation in /
and c) was considered for model selection.
The most appropriate model from the list of
candidates was chosen using an informationtheoretic criterion. This method compares
competing models through optimization of the
likelihood function and addition of a penalty for
the number of parameters used, thereby selecting
for an optimal tradeoff between bias and precision (Burnham and Anderson, 2002). In essence, this method allows the researcher to
choose the model that gives the best fit to the
data with the fewest parameters. Parameter
estimates (/, c, p) from the chosen model are
then used to describe the population. The information-theoretic approach employed by
MARK, AICc, also takes into account small
sample sizes in assessing model fit (Hurvich
and Tsai, 1989), which is a common phenomenon
in capture-recapture studies (Williams et al.,
2002). To determine the most likely model in
our set of candidates, AICc values were compared (lowest being best) as were the Akaike
weights (highest being best) given each model
with respect to others in the set. For parameter
estimates near 1.0 (/ and c), we calculated Profile
95% Confidence Intervals that use a chi-square
distribution (an option in MARK) rather than
a normal distribution (Williams et al., 2002).
RESULTS
Eight years of surveys resulted in 136 total
captures of 84 individuals. Of those captured, 30
(35.7%) were males, 43 (51.2%) were females,
nine (10.7%) were juveniles, and two (2.4%) were
adults of unknown sex (excluded from analysis).
In adults of known sex, the ratio of males to
females was 0.7, and the ratio of juveniles to all
adults was 0.12.
Model selection allocated the most support
(99%; Table 1) to models with p(effort) rather than
p(g*effort), indicating that capture probability is
equal across sexes and varies with search effort.
Many models received no support. We present
only those models that received an Akaike
weight of at least 0.01 (Table 1). A pattern existed
among p(effort) models in which those with
constant seniority performed best (combined
Akaike weight 5 0.67) followed closely by
models with sex-specific seniority (combined
Akaike weight 5 0.25) and then time-specific
seniority (combined Akaike weight 5 0.07; Table
1). This provides some evidence for sex-specific
seniority, although parsimony (fewer parameters) seemed more important for model fit
DEMOGRAPHY AND CONSERVATION OF TERRAPENE ORNATA
565
TABLE 1. Alternative models for estimation of survival (/), recapture (p), and seniority (c) parameters from
Pradel temporal symmetry modeling for adult Terrapene ornata in Carroll and Whiteside Counties, Illinois, 1996–
2003. For each model, (t) indicates that parameters are time (year) specific, (g) indicates parameters are group (sex)
specific, (.) indicates that parameters are constant over time, (effort) indicates classification based on sampling
effort, and (*) indicates an interaction. The most parsimonious model is generally considered the best for
describing the data, and is identified as the model with the lowest AICc score and the highest weight (see text for
more detail).
Model
AICc
Delta
AICc
Weight
Number of
parameters
Deviance
/(.)p(effort)c(.)
/(g)p(effort)c(.)
/(.)p(effort)c(g)
/(g)p(effort)c(g)
/(.)p(effort)c(t)
/(g)p(effort)c(t)
/(.)p(g*effort)c(.)
/(g*t)p(g*effort)c(g*t)
723.34
723.84
725.24
726.01
727.67
728.33
730.30
783.72
0.00
0.50
1.90
2.67
4.33
4.99
6.96
60.38
0.38
0.29
0.15
0.10
0.04
0.03
0.01
0
6
7
7
8
11
12
10
36
444.18
442.50
443.90
442.46
437.33
435.66
442.25
430.92
(Burnham and Anderson, 2002). Within this
framework, constant survival models out-competed sex-specific counterparts, again showing
the importance of parameter reduction for model
fit. As before, the proximity of competing
constant and sex-specific survival models makes
it difficult to rule out gender effects on survival.
Although model /(.)p(effort)c(.) garnered the
most support (38%; Table 1), the proximity of
model /(g)p(effort)c(.)( AIC 5 0.5; Table 1)
makes it impossible to choose between the two
(Burnham and Anderson, 2002). Therefore, sex
potentially affected survival, but reducing the
number of parameters required to model capture
histories provided a better fit.
TABLE 2. Estimates of survival (/), recapture (p), and
seniority (c) parameters from model /(g)p(effort)c(.)
from Pradel temporal symmetry modeling for adult
Terrapene ornata in Carroll and Whiteside Counties,
Illinois, 1996–2003. Estimates of uncertainty and upper
and lower 95% confidence intervals are shown for each.
Recapture probability p1 corresponds to 1996, p2
corresponds to 1997, p3 corresponds to years 1998 and
1999, and p4 corresponds to years 2000–2003. The 95%
CIs for / and c were calculated based on a chi-square
distribution, whereas those for estimates of p were
calculated based on a normal distribution (see text for
more detail).
Parameter
Estimate
SE
Coefficient
of
variation
/ (male)
/ (female)
p1
p2
p3
p4
c
0.90
0.99
0.16
0.36
0.28
0.08
0.95
0.08
0.06
0.04
0.07
0.05
0.03
0.04
0.09
0.06
0.25
0.19
0.18
0.38
0.04
Lower
95%
CI
Upper
95%
CI
0.75
0.87
0.09
0.24
0.19
0.05
0.86
1.00
1.00
0.26
0.49
0.38
0.15
1.00
With such a competitive group of candidate
models, averaging of parameter estimates across
models would normally be warranted (Burnham
and Anderson, 2002). However, this approach is
not feasible with our model set because of the
variable parameterizations of the top models (for
example, it is illogical to average survival
parameters from models with and without group
effects on survival). We chose to interpret data
based solely on the ‘‘best’’ model (/(.)p(effort)c(.))
for three reasons: (1) the lack of a logical method
for averaging among our top models; (2) the
relative superiority of models with constant
survival and constant seniority (Table 1); and (3)
the fact that it was the most parsimonious of the
top models. However, parameter estimates for
model /(g)p(effort)c(.) are also reported (Table 2)
because we cannot rule out the possibility of sexspecific survival. The estimate of survival from
model /(.)p(effort)c(.) was high (0.97, SE 5 0.06;
Table 3), as was the estimate for seniority (0.95,
SE 5 0.04; Table 3). The resulting estimate of
recruitment into the adult population (k) was 1.02
(SE 5 0.06), suggesting a stable population.
Because we did not model average, our estimates
of variance are likely to be underestimates
(Burnham and Anderson, 2002).
DISCUSSION
Our estimate of survival is comparable to those
found in other demographic studies of box
turtles. Using the Jolly-Seber method, Schwartz
et al. (1984) estimated an annual survival of 0.819
for all individuals in a population of Terrapene
carolina triunguis, and Doroff and Keith (1990)
estimated annual survival of adult female T.
ornata at 0.816 (95% CI 0.69–0.94) and survival of
adult males as 0.813 (0.70–0.93) in a degraded
area. S. J. Converse and J. B. Iverson (unpubl.
data) estimated an annual survival of 0.932 (SE 5
0.014) and 0.882 (SE 5 0.022) for female and male
566
K. D. BOWEN ET AL.
T. ornata, respectively, in an undisturbed area.
Our estimate of mean survival for all marked
adults (0.97) is higher than these estimates.
However, the 95% CI of our estimate (0.85–1.0)
suggests that the populations may be more
similar than indicated by a simple comparison of
mean survival. Langtimm et al. (1996) estimated
weekly survival rates of 0.937–1.0 in a population
of Terrapene carolina bauri. These estimates would
appear to be similar to ours, but the lowest
weekly survival rate extrapolated to an annual
survival rate (0.93752) is equal to 0.034.
We estimated annual recruitment into the adult
population at 1.02 (SE 5 0.06). Kazmaier et al.
(2001) estimated population growth rate at 0.981
(SE 5 0.019) for a population of Gopherus
berlandieri, and S. J. Converse and J. B. Iverson
(unpubl. data) estimated an annual recruitment
into the adult population of 1.006 (SE 5 0.065) for
an undisturbed T. ornata population. Thus,
reported growth/recruitment rates from other
populations of terrestrial turtles are similar to
ours.
Estimates of seniority can provide insight into
the effects of changes in survival and recruitment
on population growth rate (Nichols et al., 2000;
Williams et al., 2002). At a c of 0.5, for example,
survival and recruitment are equal contributors
to population growth. As c increases past 0.5,
survival is of increasing importance to population growth rate (with recruitment decreasing in
importance; Williams et al., 2002). Our estimated
seniority probability of 0.95 (SE 5 0.04) over the
eight years of the study suggests that a change in
survival will have a much greater effect on
population growth rate than will a proportional
change in recruitment into the adult population.
The importance of adult survival to population
growth rate found here is consistent with results
from other turtle populations (Heppell, 1998).
The female-biased sex ratio of our population
may be the norm for T. ornata. The relatively
small number of captured juveniles also appears
to be typical. Legler (1960) reported a sex ratio of
61 males to 103 females (30 juveniles) in a Kansas
population. Doroff and Keith (1990) found 39
males and 62 females in their Wisconsin population, Converse et al. (2002) found 41 males, 80
females, and 23 juveniles in a Nebraska population, and Blair (1976) reported that the sex ratio
was biased toward females in a Texas population.
S. J. Converse and J. B. Iverson (unpubl. data)
found that female survival was higher than male
survival in a Nebraska population of T. ornata. If
this finding were common to all populations,
a sex-based difference in survival could explain
the consistent bias in adult sex ratios. However,
this hypothesis is inconsistent with the results of
Doroff and Keith (1990) and this study. It is
possible that differences in capture probability
TABLE 3. Estimates of survival (/), recapture (p), and
seniority (c) parameters from model /(.)p(effort)c(.)
from Pradel temporal symmetry modeling for adult
Terrapene ornata in Carroll and Whiteside Counties,
Illinois, 1996–2003. Estimates of uncertainty and upper
and lower 95% confidence intervals are shown for each.
Recapture probability p1 corresponds to 1996, p2
corresponds to 1997, p3 corresponds to years 1998 and
1999, and p4 corresponds to years 2000–2003. The 95%
CIs for / and c were calculated based on a chi-square
distribution, whereas those for estimates of p were
calculated based on a normal distribution (see text for
more detail).
Parameter
Estimate
SE
Coefficient
of variation
Lower
95% CI
Upper
95% CI
/
p1
p2
p3
p4
c
0.97
0.16
0.36
0.28
0.08
0.95
0.06
0.04
0.07
0.05
0.03
0.04
0.06
0.25
0.19
0.18
0.38
0.04
0.85
0.09
0.24
0.19
0.05
0.86
1.00
0.26
0.49
0.38
0.15
1.00
are responsible for the small number of observed
juveniles. Legler (1960) suggested that juvenile T.
ornata were much more numerous than indicated
by the number captured.
Kuo and Janzen (2004) reported on the genetic
diversity of the same population of T. ornata that
we studied. They found that the level of genetic
diversity was similar to a larger reference
population in Nebraska, despite a bottleneck in
the recent past. Although genetic diversity is
currently high, they calculated that a population
size of 700 would be necessary to maintain 90%
of the allelic diversity over the next 200 years.
Kuo and Janzen (2004) suggest that it will be an
easier task to maintain high levels of genetic
diversity than to increase the level of genetic
diversity once it is depauperate and advocate
active management to increase adult survival
and the number of turtles.
Despite isolation and fragmentation of the
study site, this population appears to be healthy
in both a demographic and a genetic sense. For
several reasons, however, we suggest caution in
the management of this and similar turtle
populations. First, like other populations of
turtles, this population will be vulnerable to the
removal of adults, whether through increases in
mortality, collection for the pet trade (Dodd,
2001), or both. This is evident from a high
estimate of c and an estimate of recruitment that
is close to unity. A small decrease in adult
survival would likely lead to a population decline. In addition, the analysis of Kuo and Janzen
(2004) suggests that an increase in the size of the
population will be necessary to ensure adequate
genetic diversity for the long-term. Management
DEMOGRAPHY AND CONSERVATION OF TERRAPENE ORNATA
initiatives to increase recruitment into the adult
population, whether through more strict protection of the study area or more manipulative
efforts such as transplantation of adults from
other areas (Kuo and Janzen, 2004), may be
warranted. Headstarting will probably be of
limited use considering the high estimate of c
but could be useful if the annual survival rate of
adults is stable and high (Heppell et al., 1996).
Finally, from a methodological standpoint, the
uncertainty associated with our estimates is
a potential cause for concern. The 95% CI of our
survival estimate includes values that are considered low for at least one population of T.
ornata (Doroff and Keith, 1990) and that for our
estimate of population growth rate includes
values that would indicate population decline.
Furthermore, the model selection uncertainty
associated with our analysis means that our
estimates of variation are underestimates (Burnham and Anderson, 2002).
This study is one of only a few that presents
rigorous estimates of survival and recruitment
for an isolated turtle population in fragmented
habitat. The combined use of demographic and
genetic information to assess the conservation
status of turtle populations is also rare. We
advocate more studies of turtle populations in
areas that receive intense human use to understand and combat the current global decline
of many species (Gibbons et al., 2000).
Acknowledgments.—We thank numerous members of the Janzen Lab and Turtle Camp crews
for helping with specimen collection over the
years. Animals were collected under permits
from the Illinois DNR and the U.S. Fish and
Wildlife Service and in accordance with approved COAC protocols from Iowa State University. W. R. Clark, S. J. Converse, J. B. Iverson,
D. L. Otis, and R. J. Spencer provided helpful
comments on early drafts of the manuscript.
KDB acknowledges the support of a Graduate
College Fellowship from Iowa State University.
The fieldwork in this long-term study was
largely supported by National Science Foundation grants DEB-9629529 and DEB-0089680 to
FJJ. This manuscript was completed while FJJ
was a courtesy research associate in the Center
for Ecology and Evolutionary Biology at the
University of Oregon.
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