Biological Conservation 143 (2010) 1951–1959
Contents lists available at ScienceDirect
Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Demographic consequences of adaptive growth and the ramifications
for conservation of long-lived organisms
Ricky-John Spencer a,*, Fredric J. Janzen b
a
b
Native and Pest Animal Unit, School of Natural Sciences, University of Western Sydney, Locked Bag 1797, Penrith South DC NSW 1797, Australia
Ecology, Evolution and Organismal Biology (EEOB), Iowa State University, Ames, IA 50011, USA
a r t i c l e
i n f o
Article history:
Received 15 July 2009
Received in revised form 11 April 2010
Accepted 19 April 2010
Available online 3 June 2010
Keywords:
Turtle
Adaptive growth
Population models
Long-lived organisms
Human impact
Invasive species
Density-dependent selection
Elasticity analyses, Emydura macquarii,
Chrysemys picta
a b s t r a c t
Understanding how organisms respond to human impacts is increasingly challenging biologists. Shortlived organisms can adapt rapidly to changes in environmental hazards, but only recently have long-lived
organisms been shown to adapt to human impacts. Changes in any life-history trait, such as individual
growth rates, may affect demographic model predictions and reliability of elasticity analyses that are
often used to help manage and conserve long-lived organisms. The aim of this study was to test model
predictions of the effect of increased recruitment and density-dependent processes to manage populations of long-lived turtles in two continents. We explored how human-induced changes in juvenile density affect population growth estimates and the strength of selection on stage-based life-history traits.
Model projections undervalued the potential effect of an increase in nest survival. Sensitivity calculations
indicated greatest selection intensities for juvenile growth or maturation, whereas elasticity analyses
indicated that changes in adult survival have the largest proportional effect on population fitness.
Long-term use of the locality of our North American population as a recreational site may have increased
adult mortality of turtles and reduced the number of nest predators, inducing rapid individual growth
and early maturation. The traditional static view of turtle life history and demography thus is inappropriate even over relatively short periods of time. Anthropogenically-induced changes in demographic processes can potentially induce adaptive changes to life-history processes, which can seriously impact
the reliability of long-term projections from common demographic models. Management practices must
account for this dynamism accordingly.
Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction
Ecologists often need to predict how populations will change to
understand selective and demographic pressures and to make management recommendations. Elasticity analyses are generally used
to help set and address conservation goals for populations of
long-lived species (Crouse et al., 1987; Crowder et al., 1994; Heppell et al., 2000). Such analyses have revealed persistent demographic patterns across many taxa (Heppell et al., 2000) and
potentially provide powerful techniques to assess life histories
(Blomberg and Shine, 2000). The compulsory introduction of Turtle
Excluder Devices to commercial trawling nets is a major change to
conservation programs for marine turtles that was primarily based
on elasticity analyses (Crowder et al., 1994). However, the reliability of elasticity analysis as a management tool is questionable
because these analyses are susceptible to large and stochastic
* Corresponding author.
E-mail addresses: ricky.spencer@uws.edu.au (R.-J. Spencer), fjanzen@iastate.edu
(F.J. Janzen).
0006-3207/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.biocon.2010.04.034
changes in transition values and to rapid changes in individual
growth rates (de Kroon et al., 2000).
Density-dependent processes provide resilience and resistance
for populations in a wide range of organisms (Bradshaw et al.,
2006, Brook and Bradshaw, 2006). Density-dependent processes
such as growth, survival, and reproduction are often compensatory
if they change in response to variation in population density. Compensatory density dependence is especially important for depleted
fisheries populations because it offsets losses of individuals and allows populations to remain viable (Lorenzen and Enberg, 2002;
Minto et al., 2008). Long-lived species are typically viewed as having slow life histories (slow growth, delayed maturity and high survival), and resilience or resistance to major perturbations is
primarily reliant on adult survival and reproduction, which open
populations to exploitation (Musick, 1999). However, some longlived vertebrates compensate for increases in mortality through increased survival or fecundity, as well as though changes in growth
and maturity (Fowler, 1987; Spencer et al., 2006; Fordham et al.,
2009). Often these processes, even in long-lived organisms occur
rapidly and rapid and stochastic changes in demographic parame-
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R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
ters, such as rates survival and mortality, are increasingly associated with human-induced alterations. Under intensive anthropogenic pressures, density-dependent population processes may
not be mutually exclusive and may interact to provide an environment that is conducive to demographic compensation, adaptive
plasticity, and life-history evolution of traits like individual growth
rates. For example, many commercial fishery populations have
seen extreme responses to overharvesting of adult populations
whereby rapid changes in juvenile growth have reduced ages at
maturity and average adult body size (Lorenzen and Enberg,
2002; Hutchings, 2005). Enhanced growth rates exemplify the consequence of selection for early maturation in environments where
mortality is high and variable (Bronikowski and Arnold, 1999).
Although population models are well suited to evaluate density-dependent factors such as compensatory mortality, they are
limited at the interface where demography influences adaptive
and life-history processes. In particular, altered juvenile densities
through conservation programs or habitat modifications may unintentionally result from compensatory changes in demography.
Ensuing density-dependent selection then contributes considerably to compensatory adjustments of mortality in other life-history
stages, minimizing potential changes in population growth rates
(e.g. Fordham et al., 2009). To illustrate, Spencer et al. (2006)
experimentally simulated enhanced offspring survival of an Australian freshwater turtle, a common management practice for
imperiled turtle populations (Klemens, 2000). In this large-scale
experiment, juvenile growth of a turtle responded quickly to
changes in population density and was complemented by a positive genetic correlation between juvenile growth and body size,
suggesting that long-lived organisms may possess two means to
maintain population viability in response to substantial changes
to population structure (Spencer et al., 2006). However, management programs for long-lived organisms, such as turtles, have historically focused on increasing juvenile recruitment, despite the
effectiveness of these programs has been questioned from both
demographic (e.g. Heppell et al., 1996) and evolutionary (e.g.
Heath et al., 2003) perspectives.
How valid are these concerns? If juvenile growth is submaximal
and density dependent, management and conservation plans based
primarily on matrix model projections and elasticity analyses may
be unreliable because large increases in juvenile densities could affect both individual growth and age at maturity and may lead to
compensatory changes in life histories through phenotypic plasticity or evolutionary adaptation. Thus model projections to guide
conservation efforts may not satisfactorily reflect management
outcomes. Resolving this issue clearly has important implications
for management practices. We address this problem by analyzing
demographic and life-history data for phylogenetically divergent
turtle populations with differing levels of human impact. We also
employ complementary simulation-based models to assess the affect of changes in juvenile growth and survival schedules on
demography and life history of populations of long-lived turtles.
Murray River turtles (Emydura macquarii; Suborder Pleurodira)
have been under intense (nest and adult) mortality pressures since
foxes (Vulpes vulpes) have been introduce to Australia (Thompson,
1983; Spencer and Thompson, 2005). Fox exclusion fences have
been erected. Extensive management efforts, such as exclusion
fences and intensive fox control techniques, have been implemented around several E. macquarii populations in an attempt to
mitigate these negative impacts and increase juvenile recruitment.
We first create three models to empirically test demographic model projections and to explore how changes in juvenile growth of E.
macquarii (Spencer et al., 2006) affect population growth estimates. We then conduct sensitivity and elasticity analyses for the
deterministic model and perturbation analyses on all three models
to assess the relative importance of different life-history stages to
population growth under different juvenile growth patterns (Caswell, 2001). We next capitalize on a comparative life-history data
set for painted turtles (Chrysemys picta; Suborder Cryptodira) to
test empirically and with simulations whether changes in stagespecific survival caused by human activity has resulted in a wild
turtle population characteristically similar to a population of E.
macquarii with experimentally enhanced juvenile density. Our
comprehensive assessment of the potential demographic and
life-history impact of management practices and long-term human
activity through experimental and comparative methods has
important consequences for conservation of populations of longlived organisms.
2. Methods
2.1. Model systems
Three populations of E. macquarii on the Murray River in Australia have been studied since 1996. Over 90% of turtle nests are destroyed by foxes (Thompson, 1983; Spencer, 2002a) and these
populations have been part of a large project investigating the full
impact of foxes on turtle demography and behavior (Spencer,
2002a,b; Spencer and Thompson, 2005; Spencer et al., 2006). E.
macquarii is an omnivorous turtle that inhabits river backwaters
with abundant aquatic plants and is heavily reliant on adult turtles
for population stability (Spencer and Thompson, 2005). The juvenile population is small and even minor reductions in nest predation rates can potentially increase recruitment significantly
(Spencer et al., 2006).
Painted turtles (C. picta) are recognized as a polytypic species
distributed across North America (Starkey et al., 2003). These turtles inhabit ponds and marshes, the margins of small lakes, and river backwaters with abundant aquatic plants. Aspects of the
evolutionary ecology of C. picta have been studied in a population
at a recreational area of the Mississippi River in northern Illinois
for the past 20 years (Janzen, 1994; Janzen and Morjan, 2001).
2.2. Australia pleurodire study
2.2.1. Study sites and experimental design
We used a Before-After-Control-Impact (Underwood, 1997)
experimental program to determine the impact of foxes on population dynamics of E. macquarii (see Spencer and Thompson,
2005). Foxes were removed from around two lagoons (fox removal
sites = Snowdon’s and Hawksview) after the first nesting season,
whereas foxes were continually monitored around another lagoon
(control site = Bankview) (Spencer, 2002a). We monitored nest
predation rates around each lagoon and conducted a capturemark-recapture program of each turtle population to determine
stage-specific life-history traits (growth, fecundity and survival)
(see Spencer, 2002b; Spencer and Thompson, 2005).
2.2.2. Demographic modeling
The experimental design allowed us to project and empirically
test the outcome of potential management plans in a fully controlled and replicated fashion in the field. Data on adult survival,
growth and fecundity were determined from Spencer and Thompson (2005), and Spencer et al. (2006) estimated juvenile growth
and survival in each population. Annual nest predation rates for
each population were obtained from Spencer (2002a) and Spencer
and Thompson (2005). Egg and hatchling growth is based on mean
nest predation rates under high (0.9) and low (0.5) nest predation
conditions, as well as a mean annual rate of 0.45 for hatchling survival (Spencer et al., 2006). Growth of E. macquarii is well described
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R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
by von Bertalanffy growth equations (Spencer, 2002b; Spencer
et al., 2006)
PL2 ¼ a—ða—PL1 ÞekðdtÞ ;
where PL = plastron length, a = asymptotic size, and k = growth
coefficient. Spencer et al., 2006 estimated k under high and low nest
predation conditions (low vs. high recruitment, respectively), hence
age at maturity (i.e. probability 50% of cohort are mature at a particular size) under these different conditions is 12 year and 8 year,
respectively (Fig. 1).
We applied the outcomes for juvenile growth and survival under different densities to model plasticity of age at maturity under
different environmental conditions. We simulated growth and survival of 5000 hatchlings entering high growth/density and low
growth/density populations (see Spencer et al., 2006), generating
random PL between 24 and 35 mm for each hatchling. The proportion of females that are mature for a particular PL was determined
from Spencer (2002b) and unpublished data. Annual cohort size
and individual survival in the high-density population was derived
from the equation
Annual survival ¼ 0:043eð0:074Xhatchling plastron lengthÞ
Survival was independent of body size in the low-density population (0.55; Spencer et al., 2006). The relevant survival probability was applied to an individual for the duration of the
juvenile stage based on validation for the first 5 years of life (Spencer et al., 2006).
We modeled PL growth for a particular hatchling size using the
growth-interval equation of the von Bertalanffy model (see above).
We assigned a = 215 and k = 0.2 for the high-density population, but
used k = 0.14 for the low-density population (see Spencer et al.,
2006). We estimated annual average cohort PL by incorporating
Plastron length (mm)
200
P = 1.0
P = 0.87
P = 0.48
P = 0.13
P=0
190
180
170
160
150
140
130
5
7
9
11
13
15
17
Age (Years)
Fig. 1. Average PL of two experimentally manipulated populations of the turtle, E.
macquarii. The solid black line is the projected average growth curve (von
Bertalanffy) of hatchlings subjected to high juvenile densities and the dashed black
line is the projected growth curve of hatchlings in low-density populations. The
relative proportion of females that is mature for a particular PL is also shown,
assuming that size at maturity (175 mm PL, Spencer, 2002b) is constant under both
scenarios.
population structure (determined from initial population size and
size-dependent survival probabilities) and the growth equation to
determine the probability of maturing at a particular age. We then
compared the differences in the average (or proportional) age at
maturity between high and low juvenile density populations.
We derived square-transition matrices that contained survival,
growth and fecundity values to evaluate changes in population
growth and age at maturity. Three distinct stages were chosen:
egg/hatchling (EH), juvenile (J), and adult (A). The stage-based entries occur on the diagonal (Pi the probability of surviving and
remaining in a stage), on the subdiagonal (Gi the probability of surviving and growing to the next stage), and on the top row (Fi fecundity) of the matrix. We assumed that all individuals within a stage
were identical and that a fixed proportion of individuals grew into
the next stage each year. Moreover, while rapid juvenile growth
may impact future reproduction and survival, for simplicity we assumed that fecundity was constant for each scenario.
We derived matrices to simulate: (1) current values based on
the control site, with high nest predation, delayed maturity and
low adult survival; (2) a predictive management model of the effect of reducing red fox numbers, with transitional values based
on low nest predation, delayed maturity and high adult survival;
and (3) the actual effect of reducing foxes, with transitional values
based on low nest predation, rapid density-dependent juvenile
growth and survival, early maturation and high adult survival.
We used PopTools (Hood, 2003) to determine elasticity and sensitivity values of each matrix parameter and the intrinsic rate of increase, r, under each scenario.
2.3. North America cryptodire study
2.3.1. Growth and survival
We monitored a population of C. picta from 1995 to 2002 at the
Thomson Causeway Recreation Area (TCRA) in the Mississippi River between Illinois and Iowa (see Janzen, 1994). More than 10,000
people and 5000 recreational vehicles visit the TCRA from April to
November each year, including numerous fishermen both from
land and powerboat. We compared life history and demographic
traits of this population of C. picta to two relatively undisturbed
populations of similar latitude (Table 1). The Michigan population
at the E.S. George Reserve has been fenced in since 1930 (Congdon
et al., 2003). The Nebraska population is in the Nebraska Sandhills
at the remote Crescent Lake National Wildlife Refuge (Iverson and
Smith, 1993).
We patrolled the nesting area at the TCRA hourly between sunrise and sunset from mid-May to the beginning of July each year.
Females were hand captured after nesting and nests were then
excavated to count eggs. Methods for measuring and marking turtles were as described above for E. macquarii (Spencer and Thompson, 2005).
We also followed neonates and juveniles in the TCRA population. Aquatic trapping occurred seasonally, with C. picta predominantly captured in baited hoop or lobster traps. Hatchlings were
toe-clipped uniquely (Spencer, 2002b), weighed, and measured
Table 1
Variation in growth, survival and reproductive traits in painted turtles (Chrysemys picta). PL at maturity (plastron length at maturity (mm)), maximum PL (plastron length of the
largest individuals of the population (mm)), K (von Bertalanffy growth constant), Proportion body size at maturation (PL at maturity/Maximum PL), Annual survival (mean annual
survival rate of females), Nest predation (mean annual nest predation rate). Data for the Nebraska and Michigan populations were collated from 1: Iverson and Smith (1993). 2:
Shine and Iverson (1995). 3: J. Iverson pers com. 5. Wilbur (1975). 6. Congdon et al. (2003). Data highlighted in bold accentuate key differences between Illinois (=TCRA) and the
other two populations.
Location
Latitude
Age: maturity
PL: maturity
Maximum PL
K
Maturity PL/max PL
Annual adult survival rate
Annual nest predation rate
Nebraska1,2,3
Michigan1,2,5,6
Illinois
42
43
41
7
8
5
149
128
130
197
165
180
0.19
0.15
0.36
0.76
0.77
0.72
0.92
0.92
0.83
0.70–0.9
0.93
0.49
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R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
(PL) before release in May 2003. We tested the growth pattern of C.
picta against the von Bertalanffy growth model (Spencer, 2002b;
Spencer et al., 2006). Growth data of turtles captured one or more
trapping seasons apart were included. We used only measurements from the first time period and randomly chose a second time
period if turtles were captured multiple times throughout the
study.
The data set comprised capture-mark-recapture (CMR) profiles
over eight trapping periods (year). Survival (/) and capture (p)
probabilities and population growth rate (k) were estimated and
modeled in program Mark (White and Burnham, 1999), following
CMR methodology (Lebreton et al., 1992; Pradel, 1996). To select
the most appropriate model for describing demographic temporal
variation, we used a bias-corrected version of the Akaike’s Information Criterion, AICc (Burnham and Anderson, 1998). We tested for
overdispersion and adjusted the AICc value (QAICc) using an estimate of the variance inflation factor (i.e., ĉ) (Anderson et al.,
1994). The model with the lowest QAICc value represents the best
choice to describe temporal variation in a given demographic rate.
Annual survival rates have been estimated for the two relatively
undisturbed populations in previous studies (see Shine and
Iverson, 1995).
2.3.2. Demographic modeling
We derived a three-stage square-transition matrix, similar to
that of E. macquarii with survival, growth and fecundity values to
evaluate changes in population growth and age at maturity. All
parameter calculations were based on field observations of recaptured animals.
We created a ‘typical freshwater turtle’ model population for
our initial simulations based on annual survival and growth values
primarily derived from parameters in Table 1. We used an adult
annual survival rate (PA) of 0.90; a juvenile survival rate (Pj + Gj)
of 0.55 and age at maturity of 8 years. The egg growth (GE), nest
survival, component was 0.2 and we assumed a fecundity value
of 11 (FA). C. picta at TCRA produce an average of two annual
clutches, each with a mean of 11 eggs. Although the sex of C. picta
is determined by incubation temperature, we assumed equal sex
ratios (the mean annual sex ratio of all nests produced at TCRA between 1988 and 2002 is 0.59 ± 0.27 S.D. male (Janzen, 1994; Schwanz et al., in press).
We ran a deterministic model of 500 time steps to determine
mean population size of egg, juvenile, and adult stages based on
an initial population of 300 adults. We then applied densitydependent functions to the PA and Pj + Gj rates that realistically reflect stage-specific population sizes at TCRA. We allowed PA to fluctuate between 0.83 and 0.95 using:
1=PAðtþ1Þ ¼ a=ð1 þ expððNAðtÞx0Þ=b Þ;
where PA(t + 1) is the probability of an adult surviving at t + 1, NA(t) is the adult
population size at time t, a = 1.25, x0 = 1704, and b = 1072. This
equation and values maintains adult survival between a minimum
value of 0.83 and a maximum value of 0.95, however survival is
density dependent between the adult population sizes of 300 and
1000. We applied a similar density-dependent function, with independent values to Pj + Gj, such that the value could fluctuate from
0.35 to 0.65 between juvenile population sizes (Nj) of 1000–3000,
respectively. Both below and above those population size limits,
juvenile survival remained constant. Juvenile survival (Pj + Gj) was
determined by the function:
1=PJðtþ1Þ ¼ a=ð1 þ expððNJðtÞx0Þ=bÞ ;
where PJ(t+1) is the probability of a juvenile surviving at t + 1, NJ(t) is
the juvenile population size at time t, a = 5.04, x0 = 2910, and
b = 2979.
We also included a function to simulate potential densitydependent selection on age at maturity. We linked age at maturity
to density-dependent changes in juvenile survival, which changed
the ratio between Pj and Gj for the next time step in the simulation.
We assumed that changes in age at maturity between 0.45 and
0.65 (Pj) were determined by the linear equation:
Mc ¼ 1:35 ðPj Þ 0:73;
where Mc = change in age at maturity (years) and Pj = juvenile survival rate. We also applied an environmental stochasticity value
of 0.15 to nest survival to simulate changes in annual nest predation
in the Nebraska population (GE = 0.2 in Table 1). We ran the model
over 500 years and determined the finite rate of population growth
(k), elasticity values of each matrix parameter, stable stage distribution, and reproductive values of each stage. Population growth (k) is
related to the intrinsic rate of increase, r, where r = ln (k). The elasticity of a matrix parameter is the proportional change in k following an increase or decrease in that parameter. Elasticities can be
interpreted as proportional contributions of each matrix parameter
to k (de Kroon et al., 1986). The proportional sensitivity analysis
uses the stable stage distribution given by the right eigenvector of
the matrix, and the stage-specific reproductive values given by
the left eigenvector of the matrix with the first-stage (hatchlings)
reproductive value set at 1.0. Reproductive values estimate the expected reproductive contribution of each stage to population
growth (Crouse et al., 1987).
To simulate human impact, we applied survival values for the
TCRA population (Table 1). Population sizes were based on egg,
juvenile, and adult estimates at t = 500. Nest survival (GE) averaged
0.51 and the environmental stochasticity value was 0.30. Adult
survival (PA) remained density dependent but an environmental
stochasticity value of 0.10 was applied to simulate variability in
adult survival. We ran the model over 500 additional years to assess changes in k, elasticity values of each matrix parameter, stable
stage distribution, and reproductive values of each stage. We used
the program ULM for all demographic modeling (Legendre and Clobert, 1995).
3. Results
3.1. Australia: Pleurodira: E. macquarii
3.1.1. Modeling and elasticity
A summary of transitional stage values for survival, growth and
fecundity of E. macquarii is shown in Table 2. Under current high
nest predation conditions, juvenile recruitment is low and the
von Bertalanffy growth constant (k) was estimated at 0.13,
Table 2
Transitional stage parameters for the three demographic models used for E. macquarii.
The top matrix shows growth, survival, and fecundity values under current
conditions. The middle matrix shows values under predictive management conditions. The bottom matrix shows actual values incorporating changes in densitydependent growth and age at maturity. EH, J and A are the egg/hatchling, juvenile and
adult stages, respectively.
EH
J
A
EH
J
A
0
0.225
0
0
0.694
0.006
12
0
0.977
EH
J
A
0
0.045
0
0
0.694
0.006
12
0
0.943
EH
J
A
0
0.225
0
0
0.673
0.027
12
0
0.977
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R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
whereas k was estimated at 0.20 in the low nest predation environment (Spencer et al., 2006).
At current levels of survival, fecundity, and growth, the intrinsic
rate of increase (r) of populations of E. macquarii under high predation pressure from foxes is 0.045. The majority of the population
consists of eggs, with adults and juveniles making up only 20%. The
reproductive value for adult females was 0.98, the elasticity value
for adult survival was 0.93, and the generation time for the population was 37.5 years. In contrast, the projected outcome of increased juvenile density after fox removal significantly increased
r to +0.024, with very little change in generation time (34.0 years).
However, including density-dependent juvenile growth and a
reduction in the age at maturity dramatically affects both the population growth rate and dynamics of the population in ways not
predicted by initial projections of a reduction in fox numbers. In
this case, r was predicted to be +0.11 and the generation time
was almost half that of the current population, 19.8. For all models,
r is more sensitive to changes in adult survival than to growth or
fecundity.
Under current conditions, E. macquarii solely relies on adult survival for population stability. Adult survival elasticity was predicted to decrease from 0.93 to 0.80 after reducing fox numbers
and increasing juvenile densities, but our projections failed to account for the true reduction in adult survival elasticity value
(0.60–0.70) once density-dependent growth and reduced age at
maturity were included in the model (Fig. 2a). Fecundity elasticity
values were low, thus population dynamics are more strongly driven by adult survival rates than by fecundity or survival rates of
hatchlings or juveniles. Populations were most sensitive to changes
in juvenile growth in each model (Fig. 2b), particularly under the
predictive management scenario, where the sensitivity value was
6.7, compared to values less than 4.5 in the other models.
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
The trapping program during 2003 and 2004 captured 320 C.
picta of which 164 turtles were either female or juvenile. Turtles
considered 2 or 3 years of age were captured in large proportions
(n = 66 or 21%). CMR profiles from 421 females captured between
1995 and 2002 were developed to determine survival and growth
of C. picta. Painted turtles grew rapidly and matured early at the
TCRA. Growth was well modeled by the von Bertalanffy equation
(MSE = 23.87 RMSE = 4.89). Growth was estimated to asymptote
at 161 mm PL (±0.63), and k was high at 0.36 (±0.01). The ten
smallest mature females were between 130 and 136 mm PL, which
is predicted to equate to 5 years of age (including a year of no
growth while overwintering as neonates in the nests) based on
the growth model.
QAICc values indicated that the best model for the TCRA nesting
population was /(t) p(t) k(t); survival and capture probabilities and
population growth rate all varied over time. Survival rate of the
nesting population averaged 0.83 over the study period, but fluctuated between 0.72 and 1.0 over the 8 years (Fig. 4). Although capture probabilities also fluctuated over time, they remained above
0.5 (0.51–0.79 (0.40–0.91 C.I.)) throughout the study. Annual estimates of k fluctuated between 0.82 and 1.38 (0.67–1.60 C.I.), but
averaged 1.03 (0.98–1.08 C.I.). Recruitment into the nesting population was also variable, fluctuating between 0 and 0.38 (0–0.60
C.I) during the study. Nest predation rates varied, but averaged
0.49 (±0.27 S.D.) (Fig. 4). There was a negative linear relationship
between nest and adult survival over the study time period
(R2 = 0.41; Fig. 4).
Overall, the TCRA population most strikingly differs from other
well-studied populations of C. picta by exhibiting an earlier age at
60
Survival
Growth
Fecundity
8
Sensitivity Value
3.2. North America: Cryptodira: C. picta
7
6
5
4
3
2
Proportion of Population
Elasticity Value
3.1.2. Age at maturity and selection
Maturity of the female population takes place between 170 and
190 mm, with almost 50% of individuals mature at 180 mm. Our
projections indicate that 50% of female E. macquarii in the highdensity population mature during their 8th year and that 100% of
a female cohort are mature by age 10. However, in the low-density
population, the majority of a cohort matures between 12 and
14 years (Fig. 1).
At maturity, the two largest hatchling size classes comprised
over 80% of a cohort in the high-density population. Despite a relatively even spread of size classes for our initial simulated population (Fig. 3), the impact of hatchling size-dependent survival
becomes magnified by the age of maturity, with few or no smaller
hatchlings in the initial cohort reaching maturity.
50
40
30
20
10
1
0
Survival
Growth
Fecundity
Fig. 2. (a) Elasticity values of survival, growth and fecundity for E. macquarii under
current, predicted and actual management conditions. First bar represents values
when foxes are present. Second bar represents values when foxes are absent with
late maturation. Third bar represents values when foxes are absent (Treatment)
with early maturation. (b) Sensitivity values of survival, growth and fecundity for E.
macquarii under current, predicted and actual management conditions. Open bars
represent adult transitional stage values. Shaded bars represent juvenile transitional stage values and closed bars represent EH transitional stage values.
0
24
25
26
27
28
29
30
31
32
33
34
35
PL at Hatching (mm)
Fig. 3. Survival in the high-density population of E. macquarii. The proportion of
individuals of different hatchling sizes that survived at 0 (black), 5 (grey) and
10 years after entering an initial randomly generated population of 5000 turtles.
Survival probabilities are related to hatchling PL (see Spencer et al., 2006). (For
interpretation of the references to colour in this figure legend, the reader is referred
to the web version of this article.).
1956
R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
tion (Fig. 6). Females continued to grow rapidly in the year after
their first reproductive event (8.3 ± 0.6 mm), but growth rates remained less than 1.5 mm after the second year of reproduction.
0.9
Nest Survival Rate
0.8
0.7
0.6
3.2.2. Modeling and simulations
Under high adult survival and nest mortality conditions, population growth of C. picta over the first 500 years was positive
(k = 1.002, Fig. 5). Over 68% of the population consisted of eggs,
25% were juveniles and only 6% were adults. Adults contributed
95% of the reproductive value of the population; consequently
the elasticity value for adult survival (PA) was over 0.7 and no other
transitional stage had a value over 0.1 (Table 3).
The dynamics and composition of the population changed relatively quickly upon simulating human impacts (Fig. 5). The juvenile population trebled within 10 years and the stable stage
distribution consisted of similar numbers of eggs and juveniles
and only 4% adults (Fig. 5). Under this scenario, the population
grew 1.68% per year (k = 1.017) for the first 500 years, and k remained at 1.00 between years 600 and 1000. Eggs and juveniles
had similar reproductive values, and the reproductive value of
adults remained at 0.95 after human impact, but the elasticity value for adult survival decreased to less than 0.6 (Table 3).
Age at maturity only decreased from 8 years to 7 years over the
first 500 years under ‘typical’ freshwater turtle conditions, with
most of the decrease occurring within the first 100 years as the
newly established population reached a stable distribution. However, after simulating human impact with a rapid and large increase in the juvenile population, this strong density-dependent
selection reduces age at maturity by 1.5–2 years within 30 years,
similar to our results with E. macquarii (Fig. 1).
0.5
0.4
0.3
0.2
0.1
0.55
0
0.65
0.75
0.85
0.95
Adult Survival Rate
Fig. 4. Relationship between apparent survival of adult females (±C.I.) and nests of
the C. picta population between 1994 and 2001 at the TCRA (R2 = 0.41).
maturity, a much higher von Bertalanffy growth constant, and lower and more variable annual adult survival and nest predation rates
(Table 1).
3.2.1. Reproduction
There was little indication of females missing reproduction in a
given year. Of females recaptured on at least two occasions, 87%
produced at least one clutch in a year following a reproductive
event. Clutch size averaged 10.7 (±2.3 S.E) eggs and mean wet
egg mass was 6.6 g (±0.6 g S.E.); these traits did not vary or covary
significantly across years (p > 0.1 for all years). There was generally
a significant positive relationship between mean annual egg and
body size and clutch and body size (p < 0.001, R2 = 0.09–0.28) except in 2000, however, R2 values were less than 0.4. The positive
association between mean annual egg size and body size was generally stronger than the relationship between clutch and body size
(p < 0.001, R2 = 0.16–0.44) except in 1999.
Annual egg production (1st and 2nd clutch) remained at
approximately 20 eggs regardless of reproductive age. However,
average egg mass increased from 5 g to 7 g over the first 8 years
of reproduction, a 40% increase in resources devoted to reproduc-
4. Discussion
Understanding how organisms respond to human impacts is one
of the greatest challenges facing biologists and resource managers
today because burgeoning human populations coupled with
destructive environmental activities are currently the world’s strongest evolutionary force (Palumbi, 2001; Darimont et al., 2009).
Whether the life histories of long-lived organisms can adapt to hu-
1000
900
3500
800
3000
700
2500
600
500
2000
400
1500
300
1000
Adult Population Size (n)
Egg and Juvenile Population Size (n)
4000
200
500
100
0
0
100
200
300
400
500
600
700
800
900
0
1000
Time (years)
Fig. 5. Simulated size of adult (solid), juvenile (dashed), and egg (grey) populations over 1000 years for C. picta. The first 500 years were under relatively high and constant
adult survival and nest predation, while the second 500 were simulated under conditions of human impact at the TCRA.
R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
20
8
15
7
10
6
5
5
4
Annual egg production
Average Egg Mass (g)
25
9
0
year 1
year 2
year 3
year 5
year 8
Reproductive year
Fig. 6. Average egg mass (bars) and total egg reproduction (line) of female C. picta
between their 1st–8th reproductive years at the TCRA.
Table 3
Elasticity values of survival, growth and fecundity in the TCRA population of C. picta
for the initial 500 time runs (typical) and after we simulated human impact (human
impact).
Egg
Juvenile
Adult
Growth
Typical
Human impact
0.06
0.10
0.07
0.11
0
0
Survival
Typical
Human impact
0
0
0.09
0.10
0.72
0.59
Reproduction
Typical
Human impact
0
0
0
0.06
0.10
man-induced changes in demography is poorly understood. Yet conservation planning cannot await ‘‘perfect” data, so managers have
relied heavily on tools like elasticity analyses for guidance or have
simply implemented programs to address concerns about low juvenile recruitment. Our quantitative analyses of a manipulative field
experiment and an extensive comparative data set involving turtles
provide at least two important insights into the population biology
of imperiled long-lived organisms. First, elasticity analyses alone
were inadequate to identify all key life stages to target in an effective
management strategy. Second, manipulating turtle populations,
through habitat modification and increased urbanization or through
pre-meditated conservation actions, can lead to adaptive and potentially evolved changes in life-history traits.
Many studies have addressed the likely causes of variation in
life-history traits (e.g., Reznick et al., 1996; Bronikowski, 2000; reviewed in Roff, 1992 and Stearns, 1992), however far fewer studies
have characterized the effects of variable life histories on population-level metrics such as population growth rate and the impact
on fitness of changes in population life histories (e.g. Heppell,
1998; Bronikowski et al., 2002). Such studies are increasingly critical. Elevated levels of human activity are a major source of selection on life histories, causing contemporary evolution (e.g. Heath
et al., 2003; Phillips and Shine, 2004), and fundamental to population ecology is determining how variable life histories affect population demographics (Caswell, 2001), particularly with respect to
conservation biology (Wisdom et al., 2000). The apparent phenotypic plasticity in growth in E. macquarii and a strong quantitative
genetic basis for this trait (Spencer et al., 2006) indicates an
impressive immediate capacity to acclimatize plastically to major
demographic perturbations and a longer-term potential to evolve
adaptively. This latter inference becomes clear, as a cohort in
high-density populations attains maturity with over 80% of the
cohort consisting of individuals from the two largest hatchling size
classes (Fig. 3). Given that hatchling size (Janzen, 1993) and growth
1957
(see above) are heritable, populations could evolve rapidly under
strong selection. These findings thus suggest that long-lived organisms may possess two means for responding to major changes in
population structure to maintain population viability.
Little is known about factors that limit or regulate turtle population dynamics. Understanding the population ecology of turtles,
despite their global imperilment, therefore lags well behind that
of other taxa. We show that knowing how potentially densitydependent processes affect population dynamics of long-lived
organisms, particularly in the juvenile stage, is vitally important
for developing relevant, accurate management strategies (see also
Fordham et al., 2009 and references therein). Indeed, the predictive
model for managing E. macquarii was poorly supported by our data,
primarily because juvenile growth responds rapidly to changes in
turtle densities. de Kroon et al. (2000) identified three areas where
pitfalls may arise when elasticity helps direct population management. Importantly, the predicted effects of management efforts
may be incorrect if transition values and individual growth rates
change substantially. The predictive model that we tested essentially undervalues an increase in nest or egg survival, a stage that
is often targeted in turtle conservation but always predicted to
minimally impact population growth (e.g. Crowder et al., 1994).
The sensitivity of the rate of increase of a population to changes
in vital rates yields direct estimates of the intensity and direction of
selection (Caswell, 2001; Bronikowski et al., 2002). Based on our
sensitivity calculations for E. macquarii, the greatest selection intensities under all scenarios were for juvenile growth or maturation
(Fig. 2). Notably, the sensitivity of juvenile growth is maximal under
our predictive management scenario, where recruitment is high but
age at maturity is delayed. Indeed, the immediate response of juvenile E. macquarii under high-density conditions in our field experiment supports the prediction that more rapid growth in juveniles
should be favored because it results in earlier maturity (Reznick,
1982). Why, then, do juveniles in lower density populations grow
slowly despite strong selection to mature earlier? Both theoretical
and empirical studies of adaptive growth imply that high juvenile
growth rates likely carry fitness costs, such that individuals should
grow more slowly in certain circumstances (Arendt, 1997; Nylin
and Gotthard, 1998). Consequently, much of the observed variation
in growth rate may derive from adaptive balancing of costs and
benefits associated with growth, which may result in different optima in different environments (Gotthard, 2001). The evolution of
submaximal growth rates, particularly in environments conducive
to rapid growth, suggests the existence of trade-offs with other fitness-related traits, such as developmental, behavioral, or physiological traits. This scenario is particularly magnified in turtles,
given their long generation times and high fecundity, because any
impact on longevity has major consequences for fitness.
Ecological and life-history studies often assume that growth
rates are maximized so that variation among populations is a passive consequence of factors such as differential resources or temperature (Arendt and Reznick, 2005). Ecologists and managers
need to be aware that growth rates adapt in response to mortality
patterns, subject to constraints on the ability to acquire and process available resources. Progression to a rapid growth ecotype in
our diverse turtle populations may specifically relate to a long history dominated by anthropogenically-driven mortality or predation, rather than by resource limitation per se, because rapid
individual growth rates and early maturity are predicted by evolutionary theory to occur in populations with higher rates of adult
mortality (e.g. Bronikowski and Arnold, 1999). The combination
of changes in age-specific mortality rates and resource availability
from altered river flow may have further enhanced selection for
quick growth and early maturation in C. picta at the TCRA, importantly impacting other key traits, such as fecundity (Arendt, 1997).
Turtles at the TCRA grow much faster than most other turtles and
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R.-J. Spencer, F.J. Janzen / Biological Conservation 143 (2010) 1951–1959
also mature earlier (at a smaller size) than other populations of C.
picta. The von Bertalanffy growth constant (k = 0.36) of female C.
picta at the human-impacted TCRA is almost double that of any
other turtle (see Shine and Iverson, 1995) (Table 1).
Plasticity or life-history evolution? Spencer et al. (2006) clearly
show that growth rates in turtles can respond quickly to environmental factors, such as density or temperature. Importantly, however, they also find that turtle growth rates in the field exhibit a
strong, positive genetic correlation across different rearing environments. Moreover, strong selection for increased neonate body
size occurs in high juvenile density environments, like that at the
TCRA (Paitz et al., 2007); the vast majority of turtles at maturity
derive from the few clutches producing the largest offspring
(Fig. 3), which represents strong selection for larger eggs (see also
Janzen and Warner, 2009). Supporting this view is that older female C. picta at the TCRA increase egg mass by 40% within 6 years
of maturing, whereas clutch sizes remain constant (Fig. 6; Bowden
et al., 2004).
Growth variation in populations can instantly exemplify phenotypic plasticity, but can long-term changes in stage-specific mortality ultimately result in life-history evolution of long-lived
organisms? We suggest that, through both plasticity and life-history evolution, fast growth of C. picta at the TCRA relates to longterm reduction in nest predation rates and variable adult survival.
Our demographic models demonstrate that changes in egg and
adult mortality may lead to rapid increases in numbers in all
stages, but particularly in the juvenile population (Fig. 5; see also
Fordham et al., 2008, 2009). In turn, density-dependent changes
in growth rate impose strong mortality selection on juvenile turtles (Fig. 3). Under typical demographic conditions for C. picta
(t = 200–500 in Fig. 5), population sizes of all stages are relatively
stable and representative of the conservative life-history pattern
of turtles. Low egg survival and density-dependent regulation in
the adult stage maintains a relatively stable population, but rapid
progression to an increased juvenile population fundamentally
shifts the dynamics and life history of the population. Changing
from density-dependent regulation of the adult population to density-dependent regulation of, as well as large stochastic changes in,
recruitment causes the juvenile population to fluctuate wildly
(Fig. 5). Such extreme changes in survival are conducive to density-dependent selection for fast growth and early reproduction
(Bronikowski and Arnold, 1999). Under these conditions, a heritable basis for growth (e.g. Spencer et al., 2006) may have allowed
age at maturity to evolve relatively quickly after human impact
at the TCRA.
Consistent with a growing literature (e.g. Stevens et al., 2000;
Gamble and Simons, 2004; Fordham et al., 2009), we have shown
that the traditional static view of life history and demography of
long-lived organisms is inappropriate even over relatively short
periods of time. Anthropogenically-induced changes in juvenile
growth rates and age at maturity can lead to local adaptation of
long-lived turtles via plasticity and/or contemporary evolution,
which can seriously impact the reliability of long-term projections
from common demographic models. Thus, any change in demographic processes can potentially induce adaptive changes to lifehistory processes. Hence, we must take the sober view that these
increasingly imperiled organisms can adapt in response to, or otherwise accommodate (e.g. Bowen and Janzen, 2008), certain human
activities. Management practices must account for this dynamism
accordingly.
Acknowledgements
Australian research was supported by a Reserves Advisory Committee Environmental Trust Grant and an Australian Research
Council Small Grant; conducted under NSW NPWS Permit B1313,
USYD ACEC approval # L04/12-94/2/2017, and NSW Fisheries
#F86/2050. We thank the Webb, Griffith, Ruwolt and Delaney families for their time, help and properties. We thank M.B. Thompson
for advice and support. In the USA, thanks to John Iverson, Cathy
Pfister and Selina Heppell; numerous enthusiastic TCRA participants; ISU Committee on Animal Care; US Army Corps of Engineers, US Fish and Wildlife Service; Illinois DNR; and NSF LTREB
DEB-0089680 and DEB-0640932 for financial support.
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