Actual and Potential Use of Population Viability Analyses in Recovery of Plant Species Listed under the U.S. Endangered Species Act

Use of population viability analyses (PVAs) in endangered species recovery planning has been met with both support and criticism. Previous reviews promote use of PVA for setting scientifically based, measurable, and objective recovery criteria and recommend improvements to increase the framework's utility. However, others have questioned the value of PVA models for setting recovery criteria and assert that PVAs are more appropriate for understanding relative trade‐offs between alternative management actions. We reviewed 258 final recovery plans for 642 plants listed under the U.S. Endangered Species Act to determine the number of plans that used or recommended PVA in recovery planning. We also reviewed 223 publications that describe plant PVAs to assess how these models were designed and whether those designs reflected previous recommendations for improvement of PVAs. Twenty‐four percent of listed species had recovery plans that used or recommended PVA. In publications, the typical model was a matrix population model parameterized with ≤5 years of demographic data that did not consider stochasticity, genetics, density dependence, seed banks, vegetative reproduction, dormancy, threats, or management strategies. Population growth rates for different populations of the same species or for the same population at different points in time were often statistically different or varied by >10%. Therefore, PVAs parameterized with underlying vital rates that vary to this degree may not accurately predict recovery objectives across a species’ entire distribution or over longer time scales. We assert that PVA, although an important tool as part of an adaptive‐management program, can help to determine quantitative recovery criteria only if more long‐term data sets that capture spatiotemporal variability in vital rates become available. Lacking this, there is a strong need for viable and comprehensive methods for determining quantitative, science‐based recovery criteria for endangered species with minimal data availability. Uso Actual y Potencial del Análisis de Viabilidad Poblacional para la Recuperación de Especies de Plantas Enlistadas en el Acta de Especies En Peligro de E.U.A     

Incorporating landscape stochasticity into population viability analysis.

The importance of incorporating landscape dynamics into population viability analysis (PVA) has previously been acknowledged, but the need to repeat the landscape generation process to encapsulate landscape stochasticity in model outputs has largely been overlooked. Reasons for this are that (1) there is presently no means for quantifying the relative effects of landscape stochasticity and population stochasticity on model outputs, and therefore no means for determining how to allocate simulation time optimally between the two; and (2) the process of generating multiple landscapes to incorporate landscape stochasticity is tedious and user-intensive with current PVA software. Here we demonstrate that landscape stochasticity can be an important source of variance in model outputs. We solve the technical problems with incorporating landscape stochasticity by deriving a formula that gives the optimal ratio of population simulations to landscape simulations for a given model, and by providing a computer program that incorporates the formula and automates multiple landscape generation in a dynamic landscape metapopulation (DLMP) model. Using a case study of a bird population, we produce estimates of DLMP model output parameters that are up to four times more precise than those estimated from a single landscape in the same amount of total simulation time. We use the DLMP modeling software RAMAS Landscape to run the landscape and metapopulation models, though our method is general and could be applied to any PVA platform. The results of this study should motivate DLMP modelers to consider landscape stochasticity in their analyses.     

Demographic heterogeneity, cohort selection, and population growth

Demographic heterogeneity--variation among individuals in survival and reproduction--is ubiquitous in natural populations. Structured population models address heterogeneity due to age, size, or major developmental stages. However, other important sources of demographic heterogeneity, such as genetic variation, spatial heterogeneity in the environment, maternal effects, and differential exposure to stressors, are often not easily measured and hence are modeled as stochasticity. Recent research has elucidated the role of demographic heterogeneity in changing the magnitude of demographic stochasticity in small populations. Here we demonstrate a previously unrecognized effect: heterogeneous survival in long-lived species can increase the long-term growth rate in populations of any size. We illustrate this result using simple models in which each individual's annual survival rate is independent of age but survival may differ among individuals within a cohort. Similar models, but with nonoverlapping generations, have been extensively studied by demographers, who showed that, because the more "frail" individuals are more likely to die at a young age, the average survival rate of the cohort increases with age. Within ecology and evolution, this phenomenon of "cohort selection" is increasingly appreciated as a confounding factor in studies of senescence. We show that, when placed in a population model with overlapping generations, this heterogeneity also causes the asymptotic population growth rate lambda to increase, relative to a homogeneous population with the same mean survival rate at birth. The increase occurs because, even integrating over all the cohorts in the population, the population becomes increasingly dominated by the more robust individuals. The growth rate increases monotonically with the variance in survival rates, and the effect can be substantial, easily doubling the growth rate of slow-growing populations. Correlations between parent and offspring phenotype change the magnitude of the increase in lambda, but the increase occurs even for negative parent-offspring correlations. The effect of heterogeneity in reproductive rate on lambda is quite different: growth rate increases with reproductive heterogeneity for positive parent-offspring correlation but decreases for negative parent-offspring correlation. These effects of demographic heterogeneity on lambda have important implications for population dynamics, population viability analysis, and evolution.     

Incorporating catastrophic risk assessments into setting conservation goals for threatened Pacific salmon

Catastrophic die-offs can have important consequences for vertebrate population growth and biodiversity, but catastrophic risks are not commonly incorporated into endangered-species recovery planning. Natural (e.g., landslides, floods) and anthropogenic (e.g., toxic leaks and spills) catastrophes pose a challenge for evolutionarily significant units (ESUs) of Pacific salmon listed under the Endangered Species Act and teetering at precariously low population levels. To spread risks among Puget Sound chinook salmon populations, recovery strategies for ESU-wide viability recommend at least two viable populations of historical life-history types in each of five geographic regions. We explored the likelihood of Puget Sound chinook salmon ESU persistence by examining spatial patterns of catastrophic risk and testing ESU viability recommendations for 22 populations of the threatened Puget Sound chinook salmon ESU. We combined geospatial information about catastrophic risks and chinook salmon distribution in Puget Sound watersheds to categorize relative catastrophic risks for each population. We then analyzed similarities in risk scores among regions and compared risk distributions among strategies: (1) population groups selected using the ESU viability recommendations of having populations spread out geographically and including historical life-history diversity, and (2) population groups selected at random. Risks from individual catastrophes varied among populations, but overall risk from catastrophes was similar within geographic regions. Recovery strategies that called for two viable populations in each of five geographic regions had lower risk than random strategies; strategies that included life-history diversity had even lower risks. Geographically distributed populations have varying catastrophic-risks profiles, thus identifying and reinforcing the spatial and life-history diversity critical for populations to respond to environmental change or needed to rescue severely depleted or extirpated populations. Recovery planning can promote viability of Pacific salmon ESUs across the landscape by incorporating catastrophic risk assessments.     

Effects of life history and reproduction on recruitment time lags in reintroductions of rare plants

Reintroductions are important components of conservation and recovery programs for rare plant species, but their long-term success rates are poorly understood. Previous reviews of plant reintroductions focused on short-term (e.g., ≤3 years) survival and flowering of founder individuals rather than on benchmarks of intergenerational persistence, such as seedling recruitment. However, short-term metrics may obscure outcomes because the unique demographic properties of reintroductions, including small size and unstable stage structure, could create lags in population growth. We used time-to-event analysis on a database of unusually well-monitored and long-term (4–28 years) reintroductions of 27 rare plant species to test whether life-history traits and population characteristics of reintroductions create time-lagged responses in seedling recruitment (i.e., recruitment time lags [RTLs]), an important benchmark of success and indicator of persistence in reintroduced populations. Recruitment time lags were highly variable among reintroductions, ranging from <1 to 17 years after installation. Recruitment patterns matched predictions from life-history theory with short-lived species (fast species) exhibiting consistently shorter and less variable RTLs than long-lived species (slow species). Long RTLs occurred in long-lived herbs, especially in grasslands, whereas short RTLs occurred in short-lived subtropical woody plants and annual herbs. Across plant life histories, as reproductive adult abundance increased, RTLs decreased. Highly variable RTLs were observed in species with multiple reintroduction events, suggesting local processes are just as important as life-history strategy in determining reintroduction outcomes. Time lags in restoration outcomes highlight the need to scale success benchmarks in reintroduction monitoring programs with plant life-history strategies and the unique demographic properties of restored populations. Drawing conclusions on the long-term success of plant reintroduction programs is premature given that demographic processes in species with slow life-histories take decades to unfold.     

A stage-structured,stochastic population model for the giant kelpMacrocystis pyrifera

We have developed a population model for the giant kelpMacrocystis pyrifera (L.) C. Agardh in southern California, USA. The model includes five life-history stages and takes into account environmental and demographic stochasticity, as well as density-dependent interactions. The density of each stage is predicted on a monthly basis for up to 20 yr, and extinction probability is determined for adult sporophytes. Survival probabilities and rates of reproduction and growth are based on stage-specific responses to environmental conditions (irradiance and temperature), including the occurrence of El Niño events. The model is validated by comparing simulation results to empirical data from natural kelp populations. Results of the model provide insight into patterns observed in natural populations and have applications in resource management.To obtain a copy of the model, please contact Applied Biomathematics, 100 North Country Road, Setauket, New York 11733, USA     

Stochastic population dynamics in populations of western terrestrial garter snakes with divergent life histories

Comparative evaluations of population dynamics in species with temporal and spatial variation in life-history traits are rare because they require long-term demographic time series from multiple populations. We present such an analysis using demographic data collected during the interval 1978-1996 for six populations of western terrestrial garter snakes (Thamnophis elegans) from two evolutionarily divergent ecotypes. Three replicate populations from a slow-living ecotype, found in mountain meadows of northeastern California, were characterized by individuals that develop slowly, mature late, reproduce infrequently with small reproductive effort, and live longer than individuals of three populations of a fast-living ecotype found at lakeshore locales. We constructed matrix population models for each of the populations based on 8-13 years of data per population and analyzed both deterministic dynamics based on mean annual vital rates and stochastic dynamics incorporating annual variation in vital rates. (1) Contributions of highly variable vital rates to fitness (lambda(s)) were buffered against the negative effects of stochastic variation, and this relationship was consistent with differences between the meadow (M-slow) and lakeshore (L-fast) ecotypes. (2) Annual variation in the proportion of gravid females had the greatest negative effect among all vital rates on lambda(s). The magnitude of variation in the proportion of gravid females and its effect on lambda(s) was greater in M-slow than L-fast populations. (3) Variation in the proportion of gravid females, in turn, depended on annual variation in prey availability, and its effect on lambda(s) was 4 23 times greater in M-slow than L-fast populations. In addition to differences in stochastic dynamics between ecotypes, we also found higher mean mortality rates across all age classes in the L-fast populations. Our results suggest that both deterministic and stochastic selective forces have affected the evolution of divergent life-history traits in the two ecotypes, which, in turn, affect population dynamics. M-slow populations have evolved life-history traits that buffer fitness against direct effects of variation in reproduction and that spread lifetime reproduction across a greater number of reproductive bouts. These results highlight the importance of long-term demographic and environmental monitoring and of incorporating temporal dynamics into empirical studies of life-history evolution.     

Reliability of Absolute and Relative Predictions of Population Persistence Based on Time Series

Abstract:  Conventional population viability analysis (PVA) is often impractical because data are scarce for many threatened species. For this reason, simple count-based models are being advocated. The simplest of these models requires nothing more than a time series of abundance estimates, from which variance and autocorrelation in growth rate are estimated and predictions of population persistence are generated. What remains unclear, however, is how many years of data are needed to generate reliable estimates of these parameters and hence reliable predictions of persistence. By analyzing published and simulated time series, we show that several decades of data are needed. Predictions based on short time series were very unreliable mainly because limited data yielded biased, unreliable estimates of variance in growth rate, especially when growth rate was strongly autocorrelated. More optimistically, our results suggest that count-based PVA is sometimes useful for relative risk assessment (i.e., for ranking populations by extinction risk), even when time series are only a decade long. However, some conditions consistently lead to backward rankings. We explored the limited conditions under which simple count-based PVA may be useful for relative risk assessment.     

A population viability analysis for the Island Fox on Santa Catalina Island,California

The island fox (Urocyon littoralis) on Santa Catalina Island is among the most imperiled species on the Channel Islands due to a recent outbreak of canine distemper virus (CDV). The western subpopulation, which was not exposed to CDV, is a crucial element in the recovery of foxes by providing a source of animals for translocation and captive breeding. Using the program VORTEX, we developed a population viability analysis for the Santa Catalina Island fox to (1) address the likelihood of population persistence, (2) estimate the current susceptibility of the population to catastrophic events, and (3) evaluate the efficacy of current restoration strategies of releasing captive bred foxes and transplanting wild animals. Overall, we found the population to be susceptible to catastrophic events; a 50% increase in mortality every 20 years was sufficient to elevate the extinction risk above 5%. Current management activities entail the transplanting of 12 juvenile foxes annually, which may reduce the viability of the western subpopulation. A minimum population size of at least 150 foxes should be maintained in each subpopulation to reduce the risk of extinction due to demographic stochasticity. Releases of translocated and captive bred animals affect the speed of recovery on the eastern half of Catalina Island, but not the probability of extinction, which is near zero under current conditions. We conducted a sensitivity analysis for demographic parameters by incrementally varying survival, fecundity and density-dependence parameters, while holding all other parameters constant. Sensitivity analyses identified mortality and mean litter size as the most sensitive parameters, while the implementation of density-dependence and environmental variation of model parameters did not seem to affect population performance. We conclude that the population of island foxes on Santa Catalina is currently at a critically low population level, but recovery of the species appears possible.     

A stochastic model for estimating sustainable limits to wildlife mortality in a changing world

Human-caused mortality of wildlife is a pervasive threat to biodiversity. Assessing the population-level impact of fisheries bycatch and other human-caused mortality of wildlife has typically relied upon deterministic methods. However, population declines are often accelerated by stochastic factors that are not accounted for in such conventional methods. Building on the widely applied potential biological removal (PBR) equation, we devised a new population modeling approach for estimating sustainable limits to human-caused mortality and applied it in a case study of bottlenose dolphins affected by capture in an Australian demersal otter trawl fishery. Our approach, termed sustainable anthropogenic mortality in stochastic environments (SAMSE), incorporates environmental and demographic stochasticity, including the dependency of offspring on their mothers. The SAMSE limit is the maximum number of individuals that can be removed without causing negative stochastic population growth. We calculated a PBR of 16.2 dolphins per year based on the best abundance estimate available. In contrast, the SAMSE model indicated that only 2.3–8.0 dolphins could be removed annually without causing a population decline in a stochastic environment. These results suggest that reported bycatch rates are unsustainable in the long term, unless reproductive rates are consistently higher than average. The difference between the deterministic PBR calculation and the SAMSE limits showed that deterministic approaches may underestimate the true impact of human-caused mortality of wildlife. This highlights the importance of integrating stochasticity when evaluating the impact of bycatch or other human-caused mortality on wildlife, such as hunting, lethal control measures, and wind turbine collisions. Although population viability analysis (PVA) has been used to evaluate the impact of human-caused mortality, SAMSE represents a novel PVA framework that incorporates stochasticity for estimating acceptable levels of human-caused mortality. It offers a broadly applicable, stochastic addition to the demographic toolbox to evaluate the impact of human-caused mortality on wildlife.     

Population feedback after successful invasion leads to ecological suicide in seasonal environments

For most consumer species, winter represents a period of harsh food conditions in addition to the physiological strain that results from the low ambient temperatures. In size-structured populations, larger-bodied individuals do better during winter as they have larger energy reserves to buffer starvation periods. In contrast, smaller-bodied individuals do better under growing conditions, as they have lower maintenance costs. We study how the interplay between size-dependent life-history processes and seasonal changes in temperature and food availability shape the long-term dynamics of a size-structured consumer population and its unstructured resource. We show that the size dependence of maintenance requirements translates into a minimum body size that is needed for surviving starvation when consumers can adapt only to a limited extent to the low food densities in winter. This size threshold can lead to population extinction because adult individuals suffer only a little during winter and hence produce large numbers of offspring. Due to population feedback on the resource and intense intra-cohort competition, newborn consumers then fail to reach the size threshold for survival. Under these conditions, small numbers of individuals can survive, increase in density, and build up a population, which will subsequently go extinct due to its feedback on the resource. High juvenile mortality may prevent this ecological suicide from occurring, as it releases resource competition among newborns and speeds up their growth. In size-structured populations, annual fluctuations in temperature and food availability may thus lead to a conflict between individual fitness and population persistence.     

Setting population targets for mammals using body mass as a predictor of population persistence

Conservation planning and biodiversity assessments need quantitative targets to optimize planning options and assess the adequacy of current species protection. However, targets aiming at persistence require population‐specific data, which limit their use in favor of fixed and nonspecific targets, likely leading to unequal distribution of conservation efforts among species. We devised a method to derive equitable population targets; that is, quantitative targets of population size that ensure equal probabilities of persistence across a set of species and that can be easily inferred from species‐specific traits. In our method, we used models of population dynamics across a range of life‐history traits related to species’ body mass to estimate minimum viable population targets. We applied our method to a range of body masses of mammals, from 2 g to 3825 kg. The minimum viable population targets decreased asymptotically with increasing body mass and were on the same order of magnitude as minimum viable population estimates from species‐ and context‐specific studies. Our approach provides a compromise between pragmatic, nonspecific population targets and detailed context‐specific estimates of population viability for which only limited data are available. It enables a first estimation of species‐specific population targets based on a readily available trait and thus allows setting equitable targets for population persistence in large‐scale and multispecies conservation assessments and planning.     

Long-term demographic and genetic effects of releasing captive-born individuals into the wild

Because of continued habitat destruction and species extirpations, the need to use captive breeding for conservation purposes has been increasing steadily. However, the long-term demographic and genetic effects associated with releasing captive-born individuals with varied life histories into the wild remain largely unknown. To address this question, we developed forward-time, agent-based models for 4 species with long-running captive-breeding and release programs: coho salmon (Oncorhynchus kisutch), golden lion tamarin (Leontopithecus rosalia), western toad (Anaxyrus boreas), and Whooping Crane (Grus americana). We measured the effects of supplementation by comparing population size and neutral genetic diversity in supplemented populations to the same characteristics in unaltered populations 100 years after supplementation ended. Releasing even slightly less fit captive-born individuals to supplement wild populations typically resulted in reductions in population sizes and genetic diversity over the long term when the fitness reductions were heritable (i.e., due to genetic adaptation to captivity) and populations continued to be regulated by density-dependent mechanisms over time. Negative effects for species with longer life spans and lower rates of population replacement were smaller than for species with shorter life spans and higher rates of population replacement. Programs that released captive-born individuals over fewer years or that avoided breeding individuals with captive ancestry had smaller reductions in population size and genetic diversity over the long term. Relying on selection in the wild to remove individuals with reduced fitness mitigated some negative demographic effects, but at a substantial cost to neutral genetic diversity. Our results suggest that conservation-focused captive-breeding programs should take measures to prevent even small amounts of genetic adaptation to captivity, quantitatively determine the minimum number of captive-born individuals to release each year, and fully account for the interactions among genetic adaptation to captivity, population regulation, and life-history variation.     

Erosion of Heterozygosity in Fluctuating Populations

Abstract: Demographic, environmental, and genetic stochasticity threaten the persistence of isolated populations. The relative importance of these intertwining factors remains unresolved, but a common view is that random demographic and environmental events will usually drive small populations to the brink of extinction before genetic deterioration poses a serious threat. To evaluate the potential importance of genetic factors, we analyzed a model linking demographic and environmental conditions to the loss of genetic diversity in isolated populations undergoing natural levels of fluctuation. Nongenetic processes—environmental stochasticity and population demography—were modeled according to a bounded diffusion process. Genetic processes were modeled by quantifying the rate of drift according to the effective population size, which was predicted from the same parameters used to describe the nongenetic processes. We combined these models to predict the heterozygosity remaining at the time of extinction, as predicted by the nongenetic portion of the model. Our model predicts that many populations will lose most or all of their neutral genetic diversity before nongenetic random events lead to extinction. Given the abundant evidence for inbreeding depression and recent evidence for elevated extinction rates of inbred populations, our findings suggest that inbreeding may be a greater general threat to population persistence than is generally recognized. Therefore, conservation biologists should not ignore the genetic component of extinction risk when assessing species endangerment and developing recovery plans.     

Demographic stochasticity reduces the synchronizing effect of dispersal in predator-prey metapopulations

Dispersal may affect predator-prey metapopulations by rescuing local sink populations from extinction or by synchronizing population dynamics across the metapopulation, increasing the risk of regional extinction. Dispersal is likely influenced by demographic stochasticity, however, particularly because dispersal rates are often very low in metapopulations. Yet the effects of demographic stochasticity on predator-prey metapopulations are not well known. To that end, I constructed three models of a two-patch predator-prey system. The models constitute a hierarchy of complexity, allowing direct comparisons. Two models included demographic stochasticity (pure jump process [PJP] and stochastic differential equations [SDE]), and the third was deterministic (ordinary differential equations [ODE]). One stochastic model (PJP) treated population sizes as discrete, while the other (SDE) allowed population sizes to change continuously. Both stochastic models only produced synchronized predator-prey dynamics when dispersal was high for both trophic levels. Frequent dispersal by only predators or prey in the PJP and SDE spatially decoupled the trophic interaction, reducing synchrony of the non-dispersive species. Conversely, the ODE generated synchronized predator-prey dynamics across all dispersal rates, except when initial conditions produced anti-phase transients. These results indicate that demographic stochasticity strongly reduces the synchronizing effect of dispersal, which is ironic because demographic stochasticity is often invoked post hoc as a driver of extinctions in synchronized metapopulations.     

A case study of bats and white‐nose syndrome demonstrating how to model population viability with evolutionary effects

Ecological factors generally affect population viability on rapid time scales. Traditional population viability analyses (PVA) therefore focus on alleviating ecological pressures, discounting potential evolutionary impacts on individual phenotypes. Recent studies of evolutionary rescue (ER) focus on cases in which severe, environmentally induced population bottlenecks trigger a rapid evolutionary response that can potentially reverse demographic threats. ER models have focused on shifting genetics and resulting population recovery, but no one has explored how to incorporate those findings into PVA. We integrated ER into PVA to identify the critical decision interval for evolutionary rescue (DIER) under which targeted conservation action should be applied to buffer populations undergoing ER against extinction from stochastic events and to determine the most appropriate vital rate to target to promote population recovery. We applied this model to little brown bats (Myotis lucifugus) affected by white‐nose syndrome (WNS), a fungal disease causing massive declines in several North American bat populations. Under the ER scenario, the model predicted that the DIER period for little brown bats was within 11 years of initial WNS emergence, after which they stabilized at a positive growth rate (λ = 1.05). By comparing our model results with population trajectories of multiple infected hibernacula across the WNS range, we concluded that ER is a potential explanation of observed little brown bat population trajectories across multiple hibernacula within the affected range. Our approach provides a tool that can be used by all managers to provide testable hypotheses regarding the occurrence of ER in declining populations, suggest empirical studies to better parameterize the population genetics and conservation‐relevant vital rates, and identify the DIER period during which management strategies will be most effective for species conservation.     

Risk Analysis of Hunting of Seal Populations in the Baltic

Vulnerabilities of grey seal ( Halichoerus grypus) and ringed seal ( Phoca hispida) populations in the Baltic Sea were evaluated for potential opening of the populations for hunting. We used ecological risk analysis to assess the effects of environmental and demographic stochasticity and uncertain and partly missing population data on the modeled outcomes. The impact of different harvesting strategies on the long-term sustainability of seal populations was analyzed with four different models with increasing complexity and population detail. It appears the simpler the population model used, the more overconfident results it gave with regard to the hunting policy to be adopted. Therefore, it proves risky in population management decisions to rely on simplistic calculations based on growth rate and estimated population size alone. This is even more so if the population estimates have a wide error margin. Due to an unknown, but presumably positive number of seal kills in the Baltic at present, the sustainable harvest is likely to be close to zero for both seal species. Our risk analysis strongly suggests refraining from Baltic seal hunting now, with their current population sizes, and in the future if the development of population numbers cannot be assessed accurately enough.     

An experimental test of alternative population augmentation scenarios

Human land use is fragmenting habitats worldwide and inhibiting dispersal among previously connected populations of organisms, often leading to inbreeding depression and reduced evolutionary potential in the face of rapid environmental change. To combat this augmentation of isolated populations with immigrants is sometimes used to facilitate demographic and genetic rescue. Augmentation with immigrants that are genetically and adaptively similar to the target population effectively increases population fitness, but if immigrants are very genetically or adaptively divergent, augmentation can lead to outbreeding depression. Despite well‐cited guidelines for the best practice selection of immigrant sources, often only highly divergent populations remain, and experimental tests of these riskier augmentation scenarios are essentially nonexistent. We conducted a mesocosm experiment with Trinidadian guppies (Poecilia reticulata) to test the multigenerational demographic and genetic effects of augmenting 2 target populations with 3 types of divergent immigrants. We found no evidence of demographic rescue, but we did observe genetic rescue in one population. Divergent immigrant treatments tended to maintain greater genetic diversity, abundance, and hybrid fitness than controls that received immigrants from the source used to seed the mesocosms. In the second population, divergent immigrants had a slightly negative effect in one treatment, and the benefits of augmentation were less apparent overall, likely because this population started with higher genetic diversity and a lower reproductive rate that limited genetic admixture. Our results add to a growing consensus that gene flow can increase population fitness even when immigrants are more highly divergent and may help reduce uncertainty about the use of augmentation in conservation.     

Persistence of Different-sized Populations: An Empirical Assessment of Rapid Extinctions in Bighorn Sheep

Abstract: Theory and simulation models suggest that small populations are more susceptible to extinction than large populations, yet assessment of this idea has been hampered by lack of an empirical base. I address the problem by asking how long different-sized populations persist and present demographic and weather data spanning up to 70 years for 122 bighorn sheep ( Ovis canadensis ) populations in southwestern North America Analyses reveal that: (1) 100 percent of the populations with fewer than 50 individuals went extinct within 50 years; (2) populations with greater than 100 individuals persisted for up to 70 years; and (3) the rapid loss of populations was not likely to be caused by food shortages, severe weather, predation, or interspecific competition These data suggest that population size is a marker of persistence trajectories and they indicate that local extinction cannot be overcome because 50 individuals, even in the short term, are not a minimum viable population size for bighorn sheep.     

Counterintuitive density-dependent growth in a long-lived vertebrate after removal of nest predators

Examining the phenotypic and genetic underpinnings of life-history variation in long-lived organisms is central to the study of life-history evolution. Juvenile growth and survival are often density dependent in reptiles, and theory predicts the evolution of slow growth in response to low resources (resource-limiting hypothesis), such as under densely populated conditions. However, rapid growth is predicted when exceeding some critical body size reduces the risk of mortality (mortality hypothesis). Here we present results of paired, large-scale, five-year field experiments to identify causes of variation in individual growth and survival rates of an Australian turtle (Emydura macquarii) prior to maturity. To distinguish between these competing hypotheses, we reduced nest predators in two populations and retained a control population to create variation in juvenile density by altering recruitment levels. We also conducted a complementary split-clutch field-transplant experiment to explore the impact of incubation temperature (25 degrees or 30 degrees C), nest predator level (low or high), and clutch size on juvenile growth and survival. Juveniles in high-recruitment (predator removal) populations were not resource limited, growing more rapidly than young turtles in the control populations. Our experiments also revealed a remarkably long-term impact of the thermal conditions experienced during embryonic development on growth of turtles prior to maturity. Moreover, this thermal effect was manifested in turtles approaching maturity, rather than in turtles closer to hatching, and was dependent on population density in the post-hatching rearing environment. This apparent phenotypic plasticity in growth complements our observation of a strong, positive genetic correlation between individual body size in the experimental and control populations over the first five years of life (rG - +0.77). Thus, these Australian pleurodiran turtles have the impressive capacity to acclimate plastically to major demographic perturbations and enjoy the longer-term potential to evolve adaptively to maintain viability.