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.     

Role of Patch Size, Disease, and Movement in Rapid Extinction of Bighorn Sheep

Abstract: The controversy (  Berger 1990, 1999 ; Wehausen 1999 ) over rapid extinction in bighorn sheep ( Ovis canadensis ) has focused on population size alone as a correlate to persistence time. We report on the persistence and population performance of 24 translocated populations of bighorn sheep. Persistence in these sheep was strongly correlated with larger patch sizes, greater distance to domestic sheep, higher population growth rates, and migratory movements, as well as to larger population sizes. Persistence was also positively correlated with larger average home-range size ( p = 0.058, n = 10 translocated populations) and home-range size of rams ( p = 0.087, n = 8 translocated populations). Greater home-range size and dispersal rates of bighorn sheep were positively correlated to larger patches. We conclude that patch size and thus habitat carrying capacity, not population size per se, is the primary correlate to both population performance and persistence. Because habitat carrying capacity defines the upper limit to population size, clearly the amount of suitable habitat in a patch is ultimately linked to population size. Larger populations (250+ animals) were more likely to recover rapidly to their pre-epizootic survey number following an epizootic ( p = 0.019), although the proportion of the population dying in the epizootic also influenced the probability of recovery ( p = 0.001). Expensive management efforts to restore or increase bighorn sheep populations should focus on large habitat patches located ≥23 km from domestic sheep, and less effort should be expended on populations in isolated, small patches of habitat.     

Will Observation Error and Biases Ruin the Use of Simple Extinction Models?

Abstract: Estimating the risk of extinction for populations of endangered species is an important component of conservation biology. These estimates must be made from data that contain both environmental noise in the year-to-year transitions in population size (so-called "process error"), random errors in sampling, and possible biases in sampling ( both forms of observation errors). To determine how much faith to place in estimated extinction rates, it is important to know how sensitive they are to observation error. We used three simple, commonly employed models of population dynamics to generate simulated population time series. We then combined random observation error or systematic biases with those data, fit models to the time series data, and observed how close the extinction dynamics of the fitted models compared with the dynamics of the underlying models. We found that systematic biases in sampling rarely affected estimates of extinction risk. We also found that even moderate levels of random observation error do not significantly affect extinction estimates except over a small range of process errors, corresponding to the region where extinction risk is most uncertain. With more substantial sampling error, estimates of extinction risk degraded rapidly. Field census techniques for a variety of taxa often involve observation errors within ±32% of actual population sizes. For typical time series used in conservation, therefore, we often may not need to be overly concerned about observation errors as an extra source of imperfection in our estimated extinction rates.     

Variability in Population Abundance and the Classification of Extinction Risk

Abstract: Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50–80%; vulnerable, decline in abundance of 30–50%; least concern or near threatened, decline in abundance of 0–30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state‐space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state‐space models were low (maximum 3–14%), but 33–75% of small declines (30–50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.     

A Behaviorally Explicit Demographic Model Integrating Habitat Selection and Population Dynamics in California Sea Lions

Abstract: Although there has been a call for the integration of behavioral ecology and conservation biology, there are few tools currently available to achieve this integration. Explicitly including information about behavioral strategies in population viability analyses may enhance the ability of conservation biologists to understand and estimate patterns of extinction risk. Nevertheless, most behavioral‐based PVA approaches require detailed individual‐based data that are rarely available for imperiled species. We present a mechanistic approach that incorporates spatial and demographic consequences of behavioral strategies into population models used for conservation. We developed a stage‐structured matrix model that includes the costs and benefits of movement associated with 2 habitat‐selection strategies (philopatry and direct assessment). Using a life table for California sea lions (Zalophus californianus), we explored the sensitivity of model predictions to the inclusion of these behavioral parameters. Including behavioral information dramatically changed predicted population sizes, model dynamics, and the expected distribution of individuals among sites. Estimated population sizes projected in 100 years diverged up to 1 order of magnitude among scenarios that assumed different movement behavior. Scenarios also exhibited different model dynamics that ranged from stable equilibria to cycles or extinction. These results suggest that inclusion of behavioral data in viability models may improve estimates of extinction risk for imperiled species. Our approach provides a simple method for incorporating spatial and demographic consequences of behavioral strategies into population models and may be easily extended to other species and behaviors to understand the mechanisms of population dynamics for imperiled populations.     

Minimum viable metapopulation size, extinction debt, and the conservation of a declining species.

A key question facing conservation biologists is whether declines in species' distributions are keeping pace with landscape change, or whether current distributions overestimate probabilities of future persistence. We use metapopulations of the marsh fritillary butterfly Euphydryas aurinia in the United Kingdom as a model system to test for extinction debt in a declining species. We derive parameters for a metapopulation model (incidence function model, IFM) using information from a 625-km2 landscape where habitat patch occupancy, colonization, and extinction rates for E. aurinia depend on patch connectivity, area, and quality. We then show that habitat networks in six extant metapopulations in 16-km2 squares were larger, had longer modeled persistence times (using IFM), and higher metapopulation capacity (lambdaM) than six extinct metapopulations. However, there was a > 99% chance that one or more of the six extant metapopulations would go extinct in 100 years in the absence of further habitat loss. For 11 out of 12 networks, minimum areas of habitat needed for 95% persistence of metapopulation simulations after 100 years ranged from 80 to 142 ha (approximately 5-9% of land area), depending on the spatial location of habitat. The area of habitat exceeded the estimated minimum viable metapopulation size (MVM) in only two of the six extant metapopulations, and even then by only 20%. The remaining four extant networks were expected to suffer extinction in 15-126 years. MVM was consistently estimated as approximately 5% of land area based on a sensitivity analysis of IFM parameters and was reduced only marginally (to approximately 4%) by modeling the potential impact of long-distance colonization over wider landscapes. The results suggest a widespread extinction debt among extant metapopulations of a declining species, necessitating conservation management or reserve designation even in apparent strongholds. For threatened species, metapopulation modeling is a potential means to identify landscapes near to extinction thresholds, to which conservation measures can be targeted for the best chance of success.     

Population viability analysis for several populations using multivariate state-space models

The International Union for the Conservation of Nature and Natural Resources (IUCN), the world's largest and most important global conservation network, has listed approximately 16,000 species worldwide as threatened. The most important tool for recognizing and listing species as threatened is population viability analysis (PVA), which estimates the probability of extinction of a population or species over a specified time horizon. The most common PVA approach is to apply it to single time series of population abundance. This approach to population viability analysis ignores covariability of local populations. Covariability can be important because high synchrony of local populations reduces the effective number of local populations and leads to greater extinction risk. Needed is a way of extending PVA to model correlation structure among multiple local populations. Multivariate state-space modeling is applied to this problem and alternative estimation methods are compared. The multivariate state-space technique is applied to endangered populations of pacific salmon, USA. Simulations demonstrated that the correlation structure can strongly influence population viability and is best estimated using restricted maximum likelihood instead of maximum likelihood.     

Estimates of minimum patch size depend on the method of estimation and the condition of the habitat

Minimum patch size for a viable population can be estimated in several ways. The density-area method estimates minimum patch size as the smallest area in which no new individuals are encountered as one extends the arbitrary boundaries of a study area outward. The density-area method eliminates the assumption of no variation in density with size of habitat area that accompanies other methods, but it is untested in situations in which habitat loss has confined populations to small areas. We used a variant of the density area method to study the minimum patch size for the gopher tortoise (Gopherus polyphemus) in Florida, USA, where this keystone species is being confined to ever smaller habitat fragments. The variant was based on the premise that individuals within populations are likely to occur at unusually high densities when confined to small areas, and it estimated minimum patch size as the smallest area beyond which density plateaus. The data for our study came from detailed surveys of 38 populations of the tortoise. For all 38 populations, the areas occupied were determined empirically, and for 19 of them, duplicate surveys were undertaken about a decade apart. We found that a consistent inverse density area relationship was present over smaller areas. The minimum patch size estimated from the density-area relationship was at least 100 ha, which is substantially larger than previous estimates. The relative abundance of juveniles was inversely related to population density for sites with relatively poor habitat quality, indicating that the estimated minimum patch size could represent an extinction threshold. We concluded that a negative density area relationship may be an inevitable consequence of excessive habitat loss. We also concluded that any detrimental effects of an inverse density area relationship may be exacerbated by the deterioration in habitat quality that often accompanies habitat loss. Finally, we concluded that the value of any estimate of minimum patch size as a conservation tool is compromised by excessive habitat loss.     

The unified neutral theory of biodiversity: do the numbers add up?

Hubbell's unified neutral theory is a zero-sum ecological drift model in which population sizes change at random in a process resembling genetic drift, eventually leading to extinction. Diversity is maintained within the community by speciation. Hubbell's model makes predictions about the distribution of species abundances within communities and the turnover of species from place to place (beta diversity). However, ecological drift cannot be tested adequately against these predictions without independent estimates of speciation rates, population sizes, and dispersal distances. A more practical prediction from ecological drift is that time to extinction of a population of size N is approximately 2N generations. I test this prediction here using data for passerine birds (Passeriformes). Waiting times to speciation and extinction were estimated from genetic divergence between sister populations and a lineage-through-time plot for endemic South American suboscine passerines. Population sizes were estimated from local counts of birds in two large forest plots extrapolated to the area of wet tropical forest in South America and from atlas data on European passerines. Waiting times to extinction (ca. 2 Ma) are much less than twice the product of average population size (4.0 and 14.4 x 10(6) individuals in South America and Europe) and generation length (five and three years) for songbirds, that is, 40 and 86 Ma, respectively. Thus, drift is too slow to account for turnover in regional avifaunas. Presumably, other processes, involving external drivers, such as climate and physiographic change, and internal drivers, such as evolutionary change in antagonistic interactions, predominate. Hubbell's model is historical and geographic, and his perspective importantly links local and regional process and pattern. Ecological reality can be added to the mix while retaining Hubbell's concept of continuity of communities in space and time.     

Effects of Climate Change on Population Persistence of Desert-Dwelling Mountain Sheep in California

Abstract:  Metapopulations may be very sensitive to global climate change, particularly if temperature and precipitation change rapidly. We present an analysis of the role of climate and other factors in determining metapopulation structure based on presence and absence data. We compared existing and historical population distributions of desert bighorn sheep ( Ovis canadensis ) to determine whether regional climate patterns were correlated with local extinction. To examine all mountain ranges known to hold or to have held desert bighorn populations in California and score for variables describing climate, metapopulation dynamics, human impacts, and other environmental factors, we used a geographic information system (GIS) and paper maps. We used logistic regression and hierarchical partitioning to assess the relationship among these variables and the current status of each population (extinct or extant). Parameters related to climate—elevation, precipitation, and presence of dependable springs—were strongly correlated with population persistence in the twentieth century. Populations inhabiting lower, drier mountain ranges were more likely to go extinct. The presence of domestic sheep grazing allotments was negatively correlated with population persistence. We used conditional extinction probabilities generated by the logistic-regression model to rank native, naturally recolonized, and reintroduced populations by vulnerability to extinction under several climate-change scenarios. Thus risk of extinction in metapopulations can be evaluated for global-climate-change scenarios even when few demographic data are available.     

Effective Population Size in Red-Cockaded Woodpeckers: Population and Model Differences

Loss of genetic variability in isolated populations is an important issue for conservation biology. Most studies involve only a single population of a given species and a single method of estimating rate of loss. Here we present analyses for three different Red-cockaded Woodpecker ( Picoides borealis ) populations from different geographic regions. We compare two different models for estimating the expected rate of loss of genetic variability, and test their sensitivity to model parameters. We found that the simpler model (Reed et al. 1988) consistently estimated a greater rate of loss of genetic variability from a population than did the Emigh and Pollak (1979) model. The ratio of effective population size (which describes the expected rate of loss of genetic variability) to breeder population size varied widely among Red-cockaded Woodpecker populations due to geographic variation in demography. For this species, estimates of effective size were extremely sensitive to survival parameters, but not to the probability of breeding or reproductive success. Sensitivity was sufficient that error in estimating survival rates in the field could easily mask true population differences in effective size. Our results indicate that accurate and precise demographic data are prerequisites to determining effective population size for this species using genetic models, and that a single estimate of rate of loss of genetic variability is not valid across populations.     

Combining demographic and count-based approaches to identify source-sink dynamics of a threatened seabird.

Identifying source-sink dynamics is of fundamental importance for conservation but is often limited by an inability to determine how immigration and emigration influence population processes. We demonstrate two ways to assess the role of immigration on population processes without directly observing individuals dispersing from one population to another and apply these methods to a population of Marbled Murrelets (Brachyramphus marmoratus) in California (USA). In the first method, the rate of immigration (i) is estimated by subtracting local recruitment (recruitment from within the population due to reproduction) estimated with demographic data from total recruitment (f; recruitment from within the population plus recruitment from other populations) estimated using temporal symmetry mark-recapture models developed by R. Pradel. The second method compares population growth rates estimated with temporal symmetry models (lambdaTS) and/or population growth rates estimated from counts of individuals over multiple sampling periods (lambdaC) with growth estimates from a stage-structured projection matrix model (lambdaM). Both lambdaTS and lambdaC incorporate all demographic processes affecting population change (birth, death, immigration, and emigration), whereas matrix models are usually constructed without incorporating immigration. Thus, if lambdaTS and lambdaC are > or = 1 and lambdaM < 1, the population is sustained by immigration and is considered to be a sink. Using the first method, recruitment estimated with temporal symmetry models was high (f= 0.182, SE = 0.058), the mean adult birth rate, as estimated using the ratio of juveniles to > or = 1 year old individuals (observed during ship-based surveys) was low (bA = 0.039, SE = 0.014), and immigration was 0.160 (SE = 0.057). Using the second method, murrelet numbers in central California were stable (lambdaC = 1.058, SE = 0.047; lambdaTS = 1.064, SE = 0.033), but were projected to decline 9.5% annually in the absence of immigration (lambdaM = 0.905, SE = 0.053). Our results suggest that Marbled Murrelets in central California represent a sink population that is stable but would decline in the absence of immigration from larger populations to the north. However, the extent to which modeled immigration is due to permanent recruitment or temporarily dispersing individuals that simply mask population declines is uncertain.     

Extinction Rate Estimates for Plant Populations in Revisitation Studies: Importance of Detectability

Abstract:  Many researchers have obtained extinction-rate estimates for plant populations by comparing historical and current records of occurrence. A population that is no longer found is assumed to have gone extinct. Extinction can then be related to characteristics of these populations, such as habitat type, size, or species, to test ideas about what factors may affect extinction. Such studies neglect the fact that a population may be overlooked, however, which may bias estimates of extinction rates upward. In addition, if populations are unequally detectable across groups to be compared, such as habitat type or population size, comparisons become distorted to an unknown degree. To illustrate the problem, I simulated two data sets, assuming a constant extinction rate, in which populations occurred in different habitats or habitats of different size and these factors affected their detectability. The conventional analysis implicitly assumed that detectability equalled 1 and used logistic regression to estimate extinction rates. It wrongly identified habitat and population size as factors affecting extinction risk. In contrast, with capture-recapture methods, unbiased estimates of extinction rates were recovered. I argue that capture-recapture methods should be considered more often in estimations of demographic parameters in plant populations and communities.     

Systematic Conservation Planning and the Cost of Tackling Conservation Conflicts with Large Carnivores in Italy

Abstract:  Conservation in Europe (including the establishment of protected areas) is undertaken mainly through legislation and on densely populated private land. Consequently, conflicts of interest arise between human economic activities and biodiversity conservation. We used a systematic approach to conservation planning to explore different conservation scenarios for the Apennine populations of wolves (Canis lupus) and bears (Ursus arctos marsicanus) in Italy. The conservation measures we considered were electrified fences and guard dogs to prevent wolves and bears from preying on sheep. We used habitat suitability models of the two species as an estimate of their distributions. Across the study area, we estimated the potential intensity of conflict caused by predation on sheep and the cost of the antipredator measures. We examined scenarios for the conservation of wolves and bears that identified systems of sites where antipredator measures should be applied to either minimize the economic cost of the plan or tackle a predetermined amount of conflict. The overall cost of the conservation plans ranged between €1,486,000 and €16,876,000, depending on the scenario and on the size of the conservation target. Because potential conflict intensity (i.e., potential predation) and cost of conflict resolution were correlated, the scenarios that minimized cost also minimized the amount of conflict that was addressed. Conserving these two species by addressing their predation on sheep was up to 4.36 times more expensive than conserving them by providing suitable habitat in areas of low conflict. Yet avoiding conflicts is not always desirable because it can drastically reduce the options for conservation. Choosing a conservation plan requires consideration of the level of threat to the target species and their sensitivity to conflicts.     

Habitat Change and Plant Demography: Assessing the Extinction Risk of a Formerly Common Grassland Perennial

Abstract:  An important aim of conservation biology is to understand how habitat change affects the dynamics and extinction risk of populations. We used matrix models to analyze the effect of habitat degradation on the demography of the declining perennial plant Trifolium montanum in 9 calcareous grasslands in Germany over 4 years and experimentally tested the effect of grassland management. Finite population growth rates (λ) decreased with light competition, measured as leaf-area index above T. montanum plants. At unmanaged sites λ was <1 due to lower recruitment and lower survival and flowering probability of large plants. Nevertheless, in stochastic simulations, extinction of unmanaged populations of 100 flowering plants was delayed for several decades. Clipping as a management technique rapidly increased population growth because of higher survival and flowering probability of large plants in managed than in unmanaged plots. Transition-matrix simulations from these plots indicated grazing or mowing every second year would be sufficient to ensure a growth rate ≥1 if conditions stayed the same. At frequently grazed sites, the finite growth rate was approximately 1 in most populations of T. montanum . In stochastic simulations, the extinction risk of even relatively small grazed populations was low, but about half the extant populations of T. montanum in central Germany are smaller than would be sufficient for a probability of survival of >95% over 100 years. We conclude that habitat change after cessation of management strongly reduces recruitment and survival of established individuals of this perennial plant. Nevertheless, our results suggest extinction processes may take a long time in perennial plants, resulting in an extinction debt. Even if management is frequent, many remnant populations of T. montanum may be at risk because of their small size, but even a slight increase in size could considerably reduce their extinction risk.     

Relationship between Population Size and Fitness

Abstract:  Long-term effective population size, which determines rates of inbreeding, is correlated with population fitness. Fitness, in turn, influences population persistence. I synthesized data from the literature concerning the effects of population size on population fitness in natural populations of plants to determine how large populations must be to maintain levels of fitness that will provide adequate protection against environmental perturbations that can cause extinction. Integral to this comment on what has been done and what needs to be done, sThe evidence suggests that there is a linear relationship between log population size and population fitness over the range of population sizes examined. More importantly, populations will have to be maintained at sizes of >2000 individuals to maintain population fitness at levels compatible with the conservation goal of long-term persistence. This approach to estimating minimum viable population size provides estimates that are in general agreement with those from numerous other studies and strengthens the argument that conservation efforts should ultimately aim at maintaining populations of several thousand individuals to ensure long-term persistence.     

Minimum area requirements for an at‐risk butterfly based on movement and demography

Determining the minimum area required to sustain populations has a long history in theoretical and conservation biology. Correlative approaches are often used to estimate minimum area requirements (MARs) based on relationships between area and the population size required for persistence or between species’ traits and distribution patterns across landscapes. Mechanistic approaches to estimating MAR facilitate prediction across space and time but are few. We used a mechanistic MAR model to determine the critical minimum patch size (CMP) for the Baltimore checkerspot butterfly (Euphydryas phaeton), a locally abundant species in decline along its southern range, and sister to several federally listed species. Our CMP is based on principles of diffusion, where individuals in smaller patches encounter edges and leave with higher probability than those in larger patches, potentially before reproducing. We estimated a CMP for the Baltimore checkerspot of 0.7–1.5 ha, in accordance with trait‐based MAR estimates. The diffusion rate on which we based this CMP was broadly similar when estimated at the landscape scale (comparing flight path vs. capture‐mark‐recapture data), and the estimated population growth rate was consistent with observed site trends. Our mechanistic approach to estimating MAR is appropriate for species whose movement follows a correlated random walk and may be useful where landscape‐scale distributions are difficult to assess, but demographic and movement data are obtainable from a single site or the literature. Just as simple estimates of lambda are often used to assess population viability, the principles of diffusion and CMP could provide a starting place for estimating MAR for conservation.     

Projecting population trends of endangered amphibian species in the face of uncertainty: A pattern-oriented approach

Amphibian populations have been declining worldwide for the last three decades. Determining the risk of extinction is one of the major goals of amphibian conservation, yet few quantitative models have been developed for amphibian populations. Like most rare or threatened populations, there is a paucity of life history data available for most amphibian populations. Data on the critical juvenile life stage are particularly lacking. Pattern oriented modeling (POM) has been used successfully to estimate life history parameters indirectly when critical data lacking, but has not been applied to amphibian populations. We describe a spatially explicit, individual-based, stochastic simulation model developed to project population dynamics of pond-breeding amphibian populations. We parameterized the model with life history and habitat data collected for the endangered Houston toad (Bufohoustonensis), a species for which there is a high degree of uncertainty for juvenile and adult male survival. During model evaluation, we focused on explicitly reducing this uncertainty, evaluating 16 different versions of the model that represented the range of parametric uncertainty for juvenile and adult male survival. Following POM protocol, we compared simulation results to four population-level patterns observed in the field: population size, adult sex ratio, proportion of toads returning to their natal pond, and mean maximum distance moved. Based on these comparisons, we rejected 11 of the 16 model versions. Results of the remaining versions confirmed that population persistence depends heavily on juvenile survival, and further suggested that probability of juvenile survival is likely between 0.0075 and 0.015 (previous estimates ranged from 0.003 to 0.02), and that annual male survival is near 0.15 (previous estimates ranged up to 0.43).     

Population Trends and the Koala Conservation Debate

Abstract: A critical issue affecting the long-term management of koalas is their perceived conservation status. Koalas still occur in many areas throughout their historical range, but numbers of animals are estimated to vary from <100,000 to at least one order of magnitude higher. Complex factors limit free-ranging koala populations, including food tree preferences, history of disturbance, and Chlamydia infection, all of which make longer-term population trends of many populations difficult to predict. Lack of consensus regarding the size and viability of remaining populations and regarding the extent of and reasons for decline, overabundance or in some instances, hinders the conservation task. A reappraisal of population trends suggests that, notwithstanding localized management issues in Victoria and South Australia, overall the species is "vulnerable" on the basis of current World Conservation Union criteria. Recommendations for more effective conservation of koalas include (1) acknowledging the legitimacy of differing perspectives, (2) recognizing the uncertainty and assumptions inherent in population estimates and trends, (3) applying greater rigor and developing better standards for monitoring population trends, and (4) being cautious in assigning conservation status to national, state, and regional populations.     

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.