Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

A hierarchical model for spatial capture-recapture data

Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effective sample area is unknown. A number of methods involving the adjustment of estimates based on heuristic considerations are in widespread use. In this paper, a hierarchical model of spatially indexed capture-recapture data is proposed for sampling based on area searches of spatial sample units subject to uniform sampling intensity. The hierarchical model contains explicit models for the distribution of individuals and their movements, in addition to an observation model that is conditional on the location of individuals during sampling. Bayesian analysis of the hierarchical model is achieved by the use of data augmentation, which allows for a straightforward implementation in the freely available software WinBUGS. We present results of a simulation study that was carried out to evaluate the operating characteristics of the Bayesian estimator under variable densities and movement patterns of individuals. An application of the model is presented for survey data on the flat-tailed horned lizard (Phrynosoma mcallii) in Arizona, USA.     

Assessing tiger population dynamics using photographic capture-recapture sampling

Although wide-ranging, elusive, large carnivore species, such as the tiger, are of scientific and conservation interest, rigorous inferences about their population dynamics are scarce because of methodological problems of sampling populations at the required spatial and temporal scales. We report the application of a rigorous, noninvasive method for assessing tiger population dynamics to test model-based predictions about population viability. We obtained photographic capture histories for 74 individual tigers during a nine-year study involving 5725 trap-nights of effort. These data were modeled under a likelihood-based, "robust design" capture-recapture analytic framework. We explicitly modeled and estimated ecological parameters such as time-specific abundance, density, survival, recruitment, temporary emigration, and transience, using models that incorporated effects of factors such as individual heterogeneity, trap-response, and time on probabilities of photo-capturing tigers. The model estimated a random temporary emigration parameter of gamma" = gamma' = 0.10 +/- 0.069 (values are estimated mean +/- SE). When scaled to an annual basis, tiger survival rates were estimated at S = 0.77 +/- 0.051, and the estimated probability that a newly caught animal was a transient was tau = 0.18 +/- 0.11. During the period when the sampled area was of constant size, the estimated population size N(t) varied from 17 +/- 1.7 to 31 +/- 2.1 tigers, with a geometric mean rate of annual population change estimated as lambda = 1.03 +/- 0.020, representing a 3% annual increase. The estimated recruitment of new animals, B(t), varied from 0 +/- 3.0 to 14 +/- 2.9 tigers. Population density estimates, D, ranged from 7.33 +/- 0.8 tigers/100 km2 to 21.73 +/- 1.7 tigers/100 km2 during the study. Thus, despite substantial annual losses and temporal variation in recruitment, the tiger density remained at relatively high levels in Nagarahole. Our results are consistent with the hypothesis that protected wild tiger populations can remain healthy despite heavy mortalities because of their inherently high reproductive potential. The ability to model the entire photographic capture history data set and incorporate reduced-parameter models led to estimates of mean annual population change that were sufficiently precise to be useful. This efficient, noninvasive sampling approach can be used to rigorously investigate the population dynamics of tigers and other elusive, rare, wide-ranging animal species in which individuals can be identified from photographs or other means.     

Use of the superpopulation approach to estimate breeding population size: an example in asynchronously breeding birds

Many populations of animals are fluid in both space and time, making estimation of numbers difficult. Much attention has been devoted to estimation of bias in detection of animals that are present at the time of survey. However, an equally important problem is estimation of population size when all animals are not present on all survey occasions. Here, we showcase use of the superpopulation approach to capture-recapture modeling for estimating populations where group membership is asynchronous, and where considerable overlap in group membership among sampling occasions may occur. We estimate total population size of long-legged wading bird (Great Egret and White Ibis) breeding colonies from aerial observations of individually identifiable nests at various times in the nesting season. Initiation and termination of nests were analogous to entry and departure from a population. Estimates using the superpopulation approach were 47-382% larger than peak aerial counts of the same colonies. Our results indicate that the use of the superpopulation approach to model nesting asynchrony provides a considerably less biased and more efficient estimate of nesting activity than traditional methods. We suggest that this approach may also be used to derive population estimates in a variety of situations where group membership is fluid.     

Determining Optimal Population Monitoring for Rare Butterflies

Abstract: Determining population viability of rare insects depends on precise, unbiased estimates of population size and other demographic parameters. We used data on the endangered St. Francis' satyr butterfly (Neonympha mitchellii francisci) to evaluate 2 approaches (mark–recapture and transect counts) for population analysis of rare butterflies. Mark–recapture analysis provided by far the greatest amount of demographic information, including estimates (and standard errors) of population size, detection, survival, and recruitment probabilities. Mark–recapture analysis can also be used to estimate dispersal and temporal variation in rates, although we did not do this here. Models of seasonal flight phenologies derived from transect counts (Insect Count Analyzer) provided an index of population size and estimates of survival and statistical uncertainty. Pollard–Yates population indices derived from transect counts did not provide estimates of demographic parameters. This index may be highly biased if detection and survival probabilities vary spatially and temporally. In terms of statistical performance, mark–recapture and Pollard–Yates indices were least variable. Mark–recapture estimates were less likely to fail than Insect Count Analyzer, but mark–recapture estimates became less precise as sampling intensity decreased. In general, count‐based approaches are less costly and less likely to cause harm to rare insects than mark–recapture. The optimal monitoring approach must reconcile these trade‐offs. Thus, mark–recapture should be favored when demographic estimates are needed, when financial resources enable frequent sampling, and when marking does not harm the insect populations. The optimal sampling strategy may use 2 sampling methods together in 1 overall sampling plan: limited mark–recapture sampling to estimate survival and detection probabilities and frequent but less expensive transect counts.     

Application of adaptive cluster sampling to low-density populations of freshwater mussels

Freshwater mussels appear to be promising candidates for adaptive cluster sampling because they are benthic macroinvertebrates that cluster spatially and are frequently found at low densities. We applied adaptive cluster sampling to estimate density of freshwater mussels at 24 sites along the Cacapon River, WV, where a preliminary timed search indicated that mussels were present at low density. Adaptive cluster sampling increased yield of individual mussels and detection of uncommon species; however, it did not improve precision of density estimates. Because finding uncommon species, collecting individuals of those species, and estimating their densities are important conservation activities, additional research is warranted on application of adaptive cluster sampling to freshwater mussels. However, at this time we do not recommend routine application of adaptive cluster sampling to freshwater mussel populations. The ultimate, and currently unanswered, question is how to tell when adaptive cluster sampling should be used, i.e., when is a population sufficiently rare and clustered for adaptive cluster sampling to be efficient and practical? A cost-effective procedure needs to be developed to identify biological populations for which adaptive cluster sampling is appropriate.     

Estimating trend precision and power to detect trends across grouped count data

Ecologists commonly use grouped or clustered count data to estimate temporal trends in counts, abundance indices, or abundance. For example, the U.S. Breeding Bird Survey data represent multiple counts of birds from within each of multiple, spatially defined routes. Despite a reliance on grouped counts, analytical methods for prospectively estimating precision of trend estimates or statistical power to detect trends that explicitly acknowledge the characteristics of grouped count data are undescribed. These characteristics include the fact that the sampling variance is an increasing function of the mean, and that sampling and group-level variance estimates are generally estimated on different scales (the sampling and log scales, respectively). We address these issues for repeated sampling of a single population using an analytical approach that has the flavor of a generalized linear mixed model, specifically that of a negative binomial-distributed count variable with random group effects. The count mean, including grand intercept, trend, and random group effects, is modeled linearly on the log scale, while sampling variance of the mean is estimated on the log scale via the delta method. Results compared favorably with those derived using Monte Carlo simulations. For example, at trend = 5% per temporal unit, differences in standard errors and in power were modest relative to those estimated by simulation (< or = /11/% and < or = /16/%, respectively), with relative differences among power estimates decreasing to < or = /7/% when power estimated by simulations was > or = 0.50. Similar findings were obtained using data from nine surveys of fingernail clams in the Mississippi River. The proposed method is suggested (1) where simulations are not practical and relative precision or power is desired, or (2) when multiple precision or power calculations are required and where the accuracy of a fraction of those calculations will be confirmed using simulations.     

A new approach to the "apparent survival" problem: estimating true survival rates from mark-recapture studies

Survival estimates generated from live capture-mark-recapture studies may be negatively biased due to the permanent emigration of marked individuals from the study area. In the absence of a robust analytical solution, researchers typically sidestep this problem by simply reporting estimates using the term "apparent survival." Here, we present a hierarchical Bayesian multistate model designed to estimate true survival by accounting for predicted rates of permanent emigration. Initially we use dispersal kernels to generate spatial projections of dispersal probability around each capture location. From these projections, we estimate emigration probability for each marked individual and use the resulting values to generate bias-adjusted survival estimates from individual capture histories. When tested using simulated data sets featuring variable detection probabilities, survival rates, and dispersal patterns, the model consistently eliminated negative biases shown by apparent survival estimates from standard models. When applied to a case study concerning juvenile survival in the endangered Cape Sable Seaside Sparrow (Ammodramus maritimus mirabilis), bias-adjusted survival estimates increased more than twofold above apparent survival estimates. Our approach is applicable to any capture-mark-recapture study design and should be particularly valuable for organisms with dispersive juvenile life stages.     

Importance of the National Petroleum Reserve–Alaska for Aquatic Birds

We used data from aerial surveys (1992–2010) of >100,000 km² and ground surveys (1998–2004) of >150 km² to estimate the density and abundance of birds on the North Slope of Alaska (U.S.A.). In the ground surveys, we used double sampling to estimate detection ratios. We used the aerial survey data to compare densities of birds and Arctic fox (Vulpes lagopus), the major nest predator of birds, on the North Slope, in Prudhoe Bay, and in nearby areas. We partitioned the Prudhoe Bay oil field into 2 × 2 km plots and determined the relation between density of aquatic birds and density of roads, buildings, and other infrastructure in these plots. Abundance and density (birds per square kilometer) of 3 groups of aquatic birds—waterfowl, loons, and grebes; shorebirds; and gulls, terns, and jaegers—were highest in the National Petroleum Reserve–Alaska (NPRA) and lowest in the Arctic National Wildlife Refuge. Six other major wetlands occur in the Arctic regions of Canada and Russia, but the largest population of aquatic birds was in the NPRA. Aquatic birds were concentrated in the northern part of the NPRA. For example, an area that covered 18% of the NPRA included 53% of its aquatic birds. The aerial surveys showed that bird density was not lower and fox density was not higher in Prudhoe Bay than in surrounding areas. Density of infrastructure did not significantly affect bird density for any group of species. Our results establish that the NPRA is one of the most important areas for aquatic birds in the Arctic. Our results and those of others also indicate that oil production, as practiced in Prudhoe Bay, does not necessarily lead to substantial declines in bird density or productivity in or near the developed areas. Prioridades para la Conservación de Aves en el Norte de Alaska     

Occupancy Patterns of Regionally Declining Grassland Sparrow Populations in a Forested Pennsylvania Landscape

Organisms can be affected by processes in the surrounding landscape outside the boundary of habitat areas and by local vegetation characteristics. There is substantial interest in understanding how these processes affect populations of grassland birds, which have experienced substantial population declines. Much of our knowledge regarding patterns of occupancy and density stem from prairie systems, whereas relatively little is known regarding how occurrence and abundance of grassland birds vary in reclaimed surface mine grasslands. Using distance sampling and single‐season occupancy models, we investigated how the occupancy probability of Grasshopper (Ammodramus savannarum) and Henslow's Sparrows (A. henslowii) on 61 surface mine grasslands (1591 ha) in Pennsylvania changed from 2002 through 2011 in response to landscape, grassland, and local vegetation characteristics . A subset (n = 23; 784 ha) of those grasslands were surveyed in 2002, and we estimated changes in sparrow density and vegetation across 10 years. Grasshopper and Henslow's Sparrow populations declined 72% and 49%, respectively from 2002 to 2011, whereas overall woody vegetation density increased 2.6 fold. Henslow's Sparrows avoided grasslands with perimeter–area ratios ≥0.141 km/ha and woody shrub densities ≥0.04 shrubs/m². Both species occupied grasslands ≤13 ha, but occupancy probability declined with increasing grassland perimeter–area ratio and woody shrub density. Grassland size, proximity to nearest neighboring grassland ( = 0.2 km), and surrounding landscape composition at 0.5, 1.5, and 3.0 km were not parsimonious predictors of occupancy probability for either species. Our results suggest that reclaimed surface mine grasslands, without management intervention, are ephemeral habitats for Grasshopper and Henslow's Sparrows. Given the forecasted decline in surface coal production for Pennsylvania, it is likely that both species will continue to decline in our study region for the foreseeable future. Patrones de Ocupación de Poblaciones Regionalmente Declinantes de Gorriones de Pastizales en un Paisaje Boscoso de Pennsylvania     

Toward accurate and precise estimates of lion density

Reliable estimates of animal density are fundamental to understanding ecological processes and population dynamics. Furthermore, their accuracy is vital to conservation because wildlife authorities rely on estimates to make decisions. However, it is notoriously difficult to accurately estimate density for wide‐ranging carnivores that occur at low densities. In recent years, significant progress has been made in density estimation of Asian carnivores, but the methods have not been widely adapted to African carnivores, such as lions (Panthera leo). Although abundance indices for lions may produce poor inferences, they continue to be used to estimate density and inform management and policy. We used sighting data from a 3‐month survey and adapted a Bayesian spatially explicit capture‐recapture (SECR) model to estimate spatial lion density in the Maasai Mara National Reserve and surrounding conservancies in Kenya. Our unstructured spatial capture‐recapture sampling design incorporated search effort to explicitly estimate detection probability and density on a fine spatial scale, making our approach robust in the context of varying detection probabilities. Overall posterior mean lion density was estimated to be 17.08 (posterior SD 1.310) lions >1 year old/100 km², and the sex ratio was estimated at 2.2 females to 1 male. Our modeling framework and narrow posterior SD demonstrate that SECR methods can produce statistically rigorous and precise estimates of population parameters, and we argue that they should be favored over less reliable abundance indices. Furthermore, our approach is flexible enough to incorporate different data types, which enables robust population estimates over relatively short survey periods in a variety of systems. Trend analyses are essential to guide conservation decisions but are frequently based on surveys of differing reliability. We therefore call for a unified framework to assess lion numbers in key populations to improve management and policy decisions.     

A test on social behavior as a cause of dispersal of spruce grouse

Summary Social behavior is one hypothesized cause for dispersal. We tested this hypothesis on spruce grouse (Dendragapus canadensis) in two years by removing adults in summer and monitoring emigration of juveniles in autumn. Despite the absence of adults, juveniles emigrated from the natal range at rates comparable to juveniles on control areas. Data on density and dispersal from earlier spruce grouse studies are reviewed, which show that emigration is constant despite substantial differences in density, and that a clear female sex bias occurs across a wide range of densities. Results do not support the hypothesis that social behavior influences dispersal nor do they support a prediction of the Oedipus hypothesis that no sex bias occurs in polygynous birds. Offprint requests to: D.M. Keppie     

On estimating Hooded crow density from line transect data through exponential mixture models

Line transect sampling is a distance sampling method widely used for estimating wildlife population density. Since the usual approach assumes a model for the detection function, the estimate depends on the shape of such a function. In particular, the estimate is influenced by the so-called shoulder condition, which ensures that detection is nearly certain at small distances from the line transect. For instance, the half-normal model satisfies this condition, whereas the negative exponential model does not. The aim of this paper is to propose the exponential mixture model of the half-normal and the negative exponential in order to estimate the population density in the case where the shoulder condition is not guaranteed. Such a case study on Hooded crow is described in the paper.     

Estimation of population parameters from aerial counts of North American mallards: a cautionary tale.

The accuracy of population estimates strongly interferes with our ability to obtain unbiased estimates of population parameters based on analyses of time series of population fluctuations. Here we use long-term data on fluctuations in the size of Mallard populations collected as part of the May Breeding Waterfowl Survey covering a large section of North America. We assume a log-linear model of density dependence and use a hierarchical Bayesian state-space approach in which all parameters are assumed to be realizations from a common underlying distribution. Thus, parameters for different populations are not allowed to vary independently of each other. We then simulated independent time series of aerial counts, using the estimated parameters and adding various levels of observation error. These simulations showed that the estimates of stochastic population growth rate and strength of density dependence were biased even when moderate sampling errors were present. In contrast, the estimates of the environmental stochasticity and the carrying capacity were unbiased even for short time series and large observation error. Our results underline the importance of reducing the magnitude of sampling error in the design of large-scale monitoring programs of population fluctuations.     

Geostatistical analysis of spatial distribution of Liocarcinus depurator,Macropipus tuberculatus and Polybius henslowii (Crustacea: Brachyura) over the Galician continental shelf (NW Spain)

Geostatistics was used to analyze the spatial structure and distribution of three species of brachyurans, Liocarcinus depurator, Macropipus tuberculatus and Polybius henslowii, from sets of data collected during three survey cruises (1983 and 1984) over the Galician continental shelf. The present study investigates the feasibility of using geostatistics with data collected according to traditional methods and of thereby improving the sampling methodology. In order to investigate the spatial structure of the species studied, experimental variograms were calculated and fitted to a spherical model. The spatial structure model was used to estimate and map abundance and distribution of the populations studied using the so-called Kriging technique. Geostatistical analysis enabled the determination of spatial density gradients as well as patch size (14 to 22 km, L. depurator; 10 to 28 km, M. tuberculatus; 7.5 to 28 km, P. henslowii) along the continental shelf. Depth was revealed as a limiting factor, restricting the distribution of L. depurator and M. tuberculatus on a large scale, whereas upwelling processes and nutrient-rich waters from the rías affected the spatial structure on a smaller scale, especially in the case of P. henslowii. A spatial segregation in the distribution of the three species also emerged; this probably arose from differences in physical and biological factors that result in different habitat-exploitation patterns. The study demonstrates the existence of spatial covariance and that the variograms vary as a function of population density and geographical area. This information will be useful in improving the design of future sampling cruises.     

一种用粪便样方法估计林麝种群密度的新方法

数量(密度)特征是动物种群的一个重要特征，是定点研究一个野生种群生态的一项非常重要的基础工作．其数量和年龄结构为进一步分析其种群的密度、移动、巢域和核域，尤其是为种群动态提供依据，也是珍稀动物保护和资源动物管理利用的先行工作和重要组成部份．关于野生动物种群数量的具体统计方法，有不少的文章报道，然而从动物的空间分布格局来研究大型脊椎动物却较为少见，这主要是受研究方法的限制．为此，作者对此进行了改进，提出了一种新的研究方法：临时空间样方法，并将其应用到冶勒自然保护区林麝的数量调查研究中去．结果表明：(1)集聚分布指数CA=12．09，说明冶勒自然保护区的林麝有较高的集聚度；(2)林麝粪堆在野外的分布属于聚集分布，并与负二项分布拟合较好；(3)在冶勒自然保护区，生活有62～98只左右的林麝，其密度为：1．0～1．6只／km^2．林麝在冶勒自然保护区呈现聚积分布的原因是由于环境和人类活动因素造成的．图1表2参24     

Estimating Sustainable Bycatch Rates for California Sea Lion Populations in the Gulf of California

Abstract:  Commercial and subsistence fisheries pressure is increasing in the Gulf of California, Mexico. One consequence often associated with high levels of fishing pressure is an increase in bycatch of marine mammals and birds. Fisheries bycatch has contributed to declines in several pinniped species and may be affecting the California sea lion ( Zalophus californianus ) population in the Gulf of California. We used data on fisheries and sea lion entanglement in gill nets to estimate current fishing pressure and fishing rates under which viable sea lion populations could be sustained at 11 breeding sites in the Gulf of California. We used 3 models to estimate sustainable bycatch rates: a simple population-growth model, a demographic model, and an estimate of the potential biological removal. All models were based on life history and census data collected for sea lions in the Gulf of California. We estimated the current level of fishing pressure and the acceptable level of fishing required to maintain viable sea lion populations as the number of fishing days (1 fisher/boat setting and retrieving 1 day's worth of nets) per year. Estimates of current fishing pressure ranged from 101 (0–405) fishing days around the Los Machos breeding site to 1887 (842–3140) around the Los Islotes rookery. To maintain viable sea lion populations at each site, the current level of fishing permissible could be augmented at some sites and should be reduced at other sites. For example, the area around San Esteban could support up to 1428 (935–2337) additional fishing days, whereas fishing around Lobos should be reduced by at least 165 days (107–268). Our results provide conservation practitioners with site-specific guidelines for maintaining sustainable sea lion populations and provide a method to estimate fishing pressure and sustainable bycatch rates that could be used for other marine mammals and birds .     

Two neighborhood-free plot designs for adaptive sampling of forests

Adaptive cluster sampling (ACS) has the potential of being superior for sampling rare and geographically clustered populations. However, setting up an efficient ACS design is challenging. In this study, two adaptive plot designs are proposed as alternatives: one for fixed-area plot sampling and the other for relascope sampling (also known as variable radius plot sampling). Neither includes a neighborhood search which makes them much easier to execute. They do, however, include a conditional plot expansion: at a sample point where a predefined condition is satisfied, sampling is extended to a predefined larger cluster-plot or a larger relascope plot. Design-unbiased estimators of population total and its variance are derived for each proposed design, and they are applied to ten artificial and one real tree position maps to estimate density (number of trees per ha) and basal area (the cross-sectional area of a tree stem at breast height) per hectare. The performances—in terms of relative standard error (SE%)—of the proposed designs and their non-adaptive alternatives are compared. The adaptive plot designs were superior for the clustered populations in all cases of equal sample sizes and in some cases of equal area of sample plots. However, the improvement depends on: (1) the plot size factor; (2) the critical value (the minimum number of trees triggering an expansion); (3) the subplot distance for the adapted cluster-plots, and (4) the spatial arrangement of the sampled population. For some spatial arrangements, the improvement is relatively small. The adaptive designs may be particularly attractive for sampling in rare and compactly clustered populations with critical value of 1, subplot distance equal to the diameter of initial circular plots, or plot size factor of 2.5 for an initial basal area factor of 2.     

Effectiveness of Predator Removal for Enhancing Bird Populations

Abstract: Predation pressure on vulnerable bird species has made predator control an important issue for international nature conservation. Predator removal by culling or translocation is controversial, expensive, and time‐consuming, and results are often temporary. Thus, it is important to assess its effectiveness from all available evidence. We used explicit systematic review methodology to determine the impact of predator removal on four measurable responses in birds: breeding performance (hatching success and fledging success) and population size (breeding and postbreeding). We used meta‐analysis to summarize results from 83 predator removal studies from six continents. We also investigated whether characteristics of the prey, predator species, location, and study methodology explained heterogeneity in effect sizes. Removing predators increased hatching success, fledging success, and breeding populations. Removing all predator species achieved a significantly larger increase in breeding population than removing only a subset. Postbreeding population size was not improved on islands, or overall, but did increase on mainlands. Heterogeneity in effect sizes for the four population parameters was not explained by whether predators were native or introduced; prey were declining, migratory, or game species; or by the study methodology. Effect sizes for fledging success were smaller for ground‐nesting birds than those that nest elsewhere, but the difference was not significant. We conclude that current evidence indicates that predator removal is an effective strategy for the conservation of vulnerable bird populations. Nevertheless, the ethical and practical problems associated with predator removal may lead managers to favor alternative, nonlethal solutions. Research is needed to provide and synthesize data to determine whether these are effective management practices for future policies on bird conservation.     

Estimating the abundance of mouse populations of known size: promises and pitfalls of new methods.

Knowledge of animal abundance is fundamental to many ecological studies. Frequently, researchers cannot determine true abundance, and so must estimate it using a method such as mark-recapture or distance sampling. Recent advances in abundance estimation allow one to model heterogeneity with individual covariates or mixture distributions and to derive multimodel abundance estimators that explicitly address uncertainty about which model parameterization best represents truth. Further, it is possible to borrow information on detection probability across several populations when data are sparse. While promising, these methods have not been evaluated using mark-recapture data from populations of known abundance, and thus far have largely been overlooked by ecologists. In this paper, we explored the utility of newly developed mark-recapture methods for estimating the abundance of 12 captive populations of wild house mice (Mus musculus). We found that mark-recapture methods employing individual covariates yielded satisfactory abundance estimates for most populations. In contrast, model sets with heterogeneity formulations consisting solely of mixture distributions did not perform well for several of the populations. We show through simulation that a higher number of trapping occasions would have been necessary to achieve good estimator performance in this case. Finally, we show that simultaneous analysis of data from low abundance populations can yield viable abundance estimates.