Random median sampling to enhance the precision of population estimates

We describe the characteristics of a sampling procedure called random median sampling that was proposed to enhance the precision of population estimates. In performing random median sampling, we first select a sampling item at random from the sampling area. We roughly compare the abundance of individuals in the selected item with that of the adjacent two items in order to identify the item that has median abundance, i.e., the item that has the second largest abundance among the three items. We count the number of individuals of the item having the median abundance. This procedure is repeated n times in the sampling area (i = 1, 2, ..., n). Let m _i be the ith median abundance. The estimates of the mean abundance per sampling item and the variance of estimates are given by Σm _i /n and Σ(m _i –Σm _i /n)²/n(n – 1), respectively. This method is a local application of the median ranked set sampling that was proposed by Muttlak (J Appl Stat Sci 6:245–255, 1997). Random median sampling is effective when the correlation coefficient between adjacent items is small. If the correlation coefficient is close to zero, random median sampling reduces the variance of estimates to 45 or 32% of that in simple random sampling when the distribution follows a normal distribution or a Laplace distribution, respectively. The sample size required to achieve a given precision of estimate decreases accordingly. The effectiveness of random median sampling, however, is small if the correlation coefficient is large. The condition in which random median sampling is superior to simple random sampling is also discussed.     

Noninvasive Hair Sampling and Genetic Tagging of Co-Distributed Fishers and American Martens

ABSTRACT Estimation of abundance is important for assessing population responses to management actions. Accurate abundance estimates are particularly critical for monitoring temporal variation following reintroductions when the management goal is to attain population sizes capable of sustaining harvest. Numerous reintroductions have taken place in the Great Lakes region of North America, including efforts to restore extirpated fishers (Martes pennanti) and American martens (M. americana). We used a DNA-based noninvasive hair-snaring method based on one trap design and trapping -grid configuration, and evaluated capture—mark—recapture (CMR) analytical approaches to simultaneously estimate population size for co-distributed fishers and American martens in a 671-km² area of the Ottawa National Forest in the western Upper Peninsula of Michigan, USA. We included harvest as a final recapture period to increase probability of recapture and to evaluate potential violations of geographic closure assumptions. We used microsatellite markers to identify target species, eliminate congener species, and provide individual identity for estimation of abundance. Population estimates for fishers and martens on the study area ranged from 35 to 60 and 8 to 28, respectively. Estimators incorporating harvest data resulted in up to a 40% increase in abundance estimates relative to estimators without harvest. We considered population estimates not including harvest data the most appropriate for the study due to timing of sampling and environmental factors, but inclusion of harvested individuals was shown to be useful as a means to detect violations of the assumption of geographic closure. We suggest improvements on future CMR sampling designs for larger landscape scales of relevance to management through incorporation of habitat or historical harvest data. Noninvasive genetic methods that simultaneously estimate the numerical abundance of co-distributed species can greatly decrease assessment costs relative to traditional methods, and increase resulting demographic and ecological information.     

Scaling Occupancy Estimates up to Abundance for Wolves

Management of wildlife populations often requires reliable estimates of population size or distribution. Estimating abundance can be logistically difficult, and occupancy models have been used as a less expensive proxy for abundance estimation. Another alternative is to use independent estimates of home-range size and mean group size to directly scale occupancy estimates up to abundance. We used simulations to explore when scaling occupancy up to abundance is reliable, and as an example we applied an occupancy approach to estimate abundance of wolves (Canis lupus) from roadside snow-tracking surveys in northern Wisconsin, USA, in 2016 and 2018. Estimates of wolf abundance were plausible and compared favorably with independent estimates produced by territory mapping, and snow-tracking data requirements were lower than for territory mapping. Simulation results suggested that reasonable abundance estimates could be obtained under some conditions but also that severe positive bias could result under other conditions, especially when populations were small and dispersed, home range size was small, and areal sampling units were large. Positive bias in abundance estimates occurs because of closure assumption violations when tracks from a single wolf or pack are detected in >1 sample unit, and the sum of the sample unit areas where tracks were detected exceed the sum of the home range areas. Bias was minimized when sampling units were small relative to home range size or when sampling units were route segments that approximate point sample units, and when home ranges were highly aggregated. We conclude that, although caution is warranted when scaling occupancy estimates up to abundance, scaled occupancy models can provide feasible and reliable estimates of abundance, assuming home range size and mean group size are accurately known or estimated, sampling units are appropriately chosen, and covariates that aggregate home ranges can be used to accurately predict occupancy probability. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.     

Challenges of DNA-Based Mark-Recapture Studies of American Black Bears

Abstract: We explored whether genetic sampling would be feasible to provide a region-wide population estimate for American black bears (Ursus americanus) in the southern Appalachians, USA. Specifically, we determined whether adequate capture probabilities (p > 0.20) and population estimates with a low coefficient of variation (CV < 20%) could be achieved given typical agency budget and personnel constraints. We extracted DNA from hair collected from baited barbed-wire enclosures sampled over a 10-week period on 2 study areas: a high-density black bear population in a portion of Great Smoky Mountains National Park and a lower density population on National Forest lands in North Carolina, South Carolina, and Georgia. We identified individual bears by their unique genotypes obtained from 9 microsatellite loci. We sampled 129 and 60 different bears in the National Park and National Forest study areas, respectively, and applied closed mark-recapture models to estimate population abundance. Capture probabilities and precision of the population estimates were acceptable only for sampling scenarios for which we pooled weekly sampling periods. We detected capture heterogeneity biases, probably because of inadequate spatial coverage by the hair-trapping grid. The logistical challenges of establishing and checking a sufficiently high density of hair traps make DNA-based estimates of black bears impractical for the southern Appalachian region. Alternatives are to estimate population size for smaller areas, estimate population growth rates or survival using mark-recapture methods, or use independent marking and recapturing techniques to reduce capture heterogeneity.     

The use of faeces counts to estimate relative densities of wild boar in a Mediterranean area

The monitoring of population trends of wild ungulates is important to evaluate their population dynamics and to develop sound conservation/management plans. The wild boar Sus scrofa can impose heavy impacts on ecosytems and human activities, as well as be responsible for disease transmission. Estimating abundance of wild boars is a challenging issue, because of some peculiar biological and ecological traits of this ungulate. Indices of relative abundance could be used to evaluate its population trends. In a Mediterranean area we used faeces counts, through a two-stage stratified sampling, to estimate relative densities of wild boars, between 2007 and 2014. Faeces density estimates increased not significantly between 2007 (151.5 faeces/100 ha) and 2010 (203.8 faeces/100 ha) and decreased significantly from 2010 to 2014 (95.5 faeces/100 ha). The decrease in faeces density estimates was consistent with the increased harvest effort (number of harvest days), performed from 2010 to 2013 to limit impact on ecosystems and reduce damages to crops. The variation of faeces density estimates was also consistent with that of harvest indices (total harvest to harvest effort), with significantly positive values of Pearson and rank correlation coefficients. Results suggest that faeces density estimates achieved with the adopted sampling strategies can be effectively used as indices of relative abundance.     

Zooplankton sampling strategies for environmental studies

This study characterizes sources of variation in total zooplankton abundance estimates at seven stations within the 5–10 m depth contour of southeastern Lake Michigan which were sampled monthly, April through October, for the 1975 to 1979 period. Month, year, and station were statistically significant factors affecting abundance estimates as were all interactions. Month was the largest source of variance either as a main effect or interaction. Smallest coefficients of variation were associated with subsampling (mean 6.1%) and replicate sampling (mean 15.1%). The between-station coefficient of variation averaged 39.0% and tended to be highest during the summer. For a given station and month, the between-year coefficient of variation averaged 73.4% while the between-month coefficient of variation for a single station in a given year averaged 95.1%. A table shows the estimated number of replications necessary to detect a true difference in two population means as a function of coefficient of variation. Environmental studies designed to detect spatial alterations should conduct such analyses on a cruise-by-cruise basis. Cruises should consist of a large number of stations and be conducted at least once during each season. Studies designed to detect temporal alterations require more frequent sampling because of the greater variability associated with temporal data sets. Because spatial variability adds little to the overall variability of such data sets, only a few representative stations need be sampled during each cruise.     

Optimizing survey effort for burrow‐nesting seabirds

ABSTRACT Regular monitoring of seabird populations is necessary to improve our understanding of their responses to environmental change and inform conservation management. However, given the difficulty in accessing remote breeding sites and the limited resources typically available to land managers, conducting regular, extensive surveys of seabird populations is often not feasible. Our objective was to determine the minimum survey effort required to obtain accurate and precise population estimates of Short‐tailed Shearwaters (Ardenna tenuirostris) and Little Penguins (Eudyptula minor), two abundant burrowing seabird species in southeastern Australia, by comparing bootstrapped means and confidence intervals under different sampling regimes on four islands. We found that, in many cases, survey effort (the proportion of transects and quadrats along transects surveyed) could be reduced. For Short‐tailed Shearwaters, reducing the number of transects resulted in a maximum difference of 15% between the means at full survey effort and two levels of reduced survey effort. Means differed by <3% when we halved the number of quadrats. For Little Penguins, reducing the number of transects and quadrats by 50% resulted in differences of 7–40% and 4–34%, respectively, between the full and reduced survey effort means. Confidence intervals generally increased with decreasing survey effort for both species. Differences in required survey effort between the two species in our study may have been due to differences in burrow distribution on islands, with Short‐tailed Shearwater burrows generally uniformly distributed on each island and Little Penguin burrows typically occurring in patches. These would be influenced by island‐specific characteristics in concert with habitat preferences, population size, and seasonal variation in seabird abundance. Stratified sampling did not increase survey accuracy and simulations showed that large reductions in survey effort could be made under a pseudo‐random sampling regime, with mean abundance estimates similar at most levels of survey effort. For both species, reducing the proportion of pseudo‐random quadrats to 50% and 25% of the full survey effort produced confidence intervals of 12% and 21%, respectively, of the maximum, whereas a survey effort of 10% produced confidence intervals of up to 36% of the maximum for both species. A pseudo‐random sampling regime would maximize survey efficiency because considerably fewer quadrats would be required and allow development of more efficient sampling protocols and regimes.     

An Evaluation of Sex-Age-Kill (SAK) Model Performance

ABSTRACT The sex-age-kill (SAK) model is widely used to estimate abundance of harvested large mammals, including white-tailed deer (Odocoileus virginianus). Despite a long history of use, few formal evaluations of SAK performance exist. We investigated how violations of the stable age distribution and stationary population assumption, changes to male or female harvest, stochastic effects (i.e., random fluctuations in recruitment and survival), and sampling efforts influenced SAK estimation. When the simulated population had a stable age distribution and λ > 1, the SAK model underestimated abundance. Conversely, when λ < 1, the SAK overestimated abundance. When changes to male harvest were introduced, SAK estimates were opposite the true population trend. In contrast, SAK estimates were robust to changes in female harvest rates. Stochastic effects caused SAK estimates to fluctuate about their equilibrium abundance, but the effect dampened as the size of the surveyed population increased. When we considered both stochastic effects and sampling error at a deer management unit scale the resultant abundance estimates were within ±121.9% of the true population level 95% of the time. These combined results demonstrate extreme sensitivity to model violations and scale of analysis. Without changes to model formulation, the SAK model will be biased when λ ≠ 1. Furthermore, any factor that alters the male harvest rate, such as changes to regulations or changes in hunter attitudes, will bias population estimates. Sex-age-kill estimates may be precise at large spatial scales, such as the state level, but less so at the individual management unit level. Alternative models, such as statistical age-at-harvest models, which require similar data types, might allow for more robust, broad-scale demographic assessments.     

Monitoring of the Flat-Tailed Horned Lizard With Methods Incorporating Detection Probability

Abstract: Difficulty in monitoring the flat-tailed horned lizard (Phrynosoma mcallii) has led to controversy over its conservation status. The difficulty in detecting this species has discouraged large-scale estimates of abundance and led to uncertainty over whether the species exists in population sizes of sufficient size for long-term persistence. We incorporated detection probability into monitoring of this species using closed mark—recapture and distance-sampling methods. Density estimation from mark—recapture abundance estimates was improved using an estimate of the proportion of time lizards were on the plot. We estimated the probability of detection on the line for distance sampling and adjusted density estimates accordingly. We estimated the populations of the Yuha Basin Management Area in 2002 and the East Mesa Management Area, Imperial County, California, USA, in 2003 to be 25,514 (95% CI 14,444-38,970) and 42,619 (95% CI 23,161-67,639), respectively. Two estimates of detection probability on the line in distance sampling by different methods were 0.45 and 0.65. Density estimates derived from distance analyses for 3 East Mesa Management Area plots and the Yuha Basin Management Area were 1.55 per ha (95% CI 0.64-3.76) and 0.41 per ha (95% CI 0.22-0.7), respectively. These are the first large-scale estimates of abundance and density for P. mcallii.     

Challenges and opportunities for estimating abundance of a low-density moose population

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low-density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance-sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km²) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280-km² Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.     

Incorporating capture heterogeneity in the estimation of autoregressive coefficients of animal population dynamics using capture–recapture data

Population dynamic models combine density dependence and environmental effects. Ignoring sampling uncertainty might lead to biased estimation of the strength of density dependence. This is typically addressed using state‐space model approaches, which integrate sampling error and population process estimates. Such models seldom include an explicit link between the sampling procedures and the true abundance, which is common in capture–recapture settings. However, many of the models proposed to estimate abundance in the presence of capture heterogeneity lead to incomplete likelihood functions and cannot be straightforwardly included in state‐space models. We assessed the importance of estimating sampling error explicitly by taking an intermediate approach between ignoring uncertainty in abundance estimates and fully specified state‐space models for density‐dependence estimation based on autoregressive processes. First, we estimated individual capture probabilities based on a heterogeneity model for a closed population, using a conditional multinomial likelihood, followed by a Horvitz–Thompson estimate for abundance. Second, we estimated coefficients of autoregressive models for the log abundance. Inference was performed using the methodology of integrated nested Laplace approximation (INLA). We performed an extensive simulation study to compare our approach with estimates disregarding capture history information, and using R‐package VGAM, for different parameter specifications. The methods were then applied to a real data set of gray‐sided voles Myodes rufocanus from Northern Norway. We found that density‐dependence estimation was improved when explicitly modeling sampling error in scenarios with low process variances, in which differences in coverage reached up to 8% in estimating the coefficients of the autoregressive processes. In this case, the bias also increased assuming a Poisson distribution in the observational model. For high process variances, the differences between methods were small and it appeared less important to model heterogeneity.     

Effect of count duration on abundance estimates of Black-capped Vireos

ABSTRACT.   Distance sampling applied to point count surveys (point transects) has become a common method for estimating the absolute abundance of birds. When conducting point transects, detections of focal species are typically recorded during a fixed time interval. However, count duration has varied among studies and the effect of such variation on the resulting abundance estimates is unclear. My objective was to examine the effect of count duration on abundance estimates of male Black-capped Vireos ( Vireo atricapilla ). The abundance of these vireos in a 349-ha area in central Texas was estimated using 3-, 5-, and 6-min point transects and results were then compared to actual number present as determined by banding and territory mapping. The 3-min counts provided an estimate that was 26% greater than the actual number of male Vireos present ( N = 201), but this number was within the corresponding 95% confidence interval ( N = 157–413). Confidence intervals for the 5- and 6-min counts did not include the actual number of vireos present. The shortest count duration may have provided the most accurate abundance estimate because male Black-capped Vireos are typically active, sing intermittently, and sometimes move tens of meters between songs. Thus, shorter-duration counts may also yield the most accurate abundance estimates for other species that exhibit similar behavior. However, because behavior varies among species, I recommend that investigators collect preliminary data to establish an appropriate count duration when accurate estimates of absolute, rather than relative, abundance are important.     

Evaluating the current condition of a threatened marine mammal population: Estimating northern sea otter (Enhydra lutris kenyoni) abundance in southwest Alaska

We estimated density and abundance of the threatened southwest Alaska distinct population segment of northern sea otters (Enhydra lutris kenyoni) in two management units. We conducted aerial surveys in Bristol Bay and South Alaska Peninsula management units in 2016, and modeled sea otter density and abundance with Bayesian hierarchical distance sampling models and spatial environmental covariates (depth, distance to shore, depth × distance to shore). Spatial environmental covariates substantially impacted sea otter group density in both management units, but effects sizes differed between the two management units. Abundance (9,733 otters, 95% CrI 6,412–17,819) and density (0.82 otters/km², 95% CrI 0.54–1.49) estimates for Bristol Bay indicated a moderate population size. In contrast, abundance (546 otters, 95% CrI 322–879) and density (0.06 otters/km², 95% CrI 0.03–0.09) estimates indicated a relatively low population size in South Alaska Peninsula. Overall, our results highlight the importance of accounting for the detection process in monitoring at-risk species to reduce the uncertainty associated with making conclusions about population declines.     

Evaluating abundance estimates and evidence of breeding for Bobolinks from transect and point-count surveys

Estimating the abundance and breeding success of territorial songbirds is challenging. Various types of surveys and analyses are available, but all receive some criticism in the literature, and most methods are rarely compared with results obtained using intensive monitoring efforts. We assessed the efficacy of transect and point-count surveys to estimate the abundance of male Bobolinks (Dolichonyx oryzivorus) and detect evidence of nesting and fledging by comparing the results of those surveys to results from more intensive monitoring (i.e., spot mapping and nest monitoring). We monitored 36 fields (254 ha) of late-harvest hay, restored grassland, and fallow fields in the Luther Marsh Wildlife Management Area and on four farms in southern Ontario, Canada, in 2018. Compared to the number of territories identified based on spot mapping (197), distance sampling analysis of transect survey data provided a more accurate estimate of the abundance of male Bobolinks (230, 95% CI: 187, 282) than N-mixture models of transect (668, 95% CI: 332, 1342) and point-count (337, 95% CI: 203, 559) data. Three visits to survey transects and five to point counts did not effectively detect evidence of Bobolink breeding (i.e., nesting or fledging) in fields compared to spot mapping and nest monitoring. Distance sampling analysis of transect data appears promising for estimating the number of Bobolink territories in an area, e.g., those impacted by conservation programs. If estimates of the number of nesting Bobolinks and frequency of fledging are of interest, spot mapping and nest monitoring could be implemented at a subset of sampled fields. Our results suggest that additional studies to evaluate model-based estimates of abundance with the best available information (e.g., from spot mapping of marked or unmarked populations and nest monitoring) would be useful to ensure that robust estimates are provided to support population estimates and conservation actions.     

Using helicopter counts to estimate the abundance of Himalayan tahr in New Zealand's Southern Alps

Estimating the abundance and density of mountain ungulates is difficult because of rugged and remote terrain, high elevations, and rapidly changing weather. Helicopter surveys could overcome these problems, but researchers have seldom applied helicopter-based survey methods at large spatial scales in mountain terrain. We used helicopters to count introduced Himalayan tahr (Hemitragus jemlahicus) at 117 plots, each of 4 km², in New Zealand's Southern Alps during 2016–2019. The sampling frame was 7,844 km² and we located the plots at the vertices of an 8-km grid superimposed over the sampling frame (i.e., a systematic random sampling design). We conducted 3 repeat counts at each plot during summer–autumn. We used the repeat counts to estimate tahr abundance and density, corrected for imperfect detection, using a dynamic N-mixture model for open populations. We estimated the population of tahr in the sampling frame using design-based, finite sampling methods and model-based inference procedures. The mean estimated density of tahr on each plot varied from zero to 31.7 tahr/km². The mean densities of tahr varied among management units, ranging from 0.3 to 10.7 tahr/km², and exceeded specified intervention densities in 6 of the 7 management units. The total design-based estimate of tahr abundance in the sampling frame was 34,500 (95% CI = 27,750–42,900), with a coefficient of variation (CV) of 0.11. The corresponding model-based estimate of total abundance was similar (34,550, 95% CI = 30,250–38,700) but was substantially more precise (CV = 0.06) than the design-based estimate. The precision of the estimates for the individual management units was also better than that of the design-based estimates, with CVs of <0.20 for all but 1 management unit. Our study provides a repeatable method for sampling mountain ungulates. More generally, robust estimation of abundance and density of mountain ungulates is possible by combining aerial surveys and open population models with an objective, probabilistic sampling design.     

Evaluation of Bear Rub Surveys to Monitor Grizzly Bear Population Trends

Abstract: Wildlife managers need reliable estimates of population size, trend, and distribution to make informed decisions about how to recover at-risk populations, yet obtaining these estimates is costly and often imprecise. The grizzly bear (Ursus arctos) population in northwestern Montana, USA, has been managed for recovery since being listed under the United States Endangered Species Act in 1975, yet no rigorous data were available to evaluate the program's success. We used encounter data from 379 grizzly bears identified through bear rub surveys to parameterize a series of Pradel model simulations in Program MARK to assess the ability of noninvasive genetic sampling to estimate population growth rates. We evaluated model performance in terms of 1) power to detect gender-specific and population-wide declines in population abundance, 2) precision and relative bias of growth rate estimates, and 3) sampling effort required to achieve 80% power to detect a decline within 10 years. Simulations indicated that ecosystem-wide, annual bear rub surveys would exceed 80% power to detect a 3% annual decline within 6 years. Robust-design models with 2 simulated surveys per year provided precise and unbiased annual estimates of trend, abundance, and apparent survival. Designs incorporating one survey per year require less sampling effort but only yield trend and apparent survival estimates. Our results suggest that systematic, annual bear rub surveys may provide a viable complement or alternative to telemetry-based methods for monitoring trends in grizzly bear populations.     

Optimising sampling methods for small mammal communities in Neotropical rainforests

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Estimating effective population size from linkage disequilibrium: severe bias in small samples

Effective population size (N _e) is a central concept in evolutionary biology and conservation genetics. It predicts rates of loss of neutral genetic variation, fixation of deleterious and favourable alleles, and the increase of inbreeding experienced by a population. A method exists for the estimation of N _e from the observed linkage disequilibrium between unlinked loci in a population sample. While an increasing number of studies have applied this method in natural and managed populations, its reliability has not yet been evaluated. We developed a computer program to calculate this estimator of N _e using the most widely used linkage disequilibrium algorithm and used simulations to show that this estimator is strongly biased when the sample size is small (<‰100) and below the true N _e. This is probably due to the linkage disequilibrium generated by the sampling process itself and the inadequate correction for this phenomenon in the method. Results suggest that N _e estimates derived using this method should be regarded with caution in many cases. To improve the method’s reliability and usefulness we propose a way to determine whether a given sample size exceeds the population N _e and can therefore be used for the computation of an unbiased estimate.     

Is genomic diversity a useful proxy for census population size? Evidence from a species‐rich community of desert lizards

Species abundance data are critical for testing ecological theory, but obtaining accurate empirical estimates for many taxa is challenging. Proxies for species abundance can help researchers circumvent time and cost constraints that are prohibitive for long‐term sampling. Under simple demographic models, genetic diversity is expected to correlate with census size, such that genome‐wide heterozygosity may provide a surrogate measure of species abundance. We tested whether nucleotide diversity is correlated with long‐term estimates of abundance, occupancy and degree of ecological specialization in a diverse lizard community from arid Australia. Using targeted sequence capture, we obtained estimates of genomic diversity from 30 species of lizards, recovering an average of 5,066 loci covering 3.6 Mb of DNA sequence per individual. We compared measures of individual heterozygosity to a metric of habitat specialization to investigate whether ecological preference exerts a measurable effect on genetic diversity. We find that heterozygosity is significantly correlated with species abundance and occupancy, but not habitat specialization. Demonstrating the power of genomic sampling, the correlation between heterozygosity and abundance/occupancy emerged from considering just one or two individuals per species. However, genetic diversity does no better at predicting abundance than a single day of traditional sampling in this community. We conclude that genetic diversity is a useful proxy for regional‐scale species abundance and occupancy, but a large amount of unexplained variation in heterozygosity suggests additional constraints or a failure of ecological sampling to adequately capture variation in true population size.     

Pooling robustness in distance sampling: Avoiding bias when there is unmodelled heterogeneity

The pooling robustness property of distance sampling results in unbiased abundance estimation even when sources of variation in detection probability are not modeled. However, this property cannot be relied upon to produce unbiased subpopulation abundance estimates when using a single pooled detection function that ignores subpopulations. We investigate by simulation the effect of differences in subpopulation detectability upon bias in subpopulation abundance estimates. We contrast subpopulation abundance estimates using a pooled detection function with estimates derived using a detection function model employing a subpopulation covariate. Using point transect survey data from a multispecies songbird study, species-specific abundance estimates are compared using pooled detection functions with and without a small number of adjustment terms, and a detection function with species as a covariate. With simulation, we demonstrate the bias of subpopulation abundance estimates when a pooled detection function is employed. The magnitude of the bias is positively related to the magnitude of disparity between the subpopulation detection functions. However, the abundance estimate for the entire population remains unbiased except when there is extreme heterogeneity in detection functions. Inclusion of a detection function model with a subpopulation covariate essentially removes the bias of the subpopulation abundance estimates. The analysis of the songbird point count surveys shows some bias in species-specific abundance estimates when a pooled detection function is used. Pooling robustness is a unique property of distance sampling, producing unbiased abundance estimates at the level of the study area even in the presence of large differences in detectability between subpopulations. In situations where subpopulation abundance estimates are required for data-poor subpopulations and where the subpopulations can be identified, we recommend the use of subpopulation as a covariate to reduce bias induced in subpopulation abundance estimates.