The landscape configuration and lethality of snare poaching of sympatric guilds of large carnivores and ungulates

Poaching of wildlife presents one of the biggest conservation challenges in the 21st century. Snaring is one of the primary means of capturing target animals. To prioritise interventions intending to reduce snaring, we describe an approach for quantifying the configuration and lethality of snares. We conducted transect surveys in Murchison Falls National Park. All the snares that we recovered were made of wire with the majority (81.0%, n = 546 of 674) deriving from vehicle tire wire. The density of snares ranged from 0.08 to 4.58 snares/km², which is the highest known density in sub‐Saharan Africa. The majority (63%) of the animals caught in wire snares were unrecovered and wasted. We found that noose width, vertical drop, wire circumference, anchor height, proportion of un‐thicketed area, grass height, distance to river and village had a significant positive relationships to lethality, while snare thickness, charms, tree DBH, thicket diameter, distance to nearest road negatively affected lethality. We recommend adopting wholistic anti‐snare countermeasures such as the human heritage‐centred conservation to empower local people. Our method illustrates the opportunity to standardise temporal and spatial measurements of snare density and configuration necessary to stop illegal wildlife poaching.     

Estimation of Black Bear Abundance Using a Discrete DNA Sampling Device

Abstract We developed a snare for collection of black bear (Ursus americanus) hair that obtained a unique hair sample at each snare site, improved the quantity of collected hair compared to barbed-wire corrals, and was easy to deploy over a wide range of topographical features and habitat conditions. This device allowed us to implement intensive sampling methodology needed in mark-recapture experiments with minimal effort. By improving the quantity of hair collected, we also lowered the potential for bear identification errors at the lab. During 2003–2004, bears in 2 study areas triggered snares 1,104 times, which resulted in the collection of 981 hair samples. Of the samples we collected, 79% (775) produced valid genetic data. In 2003, 454 samples identified 79 genetically distinct individuals, and 321 samples identified 86 genetically distinct individuals in 2004. Analysis of capture-recapture data indicated that capture probabilities were affected by heterogeneity among individuals and behavioral responses, but showed little evidence of time effects. Consequently, we used the Pollock and Otto (1983) estimator for model M_bh to estimate abundance with reasonably good precision (CV: 12–14%). Density on the Steamboat and Toketee, Oregon, USA, study areas over the 2-year period averaged 19 bears/100 km² and 22 bears/100 km², respectively. Average capture and recapture probabilities over the 2 years of the study were 30% and 63%, respectively, indicating a trap-prone behavioral response. Knowledge of bear densities on the Steamboat and Toketee study areas will enable managers to set hunting quotas, advise land management agencies on habitat issues, and create a baseline database to assist in the long-term monitoring of bear trends in a changing landscape.     

Comparison of the effectivity of different snare types for collecting and retaining hair from Eurasian Lynx (Lynx lynx)

Genetic methods are increasingly being used as noninvasive tools to survey populations of wild animals. One challenge of these methods is the sampling of genetic material from the target species. Genetic material of various predators, such as bears, canids, and felids, has been successfully obtained from both hair trapped in snares and scat. However, there is currently no standard procedure for sampling genetic material from the Eurasian Lynx (Lynx lynx). We tested established and newly developed hair snares in two near-natural lynx enclosures in the Bavarian Forest National Park. All snares consisted of a wooden post; they differed in the type of material attached to the post for snaring hair: carpet (velour with 40 nails), wildcat (spruce wood with 2–3?mm deep, horizontal and diagonal ridges), wire brush, doormat, or rubber bands (250?g of rubber bands wrapped around the post). We determined the acceptance of the hair snares by the animals by observing their behavior with the aid of video cameras. The number of rubbing events on the different trap types did not significantly differ, but the rubbing duration was longer for the doormat hair snare. The wire brush hair snare collected the highest total amount of hair and — beside the carpet — the highest amount of hair per unit of time. Almost all hair trapped on the wire brush snare were retained during a 2-week exposure to the elements outside of the enclosures. The results of our study may hold for other felid species with hair characteristics similar to those of lynx.     

Estimating abundances,densities, and interspecific associations in a carnivore community

Estimating population abundances, densities, and interspecific interactions are common goals in wildlife management. Camera traps have been used to estimate the abundance and density of a single species, and are useful for carnivores that occur at low densities. Spatial capture–recapture (SCR) models can be used to estimate abundance and density from a camera trap array when all, some, or no individuals in the population can be uniquely identified. These SCR models also estimate locations of individual activity centers, the spatial patterning of which could provide important information about interspecific interactions. We used SCR models to estimate abundances, densities, and activity centers of each of 3 carnivore species (i.e., dingo [Canis familiaris], red fox [Vulpes vulpes], and feral cat) using photographs from 1 camera trap array in southeastern Australia during September to November 2015. Some dingoes and feral cats were uniquely identifiable and therefore, we used a spatial mark–resight model for these species. We could not uniquely identify fox individuals, however, so we used a spatial unmarked (SUN) model for this species. Our estimated dingo density was 0.06/km². The fox (0.25/km²) and feral cat (0.16/km²) densities are within the ranges previously reported for these species in Australia. We obtained a relatively imprecise fox density estimate because we did not have detections of uniquely identifiable individuals; hence, the SUN model should be used as a last resort. We next modeled spatial dependence among the estimated activity centers for the 3 species using a spatial pair correlation function for a marked point process. Consistent with our expectations, the activity centers of dingoes and foxes were strongly negatively associated at distances of <1,000 m. Foxes and feral cats were also negatively associated at distances of <1,500 m. Surprisingly, dingoes and feral cats were positively associated at distances of >500 m, with no association evident at distances of <500 m. Our study extends the inferences that can be made from using a camera trap array and SCR methods to include spatial patterning and interspecific interactions, and provides new insights into the carnivore community of dingoes, foxes, and feral cats in southeastern Australia. © 2019 The Authors. The Journal of Wildlife Management Published by Wiley Periodicals, Inc.     

Factors associated with black bear density and implications for management

Wildlife density estimates are important to accurately formulate population management objectives and understand the relationship between habitat characteristics and a species’ abundance. Despite advances in density and abundance estimation methods, management of common game species continues to be challenged by a lack of reliable population estimates. In Washington, USA, statewide American black bear (Ursus americanus) abundance estimates are predicated on density estimates derived from research in the 1970s and are hypothesized to be a function of precipitation and vegetation, with higher densities in western Washington. To evaluate current black bear density and landscape relationships in Washington, we conducted a 4-year capture-recapture study in 2 areas of the North Cascade Mountains using 2 detection methods, non-invasive DNA collection and physical capture and deployment of global positioning system (GPS) collars. We integrated GPS telemetry from collared bears with spatial capture-recapture (SCR) data and created a SCR-resource selection model to estimate density as a function of spatial covariates and test the hypothesis that density is higher in areas with greater vegetative food resources. We captured and collared 118 bears 132 times and collected 7,863 hair samples at hair traps where we identified 537 bears from 1,237 detections via DNA. The most-supported model in the western North Cascades depicted a negative relationship between black bear density and an index of human development. We estimated bear density at 20.1 bears/100 km², but density varied from 13.5/100 km² to 27.8 bears/100 km² depending on degree of human development. The model best supported by the data in the eastern North Cascades estimated an average density of 19.2 bears/100 km², which was positively correlated with primary productivity, with resulting density estimates ranging from 7.1/100 km² to 33.6 bears/100 km². The hypothesis that greater precipitation and associated vegetative production in western Washington supports greater bear density compared to eastern Washington was not supported by our data. In western Washington, empirically derived average density estimates (including cubs) were nearly 50% lower than managers expected prior to our research. In eastern Washington average black bear density was predominantly as expected, but localized areas of high primary productivity supported greater than anticipated bear densities. Our findings underscore the importance that black bear density is not likely uniform and management risk may be increased if an average density is applied at too large a scale. Disparities between expected and empirically derived bear density illustrate the need for more rigorous monitoring to understand processes that affect population numbers throughout the jurisdiction, and suggest that management plans may need to be reevaluated to determine if current harvest strategies are achieving population objectives. © 2019 The Wildlife Society.     

Density and genetic structure of black bears in coastal South Carolina

The frequency of black bear (Ursus americanus) sightings, vehicle collisions, and nuisance incidents in the coastal region of South Carolina has increased over the past 4 decades. To develop the statewide Black Bear Management and Conservation Strategy, the South Carolina Department of Natural Resources needed reliable information for the coastal population. Because no such data were available, we initiated a study to determine population density and genetic structure of black bears. We selected 2 study areas that were representative of the major habitat types in the study region: Lewis Ocean Bay consisted primarily of Carolina Bays and pocosin habitats, whereas Carvers Bay was representative of extensive pine plantations commonly found in the region. We established hair snares on both study areas to obtain DNA from hair samples during 8 weekly sampling periods in 2008 and again in 2009. We used genotypes to obtain capture histories of sampled bears. We estimated density using spatially explicit capture–recapture (SECR) models and used information-theoretic procedures to fit parameters for capture heterogeneity and behavioral responses and to test if density and model parameters varied by year. Model-averaged density was 0.046 bears/km² (SE = 0.011) for Carvers Bay and 0.339 bears/km² (SE = 0.056) for Lewis Ocean Bay. Next, we sampled habitat covariates for all locations in the SECR sampling grid to derive spatially explicit estimates of density based on habitat characteristics. Addition of habitat covariates had substantial support, and accounted for differences in density between Carvers Bay and Lewis Ocean Bay; black bear density showed a negative association with the area of pine forests (4.5-km² scale) and a marginal, positive association with the area of pocosin habitat (0.3-km² scale). Bear density was not associated with pine forest at a smaller scale (0.3-km²), nor with major road density or an index of largest patch size. Predicted bear densities were low throughout the coastal region and only a few larger areas had high predicted densities, most of which were centered on public lands (e.g., Francis Marion National Forest, Lewis Ocean Bay). We sampled a third bear population in the Green Swamp area of North Carolina for genetic structure analyses and found no evidence of historic fragmentation among the 3 sampled populations. Neither did we find evidence of more recent barriers to gene exchange; with the exception of 1 recent migrant, Bayesian population assignment techniques identified only a single population cluster that incorporated all 3 sampled areas. Bears in the region may best be managed as 1 population. If the goal is to maintain or increase bear densities, demographic connectivity of high-density areas within the low-density landscape matrix is a key consideration and managers would need to mitigate potential impacts of planned highway expansions and anticipated development. Because the distribution of black bears in coastal South Carolina is not fully known, the regional map of potential black bear density can be used to identify focal areas for management and sites that should be surveyed for occupancy or where more intensive studies are needed. © 2012 The Wildlife Society.     

Estimating Black Bear Density Using DNA Data From Hair Snares

ABSTRACT DNA-based mark-recapture has become a methodological cornerstone of research focused on bear species. The objective of such studies is often to estimate population size; however, doing so is frequently complicated by movement of individual bears. Movement affects the probability of detection and the assumption of closure of the population required in most models. To mitigate the bias caused by movement of individuals, population size and density estimates are often adjusted using ad hoc methods, including buffering the minimum polygon of the trapping array. We used a hierarchical, spatial capture-recapture model that contains explicit components for the spatial-point process that governs the distribution of individuals and their exposure to (via movement), and detection by, traps. We modeled detection probability as a function of each individual's distance to the trap and an indicator variable for previous capture to account for possible behavioral responses. We applied our model to a 2006 hair-snare study of a black bear (Ursus americanus) population in northern New York, USA. Based on the microsatellite marker analysis of collected hair samples, 47 individuals were identified. We estimated mean density at 0.20 bears/km². A positive estimate of the indicator variable suggests that bears are attracted to baited sites; therefore, including a trap-dependence covariate is important when using bait to attract individuals. Bayesian analysis of the model was implemented in WinBUGS, and we provide the model specification. The model can be applied to any spatially organized trapping array (hair snares, camera traps, mist nests, etc.) to estimate density and can also account for heterogeneity and covariate information at the trap or individual level.     

Estimating abundance with sparse data: tigers in northern Myanmar

As part of a national strategy for recovering tiger populations, the Myanmar Government recently proposed its first and the world’s largest tiger reserve in the Hukaung Valley, Kachin State. During November 2002–June 2004, camera-traps were used to record tigers, identify individuals, and, using capture–recapture approaches, estimate density in the reserve. Despite extensive (203 trap locations, 275–558 km² sample plots) and intensive (>4,500 trap nights, 9 months of sampling) survey efforts, only 12 independent detections of six individual tigers were made across three study sites. Due to the sparse data, estimates of tiger abundance generated by Program CAPTURE could not be made for all survey sites. Other approaches to estimating density, based on numbers of tigers caught, or derived from borrowed estimates of detection probability, offer an alternative to capture–recapture analysis. Tiger densities fall in the range of 0.2–2.2 tigers/100 km², with 7–71 tigers inside a 3,250 km² area of prime tiger habitat, where efforts to protect tigers are currently focused. Tiger numbers might be stabilized if strict measures are taken to protect tigers and their prey from seasonal hunting and to suppress illegal trade in wildlife. Efforts to monitor abundance trends in the tiger population will be expensive given the difficulty with which tiger data can be obtained and the lack of available surrogate indices of tiger density. Monitoring occupancy patterns, the subject of a separate ongoing study, may be more efficient.     

How Does Spatial Study Design Influence Density Estimates from Spatial Capture-Recapture Models?

When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR) models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2). Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.     

Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains

Obtaining estimates of animal population density is a key step in providing sound conservation and management strategies for wildlife. For many large carnivores however, estimating density is difficult because these species are elusive and wide‐ranging. Here, we focus on providing the first density estimates of the Eurasian lynx (Lynx lynx) in the French Jura and Vosges mountains. We sampled a total of 413 camera trapping sites (with two cameras per site) between January 2011 and April 2016 in seven study areas across seven counties of the French Jura and Vosges mountains. We obtained 592 lynx detections over 19,035 trap days in the Jura mountains and 0 detection over 6,804 trap days in the Vosges mountains. Based on coat patterns, we identified a total number of 92 unique individuals from photographs, including 16 females, 13 males, and 63 individuals of unknown sex. Using spatial capture–recapture (SCR) models, we estimated abundance in the study areas between 5 (SE = 0.1) and 29 (0.2) lynx and density between 0.24 (SE = 0.02) and 0.91 (SE = 0.03) lynx per 100 km². We also provide a comparison with nonspatial density estimates and discuss the observed discrepancies. Our study is yet another example of the advantage of combining SCR methods and noninvasive sampling techniques to estimate density for elusive and wide‐ranging species, like large carnivores. While the estimated densities in the French Jura mountains are comparable to other lynx populations in Europe, the fact that we detected no lynx in the Vosges mountains is alarming. Connectivity should be encouraged between the French Jura mountains, the Vosges mountains, and the Palatinate Forest in Germany where a reintroduction program is currently ongoing. Our density estimates will help in setting a baseline conservation status for the lynx population in France.     

Winter bait stations as a multispecies survey tool

Winter bait stations are becoming a commonly used technique for multispecies inventory and monitoring but a technical evaluation of their effectiveness is lacking. Bait stations have three components: carcass attractant, remote camera, and hair snare. Our 22,975 km² mountainous study area was stratified with a 5 × 5 km sampling grid centered on northern Idaho and including portions of Washington, Montana, and British Columbia. From 2010–14, we conducted 563 sampling sessions at 497 bait stations in 453 5 × 5 km cells. We evaluated the effectiveness of cameras and hair snare DNA collection at stations to detect species and individual animals, factors affecting DNA viability, the effectiveness of re‐visiting stations, and the influence of elevation, seasonality, and latency on species detections. Cameras were more effective at detecting multiple species than DNA hair snaring. Length of deployment time and elevation increased genetic species ID success but individual ID success rates were increased only by collecting hairs earlier in the season. Re‐visiting stations did not change camera or genetic species detection results but did increase the number of individual genotypes identified. Marten and fisher were detected quickly while bobcat and coyote showed longer latency to detection. Seasonality significantly affected coyote and bobcat detections but not marten, fisher, or weasel. Multispecies bait station study design should incorporate mixed elevation sites with stratified seasonality. Priority should be given to including cameras as components of bait stations over hair snares, unless there is a specific genetic goal to the study. A hair snare component should be added, however, if individual ID or genetic data are necessary. Winter stations should be deployed a minimum of 45–60 days to allow for detection of low density species and species with long latency to detection times. Hair samples should be collected prior to DNA‐degrading late season rain events. Re‐visiting stations does not change which species are detected at stations; therefore, studies with objectives to delineate species presence or distribution will be more effective if they focus on deploying more stations across a broader landscape in lieu of surveying the same site multiple times.     

A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture

Spatial capture–recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large‐scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state‐space (LESS). Based on prior knowledge about a species’ home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine (Gulo gulo)—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km²). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.     

Testing the precision and sensitivity of density estimates obtained with a camera‐trap method revealed limitations and opportunities

The use of camera traps in ecology helps affordably address questions about the distribution and density of cryptic and mobile species. The random encounter model (REM) is a camera‐trap method that has been developed to estimate population densities using unmarked individuals. However, few studies have evaluated its reliability in the field, especially considering that this method relies on parameters obtained from collared animals (i.e., average speed, in km/h), which can be difficult to acquire at low cost and effort. Our objectives were to (1) assess the reliability of this camera‐trap method and (2) evaluate the influence of parameters coming from different populations on density estimates. We estimated a reference density of black bears (Ursus americanus) in Forillon National Park (Québec, Canada) using a spatial capture–recapture estimator based on hair‐snag stations. We calculated average speed using telemetry data acquired from four different bear populations located outside our study area and estimated densities using the REM. The reference density, determined with a Bayesian spatial capture–recapture model, was 2.87 individuals/10km² [95% CI: 2.41–3.45], which was slightly lower (although not significatively different) than the different densities estimated using REM (ranging from 4.06–5.38 bears/10km² depending on the average speed value used). Average speed values obtained from different populations had minor impacts on REM estimates when the difference in average speed between populations was low. Bias in speed values for slow‐moving species had more influence on REM density estimates than for fast‐moving species. We pointed out that a potential overestimation of density occurs when average speed is underestimated, that is, using GPS telemetry locations with large fix‐rate intervals. Our study suggests that REM could be an affordable alternative to conventional spatial capture–recapture, but highlights the need for further research to control for potential bias associated with speed values determined using GPS telemetry data.     

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard Panthera pardus

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km² (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.     

Estimating population size and density of a low-density population of black bears in Rocky Mountain National Park, Colorado

Rocky Mountain National Park (RMNP) is home to a low-density black bear (Ursus americanus) population that exists at >2,400?m with a very limited growing season. A previous study (1984–1991) found bear densities among the lowest reported (1.37–1.52 bears/100?km²). Because of concerns of viability of this small population, we assessed population size and density of black bears from 2003 to 2006 to determine the current status of RMNP’s bear population. We used three approaches to estimate population size and density: (1) minimum number known, (2) occupancy modeling, and (3) catch per unit effort (CPUE). We used information from capture and remote-triggered cameras, as well as visitor information, to derive a minimum known population estimate of 20–24 individuals and a median density estimate of 1.35 bears/100?km². Bear occupancy was estimated at 0.46 (SE?=?0.11), with occupancy positively influenced by lodgepole pine stands, non-vegetated areas, and patch density but negatively influenced by mixed conifer stands. We combined the occupancy estimate with mean home-range size and overlap for bears in RMNP to derive a density estimate of 1.44 bears/100?km². We also related CPUE to density estimates for eight low-density black bear populations to estimate density in RMNP; this estimate (1.03 bears/100?km²) was comparable to the occupancy estimate and suggests that this approach may be useful for future population monitoring. The use of corroborative techniques for assessing population size of a low-density black bear population was effective and should be considered for similar low-density wildlife populations.     

Noninvasive Estimation of Black Bear Abundance Incorporating Genotyping Errors and Harvested Bear

ABSTRACT Estimating black bear (Ursus americanus) population size is a difficult but important requirement when justifying harvest quotas and managing populations. Advancements in genetic techniques provide a means to identify individual bears using DNA contained in tissue and hair samples, thereby permitting estimates of population abundance based on established mark-capture-recapture methodology. We expand on previous noninvasive population-estimation work by geographically extending sampling areas (36,848 km²) to include the entire Northern Lower Peninsula (NLP) of Michigan, USA. We selected sampling locations randomly within biologically relevant bear habitat and used barbed wire hair snares to collect hair samples. Unlike previous noninvasive studies, we used tissue samples from harvested bears as an additional sampling occasion to increase recapture probabilities. We developed subsampling protocols to account for both spatial and temporal variance in sample distribution and variation in sample quality using recently published quality control protocols using 5 microsatellite loci. We quantified genotyping errors using samples from harvested bears and estimated abundance using statistical models that accounted for genotyping error. We estimated the population of yearling and adult black bears in the NLP to be 1,882 bears (95% CI = 1,389-2,551 bears). The derived population estimate with a 15% coefficient of variation was used by wildlife managers to examine the sustainability of harvest over a large geographic area.     

Generalized spatial mark–resight models with incomplete identification: An application to red fox density estimates

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Grizzly Bear Density in Glacier National Park,Montana

Abstract: We present the first rigorous estimate of grizzly bear (Ursus arctos) population density and distribution in and around Glacier National Park (GNP), Montana, USA. We used genetic analysis to identify individual bears from hair samples collected via 2 concurrent sampling methods: 1) systematically distributed, baited, barbed-wire hair traps and 2) unbaited bear rub trees found along trails. We used Huggins closed mixture models in Program MARK to estimate total population size and developed a method to account for heterogeneity caused by unequal access to rub trees. We corrected our estimate for lack of geographic closure using a new method that utilizes information from radiocollared bears and the distribution of bears captured with DNA sampling. Adjusted for closure, the average number of grizzly bears in our study area was 240.7 (95% CI = 202–303) in 1998 and 240.6 (95% CI = 205–304) in 2000. Average grizzly bear density was 30 bears/1,000 km², with 2.4 times more bears detected per hair trap inside than outside GNP. We provide baseline information important for managing one of the few remaining populations of grizzlies in the contiguous United States.     

Spatial variation in density of American black bears in northern Yellowstone National Park

The quality and availability of resources are known to influence spatial patterns of animal density. In Yellowstone National Park, relationships between the availability of resources and the distribution of grizzly bears (Ursus arctos) have been explored but have yet to be examined in American black bears (Ursus americanus). We conducted non-invasive genetic sampling during 2017–2018 (mid-May to mid-July) and applied spatially explicit capture-recapture models to estimate density of black bears and examine associations with landscape features. In both years, density estimates were higher in forested vegetation communities, which provide food resources and thermal and security cover preferred by black bears, compared with non-forested areas. In 2017, density also varied by sex, with female densities being higher than males. Based on our estimates, the northern range of Yellowstone National Park supports one of the highest densities of black bears (20 black bears/100 km²) in the northern Rocky Mountains (6–12 black bears/100 km² in other regions). Given these high densities, black bears could influence other wildlife populations more than previously thought, such as through displacement of sympatric predators from kills. Our study provides the first spatially explicit estimates of density for black bears within an ecosystem that contains the majority of North America's large mammal species. Our density estimates provide a baseline that can be used for future research and management decisions of black bears, including efforts to reduce human–bear conflicts.     

Environmental determinants and use of space by six Neotropical primates in the northern Brazilian Amazon

The Guiana Shield has large pristine tracts of tropical forest with high biological diversity and is an area of endemism within the Amazon Basin. However, the conservation status of primates in eastern Amazonian Brazil is still poorly known. Here, we report information on relative abundance, group size, density estimates, plus the effects of environmental variables and seasonality of primates in a sustainable-use reserve in the eastern Brazilian Amazon. From a 603 km transect-based census conducted in October–December 2013 and March–June 2014 we obtained 122 sighting records of six primate species. The most common were Ateles paniscus (45 detections) and Saguinus midas (40 detections). The high calculated density estimates for Sg. midas (2.01 groups/km² or 12.05 individuals/km²) and for At. paniscus (3.44 groups/km² or 10.31 individuals/km²) underscore the conservation importance of the study area for the vulnerable At. paniscus. We found no effect of environmental variables on the number of detections of primates, except for At. paniscus and Cebus olivaceus, with the former showing a higher number of detections in more open canopy forest during the rainy season, and the later having a higher number of detections in areas with higher density of palms in the dry season.