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.     

Trap Array Configuration Influences Estimates and Precision of Black Bear Density and Abundance

Spatial capture-recapture (SCR) models have advanced our ability to estimate population density for wide ranging animals by explicitly incorporating individual movement. Though these models are more robust to various spatial sampling designs, few studies have empirically tested different large-scale trap configurations using SCR models. We investigated how extent of trap coverage and trap spacing affects precision and accuracy of SCR parameters, implementing models using the R package secr. We tested two trapping scenarios, one spatially extensive and one intensive, using black bear (Ursus americanus) DNA data from hair snare arrays in south-central Missouri, USA. We also examined the influence that adding a second, lower barbed-wire strand to snares had on quantity and spatial distribution of detections. We simulated trapping data to test bias in density estimates of each configuration under a range of density and detection parameter values. Field data showed that using multiple arrays with intensive snare coverage produced more detections of more individuals than extensive coverage. Consequently, density and detection parameters were more precise for the intensive design. Density was estimated as 1.7 bears per 100 km² and was 5.5 times greater than that under extensive sampling. Abundance was 279 (95% CI = 193–406) bears in the 16,812 km² study area. Excluding detections from the lower strand resulted in the loss of 35 detections, 14 unique bears, and the largest recorded movement between snares. All simulations showed low bias for density under both configurations. Results demonstrated that in low density populations with non-uniform distribution of population density, optimizing the tradeoff among snare spacing, coverage, and sample size is of critical importance to estimating parameters with high precision and accuracy. With limited resources, allocating available traps to multiple arrays with intensive trap spacing increased the amount of information needed to inform parameters with high precision.     

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.     

Estimates of Abundance and Harvest Rates of Female Black Bears Across a Large Spatial Extent

American black bears (Ursus americanus) are an iconic wildlife species in the southern Appalachian highlands of the eastern United States and have increased in number and range since the early 1980s. Given an increasing number of human-bear conflicts in the region, many management agencies have liberalized harvest regulations to reduce bear populations to socially acceptable levels. Wildlife managers need reliable population data for assessing the effects of management actions for this high-profile species. Our goal was to use DNA extracted from hair collected at barbed-wire enclosures (i.e., hair traps) to identify individual bears and then use spatially explicit capture-recapture methods to estimate female black bear density, abundance, and harvest rate. We established 888 hair traps across 66,678 km² of the southern Appalachian highlands in Georgia, North Carolina, South Carolina, and Tennessee, USA, in 2017 and 2018, arranged in 174 clusters of 2–9 traps/cluster. We collected 9,113 hair samples from those sites over 6 weeks of sampling, of which 1,954 were successfully genotyped to 462 individual female bears. Our spatially explicit estimator included a percent forest covariate to explain inhomogeneous bear density across the region. Densities ranged up to 0.410 female bears/km² and regional abundance was 5,950 (95% CI = 4,988–7,098) female bears. Based on hunter kill data from 2016 to 2018, mean annual harvest rates for females were 12.7% in Georgia, 17.6% in North Carolina, 17.6% in South Carolina, and 22.8% in Tennessee. Our estimated harvest rates for most states approached or exceeded theoretical maximum sustainable levels, and population trend data (i.e., bait-station indices) indicated decreasing growth rates since about 2009. These data suggest that the increased harvest goals and poor hard mast production over a series of prior years reduced bear population abundance in many states. We were able to obtain reasonable population abundance and density estimates because of spatially explicit capture-recapture methods, cluster sampling, and a large spatial extent. Continued monitoring of bear populations (e.g., annual bait-station surveys and periodic population estimation using spatially explicit methods) by state jurisdictions would help to ensure that population trajectories are consistent with management goals. © 2021 The Wildlife Society.     

Estimating grizzly and black bear population abundance and trend in Banff National Park using noninvasive genetic sampling

We evaluated the potential of two noninvasive genetic sampling methods, hair traps and bear rub surveys, to estimate population abundance and trend of grizzly (Ursus arctos) and black bear (U. americanus) populations in Banff National Park, Alberta, Canada. Using Huggins closed population mark-recapture models, we obtained the first precise abundance estimates for grizzly bears (N=?73.5, 95% CI?=?64-94 in 2006; N=?50.4, 95% CI?=?49-59 in 2008) and black bears (N=?62.6, 95% CI?=?51-89 in 2006; N=?81.8, 95% CI?=?72-102 in 2008) in the Bow Valley. Hair traps had high detection rates for female grizzlies, and male and female black bears, but extremely low detection rates for male grizzlies. Conversely, bear rubs had high detection rates for male and female grizzlies, but low rates for black bears. We estimated realized population growth rates, lambda, for grizzly bear males (λ=?0.93, 95% CI?=?0.74-1.17) and females (λ=?0.90, 95% CI?=?0.67-1.20) using Pradel open population models with three years of bear rub data. Lambda estimates are supported by abundance estimates from combined hair trap/bear rub closed population models and are consistent with a system that is likely driven by high levels of human-caused mortality. Our results suggest that bear rub surveys would provide an efficient and powerful means to inventory and monitor grizzly bear populations in the Central Canadian Rocky Mountains.     

Evidence of a large carnivore population recovery: Counting bears in Greece

Reliable population and density estimates are the cornerstone of effective conservation and management planning, as conservation priorities often arise in relation to population numbers. Despite increased public interest and costly conservation programs limited information on brown bear (Ursus arctos, Linnaeus, 1758) abundance and density in Greece exists. We carried out systematic non-invasive genetic sampling using hair traps on power poles, as part of a capture-mark-recapture study design in order to rigorously estimate abundance and density of the Pindos bear population in Greece. From 2007–2010 we identified 211 and estimated a mean of 182.3 individuals in four sampling areas; bear densities ranged from 10.0 to 54 bears/1000 km². These results indicate an important population recovery of this large carnivore in Greece in recent years; a conservative population estimate would place the population size in the entire country >450 individuals. Considering the results of the study and the increased negative interactions between humans and bears recorded currently in Greece, we suggest that systematic genetic monitoring using power poles should continue in order to collect the necessary information that will enable the definition of an effective Action Plan for the long-term conservation of this species.     

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.     

Estimating Brownian motion dispersal rate, longevity and population density from spatially explicit mark-recapture data on tropical butterflies

1.?We develop a Bayesian method for analysing mark-recapture data in continuous habitat using a model in which individuals movement paths are Brownian motions, life spans are exponentially distributed and capture events occur at given instants in time if individuals are within a certain attractive distance of the traps. 2.?The joint posterior distribution of the dispersal rate, longevity, trap attraction distances and a number of latent variables representing the unobserved movement paths and time of death of all individuals is computed using Gibbs sampling. 3.?An estimate of absolute local population density is obtained simply by dividing the Poisson counts of individuals captured at given points in time by the estimated total attraction area of all traps. Our approach for estimating population density in continuous habitat avoids the need to define an arbitrary effective trapping area that characterized previous mark-recapture methods in continuous habitat. 4.?We applied our method to estimate spatial demography parameters in nine species of neotropical butterflies. Path analysis of interspecific variation in demographic parameters and mean wing length revealed a simple network of strong causation. Larger wing length increases dispersal rate, which in turn increases trap attraction distance. However, higher dispersal rate also decreases longevity, thus explaining the surprising observation of a negative correlation between wing length and longevity.     

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.     

Density estimation in a wolverine population using spatial capture–recapture models

Classical closed-population capture–recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture–recapture models that accommodate the spatial attribute inherent in capture–recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km² area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000 km² (95% Bayesian CI: 5.9–15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies. © 2011 The Wildlife Society.     

The influence of trap type on evaluating population structure of the semifossorial and social rodent <Emphasis Type="Italic">Octodon degus</Emphasis>

Trap type may influence captures of individuals in different age-sex categories in small mammal studies, resulting in biased population and demographic information. We deployed 4 live trap types at burrow systems of the rodent, Octodon degus Molina, 1782, in central Chile to determine trap efficacy in capturing individuals of 6 demographic categories. We captured 2672 individuals in 17 709 trap days (15.1% trapping success). Tomahawks were the most efficient trap capturing half of individuals during both years, followed by mesh Sherman traps, large Sherman traps, and medium Sherman traps in 2005. All trap types equally sampled sexes. Large and medium Sherman traps provided similar demographic structure, where half of the individuals captured were pups; Tomahawk traps sampled more adults than pups. Relative captures of pups were similar across different trap types, suggesting that pups are equally sampled by each of the deployed trap types. Relative captures of adults were lower in Sherman traps, suggesting that this age class avoided solid-walled traps. For Octodon degus, the sole use of Tomahawk traps may produce sufficient, unbiased demographic data. Only 4 trap mortalities occurred (0.15%). Researchers may minimize trap mortality without compromising sufficient demographic sampling by trapping during peak animal activity.     

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.     

Stable isotopes to detect food-conditioned bears and to evaluate human-bear management

We used genetic and stable isotope analysis of hair from free-ranging black bears (Ursus americanus) in Yosemite National Park, California, USA to: 1) identify bears that consume human food, 2) estimate the diets of these bears, and 3) evaluate the Yosemite human–bear management program. Specifically, we analyzed the isotopic composition of hair from bears known a priori to be food-conditioned or non-food-conditioned and used these data to predict whether bears with an unknown management status were food-conditioned (FC) or non-food-conditioned (NFC). We used a stable isotope mixing model to estimate the proportional contribution of natural foods (plants and animals) versus human food in the diets of FC bears. We then used results from both analyses to evaluate proactive (population-level) and reactive (individual-level) human–bear management, and discussed new metrics to evaluate the overall human–bear management program in Yosemite. Our results indicated that 19 out of 145 (13%) unknown bears sampled from 2005 to 2007 were food-conditioned. The proportion of human food in the diets of known FC bears likely declined from 2001–2003 to 2005–2007, suggesting proactive management was successful in reducing the amount of human food available to bears. In contrast, reactive management was not successful in changing the management status of known FC bears to NFC bears, or in reducing the contribution of human food to the diets of FC bears. Nine known FC bears were recaptured on 14 occasions from 2001 to 2007; all bears were classified as FC during subsequent recaptures, and human–bear management did not reduce the amount of human food in the diets of FC bears. Based on our results, we suggest Yosemite continue implementing proactive human–bear management, reevaluate reactive management, and consider removing problem bears (those involved in repeated bear incidents) from the population. © 2012 The Wildlife Society.     

Mark–recapture using tetracycline and genetics reveal record-high bear density

We used tetracycline biomarking, augmented with genetic methods to estimate the size of an American black bear (Ursus americanus) population on an island in Southeast Alaska. We marked 132 and 189 bears that consumed remote, tetracycline-laced baits in 2 different years, respectively, and observed 39 marks in 692 bone samples subsequently collected from hunters. We genetically analyzed hair samples from bait sites to determine the sex of marked bears, facilitating derivation of sex-specific population estimates. We obtained harvest samples from beyond the study area to correct for emigration. We estimated a density of 155 independent bears/100 km², which is equivalent to the highest recorded for this species. This high density appears to be maintained by abundant, accessible natural food. Our population estimate (approx. 1,000 bears) could be used as a baseline and to set hunting quotas. The refined biomarking method for abundance estimation is a useful alternative where physical captures or DNA-based estimates are precluded by cost or logistics. © 2011 The Wildlife Society.     

Estimating density of ship rats in New Zealand forests by capture- mark-recapture trapping

We developed a capture-mark-recapture protocol for measuring the population density (D) of ship rats (Rattus rattus) in forest. Either mesh cage traps or Elliott box traps were set at each of six sites (48 traps per site for 5 nights) in the Orongorongo Valley on two occasions in autumn 2003. Cage traps only were set at three sites in autumn 2004. Rats were caught much more readily in cage traps than in Elliott traps and none were recaptured in Elliott traps. Additional food, bedding and trap covers reduced mortality and interference with traps. To estimate density we fitted a spatial detection model; this method avoids the need to estimate effective trapping area. Estimates were based on both a model assuming equal capture probability (Dˆ₀) and a model incorporating temporal and individual variation (Dˆ_th). Our target for precision was CV(Dˆ) ≤ 20%, but when data were pooled from multiple sites with cage traps, CV(Dˆ_th) was ~30%. Estimated density of rats (Dˆ_th) was 5 ha^-1in 2003 and 9 ha^-1 in 2004; these estimates did not differ significantly. The overall capture index in 2004 was 3 rats per 00 corrected trap-nights on snap-trap lines set after live trapping. House mice were caught in both types of live trap, but at rates high enough for density estimation only where Elliott traps were used. Field estimates of detection functions for rats captured with cage traps allowed us to simulate the performance of alternative trapping systems. We predict that a 64-trap layout at three sites with five trapping occasions would yield acceptable precision of Dˆ_th (20–23%) at the observed rat densities. Our use of Dˆ_th was conservative; slightly higher precision may be achieved by assuming constant trappability ( Dˆ₀), and future work may justify this assumption.     

Integrating sign surveys and telemetry data for estimating brown bear (Ursus arctos) density in the Romanian Carpathians

Accurate population size estimates are important information for sustainable wildlife management. The Romanian Carpathians harbor the largest brown bear (Ursus arctos) population in Europe, yet current management relies on estimates of density that lack statistical oversight and ignore uncertainty deriving from track surveys. In this study, we investigate an alternative approach to estimate brown bear density using sign surveys along transects within a novel integration of occupancy models and home range methods. We performed repeated surveys along 2‐km segments of forest roads during three distinct seasons: spring 2011, fall‐winter 2011, and spring 2012, within three game management units and a Natura 2000 site. We estimated bears abundances along transects using the number of unique tracks observed per survey occasion via N‐mixture hierarchical models, which account for imperfect detection. To obtain brown bear densities, we combined these abundances with the effective sampling area of the transects, that is, estimated as a function of the median (± bootstrapped SE) of the core home range (5.58 ± 1.08 km²) based on telemetry data from 17 bears tracked for 1‐month periods overlapping our surveys windows. Our analyses yielded average brown bear densities (and 95% confidence intervals) for the three seasons of: 11.5 (7.8–15.3), 11.3 (7.4–15.2), and 12.4 (8.6–16.3) individuals/100 km². Across game management units, mean densities ranged between 7.5 and 14.8 individuals/100 km². Our method incorporates multiple sources of uncertainty (e.g., effective sampling area, imperfect detection) to estimate brown bear density, but the inference fundamentally relies on unmarked individuals only. While useful as a temporary approach to monitor brown bears, we urge implementing DNA capture–recapture methods regionally to inform brown bear management and recommend increasing resources for GPS collars to improve estimates of effective sampling area.     

Balancing Precision and Risk: Should Multiple Detection Methods Be Analyzed Separately in N-Mixture Models?

Using multiple detection methods can increase the number, kind, and distribution of individuals sampled, which may increase accuracy and precision and reduce cost of population abundance estimates. However, when variables influencing abundance are of interest, if individuals detected via different methods are influenced by the landscape differently, separate analysis of multiple detection methods may be more appropriate. We evaluated the effects of combining two detection methods on the identification of variables important to local abundance using detections of grizzly bears with hair traps (systematic) and bear rubs (opportunistic). We used hierarchical abundance models (N-mixture models) with separate model components for each detection method. If both methods sample the same population, the use of either data set alone should (1) lead to the selection of the same variables as important and (2) provide similar estimates of relative local abundance. We hypothesized that the inclusion of 2 detection methods versus either method alone should (3) yield more support for variables identified in single method analyses (i.e. fewer variables and models with greater weight), and (4) improve precision of covariate estimates for variables selected in both separate and combined analyses because sample size is larger. As expected, joint analysis of both methods increased precision as well as certainty in variable and model selection. However, the single-method analyses identified different variables and the resulting predicted abundances had different spatial distributions. We recommend comparing single-method and jointly modeled results to identify the presence of individual heterogeneity between detection methods in N-mixture models, along with consideration of detection probabilities, correlations among variables, and tolerance to risk of failing to identify variables important to a subset of the population. The benefits of increased precision should be weighed against those risks. The analysis framework presented here will be useful for other species exhibiting heterogeneity by detection method.     

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.     

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.     

cameratrapR: An R package for estimating animal density using camera trapping data

• 1.Camera trapping plays an important role in wildlife surveys, and provides valuable information for estimation of population density. While mark-recapture techniques can estimate population density for species that can be individually recognized or marked, there are no robust methods to estimate density of species that cannot be individually identified.
• 2.We developed a new approach to estimate population density based on the simulation of individual movement within the camera grid. Simulated animals followed a correlated random walk with the movement parameters of segment length, angular deflection, movement distance and home-range size derived from empirical movement paths. Movement was simulated under a series of population densities. We used the Random Forest algorithm to determine the population density with the highest likelihood of matching the camera trap data. We developed an R package, cameratrapR, to conduct simulations and estimate population density.
• 3.Compared with line transect surveys and the random encounter model, cameratrapR provides more reliable estimates of wildlife density with narrower confidence intervals. Functions are provided to visualize movement paths, derive movement parameters, and plot camera trapping results.
• 4.The package allows researchers to estimate population sizes/densities of animals that cannot be individually identified and cameras are deployed in a grid pattern.