Geology topics

J. Andrew Royle

Publications and source records attributed to J. Andrew Royle.

At least 127 records · Page 7Linked to original sources

USGS2013

Integrating resource selection information with spatial capture--recapture

1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection. 2. In this paper we develop the first statistical framework for jointly modeling space usage, resource selection, and population density by integrating SCR data, such as from camera traps, mist-nets, or conventional catch traps, with resource selection data from telemetered individuals. We provide a framework for estimation based on marginal likelihood, wherein we estimate simultaneously the parameters of the SCR and RSF models. 3. Our method leads to increases in precision for estimating parameters of ordinary SCR models. Importantly, we also find that SCR models alone can estimate parameters of RSFs and, as such, SCR methods can be used as the sole source for studying space-usage; however, precision will be higher when telemetry data are available. 4. Finally, we find that SCR models using standard symmetric and stationary encounter probability models may not fully explain variation in encounter probability due to space usage, and therefore produce biased estimates of density when animal space usage is related to resource selection. Consequently, it is important that space usage be taken into consideration, if possible, in studies focused on estimating density using capture–recapture methods.

Methods in Ecology and Evolution↗

USGS2013

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

PLoS ONE↗

USGS2013

Spatially explicit models for inference about density in unmarked or partially marked populations

Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating that neither spatial independence nor individual recognition is needed to estimate population density—rather, spatial dependence can be informative about individual distribution and density.

Annals of Applied Statistics↗

USGS2013

Spatial capture-recapture models for jointly estimating population density and landscape connectivity

Population size and landscape connectivity are key determinants of population viability, yet no methods exist for simultaneously estimating density and connectivity parameters. Recently developed spatial capture–recapture (SCR) models provide a framework for estimating density of animal populations but thus far have not been used to study connectivity. Rather, all applications of SCR models have used encounter probability models based on the Euclidean distance between traps and animal activity centers, which implies that home ranges are stationary, symmetric, and unaffected by landscape structure. In this paper we devise encounter probability models based on “ecological distance,” i.e., the least-cost path between traps and activity centers, which is a function of both Euclidean distance and animal movement behavior in resistant landscapes. We integrate least-cost path models into a likelihood-based estimation scheme for spatial capture–recapture models in order to estimate population density and parameters of the least-cost encounter probability model. Therefore, it is possible to make explicit inferences about animal density, distribution, and landscape connectivity as it relates to animal movement from standard capture–recapture data. Furthermore, a simulation study demonstrated that ignoring landscape connectivity can result in negatively biased density estimators under the naive SCR model.

Ecology↗

USGS2013

Spatial capture-recapture

Spatial Capture-Recapture provides a revolutionary extension of traditional capture-recapture methods for studying animal populations using data from live trapping, camera trapping, DNA sampling, acoustic sampling, and related field methods. This book is a conceptual and methodological synthesis of spatial capture-recapture modeling. As a comprehensive how-to manual, this reference contains detailed examples of a wide range of relevant spatial capture-recapture models for inference about population size and spatial and temporal variation in demographic parameters. Practicing field biologists studying animal populations will find this book to be a useful resource, as will graduate students and professionals in ecology, conservation biology, and fisheries and wildlife management.

Book↗

USGS2012

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.

PLoS ONE↗

USGS2012

Presence-only modeling using MAXENT: when can we trust the inferences?

1. Recently, interest in species distribution modelling has increased following the development of new methods for the analysis of presence-only data and the deployment of these methods in user-friendly and powerful computer programs. However, reliable inference from these powerful tools requires that several assumptions be met, including the assumptions that observed presences are the consequence of random or representative sampling and that detectability during sampling does not vary with the covariates that determine occurrence probability. 2. Based on our interactions with researchers using these tools, we hypothesized that many presence-only studies were ignoring important assumptions of presence-only modelling. We tested this hypothesis by reviewing 108 articles published between 2008 and 2012 that used the MAXENT algorithm to analyse empirical (i.e. not simulated) data. We chose to focus on these articles because MAXENT has been the most popular algorithm in recent years for analysing presence-only data. 3. Many articles (87%) were based on data that were likely to suffer from sample selection bias; however, methods to control for sample selection bias were rarely used. In addition, many analyses (36%) discarded absence information by analysing presence–absence data in a presence-only framework, and few articles (14%) mentioned detection probability. We conclude that there are many misconceptions concerning the use of presence-only models, including the misunderstanding that MAXENT, and other presence-only methods, relieve users from the constraints of survey design. 4. In the process of our literature review, we became aware of other factors that raised concerns about the validity of study conclusions. In particular, we observed that 83% of articles studies focused exclusively on model output (i.e. maps) without providing readers with any means to critically examine modelled relationships and that MAXENT's logistic output was frequently (54% of articles) and incorrectly interpreted as occurrence probability. 5. We conclude with a series of recommendations foremost that researchers analyse data in a presence–absence framework whenever possible, because fewer assumptions are required and inferences can be made about clearly defined parameters such as occurrence probability.

Methods in Ecology and Evolution↗

USGS2012

Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models

1. The advent of spatially explicit capture-recapture models is changing the way ecologists analyse capture-recapture data. However, the advantages offered by these new models are not fully exploited because they can be difficult to implement. 2. To address this need, we developed a user-friendly software package, created within the R programming environment, called SPACECAP. This package implements Bayesian spatially explicit hierarchical models to analyse spatial capture-recapture data. 3. Given that a large number of field biologists prefer software with graphical user interfaces for analysing their data, SPACECAP is particularly useful as a tool to increase the adoption of Bayesian spatially explicit capture-recapture methods in practice.

Methods in Ecology and Evolution↗

USGS2012

Biodiversity of man-made open habitats in an underused country: a class of multispecies abundance models for count data

Since the 1960s, Japan has become highly dependent on foreign countries for natural resources, and the amount of managed lands (e.g. coppice, grassland, and agricultural field) has declined. Due to infrequent natural and human disturbance, early-successional species are now declining in Japan. Here we surveyed bees, birds, and plants in four human-disturbed open habitats (pasture, meadow, young planted forest, and abandoned clear-cut) and two forest habitats (mature planted forest and natural old-growth). We extended a recently developed multispecies abundance model to accommodate count data, and used the resulting models to estimate species-, functional group-, and community-level state variables (abundance and species richness) at each site, and compared them among the six habitats. Estimated individual-level detection probability was quite low for bee species (mean across species = 0.003; 0.16 for birds). Thirty-two (95% credible interval: 13-64) and one (0-4) bee and bird species, respectively, were suggested to be undetected by the field survey. Although habitats in which community-level abundance and species richness was highest differed among taxa, species richness and abundance of early-successional species were similar in the four disturbed open habitats across taxa except for plants in the pasture habitat which was a good habitat only for several exotic species. Our results suggest that human disturbance, especially the revival of plantation forestry, may contribute to the restoration of early-successional species in Japan

Biodiversity and Conservation↗

USGS2012

Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions

1. Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. MAXENT is a widely used software program designed to model and map species distribution using presence-only data. 2. We provide a critical review of MAXENT as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that MAXENT produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence ( ψ ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence-only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that MAXENT produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that MAXENT predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the MAXENT method. 4. As with MAXENT, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.

Methods in Ecology and Evolution↗

USGS2012

Dealing with incomplete and variable detectability in multi-year, multi-site monitoring of ecological populations

An ecological monitoring program should be viewed as a component of a larger framework designed to advance science and/or management, rather than as a stand-alone activity. Monitoring targets (the ecological variables of interest; e.g. abundance or occurrence of a species) should be set based on the needs of that framework (Nichols and Williams 2006; e.g. Chapters 2–4). Once such monitoring targets are set, the subsequent step in monitoring design involves consideration of the field and analytical methods that will be used to measure monitoring targets with adequate accuracy and precision. Long-term monitoring programs will involve replication of measurements over time, and possibly over space; that is, one location or each of multiple locations will be monitored multiple times, producing a collection of site visits (replicates). Clearly this replication is important for addressing spatial and temporal variability in the ecological resources of interest (Chapters 7–10), but it is worth considering how this replication can further be exploited to increase the effectiveness of monitoring. In particular, defensible monitoring of the majority of animal, and to a lesser degree plant, populations and communities will generally require investigators to account for imperfect detection (Chapters 4, 18). Raw indices of population state variables, such as abundance or occupancy (sensu MacKenzie et al. 2002), are rarely defensible when detection probabilities are < 1, because in those cases detection may vary over time and space in unpredictable ways. Myriad authors have discussed the risks inherent in making inference from monitoring data while failing to correct for differences in detection, resulting in indices that have an unknown relationship to the parameters of interest (e.g. Nichols 1992, Anderson 2001, MacKenzie et al. 2002, Williams et al. 2002, Anderson 2003, White 2005, Kéry and Schmidt 2008). While others have argued that indices may be preferable in some cases due to the challenges associated with estimating detection probabilities (e.g. McKelvey and Pearson 2001, Johnson 2008), we do not attempt to resolve this debate here. Rather, we are more apt to agree with MacKenzie and Kendall (2002) that the burden of proof ought to be on the assertion that detection probabilities are constant. Furthermore, given the wide variety of field methods available for estimating detection probabilities and the inability for an investigator to know, a priori, if detection probabilities will be constant over time and space, we believe that development of monitoring programs ought to include field and analytical methods to account for the imperfect detection of organisms.

Book chapter↗

USGS2012

Assessment of bias in US waterfowl harvest estimates

Context. North American waterfowl managers have long suspected that waterfowl harvest estimates derived from national harvest surveys in the USA are biased high. Survey bias can be evaluated by comparing survey results with like estimates from independent sources. Aims. We used band-recovery data to assess the magnitude of apparent bias in duck and goose harvest estimates, using mallards (Anas platyrhynchos) and Canada geese (Branta canadensis) as representatives of ducks and geese, respectively. Methods. We compared the number of reported mallard and Canada goose band recoveries, adjusted for band reporting rates, with the estimated harvests of banded mallards and Canada geese from the national harvest surveys. Weused the results of those comparisons to develop correction factors that can be applied to annual duck and goose harvest estimates of the national harvest survey. Key results. National harvest survey estimates of banded mallards harvested annually averaged 1.37 times greater than those calculated from band-recovery data, whereas Canada goose harvest estimates averaged 1.50 or 1.63 times greater than comparable band-recovery estimates, depending on the harvest survey methodology used. Conclusions. Duck harvest estimates produced by the national harvest survey from 1971 to 2010 should be reduced by a factor of 0.73 (95% CI = 0.71–0.75) to correct for apparent bias. Survey-specific correction factors of 0.67 (95% CI = 0.65–0.69) and 0.61 (95% CI = 0.59–0.64) should be applied to the goose harvest estimates for 1971–2001 (duck stamp-based survey) and 1999–2010 (HIP-based survey), respectively. Implications. Although this apparent bias likely has not influenced waterfowl harvest management policy in the USA, it does have negative impacts on some applications of harvest estimates, such as indirect estimation of population size. For those types of analyses, we recommend applying the appropriate correction factor to harvest estimates.

Wildlife Research↗

USGS2012

Population size of snowy plovers breeding in North America

Snowy Plovers ( Charadrius nivosus ) may be one of the rarest shorebirds in North America yet a comprehensive assessment of their abundance and distribution has not been completed. During 2007 and 2008, 557 discrete wetlands were surveyed and nine additional large wetland complexes sampled in México and the USA. From these surveys, a population of 23,555 (95% CI = 17,299 – 29,859) breeding Snowy Plovers was estimated. Combining the estimate with information from areas not surveyed, the total North American population was assessed at 25,869 (95% CI = 18,917 – 32,173). Approximately 42% of all breeding Snowy Plovers in North America resided at two sites (Great Salt Lake, Utah, and Salt Plains National Wildlife Refuge, Oklahoma), and 33% of all these were on wetlands in the Great Basin (including Great Salt Lake). Also, coastal habitats in central and southern Texas supported large numbers of breeding plovers. New breeding sites were discovered in interior deserts and highlands and along the Pacific coast of México; approximately 9% of the North American breeding population occurred in México. Because of uncertainties about effects of climate change and current stresses to breeding habitats, the species should be a management and conservation priority. Periodic monitoring should be undertaken at important sites to ensure high quality habitat is available to support the Snowy Plover population.

Waterbirds↗

USGS2012

Hierarchical distance-sampling models to estimate population size and habitat-specific abundance of an island endemic

Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Royle, D. K. Dawson, and S. Bates provides a practical solution. Here, we extend this model to estimate habitat-specific abundance and rangewide population size of a bird species of management concern, the Island Scrub-Jay ( Aphelocoma insularis ), which occurs solely on Santa Cruz Island, California, USA. We surveyed 307 randomly selected, 300 m diameter, point locations throughout the 250-km 2 island during October 2008 and April 2009. Population size was estimated to be 2267 (95% CI 1613-3007) and 1705 (1212-2369) during the fall and spring respectively, considerably lower than a previously published but statistically problematic estimate of 12 500. This large discrepancy emphasizes the importance of proper survey design and analysis for obtaining reliable information for management decisions. Jays were most abundant in low-elevation chaparral habitat; the detection function depended primarily on the percent cover of chaparral and forest within count circles. Vegetation change on the island has been dramatic in recent decades, due to release from herbivory following the eradication of feral sheep ( Ovis aries ) from the majority of the island in the mid-1980s. We applied best-fit fall and spring models of habitat-specific jay abundance to a vegetation map from 1985, and estimated the population size of A. insularis was 1400-1500 at that time. The 20-30% increase in the jay population suggests that the species has benefited from the recovery of native vegetation since sheep removal. Nevertheless, this jay's tiny range and small population size make it vulnerable to natural disasters and to habitat alteration related to climate change. Our results demonstrate that hierarchical distance-sampling models hold promise for estimating population size and spatial density variation at large scales. Our statistical methods have been incorporated into the R package unmarked to facilitate their use by animal ecologists, and we provide annotated code in the Supplement.

California↗

USGS2012

Density estimation in tiger populations: combining information for strong inference

A productive way forward in studies of animal populations is to efficiently make use of all the information available, either as raw data or as published sources, on critical parameters of interest. In this study, we demonstrate two approaches to the use of multiple sources of information on a parameter of fundamental interest to ecologists: animal density. The first approach produces estimates simultaneously from two different sources of data. The second approach was developed for situations in which initial data collection and analysis are followed up by subsequent data collection and prior knowledge is updated with new data using a stepwise process. Both approaches are used to estimate density of a rare and elusive predator, the tiger, by combining photographic and fecal DNA spatial capture–recapture data. The model, which combined information, provided the most precise estimate of density (8.5 ± 1.95 tigers/100 km 2 [posterior mean ± SD]) relative to a model that utilized only one data source (photographic, 12.02 ± 3.02 tigers/100 km 2 and fecal DNA, 6.65 ± 2.37 tigers/100 km 2 ). Our study demonstrates that, by accounting for multiple sources of available information, estimates of animal density can be significantly improved.

Ecology↗

USGS2012

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km 2 ) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km 2 (95% Cl 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% Cl 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance x sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% Cl 182–451) to 529 (95% Cl 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km 2 for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities.

Journal of Wildlife Management↗

USGS2011

A framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs

Wildlife management often hinges upon an accurate assessment of population density. Although undeniably useful, many of the traditional approaches to density estimation such as visual counts, livetrapping, or mark–recapture suffer from a suite of methodological and analytical weaknesses. Rare, secretive, or highly mobile species exacerbate these problems through the reality of small sample sizes and movement on and off study sites. In response to these difficulties, there is growing interest in the use of non-invasive survey techniques, which provide the opportunity to collect larger samples with minimal increases in effort, as well as the application of analytical frameworks that are not reliant on large sample size arguments. One promising survey technique, the use of scat detecting dogs, offers a greatly enhanced probability of detection while at the same time generating new difficulties with respect to non-standard survey routes, variable search intensity, and the lack of a fixed survey point for characterizing non-detection. In order to account for these issues, we modified an existing spatially explicit, capture–recapture model for camera trap data to account for variable search intensity and the lack of fixed, georeferenced trap locations. We applied this modified model to a fisher ( Martes pennanti ) dataset from the Sierra National Forest, California, and compared the results (12.3 fishers/100 km 2 ) to more traditional density estimates. We then evaluated model performance using simulations at 3 levels of population density. Simulation results indicated that estimates based on the posterior mode were relatively unbiased. We believe that this approach provides a flexible analytical framework for reconciling the inconsistencies between detector dog survey data and density estimation procedures.

Journal of Wildlife Management↗

USGS2011

Linking landscape characteristics to local grizzly bear abundance using multiple detection methods in a hierarchical model

Few studies link habitat to grizzly bear Ursus arctos abundance and these have not accounted for the variation in detection or spatial autocorrelation. We collected and genotyped bear hair in and around Glacier National Park in northwestern Montana during the summer of 2000. We developed a hierarchical Markov chain Monte Carlo model that extends the existing occupancy and count models by accounting for (1) spatially explicit variables that we hypothesized might influence abundance; (2) separate sub-models of detection probability for two distinct sampling methods (hair traps and rub trees) targeting different segments of the population; (3) covariates to explain variation in each sub-model of detection; (4) a conditional autoregressive term to account for spatial autocorrelation; (5) weights to identify most important variables. Road density and per cent mesic habitat best explained variation in female grizzly bear abundance; spatial autocorrelation was not supported. More female bears were predicted in places with lower road density and with more mesic habitat. Detection rates of females increased with rub tree sampling effort. Road density best explained variation in male grizzly bear abundance and spatial autocorrelation was supported. More male bears were predicted in areas of low road density. Detection rates of males increased with rub tree and hair trap sampling effort and decreased over the sampling period. We provide a new method to (1) incorporate multiple detection methods into hierarchical models of abundance; (2) determine whether spatial autocorrelation should be included in final models. Our results suggest that the influence of landscape variables is consistent between habitat selection and abundance in this system.

Animal Conservation↗