Heterogeneity In Detection Probability Research Articles

A simple framework for maximizing camera trap detections using experimental trials

Camera trap data are biased when an animal passes through a camera’s field of view but is not recorded. Cameras that operate using passive infrared sensors rely on their ability to detect thermal energy from the surface of an object. Optimal camera deployment consequently depends on the relationship between a sensor array and an animal. Here, we describe a general, experimental approach to evaluate detection errors that arise from the interaction between cameras and animals. We adapted distance sampling models and estimated the combined effects of distance, camera model, lens height, and vertical angle on the probability of detecting three different body sizes representing mammals that inhabit temperate, boreal, and arctic ecosystems. Detection probabilities were best explained by a half-normal-logistic mixture and were influenced by all experimental covariates. Detection monotonically declined when proxies were ≥6 m from the camera; however, models show that body size and camera model mediated the effect of distance on detection. Although not a focus of our study, we found that unmodeled heterogeneity arising from solar position has the potential to bias inferences where animal movements vary over time. Understanding heterogeneous detection probabilities is valuable when designing and analyzing camera trap studies. We provide a general experimental and analytical framework that ecologists, citizen scientists, and others can use and adapt to optimize camera protocols for various wildlife species and communities. Applying our framework can help ecologists assess trade-offs that arise from interactions among distance, cameras, and body sizes before committing resources to field data collection.

Comparing methods to estimate feral burro abundance

AbstractObtaining precise and unbiased estimates of feral burro (Equus asinus) abundance in the western United States is challenging due to their cryptic pelage and the rugged terrain they inhabit. Management agencies employ helicopter‐based, simultaneous double‐observer sightability surveys (hereafter denoted as DOS) to estimate abundance of burros; but the DOS method routinely produces negatively biased estimates due to residual heterogeneity in detection probability. Consequently, testing alternative methods to improve upon current procedures is warranted. Residual heterogeneity in DOS surveys can be minimized by including radio‐collared individuals in the population. Alternatively, if distance measurements are recorded, residual heterogeneity can also be reduced via a mark‐recapture distance sampling (MRDS) approach. Aerial infrared (IR) surveys offer a safer alternative than helicopter‐based surveys because they can be flown at a higher altitude and require fewer observers in the aircraft. Further, IR surveys using a distance sampling approach have been shown to generate accurate and precise estimates of feral horse (E. caballus) populations. Accordingly, we compared results of surveys using aerial IR distance sampling, the standard DOS survey, a DOS survey incorporating detections of radio‐collared individuals, and an MRDS analysis of a feral burro population with a known minimum population size in central Utah, winter 2015–2016 and spring 2016. The minimum number of burros known alive during the winter and spring surveys were 236 and 136, respectively. The average detection probability of IR surveys was P = 0.88 (SE = 0.16) and distance models produced estimates of 127 burros (95% CIs = 99–175) for the winter survey, and 94 burros (CIs = 72–134) for the spring survey. Mean detection probability of the standard DOS surveys was P = 0.78 (SE = 0.09), and model‐generated abundance estimates were 155 burros (CIs = 133–227) in winter, and 92 burros (CIs = 79–139) in spring. Incorporating detections of radio‐collared individuals in the DOS survey resulted in a decreased detection probability (P = 0.46; SE = 0.06) and increased abundance estimates to 267 (CIs = 169–571) and 155 (CIs = 128–263) for winter and spring, respectively. Mark‐recapture distance sampling produced a mean detection probability of P = 0.48 (SE = 0.12) and resulted in estimates of 282 (CIs = 178–385) and 169 (CIs = 73–310) burros in winter and spring, respectively. Our study demonstrated that aerial IR surveys conducted using standard distance sampling can produce precise estimates of burro population sizes; however, estimates were negatively biased relative to the known population size. Small sample size limits generalization of our results, but the IR‐based distance approach did not improve upon DOS surveys. Accounting for residual heterogeneity through use of radio‐collars and mark‐recapture distance sampling eliminated the negative bias from the standard DOS survey but decreased survey precision. Managers will need to decide whether unbiased but less precise abundance estimates are preferable compared to a more precise, but biased, estimate.

Understanding implications of detection heterogeneity in wildlife abundance estimation

AbstractNegative bias in mark‐recapture abundance estimators due to heterogeneity in detection (capture) probability is a well‐known problem, but we believe most biologists do not understand why heterogeneity causes bias and how bias can be reduced. We demonstrate how heterogeneity creates dependence and bias in mark‐recapture approaches to abundance estimation. In comparison, heterogeneity, and hence estimator bias, is not as problematic for distance sampling and mark‐resight methods because both techniques estimate detection probabilities based on a known quantity. We show how the introduction of a known number of individuals planted into a study population prior to a mark‐recapture survey can reduce bias from heterogeneity in detection probability. We provide examples with simulation and an analysis of motion‐sensitive camera data from a study population of introduced eastern wild turkeys (Meleagris gallopavo silvestris) of known size with a subset of telemetered birds. In choosing a method for abundance estimation, careful consideration should be given to assumptions and how heterogeneity in detection probability can be accommodated for each application.

Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects

Spatial capture-recapture (SCR) models are now widely used for estimating density from repeated individual spatial encounters. SCR accounts for the inherent spatial autocorrelation in individual detections by modelling detection probabilities as a function of distance between the detectors and individual activity centres. However, additional spatial heterogeneity in detection probability may still creep in due to environmental or sampling characteristics. if unaccounted for, such variation can lead to pronounced bias in population size estimates.In this paper, we address this issue by describing three Bayesian SCR models that use generalized linear mixed modelling (GLMM) approach to account for latent heterogeneity in baseline detection probability across detectors with: independent random effects (RE), spatially autocorrelated random effects (SARE) with components of prior covariance matrix modelled as a decreasing function of inter-detector distance, and a two-group finite mixture model (FM) to identify latent detectability classes of each detector. We test these models using a simulation study and an empirical application to a non-invasive genetic monitoring data set of female brown bears (Ursus arctos) in central Sweden.In the simulation study, all three models largely succeeded in mitigating the biasing effect of spatially heterogeneous detection probability on population size estimates. Overall, SARE provided the least biased population size estimates (median RB: -9% – 6%). When spatial autocorrelation in detection probability was high, SARE also performed best at predicting the spatial pattern of heterogeneity in detection probability. At intermediate levels of autocorrelation, spatially-explicit estimates of detection probability obtained with FM were more accurate than those generated by SARE and RE. The empirical example revealed patterns consistent with the results from the simulation study. We found that ignoring spatial heterogeneity in detection probability led to at least 22% lower estimate of bear population size compared to models that accounted for it (i.e., SARE and RE models). When the number of detections per detector is low (≤1), the GLMMs considered here may require dimension reduction of the random effects by pooling baseline detection probability parameters across neighbouring detectors (“aggregation”) to avoid over-parameterization.The added complexity and computational overhead associated with SCR-GLMMs may only be justified in extreme cases of spatial heterogeneity, e.g., large clusters of inactive detectors unbeknownst to the investigator. However, even in less extreme cases, detecting and estimating spatially heterogeneous detection probability may assist in planning or adjusting monitoring schemes.

Estimating abundance of a recovering transboundary brown bear population with capture-recapture models

Estimating the size of small populations of large mammals can be achieved via censuses, or complete counts, of recognizable individuals detected over a time period: minimum detected (population) size (MDS). However, as a population grows larger and its spatial distribution expands, the risk of under-estimating population size using MDS rapidly increases because the assumption of perfect detection of all individuals in the population is violated. The need to report uncertainty around population size estimates consequently becomes crucial. We explored these biases using the monitoring framework of the critically endangered Pyrenean brown bear that was close to extinction in the mid-1990s, with only five individuals remaining, but was subsequently bolstered by the introduction of 11 bears from Slovenia. Each year since 1996, the abundance of the population has been assessed using MDS and minimum retained (population) size (MRS), which corresponded to a reassessment of the MDS in the light of the new information collected in subsequent years (e.g., adding bears which were not detected the previous years but detected the current year). We used Pollock’s closed robust design (PCRD) capture-recapture models applied to the cross-border non-invasive sampling data from France, Spain and Andorra to provide the first published annual abundance and temporal trend estimates of the Pyrenean brown bear population since 2008. Annual population size increased fivefold between 2008 and 2020, going from 13 to 66 individuals. PCRD estimates were globally close to MRS counts and had reasonably narrow associated 95% Credibility Intervals. Even in cases where sampling effort is large compared to population size, the PCRD estimates of population size can diverge from the MDS counts. We report individual heterogeneity in detection that might stem from intraspecific home range size variation that result in individuals that move the most being most likely to be detected. We also found that cubs had a higher mortality rate than adults and subadults, because of infanticide by males, predation, maternal death, or abandonment. Overall, the PCRD capture-recapture modelling approach provides estimates of abundance and demographic rates of the Pyrenean brown bear population, together with associated uncertainty, while minimizing bias due to inter-individual heterogeneity in detection probabilities. We strongly encourage wildlife ecologists and managers to use robust approaches when researching large mammal populations. Such information is vital for informing management decision-making and assessing population conservation status.

Zero-inflated count distributions for capture-mark-reencounter data.

The estimation of demographic parameters is a key component of evolutionary demography and conservation biology. Capture–mark–recapture methods have served as a fundamental tool for estimating demographic parameters. The accurate estimation of demographic parameters in capture–mark–recapture studies depends on accurate modeling of the observation process. Classic capture–mark–recapture models typically model the observation process as a Bernoulli or categorical trial with detection probability conditional on a marked individual's availability for detection (e.g., alive, or alive and present in a study area). Alternatives to this approach are underused, but may have great utility in capture–recapture studies. In this paper, we explore a simple concept: in the same way that counts contain more information about abundance than simple detection/non‐detection data, the number of encounters of individuals during observation occasions contains more information about the observation process than detection/non‐detection data for individuals during the same occasion. Rather than using Bernoulli or categorical distributions to estimate detection probability, we demonstrate the application of zero‐inflated Poisson and gamma‐Poisson distributions. The use of count distributions allows for inference on availability for encounter, as well as a wide variety of parameterizations for heterogeneity in the observation process. We demonstrate that this approach can accurately recover demographic and observation parameters in the presence of individual heterogeneity in detection probability and discuss some potential future extensions of this method.

Integrating telemetry data at several scales with spatial capture–recapture to improve density estimates

AbstractAccurate population estimates are essential for monitoring and managing wildlife populations. Mark–recapture sampling methods have regularly been used to estimate population parameters for rare and cryptic species, including the federally listed Mojave desert tortoise (Gopherus agassizii); however, the methods employed are often plagued by violations of statistical assumptions, which have the potential to bias density estimates. By incorporating spatial information into conventional density estimation models, spatial capture–recapture (SCR) models can account for common assumption violations such as spatially heterogeneous detection probabilities and temporary emigration when animals leave plots during a survey. We conducted mark–recapture surveys at 10 1‐km2 plots in and adjacent to the Ivanpah Valley of California and Nevada from 2015 to 2019. Locality data were collected concurrently using radio‐telemetry and GPS data loggers. GPS data demonstrated that desert tortoises frequently exhibited temporary emigration outside a plot during the survey periods, thereby complicating standard approaches for closed‐model density estimation. We integrated mark–recapture survey data for subadults and adults at each plot with corresponding spatial capture locations and supplementary spatial data using a modified SCR model fitted in a Bayesian framework. We compared density estimates modeled with conventional non‐spatial methods, as well as three SCR models based on symmetrical usage areas described by various levels and types of supplementary spatial data. The conventional model consistently resulted in inflated estimates of density while the SCR models allowed us to generate spatially corrected estimates for a species where detectability and densities are low. However, we found that if not properly specified, the temporal scale of supplementary data may result in an unintended source of bias in parameter estimates. Integrating spatial data over a larger temporal scale than mark–recapture surveys were conducted resulted in higher detection probabilities and lower density estimates, due to an overestimation of space use. Our results not only demonstrate the importance of accounting for spatial information but also the value of understanding the potential for bias when integrating multiple data sets at different temporal resolutions. The methods presented can be used to enhance monitoring efforts for the Mojave desert tortoise and other species where mark–recapture methods are used.

Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture

ContextSpatial capture-recapture (SCR) models are increasingly popular for analyzing wildlife monitoring data. SCR can account for spatial heterogeneity in detection that arises from individual space use (detection kernel), variation in the sampling process, and the distribution of individuals (density). However, unexplained and unmodeled spatial heterogeneity in detectability may remain due to cryptic factors, both intrinsic and extrinsic to the study system. This is the case, for example, when covariates coding for variable effort and detection probability in general are incomplete or entirely lacking.ObjectivesWe identify how the magnitude and configuration of unmodeled, spatially variable detection probability influence SCR parameter estimates.MethodsWe simulated SCR data with spatially variable and autocorrelated detection probability. We then fitted an SCR model ignoring this variation to the simulated data and assessed the impact of model misspecification on inferences.ResultsHighly-autocorrelated spatial heterogeneity in detection probability (Moran’s I = 0.85–0.96), modulated by the magnitude of the unmodeled heterogeneity, can lead to pronounced negative bias (up to 65%, or about 44-fold decrease compared to the reference scenario), reduction in precision (249% or 2.5-fold) and coverage probability of the 95% credible intervals associated with abundance estimates to 0. Conversely, at low levels of spatial autocorrelation (median Moran’s I = 0), even severe unmodeled heterogeneity in detection probability did not lead to pronounced bias and only caused slight reductions in precision and coverage of abundance estimates.ConclusionsUnknown and unmodeled variation in detection probability is liable to be the norm, rather than the exception, in SCR studies. We encourage practitioners to consider the impact that spatial autocorrelation in detectability has on their inferences and urge the development of SCR methods that can take structured, unknown or partially unknown spatial variability in detection probability into account.

Spatial correlation structures for detections of individuals in spatial capture-recapture models.

Spatial capture-recapture (SCR) models are commonly used to estimate animal density from surveys on which detectors passively detect animals without physical capture, for example, using camera traps, hair snares, or microphones. An individual is more likely to be recorded by detectors close to its activity center, the centroid of its movement throughout the survey. Existing models to account for this spatial heterogeneity in detection probabilities rely on an assumption of independence between detection records at different detectors conditional on the animals' activity centers, which are treated as latent variables. In this paper, we show that this conditional independence assumption may be violated due to the way animals move around the survey region and encounter detectors, such that additional spatial correlation is almost inevitable. We highlight the links between the well-studied issue of unmodeled temporal heterogeneity in nonspatial capture-recapture and this variety of unmodeled spatial heterogeneity in SCR, showing that the latter causes predictable bias in the same way as the former. We address this by introducing a latent detection field into the model, and illustrate the resulting approach with a simulation study and an application to a camera-trap survey of snow leopards Panthera uncia. Our method is a unifying model for several existing SCR approaches, with special cases including standard SCR, models that account for nonspatial individual heterogeneity, and models with overdispersed detection counts.

Identifying coarse- and fine-scale drivers of avian abundance following prescribed fires

Suppression of historic fire regimes in North America has altered successional stages and shifted vegetation communities, negatively impacting wildlife diversity in forests. Prescribed fire is often used to increase habitat for wildlife populations and diversity but monitoring of responses does not always capture nuanced differences in habitat that influence wildlife communities over a range of ecological conditions and processes. We matched avian point count data with extensive vegetation sampling of unburned plots and plots that burned 12–16 months prior to evaluate the effects of coarse- and fine-scale habitat variables on the abundance of a suite of avian indicator species and nesting guilds. We estimated abundance conditional on detection probability based on repeated sampling over time intervals using N-mixture models and assessed support for coarse- and fine-scale habitat variables using multimodel inference and AIC. Six of 10 species demonstrated different abundances on burned and unburned plots. Abundances of three species were influenced solely by coarse variables, one species by fine-scale vegetation, and five species were dependent on a mix of coarse- and fine-scale variables. Even fine-scale vegetation characteristics did not fully capture the ecological processes stimulated by fire and compensatory community differences including interactions among species such as competition and predator avoidance should be considered in predicting species responses to prescribed fire. Shrub nester abundance was much higher on plots that burned the year prior, but the effect sizes of other variables were small for ground, tree, and cavity nesters, even though there were strong differences in abundances of individual species within those guilds. Detection probability was most often affected by conditions during the count including start time, temperature and wind, but also burn status and observer, suggesting potential bias in findings that do not account for heterogeneity in detection probability. We suggest variable responses of individual species within nesting guilds may obscure identification of responses in the avian community and inhibit assessment of management and restoration actions. Understanding species-specific responses to fine-scale habitat variables in the context of coarse ecological typology and the associated vegetative and wildlife community will provide the greatest insight to how prescribed fire characteristics interact to produce wildlife responses, and thus enhance its use as a restoration tool.

How sampling‐based overdispersion reveals India's tiger monitoring orthodoxy

AbstractAgencies responsible for recovering populations of iconic mammals may exaggerate population trends without adequate scientific evidence. Recently, such populations were termed as “political populations” in the conservation literature. We surmise such cases are manifested when agencies are pressured to estimate population parameters at large spatial scales for elusive species. For example, India's tiger conservation agencies depend on an extrapolation method using index‐calibration models for estimating population size. A recent study demonstrated mathematically the unreliability of this approach in practical situations. However, it continues to be applied by official agencies in Asia and promoted further by global organizations working on tiger conservation. In this article, we aim to: (a) discuss the ecological oddities in the results of India's national tiger surveys, (b) contrast these survey approaches to known statistical approaches for large scale wildlife abundance estimation, (c) demystify the mathematics underlying the problems with the survey methodology, and (d) substantiate these arguments with results from India's national tiger survey of 2014. Our analyses show that the predictions of tiger abundance reported by the 2014 survey, and consequently on tiger population trends, are misleading because of the presence of high sampling‐based overdispersion and parameter covariance due to unexplained heterogeneity in detection probabilities. We plead for designing monitoring programs to answer clearly defined scientific or management questions rather than attempt to meet extraneous social or funding related expectations.

Bias averted: personality may not influence trappability

If bold animals are more likely to be trapped than shy animals, we take a biased sample of personalities—a problem for behavioural research. Such a bias is problematic, also, for population estimation using mark-recapture models that assume homogeneity in detection probabilities. In this study, we investigated whether differences in boldness result in differences in detection probability in a native Australian rodent, the grassland melomys (Melomys burtoni). During a mark-recapture study of this species, we used modified open field tests to assess the boldness (via emergence, and interaction with a novel object) of melomys trapped on the last night of four trapping nights in each of two trapping sessions. Despite melomys showing repeatable variation in these behavioural traits, neither boldness nor emergence latency had an effect on detection probability, and we found no evidence that detection probability varied between individuals. This result suggests that any neophobia is experienced and resolved in individuals of this species on a scale of minutes, rather than the hours across which traps are made available each night. Our work demonstrates that personality-caused sampling bias may not be inevitable, even in situations where animals are required to respond to novelty to be detected, such as in baited traps. Heterogeneity in personality does not inevitably lead to heterogeneity in detection probability. Historically, passive traps were assumed a non-biased means of sampling animal populations. Increasingly behavioural ecologists suggest that personality traits, particularly individual boldness, may influence behaviour and, as a consequence, could result in sampling bias. Here, we present a comprehensive example of when animal personality has no effect on detection probability. Despite having distinct personalities, detection probabilities of a native Australian rodent, grassland melomys Melomys burtoni, were not influenced by whether they were ‘shy’ or ‘bold’. We provide evidence that heterogeneity in personality does not inevitably lead to heterogeneity in detection probability. Given that population estimation models typically assume homogeneity in detection probability between individuals, if this is a broad phenomenon, consistently similar results may improve our confidence in this assumption.

Bias and Precision of Lizard Occupancy Estimates Vary among Observers and between Methods

Heterogeneity in detection probabilities arises from many parts of a survey design and can introduce bias into estimates of site occupancy probability. We examined the effect of two important sources of heterogeneity, observer and survey method, on occupancy estimates in a lizard community in west Texas, with a special emphasis on Dunes Sagebrush Lizard (Sceloporus arenicolus). Using line transects and visual encounter surveys, nine observers surveyed 72 sites. We detected 10 species of lizards in total; all 10 were detected using visual encounter surveys and only 4 were detected using transects. We determined observers to be an important variable in occupancy models for two of three species of lizards. Insufficient detections were made of the remaining seven species to reliably model occupancy. We conducted simulations to further investigate the impacts of observer variability on occupancy estimate bias and precision by using a range of known occupancy and detectability combinations. Precision in the occupancy estimate increased as the mean observer detection probability increased. Overall, bias was minimized by conducting surveys with either 1) observers with high detection probabilities or 2) using teams of observers with mixed (high and low) detection probabilities.Modeling observer detection probabilities explicitly provided marginal improvement to the occupancy estimate in the simulations. The consistent variability in detection probability among observers has implications for the precision and bias in occupancy estimates and decisions should be made at the experimental design phase about how to approach this problem.

Modelling heterogeneity in detection probabilities in land and aerial abundance surveys in humpback whales (Megaptera novaeangliae)

AbstractThe effective management and conservation of animal populations relies on statistically‐sound and replicable surveys to obtain estimates of abundance and assess trends. Surveys of cetaceans, such as humpback whales Megaptera novaeangliae, are difficult to conduct and are particularly affected by bias in detection probability. For example, the probability of detection of whales from land decreases substantially with increased distance from the platform. This distance effect is also true for aerial surveys, combined with the problem that animals are unavailable for detection (underwater) whilst in the field of view. We present a novel approach that combines corrected double‐platform land surveys with corrected aerial surveys to obtain a robust estimate of g(0), the probability of detection on the survey line, for aerial surveys of migrating humpback whales. Several sources of heterogeneity in detection probabilities were identified within the land and aerial surveys (including group composition, bearing of first sighting, number of groups being tracked simultaneously and cloud cover). After including these into our estimate of ĝ(0), we found that only 29% of available whales are being detected on the survey line (ĝ(0) = 0.288), which is a considerably smaller estimate than many available for humpback whales using other methods. Incorporating heterogeneity into the population surveys shows that we are likely to be underestimating the population size of whales on the east coast of Australia. The implications of this result for their conservation and management in light of increased whale‐human conflict is discussed.

Evidence that climate sets the lower elevation range limit in a high-elevation endemic salamander.

A frequent assumption in ecology is that biotic interactions are more important than abiotic factors in determining lower elevational range limits (i.e., the “warm edge” of a species distribution). However, for species with narrow environmental tolerances, theory suggests the presence of a strong environmental gradient can lead to persistence, even in the presence of competition. The relative importance of biotic and abiotic factors is rarely considered together, although understanding when one exerts a dominant influence on controlling range limits may be crucial to predicting extinction risk under future climate conditions. We sampled multiple transects spanning the elevational range limit of Plethodon shenandoah and site and climate covariates were recorded. A two‐species conditional occupancy model, accommodating heterogeneity in detection probability, was used to relate variation in occupancy with environmental and habitat conditions. Regional climate data were combined with datalogger observations to estimate the cloud base heights and to project future climate change impacts on cloud elevations across the survey area. By simultaneously accounting for species’ interactions and habitat variables, we find that elevation, not competition, is strongly correlated with the lower elevation range boundary, which had been presumed to be restricted mainly as a result of competitive interactions with a congener. Because the lower elevational range limit is sensitive to climate variables, projected climate change across its high‐elevation habitats will directly affect the species’ distribution. Testing assumptions of factors that set species range limits should use models which accommodate detection biases.

Simultaneous modelling of movement, measurement error, and observer dependence in mark-recapture distance sampling: An application to Arctic bird surveys

Mark-recapture distance sampling is a promising method for surveying bird populations from aircraft in open landscapes. However, commonly available distance sampling estimators require that distances to target animals are made without error and that animals are stationary while sampling is being conducted. Motivated by a recent bird survey where these requirements were routinely violated, we describe a marginal likelihood framework for estimating abundance from double-observer data that can accommodate movement and measurement error when observations are made consecutively (as with front and rear observers), when animals are uniformly distributed during detection by the first observer, and when detections consist of both moving and stationary animals. Assuming that all animals are subject to measurement error and that some animals can move between detections, we integrate over unknown animal locations to construct a marginal likelihood for detection, movement, and measurement error parameters. Estimates of animal abundance are then obtained using a modified Horvitz–Thompson-like estimator. In addition, unmodelled heterogeneity in detection probability can be accommodated through observer dependence parameters. Using simulation, we show that our approach yields low bias compared to approaches that ignore movement and/or measurement error, including in cases where there is considerable detection heterogeneity. Applying our approach to data from a double-observer waterfowl helicopter survey in northern Canada, we are able to estimate bird density accounting for movement and measurement error and corrected for observer heterogeneity. Our approach appears promising for generating unbiased estimates of bird abundance necessary for reliable conservation and management.

Density of American black bears in New Mexico

ABSTRACTConsidering advances in noninvasive genetic sampling and spatially explicit capture–recapture (SECR) models, the New Mexico Department of Game and Fish sought to update their density estimates for American black bear (Ursus americanus) populations in New Mexico, USA, to aide in setting sustainable harvest limits. We estimated black bear density in the Sangre de Cristo, Sandia, and Sacramento Mountains, New Mexico, 2012–2014. We collected hair samples from black bears using hair traps and bear rubs and used a sex marker and a suite of microsatellite loci to individually genotype hair samples. We then estimated density in a SECR framework using sex, elevation, land cover type, and time to model heterogeneity in detection probability and the spatial scale over which detection probability declines. We sampled the populations using 554 hair traps and 117 bear rubs and collected 4,083 hair samples. We identified 725 (367 male, 358 female) individuals. Our density estimates varied from 16.5 bears/100 km2 (95% CI = 11.6–23.5) in the southern Sacramento Mountains to 25.7 bears/100 km2 (95% CI = 13.2–50.1) in the Sandia Mountains. Overall, detection probability at the activity center (g0) was low across all study areas and ranged from 0.00001 to 0.02. The low values of g0 were primarily a result of half of all hair samples for which genotypes were attempted failing to produce a complete genotype. We speculate that the low success we had genotyping hair samples was due to exceedingly high levels of ultraviolet (UV) radiation that degraded the DNA in the hair. Despite sampling difficulties, we were able to produce density estimates with levels of precision comparable to those estimated for black bears elsewhere in the United States. © 2018 The Wildlife Society.

Tidal Habitats Support Large Numbers of Invasive Blue Catfish in a Chesapeake Bay Subestuary

The introduction of a non-native freshwater fish, blue catfish Ictalurus furcatus, in tributaries of Chesapeake Bay resulted in the establishment of fisheries and in the expansion of the population into brackish habitats. Blue catfish are an invasive species in the Chesapeake Bay region, and efforts are underway to limit their impacts on native communities. Key characteristics of the population (population size, survival rates) are unknown, but such knowledge is useful in understanding the impact of blue catfish in estuarine systems. We estimated population size and survival rates of blue catfish in tidal habitats of the James River subestuary. We tagged 34,252 blue catfish during July–August 2012 and 2013; information from live recaptures (n = 1177) and dead recoveries (n = 279) were used to estimate annual survival rates and population size using Barker’s Model in a Robust Design and allowing for heterogeneity in detection probabilities. The blue catfish population in the 12-km study area was estimated to be 1.6 million fish in 2013 (95% confidence interval [CI] adjusted for overdispersion: 926,307–2,914,208 fish). Annual apparent survival rate estimates were low: 0.16 (95% CI 0.10–0.24) in 2012–2013 and 0.44 (95% CI 0.31–0.58) in 2013–2014 and represent losses from the population through mortality, permanent emigration, or both. The tagged fish included individuals that were large enough to exhibit piscivory and represented size classes that are likely to colonize estuarine habitats. The large population size that we estimated was unexpected for a freshwater fish in tidal habitats and highlights the need to effectively manage such species.

An empirical evaluation of camera trapping and spatially explicit capture-recapture models for estimating chimpanzee density.

Empirical validations of survey methods for estimating animal densities are rare, despite the fact that only an application to a population of known density can demonstrate their reliability under field conditions and constraints. Here, we present a field validation of camera trapping in combination with spatially explicit capture-recapture (SECR) methods for enumerating chimpanzee populations. We used 83 camera traps to sample a habituated community of western chimpanzees (Pan troglodytes verus) of known community and territory size in Taï National Park, Ivory Coast, and estimated community size and density using spatially explicit capture-recapture models. We aimed to: (1) validate camera trapping as a means to collect capture-recapture data for chimpanzees; (2) validate SECR methods to estimate chimpanzee density from camera trap data; (3) compare the efficacy of targeting locations frequently visited by chimpanzees versus deploying cameras according to a systematic design; (4) evaluate the performance of SECR estimators with reduced sampling effort; and (5) identify sources of heterogeneity in detection probabilities. Ten months of camera trapping provided abundant capture-recapture data. All weaned individuals were detected, most of them multiple times, at both an array of targeted locations, and a systematic grid of cameras positioned randomly within the study area, though detection probabilities were higher at targeted locations. SECR abundance estimates were accurate and precise, and analyses of subsets of the data indicated that the majority of individuals in a community could be detected with as few as five traps deployed within their territory. Our results highlight the potential of camera trapping for cost-effective monitoring of chimpanzee populations.

Evaluating genetic capture‐recapture using a chimpanzee population of known size

ABSTRACTGenetic capture‐recapture (CR) estimates of population size have potential for aiding the conservation and management of rare or elusive animals. To date, few studies have explored the performance of genetic CR estimates by implementing them in a population of known size. We evaluated the accuracy and precision of genetic CR estimates by genotyping fecal samples collected opportunistically over the territory of a well‐studied group of approximately 190 previously identified and genotyped eastern chimpanzees (Pan troglodytes schweinfurthii) in Kibale National Park, Uganda. We compared the performance of genetic CR estimates based on 3‐month and 3‐year sampling periods to explore the impact of lengthened sample periods, which are expected to increase accuracy and precision of estimates but also increase the chances of violating population closure assumptions. We compared the effects of using spatial and non‐spatial models and equal or heterogeneous detection probabilities upon estimates. Over the 3‐year period, we detected 54% of the group members and produced population size estimates with more accuracy and narrower confidence intervals than the 3‐month sampling period. The population remained effectively closed over the 3 years and detection heterogeneity was linked to age but not sex. Non‐spatial methods estimated group size more accurately than spatially explicit methods, which had a stronger tendency to underestimate population size. This study suggests that genetic CR may produce accurate and precise population size estimates if substantial effort is allocated to sample collection and genotyping. © 2016 The Wildlife Society.