scholarly journals Integrating spatial capture-recapture models with variable individual identifiability

2020 ◽  
Author(s):  
Joel S. Ruprecht ◽  
Charlotte E. Eriksson ◽  
Tavis D. Forrester ◽  
Darren A. Clark ◽  
Michael J. Wisdom ◽  
...  
Keyword(s):  
Puma Concolor ◽  
Individual Recognition ◽  
Population Monitoring ◽  
Lynx Rufus ◽  
Individual Identification ◽  
Telemetry Data ◽  
Density Estimates ◽  
Gps Collars ◽  
Remote Cameras ◽  
Capture Recapture

AbstractMany applications in ecology depend on unbiased and precise estimates of animal population density. Spatial capture recapture models and their variants have become the preferred tool for estimating densities of carnivores. Within the spatial capture-recapture family are variants that require individual identification of all encounters (spatial capture-recapture), individual identification of a subset of a population (spatial mark-resight), or no individual identification (spatial count). In addition, these models can incorporate telemetry data (all models) and the marking process (spatial mark-resight). However, the consistency of results among methods and the relative precision of estimates in a real-world setting are unknown. Consequently, it is unclear how much and what type of data are needed to achieve satisfactory density estimates. We tested a suite of models to estimate population densities of black bears (Ursus americanus), bobcats (Lynx rufus), cougars (Puma concolor), and coyotes (Canis latrans). For each species we genotyped fecal DNA collected with detection dogs. A subset of individuals from each species were affixed with GPS collars bearing unique markings to be resighted by remote cameras set on a 1 km grid. We fit 10 models for each species ranging from those requiring no animals to be individually recognizable to others that necessitate full individual recognition. We then assessed the contribution of incorporating telemetry data to each model and the marking process to the mark-resight model. Finally, we developed an integrated hybrid model that combines camera, physical capture, genetic, and GPS data into a single hierarchical model. Importantly, we find that spatial count models that do not individually identify animals fail in all cases whether or not telemetry data are included. Results improved as models contained more information on individual identity. Models where a subset of individuals were identifiable yielded qualitatively similar results, but can produce quantitatively divergent estimates, suggesting that long-term population monitoring should use a consistent method across years. Incorporation of telemetry data and the marking process can produce more accurate and precise density estimates. Our results can be used to guide future study designs to efficiently estimate carnivore densities for better understanding of population dynamics, predator-prey relationships, and community assemblages.

scholarly journals Identifying individual cougars (Puma concolor) in remote camera images – implications for population estimates

2018 ◽  
Vol 45 (3) ◽  
pp. 274 ◽  
Author(s):  
Peter D. Alexander ◽  
Eric M. Gese
Keyword(s):  
Puma Concolor ◽  
Camera Traps ◽  
Camera Trapping ◽  
Field Methods ◽  
Density Estimates ◽  
North West ◽  
Photo Identification ◽  
Matching Process ◽  
Capture Recapture ◽  
Remote Camera

Context Several studies have estimated cougar (Puma concolor) abundance using remote camera trapping in conjunction with capture–mark–recapture (CMR) type analyses. However, this methodology (photo-CMR) requires that photo-captured individuals are individually recognisable (photo identification). Photo identification is generally achieved using naturally occurring marks (e.g. stripes or spots) that are unique to each individual. Cougars, however, are uniformly pelaged, and photo identification must be based on subtler attributes such as scars, ear nicks or body morphology. There is some debate as to whether these types of features are sufficient for photo-CMR, but there is little research directly evaluating its feasibility with cougars. Aim We aimed to examine researchers’ ability to reliably identify individual cougars in photographs taken from a camera-trapping survey, in order to evaluate the appropriateness of photo-CMR for estimating cougar abundance or CMR-derived parameters. Methods We collected cougar photo detections using a grid of 55 remote camera traps in north-west Wyoming, USA. The photo detections were distributed to professional biologists working in cougar research, who independently attempted to identify individuals in a pairwise matching process. We assessed the level to which their results agreed, using simple percentage agreement and Fleiss’s kappa. We also generated and compared spatially explicit capture–recapture (SECR) density estimates using their resultant detection histories. Key results There were no cases where participants were in full agreement on a cougar’s ID. Agreement in photo identification among participants was low (n = 7; simple agreement = 46.7%; Fleiss’s kappa = 0.183). The resultant SECR density estimates ranged from 0.7 to 13.5 cougars per 100 km2 (n = 4; s.d. = 6.11). Conclusion We were unable to produce reliable estimates of cougar density using photo-CMR, due to our inability to accurately photo-tag detected individuals. Abundance estimators that do not require complete photo-tagging (i.e. mark–resight) were also infeasible, given the lack of agreement on any single cougar’s ID. Implications This research suggested that there are substantial problems with the application of photo-CMR to estimate the size of cougar populations. Although improvements in camera technology or field methods may resolve these issues, researchers attempting to use this method on cougars should be cautious.

scholarly journals Leopard Panthera pardus fusca Density in the Seasonally Dry, Subtropical Forest in the Bhabhar of Terai Arc, Nepal

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Kanchan Thapa ◽  
Rinjan Shrestha ◽  
Jhamak Karki ◽  
Gokarna Jung Thapa ◽  
Naresh Subedi ◽  
...  
Keyword(s):  
Winter Season ◽  
Population Monitoring ◽  
Panthera Pardus ◽  
Density Estimates ◽  
Seasonally Dry ◽  
Abundance Estimates ◽  
Capture Recapture ◽  
Modelling Techniques ◽  
Wildlife Reserve

We estimated leopard (Panthera pardus fusca) abundance and density in the Bhabhar physiographic region in Parsa Wildlife Reserve, Nepal. The camera trap grid, covering sampling area of 289 km2 with 88 locations, accumulated 1,342 trap nights in 64 days in the winter season of 2008-2009 and photographed 19 individual leopards. Using models incorporating heterogeneity, we estimated 28 (±SE 6.07) and 29.58 (±SE 10.44) leopards in Programs CAPTURE and MARK. Density estimates via 1/2 MMDM methods were 5.61 (±SE 1.30) and 5.93 (±SE 2.15) leopards per 100 km2 using abundance estimates from CAPTURE and MARK, respectively. Spatially explicit capture recapture (SECR) models resulted in lower density estimates, 3.78 (±SE 0.85) and 3.48 (±SE 0.83) leopards per 100 km2, in likelihood based program DENSITY and Bayesian based program SPACECAP, respectively. The 1/2 MMDM methods have been known to provide much higher density estimates than SECR modelling techniques. However, our SECR models resulted in high leopard density comparable to areas considered better habitat in Nepal indicating a potentially dense population compared to other sites. We provide the first density estimates for leopards in the Bhabhar and a baseline for long term population monitoring of leopards in Parsa Wildlife Reserve and across the Terai Arc.

scholarly journals Spatial Mark-Resight for Categorically Marked Populations with an Application to Genetic Capture-Recapture

2018 ◽  
Author(s):  
Ben C. Augustine ◽  
Frances E. C. Stewart ◽  
J. Andrew Royle ◽  
Jason T. Fisher ◽  
Marcella J. Kelly
Keyword(s):  
Individual Heterogeneity ◽  
Positive Bias ◽  
Telemetry Data ◽  
Detection Function ◽  
Noninvasive Methods ◽  
Data Set ◽  
Density Estimates ◽  
Mark Recapture ◽  
Capture Recapture ◽  
Scr Model

AbstractThe estimation of animal population density is a fundamental goal in wildlife ecology and management, commonly met using mark recapture or spatial mark recapture (SCR) study designs and statistical methods. Mark-recapture methods require the identification of individuals; however, for many species and sampling methods, particularly noninvasive methods, no individuals or only a subset of individuals are individually identifiable. The unmarked SCR model, theoretically, can estimate the density of unmarked populations; however, it produces biased and imprecise density estimates in many sampling scenarios typically encountered. Spatial mark-resight (SMR) models extend the unmarked SCR model in three ways: 1) by introducing a subset of individuals that are marked and individually identifiable, 2) introducing the possibility of individual-linked telemetry data, and 3) introducing the possibility that the capture-recapture data from the survey used to deploy the marks can be used in a joint model, all improving the reliability of density estimates. The categorical spatial partial identity model (SPIM) improves the reliability of density estimates over unmarked SCR along another dimension, by adding categorical identity covariates that improve the probabilistic association of the latent identity samples. Here, we combine these two models into a “categorical SMR” model to exploit the benefits of both models simultaneously. We demonstrate using simulations that SMR alone can produce biased and imprecise density estimates with sparse data and/or when few individuals are marked. Then, using a fisher (Pekania pennanti) genetic capture-recapture data set, we show how categorical identity covariates, marked individuals, telemetry data, and jointly modeling the capture survey used to deploy marks with the resighting survey all combine to improve inference over the unmarked SCR model. As previously seen in an application of the categorical SPIM to a real-world data set, the fisher data set demonstrates that individual heterogeneity in detection function parameters, especially the spatial scale parameter σ, introduces positive bias into latent identity SCR models (e.g., unmarked SCR, SMR), but the categorical SMR model provides more tools to reduce this positive bias than SMR or the categorical SPIM alone. We introduce the possibility of detection functions that vary by identity category level, which will remove individual heterogeneity in detection function parameters than is explained by categorical covariates, such as individual sex. Finally, we provide efficient SMR algorithms that accommodate all SMR sample types, interspersed marking and sighting periods, and any number of identity covariates using the 2-dimensional individual by trap data in conjunction with precomputed constraint matrices, rather than the 3-dimensional individual by trap by occasion data used in SMR algorithms to date.

scholarly journals Improving the random encounter model method to estimate carnivore densities using data generated by conventional camera-trap design

Oryx ◽  
2019 ◽  
pp. 1-6 ◽  
Author(s):  
Germán Garrote ◽  
Ramón Pérez de Ayala ◽  
Antón Álvarez ◽  
José M. Martín ◽  
Manuel Ruiz ◽  
...  
Keyword(s):  
Correction Factor ◽  
Individual Recognition ◽  
Camera Traps ◽  
Individual Identification ◽  
Iberian Lynx ◽  
Surrounding Area ◽  
Lynx Pardinus ◽  
Trap Design ◽  
Capture Recapture ◽  
Using Data

Abstract The random encounter model, a method for estimating animal density using camera traps without the need for individual recognition, has been developed over the past decade. A key assumption of this model is that cameras are placed randomly in relation to animal movements, requiring that cameras are not set only at sites thought to have high animal traffic. The aim of this study was to define a correction factor that allows the random encounter model to be applied in photo-trapping surveys in which cameras are placed along tracks to maximize capture probability. Our hypothesis was that applying such a correction factor would compensate for the different rates at which lynxes use tracks and the surrounding area, and should thus improve the estimates obtained with the random encounter model. We tested this using data from a well-known Iberian lynx Lynx pardinus population. Firstly, we estimated Iberian lynx densities using a traditional camera-trapping design followed by spatially explicit capture–recapture analyses. We estimated the differential use rate for tracks vs the surrounding area using data from a lynx equipped with a GPS collar, and subsequently calculated the correction factor. As expected, the random encounter model overestimated densities by 378%. However, the application of the correction factor improved the estimate and reduced the error to 16%. Although there are limitations to the application of the correction factor, the corrected random encounter model shows potential for density estimation of species for which individual identification is not possible.

scholarly journals Spatially Explicit Capture-Recapture Through Camera Trapping: A Review of Benchmark Analyses for Wildlife Density Estimation

2020 ◽  
Vol 8 ◽  
Author(s):  
Austin M. Green ◽  
Mark W. Chynoweth ◽  
Çağan Hakkı Şekercioğlu
Keyword(s):  
Density Estimation ◽  
Study Design ◽  
New Technologies ◽  
Model Development ◽  
Population Monitoring ◽  
Camera Traps ◽  
Camera Trapping ◽  
Individual Identification ◽  
Spatially Explicit ◽  
Capture Recapture

Camera traps have become an important research tool for both conservation biologists and wildlife managers. Recent advances in spatially explicit capture-recapture (SECR) methods have increasingly put camera traps at the forefront of population monitoring programs. These methods allow for benchmark analysis of species density without the need for invasive fieldwork techniques. We conducted a review of SECR studies using camera traps to summarize the current focus of these investigations, as well as provide recommendations for future studies and identify areas in need of future investigation. Our analysis shows a strong bias in species preference, with a large proportion of studies focusing on large felids, many of which provide the only baseline estimates of population density for these species. Furthermore, we found that a majority of studies produced density estimates that may not be precise enough for long-term population monitoring. We recommend simulation and power analysis be conducted before initiating any particular study design and provide examples using readily available software. Furthermore, we show that precision can be increased by including a larger study area that will subsequently increase the number of individuals photo-captured. As many current studies lack the resources or manpower to accomplish such an increase in effort, we recommend that researchers incorporate new technologies such as machine-learning, web-based data entry, and online deployment management into their study design. We also cautiously recommend the potential of citizen science to help address these study design concerns. In addition, modifications in SECR model development to include species that have only a subset of individuals available for individual identification (often called mark-resight models), can extend the process of explicit density estimation through camera trapping to species not individually identifiable.

Spatially explicit capture–recapture analysis of bobcat (Lynx rufus) density: implications for mesocarnivore monitoring

2015 ◽  
Vol 42 (5) ◽  
pp. 394 ◽  
Author(s):  
Daniel H. Thornton ◽  
Charles E. Pekins
Keyword(s):  
Density Estimation ◽  
Large Scale ◽  
Local Density ◽  
Camera Traps ◽  
Camera Trapping ◽  
Lynx Rufus ◽  
Spatially Explicit ◽  
Density Estimates ◽  
Capture Recapture ◽  
Study Sites

Context Accurate density estimation is crucial for conservation and management of elusive species. Camera-trapping may provide an efficient method for density estimation, particularly when analysed with recently developed spatially explicit capture–recapture (SECR) models. Although camera-traps are employed extensively to estimate large carnivore density, their use for smaller carnivores has been limited. Moreover, while camera-trapping studies are typically conducted at local scales, the utility of analysing larger-scale patterns by combining multiple camera studies remains poorly known. Aims The goal of the present study was to develop a better understanding of the utility of SECR models and camera-trapping for the estimation of density of small carnivores at local and regional scales. Methods Based on data collected from camera-traps, we used SECR to examine density of bobcats (Lynx rufus) at four study sites in north-central Texas. We then combined our density estimates with previous estimates (from multiple methodologies) across the bobcat’s geographic range, and used linear regression to examine drivers of range-wide density patterns. Key results Bobcat densities averaged 13.2 per 100 km2 across all four study sites, and were lowest at the site in the most heavily modified landscape. Bobcat capture probability was positively related to forest cover around camera-trap sites. At the range-wide scale, 53% of the variation in density was explained by just two factors: temperature and longitude. Conclusions Our results demonstrate the utility of camera-traps, combined with SECR, to generate precise density estimates for mesocarnivores, and reveal the negative effects of landscape disturbance on bobcat populations. The associations revealed in our range-wide analysis, despite variability in techniques used to estimate density, demonstrate how a combination of multiple density estimates for a species can be used for large-scale inference. However, improvement in our understanding of biogeographic density patterns for mesocarnivores could be obtained from a greater number of camera-based density estimates across the range of a species, combined with meta-analytic techniques. Implications Camera-trapping and SECR should be more widely applied to generate local density estimates for many small and medium-sized carnivores, where at least a portion of the individuals are identifiable. If such estimates are more widely obtained, meta-analytic techniques could be used to test biogeographic predictions or for large-scale monitoring efforts.

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

Ecosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Corey I. Mitchell ◽  
Kevin T. Shoemaker ◽  
Todd C. Esque ◽  
Amy G. Vandergast ◽  
Steven J. Hromada ◽  
...  
Keyword(s):  
Telemetry Data ◽  
Density Estimates ◽  
Capture Recapture

Kinship and social structure of bobcats (Lynx rufus) inferred from microsatellite and radio-telemetry data

2006 ◽  
Vol 269 (4) ◽  
pp. 494-501 ◽  
Author(s):  
J. E. Janečka ◽  
T. L. Blankenship ◽  
D. H. Hirth ◽  
M. E. Tewes ◽  
C. W. Kilpatrick ◽  
...  
Keyword(s):  
Social Structure ◽  
Radio Telemetry ◽  
Lynx Rufus ◽  
Telemetry Data

scholarly journals Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture-recapture modelling

2021 ◽  
Author(s):  
Soumen Dey ◽  
Richard Bischof ◽  
Pierre P. A. Dupont ◽  
Cyril Milleret
Keyword(s):  
Home Range ◽  
Space Use ◽  
Relative Bias ◽  
Diagnostic Tools ◽  
List Type ◽  
Detection Function ◽  
Density Estimates ◽  
Use Patterns ◽  
Wide Range ◽  
Capture Recapture

AbstractSpatial capture-recapture (SCR) is now used widely to estimate wildlife densities. At the core of SCR models lies the detection function, linking individual detection probability to the distance from its latent activity center. The most common function (half-normal) assumes a bivariate normal space use and consequently detection pattern. This is likely an oversimplification and misrepresentation of real-life animal space use patterns, but studies have reported that density estimates are relatively robust to misspecified detection functions. However, information about consequences of such misspecification on space use parameters (e.g. home range area), as well as diagnostic tools to reveal it are lacking.We simulated SCR data under six different detection functions, including the half-normal, to represent a wide range of space use patterns. We then fit three different SCR models, with the three simplest detection functions (half-normal, exponential and half-normal plateau) to each simulated data set. We evaluated the consequences of misspecification in terms of bias, precision and coverage probability of density and home range area estimates. We also calculated Bayesian p-values with respect to different discrepancy metrics to assess whether these can help identify misspecifications of the detection function.We corroborate previous findings that density estimates are robust to misspecifications of the detection function. However, estimates of home range area are prone to bias when the detection function is misspecified. When fitted with the half-normal model, average relative bias of 95% kernel home range area estimates ranged between −25% and 26% depending on the misspecification. In contrast, the half-normal plateau model (an extension of the half-normal) returned average relative bias that ranged between −26% and −4%. Additionally, we found useful heuristic patterns in Bayesian p-values to diagnose the misspecification in detection function.Our analytical framework and diagnostic tools may help users select a detection function when analyzing empirical data, especially when space use parameters (such as home range area) are of interest. We urge development of additional custom goodness of fit diagnostics for Bayesian SCR models to help practitioners identify a wider range of model misspecifications.

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