Spatially Explicit Capture–recapture Model Research Articles

Occupancy data improves parameter precision in spatial capture-recapture models.

Population size is one of the basic demographic parameters for species management and conservation. Among different estimation methods, spatially explicit capture–recapture (SCR) models allow the estimation of population density in a framework that has been greatly developed in recent years. The use of automated detection devices, such as camera traps, has impressively extended SCR studies for individually identifiable species. However, its application to unmarked/partially marked species remains challenging, and no specific method has been widely used. We fitted an SCR‐integrated model (SCR‐IM) to stone marten Martes foina data, a species for which only some individuals are individually recognizable by natural marks, and estimate population size based on integration of three submodels: (1) individual capture histories from live capture and transponder tagging; (2) detection/nondetection or “occupancy” data using camera traps in a bigger area to extend the geographic scope of capture–recapture data; and (3) telemetry data from a set of tagged individuals. We estimated a stone marten density of 0.352 (SD: 0.081) individuals/km2. We simulated four dilution scenarios of occupancy data to study the variation in the coefficient of variation in population size estimates. We also used simulations with similar characteristics as the stone marten case study, comparing the accuracy and precision obtained from SCR‐IM and SCR, to understand how submodels' integration affects the posterior distributions of estimated parameters. Based on our simulations, we found that population size estimates using SCR‐IM are more accurate and precise. In our stone marten case study, the SCR‐IM density estimation increased the precision by 37% when compared to the standard SCR model as regards to the coefficient of variation. This model has high potential to be used for species in which individual recognition by natural markings is not possible, therefore limiting the need to rely on invasive sampling procedures.

Assessing small-mammal trapping design using spatially explicit capture recapture (SECR) modeling on long-term monitoring data.

Few studies have evaluated the optimal sampling design for tracking small mammal population trends, especially for rare or difficult to detect species. Spatially explicit capture-recapture (SECR) models present an advancement over non-spatial models by accounting for individual movement when estimating density. The salt marsh harvest mouse (SMHM; Reithrodontomys raviventris) is a federal and California state listed endangered species endemic to the San Francisco Bay-Delta estuary, California, USA; where a population in a subembayment has been continually monitored over an 18-year period using mark-recapture methods. We analyzed capture data within a SECR modeling framework that allowed us to account for differences in detection and movement between sexes. We compared the full dataset to subsampling scenarios to evaluate how the grid size (area) of the trap design, trap density (spacing), and number of consecutive trapping occasions (duration) influenced density estimates. To validate the subsampling methods, we ran Monte Carlo simulations based on the true parameter estimates for each specific year. We found that reducing the area of the trapping design by more than 36% resulted in the inability of the SECR model to replicate density estimates within the SE of the original density estimates. However, when trapping occasions were reduced from 4 to 3-nights the density estimates were indistinguishable from the full dataset. Furthermore, reducing trap density by 50% also resulted in density estimates comparable to the full dataset and was a substantially better model than reducing the trap area by 50%. Overall, our results indicated that moderate reductions in the number of trapping occasions or trap density could yield similar density estimates when using a SECR approach. This approach allows the optimization of field trapping efforts and designs by reducing field efforts while maintaining the same population estimate compared to the full dataset. Using a SECR approach may help other wildlife programs identify sampling efficiencies without sacrificing data integrity for long term monitoring of population densities.

Integrating multiple datasets into spatially-explicit capture-recapture models to estimate the abundance of a locally scarce felid

The conservation of animal populations often requires the estimation of population size. Low density and secretive behaviour usually determine scarce data sources and hampers precise abundance estimations of carnivore populations. However, joint analysis of independent scarce data sources in a common modeling framework allows unbiased and precise estimates of population parameters. We aimed to estimate the density of the European wildcat (Felis silvestris) in a protected area of Spain, by combining independent datasets in a spatially-explicit capture-recapture (SCR) framework. Data from live-capture with individual identification, camera-trapping without individual identification and radio-tracking concurrently obtained were integrated in a joint SCR and count data model. Ten live captures of five wildcats were obtained with an effort of 2034 trap-days, whereas seven wildcat independent events were recorded in camera traps with 3628 camera-days. Two wildcats were radio-tagged and telemetry information on their movements was obtained. The integration of the different data sources improved the precision obtained by the standard SCR model. The mean (± SD) density estimated with the integrated model (0.038 ± 0.017 wildcats/km2, 95% highest posterior density 0.013–0.082) is among the lowest values ever reported for this species, despite corresponding to a highly protected area. Among the likely causes of such low density, low prey availability could have triggered an extinction vortex process. We postulate that the estimated low density could represent a common situation of wildcat populations in the southern Iberia, highlighting the need for further studies and urgent conservation actions in the furthermost southwestern range of this species in Europe.

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

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.

A Spatially Explicit Capture–Recapture Model for Partially Identified Individuals When Trap Detection Rate Is Less than One

Spatially explicit capture–recapture (SECR) models have gained enormous popularity to solve abundance estimation problems in ecology. In this study, we develop a novel Bayesian SECR model that disentangles two processes: one is the process of animal arrival within a detection region, and the other is the process of recording this arrival by a given set of detectors. We integrate this complexity into an advanced version of a recent SECR model involving partially identified individuals (Royle JA. Spatial capture-recapture with partial identity. arXiv preprint arXiv:1503.06873, 2015). We assess the performance of our model over a range of realistic simulation scenarios and demonstrate that estimates of population size N improve when we utilize the proposed model relative to the model that does not explicitly estimate trap detection probability (Royle JA. Spatial capture-recapture with partial identity. arXiv preprint arXiv:1503.06873, 2015). We confront and investigate the proposed model with a spatial capture–recapture dataset from a camera trapping survey of tigers (Panthera tigris) in Nagarahole study area of southern India. Detection probability is estimated at 0.489 (with 95% credible interval (CI) [0.430, 0.543]) which implies that the camera traps are performing imperfectly and thus justifying the use of our model in real world applications. We discuss possible extensions, future work and relevance of our model to other statistical applications beyond ecology. AMS classification codes: 62F15, 92D40

Using non-invasively collected genetic data to estimate density and population size of tigers in the Bangladesh Sundarbans

Population density is a key parameter to monitor endangered carnivores in the wild. The photographic capture-recapture method has been widely used for decades to monitor tigers, Panthera tigris, however the application of this method in the Sundarbans tiger landscape is challenging due to logistical difficulties. Therefore, we carried out molecular analyses of DNA contained in non-invasively collected genetic samples to assess the tiger population in the Bangladesh Sundarbans within a spatially explicit capture-recapture (SECR) framework. By surveying four representative sample areas totalling 1994 km2 of the Bangladesh Sundarbans, we collected 440 suspected tiger scat and hair samples. Genetic screening of these samples provided 233 authenticated tiger samples, which we attempted to amplify at 10 highly polymorphic microsatellite loci. Of these, 105 samples were successfully amplified, representing 45 unique genotype profiles. The capture-recapture analyses of these unique genotypes within the SECR model provided a density estimate of 2.85 ± SE 0.44 tigers/100 km2 (95% CI: 1.99–3.71 tigers/100 km2) for the area sampled, and an estimate of 121 tigers (95% CI: 84–158 tigers) for the total area of the Bangladesh Sundarbans. We demonstrate that this non-invasive genetic surveillance can be an additional approach for monitoring tiger populations in a landscape where camera-trapping is challenging.

Characterizing recolonization by a reintroduced bear population using genetic spatial capture-recapture

Many large carnivores are recolonizing range as a result of improved management and conservation policy, habitat restoration, and reintroduction programs. American black bears (Ursus americanus) are projected to recolonize portions of the United States, but few studies have characterized or provided practical methods for monitoring this process. We used noninvasive hair sampling at 4 proximal study areas along the Kentucky–Virginia, USA, border during 2012–2013 to estimate demographics and population genetics, and investigate recolonization patterns of an American black bear population that was founded by 55 bears reintroduced to a fragmented mountainous landscape during the 1990s and subjected to harvest 6 years post-reintroduction. Using spatially explicit capture–recapture (SECR) models, we estimated a density of 0.26 bear/km2, or minimum abundance of 482 bears, distributed among 2 primary core areas previously identified by occupancy analysis: a southern and northern core area. The southern core area was established by a founder adult female that exhibited post-release dispersal, but moderate asymmetrical gene flow (Nm = 6 bears) from the northern core area mitigated deleterious genetic consequences typical of such founder events. Effective number of breeders (NB = 62 bears) was similar to the number of founders, suggesting that genetically, the population remains mostly the product of reintroduction. Despite limited connectivity with other populations in the region, genetic diversity (HE = 0.78) was retained because of rapid population growth during the 16 years post-reintroduction (λ = 1.14/year). This bear population exhibited demographic characteristics indicative of continued recolonization, including a significantly female-biased sex ratio (0.53M:1.00F) and female density decreasing with increasing distance from the reintroduction release areas in the northern core. Few bear detections at 2 peripheral study areas and results from SECR model detection function transformation suggested recolonization may continue to the southwest and northeast along prominent linear mountain ridges. Although the population has grown and is genetically stable, because of relatively low population density and recolonization direction, we suggest monitoring demographic vital rates to evaluate harvest sustainability and population viability. Our study demonstrates the utility of noninvasive genetic sampling in conjunction with SECR models to characterize and monitor recolonizing bear populations, which may also be useful for management of expanding populations of other large carnivores. © 2016 The Wildlife Society.

Density‐dependent home‐range size revealed by spatially explicit capture–recapture

The size of animal home ranges often varies inversely with population density among populations of a species. This fact has implications for population monitoring using spatially explicit capture–recapture (SECR) models, in which both the scale of home‐range movements σ and population densityDusually appear as parameters, and both may vary among populations. It will often be appropriate to model a structural relationship between population‐specific values of these parameters, rather than to assume independence. We suggest re‐parameterizing the SECR model usingkp= σp√Dp, wherekprelates to the degree of overlap between home ranges and the subscriptpdistinguishes populations. We observe thatkpis often nearly constant for populations spanning a range of densities. This justifies fitting a model in which the separatekpare replaced by the single parameterkand σpis a density‐dependent derived parameter. Continuous density‐dependent spatial variation in σ may also be modelled, using a scaled non‐Euclidean distance between detectors and the locations of animals. We illustrate these methods with data from automatic photography of tigersPanthera tigrisacross India, in which the variation is among populations, from mist‐netting of ovenbirdsSeiurus aurocapillain Maryland, USA, in which the variation is within a single population over time, and from live‐trapping of brushtail possumsTrichosurus vulpeculain New Zealand, modelling spatial variation within one population.Possible applications and limitations of the methods are discussed. A model in whichkpis constant, while density varies, provides a parsimonious null model for SECR. The parameterkof the null model is a concise summary of the empirical relationship between home‐range size and density that is useful in comparative studies. We expect deviations from this model, particularly the dependence ofkpon covariates, to be biologically interesting.

Evaluating heterogeneity of sex-specific capture probability and precision in camera-trap population estimates of tigers

Population estimates of tigers (Panthers tigris) are affected by movement patterns of the sexes. We evaluated sex-specific heterogeneity to estimate the tiger population at Ranthambhore Tiger Reserve, western Rajasthan, India. We also compared population attributes obtained using a likelihood-based spatially explicit capture–recapture (SECR) method with traditional approaches that use alternate information on animal space used to estimate an effective sampled area around the traps. We obtained 122 tiger photo-captures of 27 individuals (14 F and 13 M) from 165 camera-trapping sites in a 215-km2 area over 3,300 trap-nights, for 20 sampling occasions in 2010. The capture probability of males was about 2 times greater than that of female tigers, but overall detection probability was high, with total population estimate of about 30.2 tigers (SE = 3.7). Male and female tiger density estimates/100 km2 were 2.8 (SE = 0.8) and 4.1 (SE = 1.1), respectively, and overall density estimates of 6.1 (SE = 1.2) individuals/100 km2 were obtained using the SECR method. Non-spatial models using the full mean maximum distance moved (MMDM) to estimate an effective sampled area underestimated the density by 9% compared with the SECR model as a baseline, and 1/2MMDM models overestimated the density by 25% as compared with results from the SECR model. We conclude that to assess the reliability of abundance estimates, heterogeneity in sex-specific capture probabilities should be considered. We recommend using an SECR model because these methods appear to be minimally biased for estimating an effective sampled area and can be applied consistently among studies of various taxa. © 2014 The Wildlife Society.

Estimating cougar densities in northeast Oregon using conservation detection dogs

Estimating densities of cougar (Puma concolor) is important for managing cougars and their prey but remains challenging because of cougar's elusive and solitary behavior. To evaluate a non-invasive, genetic capture–recapture method to estimate cougar population size and density, we surveyed a 220-km2 area using conservation detection dogs trained to locate scat over a 4-week sampling period in northeast Oregon. We collected 272 scat samples and conducted DNA analysis on 249 samples from which we determined individual identification from 73 samples that represented 21 cougars (9 males and 12 females). We evaluated 4 models to estimate cougar densities: Huggins closed population capture–recapture (Huggins), CAPWIRE, multiple detections with Poisson (MDP), and spatially explicit capture–recapture (SECR). Population estimates for cougars using our study area were 26 (95% CI = 22–35, 9 males and 17 females) from Huggins models, 24 (95% CI = 21–30, 9 males and 15 females) from CAPWIRE, and 27 (95% CI = 24–42, 9 males and 18 females) from the MDP model. We accounted for the edge effect in density estimates caused by individuals whose home ranges included only a portion of the survey grid by buffering the study area using the mean home range radius of 8 cougars equipped with global positioning system collars on or near the study area. We estimated densities of 4.6 cougars/100 km2 (95% CI = 3.8–8.3) for the Huggins model, 4.8 cougars/100 km2 (95% CI = 4.2–7.8) for the MDP model, 4.2 cougars/100 km2 (95% CI = 3.3–5.3) for the CAPWIRE model, and 5.0 cougars/100 km2 (95% CI = 3.2–7.7) for the SECR model. Our results suggested estimating cougar densities using scat detection dogs could be feasible at a broader scale with less effort than other methods currently being used. © 2014 The Wildlife Society.

Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies

Camera traps have become the main method for estimating jaguar (Panthera onca) densities. Over 74 studies have been carried out throughout the species range following standard design recommendations. We reviewed the study designs used by these studies and the results obtained. Using simulated data we evaluated the performance of different statistical methods for estimating density from camera trap data including the closed-population capture–recapture models Mo and Mh with a buffer of ½ and the full mean maximum distance moved (MMDM) and spatially explicit capture–recapture (SECR) models under different study designs and scenarios. We found that for the studies reviewed density estimates were negatively correlated with camera polygon size and MMDM estimates were positively correlated. The simulations showed that for camera polygons that were smaller than approximately one home range density estimates for all methods had a positive bias. For large polygons the Mh MMDM and SECR model produced the most accurate results and elongated polygons can improve estimates with the SECR model. When encounter rates and home range sizes varied by sex, estimates had a negative bias for models that did not include sex as a covariate. Based on the simulations we concluded that the majority of jaguar camera trap studies did not meet the requirements necessary to produce unbiased density estimates and likely overestimated true densities. We make clear recommendations for future study designs with respect to camera layout, number of cameras, study length, and camera placement. Our findings directly apply to camera trap studies of other large carnivores.

Evaluating the potential biases in carnivore capture–recapture studies associated with the use of lure and varying density estimation techniques using photographic‐sampling data of the Malagasy civet

AbstractEstimating density of elusive carnivores with capture–recapture analyses is increasingly common. However, providing unbiased and precise estimates is still a challenge due to uncertainties arising from the use of (1) bait or lure to attract animals to the detection device and (2) ad hoc boundary‐strip methods to compensate for edge effects in area estimation. We used photographic‐sampling data of the Malagasy civet Fossa fossana collected with and without lure to assess the effects of lure and to compare the use of four density estimators which varied in methods of area estimation. The use of lure did not affect permanent immigration or emigration, abundance and density estimation, maximum movement distances, or temporal activity patterns of Malagasy civets, but did provide more precise population estimates by increasing the number of recaptures. The spatially‐explicit capture–recapture (SECR) model density estimates ±SE were the least precise as they incorporate spatial variation, but consistent with each other (Maximum likelihood‐SECR = 1.38 ± 0.18, Bayesian‐SECR = 1.24 ± 0.17 civets/km2), whereas estimates relying on boundary‐strip methods to estimate effective trapping area did not incorporate spatial variation, varied greatly and were generally larger than SECR model estimates. Estimating carnivore density with ad hoc boundary‐strip methods can lead to overestimation and/or increased uncertainty as they do not incorporate spatial variation. This may lead to inaction or poor management decisions which may jeopardize at‐risk populations. In contrast, SECR models free researchers from making subjective decisions associated with boundary‐strip methods and they estimate density directly, providing more comparable and valuable population estimates.