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30 results for “spatial capture-recapture”
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>
Spatial capture-recapture data of Darwin's frogs captured between 2014-2017
<p>Search-encounter spatial capture-recapture data from <em>R. darwinii</em> individuals captured between 2014-2017 at two plots located near Neltume, Southern Chile.</p> <p>x and y coordinates in meters are provided for each capture as text files. These are matrices with 64 columns (secondary capture occasions) and 311 rows (frogs). The 16 primary capture occasions are separated by a 3-month period, and each of these occasion is composed of four secondary capture occasions. With these data you can reconstruct the capture-history matrix used in non-spatial capture-recapture models.</p> <p>Snout-to-vent length (SVL) during each capture occasion are provided in mm for each frog. With these data you can reconstruct the age of the individuals (juveniles or adults).</p> <p>Id data is provided for each individual. The first column represents the frog’s code, and the second one represents the plot where the frog was captured (1= HUI1, 2= HUI2).</p> <p>Any question can be addressed to andresvalenzuela.zoo@gmail.com</p>
Spatial partial identity model for spatial capture-recapture analysis of large carnivores in Kasungu National Park, Malawi
<p>Overview:</p> <p>Decline in global carnivore populations has led to increased demand for assessment of carnivore densities in understudied habitats. Spatial capture-recapture is used increasingly to estimate species densities, where individuals are often identified from their unique pelage patterns. However, uncertainty in bilateral individual identification can lead to the omission of capture data and reduce the precision of results. The recent development of the two-flank spatial partial identity model (SPIM), offers a cost-effective approach which can reduce uncertainty in individual identity assignment and provide robust density estimates. We conducted camera trap surveys annually between 2016 and 2018 in Kasungu National Park, Malawi, a primary miombo woodland and a habitat lacking baseline data on carnivore densities. We used SPIM to estimate density for leopard (<em>Panthera pardus</em>) and spotted hyaena (<em>Crocuta crocuta</em>), and report on the status of other large carnivores.</p> <p>Usage notes:</p> <p>These data are to estimate density for leopard and spotted hyaena in KNP, Malawi. They are provided as an example for using the spatial partial identity model for spatial capture-recapture analysis in populations where individuals are partially identified.</p> <p>Methods:</p> <p>Individual leopards and spotted hyaena were identified from photographs using their unique pelage patterns (Henschel & Ray, 2003). A database was maintained of identified individuals, with partial (single flank) or complete (two flank) identities, to build capture histories for SCR analysis. We identified individuals from left flank captures for both species, due to higher numbers of identified left flank individuals recorded during preliminary surveys. Complete identities were added where flanks were certain to come from the same individual (from baited stations outside of survey time, live captures, dual camera trap stations and multiple passes of a single camera trap). Leopards were sexed by visual determination of external genitalia, presence of the dewlap, frontal bossing and overall body size (Henschel & Ray, 2003; Devens <em>et al</em>. 2018). Sexing was not possible for spotted hyaena due to difficulties in determining sex from external genitalia and body size. Capture histories were developed for spatial captures and trap effort, with each day (24 hours) treated as a separate sampling occasion (Goldberg <em>et al</em>. 2015). Trap effort was measured through a binary matrix of active-inactive days, to improve estimates of detection probability, and included the spatial location of each camera location.</p> <p>Density was modelled using the package <em>SPIM </em>(Augustine, 2018) in R v.3.5.2<em> </em>(R Development Core Team, 2018) to resolve the complete identity of individuals from single-flank samples probabilistically (see Augustine <em>et al</em>. 2018 for complete description of spatial partial identity model), and a Bernoulli observation model fitted, whereby an individual may be captured in each trap only once during each sampling occasion (Royle <em>et al</em>. 2013; Augustine <em>et al</em>. 2018). For Markov Chain Monte Carlo simulations, a single chain of 50,000 iterations per single session analysis was undertaken, with a burn-in of 500 iterations and data augmentation of 100-130 individuals for leopard and 125-250 for spotted hyaena. Analysis was conducted with an increasing buffer width from 10,000 to 25,000 metres (leopard) and 10,000 to 40,000 metres (spotted hyaena), using 5,000 metre increments, until density estimates stabilised (Chase-Grey <em>et al</em>. 2013; Devens <em>et al</em>. 2018).</p>
Forecasting wildlife movement with spatial capture-recapture
<ol> <li>Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture-recapture modeling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time.</li> <li>We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased, and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the performance of the models' abundance estimators and movement forecasts when fit to data simulated under different movement models. We also fit the models to spatial capture–recapture data collected on North Atlantic right whales off the Atlantic Coast of the southeastern United States.</li> <li>Random walk movement models improved abundance estimation and movement forecasts in our simulation study and received greater support from the data in the right whale case study than did activity center movement models.</li> <li>Forecasts of wildlife movement made under integrated spatial capture–recapture movement models will be most valuable when individuals have been observed recently, when sampling for individuals is extensive and efficient, and when the scale of individuals' movements is small relative to the scale of the study area and sampling process. </li> </ol>
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
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Forecasting wildlife movement with spatial capture-recapture
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Acoustic spatial capture-recapture study on vocalising bowhead whales
<h1>Acoustic spatial capture-recapture study on vocalising bowhead whales</h1> <p>Contains the code and data used for the analysis of the case study data and run the simulation study. <br>The results of this study have been peer-reviewed and published (DOI: https://doi.org/10.1007/s13253-023-00563-0) and also appear in Chapter 2 and 3 of the PhD thesis "Advancements in methods for estimating the abundance of marine megafauna using novel sampling techniques" by Felix T Petersma. <br>We provide some information in support of the files presented here; however, for extensive background we kindly refer you to the publication.</p> <p>Most of the functionality of the model fitting is contained in the 'ascrRcpp'-package. <br>This package has been included pre-built and can be installed using the file 'ascrRcpp_1.0.tar.gz'.<br>Once this package is installed, all scripts included in this location should run fine, given that all other packages that are used will be installed as well.</p> <p> </p>
Spatially explicit genetic capture-recapture data from black bears in Ontario, Canada, 2017-2019
<p>The Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry sampled black bear (<em>Ursus americanus</em>) DNA at baited barbed wire hair corrals on 77 independent study areas in Ontario Canada, 2017-2019. Spatially explicit capture-recapture data from these surveys (>12 000 independent, spatially referenced detections of nearly 4000 individual bears) are archived here. This data set will be cited in manuscripts presenting different analyses of the entire data set or subsets thereof.</p>
Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
<p>Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and thus omit linkages between individual-level and population-level processes. We describe an integrated spatial capture-recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km<sup>2</sup> survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture-recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from < 0.75 bears/625 km<sup>2</sup> grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53–69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multi-year integrated population model using capture-recapture and telemetry data (2008–2016; Regehr et al. 2018). Overall, the SCR-movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR-movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.</p>
Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
<p>Over the last decade, spatial capture-recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies -- individual animals observed at specific locations and times -- can provide a rich source of information about how these processes relate to demographic rates. We developed SCR models that integrate complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using Bayesian analysis methods. We evaluated their performance through a simulation study, where we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T=25 to T=15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but movement models developed here could be extended to open SCR models. The movement process models could also be extended to accommodate additional "building blocks'' of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.</p>
R code and simulation output for Efford, M. G. & Boulanger, J. 2019. Fast evaluation of study designs for spatially explicit capture-recapture. Methods in Ecology and Evolution
<p>R code and simulation output for Efford, M. G. & Boulanger,<br> J. 2019. Fast evaluation of study designs for spatially explicit<br> capture-recapture. Methods in Ecology and Evolution In press.</p> <p>R code draws on previously published R packages 'secr' and 'secrdesign' available from CRAN:</p> <p><a href="https://CRAN.R-project.org/package=secr">https://CRAN.R-project.org/package=secr</a></p> <p><a href="https://CRAN.R-project.org/package=secrdesign">https://CRAN.R-project.org/package=secrdesign</a></p>
Spatially explicit genetic capture-recapture data from black bears in Ontario, Canada
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Data for fitting spatial capture-recapture (SCR) models to estimate spatially explicit demographics of Mojave desert tortoises on a demography plot in California, USA
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Integrated animal movement and spatial capture-recapture models: simulation, implementation, and inference
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Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
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Using spatial capture-recapture methods to estimate long-term spatiotemporal variation of a wide-ranging marine species
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Data from: Examining disease prevalence for species of conservation concern using non-invasive spatial capture-recapture techniques
1. Non-invasive techniques have long been used to estimate wildlife population abundance and density. However, recent technological breakthroughs have facilitated non-invasive estimation of the proportion of animal populations with certain diseases. Giraffes Giraffa camelopardalisare increasingly becoming recognized as a species of conservation concern with decreasing population trajectories across their range in Africa. 2. Diseases may be an important component impacting giraffe population declines, and the emerging 'Giraffe Skin Disease' (GSD), characterized by the appearance of wrinkled skin and alopecic lesions on the limbs, neck, and chest of infected giraffe, may hinder movement causing increased susceptibility to predation. 3. We examined the prevalence of GSD in Tanzania's Ruaha National Park over a 4-month period in 2015, using photographic capture–recapture surveys via road-based transects. We divided the study area into five circuitous survey units, each approximately 100 km in length ($\bar x$ = 99.22 km, SD = 3.72), and surveyed for giraffes for four months. From these surveys, we developed a database of spatially-explicit giraffe photographs. 4. We processed these photos for individual identification and fitted spatial capture–recapture models to predict the spatial configuration of giraffe abundance and GSD prevalence within the study area. 5. Our results indicated that >86% of the giraffe population showed signs of GSD and that the disease was more prevalent in the northern and north-eastern portion of Ruaha National Park. 6. Synthesis and applications. Our research shows that data from non-invasive surveys can be used in spatial capture–recapture (SCR) models to estimate the proportion of a population affected by a visible disease. Researchers and conservationists can use SCR models to better examine the variation in parameters associated with these populations such as sex and age class, movement, and encounter rate, which may be linked to the prevalence of the disease, while incorporating broad spatial and temporal dimensions of the population in such areas. We discuss the implications of this research for conservation of threatened species with an emphasis on disease ecology and vulnerability to predations, and more broadly, for wildlife conservation.
Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of "marked" wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of "unmarked" wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates.
Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus
Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.
Data from: Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks
<p>River otters (Lontra canadensis) were extirpated from New Mexico by the 1950s. A limited reintroduction occurred during 2008–2010 in which 33 otters sourced from Washington (WA) were translocated to the Upper Rio Grande Basin (URG) of New Mexico. We conducted a noninvasive genetic capture-recapture survey during the winter of 2018 by collecting fecal DNA samples from river otter scats found at latrines in the URG dendritic network of perennial waterways. Our objectives were to: 1) estimate genetic diversity and effective population size; 2) genetic divergence from the WA source population and potential connectivity with regionally proximal populations; 3) spatially explicit population density and size; and 4) population growth rate since the founder event. Between February and April 2018, we collected 1,184 fecal DNA samples from 622 individual scats at 20 latrines; genotyping was attempted at 10 otter-specific microsatellite loci for a subsample of 543 samples. A bottlenecking founder effect was strongly supported, which, combined with genetic drift, reduced genetic diversity and effective population size by 20–26% and 106–170%, respectively, compared with the WA source population. Estimated population density from spatial capture-recapture models was 0.23–0.28 otter/km of waterway, or 1 otter/3.57–4.35 km of waterway, corresponding to a total population size of 83–100 otters across 359 km of the perennial dendritic network from La Mesilla, New Mexico to Alamosa National Wildlife Refuge, Colorado. Estimated average annual population growth rate since the founder event was 1.12–1.15/year. Despite successful population establishment, the URG river otter population remains small, is genetically degraded, and does not yet meet the criteria for long-term reintroduction success. Projections suggested that the population could reach the recommended minimum viable population size of ≥400 otters by the years 2030–2033, though sufficient habitat may not exist in the URG Basin to support that many otters. </p>
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