Optimal sampling design for spatial capture–recapture

Dupont, Gates; Royle, J. Andrew; Nawaz, Muhammad Ali; Sutherland, Chris

doi:10.1002/ecy.3262

Cited by 27 publications

(16 citation statements)

References 19 publications

(30 reference statements)

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“…Efford and Boulanger ( 2019 ) proposed that an evaluation of expected sample sizes of individuals and recaptures using simulations can be related to expected precision, which can be used to inform optimal SCR sampling design. Similarly, Dupont et al ( 2021 ) further identified a genetic algorithm that optimized an objective function related to estimator precision to more quickly identify an optimal design based on a goal of maximizing the number of individuals detected, the number of spatial recaptures, or a balance of these two criteria. Finally, multiple study areas can be used to better capture variability in landscape features when landscape heterogeneity in density is being evaluated or when inference across a larger landscape is of interest (Short Bull et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Precision and bias of spatial capture–recapture estimates: A multi‐site, multi‐year Utah black bear case study

Schmidt

Graves

Pederson

et al. 2022

Ecological Applications

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Spatial capture-recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low-density, wide-ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi-site, multi-year Utah black bear (Ursus americanus) capture-recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km 2 . Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial

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Section: Discussionmentioning

confidence: 99%

Precision and bias of spatial capture–recapture estimates: A multi‐site, multi‐year Utah black bear case study

Schmidt

Graves

Pederson

et al. 2022

Ecological Applications

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“…Hierarchical survey designs that use clusters of camera stations such that spatial recaptures can occur within widely spaced groups of cameras could extend the spatial area sampled by survey grids ( Efford and Fewster 2013 ; Sun et al 2014 ). Optimization criteria and functions are available to help predict the most informative detector station layouts for a given survey area ( Dupont et al 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…We did not evaluate this in the present study, but sambar deer density estimation at site BL may have benefited from this approach. Clustered survey designs can increase the area covered by a camera trap survey and the number of individuals detected without biasing spatial capture–recapture parameter estimates ( Sun et al 2014 ; Efford and Boulanger 2019 ; Dupont et al 2021 ). The optimal allocation of effort across cluster size and number of clusters can be explored using optimization algorithms and simulations ( Sun et al 2014 ; Efford and Boulanger 2019 ; Dupont et al 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Estimating deer density and abundance using spatial mark–resight models with camera trap data

Bengsen

Forsyth

Ramsey

et al. 2022

Journal of Mammalogy

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Globally, many wild deer populations are actively studied or managed for conservation, hunting, or damage mitigation purposes. These studies require reliable estimates of population state parameters, such as density or abundance, with a level of precision that is fit for purpose. Such estimates can be difficult to attain for many populations that occur in situations that are poorly suited to common survey methods. We evaluated the utility of combining camera trap survey data, in which a small proportion of the sample is individually recognizable using natural markings, with spatial mark–resight (SMR) models to estimate deer density in a variety of situations. We surveyed 13 deer populations comprising four deer species (Cervus unicolor, C. timorensis, C. elaphus, Dama dama) at nine widely separated sites, and used Bayesian SMR models to estimate population densities and abundances. Twelve surveys provided sufficient data for analysis and seven produced density estimates with coefficients of variation (CVs) ≤ 0.25. Estimated densities ranged from 0.3 to 24.6 deer km−2. Camera trap surveys and SMR models provided a powerful and flexible approach for estimating deer densities in populations in which many detections were not individually identifiable, and they should provide useful density estimates under a wide range of conditions that are not amenable to more widely used methods. In the absence of specific local information on deer detectability and movement patterns, we recommend that at least 30 cameras be spaced at 500–1,000 m and set for 90 days. This approach could also be applied to large mammals other than deer.

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“…The number of traps may determine the number of individuals encountered and the frequency of detections, whereas the trap spacing may determine the frequency and spatial range of spatial recaptures. In the sampling design phase, the choice between deploying an extensive trapping array (i.e., large area, low trap density) versus an intensive array (i.e., small area, high trap density) may have substantial implications for detection probability and number of spatial recaptures (Wilton et al 2014, Dupont et al 2021. While empirical evaluations of SCR design are lacking, simulation studies suggest that SCR methods perform well when the trapping area is larger than a home range, and when trap spacing is close to 2-3σ, which, in theory, equates roughly to the radius of a typical home range, making spatial recaptures possible (Sollmann et al 2012, Sun et al 2014.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of spatial capture–recapture study design

Fleming

Grant²,

Sterrett

et al. 2021

Ecological Applications

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A principal challenge impeding strong inference in analyses of wild populations is the lack of robust and long-term data sets. Recent advancements in analytical tools used in wildlife science may increase our ability to integrate smaller data sets and enhance the statistical power of population estimates. One such advancement, the development of spatial capture-recapture (SCR) methods, explicitly accounts for differences in spatial study designs, making it possible to equate multiple study designs in one analysis. SCR has been shown to be robust to variation in design as long as minimal sampling guidance is adhered to. However, these expectations are based on simulation and have yet to be evaluated in wild populations.Here we conduct a rigorously designed field experiment by manipulating the arrangement of artificial cover objects (ACOs) used to collect data on red-backed salamanders (Plethodon cinereus) to empirically evaluate the effects of design configuration on inference made using SCR. Our results suggest that, using SCR, estimates of space use and detectability are sensitive to study design configuration, namely the spacing and extent of the array, and that caution is warranted when assigning biological interpretation to these parameters. However, estimates of population density remain robust to design except when the configuration of detectors grossly violates existing recommendations.

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Optimal sampling design for spatial capture–recapture

Cited by 27 publications

References 19 publications

Precision and bias of spatial capture–recapture estimates: A multi‐site, multi‐year Utah black bear case study

Precision and bias of spatial capture–recapture estimates: A multi‐site, multi‐year Utah black bear case study

Estimating deer density and abundance using spatial mark–resight models with camera trap data

Experimental evaluation of spatial capture–recapture study design

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