Consequences of ignoring group association in spatial capture–recapture analysis

Bischof, Richard; Dupont, Pierre; Milleret, Cyril; Chipperfield, Joseph D.; Royle, J. Andrew

doi:10.2981/wlb.00649

Cited by 44 publications

(97 citation statements)

References 36 publications

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“…Different sources of heterogeneity can bias noninvasive genetic population density estimates, e.g. sex, age class, social status, individual differences in behaviour or space use (Boulanger et al 2004;Bischof et al 2020). Furthermore, differences in defecation rates can result in heterogeneous detection probabilities (Marucco et al 2009;Lunt & Mhlanga 2011), an aspect that in our opinion should be addressed in future studies.…”

Section: Discussionmentioning

confidence: 94%

“…In contrast, adult stags live solitary or in bachelor herds most of the year and therefore potentially show a different space use compared to the juvenile and subadult males. This aspect could introduce bias (Hickey & Sollmann 2018;Bischof et al 2020) that would not be accounted for by sex-specific estimation of population density. Further research, e.g.…”

Section: Discussionmentioning

confidence: 99%

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Estimating red deer (Cervus elaphus) population size based on non-invasive genetic sampling

Ebert¹,

Sandrini²,

Welter³

et al. 2021

Eur J Wildl Res

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Some deer species are of conservation concern; others are officially managed as a food source or for their trophies, whereas in many regions, deer are regarded as overabundant or even as a nuisance causing damages. Regardless of local management issues, in most cases, reliable data on deer population sizes and sex ratios are lacking. Non-invasive genetic approaches are promising tools for the estimation of population size and structure. We developed and tested a non-invasive genetic approach for red deer (Cervus elaphus) population size and density estimation based on faeces collected from three free-ranging red deer populations in south-western Germany. Altogether, we genotyped 2762 faecal samples, representing 1431 different individuals. We estimated population density for both sexes separately using two different approaches: spatially explicit capture-recapture (SECR) approach and a single-session urn model (CAPWIRE). The estimated densities of both approaches were similar for all three study areas, ranging between total densities of 3.3 (2.5–4.4) and 8.5 (6.4–11.3) red deer/km2. The estimated sex ratios differed significantly between the studied populations (ranging between 1:1.1 and 1:1.7), resulting in considerable consequences for management. In further research, the issues of population closure and approximation of the effectively sampled area for density estimation should be addressed. The presented approach can serve as a valuable tool for the management of deer populations, and to our knowledge, it represents the only sex-specific approach for estimation of red deer population size and density.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Estimating red deer (Cervus elaphus) population size based on non-invasive genetic sampling

Ebert¹,

Sandrini²,

Welter³

et al. 2021

Eur J Wildl Res

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show abstract

“…(2017) were unsure to what extent the measures of uncertainty in their study of a community western chimpanzees ( Pan troglodytes verus ) were underestimated. Bischof, Dupont, Milleret, Chipperfield, and Royle (2020) found that SCR models are robust to moderate levels of aggregation and cohesion, with low to moderate aggregation and cohesion not impacting the bias and precision of density and

σ

estimates. Inferences from SCR density estimates for species with small group sizes can be trusted even if grouping is ignored (Bischof et al., 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Bischof, Dupont, Milleret, Chipperfield, and Royle (2020) found that SCR models are robust to moderate levels of aggregation and cohesion, with low to moderate aggregation and cohesion not impacting the bias and precision of density and

σ

estimates. Inferences from SCR density estimates for species with small group sizes can be trusted even if grouping is ignored (Bischof et al., 2020). Although the fission–fusion social structure of caribou leads to frequent exchanges of individuals between groups, boreal caribou were rarely resampled together as a group or as a pair in our study (unpublished data).…”

Section: Discussionmentioning

confidence: 99%

An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou

McFarlane

Manseau

Steenweg

et al. 2020

Ecology and Evolution

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Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture–recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from noninvasive genetic sampling of seven boreal caribou populations ( Rangifer tarandus caribou ), which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of nonindependence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates. Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset ( n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures was strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.

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“…However, misspecifications may have consequences for estimates of other important parameters besides density/population size, such as home range area, habitat use/selection, movement, and connectivity, all of which feature increasingly in the SCR literature (Efford et al, 2016; Royle et al, 2013b; Gardner et al, 2018; Royle et al, 2013a). Furthermore, even in the absence of systematic bias, misspecifications could impact the confidence one can place in those estimates by affecting the associated precision and coverage probability (Bischof et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

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

Dey

Bischof

Dupont

et al. 2021

Preprint

Self Cite

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Spatial 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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Consequences of ignoring group association in spatial capture–recapture analysis

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 44 publications

References 36 publications

Estimating red deer (Cervus elaphus) population size based on non-invasive genetic sampling

Estimating red deer (Cervus elaphus) population size based on non-invasive genetic sampling

An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou

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

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