Integrating dead recoveries in open‐population spatial capture–recapture models

Dupont, Pierre; Milleret, Cyril; Tourani, Mahdieh; Brøseth, Henrik; Bischof, Richard

doi:10.1002/ecs2.3571

Cited by 7 publications

(8 citation statements)

References 45 publications

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“…However, we also want to highlight the possibilities of implementing other types of data into the SCR framework for future studies. Multiple data sources, such as recoveries of dead animals, can also be integrated in the SCR framework to increase the precision of estimates (Dupont et al 2021 ). Several methods were recently proposed for incorporating detections of unidentified individuals, leading to more precise estimation (Jiménez et al 2019 , 2021 ; Tourani et al 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling

et al. 2021

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Spatial capture–recapture modelling (SCR) is a powerful tool for estimating density, population size, and space use of elusive animals. Here, we applied SCR modelling to non-invasive genetic sampling (NGS) data to estimate red fox (Vulpes vulpes) densities in two areas of boreal forest in central (2016–2018) and southern Norway (2017–2018). Estimated densities were overall lower in the central study area (mean = 0.04 foxes per km2 in 2016, 0.10 in 2017, and 0.06 in 2018) compared to the southern study area (0.16 in 2017 and 0.09 in 2018). We found a positive effect of forest cover on density in the central, but not the southern study area. The absence of an effect in the southern area may reflect a paucity of evidence caused by low variation in forest cover. Estimated mean home-range size in the central study area was 45 km2 [95%CI 34–60] for females and 88 km2 [69–113] for males. Mean home-range sizes were smaller in the southern study area (26 km2 [16–42] for females and 56 km2 [35–91] for males). In both study areas, detection probability was session-dependent and affected by sampling effort. This study highlights how SCR modelling in combination with NGS can be used to efficiently monitor red fox populations, and simultaneously incorporate ecological factors and estimate their effects on population density and space use.

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Section: Discussionmentioning

confidence: 99%

Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling

et al. 2021

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“…Using simulations, we show that the model produces sound inferences on the role of spatial covariates and density dependence in explaining spatial variation in survival. In addition, the model allows for integrating spatial dead recoveries (Dupont et al 2021) and estimates multiple competing sources of mortality with potentially different spatial determinants. The model overcomes a challenge faced by other methods, namely to a obtain population-level assessment of spatial determinants of variation in survival (Royle et al 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The model is composed of four sub-models for 1) density and inter-annual movement, 2) demography, 3) live detections, and 4) dead recoveries. (Royle et al 2014, Bischof et al 2020a, Milleret et al 2020, 2021, Dupont et al 2021). We created two versions of the model.…”

Section: Methodsmentioning

confidence: 99%

Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models

Milleret

Dey

Dupont

et al. 2022

Preprint

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Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor thoroughly tested. We developed a Bayesian OSPCR model that not only accounts for spatial variation in survival using spatial covariates, but also estimates local density-dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density-dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to positive bias in abundance estimates (up to 10% relative bias). We provide a set of features in R package nimbleSCR that allow computationally efficient fitting of Bayesian OPSCR models with spatially varying survival. The ability to make population-level inferences of spatial variation in survival is an essential step towards a fully spatially-explicit OPSCR model that can disentangle the role of multiple spatial drivers on population dynamics.Open Research statementcode to reproduce the analysis is available on github; https://github.com/Cyril-Milleret/Public/tree/master/SpatialSurvivalOPSCR

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“…The first option is using Equation () to fit data that typically arise from area searches for DNA samples of animals, where any individual can be detected more than once within the detection region. The second option is modeling the case where only a single detection per individual is possible within

\boldsymbol{o}

(e.g., dead recoveries, Dupont et al, 2021):

\mathrm{log}\mathbb{\mathrm{P}} \left({\boldsymbol{y}}_i|{\boldsymbol{s}}_i,\uptheta, \upsigma \right)=-\mathrm{log}\Lambda \left(\boldsymbol{o}|{\boldsymbol{s}}_i,\uptheta, \upsigma \right)+\mathrm{log}\uplambda \left({\boldsymbol{y}}_i|{\boldsymbol{s}}_i,\uptheta, \upsigma \right).

If this kind of sampling is repeated over a number of occasions, individual detection locations can be modeled using a binomial point process.…”

Section: Methodsmentioning

confidence: 99%

A flexible and efficient Bayesian implementation of point process models for spatial capture–recapture data

Zhang

Chipperfield

Illian

et al. 2022

Ecology

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Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway.

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Integrating dead recoveries in open‐population spatial capture–recapture models

Abstract: Integrating dead recoveries in openpopulation spatial capture-recapture models.

Cited by 7 publications

References 45 publications

Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling

Estimating red fox density using non-invasive genetic sampling and spatial capture–recapture modelling

Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models

A flexible and efficient Bayesian implementation of point process models for spatial capture–recapture data

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