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Milleret C, Dey S, Dupont P, Brøseth H, Turek D, de Valpine P, Bischof R. Estimating spatially variable and density-dependent survival using open-population spatial capture-recapture models. Ecology 2023; 104:e3934. [PMID: 36458376 PMCID: PMC10078101 DOI: 10.1002/ecy.3934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/07/2022] [Indexed: 12/05/2022]
Abstract
Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only 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 OPSCR 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 up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause-specific mortality of female wolverines (Gulo gulo) in central Sweden and Norway. The ability to make population-level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.
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Affiliation(s)
- Cyril Milleret
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Soumen Dey
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Pierre Dupont
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
| | - Henrik Brøseth
- Norwegian Institute for Nature Research (NINA), Trondheim, Norway
| | - Daniel Turek
- Department of Mathematics and Statistics, Williams College, Williamstown, Massachusetts, USA
| | - Perry de Valpine
- Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, California, USA
| | - Richard Bischof
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
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Zhang W, Chipperfield JD, Illian JB, Dupont P, Milleret C, de Valpine P, Bischof R. A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Ecology 2023; 104:e3887. [PMID: 36217822 PMCID: PMC10078592 DOI: 10.1002/ecy.3887] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 07/29/2022] [Accepted: 08/29/2022] [Indexed: 02/01/2023]
Abstract
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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Affiliation(s)
- Wei Zhang
- Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, California, USA.,School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Joseph D Chipperfield
- Faculty of Life Sciences and Natural Resource Management, Norwegian University of Life Sciences, Trondheim, Norway.,Norwegian Institute for Nature Research, Høyteknologisenteret, Bergen, Norway
| | - Janine B Illian
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Pierre Dupont
- Faculty of Life Sciences and Natural Resource Management, Norwegian University of Life Sciences, Trondheim, Norway
| | - Cyril Milleret
- Faculty of Life Sciences and Natural Resource Management, Norwegian University of Life Sciences, Trondheim, Norway
| | - Perry de Valpine
- Department of Environmental Science, Policy and Management, University of California Berkeley, Berkeley, California, USA
| | - Richard Bischof
- Faculty of Life Sciences and Natural Resource Management, Norwegian University of Life Sciences, Trondheim, Norway
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Jiménez J, Díaz‐Ruiz F, Monterroso P, Tobajas J, Ferreras P. Occupancy data improves parameter precision in spatial capture-recapture models. Ecol Evol 2022; 12:e9250. [PMID: 36052294 PMCID: PMC9412271 DOI: 10.1002/ece3.9250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/22/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- José Jiménez
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ciudad RealSpain
| | - Francisco Díaz‐Ruiz
- Departamento de Biología Animal, Facultad de CienciasUniversidad de MálagaMálagaSpain
| | - Pedro Monterroso
- CIBIO, Centro de Investigacão em Biodiversidade e Recursos Genéticos, InBIO Laboratório AssociadoUniversidade do PortoVairãoPortugal
- BIOPOLIS Program in Genomics, Biodiversity and Land PlanningCIBIOVairãoPortugal
| | - Jorge Tobajas
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ciudad RealSpain
- Departamento de Botánica, Ecología y Fisiología VegetalUniversidad de CórdobaCórdobaSpain
| | - Pablo Ferreras
- Instituto de Investigación en Recursos Cinegéticos (IREC, CSIC‐UCLM‐JCCM)Ciudad RealSpain
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Comparison of methods for estimating density and population trends for low-density Asian bears. Glob Ecol Conserv 2022. [DOI: 10.1016/j.gecco.2022.e02058] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
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Lindsø LK, Dupont P, Rød-Eriksen L, Andersskog IPØ, Ulvund KR, Flagstad Ø, Bischof R, Eide NE. Estimating red fox density using non-invasive genetic sampling and spatial capture-recapture modelling. Oecologia 2022; 198:139-151. [PMID: 34859281 PMCID: PMC8803778 DOI: 10.1007/s00442-021-05087-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 11/20/2021] [Indexed: 11/28/2022]
Abstract
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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Affiliation(s)
- Lars K Lindsø
- Norwegian Institute for Nature Research, Høgskoleringen 9, 7034, Trondheim, Norway.
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Universitetstunet 3, 1430, Ås, Norway.
- Centre for Ecological and Evolutionary Synthesis (CEES), The Department of Biosciences, University of Oslo, Blindernveien 31, 0371, Oslo, Norway.
| | - Pierre Dupont
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Universitetstunet 3, 1430, Ås, Norway
| | - Lars Rød-Eriksen
- Norwegian Institute for Nature Research, Høgskoleringen 9, 7034, Trondheim, Norway
| | | | | | - Øystein Flagstad
- Norwegian Institute for Nature Research, Høgskoleringen 9, 7034, Trondheim, Norway
| | - Richard Bischof
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Universitetstunet 3, 1430, Ås, Norway
| | - Nina E Eide
- Norwegian Institute for Nature Research, Høgskoleringen 9, 7034, Trondheim, Norway
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