Unifying population and landscape ecology with spatial capture–recapture

Royle, J. Andrew; Fuller, Angela K.; Sutherland, Chris

doi:10.1111/ecog.03170

Cited by 121 publications

(169 citation statements)

References 86 publications

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“…1, 2). Genetic tags, a unique sequence of DNA loci used to identify individuals and their species, sex, and lineage, combined with modern analytical methods (e.g., spatial capture recapture [SCR]; Royle et al 2017) have emerged as one of the most promising approaches to meet these demands, particularly for ecological process distributed across large spatial extents and for large, elusive, sensitive, unmarked, or low-density species (Taberlet et al 1999, Lukacs and Burnham 2005, Schwartz et al 2007, Proctor et al 2010Fig. 3).…”

Section: Introductionmentioning

confidence: 99%

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Lamb

Ford

Proctor³

et al. 2019

Ecological Applications

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The Anthropocene is an era of marked human impact on the world. Quantifying these impacts has become central to understanding the dynamics of coupled human‐natural systems, resource‐dependent livelihoods, and biodiversity conservation. Ecologists are facing growing pressure to quantify the size, distribution, and trajectory of wild populations in a cost‐effective and socially acceptable manner. Genetic tagging, combined with modern computational and genetic analyses, is an under‐utilized tool to meet this demand, especially for wide‐ranging, elusive, sensitive, and low‐density species. Genetic tagging studies are now revealing unprecedented insight into the mechanisms that control the density, trajectory, connectivity, and patterns of human–wildlife interaction for populations over vast spatial extents. Here, we outline the application of, and ecological inferences from, new analytical techniques applied to genetically tagged individuals, contrast this approach with conventional methods, and describe how genetic tagging can be better applied to address outstanding questions in ecology. We provide example analyses using a long‐term genetic tagging dataset of grizzly bears in the Canadian Rockies. The genetic tagging toolbox is a powerful and overlooked ensemble that ecologists and conservation biologists can leverage to generate evidence and meet the challenges of the Anthropocene.

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Section: Introductionmentioning

confidence: 99%

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Lamb

Ford

Proctor³

et al. 2019

Ecological Applications

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“…Moreover, methods that simultaneously estimate local densi-ties and resistance to individual movement, such as the ecological distance parameterization of spatial capturerecapture models (SCR) (Royle et al 2013), capture interdependencies between density and connectivity that could ultimately affect population viability . Specifically, SCR-based landscape connectivity metrics (Sutherland et al 2015;Morin et al 2017;Royle et al 2018) describe the capacity of individuals to move through the landscape with respect to their distribution across the landscape and thus are valuable objectives for a reserve-design optimization framework. For example, density-weighted connectivity (Sutherland et al 2015;Morin et al 2017), which can be derived from population densities and functional connectivity estimated from SCR models, was recently used as an optimization objective in landscape conservation (Xue et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Reserve design to optimize functional connectivity and animal density

Gupta

Dilkina

Morin

et al. 2019

Conservation Biology

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Ecological distance‐based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve‐design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget‐constrained optimization problem and conducted a simulation to explore 3 different SCR‐informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density‐weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space‐use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home‐range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long‐term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density‐weighted connectivity or preemptively imposing home‐range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high‐density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home‐range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.

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“…• measure population abundance, density, and distribution of marked and unmarked populations (e.g., Goswami, Madhusudan, & Ullas Karanth, 2007;Heilbrun, Silvy, Peterson, & Tewes, 2006;Karanth & Nichols, 1998;Rowcliffe & Carbone, 2008;Royle, Fuller, & Sutherland, 2018;Whittington, Low, & Hunt, 2019;Whittington, Hebblewhite, & Chandler, 2018) • examine multi-species dynamics (Swanson et al, 2015),…”

Section: The D Iver S It Y Of C Amer a Tr Ap Re S E Arch Projec Tsmentioning

confidence: 99%

Design patterns for wildlife‐related camera trap image analysis

Greenberg

Godin

Whittington

2019

Ecology and Evolution

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This paper describes and explains design patterns for software that supports how analysts can efficiently inspect and classify camera trap images for wildlife‐related ecological attributes. Broadly speaking, a design pattern identifies a commonly occurring problem and a general reusable design approach to solve that problem. A developer can then use that design approach to create a specific software solution appropriate to the particular situation under consideration. In particular, design patterns for camera trap image analysis by wildlife biologists address solutions to commonly occurring problems they face while inspecting a large number of images and entering ecological data describing image attributes. We developed design patterns for image classification based on our understanding of biologists' needs that we acquired over 8 years during development and application of the freely available Timelapse image analysis system. For each design pattern presented, we describe the problem, a design approach that solves that problem, and a concrete example of how Timelapse addresses the design pattern. Our design patterns offer both general and specific solutions related to: maintaining data consistency, efficiencies in image inspection, methods for navigating between images, efficiencies in data entry including highly repetitious data entry, and sorting and filtering image into sequences, episodes, and subsets. These design patterns can inform the design of other camera trap systems and can help biologists assess how competing software products address their project‐specific needs along with determining an efficient workflow.

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Unifying population and landscape ecology with spatial capture–recapture

Cited by 121 publications

References 86 publications

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

Reserve design to optimize functional connectivity and animal density

Design patterns for wildlife‐related camera trap image analysis

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