Characterizing spatio-temporal variation in survival and recruitment with integrated population models

Chandler, Richard B.; Hepinstall‐Cymerman, Jeffrey; Merker, Samuel A.; Abernathy-Conners, Heather; Cooper, Robert J.

doi:10.1642/auk-17-181.1

Cited by 38 publications

(60 citation statements)

References 90 publications

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“…Constructing family pedigrees is most practical in small, fragmented, or isolated populations (Proctor et al 2018b), but that is where it can be very useful in understanding fitness correlates and monitoring the efficacy of connectivity management (Pemberton 2008, Ford et al 2011, Proctor et al 2018a). Further, Chandler et al (2018) provide an innovative and complementary SCR approach to link survival and recruitment rates with landscape attributes and extrapolate results across space. Chandler et al (2018) marked individuals with physical tags, but we highlight this important work here because it is the first analysis of its kind, that we are aware of, and could easily be applied to genetically tagged individuals.…”

Section: Why and How Does Population Density Change Across The Landscmentioning

confidence: 99%

“…Further, Chandler et al (2018) provide an innovative and complementary SCR approach to link survival and recruitment rates with landscape attributes and extrapolate results across space. Chandler et al (2018) marked individuals with physical tags, but we highlight this important work here because it is the first analysis of its kind, that we are aware of, and could easily be applied to genetically tagged individuals. Quantifying relationships between density, fitness, and habitat is one of the most significant developments in modern applied ecology and conservation, with genetic tagging playing a leading role in this scientific advance.…”

Section: Why and How Does Population Density Change Across The Landscmentioning

confidence: 99%

“…Combined, these parameters provide an explicit estimate of realized k that is often more robust than realized k derived from comparing point estimates of density, especially for low-density populations and threatened species (Sibly and Hone 2002). Further, linking the demographic components of k to habitat features presents a powerful opportunity to explore the influence of habitat on population dynamics (Chandler et al 2018).…”

Section: Why and How Does Population Density Change Across The Landscmentioning

confidence: 99%

“…Further, Chandler et al. () provide an innovative and complementary SCR approach to link survival and recruitment rates with landscape attributes and extrapolate results across space. Chandler et al.…”

Section: Introductionmentioning

confidence: 99%

“…Further, linking the demographic components of λ to habitat features presents a powerful opportunity to explore the influence of habitat on population dynamics (Chandler et al. ).…”

Section: Introductionmentioning

confidence: 99%

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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: Why and How Does Population Density Change Across The Landscmentioning

confidence: 99%

Section: Why and How Does Population Density Change Across The Landscmentioning

confidence: 99%

Section: Why and How Does Population Density Change Across The Landscmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Further, linking the demographic components of λ to habitat features presents a powerful opportunity to explore the influence of habitat on population dynamics (Chandler et al. ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Lamb

Ford

Proctor³

et al. 2019

Ecological Applications

View full text Add to dashboard Cite

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Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models

Weegman

Arnold

Clark³

et al. 2021

Ecological Applications

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Integrated population models (IPMs) are widely used to combine disparate data sets in joint analysis to better understand population dynamics and provide guidance for conservation activities. An often-cited assumption of IPMs is independence among component data sets within the combined likelihood. Dependency among data sets should lead to underestimation of variance and bias because individuals contribute data to more than one data set. In practice, studied individuals often occur in multiple data sets in IPMs (i.e., overlap), which is one way for the independence assumption to be violated. Such cases have the potential to dissuade practitioners and limit application of IPMs to solve emerging ecological problems. We assessed precision and bias of demographic rates estimated from IPMs using a complete gradient (0-100%) of overlap among data sets, wide ranges in demographic rates (e.g., survival 0.1-0.8) and sample sizes (100-1,200 individuals) and variable data sources. We compared results from our simulations with those from IPMs constructed using empirical data on tree swallows (Tachycineta bicolor) where data sets either had complete overlap or included different individuals. Contrary to previous investigators, we found no substantive bias or uncertainty in any demographic rate from IPMs derived from data sets with complete overlap. While variability in demographic rates was greater at low sample sizes (i.e., low capture, recapture, and survey probabilities), there were negligible differences in the posterior mean or root mean square error of demographic rates among IPMs with strong dependence vs. complete independence among data sets. Our simulations suggest IPMs can be designed using only capturerecapture data or harvest and capture-recovery data where population estimates are obtained from the same data as survival and productivity data. While we encourage researchers to carefully consider the modeling approach best suited for their data sets, our results suggest that dependence among data sets does not generally compromise IPM estimates. Thus, violation of the independence assumption should not dissuade researchers from the application of IPMs in ecological research.

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A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture

Milleret

Dupont

Bonenfant

et al. 2018

Ecology and Evolution

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Spatial capture–recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large‐scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state‐space (LESS). Based on prior knowledge about a species’ home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half‐normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57‐fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore—the wolverine (Gulo gulo)—with an unprecedented resolution and across the species’ entire range in Norway (> 200,000 km2). Our approach helps overcome a major computational obstacle to population and landscape‐level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.

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Characterizing spatio-temporal variation in survival and recruitment with integrated population models

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 38 publications

References 90 publications

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

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

Partial and complete dependency among data sets has minimal consequence on estimates from integrated population models

A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture

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