ArticleEcology2022
An integrated path for spatial capture-recapture and animal movement modeling.
Article in Ecology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- A non-Euclidean movement model to quantify landscape connectivity with open spatial capture-recapture models.Ecology · 2026Article
- The Range-Resident Logistic Model: A New Framework to Formalise the Population-Dynamics Consequences of Range Residency.Ecology letters · 2025Article
- A ERSF-VIPA framework: scalable wildlife movement modelling for conflict mitigation.Movement ecology · 2025Article
- Indigenous Knowledge and Community-Derived Counts Produce Robust Wildlife Population Estimates: Roosevelt Elk in Karuk Aboriginal Territory.Ecology and evolution · 2025Article
- Mechanistic home range capture-recapture models for the estimation of population density and landscape connectivity.Ecology · 2025Article
- Joint inference for telemetry and spatial survey data.Ecology · 2024Article
- Combining potential and realized distribution modeling of telemetry data for a bycatch risk assessment.Ecology and evolution · 2024Article
- Crabs ride the tide: incoming tides promote foraging of Giant Mud Crab (Scylla serrata).Movement ecology · 2023Article
- Integrated animal movement and spatial capture-recapture models: Simulation, implementation, and inference.Ecology · 2022Article
- Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model.Ecology · 2022Article
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Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ecologists and conservation biologists increasingly rely on spatial capture-recapture (SCR) and movement modeling to study animal populations. Historically, SCR has focused on population-level processes (e.g., vital rates, abundance, density, and distribution), whereas animal movement modeling has focused on the behavior of individuals (e.g., activity budgets, resource selection, migration). Even though animal movement is clearly a driver of population-level patterns and dynamics, technical and conceptual developments to date have not forged a firm link between the two fields. Instead, movement modeling has typically focused on the individual level without providing a coherent scaling from individual- to population-level processes, whereas SCR has typically focused on the population level while greatly simplifying the movement processes that give rise to the observations underlying these models. In our view, the integration of SCR and animal movement modeling has tremendous potential for allowing ecologists to scale up from individuals to populations and advancing the types of inferences that can be made at the intersection of population, movement, and landscape ecology. Properly accounting for complex animal movement processes can also potentially reduce bias in estimators of population-level parameters, thereby improving inferences that are critical for species conservation and management. This introductory article to the Special Feature reviews recent advances in SCR and animal movement modeling, establishes a common notation, highlights potential advantages of linking individual-level (Lagrangian) movements to population-level (Eulerian) processes, and outlines a general conceptual framework for the integration of movement and SCR models. We then identify important avenues for future research, including key challenges and potential pitfalls in the developments and applications that lie ahead.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.