Evidence map›Paper›PMID 34270790›Full record

ArticleEcology2022

An integrated path for spatial capture-recapture and animal movement modeling.

Brett T McClintock, Briana Abrahms, Richard B Chandler, Paul B Conn, Sarah J Converse, Robert L Emmet, Beth Gardner, Nathan J Hostetter, Devin S Johnson

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Brett T McClintockMarine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA.
Briana AbrahmsDepartment of Biology, University of Washington, Life Sciences Building, Box 351800, Seattle, Washington, USA.
Richard B ChandlerWarnell School of Forestry and Natural Resources, University of Georgia, 180 E. Green St., Athens, Georgia, USA.
Paul B ConnMarine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA.
Sarah J ConverseU.S. Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, School of Environmental and Forest Sciences & School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, Washington, USA.
Robert L EmmetQuantitative Ecology and Resource Management, University of Washington, Seattle, Washington, USA.
Beth GardnerSchool of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA.
Nathan J HostetterWashington Cooperative Fish and Wildlife Research Unit, School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA.
Devin S JohnsonMarine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, Washington, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

EcologyMovementAnimalsPopulation Densityanimal movementdensity and distributionintegrated population modelmark-recapturemovement ecologypopulation dynamicspopulation ecologyspatial capture-recapture

Identifiers

PMID34270790
PMCPMC9786756

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.