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Record W4406755848 · doi:10.1111/rec.14354

The role of habitat in predator–prey dynamics with applications to restoration

2025· article· en· W4406755848 on OpenAlexaff
Robert J. Lennox, Marius Kambestad, Saron Berhe, Kim Birnie‐Gauvin, Steven J. Cooke, Lotte S. Dahlmo, Sindre Håvarstein Eldøy, Jan Grimsrud Davidsen, Erlend M. Hanssen, Lene K. Sortland, Dylan Shea, Cecilie I. Nilsen, Helge Skoglund, Gaute Velle, Gustav Hellström, Petter Lundberg, Claudia Junge, Keno Ferter, Knut Wiik Vollset

Bibliographic record

VenueRestoration Ecology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsCarleton UniversityOcean Tracking NetworkDalhousie University
FundersMiljødirektoratetNorges Forskningsråd
KeywordsPredationHabitatEcologyContext (archaeology)EcosystemCompetition (biology)Apex predatorRestoration ecologyPredatorBiology

Abstract

fetched live from OpenAlex

Habitat is a powerful force in ecosystems, and the quantity and quality of habitat can shape ecosystem structure and function. Among the many important roles that habitat plays is as a mediator of ecological interactions, including predator–prey dynamics. In the context of ecosystem restoration, there is great potential to better understand how predator–prey dynamics are influenced by habitat and whether this has implications for how ecosystems are managed. We consider the ways in which habitat serves as an important mediator of interactions between predators and their prey and present four ways in which habitat acts as an intermediary that enhances or diminishes this relationship. We found that habitat provides refuge from predators and shapes the physical traits of prey as they use their surroundings to protect themselves. We also discuss how habitat creates physical resistance and sets the cost of predation for predators and how habitat facilitates apparent competition within a community context. These roles of habitat are well established in ecology, but we believe they are underdeveloped from an applied perspective. We conclude that habitat must be appropriately considered in the context of how it mediates predation. Given the ways that habitat influences predation, restoration efforts should consider if and how physical measures may positively or negatively affect species interactions and whether this could lead to success or failure of overall programs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.237
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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