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Record W4410480619 · doi:10.1002/jwmg.70040

Mixed evidence for disturbance‐mediated apparent competition for declining caribou in western British Columbia, Canada

2025· article· en· W4410480619 on OpenAlexafffundabout
Katie Tjaden‐McClement, Tazarve Gharajehdaghipour, Carolyn R. Shores, Shane White, Robin Steenweg, Mathieu Bourbonnais, Zoe Konanz, A. Cole Burton

Bibliographic record

VenueJournal of Wildlife Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsEnvironment and Climate Change CanadaGovernment of British ColumbiaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDisturbance (geology)Competition (biology)GeographyEcologyFisheryBiology

Abstract

fetched live from OpenAlex

Abstract Understanding causal mechanisms of decline for species at risk is critical for effective conservation. Caribou (Rangifer tarandus) face threats from habitat loss and degradation due to human activities, and many caribou populations across Canada have experienced dramatic declines in recent decades. Disturbance‐mediated apparent competition (DMAC) has been implicated in many of these declines, but its generality has been questioned, particularly for low‐productivity caribou ranges. The DMAC hypothesis leads to the following predictions: 1) a vegetation productivity pulse after disturbance, 2) primary ungulate prey attraction to disturbed areas, 3) predator attraction to primary prey and disturbance, and 4) increased caribou predation risk due to overlapping habitat use with primary prey and predators. We tested these predictions for the declining Itcha‐Ilgachuz caribou population, located in the low‐productivity Chilcotin Plateau region of west‐central British Columbia, Canada. We used a remotely sensed productivity index to examine vegetation recovery patterns after disturbance and used camera traps and Bayesian mixed effects negative binomial regression models to estimate the responses of primary prey, predator, and caribou relative abundance to landscape disturbances <40 years old, interacting species, and other habitat features. We identified a productivity pulse in harvested and burnt forest patches, but overall productivity was lower than in other caribou ranges where DMAC occurs. Primary prey, moose (Alces alces) and mule deer (Odocoileus hemionus), showed strong positive responses to burnt areas and weak positive responses to harvested forest. For predators, wolves (Canis lupus), black bears (Ursus americanus), and grizzly bears (Ursus arctos) were positively associated with primary prey species, while coyotes (Canis latrans) and Canada lynx (Lynx canadensis) were more strongly associated with snowshoe hare (Lepus americanus), and wolverines (Gulo gulo) were not associated with any focal prey species. Wolves, grizzly bears, and wolverines were not associated with habitat disturbance, but black bears, coyotes, and lynx responded positively to burned and harvested areas. Caribou did not have reduced relative abundance in harvested forests or burns, potentially increasing their overlap with predators. Overall, we found mixed support for DMAC for the Itcha‐Ilgachuz caribou population, with stronger evidence for a pathway mediated by disturbance from forest fire, rather than forest harvest. We recommend further research and action on wildfire management for the recovery of this population, including monitoring population trends of caribou and interacting species in response to habitat management. Our results emphasize the context‐dependency of mechanisms of decline for caribou and underscore the need for population‐specific knowledge to effectively conserve threatened species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.253
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2025
Admission routes3
Has abstractyes

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