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Record W4413035512 · doi:10.1111/csp2.70113

When is habitat recovered? Understanding the mechanisms of population decline to evaluate habitat recovery for boreal caribou

2025· article· en· W4413035512 on OpenAlexafffund
Craig A. DeMars, Melanie Dickie, Doug W. Lewis, Thomas J. Habib, Mark K. L. Wong, Robert Serrouya

Bibliographic record

VenueConservation Science and Practice · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsAlberta Pacific Forest IndustriesGovernment of British ColumbiaMinistry of ForestsAlberta Biodiversity Monitoring Institute
FundersAlberta-Pacific Forest Industries
KeywordsHabitatBorealEcologyGeographyPopulationWoodland caribouHabitat destructionPopulation declineEnvironmental scienceBiologyDemography

Abstract

fetched live from OpenAlex

Abstract Recovering habitat is a central objective for conserving species imperiled by habitat alteration. Yet, determining when habitat is recovered is challenging. For terrestrial wildlife, habitat recovery often focuses on regenerating vegetation, but vegetation changes may provide limited insight as to whether and when habitat is recovered. To be effective as a conservation action, habitat recovery should be linked to demographic responses of the focal species. Moreover, we suggest that habitat recovery be linked to changes in the strength of mechanisms driving population decline. Here, we illustrate such a framework using boreal woodland caribou ( Rangifer tarandus caribou ), which are threatened by altered predator–prey dynamics stemming from habitat alteration. Monitoring habitat recovery is challenging for boreal caribou because demographic effects may take decades to manifest and the spatial scale for demographic monitoring is larger than typical disturbance features or restoration projects. To address these challenges, we propose a continuum of habitat recovery where interim, multi‐scale indicators are linked to primary mechanisms underlying caribou population declines. Because habitat recovery varies geographically, indicators may need to be refined on a regional basis. Developing stronger inferences on recovery indicators will require adaptive management, where habitat recovery is implemented over larger spatial extents and longer timeframes.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.332
Teacher spread0.271 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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