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Record W4412863045 · doi:10.1111/conl.13123

Future Climate‐Driven Ecological Disruption in a Network of Marine Protected Areas on Canada's East Coast

2025· article· en· W4412863045 on OpenAlexafffundabout
Amy L. Irvine, Gabriel Reygondeau, Ryan R. E. Stanley, Yulia Egorova, Derek P. Tittensor

Bibliographic record

VenueConservation Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans CanadaThe Audio Recording AcademyDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans CanadaJarislowsky Foundation
KeywordsMarine protected areaGeographyEast coastClimate changeFisheryMarine reserveEnvironmental resource managementEcologyEnvironmental scienceHabitatFishingPhysical geographyBiology

Abstract

fetched live from OpenAlex

ABSTRACT Climate‐induced species range shifts alter ecological assemblages, yet little is known of the consequences for ecosystem functioning. We combine species distribution model (SDM) projections with species traits to develop a spatially explicit risk index for assessing climate change impacts on ecosystem functioning. The “Climate Ecological Disruption Index” (CEDI) is an easy‐to‐interpret metric that builds on existing approaches to quantifying functional diversity, providing a novel foundation for evaluating functional consequences of climate‐induced species range shifts and identifying areas at risk. We applied CEDI to a marine protected area network on Canada's east coast, where it indicated high potential for ecological disruption, with a maximum value of 0.35 (more than one‐third turnover in functional groups). Our approach is generalizable, aiding spatial conservation planning by translating projected species range shifts from SDMs into potential ecological disruption, thereby supporting the integration of climate resilience into management strategies and informing conservation planning efforts in a warming world.

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 score0.641
Threshold uncertainty score0.906

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 science0.0000.001
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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations3
Published2025
Admission routes3
Has abstractyes

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