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Record W3185972423 · doi:10.1111/gcb.15768

Global assessment of marine and freshwater recreational fish reveals mismatch in climate change vulnerability and conservation effort

2021· article· en· W3185972423 on OpenAlexafffund
Elizabeth A. Nyboer, Hsien‐Yung Lin, Joseph Bennett, Joseph Gabriel, William M. Twardek, Auston D. Chhor, Lindsay Daly, Sarah J. Dolson, Eric Guitard, Peter E. Holder, Christina M. Mozzon, Alexandria Trahan, Dennis Zimmermann, Kathleen Kesner‐Reyes, Cristina Garilao, Kristin Kaschner, Steven J. Cooke

Bibliographic record

VenueGlobal Change Biology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsUniversity of OttawaLaurentian UniversityCarleton University
FundersFonds de recherche du Québec – Nature et technologies
KeywordsClimate changeFish migrationFisheryGeographyMarine protected areaVulnerability (computing)Conservation statusVulnerability assessmentEcologyHabitatPsychological resilienceBiology

Abstract

fetched live from OpenAlex

Recreational fisheries contribute substantially to the sociocultural and economic well-being of coastal and riparian regions worldwide, but climate change threatens their sustainability. Fishery managers require information on how climate change will impact key recreational species; however, the absence of a global assessment hinders both directed and widespread conservation efforts. In this study, we present the first global climate change vulnerability assessment of recreationally targeted fish species from marine and freshwater environments (including diadromous fishes). We use climate change projections and data on species' physiological and ecological traits to quantify and map global climate vulnerability and analyze these patterns alongside the indices of socioeconomic value and conservation effort to determine where efforts are sufficient and where they might fall short. We found that over 20% of recreationally targeted fishes are vulnerable to climate change under a high emission scenario. Overall, marine fishes had the highest number of vulnerable species, concentrated in regions with sensitive habitat types (e.g., coral reefs). However, freshwater fishes had higher proportions of species at risk from climate change, with concentrations in northern Europe, Australia, and southern Africa. Mismatches in conservation effort and vulnerability were found within all regions and life-history groups. A key pattern was that current conservation effort focused primarily on marine fishes of high socioeconomic value rather than on the freshwater and diadromous fishes that were predicted to be proportionately more vulnerable. While several marine regions were notably lacking in protection (e.g., Caribbean Sea, Banda Sea), only 19% of vulnerable marine species were without conservation effort. By contrast, 72% of freshwater fishes and 33% of diadromous fishes had no measures in place, despite their high vulnerability and cultural value. The spatial and taxonomic analyses presented here provide guidance for the future conservation and management of recreational fisheries as climate change progresses.

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)0.001
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.304
Teacher spread0.264 · 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

Citations37
Published2021
Admission routes2
Has abstractyes

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