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Record W4400461731 · doi:10.1007/s10228-024-00987-9

Climate change and Arctic charr (Salvelinus alpinus) in North America: modelling possible changes in range with different climate scenarios and interspecific interactions

2024· article· en· W4400461731 on OpenAlexaffabout
Cassandra K. Bommersbach, Gabrielle Grenier, Haley K. Gendron, Les N. Harris, Muhammad Yamin Janjua, Nicholas E. Mandrak, Ross F. Tallman

Bibliographic record

VenueIchthyological Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of ManitobaUniversity of TorontoFisheries and Oceans Canada
Fundersnot available
KeywordsSalvelinusArcticClimate changeRange (aeronautics)LatitudeEcologyFontinalisSpecies distributionEnvironmental changeBiologyGeographyFisheryTroutFish <Actinopterygii>Habitat

Abstract

fetched live from OpenAlex

Abstract One of the greatest challenges for researchers today is understanding climate-change impacts on fish populations, particularly in vulnerable and understudied ecosystems such as the Canadian Arctic. Among other impacts, northern fishes will undergo thermal stress as atmospheric and sea surface temperatures are projected to rise globally. Models that consider how both environmental factors such as temperature and potential species interactions will impact population extirpation and species’ range contraction can help project the future distribution of a species in the face of a warming climate. Here, we investigate the climate-change impacts of rising temperatures and the potential northward distributional shift of brook charr ( Salvelinus fontinalis ) on Arctic charr ( Salvelinus alpinus ), Canada’s northernmost freshwater fish species. Specifically, we used a logistic regression model to establish baseline relationships between the current distribution of Arctic charr and the variables, degree-days (a key climate variable), geographical location, and brook charr occurrence. We developed the model applying the expected changes in degree-days to 2050 (25–50% increase from the average of 1976–2005) and 2080 (50–100% increase) while incorporating the historical distribution of Arctic charr to estimate the change in Arctic charr distribution over that time. We found that growing degree-days, longitude, latitude, and brook charr occurrences correctly classified 93% of Arctic charr historical occurrences in Canada. We estimate that in a high-carbon scenario, where degree-days are expected to increase by 50 to 100%, Arctic charr range is projected to decrease by 18% in Canada by 2051–2080 and decrease even further by 3% with the presence of brook charr. The Canadian high Arctic may provide refuge for Arctic charr, likely maintaining temperatures optimal for species persistence. Regardless, management that considers the climate stresses on Arctic charr populations will be important to preserve this highly valued resource that is pivotal for food security and traditional ways of life for northerners.

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)0.001
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 score0.063
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.104
GPT teacher head0.327
Teacher spread0.223 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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