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Poleward Bound: Adapting to climate-driven species redistribution

2020· preprint· en· W4234795720 on OpenAlexaff
Jess Melbourne-Thomas, Asta Audzijonytė, M Brasier, Katie Cresswell, Hannah E. Fogarty, Marcus Haward, Alistair J. Hobday, Heather L. Hunt, SD Ling, Phillipa C. McCormack, Tero Mustonen, Kaisu Mustonen, Janet A. Nye, Michael Oellermann, Rowan Trebilco, Ingrid van Putten, Cecilia Villanueva, Reg Watson, GT Pecl

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsClimate changeEnvironmental resource managementRedistribution (election)EcosystemBusinessFutures contractIndigenousNatural resource economicsEnvironmental planningEcologyGeographyEnvironmental scienceEconomicsPolitical science

Abstract

fetched live from OpenAlex

One of the most pronounced effects of climate change on the world's oceans is the (generally) poleward movement of species and fishery stocks in response to increasing water temperatures. In some regions, such redistributions are already having diverse impacts on marine socioecological systems, including profoundly altering ecosystem structure and function, challenging domestic and international fisheries, and impacting on human communities. Such effects are expected to become increasingly widespread as waters continue to warm and species ranges continue to shift. Actions taken over the coming decade (2021-2030) can help us manage and adapt to species redistributions and minimise negative impacts on ecosystems and human communities, achieving a more sustainable future in the face of ecosystem change. We describe key drivers related to climate-driven species redistributions that are likely to have a high impact and influence on whether a sustainable future is reached by 2030. We posit two different futures-a 'business as usual' future and a technically achievable and more sustainable future, aligned with the Sustainable Development Goals. We then identify concrete actions that provide a pathway towards the more sustainable 2030 and that acknowledge and include Indigenous perspectives. Achieving this sustainable future will depend on improved monitoring and detection, and on adaptive, cooperative management to proactively respond to the challenge of species redistribution. We synthesise examples of such actions as the basis of a strategic approach to tackle this global-scale challenge for the benefit of humanity and ecosystems.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

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)0.0030.001

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 head0.055
GPT teacher head0.231
Teacher spread0.176 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations10
Published2020
Admission routes1
Has abstractyes

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