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Record W3014771569 · doi:10.1111/ele.13504

Winter in water: differential responses and the maintenance of biodiversity

2020· article· en· W3014771569 on OpenAlexafffund
Bailey C. McMeans, Kevin S. McCann, Matthew M. Guzzo, Timothy Bartley, Carling Bieg, Paul J. Blanchfield, Timothy Fernandes, Henrique C. Giacomini, Trevor A. Middel, Michael D. Rennie, Mark S. Ridgway, Brian J. Shuter

Bibliographic record

VenueEcology Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsLakehead UniversityTrent UniversityMinistry of Natural Resources and ForestryAmgen (Canada)International Institute for Sustainable DevelopmentUniversity of GuelphFisheries and Oceans CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiodiversityEcologyEnvironmental scienceDifferential (mechanical device)BiologyEnvironmental resource managementEngineering

Abstract

fetched live from OpenAlex

The ecological consequences of winter in freshwater systems are an understudied but rapidly emerging research area. Here, we argue that winter periods of reduced temperature and light (and potentially oxygen and resources) could play an underappreciated role in mediating the coexistence of species. This may be especially true for temperate and subarctic lakes, where seasonal changes in the thermal environment might fundamentally structure species interactions. With climate change already shortening ice-covered periods on temperate and polar lakes, consideration of how winter conditions shape biotic interactions is urgently needed. Using freshwater fishes in northern temperate lakes as a case study, we demonstrate how physiological trait differences (e.g. thermal preference, light sensitivity) drive differential behavioural responses to winter among competing species. Specifically, some species have a higher capacity for winter activity than others. Existing and new theory is presented to argue that such differential responses to winter can promote species coexistence. Importantly, if winter is a driver of niche differences that weaken competition between, relative to within species, then shrinking winter periods could threaten coexistence by tipping the scales in favour of certain sets of species over others.

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 score0.122
Threshold uncertainty score0.789

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.170
Teacher spread0.163 · 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

Citations119
Published2020
Admission routes2
Has abstractyes

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