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Record W4413497593 · doi:10.1111/ele.70200

Impacts of Changing Winters on Lake Ecosystems Will Increase With Latitude

2025· article· en· W4413497593 on OpenAlexaff
Ted Ozersky, Amanda Poste, Milla Rautio, Eva Leu

Bibliographic record

VenueEcology Letters · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNorsk Institutt for VannforskningNorges ForskningsrådNational Science Foundation
KeywordsEcologyEcosystemLatitudeEnvironmental scienceHigh latitudeEnvironmental resource managementGeographyBiology

Abstract

fetched live from OpenAlex

Climate warming is especially pronounced in winter and at high latitudes. Warming winters are leading to the loss of lake ice and changing snow cover on lakes. Historically, lake scientists have paid less attention to the ice cover period, leading to data and theory gaps about the role of winter conditions in lake ecosystem function and the consequences of changing winters. Here we use simple models to show that the latitudinal interaction between ice cover duration and light flux seasonality has profound and underappreciated implications for lakes. Our models focus on light and temperature, two key drivers of ecosystem processes. We show that the relative amount of light arriving in lakes during ice cover increases non-linearly with latitude and that the light climate of high latitude lakes is much more sensitive to changing winter conditions than that of lower latitude lakes. We also demonstrate that the synchronicity between high light and warm temperatures may decrease with latitude, with implications for primary and secondary production. Our results suggest that ice loss may lead to greater relative change to productivity and biotic interactions in higher latitude lakes and also offer several testable predictions for understanding the consequences of climate-induced changes across latitudinal gradients.

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 score0.059
Threshold uncertainty score0.975

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 science0.0000.000
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 head0.003
GPT teacher head0.191
Teacher spread0.189 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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