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Extreme warming restructures habitat distribution and productivity along local gradients in stress and biodiversity

2021· preprint· en· W4239659115 on OpenAlexaff
Matthew A. Whalen, Sam Starko, Sandra C. Lindstrom, Patrick T. Martone

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAbiotic componentEcosystemEcologyEnvironmental scienceBiomass (ecology)HabitatIntertidal zoneGlobal warmingProductivityBiodiversityClimate changeBiology

Abstract

fetched live from OpenAlex

Significant questions remain about how ecosystems that are structured by abiotic stress will be affected by climate warming. A well-supported hypothesis states that warming will cause species to shift along abiotic gradients, such that distributions track changing local conditions. Here, we investigated the impacts of a multi-year heatwave on community dynamics and zonation in one such ecosystem: rocky intertidal communities. We demonstrate that, while populations generally shifted downslope towards reduced abiotic stress, species were impacted to varying degrees, leading to complex changes in community and ecosystem dynamics. Warming generally shifted primary production away from upper elevations through synchronized seaweed declines and replacement by invertebrates, while high producer biomass was maintained at lower elevations through compensatory dynamics that resulted in novel community composition. Our results illustrate that, rather than shifting community zonation uniformly along local gradients, warming will restructure habitat archetypes and redirect pathways for energy transfer in stress-structured systems.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.195
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; 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

Citations1
Published2021
Admission routes1
Has abstractyes

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