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Predator–prey interactions under climate change: the importance of habitat vs body temperature

2008· article· en· W2131374525 on OpenAlexfundno aff
Bernardo R. Broitman, P. Lauren Szathmary, K. A. S. Mislan, Carol A. Blanchette, Brian Helmuth

Bibliographic record

VenueOikos · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
FundersKwame Nkrumah University of Science and TechnologyMcGill UniversityNational Science Foundation
KeywordsPredationPredatorEcologyHabitatBiologyApex predatorMytilusMusselClimate changeTrophic level

Abstract

fetched live from OpenAlex

Habitat temperature is often assumed to serve as an effective proxy for organism body temperature when making predictions of species distributions under future climate change. However, the determinants of body temperature are complex, and organisms in identical microhabitats can occupy radically different thermal niches. This can have major implications of our understanding of how thermal stress modulates predator–prey relationships under field conditions. Using body temperature data from four different sites on Santa Cruz Island, California, we show that at two sites the body temperatures of a keystone predator (the seastar Pisaster ochraceus ) and its prey (the mussel Mytilus californianus ) followed very different trajectories, even though both animals occupied identical microhabitats. At the other two sites, body temperatures of predator and prey were closely coupled across a range of scales. The dynamical differences between predator and prey body temperatures depended on the location of pairs of sites, at the extremes of a persistent landscape‐scale weather pattern observed across the island. Thus, the well understood predator–prey interaction between Pisaster and Mytilus cannot be predicted based on habitat‐level information alone, as is now commonly attempted with landscape‐level (‘climate envelope’) models.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.023
GPT teacher head0.262
Teacher spread0.239 · 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 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

Citations88
Published2008
Admission routes1
Has abstractyes

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