Species’ thermal ranges predict changes in reef fish community structure during 8 years of extreme temperature variation
Bibliographic record
Abstract
Abstract Aim To assess whether observed thermal bounds in species’ latitudinal ranges (i.e., realized thermal niches) can be used to predict patterns of occurrence and abundance changes observed during a marine heatwave, relative to other important life history and functional traits. Location Rottnest Island, Western Australia. Methods A time series of standardized quantitative surveys of reef fishes spanning 8 years of pronounced ocean temperature change is used to test whether accurate predictions on shifts in species occupancy and abundance are possible using species traits. Results Species‐level responses in occurrence and abundance were closely related to the mid‐point of their realized thermal niche, more so than body size, range size or trophic level. Most of the species that disappeared from survey counts during the heatwave were characterized by geographic ranges that did not extend to latitudes with temperatures equivalent to the ocean temperature peak during the heatwave. We thus find support for the hypothesis that current distribution limits are set directly or indirectly by temperature and are highly responsive to ocean temperature variability. Main conclusions Our study shows that reef fish community structure can change very quickly when exposed to extreme thermal anomalies, in directions predicted from the realized thermal niche of the species present. Such predictions can thus identify species that will be most responsive to changing ocean climate. Continued warming, coupled with periodic extreme heat events, may lead to the loss of ecosystem services and ecological functions, as mobile species relocate to more hospitable climes, while less mobile species may head towards extinction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".