Final thermal conditions override the effects of temperature history and dispersal in experimental communities
Bibliographic record
Abstract
Predicting the effect of climate change on biodiversity is a multifactorial problem that is complicated by potentially interactive effects with habitat properties and altered species interactions. In a microcosm experiment with communities of microalgae, we analysed whether the effect of rising temperature on diversity depended on the initial or the final temperature of the habitat, on the rate of change, on dispersal and on landscape heterogeneity. We also tested whether the response of species to temperature measured in monoculture allowed prediction of the composition of communities under rising temperature. We found that the final temperature of the habitat was the primary driver of diversity in our experimental communities. Species richness declined faster at higher temperatures. The negative effect of warming was not alleviated by a slower rate of warming or by dispersal among habitats and did not depend on the initial temperature. The response of evenness, however, did depend on the rate of change and on the initial temperature. Community composition was not predictable from monoculture assays, but higher fitness inequality (as seen by larger variance in growth rate among species in monoculture at higher temperatures) explained the faster loss of biodiversity with rising temperature.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".