The realised velocity of climate change reveals remarkable idiosyncrasy of species’ distributional shifts
Bibliographic record
Abstract
To date, our understanding of how species have shifted in response to recent climate warming has been based on a few studies with a limited number of species. Here we present a comprehensive, global overview of species’ distributional responses to changing climate across a broad variety of taxa (animals, plants, and fungi). We characterise species’ responses using a metric that describes the realised velocity of climate change: how closely species’ responses have tracked changing climate through time. In contrast to existing ‘climate velocity’ metrics that have focused on space, we focus on species and index their responses to a null expectation of change in order to examine drivers of inter-specific variation. Here we show that species are tracking climate on average, but not sufficiently to keep up with the pace of climate change. Further, species responses are highly idiosyncratic, and thus highlight that projections assuming uniform responses may be misleading. This is in stark contrast to species’ present-day and historical climate niches, which show strong evidence of the imprint of evolutionary history and functional traits. Our analyses are a first step in exploring the vast wealth of empirical data on species’ historic responses to recent climate change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".