Elevational shifts, biotic homogenization and time lags in vegetation change during 40 years of climate warming
Bibliographic record
Abstract
Many species show evidence of climate‐driven distribution shifts towards higher elevations, but given the tremendous variation among species and regions, we lack an understanding of the community‐level consequences of such shifts. Here we test for signatures of climate warming impacts using a repeat survey of semi‐permanent vegetation plots in 1970 and 2012 in a montane protected area in southern Québec, Canada, where daily maximum and minimum temperatures have increased by ∼1.6°C and ∼2.5°C over the same time period. As predicted, the abundance‐weighted mean elevations of species distributions increased significantly over time (9 m/decade). A community temperature index (CTI) was calculated as the abundance‐weighted mean of the median temperature across occurrences within each species geographic range in eastern North America. CTI did not vary significantly over time, although the raw magnitude of change (+ 0.2°C) matched the expectation based on the upward shift in distributions of 9 m/decade. Species composition of high elevation sites converged over time toward that observed at low elevation, although compositional changes at low elevation sites were more modest. As a consequence, the results of a multivariate analysis showed a decline in among‐plot compositional variability (i.e. beta diversity) over time, thus providing some of the first empirical evidence linking climate warming with biotic homogenization. Finally, plot‐scale species richness showed a marked increase of ∼25% on average. Overall, elevational distribution shifts, biodiversity change, and biotic homogenization over the past four decades have been consistent with predictions based on climate warming, although the rate of change has been relatively slow, suggesting substantial time lags in biotic responses to 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".