High community turnover and dispersal limitation relative to rapid climate change
Bibliographic record
Abstract
Abstract Aim Many competing hypotheses seek to identify the mechanisms behind species richness gradients. Yet, the determinants of species turnover over broad scales are uncertain. We test whether environmental dissimilarity predicts biotic turnover spatially and temporally across an array of environmental variables and spatial scales using recently observed climate changes as a pseudo‐experimental opportunity. Location Canada. Methods We used an extensive database of observation records of 282 Canadian butterfly species collected between 1900 and 2010 to characterize spatial and temporal turnover based on Jaccard indices. We compare relationships between spatial turnover and differences in an array of relevant environmental conditions, including aspects of temperature, precipitation, elevation, primary productivity and land cover. Measurements were taken within 100‐, 200‐ and 400‐km grid cells, respectively. We tested the relative importance of each variable in predicting spatial turnover using bootstrap analysis. Finally, we tested for effects of temperature and precipitation change on temporal turnover, including distinctly accounting for turnover under individual species’ potential dispersal limitations. Results Temperature differences between areas correlate with spatial turnover in butterfly assemblages, independently of distance, sampling differences or the spatial resolution of the analysis. Increasing temperatures are positively related to biotic turnover within quadrats through time. Limitations on species dispersal may cause observed biotic turnover to be lower than expected given the magnitude of temperature changes through time. Main conclusions Temperature differences can account for spatial trends in biotic dissimilarity and turnover through time in areas where climate is changing. Butterfly communities are changing quickly in some areas, probably reflecting the dispersal capacities of individual species. However, turnover is lower through time than expected in many areas, suggesting that further work is needed to understand the factors that limit dispersal across broad regions. Our results illustrate the large‐scale effects of climate change on biodiversity in areas with strong environmental gradients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".