Data & R code for: Paquette & Hargreaves 'Biotic interactions are more often important at species' warm vs. cool range edges'
Bibliographic record
Abstract
Predicting which ecological factors constrain species distributions is a fundamental ecological question and critical to forecasting geographic responses to global change. Darwin hypothesized that abiotic factors generally impose species’ high-latitude and high-elevation (typically cool) range limits, whereas biotic interactions more often impose species’ low-latitude/low-elevation (typically warm) limits, but empirical support has been mixed. Here, we clarify three predictions arising from Darwin’s hypothesis, and show that previously mixed support is partially due to researchers testing different predictions. Using a comprehensive literature review (885 range limits), we find that biotic interactions, including competition, predation, and parasitism, contributed to >60% of range limits, and influenced species’ warm limits more often than cool limits. Abiotic factors contributed more often than biotic interactions to cool range limits, but temperature contributed frequently to both cool and warm limits. Our results suggest that most range limits will be sensitive to climate warming, but warm-limit responses will depend strongly on biotic interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.197 | 0.131 |
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".