Biogeography, macroecology and species' traits mediate competitive interactions in the order<scp>L</scp>agomorpha
Bibliographic record
Abstract
Abstract In addition to abiotic determinants, biotic factors, including competitive, interspecific interactions, limit species' distributions. Environmental changes in human disturbance, land use and climate are predicted to have widespread impacts on interactions between species, especially in the orderLagomorpha due to the higher latitudes and more extreme environmental conditions they occupy. We reviewed the published literature on interspecific interactions in the orderLagomorpha and compared the biogeography, macroecology, phylogeny and traits of species known to interact with those of species with no reported interactions, to investigate how projected future environmental change may affect interactions and potentially alter species' distributions. Thirty‐three lagomorph species have competitive interactions reported in the literature; the majority involve hares (Lepussp.) or the eastern cottontail rabbit (Sylvilagus floridanus). Key regions for interactions are located between 30–50°Nof theEquator, and include easternAsia (southernRussia on the border ofMongolia) andNorthAmerica (north‐westernUSA). Closely related, large‐bodied, similarly sized species occurring in regions of human‐modified, typically agricultural landscapes, or at high elevations, are significantly more likely to have reported competitive interactions than other lagomorph species. We identify species' traits associated with competitive interactions, and highlight some potential impacts that future environmental change may have on interspecific interactions. Our approach using bibliometric and biological data is widely applicable, and with relatively straightforward methodologies, can provide insights into interactions between species. Our results have implications for predicting species' responses to global change, and we advise that capturing, parameterizing and incorporating interspecific interactions into analyses (e.g. species distribution modelling) may be more important than suggested by the literature.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".