Effects of species interactions on the potential for evolution at species' range limits
Bibliographic record
Abstract
Species' ranges are limited by both ecological and evolutionary constraints. While there is a growing appreciation that ecological constraints include interactions among species, like competition, we know relatively little about how interactions contribute to evolutionary constraints at species' niche and range limits. Building on concepts from community ecology and evolutionary biology, we review how biotic interactions can influence adaptation at range limits by impeding the demographic conditions that facilitate evolution (which we term a 'demographic pathway to adaptation'), and/or by imposing evolutionary trade-offs with the abiotic environment (a 'trade-offs pathway'). While theory for the former is well-developed, theory for the trade-offs pathway is not, and empirical evidence is scarce for both. Therefore, we develop a model to illustrate how fitness trade-offs along biotic and abiotic gradients could affect the potential for range expansion and niche evolution following ecological release. The model shows that which genotypes are favoured at species' range edges can depend strongly on the biotic context and the nature of fitness trade-offs. Experiments that characterize trade-offs and properly account for biotic context are needed to predict which species will expand their niche or range in response to environmental change. This article is part of the theme issue 'Species' ranges in the face of changing environments (Part II)'.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".