Bibliographic record
Abstract
What causes species’ niche and range margins to shift is not only a fundamental theoretical question, but also directly affects how we assess the resilience of natural populations in current and future environments. Yet despite the urgent need for theory that can predict evolutionary and ecological responses in times of accelerated climate change, the assumptions of current eco-evolutionary theory remain restrictive, with predictions neglecting important interactions between ecological and evolutionary forces. In this study, I provide quantitative, testable predictions on limits to adaptation in changing environments, which arise from the feedback between selection, genetic drift, and population dynamics. This eco-evolutionary feedback creates a tipping point beyond which adaptation fails as genetic drift overwhelms selection, and species’ ranges contract from the margins or fragment abruptly—even under gradual environmental change. This “expansion threshold” is determined by three parameters: two quantifying the effects of spatial and temporal variability on the fitness of the population and one capturing the impact of genetic drift: the reduction of local genetic diversity across generations in finite populations. Genetic drift is strong in populations with small “neighborhood size.” Increasing dispersal, such as via assisted migration, enlarges neighborhood size, counteracting the loss of genetic variation due to genetic drift. This increases adaptive potential and can facilitate evolutionary rescue in changing environments. Conversely, beyond the expansion threshold, local genetic variance becomes depleted, increasing extinction probability. The theory provides general predictions for species’ range and niche dynamics beyond standard ecological niche models, highlighting the fundamental impact of eco-evolutionary 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".