Invasions and extinctions through the looking glass of evolutionary ecology
Bibliographic record
Abstract
Invasive and endangered species reflect opposite ends of a spectrum of ecological success, yet they experience many similar eco-evolutionary challenges including demographic bottlenecks, hybridization and novel environments. Despite these similarities, important differences exist. Demographic bottlenecks are more transient in invasive species, which (i) maintains ecologically relevant genetic variation, (ii) reduces mutation load, and (iii) increases the efficiency of natural selection relative to genetic drift. Endangered species are less likely to benefit from admixture, which offsets mutation load but also reduces fitness when populations are locally adapted. Invading species generally experience more benign environments with fewer natural enemies, which increases fitness directly and also indirectly by masking inbreeding depression. Adaptive phenotypic plasticity can maintain fitness in novel environments but is more likely to evolve in invasive species encountering variable habitats and to be compromised by demographic factors in endangered species. Placed in an eco-evolutionary context, these differences affect the breadth of the ecological niche, which arises as an emergent property of antagonistic selection and genetic constraints. Comparative studies of invasions and extinctions that apply an eco-evolutionary perspective could provide new insights into the environmental and genetic basis of ecological success in novel environments and improve efforts to preserve global biodiversity.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".