Ecological and Evolutionary Dynamics of Invasive Species Under Global Change
Bibliographic record
Abstract
The Anthropocene is characterized by accelerating changes in climate, land use, pollution, and global connectivity, largely reshaping ecosystems across spatial and temporal scales (Keys et al. 2019;Willcock et al. 2023).These rapid transformations frequently outpace the adaptive capacity of native species, contributing to widespread biodiversity loss and, possibly, to a 6th mass extinction (Barnosky et al. 2011;Hoffmann and Sgrò 2011).In contrast, invasive species often thrive in disturbed environments, thereby further exacerbating ecological disruptions across diverse ecosystems (Gu et al. 2023).As such, biological invasions have emerged not only as a consequence of global change but also as a significant driver of further environmental degradation (Sage 2020).Increasingly, evidence indicates that the interactions between biological invasions and other global change drivers are complex, nonlinear, and can often produce unexpected economic, ecological, or evolutionary outcomes (Ricciardi et al. 2021;Hu et al. 2025).For example, global change-induced environmental extremes can result in rapid evolution in invasive species, which can enhance the probability of invasion success and alter species interactions and ecosystem functioning (Moran and Alexander 2014; Borden and Flory 2021).Thus, there exists an urgent need to deepen our understanding of the complex ecological and evolutionary dynamics underlying interactions between biological invasions and global change, and, more importantly, to develop effective management solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".