Understanding and managing introduction pathways into protected areas in a changing climate
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework 2030 calls for the conservation of 30% of the world’s ecosystems, focusing on protecting areas vital to biodiversity, identifying and managing invasive species introduction pathways, and minimizing the impacts of climate change on biodiversity. While protected areas (PAs) have historically limited the introduction, establishment, and spread of non-native species, climate change is likely to increase their susceptibility to invasion. Yet we know little about how pathways may shift in the future, making it difficult for managers to plan appropriately. This paper explores how climate change may affect primary and secondary pathways of introduction and presents an adaptive management approach to avoid, minimize, and mitigate impacts. Climate change has influenced introduction pathways by modifying human behaviors (e.g., forced migration and shifting travel and vacation destinations), and by altering transportation routes, natural dispersal mechanisms, and the environmental conditions along these pathways and in donor and receiver regions. These changes increase the risk of non-native species introductions and their subsequent spread within PAs. Implementing climate-smart adaptive biosecurity, an iterative process that includes the incorporation of new technologies and perspectives, will become increasingly important for invasive species prevention and management of PAs as it provides flexibility in management response and maximizes positive outcomes when resources are limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".