Effects of climate change on the distribution of invasive alien species in Canada: a knowledge synthesis of range change projections in a warming world
Bibliographic record
Abstract
The interactive effects of climate change and invasive alien species (IAS) pose serious threats to biodiversity, ecosystems and human well-being worldwide. In particular, IAS are predicted to experience widespread changes in distribution in response to climate change, with many expanding their ranges into new areas. However, the two drivers of global change are seldom considered together in policy and management. We conducted a knowledge synthesis to assess the state of research on IAS range shifts under climate change in Canada. We found that the study of IAS distribution changes caused by climate change is a relatively new field of inquiry that integrates research in the areas of ecology, conservation biology, and environmental sciences. The multidisciplinary dimensions of the issue are largely overlooked in the scholarly literature, with most studies having a purely natural science perspective. Very little original research has occurred in the field to date; instead literature reviews are common. Research focuses on modeling range changes of current IAS threats, rather than predicting potential future IAS threats. The most commonly studied IAS already occur in Canada as native species that have spread beyond their range (e.g., lyme disease, mountain pine beetle, smallmouth bass) or as established invaders (e.g., gypsy moth). All of these IAS are expected to expand northward with climate change, resulting in widespread negative impacts on forest and freshwater biodiversity, carbon sequestration, and public health. Many barriers to predicting IAS range change under climate change are identified in the literature, including the complexity of the issue, lack of ecological data, and failure to integrate climate change – IAS interactions into research, policy, and management. Recommendations for increased research and monitoring, and the need for policy and management reform predominate in the literature.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".