Predicting Freshwater Invasion Risks Under Current and Future Conditions
Bibliographic record
Abstract
The establishment of species beyond their native range resulting from human activities has caused significant impacts on biodiversity and ecosystems globally. Freshwater ecosystems are particularly at risk of the effects of species invasions, leading to declines in native species and contributing to global biological homogenization. Global changes are predicted to affect invasion dynamics and create opportunities for introduced species to survive and establish where they were unable to historically. The central aim of my research is to assess the impacts of climate and human population change on modelled risks of invasive species at global, national, and biogeographic scales, with a focus on freshwater organisms. I have evaluated the impact of climate change on freshwater and terrestrial invasion vulnerabilities between ecoregions using climate-match modelling. I found climate change will create new opportunities for non-native species at a global scale, particularly at higher latitudes. I conducted a structured evaluation of the data inputs and scoring methods on predictions of non-native freshwater species survival using climate matching, finding it to be an accurate predictor of non-native species’ survival and that a climatch score of >= 6 has high prediction sensitivity. These findings were implemented in an assessment of the arrival and survival risk of aquatic species in trade within Canada under current and future conditions. My research found increased vulnerabilities of biogeographic regions to biological invaders and that climate-matching models are sensitive to data inputs and recommend inputs to achieve stronger predictive power of introduced species survival. I have conducted an analysis and produced a tool demonstrating how to conduct climate matching incorporating climate-change projections. I have published an R package that facilitates a complete climate-matching workflow using Euclidean distance metrics with the widely accepted algorithm Climatch, which adds the versatility of the use of climate-change projections, user-preferred data sources, and batch load many species or regional matches at once. The findings in this dissertation can be used to inform mitigation of biological invasions currently and in the future.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".