Combining global climate and regional landscape models to improve prediction of invasion risk
Bibliographic record
Abstract
Abstract Aim It is widely acknowledged that species distributions result from a variety of biotic and abiotic factors operating at different spatial scales. Here, we aimed to (1) determine the extent to which global climate niche models (CNMs) can be improved by the addition of fine‐scale regional data; (2) examine climatic and environmental factors influencing the range of 15 invasive aquatic plant species; and (3) provide a case study for the use of such models in invasion management on an island. Location Global, with a case study of species invasions in Ireland. Methods Climate niche models of global extent (including climate only) and regional environmental niche models (with additional factors such as human influence, land use and soil characteristics) were generated usingmaxentfor 15 invasive aquatic plants. The performance of these models within the invaded range of the study species in Ireland was assessed, and potential hotspots of invasion suitability were determined. Models were projected forward up to 2080 based on two climate scenarios. Results While climate variables are important in defining the global range of species, factors related to land use and nutrient level were of greater importance in regional projections. Global climatic models were significantly improved at the island scale by the addition of fine‐scale environmental variables (area under the curve values increased by 0.18 and true skill statistic values by 0.36), and projected ranges decreased from an average of 86% to 36% of the island. Main conclusions Refining CNMs with regional data on land use, human influence and landscape may have a substantial impact on predictive capacity, providing greater value for prioritization of conservation management at subregional or local scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".