<scp>BioLake</scp>: A first assessment of lake temperature‐derived bioclimatic predictors for aquatic invasive species
Bibliographic record
Abstract
Abstract Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature‐derived “BioLake” bioclimatic layers for distribution modeling of aquatic species and compared “climatch” climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization.
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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) | 0.001 | 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) | 0.006 | 0.002 |
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".