How well do species distribution models predict occurrences in exotic ranges?
Bibliographic record
Abstract
Abstract Aim Species distribution models (SDMs) are widely used predictive tools to forecast potential biological invasions. However, the reliability of SDMs extrapolated to exotic ranges remains understudied, with most analyses restricted to few species and equivocal results. We examine the spatial transferability of SDMs for 647 non‐indigenous species extrapolated across 1,867 invaded ranges, and identify what factors may help differentiate predictive success from failure. Location Global. Time period Current. Major taxa studied Six hundred and forty‐seven terrestrial species; eight taxonomic classes. Methods We performed a large‐scale assessment of the transferability of SDMs using two modelling approaches: generalized additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of species and extrapolated them to exotic ranges. We examined the influence of general factors and factors related to biological invasions on spatial transferability. Results Despite both modelling approaches performing well in the range of the species used for fitting, we observed moderate to low spatial transferability on average (mean area under the receiver operating characteristic curve [AUC] ~ .7) when extrapolating to their invaded ranges. Transferability differed between taxonomic classes and invaded continents and was positively influenced by the performance of the model and environmental generalism in the native range, and the year of first record. Models performed worse with greater environmental coverage in the exotic range, gross domestic product and number of occurrences in the native range, geographic distance between ranges and when extrapolating to islands. Main conclusions After controlling for sampling bias, half of SDMs were only weakly predictive, which should affect how SDM‐based forecasts are interpreted. Performance differed based on characteristics of the data, species, and ranges, and can suggest when SDMs may be reliable and when we should be most cautious. These considerations touch directly upon the potential use of SDMs for management of biological invasions. We discuss possible mechanisms of these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".