A practical overview of transferability in species distribution modeling
Bibliographic record
Abstract
Species distribution models (SDMs) are basic tools in ecology, biogeography, and biodiversity. The usefulness of SDMs has expanded beyond the realm of ecological sciences, and their application in other research areas is currently frequent, e.g., spatial epidemiology. In any research area, the principal interest in these models resides in their capacity to predict species response in new scenarios, i.e., the models’ transferability. Although the transferability of SDMs has been the subject of interest for many years, only in the 2000s did this topic gain particular attention. This article reviews the concept of the transferability of SDMs to new spatial scenarios, temporal periods, and (or) spatial resolutions, along with the potential constraints of the model’s transferability, and more specifically: (i) the type of predictors and multicollinearity, (ii) the model complexity, and (iii) the species’ intrinsic traits. Finally, we describe a practicable analytical protocol to be assessed before transferring a model to a new scenario. This protocol is based on three fundamental pillars: the environmental equilibrium of the species with the environment, the environmental similarity between the new scenario, and the areas used to model parametrisation and the correlation structure among predictors.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.046 | 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; both teacher heads agree on what is shown here.
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".