An evaluation of mapped species distribution models used for conservation planning
Bibliographic record
Abstract
The widespread use of spatial planning tools in conjunction with increases in the availability of geographic information systems and associated data has led to the rapid growth in the exploration and application of species distribution models. Conservation professionals can choose from a considerable number of modelling techniques, but there has been relatively little evaluation of predictive performance, data requirements, or type of inference of these models. Empirical data for woodland caribou Rangifer tarandus caribou was used to examine four species distribution models, namely a qualitative habitat suitability index and quantitative resource selection function, Mahalanobis distance and ecological niche models. Models for three sets of independent variables were developed and then a temporally independent set of caribou locations evaluated predictive performance. The similarity of species distribution maps among the four modelling approaches was also quantified. All of the quantitative species distribution models were good predictors of the validation data set, but the spatial distribution of mapped habitats differed considerably among models. These results suggest that choice of model and variable set could influence the identification of areas for conservation emphasis. Model choice may be limited by the type of species locations or desired inference. Conservation professionals should choose a model and variable set based on the question, the ecology of the species and the availability of requisite data.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".