Mapping area of habitat for inland wetland species
Bibliographic record
Abstract
Area of habitat (AOH) maps provide a high-resolution representation of the habitat available in a species' range and can support conservation policy and planning processes. However, until recently, there was insufficient knowledge on the distribution of inland wetlands and freshwater biodiversity to develop AOH mapping methods specifically tailored to inland wetlands. We used a combined empirical and thematic approach to translate inland wetland habitat classes in the International Union for Conservation of Nature (IUCN) Habitats Classification Scheme into spatially explicit wetland-cover types derived from the Global Lakes and Wetlands Database 2 and the World Karst Aquifer Map. The AOH was subsequently estimated as the area in the mapped range that corresponded to each species' habitat and elevation associations. We developed and tested the method with IUCN Red List assessment data, range maps, and point locality data for fishes, odonates, decapod crustaceans, and mollusks (22,876 species). The method performed well in comparison with similar methods already developed for terrestrial mammals, birds, amphibians, and reptiles. The mean map prevalence (proportion of area in a species' range that was AOH) was 18-32% for each taxonomic group. Based on data on known localities of occurrence, 78-100% of species per taxonomic group had a higher classification accuracy than expected if AOH were distributed in the range at random. This represents an increased accuracy in the distribution of wetland species. Our study represents the first attempt to distinguish between inland wetland habitat subclasses and to include subterranean habitats in an AOH mapping method. Our method will facilitate the inclusion of previously underrepresented taxa in key conservation tools and analyses and is expected to increase the accuracy of AOH mapping for any species associated with inland wetlands.
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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 | 0.000 |
| 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) | 0.003 | 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".