Categorizing species by niche characteristics can clarify conservation planning in rapidly‐developing landscapes
Bibliographic record
Abstract
Abstract In biodiversity‐rich landscapes that are developing rapidly, it is generally impossible to delineate land use and prioritize conservation actions in relation to the full variability of species and their responses to anthropogenic activity. Consequently, conservation policy often focuses on protecting habitat used by a few flagship, indicator or umbrella species like tigers Panthera tigris and Asian elephants Elephas maximus, which potentially leaves out species that do not share these habitat preferences. We demonstrate an empirical approach that clustered 14 mammals into surrogate groups that reflect their unique conservation needs. We surveyed a 787 km2 multiple‐use area in the Shencottah Gap of the Western Ghats, India, using foot surveys and camera‐trap surveys. Using ecological niche factor analysis, we generated indices of species prevalence (marginality and tolerance) and habitat preferences (factor correlations to marginality axis). We then clustered species by both of the above index types to reveal four clusters based on prevalence and four clusters based on habitat preference. Most clusters contained at least one threatened species. Low‐prevalence lion‐tailed macaques Macaca silenus and tigers were strongly associated with closed forests and low human disturbance. But elephants, sloth bears Melursus ursinus and gaur Bos gaurus were more tolerant of anthropogenic impact, and sloth bears and gaur preferred open forests and grasslands. Dhole Cuon alpinus and sambar Rusa unicolor were associated with highly anthropogenic habitat (farmland, cash crop and forestry plantations) with high human use. Thus, reliance on flagship species for conservation planning can both underestimate and overestimate the ability of other species to persist in multiple‐use landscapes; protecting flagship species would only protect species with similar habitat preferences. For species that avoid human impacts more than the flagship species, core habitat must be protected from human disturbance. For more tolerant species, conservation in anthropogenic habitat may hinge on policies that bolster coexistence with humans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".