Habitat suitability modeling of amphibian species in southern and central China: environmental correlates and potential richness mapping
Bibliographic record
Abstract
Successful wildlife management must take into account suitable habitat areas. Information on the correlation between distribution ranges and environmental conditions would, therefore, improve the efficacy of in-situ conservation of wildlife. In this contribution, correlations between environmental factors and the distribution of 51 amphibians in southern and central China were investigated. Ecological niche factor analysis (ENFA) at a spatial resolution of 1° latitude×1° longitude identified a mixture of climatic and habitat factors as important predictors of the occurrence of individual species. The aims of the present work were (i) to evaluate potential distributions of amphibians based on the suitability of areas; (ii) to identify the major environmental descriptors upon which they depend; and (iii) to identify areas of potential high richness that have been overlooked in available inventories. Most of the predicted species ranges of species covered the majority of southern and central China. Six richness hotspots were predicted, of which four have been described previously, but two overlooked (SE Fujian and SE Qinghai). The prediction model was considered to be relatively accurate and it is recommended that these two new potential hotspots should be subjected to further evaluation and sampling efforts. Amphibians have high ecological preference for high humidity and precipitation, and low annual frost days. ENFA is a useful tool in wildlife conservation assessment because it is able to identify potential hotspots where studies on the correlations between environmental descriptors and the occurrence of particular species could be focused.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".