Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan
Bibliographic record
Abstract
The lack of information regarding biodiversity states hampers designing and implementation conservation strategies and future targets. Northern Pakistan consists of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity to flourish in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, overlap and distribution patterns in Pakistan, we collected observational data for nine amphibian species across several distinct ecoregions by surveying 87 randomly selected locations from 2016 to 2018 in District Rawalpindi and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between species occurred at significantly higher density in tropical and subtropical coniferous forests than in other ecoregion types. We found species such as Minervarya spp., Hoplobatrachus tigerinus and Euphlyctis spp. showed preference for the lowlands in proximal, central and southern parts of the study area proximal to urban settlements, little vegetation and higher average temperatures. The toads Duttaphrynus bengalensis and D. stomaticus had scattered distributions throughout the study area with no clear preference for elevation. Sphaerotheca pashchima showed a patchy distribution in the midwestern extent of the study area as well as the foothills to the north. Microhyla nilphamariensis also showed a wide distribution throughout the study area with a preference for both lowlands and montane terrain. Endemic frogs (Nanorana vicina and Allopaa hazarensis) were observed only in locations with higher elevations, higher density of streams and lower average temperatures as compared to the other seven species sampled.
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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 | 0.000 | 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) | 0.001 | 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".