Revisiting current distribution and future habitat suitability models for the endemic Malabar Tree Toad (Pedostibes tuberculosus) using citizen science data
Bibliographic record
Abstract
Climate change is one of the major drivers of biodiversity loss. Among vertebrates, amphibians are one of the more sensitive groups to climate change due to their unique ecology, habitat requirements, narrow thermal tolerance and relatively limited dispersal abilities. We projected the influence of climate change on an endemic toad, Malabar Tree Toad ( Pedostibes tuberculosus ; hereafter MTT) from the Western Ghats biodiversity hotspot, India, for two different shared socio-economic pathways (SSP) using multiple modeling approaches for current and future (2061–2080) scenarios. The data used predominantly comes from a citizen science program, ‘Mapping Malabar Tree Toad’ which is a part of the Frog Watch citizen science program, India Biodiversity Portal. We also evaluated the availability of suitable habitats for the MTT in Protected Areas (PAs) under the current and future scenarios. Our results show that annual precipitation was the most important bioclimatic variable influencing the distribution of MTT. We used MaxEnt (MEM) and Ensemble (ESM) modeling algorithms. The predicted distribution of MTT with selected environmental layers using MEM was 4556.95 km 2 while using ESM was 18,563.76 km 2 . Overlaying PA boundaries on predicted distribution showed 37 PAs with 32.7% (1491.37 km 2 ) and 44 PAs with 21.9% (4066.25 km 2 ) coverage for MEM and ESM respectively. Among eight future climate scenarios, scenarios with high emissions showed a decreased distribution range from 33.5 to 68.7% of predicted distribution in PAs, while scenarios with low emissions showed an increased distribution range from 1.9 to 111.3% in PAs. PAs from the Central Western Ghats lose most suitable areas with a shift of suitable habitats towards the Southern Western Ghats. This suggests that MTT distribution may be restricted in the future and existing PAs may not be sufficient to conserve their habitats. Restricted and discontinuous distribution along with climate change can limit the dispersal and persistence of MTT populations, thus enhanced surveys of MTT habitats within and outside the PAs of the Western Ghats are an important step in safeguarding the persistence of MTT populations. Overall, our results demonstrate the use of citizen science data and its potential in modeling and understanding the geographic distribution and the calling phenology of an elusive, arboreal, and endemic amphibian species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".