Functional connectivity of an endemic tree frog in a highly threatened tropical dry forest in Mexico
Bibliographic record
Abstract
The increase in anthropogenic activities that lead to fragmentation and habitat loss, could result in a reduction of connectivity among habitat patches of terrestrial species. We used ecological niche models, circuit and graph theories to evaluate functional connectivity among home-range patches and suitable habitat patches of the Mexican Leaf Frog (Agalychnis dacnicolor), in a heterogeneous landscape of tropical dry forest (TDF) in central-western Mexico. We found high connectivity among home-range patches within the Chamela-Cuixmala Biosphere Reserve (CCBR) and among those surrounding the CCBR. Similarly, suitable habitat patches along the Pacific slope (except those in the South) were well-connected. Conversely, we detected weak connectivity in the southern and eastern parts of the study area, which is in accordance with the poor habitat quality and fragmentation that characterize that zone. Suitable habitat patches with the largest areas of TDF were the most important in maintaining functional connectivity, but only one patch was within a natural protected area. Our results highlight the importance of conserving large and continuous patches of habitat in a very threatened landscape to maintain connectivity in A. dacnicolor and probably in other anurans.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".