Considering plant-ungulate interaction contribute to maximizing conservation efficiency under climate change
Bibliographic record
Abstract
Climate change poses a major threat to biodiversity, thus understanding how these impacts manifest, and how they might be mitigated is a major priority for conservation biologists. Yet understanding the impacts is complex, due to the nuanced impacts on species directly, as well as resources they depend on. In this study, we examined how biotic interactions, specifically plant availability, effects the distribution patterns of an ungulate, i.e., Marco Polo sheep ( Ovis ammon polii ). Our findings suggest that plant availability is a major predictor of the sheep's range. The species distribution models (SDMs) incorporating biotic interactions, i.e., plant availability, increases accuracy in predicting the underlying implications of climate change on ungulates compared to models that exclude these interactions. Our results reveal discrepancies in ungulate spatial distribution patterns, with future suitable habitat contraction being less pronounced when incorporating biotic variables than without biotic variables (27% vs. 33%). Therefore, ignoring biotic interaction may overestimate the impacts of climate change, resulting in the inefficient allocation of scarce conservation resources. Additionally, our results indicate the importance of protected areas (PAs) as important climatic refugia, though less than half of the range is currently within PAs. This study emphasizes the non-negligible role of biotic interactions in forecasting the geographical distribution of ungulates, which has critical implications for the future wildlife conservation.
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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 | 0.000 |
| 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) | 0.002 | 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 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".