Climate-driven elevational range shift and habitat loss of Ageratina adenophora in Nepal: Predicting invasion using ensemble modeling
Bibliographic record
Abstract
Invasive Alien Plants (IAPs) pose significant risk to ecology and socio-economic settings globally. A better understanding of potential spread and its consequences is crucial for developing effective IAPs management strategies. This study aims to assess current and future distribution under climate change scenarios of highly invasive weed, Ageratina adenophora in Nepal integrating bioclimatic, topographic and anthropogenic factors. Multiple data sources were used to collect the different data and employ ensemble modeling using 11 key environmental predictors to understand the current and future distribution. At present, A. adenophora covers about 34,194. sq.km. area of Nepal. The results revealed that A. adenophora is widely spread in the Middle Mountains (24,910 sq.km.) but faces habitat reduction in this region including lower elevation like Chure (−13.23 sq.km.) under future climate change scenarios. In contrast, the habitat is expected to expand significantly in higher elevation regions including High Himalayan (+32.54 sq.km.) and High Mountain (+5898.75 sq.km). However, this expansion is accompanied by the total habitat reduction by 30–50 % (−10,744 sq.km.) in the 2050–2070 time period. The minimum temperature of the coldest month, proximity to roads, better soil organic matter and topographic factor were identified as the major drivers of its spread in Nepal. As this study used nationwide samples, ensuring diverse environmental conditions were captured, we believe the findings are more accurate and applicable on a national scale. Observing these trends, we emphasize immediate and effective mitigation measures which include- early detection, regular monitoring, prioritizing high-risk zone and development of region-specific integrated management plan to minimize the ecological and socio-economic impact of IAPS. Moreover, we recommend that future studies should incorporate land-use changes and socio-economic factors for a better understanding of invasion dynamics.
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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".