Disrupted montane forest recovery hinders biodiversity conservation in the tropical Andes
Bibliographic record
Abstract
Abstract Aim Andean montane forests are biodiversity hotspots and large carbon stores and they provide numerous ecosystem services. Following land abandonment after centuries of forest clearing for agriculture in the Andes, there is an opportunity for forest recovery. Field‐based studies show that forests do not always recover. However, large‐scale and long‐term knowledge of recovery dynamics of Andean forests remains scarce. This paper analyses tropical montane forest recovery trajectories over a 15‐year time frame at the landscape and tropical Andean scale to inform restoration planning. Methods We first detect “potential recovery” as areas that have experienced a forest transition between 2000 and 2005. Then, we use Landsat time series analysis of the normalized difference water index (NDWI) to classify four “realized recovery” trajectories (“ongoing”, “arrested”, “disrupted” and “no recovery”) based on a sequential pattern of 5‐yearly Z ‐score anomalies for 2005–2020. We compare these results against an analysis of change in tree cover to validate against other datasets. Results Across the tropical Andes, we detected a potential recovery area of 274 km 2 over the period. Despite increases in tree cover, most areas of the Andes remained in early successional states (10–25% tree cover), and NDWI levelled out after 5–10 years. Of all potential forest recovery areas, 22% showed “ongoing recovery”, 61% showed either “disrupted” or “arrested recovery”, and 17% showed “no recovery”. Our method captured forest recovery dynamics in a Peruvian arrested succession context and in landscape‐scale tree‐planting efforts in Ecuador. Main conclusions Forest recovery across the Andes is mostly disrupted, arrested or unsuccessful, with consequences for biodiversity recovery and provision of ecosystem services. Low‐recovery areas identified in this study might be good candidates for active restoration interventions in this UN Decade on Restoration. Future studies could determine restoration strategies and priorities and suggest management strategies at a local planning scale across key regions in the biodiversity hotspot.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".