An actor-centered, scalable land system typology for addressing biodiversity loss in the world’s tropical dry woodlands
Bibliographic record
Abstract
Land use is a key driver of the ongoing biodiversity crisis and therefore also a major opportunity for its mitigation. However, appropriately considering the diversity of land-use actors and activities in conservation assessments and planning is challenging. As a result, top-down conservation policy and planning are often criticized for a lack of contextual nuance widely acknowledged to be required for effective and just conservation action. To address these challenges, we have developed a conceptually consistent, scalable land system typology and demonstrated its usefulness for the world's tropical dry woodlands. Our typology identifies key land-use actors and activities that represent typical threats to biodiversity and opportunities for conservation action. We identified land systems in a hierarchical way, with a global level allowing for broad-scale planning and comparative work. Nested within it, a regionalized level provides social-ecological specificity and context. We showcase this regionalization for five hotspots of land-use change and biodiversity loss in dry woodlands in Argentina, Bolivia, Mozambique, India, and Cambodia. Unlike other approaches to present land use, our typology accounts for the complexity of overlapping land uses. This allows, for example, assessment of how conservation measures conflict with other land uses, understanding of the social-ecological co-benefits and trade-offs of area-based conservation, mapping of threats, or targeting area-based and actor-based conservation measures. Moreover, our framework enables cross-regional learning by revealing both commonalities and social-ecological differences, as we demonstrate here for the world's tropical dry woodlands. By bridging the gap between global, top-down, and regional, bottom-up initiatives, our framework enables more contextually appropriate sustainability planning across scales and more targeted and social-ecologically nuanced interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".