Ubi es, room to roam? Extension of the LPB-RAP model capabilities for potential habitat analysis
Bibliographic record
Abstract
• Dynamic, long-term simulation-based analysis of ecological connectivity. • Comparison between SSP-RCP projection and potential FLR measures implementation. • For smallholder-dominated forest landscapes in spatiotemporal high-resolution. • For landscape planning and policy development towards mitigation and adaptation. The Anthropocene presents challenges for preserving and restoring ecosystems in human-altered landscapes. Policy development and landscape planning must consider long-term developments to maintain and restore functional ecosystems, ideally by using wildlife umbrella species as proxies. Forest and Landscape Restoration (FLR) aims to support both environmental and human well-being. However, the impact of FLR on wildlife umbrella species and their movement potential should be assessed in its potential magnitude for effective conservation. For this purpose, we introduce the LPB-RAP model expanded for potential habitat analysis in smallholder-dominated forest landscapes. It focuses on ecosystem fragmentation and landscape connectivity using Circuit Theory-based methods. LPB-RAP, based on a Monte Carlo framework, enables comprehensive habitat analysis for different SSP-RCP and policy scenarios with a broad analysis spectrum for anthropo- and biosphere aspects. It simulates dichotomous landscapes with and without potential FLR for consideration in long-term planning horizons. As an implementation example serves Ecuador's Esmeraldas province, using the Jaguar ( Panthera onca ) as a target umbrella species within an SSP2-RCP4.5 narrative. The simulation period covers 2018 to 2100 in annual and hectare resolution. The years 2024 and 2070 were chosen as probing dates for the extended habitat analysis. Results indicate that an agroforestry-based FLR scenario to increase forest cover while benefiting forest ecosystems and people would only marginally improve the movement potential for female Jaguars due to their avoidance of human-disturbed areas. Additional measures, including habitat corridors, are needed to enhance movement potential amidst increasing habitat fragmentation and loss, including stakeholders of all scales.
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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) | 0.000 | 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".