Increasing Forest Cover and Connectivity Both Inside and Outside of Protected Areas in Southwestern Costa Rica
Bibliographic record
Abstract
While protected areas (PAs) are an important conservation strategy to protect vulnerable ecosystems and species, recent analyses question their effectiveness in curbing deforestation and maintaining landscape connectivity. The spatial arrangement of forests inside and outside of PAs may affect ecosystem functioning and wildlife movement. The Osa Peninsula—and Costa Rica in general—are unique conservation case studies due to their high biodiversity, extensive PA network, environmental policies, and payment for ecosystem services (PES) programs. This study explores the relationship between forest management initiatives—specifically PAs, the 1996 Forest Law, and PES—and forest cover and landscape metrics in the Osa Conservation Area (ACOSA). The Google Earth Engine API was used to process Surface Reflectance Tier 1 Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager data for 1987, 1998, and 2019, years with relatively cloud-free satellite imagery. Land use/land cover (LULC) maps were generated with the pixel-based random forest machine learning algorithm, and Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and functional landscape metrics were calculated. The LULC maps are the first to track land use change, from 1987 to 2019 and the first to separately classify mature and secondary forest in the region, and have already proven useful for conservation efforts. The results suggest that forest cover, NDVI, EVI, and structural connectivity increased from 1987 to 2019 across the study area, both within and surrounding the PAs, suggesting minimal deforestation encroachment and local leakage. These changes may have contributed to the increasing vertebrate abundance observed in the region. PAs, especially national parks with stricter conservation regulations, displayed the highest forest cover and connectivity. Forest cover increased in properties receiving PES payments. Following the Forest Law’s 1996 deforestation ban, both forest conversion and reforestation rates decreased, suggesting the law curbed deforestation but did not drive reforestation across the region. Connectivity outside of PAs slightly declined following the adoption of the law, so the subsequent forest growth likely occurred in mostly previously unforested areas. Forest expansion alone does not ensure connectivity. We highlight the importance of developing policies, PES programs, and monitoring systems that emphasize conserving and restoring large, connected forest patches for biodiversity conservation and landscape resilience. Resumen: Aunque las áreas protegidas (APs) son una importante estrategia de conservación para proteger ecosistemas y especies vulnerables, algunos análisis recientes cuestionan su eficacia para frenar la deforestación y mantener la conectividad del paisaje. La distribución espacial de los bosques dentro y fuera de las AP puede afectar el funcionamiento de los ecosistemas y los movimientos de la fauna. La Península de Osa–y Costa Rica en general–constituyen casos de estudio únicos de conservación debido a su elevada biodiversidad, su extensa red de AP, sus políticas medioambientales y sus programas de Pago por Servicios Ambientales (PSA). Este estudio explora la relación entre APs, la Ley Forestal de 1996, PSA, cobertura y métricas del paisaje en el Área de Conservación Osa (ACOSA). Se utilizó la plataforma Google Earth Engine API para procesar datos de Reflectancia Superficial Tier 1 Landsat 5 Thematic Mapper y Landsat 8 Operational Land Imager para 1987, 1998 y 2019, años con imágenes satelitales relativamente libres de nubes. Se generaron mapas de uso del suelo con el algoritmo de aprendizaje automático basado en pixeles Random Forest, y se calcularon el índice de vegetación de diferencia normalizada (NDVI), el índice de vegetación mejorado (EVI) y las métricas de paisaje funcionales. Estos mapas, los primeros en clasificar por separado los bosques maduros y secundarios de la región, han demostrado su utilidad para los esfuerzos de conservación. Los resultados sugieren que la cobertura forestal, el NDVI, el EVI y la conectividad estructural aumentaron entre 1987 y 2019 en toda la región de estudio, tanto dentro de las AP como en sus alrededores, lo que sugiere una expansión mínima de la deforestación dentro y fuera de las AP. Estos cambios pueden haber contribuido al aumento de la abundancia de vertebrados observado en la región. Las AP, especialmente los parques nacionales con regulaciones de conservación más estrictas, mostraron la mayor cobertura forestal y conectividad. La cobertura forestal aumentó en aquellas propiedades que recibieron PSA. Tras la prohibición de la deforestación por la Ley Forestal de 1996, disminuyeron tanto las tasas de conversión forestal como las de reforestación, lo que sugiere que la ley frenó la deforestación, pero no impulsó la reforestación. La conectividad fuera de las AP disminuyó ligeramente tras la entrada en vigor de la ley, lo que sugiere que el crecimiento forestal posterior se produjo en zonas que antes no estaban forestadas. Por lo tanto, la expansión forestal por sí sola no garantiza la conectividad. Resaltamos la importancia de desarrollar políticas, programas PSA y sistemas de monitoreo que hagan hincapié en la conservación y restauración de grandes zonas forestales conectadas para apuntalar la conservación de la biodiversidad y la resiliencia del paisaje.
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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".