An ecological modelling approach to support Peru wildlife conservation planning based on geospatial datasets and remote sensing information

• Autores: Cotrina Sanchez, Alexander, Rojas Briceño, Nilton, Guzman Valqui, Betty Karina, Valentini, Riccardo, Vaglio Laurin, Gaia
• Formato: artículo
• Fecha de Publicación: 2026
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/3137
• Enlace del recurso: http://hdl.handle.net/20.500.12955/3137; https://doi.org/10.1111/ddi.70206
• Nivel de acceso: acceso abierto
• Materia: Conservation planning; Planificación de conservación Forest; Bosque GEDI; GEDI LiDAR; LiDAR Peru; Perú Species distribution models; Modelos de distribución de especies Wildlife; Vida silvestre; https://purl.org/pe-repo/ocde/ford#4.01.00; Biodiversity; Biodiversidad; Remote sensing, Teledetección; Habitat, Hábitat; Deforestation; Deforestación; Especie amenazada; Threatened species

Peru, a megadiverse country, has developed conservation plans for some threatened wildlife species. This study produced spatially explicit data integrating Species Distribution Models (SDMs) into a geospatial analysis of connectivity within the protected areas (PAs) network. In addition, a deforestation analysis around selected PAs was performed evaluating the related conservation implications. The use of lidar-derived vegetation vertical structure metrics from the spaceborne Global Ecosystem Dynamics Investigation (GEDI) mission was tested as an innovative data source to support ecological modelling. This country-level analysis is a useful approach to support conservation in high-biodiversity areas. Location: Peru. Methods: Occurrence data of seven threatened wildlife species were used to compute SDMs in MaxEnt using three variable sets: (i) bioclimatic and topographic, (ii) GEDI vegetation structure metrics joined with Normalized Difference Vegetation Index (NDVI), and (iii) a combination of both. MaxEnt was explicitly calibrated by testing 126 candidate models per species across feature-class and regularization multiplier combinations. SDMs combined with auxiliary data were used to identify core areas, then connected through main ecological corridors (ECs) using geospatial analysis. Deforestation rates were computed in the buffer zones (BZ) of Protected Natural Areas (PNAs) identified as core areas. GEDI lidar-derived data were also used to compare forest degradation between two PNAs and their BZ. Results: This ecological modelling effort identified several core conservation areas, as well as the main ecological corridors interconnecting them. The study showed that highly suitable habitats are currently poorly represented by the present Peru protected areas network, particularly for primates. Test Area Under Curve (AUC) values ranged from 0.867 to 0.995; the Biotopveg set, integrating bioclimatic, topographic, GEDI, and NDVI variables was optimal for three species and the bioclimatic-topographic set for four, suggesting a species-specific contribution of vegetation structural data. GEDI data were used to detect forest degradation gradients, in accordance with known anthropogenic impacts. Deforestation analysis showed that even if indirect use protected areas resulted in less affected by deforestation in their surroundings, notable exceptions occur, calling for additional measures to support human-wildlife coexistence. Main Conclusions: Ecological modelling based on SDMs and spatial analyses can support species conservation plans and landscape connectivity at broader planning scales. GEDI provides valuable data as input in SDMs and supports detecting forest degradation.