Predicting Polylepis distribution: vulnerable and increasingly important Andean woodlands

• Autores: Zutta, Brian R., Rundel, Phillip W., Saatchi, Sassan, Casana, Jorge D., Gauthier, Paul Gauthier, Soto, Aldo, Velazco, Yessenia, Buermann, Wolfgang
• Formato: artículo
• Fecha de Publicación: 2012
• Institución: Universidad Nacional Mayor de San Marcos
• Repositorio: Revista UNMSM - Revista Peruana de Biología
• Lenguaje: español
• OAI Identifier: oai:ojs.csi.unmsm:article/849
• Enlace del recurso: https://revistasinvestigacion.unmsm.edu.pe/index.php/rpb/article/view/849
• Nivel de acceso: acceso abierto
• Materia: Maxent; MODIS; Polylepis; QSCAT; remote sensing; species distribution modeling; WorldClim.; distribución; sensores remotos

Polylepis woodlands are a vital resource for preserving biodiversity and hydrological functions, which will be altered by climate change and challenge the sustainability of local human communities. However, these highaltitude Andean ecosystems are becoming increasingly vulnerable due to anthropogenic pressure including fragmentation, deforestation and the increase in livestock. Predicting the distribution of native woodlands has become increasingly important to counteract the negative effects of climate change through reforestation and conservation. The objective of this study was to develop and analyze the distribution models of two species that form extensive woodlands along the Andes, namely Polylepis sericea and P. weberbaueri. This study utilized the program Maxent, climate and remotely sensed environmental layers at 1 km resolution. The predicted distribution model for P. sericea indicated that the species could be located in a variety of habitats along the Andean Cordillera, while P. weberbaueri was restricted to the high elevations of southern Peru and Bolivia. For both species, elevation and temperature metrics were the most significant factors for predicted distribution. Further model refinement of Polylepis and other Andean species using increasingly available satellite data demonstrate the potential to help define areas of diversity and improve conservation strategies for the Andes.