Modelos de distribución de especies endémicas y amenazadas: el caso de Glandora nitida
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2024-09-06
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[ES] En este Trabajo Fin de Grado se llevó a cabo un estudio sobre la especie endémica Glandora nitida, centrándose en la modelización de su nicho ecológico, mediante el uso de Modelos de Distribución de Especies (SDM). Se utilizó el programa MaxEnt, empleando datos de presencia de la especie y variables
ambientales, tanto climáticas como topográficas y edáficas. Las variables topográficas incluyeron pendiente, orientación y sombreado. Como variable edáfica se utilizó la acides del suelo. Las 19 variables bioclimáticas se obtuvieron de CHELSA, con una resolución de 1 km. Se generó un mapa de distribución potencial actual para Glandora nitida y se identificaron las variables más relevantes en la construcción del modelo. Este trabajo permite avanzar en el conocimiento de dicha especie amenazada. Asimismo, proporciona información valiosa para proponer medidas de conservación y gestión adecuadas, con el objetivo de mejorar su estado de conservación.
[EN] In this Final Degree Project, a study was carried out on the endemic species Glandora nitida, focusing on the modeling of its ecological niche, through the use of Species Distribution Models (SDM). The MaxEnt program was used, using data on the presence of the species and environmental variables, both climatic, topographical and edaphic. Topographic variables included slope, aspect, and shading. Soil acidity was used as edaphic variable. The 19 bioclimatic variables were obtained from CHELSA, with a resolution of 1 km. A current potential distribution map for Glandora nitida was generated and the most relevant variables in the construction of the model were identified. This work allows us to advance our knowledge of this threatened species. Likewise, it provides valuable information to propose appropriate conservation and management measures, with the aim of improving its conservation status.
[EN] In this Final Degree Project, a study was carried out on the endemic species Glandora nitida, focusing on the modeling of its ecological niche, through the use of Species Distribution Models (SDM). The MaxEnt program was used, using data on the presence of the species and environmental variables, both climatic, topographical and edaphic. Topographic variables included slope, aspect, and shading. Soil acidity was used as edaphic variable. The 19 bioclimatic variables were obtained from CHELSA, with a resolution of 1 km. A current potential distribution map for Glandora nitida was generated and the most relevant variables in the construction of the model were identified. This work allows us to advance our knowledge of this threatened species. Likewise, it provides valuable information to propose appropriate conservation and management measures, with the aim of improving its conservation status.