Optimisation of the geobiodiversity index for protected natural areas in northern Portugal

Abstract

Most studies on geodiversity and geoheritage have focused exclusively on analyzing and evaluating abiotic elements. Although some authors have emphasized the important links between abiotic and biotic elements, the focus has mostly been on abiotic elements alone and not on the interrelationship between the two. In this sense, our proposal is to analyze the potential of geodiversity in a broader sense. In other words, all elements, including biodiversity, should be considered. In the context of the project ‘Clictour - Resilient Tourism to climate change in protected areas in the north of Portugal’, we selected three protected areas in the northwest of Portugal to carry out this work: the Peneda-Gerês National Park, the Alvão Natural Park and the North Coast Natural Park. Building upon a methodological framework originally developed for the Brazilian Cerrado biome, this study adapts and applies the geobiodiversity index to three protected areas located in northwestern Portugal. The approach enables the identification of spatial patterns and zones of higher relevance by integrating key components of both geodiversity and biodiversity, contributing to a more comprehensive understanding of the natural heritage and conservation value of these landscapes. The methodological procedures were centered on segmenting the study area into hexagonal cells. The parks were subjected to a spatial matrix analysis in GIS and geostatistical environments, capable of articulating the various elements and generating an agglutinating index for each cell. The results reveal the existence of a high index value for geobiodiversity in various areas within the Peneda-Gerês National Park, as well as in the Alvão Natural Park and the North Coast Natural Park. It also shows the importance of integrating the analysis of geodiversity and biodiversity, especially in protected natural areas. This study points to the importance of the hexagonal cell compartmentalization approach, which can provide a multi-level and multi-scale approach to complex spatial analysis studies.