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Combining social media photographs and species distribution models to map cultural ecosystem services: The case of a Natural Park in Portugal

Clemente, Pedro, Calvache, Marta, Antunes, Paula, Santos, Rui, Cerdeira, Jorge Orestes, Martins, Maria João
Ecological indicators 2019 v.96 pp. 59-68
aesthetics, biogeography, cost effectiveness, decision making, digital images, ecosystem services, environmental indicators, infrastructure, issues and policy, land use planning, models, photographs, recreation, social networks, Portugal
Developing spatially explicit models of Ecosystem Services (ES) distribution and diversity across the territory has been increasingly attracting the interest of researchers and policy-makers due to its potential to operacionalize and mainstream the ES concept into existing planning and policy tools.In this paper we explore the use of social media photographs to model the spatial distribution of people preferences for cultural ecosystem services (CES), map their hotspots, identify the determinant variables as well as the spatial correlation between CES. This research was applied in the Sudoeste Alentejano and Costa Vicentina Natural Park (PNSACV) located in Southwestern Alentejo, Portugal.A collection of 1378 geo-tagged digital images taken inside the Park and posted in the Flickr web platform between 2004 and 2015 were analyzed and classified according to a tailored list of CES. To model CES spatial distribution it was used a species distribution model – Maxent – adapted to combine the observation of CES occurrence with biophysical and infrastructural variables.This method allowed us to identify and map the social preferences for CES in this area. The distance to the ocean and distance to touristic and cultural infrastructure were the most determinant variables to explain CES distribution in PNSACV. Another relevant result of this study was the identification of pairs of CES (such as Recreation & Aesthetics services) with a significant spatial overlap.Using social media data can be an expedite and cost-effective way to identify and map CES, although this approach embodies some challenges and biases that need to be considered. The use of species distribution models, such as Maxent, can be particularly valuable to support the design of future scenarios and assist decision-making on land use planning.