Biology subjects

Alcaraz-Segura, D.

Publications and source records attributed to Alcaraz-Segura, D..

4 recordsLinked to original sources

bioRxiv2025

What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media

High mountain protected areas (PAs) are increasingly recognized not only for their role in conserving biodiversity but also for their contribution to the provision of cultural ecosystem services (CES). Despite their relevance, CES remain underrepresented in conservation planning, particularly due to challenges in quantifying their spatial distribution. This study combines geolocated social media data and ecological niche models (ENMs) to assess the spatial patterns and key drivers of CES supply across eight mountain PAs spanning distinct biogeographical regions in Spain and Portugal. Using deep learning techniques to classify more than 200,000 photographs into ten CES categories, we evaluated model performance under two modeling approaches and identified the most influential environmental and social predictors. Most CES categories exhibited good model performance (Boyce index > 0.5), though variation existed across services and regions. Nature & Landscape and Gastronomy CES showed strong associations with park boundaries and human settlements, respectively, while Religious and Cultural CES were spatially linked to culturally significant landmarks.. Our findings demonstrate the potential of combining social media data with ENMs to map CES distributions and reveal both universal and context-dependent drivers. This approach offers valuable insights for integrating CES into PA management and spatial planning, supporting more holistic and culturally inclusive conservation strategies.

ecology↗

bioRxiv2025

Spatial modeling of Cultural Ecosystem Services from social media data: Systematic review of operability, opportunities, limitations and ways forward

O_LIThis systematic review explores the use of social media data to spatially model Cultural Ecosystem Services (CES), which are non-material benefits provided by ecosystems that support human well-being. Based on a search of 510 scientific articles in Web of Science and Scopus, we carefully selected and analyzed those that focused on spatial modeling of CES using social media data. We aimed to (a) identify the diversity of CES assessed, (b) analyse the social media platforms used as data sources, (c) evaluate the modelling frameworks employed, and (d) summarise the predictor variables included in these models. C_LIO_LIWe found that the most studied CES were those related to physical and psychological experiences (62%; especially recreation) and the main predictor variables were the presence of natural elements (52%), land use and land cover maps, and topographic variables, often weighted by applying distance-based metrics (24%). Twenty-four social media data sources were identified but Flickr was by far the most widely used one (40%). MaxEnt (37%) and Random Forest (16%) were the most commonly used modeling tools. The most commonly used metrics to assess model performance were AUC-ROC, AIC, and R2 values. C_LIO_LIWhile the use of social media offers an opportunity to study CES and provide cost-effective and scalable insights, this article discusses some limitations and considerations raised from the literature review to be taken into account when using this type of data. These include the quality and representativeness of social media data, the importance of a clear definition of CES, a proper labeling of social media data, and an appropriate selection of spatial modeling techniques. C_LIO_LIFuture research should address these limitations and considerations by integrating different data sources and refining methodologies to improve the accuracy and applicability of CES models. This review provides a comprehensive overview of current practices and highlights areas for further investigation in the spatial modeling of CES using social media data. C_LI

ecology↗

bioRxiv2021

Deep learning assessment of cultural ecosystem services from social media images

Crowdsourced social media data has become popular in the assessment of cultural ecosystem services (CES). Advances in deep learning show great potential for the timely assessment of CES at large scales. Here, we describe a procedure for automating the assessment of image elements pertaining to CES from social media. We focus on a binary (natural, human) and a multiclass (posing, species, nature, landscape, human activities, human structures) classification of those elements using two Convolutional Neural Networks (CNNs; VGG16 and ResNet152) with the weights from two large datasets - Places365 and ImageNet -, and our own dataset. We train those CNNs over Flickr and Wikiloc images from the Peneda-Geres region (Portugal) and evaluate their transferability to wider areas, using Sierra Nevada (Spain) as test. CNNs trained for Peneda-Geres performed well, with results for the binary classification (F1-score > 80%) exceeding those for the multiclass classification (> 60%). CNNs pre-trained with Places365 and ImageNet data performed significantly better than with our data. Model performance decreased when transferred to Sierra Nevada, but their performances were satisfactory (> 60%). The combination of manual annotations, freely available CNNs and pre-trained local datasets thereby show great relevance to support automated CES assessments from social media.

ecology↗

bioRxiv2018

Automatic whale counting in satellite images with deep learning

Despite their interest and threat status, the number of whales in worlds oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution orthoimages. Since deep convolutional neural networks (CNNs) achieve great performance in object-recognition in images, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales from space based on open data and tools. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 84% in detecting and 97% in counting 80 whales. Applying this cost-effective method worldwide could facilitate the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process.

ecology↗