Salvador Arenas-Castro

The Przewalski´s horse (Extinct in the Wild in 1996) is currently listed as Endangered. The first individuals were reintroduced to the Great Gobi B Strictly Protected Area (GGBSPA) in Mongolia in 1997. We observed selected horse groups in the GGBSPA during two seasons in one year (2019) and used ecological niche models to (1) model habitat selection for feeding and resting with generalised linear mixed models; (2) identify the influence of origin (Wild-born vs. Reintroduced); and (3) discuss the potential influence of human presence on the habitat used by the horses for these behaviours. We used three types of satellite-derived predictors: (i) topography (ALOS); (ii) vegetation indexes (Landsat); and (iii) land cover (Copernicus). We assessed the spatial similarity between Reintroduced vs. Wild-born models using pairwise comparisons of the two response variables (feeding and resting). We found significant differences and an apparent shift between the areas preferred for feeding and resting by Wild-born and Reintroduced horses, both over the selected periods and between the groups of different origins. For Wild-born horses, the probability of feeding increased with increasing vegetation quality, while Reintroduced horses tended to feed in worse areas in Late Spring. Surprisingly, an opposite trend was observed in Autumn. On the contrary, no clear pattern emerged from the full model that combined both seasons. Our research provides valuable insights that can inform the refinement of conservation practices, offering practical approaches for enhanced management of the species. We propose that habitat suitability, anthropogenic pressure, and reintroduced group size should be considered key factors for the successful reintroduction of Przewalski´s horses. Additionally, our results caution against simple sample-size maximisation and show that stratified, well-defined datasets might prevent dilution of key drivers in habitat-use models for reintroduced ungulates.

Monitoring biodiversity in protected areas is essential to mitigate biodiversity loss and evaluate the effectiveness of conservation policies. Integrating satellite remote sensing technologies, ecological niche models, and time-series analyses of biodiversity trends offers a fast and robust approach for assessing habitat suitability changes and species vulnerability over time. In this study, we implemented a framework combining these tools to monitor biodiversity in the Montesinho/Nogueira Special Conservation Area (Northeast Portugal). Using the MaxEnt algorithm, we generated ecological niche models for 342 species based on a time series (2001–2023) of remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. We analysed habitat suitability trends with the Mann-Kendall test to detect changes in habitat quality, as a metric of species vulnerability for individual species, five major taxonomic groups (vascular flora, amphibians, reptiles, birds, and mammals), functional groups (e.g. climate affinity, habitat type, diet, activity, reproduction), and conservation status (regional and European levels). Our study revealed a significant decline in habitat suitability over the past two decades, impacting all taxonomic groups and ecological functions. We observed a high variability in habitat suitability trends among species and taxonomic/functional groups, highlighting the complexity of biodiversity responses to environmental changes. Functional traits such as climatic affinity, trophic level or habitat specialisation were associated with variable rates of habitat decline, with species of Atlantic affinity, species associated with croplands and wetlands, and species specialised in insectivorous diets being at higher risk. Overall, these findings emphasise the need for comprehensive biodiversity monitoring programmes and demonstrate the utility of our approach to inform evidence-based conservation strategies in protected areas globally.

Environmental and human factors shape the distribution and habitat of Mediterranean woody plants, influencing their population structure and conservation. The Iberian wild pear ( Pyrus bourgaeana ), a keystone fleshy‐fruited species supporting wildlife during resource‐scarce periods, is one such example. However, its habitat association and spatial patterns in Mediterranean managed woodland areas remain poorly understood. This study investigates its associations with microtopography and habitat heterogeneity in two key managed areas—dehesa (DEH) and abandoned olive grove (AOG)—within its core range in southern Spain. All trees ≥5 cm in diameter were mapped in a 223.3‐ha plot, classified by size and age, and analyzed using spatial analysis, generalized linear models, and the torus translation test. Results showed stronger tree clustering in the AOG, with overall aggregation at small to intermediate scales (10–30 m), especially in older and larger trees. Positive associations were found between tree age, size, and microtopographic features. This light‐demanding species preferred higher elevations and sunnier slopes. Human activities have significantly altered the species' structure: forest conversion to olive groves eliminated the Iberian wild pears, but abandonment allowed some regeneration via animal‐dispersed seeds. However, increased ungulate density from hunting and cattle grazing, particularly in DEH, now severely limits regeneration, causing local declines. These findings highlight how historical land use and current grazing pressures affect the species viability and underscore the importance of targeted conservation strategies to preserve its role in Mediterranean ecosystems.

A pesar de su gran valor ecológico y socioeconómico, la exposición del bosque mediterráneo de encinas (Quercus ilex subsp. ballota) a factores adversos como el manejo intensivo, los puede hacer más vulnerables a eventos como el cambio global. En este estudio, analizamos las características estructurales, de composición y funcionales de diferentes tipos de encinares según su densidad de arbolado (abierto-ABI, intermedio-INT y denso-DEN) a escala regional (Andalucía, España). Para ello, aplicamos un enfoque estadístico-descriptivo y analítico a través de modelos lineales generalizados (GLMs), así como análisis de tendencias espaciales y temporales mediante el uso de datos recogidos in-situ de parcelas del Inventario Forestal Nacional (IFN) e Índices de Vegetación (IVs) derivados de imágenes de satélite (Landsat). Aridez, elevación y los valores mínimos del índice de diferencia normalizada de vegetación (NDVImin) fueron las variables más importantes en la explicación de los tres tipos de bosque. Los GLMs mostraron una relación significativa entre los valores máximos del NDVI con las variables medidas en campo (R2 adj. ~ 0.2). Además, el análisis multitemporal del máximo del índice de vegetación mejorado (EVImáx) para un periodo de 23 años (2001-2023) mostró una relación significativa entre las dinámicas de los IVs y la precipitación, siendo la respuesta de los distintos tipos de bosque similar a los eventos de sequía. Confirmamos, además, las ventajas de la combinación de datos del IFN con teledetección en el análisis de la dinámica forestal.

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.

Biodiversity loss is accelerating due to human actions, and decision‐making for conservation needs to be streamlined. Ex situ biodiversity modeling and monitoring based on satellite time‐series data could be an affordable and cost‐efficient tool for improving the prioritization of conservation areas. We developed a set of dynamic indicators for conservation prioritization based on a habitat suitability index (HSI) trend analysis of 6 flagship species (two vascular plants, bird, amphibian, reptile, and mammal) over 19 years (2001–2019) in Andalucía (southern Spain). The HSI models were derived from ecological niche models (MaxEnt) and satellite time‐series data (MODIS) as predictors. Based on the annual HSI models of all species and using the spatial conservation prioritization tool Marxan, we derived interannual dynamic indicators of habitat quality for conservation prioritization. Overall, models showed a generalized habitat regression. The best predictors of habitat quality were related to vegetation composition and structure (land cover), climate (land surface temperature), and energy balance (evapotranspiration), matching with the ecology of climate (such as Abies pinsapo ) or vegetation‐dependent (such as Alytes dickhilleni ) species. Marxan identified interannual dynamics for the priority areas outside and inside protected areas. Interannual variation in habitat quality led to shifting conservation priorities across Andalucia from 2001 to 2019. Only 10.5% of the region and 20% of protected areas showed high spatial stability. Stable zones appeared both inside and outside protected areas. The south and northeast consistently exhibited high‐priority regions. The legacy indicator highlighted areas of historical importance that have since declined in importance. New high‐value areas emerged in the south. Static and dynamic approaches to conservation planning differed significantly. Many areas prioritized in 2019 alone ranked lower when long‐term trends were considered. Our multiscale method underscores the need to integrate temporal dynamics into effective conservation strategies to achieve long‐term conservation objectives in an efficient way.