José Carlos Pérez-Girón

Species distribution models (SDMs) are widely used to predict the spread of invasive species, yet their accuracy over time and the influence of climate data resolution remain unclear. Here, we examine the capacity of SDMs to predict the distribution and short‐term expansion of the invasive gall wasp Dryocosmus kuriphilus , and compare the performance of locally sourced, annually resolved climate data with global climatic datasets. We modelled the annual distribution and expansion of D. kuriphilus using SDMs. Three climate datasets: regional interpolations, temporally explicit CHELSA time series, and long‐term CHELSA averages, were compared to test their influence on model accuracy. Habitat suitability was estimated with GLM, MaxEnt, and Random Forest, and model performance was evaluated with the Boyce index. Temporal transferability was assessed by projecting early‐year models to subsequent years and analysing accuracy patterns via multifactorial ANOVA. Model accuracy, measured with the Boyce index, improved over time, surpassing 0.7 for most datasets and algorithms. Differences among climate datasets were minor, although regional data slightly enhanced early predictions. MaxEnt consistently achieved the highest and most stable performance, while Random Forest and GLM were more variable. Models projected beyond their calibration years rapidly lost predictive power, showing limited temporal transferability. ANOVA confirmed that neither climate dataset nor algorithm significantly influenced accuracy, though MaxEnt performed best overall. SDMs are valuable tools for invasive species management when used adaptively. Accuracy improves with more occurrence data but declines when projecting future expansion, highlighting the need for regular updates. Incorporating fine‐scale, temporally explicit data enhances early detection, monitoring, and rapid‐response interventions. Continuously updated SDMs support timely management decisions, including invasion monitoring, prioritisation of actions, and integration of biological control or other mitigation strategies.

National and international regulations enforce monitoring programmes of water quality to guide management actions of inland water ecosystems. Our study evaluates the effect of spectral and spatial resolutions on the estimation of chlorophyll‐ a concentrations in mountain lakes, and derives implications for addressing the adjacency effect, which is critical and understudied in small water bodies. Five lakes in Sierra Nevada (Spain) were repeatedly sampled during 2020, 2021, and 2023, and a total of 100 chlorophyll‐ a samples with suitable coincident satellite imagery were analyzed. Laboratory‐obtained chlorophyll‐ a concentrations were modeled comparing up to 86 spectral indices and bands as predictors from three satellites: Sentinel‐2 (12 bands, 20 m/pixel), Planet (8 bands, 3 m/pixel) and WorldView‐3 (11 bands, 1.24 m/pixel). Our results showed that multivariate models for estimating chlorophyll‐ a using spectral indices did not perform significantly better than using bands alone. The best models always had multiple predictors and included green and near‐infrared bands. Models based on Sentinel‐2 and Planet ( R _adj ² > 0.45) outperformed those of WorldView‐3 ( R _adj ² ∼ 0.37), confirming that the latter performed worst despite higher spatial resolution. Regarding distance to shoreline, the Planet model showed the most consistent performance, with stable R _adj ² values and low RMSE even at 3 m from shore with a high level of accuracy ( R _adj ² ∼ 0.3; RMSE ∼ 1.15 μg L ⁻¹ ). Data and models are released to facilitate near‐real‐time monitoring of these vulnerable ecosystems, where field sampling is extremely challenging.

Oak forests are native ecosystems that support diverse life forms and provide essential services. This study focused on elucidating the taxonomic characterization and diversity of fungal species in Atlantic oak woodlands dominated by Quercus petraea at its southwestern distribution limit in the Iberian Peninsula. Sampling was conducted in three protected natural areas to assess fungal root diversity by macroscopic and microscopic methods, including histological analysis of root sections. Additionally, metabarcoding techniques were used to sequence the internal transcribed spacer (ITS) region, targeting fungal diversity. The analysis identified 1,043 Amplicon Sequence Variants (ASVs), corresponding to 297 species. Although fungal richness was consistent across locations, community composition was geographically specific. Tree social classes also influenced fungal diversity, which was higher in the intermediate trees in some areas. Functional guilds varied markedly, with symbiotic fungi (notably ectomycorrhizal fungi and dark septate endophytes) most abundant based on sequencing reads, while saprotrophs were the most diverse genus. These findings highlight the structural and ecological complexity of oak-associated fungal communities and their potential importance in forest conservation and ecosystem functioning.

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.

This 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. We 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. While 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. Future 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.