Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally.

BirdNET is a popular machine learning tool for automated recognition of bird sounds. However, evidence on how to optimize its settings for accurate bird monitoring remains limited. Here, we evaluate how BirdNET settings influence model performance in identifying bird vocalizations and characterizing bird communities, using 4224 1‐min recordings from 67 recording locations worldwide. Giving equal importance to recall and precision, a low confidence score threshold (0.1–0.3) appears optimal for detecting bird vocalizations, whereas higher thresholds (around 0.5) are more suitable for characterizing bird communities. Based on our findings, we recommend increasing the Overlap parameter from its default value of 0 to 2 s, as this consistently improves BirdNET performance in detecting both bird vocalizations and species presence. The effect of the Sensitivity parameter varied across regions. However, a value of 0.5 maximizes global performance for community‐level analyses across all confidence thresholds, and a value of 1.5 generally yields better results for vocalization‐level studies, particularly at low confidence thresholds. Our findings offer practical guidance for selecting BirdNET settings in passive acoustic bird surveys, enhancing both the identification of bird vocalizations and the characterization of bird communities.

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy.

Silvopasture, a traditional agroforestry practice, combines the presence of trees, shrubs, herbage, and livestock in time and space to provide multiple ecosystem services that contribute to human well-being. However, the abandonment of traditional agroforestry practices across Europe has led to substantial changes in vegetation characteristics, mainly due to woody plant expansion and, as a consequence, changes in wildlife that rely on open habitats. This study examines the effects of a 20-year abandonment of silvopastoral practices (i.e., livestock grazing and fuelwood harvesting) in a typical agroforestry Mediterranean landscape (kermes oak shrubland, natural grassland, and olive groves) on European hare (Lepus europaeus) habitat use. We estimated tree, shrub, and herb cover using a densitometer and hare habitat use using pellet counts within 2004-m² rectangular plots in 2002, 2011, and 2021. Hare pellet density in olive groves was significantly lower in 2021 compared to 2002, while the opposite trend was found in grassland for the same period. Woody plant cover expanded from 2002 to 2021. We suggest that the woody plant encroachment that followed the abandonment of traditional silvopastoral practices in the area is the main driver behind the reported decline in hare use of the habitat, as it became less open and therefore less favorable for the species. Maintaining a mosaic of open and closed habitats at the landscape level, which was once provided by silvopastures, is vital for the conservation of this species.

Seasonal diets of goats, sheep and European hares (Lepus europaeus) were examined using microhistological analysis of feces collected when these herbivores grazed together in a typical Mediterranean shrubland. Approximately half of the total diet content of goats was shrubs (mainly kermes oak, Quercus coccifera), while that of hares was grasses (mostly brush grass, Chrysopogon gryllus). Sheep had a more balanced diet consisting mainly of grasses, forbs, and shrubs. Dietary overlap between goats and sheep was high throughout the year. In contrast, there was very low dietary overlap between small ruminants and hares. Dietary diversity was high in spring and low in winter across all species, with sheep in general displaying higher dietary diversity across all seasons than goats and hares. Goats had intermediate and hares had low dietary diversity across all seasons. Communal grazing by small ruminants and hares ensures that there is a more uniform use of the available forage resources than if a single herbivore is left to graze an area.

In recent decades, climate change has significantly influenced the frequency and intensity of wildfires across Mediterranean pine forests. The loss of forest cover can bring long-term ecological changes that impact the overall biodiversity and alter species composition. Understanding the long-term impact of wildfires requires effective and cost-efficient methods for monitoring the postfire ecosystem dynamics. Passive acoustic monitoring (PAM) has been increasingly used to monitor the biodiversity of vocal species at large spatial and temporal scales. Using acoustic indices, where the biodiversity of an area is inferred from the overall structure of the soundscape, rather than the more labor-intensive identification of individual species, has yielded mixed results, emphasizing the importance of testing their efficacy at the regional level. In this study, we examined whether widely used acoustic indicators were effective at capturing changes in the avifauna diversity in Pinus halepensis forest stands with different fire burning histories (burnt in 2001, 2009, and 2018 and unburnt for >20 years) on the Sithonia Peninsula, Greece. We recorded the soundscape of each stand using two–three sensors across 11 days of each season from March 2022 to January 2023. We calculated for each site and season the following five acoustic indices: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Normalized Difference Soundscape Index (NDSI), and Bioacoustic Index (BI). Each acoustic index was then assessed in terms of its efficacy at predicting the local avifauna diversity, as estimated via two proxies—the species richness (SR) and the Shannon Diversity Index (SDI) of vocal bird calls. Both the SR and SDI were calculated by having an expert review the species identification of calls detected within the same acoustic dataset by the BirdNET convolutional neural network algorithm. A total of 53 bird species were identified. Our analysis shows that the BI and NDSI have the highest potential for monitoring the postfire biodiversity dynamics in Mediterranean pine forests. We propose the development of regional-scale acoustic observatories at pine and other fire-prone Mediterranean habitats, which will further improve our understanding of how to make the best use of acoustic indices as a tool for rapid biodiversity assessments.