Solar photovoltaic (PV) has become the second renewable energy source, giving rise to potential conflicts with biodiversity conservation. However, the information available about the impacts and mitigation measures of solar PV energy is scarce and scattered, and a rigorous and comprehensive review on the topic is lacking. Here, we review the state of knowledge on its impacts and mitigation measures and identify main knowledge gaps. For that, we reviewed more than 2000 articles, out of which only 180 assessed the impacts of solar PV ( N = 138) and/or propose mitigation measures (65). Even though Asia and Europe head the list of regions with the highest PV installed capacity (59% and 22%, respectively), a large portion of the existing knowledge is drawn from North American environmental contexts (48% of the studies), specifically from deserts (41%). Impacts were addressed on plants (26%), arthropods (14%), birds (10%), microorganisms (10%), reptiles (7%), mammals (4%), and bats (1%), but also on abiotic factors (e.g., humidity and temperature; 20%) and ecosystem services (3%). Most studies addressed the impact of PV on habitat alteration at landscape (33%) and microhabitat scale (20%), and on microclimate at microhabitat scale (17%), but other topics have been scarcely addressed (e.g., impact on microclimate at landscape scale or the potential of agrivoltaic systems). Lastly, 53% of the studies employed a single PV facility, and preconstruction situations were rarely reported (8%). There is a strong environmental context bias in the current understanding of PV impacts, which might not be extrapolable to other environmental situations like farmlands, where most of the solar PV capacity is being installed. Moreover, standardized and robust sampling designs are lacking to address cumulative, long‐term, and long‐scale impacts and produce comparable findings across contexts. Given the lack of empirical evidence and the irrepressible development of PV energy, it is advisable to apply an iterative monitoring and adaptive process to guarantee a safe energy transition. This review may provide useful guidance on prioritizing research efforts for a smooth shift to renewable energy.

The efficient analyses of sound recordings obtained through passive acoustic monitoring (PAM) might be challenging owing to the vast amount of data collected using such technique. The development of species-specific acoustic recognizers (e.g., through deep learning) may alleviate the time required for sound recordings but are often difficult to create. Here, we evaluate the effectiveness of BirdNET, a new machine learning tool freely available for automated recognition and acoustic data processing, for correctly identifying and detecting two cryptic forest bird species. BirdNET precision was high for both the Coal Tit (Peripatus ater) and the Short-toed Treecreeper (Certhia brachydactyla), with mean values of 92.6% and 87.8%, respectively. Using the default values, BirdNET successfully detected the Coal Tit and the Short-toed Treecreeper in 90.5% and 98.4% of the annotated recordings, respectively. We also tested the impact of variable confidence scores on BirdNET performance and estimated the optimal confidence score for each species. Vocal activity patterns of both species, obtained using PAM and BirdNET, reached their peak during the first two hours after sunrise. We hope that our study may encourage researchers and managers to utilize this user-friendly and ready-to-use software, thus contributing to advancements in acoustic sensing and environmental monitoring.

The assessment of the effects of wind farms on bird populations is commonly based on collision fatality records. This could undervalue the effect of wind farms on small‐sized birds. We evaluate the effect of wind turbines on occurrence, abundance and population trends of a threatened small passerine species, the Dupont's larkChersophilus duponti. To our knowledge, this is one of the first studies addressing the effect of wind farms on population trends using time‐series data from multiple wind farms. We estimated population trends by fitting a switching linear trend model with the softwaretrim(Trend & Indices for Monitoring data). We used multiannual data surveys of five populations in the presence of wind farms and nine in their absence (2008–2016 period). Furthermore, we fitted a logistic and a negative binomial regression model to test the effect of wind farm proximity on species occurrence and abundance in 2016, respectively. We incorporated local connectivity and habitat availability estimates in both models as predictors. Results showed a negative trend overall, but that was significantly more regressive in the presence of wind farms: 21.0% vs. 5.8% average annual decline in the absence of wind farms. Dupont's lark occurrence and abundance in 2016 were negatively affected by measures of population isolation and positively affected by the distance to wind farms. These results highlight the negative effect of isolation and wind farm proximity on Dupont's lark population parameters. Taking into account the metapopulation structure exhibited by the species in the study area, this work established a 4.5‐km threshold distance from wind farms, beyond which Dupont's lark populations should be unaffected. Synthesis and applications. This work highlights the negative impact of wind farms on small‐sized birds and provides a 4.5‐km threshold distance that should be taken into account in the design of future wind energy projects. Moreover, we suggest an analytical approach based on population trends, species abundance and occurrence variation in relation to wind farms, useful for the assessment of wind farm impacts on small‐sized birds.