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Wind turbine development is growing exponentially and faster than other sources of renewable energy worldwide. While multi-turbine facilities have small physical footprint, they are not free from negative impacts on wildlife. This is particularly true for bats, whose population viability can be threatened by wind turbines through mortality events due to collisions. Wind turbine curtailment (hereafter referred to as "blanket curtailment") in non-winter periods at low wind speeds and mild temperatures (i.e. when bats are active and wind energy production is low) can reduce fatalities, but show variable and incomplete effectiveness because other factors affect fatality risks including landscape features, rain, turbine functioning, and seasonality. The combined effects of these drivers, and their potential as criteria in algorithm-based curtailment, have so far received little attention. We compiled bat acoustic data recorded over four years at 34 wind turbine nacelles in France from post-construction regulatory studies, including 8619 entire nights (251 ± 58 nights per wind turbine on average). We modelled nightly bat activity in relation to its multiple drivers for three bat guilds, and assessed whether curtailment based on algorithm would be more efficient to limit bat exposure than blanket curtailment based on various combinations of unique wind speed and temperature thresholds. We found that landscape features, weather conditions, seasonality, and turbine functioning determine bat activity at nacelles. Algorithm-based curtailment is more efficient than blanket curtailment, and has the potential to drastically reduce bat exposure while sustaining the same energy production. Compared to blanket curtailment, the algorithm curtailment reduces average exposure by 20 to 29 % and 7 to 12 % for the high-risk guilds of long- and mid-range echolocators, and by 24 to 31 % for the low-risk guild of short-range echolocators. These findings call for the use of algorithm curtailment as both power production and biodiversity benefits will be higher in most situations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: France Energie Eolienne (FEE) is an association of >300 members, professionals of the wind energy sector in France, who have built >90 % of the turbines installed on the French territory and operate >85 % of them. The scientific question was defined with FEE whose members provided the data. FEE had no role in data preparation and analysis, interpretation and discussion of the results, and decision to publish. The Office Français de la Biodiversité (OFB) is a public institution dedicated to the protection and restoration of biodiversity in metropolitan and overseas France, under the authority of the French Ministry of Ecological Transition and Agriculture and Food. OFB and FEE proofread an early version of the manuscript and this proofreading did not influence the interpretations, discussions and conclusions of the study. Corrections made to the first draft following these proofreadings can be found at doi:https://doi.org/10.5281/zenodo.6818161. Authors take complete responsibility for the accuracy of their analysis and their interpretations and discussions. None of the authors was employed by any of the funders prior to, or at the time of the submission of the article. We declare having no other competing interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Despite the key importance of the landscape matrix for bats, we still not fully understand how the effect of forest composition interacts at combined stand and landscape scales to shape bat communities. In addition, we lack detailed knowledge on the effects of local habitat structure on bat-prey relationships in forested landscapes. We tested the assumptions that (i) forest composition has interacting effects on bats between stand and landscape scales; and (ii) stand structure mediates prey abundance effects on bat activity. Our results indicated that in conifer-dominated landscapes (> 80% of coniferous forests) bat activity was higher in stands with a higher proportion of deciduous trees while bats were less active in stands with a higher proportion of deciduous trees in mixed forest landscapes (~ 50% of deciduous forests). Moth abundance was selected in the best models for six among nine bat species. The positive effect of moth abundance on Barbastella barbastellus was mediated by vegetation clutter, with dense understory cover likely reducing prey accessibility. Altogether, our findings deepen our understanding of the ecological processes affecting bats in forest landscapes and strengthen the need to consider both landscape context and trophic linkage when assessing the effects of stand-scale compositional and structural attributes on bats.