1. Renewable energy is growing at a rapid pace globally, but as yet there has been little research on the effects of ground-mounted solar photovoltaic (PV) developments on bats, many species of which are threatened or protected.
2. We conducted a paired study at 19 ground-mounted solar PV developments in southwest England. We used static detectors to record bat echolocation calls from boundaries (i.e., hedgerows) and central locations (open areas) at fields with solar PV development, and simultaneously at matched sites without solar PV developments (control fields). We used generalized linear mixed-effect models to assess how solar PV developments and boundary habitat affected bat activity and species richness.
3. The activity of six of eight species/species groups analysed was negatively affected by solar PV panels, suggesting that loss and/or fragmentation of foraging/commuting habitat is caused by ground-mounted solar PV panels. Pipistrellus pipistrellus and Nyctalus spp. activity was lower at solar PV sites regardless of the habitat type considered. Negative impacts of solar PV panels at field boundaries were apparent for the activity of Myotis spp. and Eptesicus serotinus, and in open fields for Pipistrellus pygmaeus and Plecotus spp.
4. Bat species richness was greater along field boundaries compared with open fields, but there was no effect of solar PV panels on species richness.
5. Policy Implications: Ground-mounted solar PV developments have a significant negative effect on bat activity, and should be considered in appropriate planning legislation and policy. Solar PV developments should be screened in Environmental Impact Assessments for ecological impacts, and appropriate mitigation (e.g., maintaining boundaries, planting vegetation to network with surrounding foraging habitat) and monitoring should be implemented to highlight potential negative effects.

Sampling design

We implemented a paired study design across 19 solar PV sites to assess whether bat species richness and activity were higher in fields and along boundary habitats that contained PV panels, compared with ‘empty’, matched control sites. This resulted in 19 sampling points for solar boundary habitat, 19 for solar open habitat, 19 for control boundary habitat, and 19 for control open habitat. All sites were located in south-west England, where the highest concentration of solar PV sites and greatest bat species richness in the UK coincide (Mathews et al. 2018; Department for Business Energy and Industrial Strategy 2021). Where private land was entered, permissions were granted by land owners and the relevant solar farm companies. No ethical approval was required for this study as we passively monitored bats through acoustic recordings.

The control sites were within the same land management boundary as the solar PV site, and matched as closely as possible in plot size, habitat type, land use and boundary habitats. There was no difference in the average size of solar PV and control fields (solar PV mean = 59.6 ha, SD = 32.0; control mean = 53.2 ha, SD = 28.4; paired t-test: t(18) = 1.3, P = 0.203) (see Appendix S3 in Supporting Information). All solar PV sites were on grassland that was either grazed or managed through mowing or were on cut arable crops. Field boundaries corresponded to hedgerows, treelines, woodland, or vegetated ditches and were exactly matched. The paired fields were a minimum of 500 m apart and not adjacent to each other to maximise the chances of obtaining independent data within comparable landscapes (Froidevaux, Louboutin & Jones 2017).

Bat echolocation call recording and species identification 

Fieldwork was completed between July and October 2019 and the same period in 2020. Bat activity was monitored for seven consecutive nights at each site (30 minutes before sunset to 30 minutes after sunrise), simultaneously across the four locations (open and boundary habitats within the field with solar PV panels and paired control). Recordings were made using SM3 bat detectors (Wildlife Acoustics, Inc., Maynard, MA, USA). All detector microphones (SMM-U2f (frequency response +/- 6dB 20-100 kHz see https://www.wildcare.co.uk/amfile/file/download/file/56/product/94208/), Wildlife Acoustics)) were elevated to 1.27 m using identical tripods. Detectors were set to auto trigger between 8–120 kHz, 1–88 dB and recorded for a maximum of 10 s (384 kHz, sampling rate). A detector was placed within the centre of the control and solar fields, and along the associated boundary habitats of the control and solar fields.  Detectors recording the open and boundary habitat within the solar and control field were a minimum of 50 m apart.

Sampling took place during optimal weather condition for bats to forage (i.e., no rain, low wind speed and temperature >10°C). The mean (+ SD) temperature at dusk over the recording period was 16.2 ± 3.1 °C (https://www.timeanddate.com/weather/).

Sound files were analysed using zero crossing software Kaleidoscope Pro (v. 5.4.1, Wildlife Acoustics, Inc.) with Bats Of Europe Classifiers (United Kingdom) (v. 5.4.0) selected.  All 10 s recordings were automatically scanned and the call sequences were identified and then manually checked to confirm the species (Barbastella barbastellus, Eptesicus serotinus, Pipistrellus nathusii, P. pipistrellus, P. pygmaeus, Rhinolophus ferrumequinum and R. hipposideros) or species group (Nyctalus spp., Myotis spp., Plecotus spp.). The grouping of Myotis spp. is widely used due to the difficulty of separating the echolocation calls of the different species (Russ 2012).  Similarly Nyctalus noctula and N. leisleri, as well as Plecotus auritus and P. austriacus could not always be separated so these calls were grouped as Nyctalus spp. and Plecotus spp., respectively. All files which Kaleidoscope Pro could not automatically assign a species to were identified manually (Russ 2012).

All files which Kaleidoscope Pro classified as ‘Noise’ (195,375 files) were run through the full spectrum software Bat Classify (https://bitbucket.org/chrisscott/batclassify/src/master/). This was to ensure no call sequences within the large number of ‘Noise’ labelled files were missed.  Following analysis, 0.5% of labelled files were randomly checked to ensure that the automated identification was reliable (Rowse, Harris & Jones 2018).  For all call sequences with >80% certainty in the automated identification, the classification to species was accepted, except for Myotis species where >50% certainty was accepted to ensure call sequences were not excluded from the dataset.  These parameters were designed to apply a precautionary approach based on the Precision-Recall metric of the Bat Classify software (https://bitbucket.org/chrisscott/batclassify/src/master/).

Statistical analysis

All analyses were performed in R statistical software v.4.1.1 (R Core Team 2021) and all statistical tests were considered significant at p < 0.05. We performed generalized linear mixed-effect models (GLMMs) with “glmmTMB” package (Brooks et al. 2017) to assess the effects of PV panels on species-specific bat activity and bat species richness in agricultural landscapes. Echolocation call sequence data were pooled by site and location over the seven-night period, and we defined bat activity as total number of bat call sequences for species or species groups. Due to their low occurrences (<40% of the sites), R. hipposideros and P. nathusii were disregarded for the analysis on species-specific activity. GLMMs on bat species were fitted with a Gaussian distribution (since diagnostic plots were largely unsatisfactory with Poisson or negative binomial distributions) and we applied a squared transformation to the response variable to meet the normality assumption. GLMMs on bat activity were fitted with a negative binomial distribution and we employed zero-inflated models when necessary. We included the presence/absence of PV panels (treatment: solar vs. control site) in interaction with the habitat type surveyed (boundary vs. open field) as explanatory variables while pair IDs were considered as random factors to account for the paired-sampling design.

We also included in the models, landscape variables that could potentially affect bat activity in agricultural landscapes, including the proportion of urban, arable land, grassland and broadleaf woodland, and the Euclidean distance to the nearest watercourse. For area-based landscape variables, we considered eight spatial scales (buffers ranging from 250 m to 10 km radii) to qualify local habitats around each site, and to encompass the wide foraging ranges of the bat species studied (Laforge et al. 2021). Landscape variables were derived in QGIS using the Land Cover Map (Environmental Information Data Centre 2019) (20 m resolution) supplied by the Centre of Ecology and Hydrology. When comparing solar PV sites with control sites no statistical differences occurred in the distance to the nearest water source, or in cover of arable land, grassland, broadleaved woodland or urban areas at the different spatial scales with the exception of cover of grassland and arable habitat surrounding the control and solar PV site at the 250m and 500m scales (Appendix S2). To reduce the number of landscape variables and avoid model overparameterisation, we assessed independently the relationships between the response variables and each landscape variable using GLMMs with the same model structure as described above (i.e., including the same random effect and the interaction and using the same distribution family). We compared the second-order Akaike information criterion (AICc) of each model with the model that included the interaction only and retained in the final models only landscape variables at their best scale of effect (Martin 2018) that led to lower AICc (i.e. ΔAICc ≥ 2) (Burnham & Anderson 2002). For highly correlated variables (Spearman coefficient correlation |r| > 0.7), we retained the one leading to lower AICc. From the final full models, we finally ran post hoc pairwise comparisons corrected for multiple testing using the Tukey method in the “lsmeans” package (Lenth 2014). Residual diagnostics were checked with the “DHARMa” package (Hartig 2022). We also checked for multicollinearity, overdispersion, influential outlier, and zero inflation with the “performance” package (Lüdecke et al. 2023).

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Notes

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