Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.04.16.718868

Assessing the impact of artificial night lighting regulations designed to protect astronomical observatories on seabirds and bats

Abstract

Artificial light at night is a rapidly increasing driver of global change, affecting both astronomical observations and biodiversity. Regulations such as the Canary Islands "Sky Law" were designed to protect astronomical observations by controlling light intensity and spectral composition, yet their ecological effectiveness remains largely untested. Here, we experimentally assessed whether lighting conditions permitted under this law influence the behaviour of two sensitive nocturnal taxa: seabirds and bats. Field experiments were conducted in Tenerife, Canary Islands, using controlled lighting treatments that varied in intensity (low vs. high) and spectrum (PC amber ~1800K vs. white ~2700K), including a no-light control. We monitored the behaviour of breeding adult Corys shearwaters (Calonectris borealis) using GPS tracking and passive acoustic recording, and quantified bat activity through ultrasonic detectors. Behavioural responses included flight characteristics, colony attendance, vocal activity in shearwaters, and species-specific movement and feeding activity in bats. Generalised linear mixed models were used to evaluate treatment effects while accounting for environmental covariates. Across 211 shearwater flights and extensive acoustic datasets, we found no consistent or significant effects of light treatments on seabird flight behaviour, vocal activity, or bat movement and feeding activity. Instead, environmental variables such as moonlight, seasonality, and interannual variation were stronger predictors of behavioural responses. These results suggest that lighting conditions currently permitted under the Sky Law may have limited ecological impact on the studied taxa under the conditions tested. Further research in less disturbed environments and with broader spectral contrasts is needed to better assess the ecological implications of astronomically motivated lighting regulations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

de Tena, C., Rodriguez, B., Garcia, D., de la Paz, J. F., Rodriguez, A.. 2026-04-18. Assessing the impact of artificial night lighting regulations designed to protect astronomical observatories on seabirds and bats. https://doi.org/10.64898/2026.04.16.718868

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Floristic composition, phenology, and conservation value of four peat bogs in Bucovina, with the presence of Betula nana

This paper presents a comparative analysis of the floristic composition and site characteristics of four peat bogs in Bucovina, Romania: Poiana Stampei, Romanesti, Saru Dornei, and Gaina-Lucina. The research was based on phytosociological releves on 25 msq plots and direct field phenological observations, on six field visits from May to August 2026. Vegetation was characterised using the Braun Blanquet method, and floristic similarity between sites was assessed with the Sorensen and Bray Curtis indices. All four plots shared a common core of taxa characteristic of peatland vegetation: Sphagnum spp., Carex rostrata, Drosera rotundifolia, Eriophorum vaginatum, and Vaccinium species. Species richness was 13 taxa at Poiana Stampei, Romanesti, and Saru Dornei, and 12 at Gaina-Lucina. Romanesti and Saru Dornei showed the highest floristic similarity (descriptive values, not statistically tested, given a single releve per site), while Gaina-Lucina differed most markedly, not through species richness, which was similar across sites, but through species identity and through the presence of Betula nana, a glacial relict absent from the other sites. The results provide a descriptive basis for future research on the floristic composition and conservation of these habitats.

ecology↗

Long-Term Surveillance Reveals Establishment of Aedes albopictus in Eastern Nebraska, USA

Aedes albopictus (Skuse), the Asian tiger mosquito, is a highly competent arboviral vector whose range has expanded substantially across the United States over the past four decades. Despite predictive models placing Nebraska within the species' climatically suitable range, its establishment status in the state has remained poorly characterized. Here, we report results from a nine-year mosquito surveillance program (2017-2025) conducted across 44 Nebraska counties in collaboration with the Nebraska Department of Health and Human Services. Ae. albopictus was detected in five counties, with sustained, annually increasing populations documented in Richardson, Douglas, and Lancaster counties. Richardson County recorded continuous detections during 2017-2025, with proportional representation rising to 60.50% of collected mosquitoes by 2025. In Douglas and Lancaster counties, temporal advancement of first seasonal detection in 2024 and 2025 provide evidence consistent with successful overwintering rather than annual reintroduction. A cumulative degree-day model predicted adult emergence in mid-May across all county-year combinations, consistently preceding trap deployment by two to seven weeks and revealing a systematic early-season surveillance gap. Generalized linear mixed-effects models indicated that trap-level detection persistence, rather than urban location, was the primary predictor of yearly Ae. albopictus positivity, suggesting that current invasion dynamics are driven by focal source populations. These findings provide strong evidence for the establishment of Ae. albopictus in eastern Nebraska and highlight the need for earlier seasonal surveillance and standardized criteria to define establishment in northward-expanding vector populations.

ecology↗

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗