The use of small Unmanned Aircraft Systems (UAS; also known as “drones”) for professional and personal-leisure use is increasing enormously. UAS operate at low altitudes (<500 m) and in any terrain, thus they are susceptible to interact with local fauna, generating a new type of anthropogenic disturbance that has not been systematically evaluated. To address this gap, we performed a review of the existent literature about animals’ responses to UAS flights and conducted a pooled analysis of the data to determine the probability and intensity of the disturbance, and to identify the factors influencing animals’ reactions towards the small aircraft. We found that wildlife reactions depended on both the UAS attributes (flight pattern, engine type and size of aircraft) and the characteristics of animals themselves (type of animal, life-history stage and level of aggregation). Target-oriented flight patterns, larger UAS sizes, and fuel-powered (noisier) engines evoked the strongest reactions in wildlife. Animals during the non-breeding period and in large groups were more likely to show behavioral reactions to UAS, and birds are more prone to react than other taxa. We discuss the implications of these results in the context of wildlife disturbance and suggest guidelines for conservationists, users and manufacturers to minimize the impact of UAS. In addition, we propose that the legal framework needs to be adapted so that appropriate actions can be undertaken when wildlife is negatively affected by these emergent practices.

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Summary1. Many animals and plant species have advanced spring phenology in response to climate warming. The majority of avian phenological studies are based on arrival dates. Consequently, knowledge on bird phenology is mainly based on migratory species. In addition, arrival dates of migratory birds may be substantially affected by en route climate conditions, thus failing to provide good indicators for spring phenology on the breeding grounds. Correlating arrival dates with other phenological data or with environmental covariates may be meaningless in these cases. 2. We propose the date of highest singing activity, quantified by detection probability, as a powerful proxy for breeding phenology that is applicable to migratory and sedentary bird species alike. In contrast to arrival dates, breeding phenology is mainly (non-migrants) or at least partially (migrants) influenced by conditions experienced within the breeding area. 3. We developed a new method for flexible estimation of peak detectability date in spring by combining multiseason site occupancy with semi-parametric regression modelling (thin-plate splines). We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland. 4. We found substantial differences among species in the date of spring peak detectability: late February to midApril in sedentary and short-distance migratory species and mid-April to late May in long-distance migrants. Among 10 species with data for >9 years, five showed a trend in detectability peaks towards an earlier spring phenology by nine to 17 days within 10 years. The mean shift over all species was c. 3Á5 days per 10 years. 5. Our approach is widely applicable, especially for temporally and spatially large-scale data from monitoring or citizen-science programmes. Besides using the detectability peak as measure of phenology, the estimated seasonal pattern in detectability can help designing monitoring programmes for improved efficiency. Our approach may be applied to any species with pronounced acoustic displays or other behavioural traits strongly influencing detectability during the breeding period. We believe that it can contribute substantially to unravelling how species and communities respond to environmental change.

Many studies in ecology and management aim at quantifying absolute abundance based on counts at a set of surveyed sites. As time for data collection is typically limited, methods for reliable estimation of occupancy or abundance from low‐cost data are desirable. Time‐to‐detection (TTD) models have shown promise for the estimation of occupancy. However, they remain heavily underutilized, and restricted to inference about occupancy, rather than abundance. We developed a binomial N‐mixture model for species‐level TTD protocols that allows estimation of abundance with multiple‐ or single‐visit data. An extension of the multi‐visit version allows estimating availability per visit, given temporary emigration is random. We provide JAGS code and a new function (nmixTTD) in the R package unmarked for fitting a variety of such models. Simulations showed accurate parameter estimation from single‐visit species‐level TTD data if individual detection probability is high (≥~0.7) and the number of visited sites is in the hundreds (≥~300). Additional visits improved the accuracy of estimates considerably. A comparison with the Royle‐Nichols‐ and the classic binomial N‐mixture‐model revealed that the performance of our model is between these two, but requires data that are less expensive and less error‐prone than count data required for binomial N‐mixture‐models. In a case study, we found similar results when analysing data with the Royle‐Nichols‐, the binomial N‐mixture‐model or the multi‐visit version of our TTD model. Analysing single‐visit data with our model yielded lower abundance and higher detectability estimates. Presumably these differences are due to temporary emigration, as the single‐visit method estimates the abundance of individuals available at one sampling occasion, whereas the multi‐visit methods refer to the superpopulation, that is, the number of individuals present over the study period. Our new TTD‐N‐mixture model shows promise because it enables estimation of abundance, corrected for imperfect detection, for single‐ and multiple‐visit data, based on data that are less expensive and that will be available in large quantities in the near future thanks to technical advances like autonomous recording units. The effects of unmodelled heterogeneity in detection rate and imperfect availability require further study.

Article impact statement: Protected areas are needed to facilitate waterbird distribution change in response to climate warming in the Western Palearctic.