2025 Publications
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Wilson JP, Amano T & Fuller RA (2025) Inconsistent scientific methods hamper the management of drone use near birds. Journal of Wildlife Management, 89, e22692.
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Drone use has increased in the last decade, prompting efforts to manage their operation near wildlife. These efforts are hampered by variations in methods among studies, leading to evidence that is fragmented, inconsistent, and incomplete. To address this, we extracted evidence from 194 studies involving drones and birds, covering 314 species, including 61 studies on drone-induced bird disturbance that encompassed 206 species. We summarized the results of these 61 studies, identified evidence gaps, and developed a standard method for characterizing drone-induced bird disturbance. Drone-induced bird disturbance varied with species, breeding status, and distance of the drone from the birds. Key evidence gaps include a lack of studies on small terrestrial species likely to occur in urban environments where drones are often used and limited research in Africa, Asia, and South America. Methods were inconsistent among studies, with only 20% of studies reporting the often-recommended response variable of flight initiation distance (FID). We conclude that 1) managers should use evidence, including our database, to inform regulations, such as buffer distances, that account for species, breeding status, and drone type, 2) researchers should target contexts where interactions between drones and birds are likely but few studies exist, such as urban environments, and 3) researchers investigating drone-induced bird disturbance should conduct horizontal and vertical approaches directly towards birds, and record the FID along with predictors describing the environment, target, and stimulus.
Submitted to Remote Sensing in Ecology and Conservation on 11 Feb 2024; desk rejected on 12 Feb; submitted to Journal of Wildlife Management 13 Feb 2024; R&R decision on 6 Mar 2024; resubmitted 6 Apr 2024; further minor revision requested 23 May 2024; resubmitted on 9 Jul 2024; further minor revision requested 12 Aug 2024; resubmitted 5 Sep 2024; accepted 8 Oct 2024.
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Backstrom LJ, Callaghan CT, Worthington H, Fuller RA & Johnston A (2025) Estimating sampling biases in citizen science datasets. Ibis, 167, 73-87.
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The rise of citizen science (also called community science) has led to vast quantities of species observation data collected by members of the public. Citizen science data tend to be unevenly distributed across space and time, but the treatment of sampling bias varies between studies, and interactions between different biases are often overlooked. We present a method for conceptualizing and estimating spatial and temporal sampling biases, and interactions between them. We use this method to estimate sampling biases in an example ornithological citizen science dataset from eBird in Brisbane City, Australia. We then explore the effects of these sampling biases on subsequent model inference of population trends, using both a simulation study and an application of the same trend models to the Brisbane eBird dataset. We find varying levels of sampling bias in the Brisbane eBird dataset across temporal and spatial scales, and evidence for interactions between biases. Several of the sampling biases we identified differ from those described in the literature for other datasets, with protected areas being undersampled in the city, and only limited seasonal sampling bias. We demonstrate variable performance of trend models under different sampling bias scenarios, with more complex biases being associated with typically poorer trend estimates. Sampling biases are important to consider when analysing ecological datasets, and analysts can use this method to ensure that any biologically relevant sampling biases are detected and given due consideration during analysis. With appropriate model specification, the effects of sampling biases can be reduced to yield reliable information about biodiversity.
Submitted to Methods in Ecology and Evolution on 31 Jan 2023; rejected after review on 5 Apr 2023; submitted to Ibis on 12 Jun 2023; Reject & Resubmit decision on 20 Aug 2023; resubmitted 17 Feb 2024; further minor revision requested 31 Mar 2024; resubmitted 26 Apr 2024; accepted 15 Jun 2024.