Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population. Issue 2 (5th October 2020)

• Record Type: Journal Article
• Authors: Ditmer, Mark A.; Iannarilli, Fabiola; Tri, Andrew N.; Garshelis, David L.; Carter, Neil H.
• Editors: Prugh, Laura
• Is Part Of: Journal of animal ecology. Volume 90:Issue 2(2021)
• Page Start: 330
• Page End: 342
• Publication Date: 2020-10-05
• Subjects: bears -- human–wildlife interactions -- occupancy model -- range expansion -- spatial bias -- species monitoring; Animal ecology -- Periodicals; 591.7
• DOI: 10.1111/1365-2656.13338
• Languages: English
• ISSNs: 0021-8790

Abstract

Abstract: The integration of citizen scientists into ecological research is transforming how, where, and when data are collected, and expanding the potential scales of ecological studies. Citizen‐science projects can provide numerous benefits for participants while educating and connecting professionals with lay audiences, potentially increasing the acceptance of conservation and management actions. However, for all the benefits, collection of citizen‐science data is often biased towards areas that are easily accessible (e.g. developments and roadways), and thus data are usually affected by issues typical of opportunistic surveys (e.g. uneven sampling effort). These areas are usually illuminated by artificial light at night (ALAN), a dynamic sensory stimulus that alters the perceptual world for both humans and wildlife. Our goal was to test whether satellite‐based measures of ALAN could improve our understanding of the detection process of citizen‐scientist‐reported sightings of a large mammal. We collected observations of American black bears Ursus americanus ( n = 1, 315) outside their primary range in Minnesota, USA, as part of a study to gauge population expansion. Participants from the public provided sighting locations of bears on a website. We used an occupancy modelling framework to determine how well ALAN accounted for observer metrics compared to other commonly used metrics (e.g. housing density). Citizen scientists reported 17% of bear sightings were under artificially lit conditions and monthly ALAN estimates did the best job accounting for spatial bias in detection of all observations, based on AIC values and effect sizes ( β ^ = 0.81, 0.71–0.90 95% CI). Bear detection increased with elevated illuminance; relative abundance was positively associated with natural cover, proximity to primary bear range and lower road density. Although the highest counts of bear sightings occurred in the highly illuminated suburbs of the Minneapolis‐St. Paul metropolitan region, we estimated substantially higher bear abundance in another region with plentiful natural cover and low ALAN (up to ~375% increased predicted relative abundance) where observations were sparse. We demonstrate the importance of considering ALAN radiance when analysing citizen‐scientist‐collected data, and we highlight the ways that ALAN data provide a dynamic snapshot of human activity. Abstract : Remotely‐sensed artificial nightlight improves inference in ecological studies using wildlife sightings collected by citizen scientists through better accounting of sampling bias.