Mismatched spatial scales can limit the utility of citizen science data for estimating wildlife‐habitat relationships

Abstract

While most carnivore populations are declining worldwide, some species are successfully living in human-modified landscapes. For example, coyotes (Canis latrans) have expanded their range across North America and into many urban areas making it important to understand factors influencing broad-scale patterns of occurrence. We used citizen science data in the form of coyote observations by archery deer hunters from throughout the state of Illinois to evaluate factors affecting coyote detection and occupancy. Our statewide participant-level occupancy estimate (0.63) was 58% greater than our naïve occupancy estimate (0.40) while detection probability was <0.25, highlighting the importance of using modeling frameworks that account for imperfect detection when modeling occupancy of cryptic species with low detection rates. Time period (AM/PM) had the largest effect on detection of coyotes, with detections greater in the AM. The number of hours hunted (analogous to effort) also impacted coyote detection, with more hours hunted increasing coyote detections. In contrast, none of the landscape covariates examined had strong effects on coyote occupancy. While coyote ubiquity and generalist habitat use may at least partially explain our results, we suspect it also is because the landscape covariates were measured at the county level, as more precise participant location data were unavailable, whereas participants effectively surveyed a much smaller area. Since scale affects the strength and direction of species-habitat relationships, this scale mismatch is likely an important limitation when using many sources of citizen scientist observations to infer species-habitat relationships for widespread generalist species when precise participant location data are unavailable.

Using citizen science to understand and map habitat suitability for a synurbic mammal in an urban landscape: the hedgehog Erinaceus europaeus

“…The highest traffic volumes were located towards the edge of the study area, coinciding with sites of high suitability due to other characteristics, such as high garden cover and lower urban pressure. As traffic volume data were only available at the local authority level, they may be too broad to detect hedgehogs’ habitat relationships, a problem that has previously been highlighted (Bauder et al 2021). More detailed research on fine‐scale spatial relationships between hedgehogs and traffic within otherwise suitable suburban habitat is therefore important.…”

Improving sightings-derived residency estimation for whale shark aggregations: A novel metric applied to a global data set

“…It is important to understand the limitations of citizen science and ensure any assumptions are met before employing this method (e.g. Bauder et al, 2021;Jäckel et al, 2021).…”

An evaluation of isolation by distance and isolation by resistance on genetic structure of the Persian squirrel (Sciurus anomalus) in the Zagros forests of Iran

“…Bayesian analysis of clustering using STRUCTURE (Pritchard et al, 2000) identified three as the most likely value of K. However, the sampled individuals did not group into distinct subpopulations; instead, all individuals were inferred to share some degree of admixture from all three genetic clusters. This suggests that our samples Therefore, our analyses suggest spatial separation may be the main factor that limits gene flow and dispersal of the Persian squirrel in this area (Bauder, Cervantes, et al, 2021). In many mammal species, IBR models that account for the heterogeneity in species' distributions and migration rates better explain the genetic structure of populations in comparison to IBD models (Coulon et al, 2006 Nonetheless, there are several other mammals, including some with high dispersal ability like Goitered gazelle, Gazella subgutturosa, or white-tailed deer, Odocoileus virginianus, for which distributions are better explained by geographical distance than landscape matrix (Bauder, Cervantes, et al, 2021;Khosravi et al, 2018;Mullins et al, 2014).…”