Using community science data to investigate urban Coyotes (Canis latrans) in Atlanta, Georgia, USA

Abstract

Coyote activity was investigated in Atlanta, Georgia from 2015 to 2018 using publicly collected data. More than 500 reports were received annually (1,672 total) and analysis revealed bias toward areas of higher income and education. Human-coyote encounters, defined as an interaction at close range, were rare (196; 12% of observations), but 124 of those reports (63%) indicated the presence of pets nearby. Coyotes were less likely to be observed in areas of high human population density, farmland, and managed clearing, and more likely to be seen in riparian wetlands and areas of low-density development (e.g., parks, golf courses, large-lot homes). Coyote sightings are now relatively common in Atlanta and their presence is generally benign. However, negative coyote interactions do occur and pets should be kept under close supervision and coyote access to anthropogenic food resources prevented. This study demonstrates the effectiveness of using community science to understand urban coyotes.

A ten-year community reporting database reveals rising coyote boldness and associated human concern in Edmonton, Canada

“…First, reports were collected non-randomly and non-independently, which introduces several potential biases inherent to community reporting databases (Poessel et al 2013, Sullivan et al 2014). These biases include greater tendencies for repeat reporting by some residents with particularly strong views about coyotes, uneven advertising of the reporting database across neighborhoods or over years, potentially higher reporting from affluent neighborhoods with higher education levels (Wine et al 2015, Mowry et al 2020), and varying visibility of coyotes across seasons, time of day or land cover types due to differences in vegetative cover, human activity and daylight (Quinn 1995, Poessel et al 2013). We attempted to mitigate these effects by focusing on measures of coyote boldness and human concern, rather than spatiotemporal influences on the number or distribution of reports.…”

“…The coyote ( Canis latrans ) is a common example of an urban-adapted species that is also the largest carnivore that is common in cities across North America (Magle et al 2019; Schell et al 2020). Coyotes thrive in urban areas largely by avoiding interactions with humans (Mowry et al 2020, Drake et al 2021) while benefitting from reduced competition with other predators (Prugh et al 2009), less human persecution in urban compared to rural areas (Collins and Kays 2011), and abundant urban food resources such as rodents, garbage, compost and fruit trees (Fedriani et al 2001, Murray et al 2015a, Sugden et al 2021). Urban coyotes can potentially also improve human quality of life by regulating populations of rodents and smaller predators like feral cats (Crooks and Soule 1999; Gerht et al 2013), supporting a sense of connection with nature (Cox and Gaston 2018), and providing an aesthetic enjoyment that is often inherent in seeing wild animals (Soulsbury and White 2015).…”

“…Such databases allow researchers to gather information over many years and large geographic areas, while simultaneously engaging and educating members of the public (Weckel et al 2010, Frigerio et al 2018). Previous studies of this sort have collected voluntary reports of coyote activity using public surveys (Weckel et al 2010), city reporting databases (Lukasik and Alexander 2011, Poessel et al 2013), websites (Wine et al 2015, Mowry et al 2020), and mobile phone apps (Mueller et al 2019, Drake et al 2021). Past analyses of these datasets have shown that reporting varies across levels of urban development, land cover types, and coyote seasons (e.g., breeding, pup-rearing, dispersal; Weckel et al 2010, Poessel et al 2013), as well as with human demographic variables such as household income and education (Wine et al 2015, Mowry et al 2020).…”

“…Previous studies of this sort have collected voluntary reports of coyote activity using public surveys (Weckel et al 2010), city reporting databases (Lukasik and Alexander 2011, Poessel et al 2013), websites (Wine et al 2015, Mowry et al 2020), and mobile phone apps (Mueller et al 2019, Drake et al 2021). Past analyses of these datasets have shown that reporting varies across levels of urban development, land cover types, and coyote seasons (e.g., breeding, pup-rearing, dispersal; Weckel et al 2010, Poessel et al 2013), as well as with human demographic variables such as household income and education (Wine et al 2015, Mowry et al 2020). The studies that have assessed different levels of conflict from public reports, based on coyote behaviour, have shown that bold coyote behaviour is most prominent during the coyote pup rearing season (Lukasik and Alexander 2011, Drake et al 2021).…”

Predicting future distributions and dispersal pathways for precautionary management of human-raccoon dog conflicts in metropolitan landscapes

“…Citizen science has emerged as a powerful tool for addressing many of the challenges faced by conservationists (McKinley et al 2017). For instance, research on urban coyotes demonstrated the effectiveness of using citizen science to understand urban wildlife (Mowry et al 2021) through the collection of a large amount of data from citizen scientists, which can be used to implement adaptive management. Precautionary management has the potential to be implemented in an adaptive management approach to deal with the uncertainties that arise from environmental changes, improvements in sampling methods, or species adaptations (Richardson et al 2020).…”