The wildlife nextdoor: Socioeconomics and race predict social media carnivore reports

Social media and other internet-based, community generated datasets are emerging as valuable tools in advancing our understanding of biodiversity distributions across urban environments. However, it is unclear how best to harness these data for managing and mitigating human-wildlife conflicts in an urbanizing world. In this study, we analyzed 2584 posts and comments on carnivore sightings, human-carnivore interactions, and attitudes towards carnivores via the neighborhood-based social media platform Nextdoor, focusing on 52 peri-urban neighborhoods near the Angeles National Forest in California. We focused on the two most frequently discussed species: coyote (Canis latrans) and American black bear (Ursus americanus). We analyzed social-ecological covariates as potential predictors of carnivore reports, and also compared sightings of these species to data collected on the biodiversity logging application, iNaturalist. We found that whiter, wealthier, less densely populated neighborhoods closer to the national forest tended to report more black bear sightings and conflict, while coyote conflict and sightings did not show a clear relationship with metrics of racial makeup or urban intensity. However, whiter, wealthier neighborhoods had higher percentages of the population registered to Nextdoor, indicating a possible racial bias in participation. Comments expressing positive attitudes towards black bears were almost five times more common than positive attitudes towards coyotes. Finally, the number of Nextdoor reports for both species were 11 times more numerous than observations on iNaturalist within the same window of time and locations. We conclude that Nextdoor can be a viable data platform for predicting human-wildlife interactions. However, potential utility for coexistence will be nullified if researchers and managers do not fully account for the socioeconomic and racial biases influencing who participates in the reporting process. Building a more inclusive and accessible platform could therefore be beneficial for equity in wildlife data reporting and for engaging a diverse public in urban nature.