Mapping Urban Coyote Ecology in Los Angeles: Insights from Citizen Science and Human Mobility Data

Abstract

Understanding how urban coyotes (Canis latrans) respond to human activities is a critical challenge in urban ecology, especially in an era of rapid urbanization. As coyotes adapt to urban environments, the list of citizen reports on coyote occurrence together with their locations also becomes more frequent and diverse, offering new opportunities to study their behaviors on a larger scale for human-coyote interaction. This study investigates the spatial and temporal distributions of coyotes in Los Angeles County by integrating citizen science data from iNaturalist together with environmental, socioeconomic and human mobility datasets. We develop a species distribution model using Random Forest and Geographically Weighted Regression(GWR) to identify key ecological and anthropogenic drivers. Furthermore, we employ structural equation modeling (SEM) to explore how time-varying human visitor flows, particularly during the Covid-19 pandemic, influence urban coyote visibility across neighborhoods.

Our findings reveal that spatial patterns of coyote occurrence are strongly influenced by environmental and socioeconomic variables. The Random Forest and GWR models highlight that socioeconomic conditions such as poverty rate and population density are key predictors of the use of coyote habitat, with lower income and high density areas showing higher incidence. Furthermore, the spatial heterogeneity in the correlation between seasonal environmental factors and socioeconomic variables reflects the adaptive habitat selection strategies of coyotes at different times of the year. SEM further reveals that coyote observations increase significantly with human inflow in real time during and after the pandemic, while declining in response to sustained human absence. This suggests that coyote behavior is more shaped by short-term human mobility patterns than by long-term redistribution. Importantly, we demonstrate that citizen science data, while subject to reporting biases, correlate strongly with ecological suitability and human mobility patterns, offering a unique perspective on urban wildlife dynamics using spatial data science approaches.