Data and code from: Impacts of urban heterogeneity in environmental and societal characteristics on coyote survival

Survival links individual-level responses to population, community, and ecosystem dynamics. Thus, understanding species’ survival responses to environmental change in urban areas is key for gaining insights into the ecology and management of wildlife in these rapidly expanding environments. Despite existing research on the broad-scale effects of urbanization on wildlife survival, little is known about how heterogeneity within urban areas impacts survival. We assessed the role of environmental and societal characteristics known to structure the distribution of resources and risks important for urban wildlife ecology on coyote (Canis latrans) survival in the Chicago metropolitan area. We conducted a time-varying Cox proportional hazards survival analysis using the telemetry data of collared animals. We found that human population density had a positive effect on survival rate, and that at high human population densities, median income had a negative effect on survival rate. We also found ..., , , # Data and code from: Impacts of urban heterogeneity in environmental and societal characteristics on coyote survival

[https://doi.org/10.5061/dryad.rfj6q57j4](https://doi.org/10.5061/dryad.rfj6q57j4)

Time-varying Cox proportional hazards model to evaluate the effect of environmental and societal characteristics on coyote survival in the Chicago metropolitan area.

## Description of the data and file structure

Files containing data collected at three spatial scales (250 m, 1 km, and 2km) - **model_df_250m_dryad.rdata**, **model_df_1km_dryad.rdata**, **model_df_2km_dryad.rdata**

Each data frame contains the following columns:

* id - animal ID
* sex - female (\"f\") or male (\"m\")
* track_duration - difference between mortality date or last location and date of first location (used to estimate survival time)
* status - postmortem recovery (1) or censored (0)
* tstart - estimated \"age\" at time of location based on date of first location
* tstop - estimated \"age\" at time of next loc...,