Urbanization facilitates intrapopulation dietary niche diversity in a generalist carnivore

The niche variation hypothesis predicts that members of generalist species are specialists, and that the degree of individual specialization correlates positively with resource diversity. Urban landscapes are highly heterogeneous in resource diversity; therefore, we predicted urbanization would be associated with narrower individual dietary niches and greater differentiation among individuals of generalist species. We used stable isotope analysis to compare diets of urban and nonurban coyote (Canis latrans) populations in San Francisco and Marin County, California, USA. Urban coyotes had dietary niches nearly three times narrower than nonurban coyotes and greater among-individual variation in isotope values. Within-individual differences explained 18% of total δ13C variation in urban coyotes compared to 58% in nonurban coyotes, and 34% versus 44% of δ15N variation, indicating stronger individual specialization in the urban population. Our findings suggest urbanization facilitates intrap..., , # Urbanization facilitates intrapopulation dietary niche diversity in a generalist carnivore

https://doi.org/10.5061/dryad.b5mkkwhq8

This repository contains all raw data, plots, and scripts for the data analysis implemented in Caspi *et al.* 2025 titled *Urbanization facilitates intrapopulation dietary niche diversity in a generalist carnivore* and published in *Ecology Letters*. The GitHub repository associated with this manuscript is available at [https://github.com/tcaspi/SF-Coyote-Stable-Isotopes/tree/main](https://github.com/tcaspi/SF-Coyote-Stable-Isotopes/tree/main).

Please find below a description of all raw and clean data sets and the scripts used to clean the raw data, run the models, and create the figures presented in the manuscript. These files can be accessed by downloading the `SF-Coyote-Stable-Isotopes-main.zip`. Model output is available for download on Dryad.

## Data Files

In the `Data` folder, you will find a number of files:

`20240102_data.csv`; `20240910_data...,