Integrating human trail use in montane landscapes reveals larger zones of human influence for wary carnivores

Coexistence between humans and wildlife is necessary for many conservation goals but is difficult to achieve in landscapes with increasing human populations and species that are often wary of people and may also threaten human safety. In these contexts, coexistence may be enhanced by identifying geographic areas where animal movement is particularly important and changes to human use via trail design could support both wildlife conservation and human safety. 
We used camera trap data to monitor the spatial distribution of grizzly bears (Ursus arctos), gray wolves (Canis lupus), and humans within the central Canadian Rocky Mountains, where anthropogenic development and human activity have gradually encroached on limited wildlife habitat. We quantified spatial variation in human use, and then incorporated this output into models for the detection rates of bears and wolves. We interpolated metrics of human use throughout the study area using inverse distance weighted averages of human dete..., , , # Integrating human trail use in montane landscapes reveals larger zones of human influence for wary carnivores

[https://doi.org/10.5061/dryad.s7h44j1h8](https://doi.org/10.5061/dryad.s7h44j1h8)

## Description of the data and file structure

This Dryad repository contains all data necessary to replicate the analyses conducted by Thompson et al. (2024)* Journal of Applied Ecology*. These data were acquired through a decade-long study that involved deploying over 1,000 remote cameras that photographed wildlife and humans passing in front of them. Camera images were processed by countless volunteers and Parks staff. 

### Files and variables

#### File: spatial-zoi.zip

**Description:** This ZIP file contains the following files:

* `human_detections_SAMPLE.csv`: CSV file containing all detections of human recreationalists from a subset of cameras in our study area. Column headings:
* t: time of observation (in days)
* rec_type: type of recreation associated with each observation; no...