Marsh interspersion and muskrat (Ondatra zibethicus) habitat use

Muskrat (Ondatra zibethicus) populations have been declining in North America for decades. The precise cause of these widespread declines has not yet been identified. Over a similar timeframe, wetlands across large regions of North America have been experiencing an invasion of hybrid cattail *Typha *x glauca. This invasion is associated with many negative consequences for wetlands, including a reduction in biodiversity, open water habitat, and interspersion of water and vegetation. Muskrats are strongly tied to wetlands, especially where there is a high degree of interspersion of water and emergent vegetation. Therefore, a widespread reduction in interspersion caused by *T. *x *glauca *invasions may be contributing to widespread muskrat population declines. We sought to understand the impact of reduced marsh interspersion on fine-scale muskrat habitat use which will shed more light on broad-scale population trends. We measured intensity of habitat use by muskrats in a large, Typha-domin..., We used camera traps to measure intensity of use by muskrats along a gradient of marsh interspersion. We used aerial imagery and land cover classifications in ArcGIS Pro to measure interspersion (\"interspersion\"). We used Pearson correlation to determine correlations between intensity of use and interspersion along with other predictor variables, as well as zero-inflated negative binomial models to model intensity of use using these predictor variables. Aside from interspersion, other predictor variables used in our statistical analyses included surrounding water area (\"water area\"), whether the surrounding habitat was channelized (\"channelization\"), season (\"sampling period\"),  an index of camera viewshed obstruction (\"viewshed obstruction\"), and an index of the extent of surface water visible within the viewshed (\"sample area\"). We used R to analyze the data using the following packages: dplyr, Hmisc, pscl, and PerformanceAnalytics.
, , # Data

[https://doi.org/10.5061/dryad.866t1g1wp](https://doi.org/10.5061/dryad.866t1g1wp)

\"Melvin_Bowman_Data\" dataset relates to main predictor variable (interspersion), response variable (intensity of use), and other predictor variables.

## Description of the data and file structure

**Melvin_Bowman_Data**

**cameraID**: Unique identifier for each camera.
**sampling_period**: A sequential number indicating the time period for which the camera was active.
            1 = June 2021
            2 = July 2021
            3 = August 2021
            4 = September 2021

**latitude/longitude:** Location of camera for respective sampling period.
**interspersion**: Length of vegetation-water edge in meters within sample cell surrounding camera.
**water_area**: Areal coverage of water in square meters within sample cell.
**channelization**: Main water feature within sample cell is channelized (1) or non-channelized (0).*
**viewshed_obstruction**: an index of low (0) to high (4) obstruction o..., , **Changes after May 19, 2025:** We have simply updated the names of the variables for clarity, as they are shown in the new READ-ME, by recommendation of one of our manuscript reviewers.