DOI: 10.5061/dryad.866t1g1wp
title: Marsh interspersion and muskrat (Ondatra zibethicus) habitat use

description: 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-dominated marsh in south-central Ontario using camera traps,
 stratifying camera placement along a gradient of marsh interspersion. We
 found no correlation between interspersion and intensity of use,
 suggesting that factors other than interspersion may drive intensity of
 use. The best predictor of intensity of use in our study was the presence
 of channelized water features. Our study site, like most marshes in the
 region, was highly dominated by *T. *x glauca. Further research is needed
 to determine the impact of *T. *x *glauca *invasions on muskrats, as well
 as the cause of widespread muskrat declines.   
description: 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.
description: # 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 of viewshed due to vegetation. **sample_area**: an index of water surface extent sampled by the camera relative to a standard viewshed. The standard viewshed is from a Reconyx Hyperfire 2 where water makes up approximately half of the viewshed. **intensity_of_use:** total number of muskrat events during sample period. ## Sharing/Access information ## Code/Software We used R version 4.2.3 and RStudio v. 2022.12 to analyze the data along with packages dplyr, Hmisc, pscl, and PerformanceAnalytics.