Quantifying microhabitat selection of snowshoe hares using forest metrics from UAS-based LiDAR

This dataset and code was used to evaluate the influence of forest structure (mapped using high-resolution light detection and ranging [LiDAR] data, collected on unoccupied aerial systems [UAS]) on snowshoe hare (Lepus americanus) microhabitat use within the temperate-boreal forests of northern New England. We mapped 14 forested stands (each 20 ha) using UAS-LiDAR (>300 returns/m2) and conducted fecal pellet surveys to estimate snowshoe hare microhabitat selection during the leaf-off and leaf-on seasons of 2022. We used the LiDAR point cloud data to generate traditional forest metrics (canopy closure, sapling prevalence, and shrub prevalence) that are often used in snowshoe hare habitat studies and three-dimensional metrics that we hypothesized to represent predator exposure (viewshed and lacunarity) and food availability (lacunarity). The data and code herein was used to evaluate which of these most influenced snowshoe hare microhabitat use during the leaf-off and leaf-on seasons.
, See materials and methods in 10.1002/wlb3.01529.
, # Quantifying microhabitat selection of snowshoe hares using forest metrics from UAS-based LiDAR

Dataset DOI: [10.5061/dryad.zcrjdfns6](https://doi.org/10.5061/dryad.zcrjdfns6)

## Description of the data and file structure

For a complete description of the experimental efforts for which the data was collected, see: 10.1002/wlb3.01529.

### Files and variables

The code `WLB-2025-01529.R2.Rmd` does not contain external data (see below).

The empirical snowshoe hare data `pellets.csv` file is a data.frame that contains 1,389 observations of the pellet count response data and covariates to model snowshoe hare (*Lepus americanus*) microhabitat use. Below is a description of these data:

*Response, spatial (study, x_scal, y_scal), temporal (season), offset, and random variables**

* count: Counts of snowshoe hare pellets from 1 m^2^ circular plots
* study: fixed effect factor variable with two levels (\"NB\", \"WMNF\") to account for density differences between study regions
* x_scal: scaled ...,