DOI: 10.5061/dryad.bcc2fqzgb
title: Vegetation and vantage point influence visibility across diverse ecosystems: implications for animal ecology

description: Visual information can influence animal behavior and habitat use in
 diverse ways. Visibility is the property that relates 3D habitat structure
 to accessibility of visual information. Despite the importance of
 visibility in animal ecology, this property remains largely unstudied. Our
 objective was to assess how habitat structure from diverse environments
 and animal position within that structure can influence visibility. We
 gathered terrestrial lidar data (1 cm at 10 m) in four ecosystems (forest,
 shrub-steppe, prairie, and desert) to characterize viewsheds (i.e.,
 estimates of visibility based on spatially explicit sightlines) from
 multiple vantage points. Both ecosystem-specific structure and animal
 position influenced potential viewsheds. Generally, as height of the
 vantage point above the ground increased, viewshed extent also increased,
 but the relationships were not linear.  In low-structure
 ecosystems (prairie, shrub-steppe, and desert), variability in viewsheds
 decreased as vantage points increased to heights above the vegetation
 canopy. In the forest, however, variation in viewsheds was highest at
 intermediate heights, and markedly lower at the lowest and highest vantage
 points. These patterns are likely linked to the amount, heterogeneity, and
 distribution of vegetation structure occluding sightlines. Our work is the
 first to apply a new method that can be used to estimate viewshed
 properties relevant to animals (i.e., viewshed extent and variability). We
 demonstrate that these properties differ across terrestrial landscapes in
 complex ways that likely influence many facets of animal ecology and
 behavior.   
description: We gathered terrestrial lidar data in four ecosystems in the
 western USA. The sites were a forest (the University of Idaho Experimental
 Forest), a shrub-steppe (in the Lemhi Valley of Idaho), a prairie (a
 remnant of Palouse prairie at the Dave Skinner Ecological Preserve and
 Thorn Creek Native Seed Farm near Moscow, Idaho), and a desert (Gold Butte
 National Monument, Nevada). In each ecosystem, 12-m radius plots were
 established. In the forest, 10 plots were established that covered a wide
 range of stand types. In the shrub-steppe and desert (n = 6, n = 5
 respectively), plots were located in areas of known animal activity.
 Activity was determined by identification of tracks and scat. The prairie
 site was constrained by its small size; 6 plots were measured in areas
 that did not including encroaching vegetation. At each
 plot, we collected lidar data using a Leica BLK360 terrestrial laser
 scanner set to its standard point density (1 cm at 10 m) on a tripod 1.3 m
 above the ground. The scanner height was occasionally lowered to
 accommodate uneven terrain or vegetation. Highly reflective targets were
 deployed in the plots. We then gathered scans at 15-25 locations. The
 number of scans was determined by vegetation structure where greater
 complexity required more scans. Single large point
 clouds detailing each plot were created by stitching the individual scans
 together using ReCap Pro v6.0 and CloudCompare v2.11.3. First, targets
 were identified in ReCap Pro which created a rough alignment of the scans.
 The oriented scans were moved into CloudCompare and the iterative closest
 point algorithm tool was used for fine-scale alignment. Once aligned, the
 scans were merged in CloudCompare; the targets were removed using the
 segment tool. ReCap Pro was used to define the center ground point as
 0,0,0. The program randomly assigned the X,Y orientation and Z was
 elevation. The final point clouds ranged in size from 20-97 million
 points. In each plot, we measured 10-m viewsheds using
 the R package, <em>viewshed3d</em> at 5 X,Y locations and
 multiple Z locations. The first X,Y was located at the plot center (0,0).
 The other four were placed two meters from the center along the X,Y axes
 (0,2; 0, -2; 2,0; -2,0), which ensured the sightlines were not measured
 beyond the point cloud extent. Z positions at each X,Y were 0.25, 0.75,
 1.5, 5, and 10 m from the ground representing perspectives of wildlife in
 the ecosystems. In the forest, additional Z perspectives were placed every
 5 m (from 5-30m). The additional Z positions were not measured above 10 m
 in the other ecosystems because the viewsheds could not interact with the
 point clouds. Spherical viewsheds were estimated at these locations using
 viewshed3d which measures sightlines until they encounter an obstruction
 (i.e., lidar data point). The angular resolution of the sightlines was set
 to 0.6 degrees. Measurements are reported as a viewshed graph that
 displays the percent of unobstructed sightlines as a function of distance
 from the designated vantage point. We calculated the area under the curve
 and defined it as the viewshed coefficient for analysis. To estimate
 variability of the sightlines, we segmented the point cloud at each
 vantage point into 20 segments of 18<sup>o</sup> and estimated
 the viewshed coefficient of each segment. We calculated the standard
 deviation and mean of these 20 segments from which we calculated a
 coefficient of variation. Vertical structural heterogeneity was assessed
 by calculating roughness, the standard deviation of the canopy heights
 within 10 m of each X,Y position using the lidR package.
 Point cloud naming convention: XXX_#.laz. Letters indicate the
 site (UIEF = forest, SB = shrub-steppe, PP = prairie, GBNM = desert).
 Numbers are plot numbers. Forest plot numbers correspond to existing plot
 numbers established by other researchers in the University of Idaho
 Experimental Forest. Plot numbers in the other ecosystems assigned in
 order of lidar data collection.
description: The point cloud files are stored as .laz files which can be
 opened by the open source softwares Program R and CloudCompare. This file
 type can also be opened by proprietary softwares such as Cyclone. The
 viewshed measurement code was written for and executable in Program
 R.