DOI: 10.5061/dryad.pvmcvdnwx
title: Data from: Methods to account for incomplete viewsheds in distance sampling

description: Conventional distance sampling is a logistically feasible method for
 estimating the densities of unmarked animals. The probability density
 function (PDF) of the sampling area specifies the expected proportion of
 the population that occurs at each distance from the observer and is a
 fundamental component of distance sampling models. Current approaches set
 this PDF either to equal probability at each distance, for line transects,
 or an increasing probability for point transects (because sampling area
 increases with radial distance from a point). Geographic Information
 Systems allow measurements of the area viewable from a given location
 (i.e., the viewshed), the structure of which may not reflect theoretical
 PDFs for either line (rectangular) or point (circular) transects. We
 simulated three datasets to test how variation in the viewshed structure
 affects estimates of detection probability, abundance, and density. We
 then implemented a novel application of Bayesian distance sampling models
 to test the magnitude of parameter bias recovered by accounting for
 incomplete viewsheds. Lastly, we compared parameter estimates from
 Bayesian hierarchical models that used either traditional or custom PDFs
 to analyze a dataset of 95 county-level spotlight surveys of white-tailed
 deer (Odocoileus virginianus) in Iowa, USA. For empirical data, viewable
 sampling area decreased with distance at an average rate of 3% every 100 m
 (range from 1–7% among counties). Our model correction decreased
 county-level density estimates by an average of 18% (range from 13–27%
 among counties), which depended on how sharply visibility
 declined. We suggest incomplete viewsheds be handled by
 considering the expected distribution of animals inside and outside of the
 viewshed. More generally, we show that customizing a PDF to more
 accurately reflect the study system improves density estimates and offers
 flexibility when the distribution of animals from the observer deviates
 from traditional assumptions.
description: Deer were sampled with nocturnal spotlight surveys along gravel
 roads in Iowa, USA.
description: # data for: Methods to account for incomplete viewsheds in distance
 sampling
 [https://doi.org/10.5061/dryad.pvmcvdnwx](https://doi.org/10.5061/dryad.pvmcvdnwx) ## Description of the data and file structure Deer were sampled with nocturnal spotlight surveys conducted along gravel roads in Iowa, USA. ### Files and variables #### File: df6.csv **Description:** Dataset with each row representing a county, which is the level of site in this study. ##### Variables * row_id: Unique identifier for rows. * county: County that the transect was conducted in. This is the level of site. * dummy_county: Dummy variable for site id. * mean_group_size: Mean number of deer per group/detection. * total_area: Number of 30x30 m pixels within the viewshed. * box_area: Number of 30x30 m pixels within the rectangular sample area. #### File: df5.csv **Description:** Dataset with each row representing a detected group of deer. ##### Variables * row_id: Unique identifier for rows. * dist_to_road: Distance between the detected group of deer and the nearest road. * deer_count: Number of deer per detection. * county: Name of the county of the sample. This is the site. * binary_viewshed: Specifies whether the deer was located within the estimated viewshed. * percent_area_0to80: Percent of the viewshed within the first distance bin. * percent_area_80to160: Percent of the viewshed within the second distance bin. * percent_area_160to240: Percent of the viewshed within the third distance bin. * percent_area_240to320: Percent of the viewshed within the fourth distance bin. * percent_area_320to400: Percent of the viewshed within the fifth distance bin. * total_area: Number of 30x30 m pixels within the viewshed. * box_area: Number of 30x30 m pixels within the rectangular study area. * dummy_county: Dummy variable for site. ## Code/software We provide two R scripts to analyze this data: one for the simulation study and one for the empirical study.