Data from: Methods to account for incomplete viewsheds in distance sampling

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.