Variance propagation for density surface models

Mark V. Bravington, David Lawrence Miller*, Sharon Louise Hedley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
3 Downloads (Pure)

Abstract

Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilities—perhaps depending on covariates—are estimated based on details of individual encounters; next, local densities are estimated using a GAM, by fitting local encounter rates to location and/or spatially varying covariates while allowing for the estimated detectabilities. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as an extra random effect in the GAM stage. In effect, we refit an approximation to the detection function model at the same time as fitting the spatial model. This allows straightforward computation of the overall variance via exactly the same software already needed to fit the GAM. A further extension allows for spatial variation in group size, which can be an important covariate for detectability as well as directly affecting abundance. We illustrate these models using point transect survey data of Island Scrub-Jays on Santa Cruz Island, CA, and harbour porpoise from the SCANS-II line transect survey of European waters. Supplementary materials accompanying this paper appear on-line.
Original languageEnglish
Number of pages18
JournalJournal of Agricultural, Biological and Environmental Statistics
VolumeFirst Online
Early online date23 Feb 2021
DOIs
Publication statusE-pub ahead of print - 23 Feb 2021

Keywords

  • Abundance estimation
  • Distance sampling
  • Generalized additive models
  • Line transect sampling
  • Point transect sampling
  • Spatial modelling

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