A comparison of adaptive sampling designs and binary spatial models : a simulation study using a census of Bromus inermis

Abstract

e Commonly in environmental and ecological studies, species distribution data are recorded as presence or absence throughout a spatial domain of interest. Field based studies typically collect observations by sampling a subset of the spatial domain. We consider the effects of six different adaptive and two non-adaptive sampling designs and choice of three binary models on both predictions to unsampled locations and parameter estimation of the regression coefficients (species-environment relationships). Our simulation study is unique compared to others to date in that we virtually sample a true known spatial distribution of a nonindigenous plant species, Bromus inermis. The census of B. inermis provides a good example of a species distribution that is both sparsely (1.9 % prevalence) and patchily distributed. We find that modeling the spatial correlation using a random effect with an intrinsic Gaussian conditionally autoregressive prior distribution was equivalent or superior to Bayesian autologistic regression in terms of predicting to un-sampled areas when strip adaptive cluster sampling was used to survey B. inermis. However, inferences about the relationships between B. inermis presence and environmental predictors differed between the two spatial binary models. The strip adaptive cluster designs we investigate provided a significant advantage in terms of Markov chain Monte Carlo chain convergence when trying to model a sparsely distributed species across a large area. In general, there was little difference in the choice of neighborhood, although the adaptive king was preferred when transects were randomly placed throughout the spatial domain.

Spatial properties of sessile benthic organisms and the design of repeat visual survey transects

“…In deep‐water benthic ecosystems, where researchers often find high diversity and low overall cover of single species (e.g. Monk et al, ; Schlacher et al, ), and where the spatial precision of transects is problematic, it is particularly important that sampling designs take into account the spatial properties of organisms (Irvine et al, ; Legendre et al, ). Here, geostatistical models are used to quantify the spatial properties of potential indicator species at a long‐term, deep‐water benthic monitoring site, and highlight how these properties influence survey outcomes.…”