Early detection of an invading nonindigenous plant species (NIS) may be critical for efficient and effective management. Adaptive survey sampling methods may provide unbiased sampling for best estimates of distribution of rare and spatially clustered populations of plants in the early stages of invasion. However, there are few examples of these methods being used for nonnative plant surveys in which travelling distances away from an initial or source patch, or away from a road or trail, can be time consuming due to the topography and vegetation. Nor is there guidance as to which of the many adaptive methods would be most appropriate as a basis for invasive plant mapping and subsequent management. Here we used an empirical complete census of four invader species in early to middle stages of invasion in a management area to assess the effectiveness and efficiency of three nonadaptive methods, four adaptive cluster methods, and four adaptive web sampling methods that all originated from transects. The adaptive methods generally sampled more NIS-occupied cells and patches than standard transect approaches. Sampling along roads only was time-efficient and effective, but only for species with restricted distribution along the roads. When populations were more patchy and dispersed over the landscape the adaptive cluster starting at the road generally proved to be the most time-efficient and effective NIS detection method.

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.

Non-native invasive plant species (NIS) pose a significant threat to native biological diversity. Their management and control are mandated by Executive Order 13112 on all federal lands in the United States, including Army training lands.A key component of any NIS management strategy is knowing the distribution of NIS across the management landscape. Survey or sampling methods are often needed because Army installations are too large to inventory completely. Efficient sampling is crucial because early-detection, rapid-response management approaches rely on detection of newly established (but rare) NIS populations. Adaptive sampling is an alternative to conventional sampling that capitalizes on the spatial clustering of biological populations. Adaptive sampling has the potential to be efficient at sampling rare and clustered populations, but its use has been limited by a lack of tools to aid implementation in the field.This manual describes an adaptive cluster sampling design called the Random Transect with Adaptive Clustering Sampling Design (RTAC) and a user-friendly global positioning system (GPS) interface developed to aid implementation of the sampling design in the field. The GPS user interface described here is a customized application developed for ESRI's ArcPad ® mobile geographical information software (GIS) for field applications.