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1 April 2012 Comparison of Transect-Based Standard and Adaptive Sampling Methods for Invasive Plant Species
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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.

Management Implications: It is often not possible or cost-effective to conduct a complete inventory of potentially invasive plant species in large management areas, particularly at the early stages of invasion when populations may be infrequent and dispersed on the landscape. Detection at the early stages of invasion may be crucial for effective and cost-effective management. Thus managers must have survey methods that are effective and efficient for estimating the distribution of invading species. To accomplish different survey goals, which may include finding early invading populations, locating many different invasive plant species, finding the most populations of a single species, or collecting information to characterize species distributions, knowing which survey technique to use is critical. We tested three standard and eight adaptive survey methods on a virtual landscape populated with four empirically censused invasive plant species: Canada thistle, Dalmatian toadflax, smooth brome, and common St. Johnswort. The species exhibited somewhat different growth forms, reproductive patterns, and seed dispersal distances and were in different stages of invasion. Random transects with adaptive cluster sampling generally performed best when the survey goal was to find the largest number of populations in the shortest amount of time for species that were well established and occupied areas away from the road. If the species was in the early stages of invasion and only occupied roadside habitat, surveying along roads performed best. When the survey goal was to accurately assess the proportion of the landscape infested by each species, stratified random targeted transects without adaptive sampling performed best for all species. However, managers should be aware that adaptive sampling methods overestimate infested area. This study indicates that adaptive sampling methods can improve nonindigenous species patch detection for management, but regardless of the sampling method, detection remains relative low (maximum of 33% of patches) with typical management constraints and therefore seriously challenges the concept of early detection and rapid response.

Weed Science Society of America
Bruce D Maxwell, Vickie Backus, Matthew G Hohmann, Kathryn M Irvine, Patrick Lawrence, Erik A Lehnhoff, and Lisa J Rew "Comparison of Transect-Based Standard and Adaptive Sampling Methods for Invasive Plant Species," Invasive Plant Science and Management 5(2), 178-193, (1 April 2012).
Received: 28 March 2011; Accepted: 1 January 2012; Published: 1 April 2012

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