Utility of a Probabilistic Sampling Design to Determine Bull Trout Population Status Using Redd Counts in Basins of the Columbia River Plateau

Abstract

Redd counts are commonly used to monitor the current population status, trends in abundance, and distribution of bull trout Salvelinus confluentus. In many cases redd counts are conducted at subjectively selected sites, and there has been limited evaluation of statistical sampling designs. We evaluated the utility of the generalized random tessellation stratified (GRTS) sampling design to determine bull trout population status through redd counts. We tested a sampling effort that would be economically practical to implement on a continuous basis in multiple drainages within the southeastern Washington and Oregon portions of the Columbia River plateau. We evaluated the logistics of a pilot application of the GRTS design, compared GRTS‐based estimates of redd abundance with those from census surveys, determined the precision of the GRTS estimates and the associated power for abundance comparisons, and compared the performance of the GRTS design with that of other probability sampling designs through simulation. A target of 50 sites per basin can be sampled by a two‐person survey crew multiple times over the spawning season. At that level of effort, the precision of redd abundance estimates ranges from 15% to 35%, depending on the patchiness of the redd distribution and the extent of the target population. These levels of precision are suitable for detecting a 30–70% change in redd abundance. Direct comparisons of GRTS‐based estimates with those obtained from a census showed mixed results. However, in a simulation study with three other probability sampling designs, GRTS consistently outperformed all but systematic sampling, which provided slightly better precision at intermediate sample sizes. Depending on the scale of inference, GRTS is useful in monitoring bull trout conservation units through redd counts, though a census may provide a more practical design for monitoring core areas as defined by the U.S. Fish and Wildlife Service.

Optimization of a survey using spatially balanced sampling: a single-year application of clam monitoring in the Arcachon Bay (SW France)

“…For example, it has been used to determine bull trout (Salvelinus confluentus) population status through counts in basins of the Columbia River Plateau in the USA (Jacobs et al, 2009) and to develop ArcGIS tools via a forest biodiversity survey in a case study in Hunan Province, China (Li et al, 2012). Here, we illustrate the use of GRTS, and provide a comparison with StRS for bivalve surveys.…”

Proceedings of the coast redwood forests in a changing California: A symposium for scientists and managers

“…At low redd abundance none of their sampling designs were accurate. In a GRTS sampling design for bull trout in the Columbia Basin, Jacobs et al (2009) found that accuracy ranged from 15 percent to 35 percent and was dependent on redd distributions within basins and that there was no reduction in accuracy with sample sizes between 10 and 50 sites. Our results are similar in that increased sample size appears to only marginally improve the precision of our estimates.…”

The spatial distribution of salmon and steelhead redds and optimal sampling design

“…Redd surveys occurred weekly from mid-September through early November for fall Chinook salmon, and every 2 weeks from early March through late May for winter steelhead. During each foot survey, newly identified redds were flagged and georeferenced using recreationalgrade global positioning system (GPS) units (Courbois et al 2008;Jacobs et al 2009). The reported accuracies from the GPS units generally ranged from 2 to 15 m. In subsequent surveys, previously flagged redds were inspected to determine whether they should be classified as "still visible" or "not visible".…”

“…This methodology allowed us to track the new redds and all visible redds in each survey. The individual redd coordinates were assembled into a geographic information system (GIS) database (Isaak and Thurow 2006;Jacobs et al 2009). GPS points were moved to the closest point on the 1 : 24 000 stream layer, except in cases where the coordinate was clearly in error owing to poor satellite coverage.…”

“…While probability sampling can guarantee unbiased estimates, variance can be high owing to the patchy distributions common in salmonid spawning locations (e.g., Irvine et al 1992;Isaak and Thurow 2006). Stratified designs that concentrate sampling effort in areas with higher redd density and spatially balanced designs that ensure an even spatial distribution of sample reaches have been shown to substantially reduce estimator variance (Courbois et al 2008;Gallagher et al 2010;Jacobs and Nickelson 1998;Jacobs et al 2009). These results align with general sampling theory that predicts increased accuracy for designs that sample units with higher predicted variance more intensively (e.g., Cochran 1977) and constrain samples to be representative with respect to auxiliary variables that are related to the variable of interest (e.g., Tillé 2011).…”