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.
Freshwater habitat quality is broadly recognized as fundamental to the viability of salmonid populations. Temporal trends in freshwater habitat have rarely been quantified, however, perhaps owing to a lack of methodical and rigorous time series data sets. We present an approach for evaluating change in freshwater habitat using data from a long‐term program to monitor salmonid populations and their habitats in coastal drainages of Oregon. Our goals were to (1) evaluate the presence and magnitude of an underlying linear trend in freshwater habitat condition across coastal watersheds in Oregon and (2) determine the effectiveness of the current sampling design for meeting the monitoring objectives. Four features were selected to characterize freshwater habitat: percent of pool area, large wood volume, quantity of fine sediment, and stream size. We developed a statistical model to describe the trend in these features that incorporated an error structure to account for site, year, and site‐by‐year variability. Spatial variability accounted for most of the overall variation, and temporal variability was minimal. Trends were detected among several of the habitat metrics and these varied by geographic region. To evaluate the efficacy of the sampling design, we generated simulated data sets with hypothesized trends of 1–2% per year and estimated trend detection power under two different survey designs, variance structures, and monitoring durations. We conclude that the power to detect trends is sensitive to the duration of the monitoring program and the structure and magnitude of the variance. The monitoring program was effective in detecting subtle trends while providing a robust data set with which to address multiple monitoring objectives. Such a monitoring program is critical to assessing the viability of salmonid populations in the Pacific Northwest and tracking recovery and conservation efforts.
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