A common concern when designing surveys for rare species is ensuring sufficient detections for analytical purposes, such as estimating frequency on the landscape or modeling habitat relationships. Strict design-based approaches provide the least biased estimates but often result in low detection rates of rare species. Here, we demonstrate how model-based stratification can improve the probability of detecting five rare epiphytic macrolichens (Nephroma laevigatum, N. occultum, N. parile, Lobaria scrobiculataa, and Psuedocyphelaria rainierensis) in the Pacific Northwest. We constructed classification tree models for four more common lichens (L. oregana, L. pulmonaria, P. anomala, and P. anthraspis) that are associated with the rare species, then used the models to generate strata for sampling for the five lichen species considered rare. The classification tree models were developed using topographic and bio-climatic variables hypothesized to have direct relationships to the presence of the modeled lichen species. When the expected detection rates using the model-based stratification approach was tested on an independent data set, it resulted in two-to fivefold gains in detection compared to the observed detection rates for four of the five tested rare species.

No abstract

We show how simple statistical analyses of systematically collected inventory data can be used to provide reliable information about the distribution and habitat associations of rare species. Using an existing design‐based sampling grid on which epiphytic macrolichens had been inventoried in the Northwest Forest Plan area of the U.S. Pacific Northwest, we (1) estimate frequencies and standard errors for each of 25 lichen species having special management designation (i.e., Survey and Manage), (2) assess the probability that individual species were associated with specific land allocation and forest stand age classifications, and (3) provide estimates of sample sizes necessary to ensure sufficient detections for these analyses. We conclude with a discussion of management and conservation information needs that extant data can satisfy and identify advantages and limitations of random vs. nonrandom sampling strategies. Combining design‐assisted and model‐assisted approaches can overcome some of the limitations of either single strategy.

No abstract

/ This paper identifies lessons learned and issues raised during the development of an ecosystem monitoring strategy intended to support the Northwest Forest Plan. Adaptive ecosystem management, which requires monitoring as essential feedback to management, recognizes that action is necessary or appropriate, although knowledge may be imperfect. We suggest that this principle be explicitly acknowledged in the design of monitoring programs, and we coin the term adaptive monitoring design. Adaptive monitoring design is an iterative process that refines the specifications for monitoring over time as a result of experience in implementing a monitoring program, assessing results, and interacting with users. An adaptive design therefore facilitates ecosystem management. We also discuss lessons of temporal and spatial scales raised by the consideration of a design for ecosystem management. Three additional issues-integration of information from different sources, institutional infrastructure, and the roles of individuals working in an interagency setting-are also identified, but not developed in detail. KEY WORDS: Northwest Forest Plan; Monitoring; Ecosystem management; Adaptive management; Adaptive monitoring