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Regulations governing human activities in streams and riparian zones frequently differ depending on whether or not a stream reach supports fish. Fish presence or absence is usually determined by sampling or by assuming the presence of fish if the stream exhibits certain physical characteristics. Field surveys of fish occurrence in streams are time consuming and expensive. Inference of fish presence from simple thresholds of physical attributes, such as gradient or channel width alone, is inaccurate. We attempted to improve the accuracy and efficiency of this determination by developing a geographical information systems (GIS)‐based predictive model. A 10‐m digital elevation model incorporated field data on fish distribution from 517 streams in western Washington State and GIS‐derived representations of the physical characteristics of stream networks. A model predicting the upstream extent of fish occurrence was derived using logistic regression models coupled with a heuristic “stopping rule.” Candidate variables included stream gradient, upstream basin area, elevation, and mean annual precipitation. When assessed against independent survey data, 91.9% of the occupied fish habitat was correctly classified by the model. Errors were generally small, but occasional large errors did occur and were most frequently associated with barriers to fish movement. Smaller errors occurred in marginal habitats, streams of low topographic relief, and streams that originated from headwater ponds. Use of this type of model, coupled with targeted field survey in areas most likely to be associated with model error, would greatly improve the efficiency and accuracy of current classification schemes.

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We compared stream ecosystem responses to two types of disturbances: flood and debris flows. A large storm in February 1996 disturbed four similarly sized sub‐watersheds of the Calapooia River, in the Cascade Mountains of Oregon, USA. All sub‐watersheds had flood and in two, 31 and 81% of the perennial channel, a debris flow affected the channel. For 8 years, we used a suite of approaches: stream temperature, nutrient regime, periphyton and macroinvertebrate assemblages, and resident trout abundance and habitat to evaluate the persistence of instream impacts. Differences in stream temperatures and nitrate‐nitrogen concentrations were evident, probably, due to the removal of vegetation and modification of riparian soils at the debris flow sites. Instream biological responses varied. After the event, fish, including trout, were rare with no fish at the debris flow sites. Within 6 years, trout densities (Oncorhynchus mykiss and Oncorhynchus clarki) were similar and young‐of‐the‐year trouts were common. In contrast, periphyton and macroinvertebrate assemblages differed. Periphyton biomass was lower and nitrogen‐fixing periphyton was more abundant at the debris flow sites. Macroinvertebrate assemblage diversity was higher at the debris flow sites due to fewer dominant taxa. Macroinvertebrate functional feeding groups also differed with fewer gatherers and more scrapers at the debris flow sites. Debris flow impacts related to loss of riparian canopy will probably persist until mature red alder stands are re‐established along stream‐reaches affected by debris flows to provide nitrogen input and shade. Copyright © 2011 John Wiley & Sons, Ltd.

BackgroundManagers of landscapes dedicated to forest commodity production require information about how practices influence biological diversity. Individual species and communities may be threatened if management practices truncate or simplify forest age classes that are essential for reproduction and survival. For instance, the degradation and loss of complex diverse forest in young age classes have been associated with declines in forest-associated Neotropical migrant bird populations in the Pacific Northwest, USA. These declines may be exacerbated by intensive forest management practices that reduce hardwood and broadleaf shrub cover in order to promote growth of economically valuable tree species in plantations.Methodology and Principal FindingsWe used a Bayesian hierarchical model to evaluate relationships between avian species richness and vegetation variables that reflect stand management intensity (primarily via herbicide application) on 212 tree plantations in the Coast Range, Oregon, USA. Specifically, we estimated the influence of broadleaf hardwood vegetation cover, which is reduced through herbicide applications, on bird species richness and individual species occupancy. Our model accounted for imperfect detection. We used average predictive comparisons to quantify the degree of association between vegetation variables and species richness. Both conifer and hardwood cover were positively associated with total species richness, suggesting that these components of forest stand composition may be important predictors of alpha diversity. Estimates of species richness were 35–80% lower when imperfect detection was ignored (depending on covariate values), a result that has critical implications for previous efforts that have examined relationships between forest composition and species richness.Conclusion and SignificanceOur results revealed that individual and community responses were positively associated with both conifer and hardwood cover. In our system, patterns of bird community assembly appear to be associated with stand management strategies that retain or increase hardwood vegetation while simultaneously regenerating the conifer cover in commercial tree plantations.

As part of a habitat management planning process for commercially managed forests, we developed and evaluated habitat occupancy models for the orange‐crowned warbler (Vermivora celata), a conservation priority species in Oregon and Washington, USA. We used repeated surveys to classify a random sample of managed conifer stands at the McKenzie, PeEll, and Tolt study sites in western Oregon and Washington as either occupied or unoccupied during 1994–1995. We modeled occupancy and detection probabilities as a function of stand‐level habitat characteristics subject to manipulation by management activities. The best‐fitting model indicated that orange‐crowned warblers were 2 times (95% CI: 0.99‐5.1) and 3.8 times (95% CI: 1.5–6.1) as likely to occupy a stand for every 5% increase in evergreen shrub cover and 5‐m decrease in canopy lift (ht to lowest live branch), respectively. Management actions that maintain evergreen shrub cover >10% and permit development of low canopy lifts (4–10 m) should promote habitat occupancy by the orange‐crowned warbler in commercial forests in western Oregon and Washington.

Identification of thresholds (state changes over a narrow range of values) is of basic and applied ecological interest. However, current methods of estimating thresholds in occupancy ignore variation in the observation process and may lead to erroneous conclusions about ecological relationships or to the development of inappropriate conservation targets. We present a model to estimate a threshold in occupancy while accounting for imperfect species detection. The threshold relationship is described by a break-point (threshold) and the change in slope (threshold effect). Imperfect species detection is incorporated by jointly modeling species occurrence and species detection. We used WinBUGS to evaluate the model through simulation and to fit the model to avian occurrence data for three species from 212 sites with two replicate surveys in 2007-2008. To determine if accounting for imperfect detection changed the inference about thresholds in avian occupancy in relation to habitat structure, we compared our model to results from a commonly used threshold model (segmented logistic regression). We fit this model in both frequentist and Bayesian modes of inference. Results of the simulation study showed that 95% posterior intervals contained the true value of the parameter in approximately 95% of the simulations. As expected, the simulations indicated more precise threshold and parameter estimates as sample size increased. In the empirical study, we found evidence for threshold relationships for four species by covariate combinations when ignoring species detection. However, when we included variation from the observation process, threshold relationships were not supported in three of those four cases (95% posterior intervals included 0). In general, confidence intervals for the threshold effect were larger when we accounted for species nondetection than when we ignored nondetection. This model can be extended to investigate abundance thresholds as a function of ecological and anthropogenic factors, as well as multispecies hierarchical models.

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