Howard B. Stauffer

WHAT CAN HABITAT PREFERENCE MODELS TELL US? TESTS USING A VIRTUAL TROUT POPULATION

Habitat selection (“preference”) models are widely used to manage fish and wildlife. Their use assumes that (1) habitat with high animal densities (highly selected habitat) is high quality habitat, and low densities indicate low quality habitat; and (2) animal populations respond positively to the availability of highly selected habitat. These assumptions are increasingly questioned but very difficult to test. We evaluated these assumptions in an individual-based model (IBM) of stream trout that reproduces many natural complexities and habitat selection behaviors. Trout in the IBM select habitat to maximize their potential fitness, a function of growth potential (including food competition) and mortality risks. We know each habitat cells intrinsic habitat quality, the fitness potential a trout in the cell would experience in the absence of competition. There was no strong relation between fitness potential and the density of fish in the IBM; cells where fitness potential was high but density low were com...

Monitoring Martes Populations in California: Survey Design and Power Analysis

Fishers (Martes pennanti) and American martens (M. americana) have been protected from trapping in California since the mid-1900s, yet in portions of each of their historic ranges their numbers are extremely low, perhaps due to the effects of timber harvest. We propose a method capable of detecting declines in the occurrence and distribution of fishers or martens using baited track-plate stations. The proposed sampling unit is a small grid of stations that has a high probability of detecting animals when they are present. These multistation units are sufficiently spaced to meet the assumption of independence for a binomial model. We propose a stratified random sampling design with strata sampled for proportions of occurrence at discrete points in time. Stratification is based on variation in occurrence by region and is estimated from preliminary survey data. A previously pub- lished bias adjustment is applied to the proportion of units with detections to adjust for possible failure to detect resident individuals at a sampling unit. A Monte Carlo simulation model was developed to determine the sample size necessary to detect 20 and 50% declines, with 80% power, in the proportion of sampling units with occurrence. We assume a 10-yr sampling interval. Sensitivity analysis, using a range of values for means and standard deviations of strata proportions, determined that power was much more sensitive to changes in mean than the standard deviation. When the best current estimates of the fisher strata proportions were input for 10 strata (five regional and two habitat) in California, 115 and 17 sampling units per stratum were necessary to detect 20 and 50% declines, respectively. For some circumstances this sampling effort was also sufficient to achieve strata estimates with 5% error and to detect statistical differences between individual stratum proportions. The steps in the process of implementing a monitoring program for Pacific fishers in California are outlined as an example of the planning and preparation necessary to monitor changes in the distribution of a rare forest carnivore.

RANKING HABITAT FOR MARBLED MURRELETS: NEW CONSERVATION APPROACH FOR SPECIES WITH UNCERTAIN DETECTION

An essential element in the conservation of rare species is the ranking of some aspects of habitat quality. We developed a method to rank the importance of individual habitat patches to Marbled Murrelets (Brachyramphus marmoratus) in 26 old-growth forest stands in northern California, using estimates of stand occupancy as an index of nesting activity. We used survey data collected in the stands from 1992 to 1997. The analysis was based on an adjustment that incorporates uncertainty of detection into a binomial model. Maximum likelihood estimators were used for the proportion P of the stands occupied by murrelets and the conditional probability p of detection with each visit to an occupied survey station, and bootstrapping methods were used for error estimates. We were able to rank a single stand most important, three other stands second in importance, and eight additional stands third in importance to murrelet nesting activity. For the murrelets in our study area, these results provided information useful in negotiations between government agencies and a private company in efforts to preserve some of the stands. Our methodology also has potential application for other flora and fauna of management concern, when sampling for presence or absence with uncertain detection. This technique can be applied at a variety of scales depending upon the species and habitat. Although conservation issues require consideration of many factors, including political, social, economic, and biological, our methods are helpful in providing science-based information from sample data to assist in the decision-making process.