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Potential immediate and prolonged impact of timber harvests on stream-associated amphibians (SAA; torrent (Rhyacotriton spp.) and giant salamanders (Dicamptodon spp.) and coastal tailed frogs (Ascaphus truei)) in Oregon and Washington, USA is a management concern, in part because of widespread commercial management of forests across their geographic distributions. Relationships between SAA occupancy and detection probabilities, environmental variables, and management effects were examined in 141 randomly selected perennial streams in commercial forests west of the Cascade Crest in Oregon and Washington from July to October, 2006. Giant salamander occupancy varied by stream substrate type (i.e., consolidated vs. unconsolidated geologies) and was positively associated with stand age. Torrent salamander occupancy varied by species, was positively associated with channel gradient, and was reduced in the youngest and oldest sampled stands. Tailed frog occupancy was negatively associated with the presence of crayfish, negatively associated with low and high bankfull widths, and positively associated with stand age, although stands less than 5 years old had occupancy rates >50% in the absence of crayfish. Mean detection rates were less than 1 for all three genera (0.85, 0.66, and 0.63 for Dicamptodon, Rhyacotriton, and Ascaphus, respectively), which indicates that conclusions from previous research about relationships between SAA occupancy, environmental variables, and forest practices have an unknown element of uncertainty, particularly if detection rates co-vary with habitat change. We believe that our study is the first to present unbiased estimates of occupancy and detection parameters for SAA on forested landscapes in Oregon and Washington.

In studies of animal space use, researchers often use kernel-based techniques for estimating the size of an animal's home range and its utilization distribution from radiotracking data. However, the kernel estimator is highly sensitive to the bandwidth value used. Previous ecological studies recommended least-squares cross-validation (LSCV) as the default bandwidth selection method, but some statisticians consider this technique inferior to newer methods. We used simulations to compare the performance of the scaling LSCV and reference approaches to plug-in and solve-the-equation (STE) bandwidth methods. We generated samples of 20, 50, and 150 points from mixtures of 2, 4, and 16 bivariate normal distributions. We selected the ranges of potential variances for these distributions to create 4 distribution types with varied levels of clumping to simulate the diversity of location patterns expected from radiotracking data. For most distribution types, plug-in and STE methods performed as well or better than LSCV in % absolute error of home-range size estimates and overlap of estimated and true utilization distributions. Although the relative differences usually were small, the plug-in and STE approaches provide good alternatives to LSCV. However, LSCV performed better with distribution types composed entirely of tight clumps of points. The reference bandwidth performed poorly for most distributions. Surprisingly, it often had the lowest absolute error at outer contours for distributions consisting of a single very tight cluster surrounded by more dispersed points. Although our results demonstrate the utility of plug-in and STE approaches, no method was best across all distributions. Rather, choice of a bandwidth selection method may vary depending on the study goals, sample size, and patterns of space use by the study species. In general, we recommend plug-in and STE approaches for estimating relatively smooth outer contours. The LSCV approach is better at identifying tight clumps, including areas of peak use, although risk of LSCV failure also increases when a distribution has a very tight cluster of points. When planning to use kernel methods, researchers should consider these factors to make preliminary decisions about the bandwidth method expected to be most appropriate in their study.

The degree of space-use overlap among adjacent individuals is a central focus of many wildlife investigations. We studied the comparability of minimum convex polygon and fixed-kernel home-range overlap indices and Volume of Intersection (VI) scores using simulated data. We simulated pairs of point patterns to represent telemetry locations of adjacent individuals and varied the amount of potential overlap in the simulation region (100%, 50%, and 10%) and the point distribution (random, loosely clumped, and tightly clumped). We created 1,000 pairs of point sets (60 points in each individual set) for each of the 9 potential overlap and point distribution combinations. In all 9 treatment combinations, VI scores were highest followed by kernel and then polygon estimates. Raw differences among estimates within a treatment were greatest when there was 50% potential overlap, and overlap indices decreased as the degree of clumping increased. The relative differences among overlap indices within a treatment were affected most by potential overlap; differences generally were greatest at 10% and least at 100%. Correlation between index values was lowest for random point patterns, and highest for loosely clumped and tightly clumped point patterns. Although the VI tended to indicate the most overlap and minimum convex polygon the least, there was no consistent correction factor among techniques because of the interacting effects of the overlap index, distribution pattern, and potential overlap. Interpretation of overlap measures requires careful consideration of assumptions and properties of animals under study.

Habitat use analyses have traditionally relied on telemetry relocation points as the primary source of data. Home-range estimations have generally been disregarded as a means of quantifying habitat use and availability. Seasonal habitat use of white-tailed deer (Odocoileus virginianus)calculated from a home-range estimator using an adaptive kernel method was compared with relocation point data from Sand Lake National Wildlife Refuge, South Dakota from 1 January 1993 to 27 June 1994, to determine if an adaptive kernel home-range estimator approximates traditional methods of quantifying habitat use. No differences between proportional area of seasonal home-ranges and point data were detected (P>0·05), indicating that habitat use calculated from an adaptive kernel home-range estimate was similar to habitat use calculated from relocation points. Potential benefits of home-range estimations include consideration of radio-telemetry error, biological independence of observations, and complete utilisation distributions. Given the success of home-range estimators at quantifying habitat use, a new alternative to habitat use analysis is offered. In situations where inherent telemetry error is unavoidable, using proportional area of home-range contours to define habitat use avoids the pitfalls associated with telemetry relocation point data.

We compared diurnal and 24-h habitat use patterns of white-tailed deer (Odocoileus virginianus) at Sand Lake National Wildlife Refuge, South Dakota from 1 January to 20 December 1993. Deer were sampled during two periods, diurnal (i.e. 0800 to 1959 h) and 24-h to determine if 24-h habitat use could be estimated from diurnal use patterns at the population level. No differences between diurnal and 24-h use were detected (P=0·101) during winter, spring, summer, or fall, indicating that 24-h habitat use could be estimated (r2=0·95,P

Private lands offer some of the best opportunities and greatest challenges for conservation planning, especially at landscape scales. The large landscapes contain significant amounts of private lands that are often the most productive lands and offer some of the best potential for biodiversity conservation and other conservation objectives. The use of models for conservation planning of private lands at landscape scales generally parallel similar efforts on public lands. However, conservation planning on private lands presents various challenges not inherent in planning for public lands. Foremost are concerns for private property rights and proprietary information that complicate planning and model development. Distrust of planning conducted by agencies and lack of funding for planning conducted by private landowners unless paid for independently are formidable challenges. Mixed-ownership landscapes present additional challenges with respect to linking effective planning processes for both public and private lands. Various solutions exist addressing these challenges, including the use of an ecosystem diversity focus in planning and linking planning processes and outputs to existing programs used by private landowners including various Farm Bill programs. Finally, generating interest in conservation planning by private landowners and identifying suitable incentives that can help balance conservation objectives with economic and other objectives of private lands holds promise.

Ecological objectives relating to sustainability and land management typically focus on maintaining and enhancing biological diversity and ecosystem integrity, objectives that require planning at landscape levels. Nearly all landscapes of sufficient size to address these objectives contain a diversity of landowners, including many private lands. For conservation planning to be effective in these mixed-ownership landscapes, a number of ecological principles and management considerations should be recognized. Ecological objectives need to be addressed in an ecosystem-management context in which social and economic objectives are integrated with ecological objectives. To accomplish this integration, the most effective approach focuses on a coarse filter; an approach that strives to meet ecological objectives through the identification of an appropriate mix of ecological communities correctly configured within the landscape. One effective coarse-filter approach has identified ecological site complexity as well as an understanding of historical disturbance regimes that shaped the inherent diversity and function of the ecological communities. Monitoring effectiveness of coarse-filter planning will need to be hierarchical to address all levels of biological diversity.

Publisher Summary Animal space use and movements are best understood through direct observation. This chapter describes a variety of techniques used to analyze animal space use, including home range estimation, analysis of site fidelity, and animal interactions. It also provides limitations of using many traditional techniques. The chapter provides a discussion of modeling animal movements using a combination of advanced descriptive and visualization approaches, general movement models, and biological models. The importance of sample size and autocorrelation of data in estimating home ranges and animal movements cannot be overemphasized. When considering tradeoffs between sample size and independence, obtaining an adequate sample size is more important than independence between data points. Any analysis of radiotelemetry data must consider the full range of biological and environmental factors that may impact the results. Home range estimation and animal movement analyses must answer difficult questions regarding the internal anatomy of home range boundaries and movement pathways through the judicious use of biological and environmental information. Also, radiotelemetry studies should go beyond simple, general descriptions and use a model-driven approach to examine the key factors determining why and how an animal uses space.

Kernel methods for estimating home range are being used increasingly in wildlife research, but the effect of sample size on their accuracy is not known. We used computer simulations of 10-200 points/ home range and compared accuracy of home range estimates produced by fixed and adaptive kernels with the reference (REF) and least-squares cross-validation (LSCV) methods for determining the amount of smoothing. Simulated home ranges varied from simple to complex shapes created by mixing bivariate normal distributions. We used the size of the 95% home range area and the relative mean squared error of the surface fit to assess the accuracy of the kernel home range estimates. For both measures, the bias and variance approached an asymptote at about 50 observations/home range. The fixed kernel with smoothing selected by LSCV provided the least-biased estimates of the 95% home range area. All kernel methods produced similar surface fit for most simulations, but the fixed kernel with LSCV had the lowest frequency and magnitude of very poor estimates. We reviewed 101 papers published in The Journal of Wildlife Management (JWM) between 1980 and 1997 that estimated animal home ranges. A minority of these papers used nonparametric utilization distribution (UD) estimators, and most did not adequately report sample sizes. We recommend that home range studies using kernel estimates use LSCV to determine the amount of smoothing, obtain a minimum of 30 observations per animal (but preferably >50), and report sample sizes in published results.