Often resource selection functions (RSFs) are developed by comparing resource attributes of used sites to unused or available ones. We present alternative approaches to the analysis of resource selection based on the utilization distribution (UD). Our objectives are to describe the rationale for estimation of RSFs based on UDs, offer advice about computing UDs and RSFs, and illustrate their use in resource selection studies. We discuss the 3 main factors that should be considered when using kernel UD‐based estimates of space use: selection of bandwidth values, sample size versus precision of estimates, and UD shape and complexity. We present 3 case studies that demonstrate use of UDs in resource selection modeling. The first example demonstrates the general case of RSF estimation that uses multiple regression adjusted for spatial autocorrelation to relate UD estimates (i.e., the probability density function) to resource attributes. A second example, involving Poisson regression with an offset term, is presented as an alternative for modeling the relative frequency, or probability of use, within defined habitat units. This procedure uses the relative frequency of locations within a habitat unit as a surrogate of the UD and requires relatively fewer user‐defined options in the modeling of resource selection. Last, we illustrate how the UD can also be used to enhance univariate resource selection analyses, such as compositional analysis, in cases where animals use their range nonrandomly. The UD helps overcome several common shortcomings of some other analytical techniques by treating the animal as the primary sampling unit, summarizing use in a continuous and probabilistic manner, and relying on the pattern of animal space use rather than using individual sampling points. However, several drawbacks are apparent when using the UD in resource selection analyses. Choice of UD estimator is important and sensitive to sample size and user‐defined options, such as bandwidth and software selection. Extensions to these procedures could consider behavioral‐based approaches and alternative techniques to estimate the UD directly.

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Ovenbirds (Seiurus aurocapillus), Kentucky Warblers (Oporornis formosus), and Worm-eating Warblers (Helmitheros vermivorus) were censused in central Missouri to determine breeding population densities in three forest tracts large enough to satisfy minimum area requirements estimated in an earlier study. Densities of Kentucky Warblers and Ovenbirds were significantly higher in a large forest tract (>800 ha) than in two 300 ha forests. Worm-eating Warblers bred only in the large forest. Kentucky Warblers and Ovenbirds had larger territories in the two sites with lower population density. The three sites had similar vegetation structure but significantly different topography and edge/area ratios. Habitat selection, analyzed with principal component analysis and log-linear models, was significantly different among the three species and different among the three sites. Edge/ interior ratio and topographic features contribute to differences in population densities observed among the three sites. Area requirements may be larger than those estimated by the incidence function.

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Question: Static sampling designs for collecting spatial data efficiently are being readily utilized by ecologists, however, most ecological systems involve a multivariate spatial process that evolves dynamically over time. Efficient monitoring of such spatio‐temporal systems can be achieved by modeling the dynamic system and reducing the uncertainty associated with the effect of design choice at future observation times. However, can we combine traditional techniques with dynamic methods to find optimal dynamic sampling designs for monitoring the succession of a herbaceous community? Location: Lower Hamburg Bend Conservation Area, Missouri, USA (40°34′42″ lat. 95°45′38″ long.). Methods: The dynamic nature of the system under study is modeled in such a way that uncertainty in the measurements and temporal process can both be accounted for. Both fixed and roving monitoring locations were used in conjunction with a spatio‐temporal statistical model to efficiently determine optimal locations of roving monitors over time based on the reduction of uncertainty in predictions. Results: During the first 3 years of the study, roving monitors where held at fixed locations to allow for statistical parameter estimation from which to make predictions. Optimal monitoring locations for the remaining 2 years were selected based on the overall reduction in prediction uncertainty. Conclusions: The dynamic and adaptive vegetation monitoring scheme allowed for the efficient collection of data that will be utilized for many future ecological studies. By optimally placing an additional set of monitoring locations, we were able to utilize information about the system dynamics when informing the data collection process.