Woody plant abundance is widely recognized to have increased in savannas and grasslands worldwide. The lack of information on the rates, dynamics, and extent of increases in shrub abundance is a major source of uncertainty in assessing how this vegetation change has influenced biogeochemical cycles. Projecting future consequences of woody cover change on ecosystem function will require knowledge of where shrub cover in present-day stands lies relative to the realizable maximum for a given soil type within a bioclimatic region. We used time-series aerial photography (1936, 1966, and 1996) and field studies to quantify cover and biomass of velvet mesquite (Prosopis velutina Woot.) following its proliferation in a semidesert grassland of Arizona. Mapping of individual shrubs indicated an encroachment phase characterized by high rates of bare patch colonization. Upon entering a stabilization phase, shrub cover increases associated with recruitment and canopy expansion were largely offset by contractions in canopy area of other shrub patches. Instances of shrub disappearance coincided with a period of below-average rainfall (1936-1966). Overall, shrub cover (mean +/- SE) on sandy uplands with few and widely scattered shrubs in 1902 was dynamically stable over the 1936-1996 period averaging approximately 35% +/- 5%. Shrub cover on clayey uplands in 1936 was 17% +/- 2% but subsequently increased twofold to levels comparable to those on sandy uplands by 1966 (36% +/- 7%). Cover on both soils then decreased slightly between 1966 and 1996 to 28% +/- 3%. Thus, soil properties influenced the rate at which landscapes reached a dynamic equilibrium, but not the apparent endpoint. Although sandy and clayey landscapes appear to have stabilized at comparable levels of cover, shrub biomass was 1.4 times greater on clayey soils. Declines in shrub cover between 1966 and 1996 were accompanied by a shift to smaller patch sizes on both sandy and clayey landscapes. Dynamics observed during the stabilization phase suggest that density-dependent regulation may be in play. If woody cover has transitioned from directional increases to a dynamic equilibrium, biomass projections will require monitoring and modeling patch dynamics and stand structure rather than simply changes in total cover.

Identifying factors that may be responsible for regulating the size of animal populations is a cornerstone in understanding population ecology. The main factors that are thought to influence population size are either resources (bottom-up), or predation (top-down), or interspecific competition (parallel). However, there are highly variable and often contradictory results regarding their relative strengths and influence. These varied results are often interpreted as indicating "shifting control" among the three main factors, or a complex, nonlinear relationship among environmental variables, resource availability, predation, and competition. We argue here that there is a "missing link" in our understanding of predator-prey dynamics. We explore whether the landscape-of-fear model can help us clarify the inconsistencies and increase our understanding of the roles, extent, and possible interactions of top-down, bottom-up, and parallel factors on prey population abundance. We propose two main predictions derived from the landscape-of-fear model: (1) for a single species, we suggest that as the makeup of the landscape of fear changes from relatively safe to relatively risky, bottom-up impacts switch from strong to weak as top-down impacts go from weak to strong; (2) for two or more species, interspecific competitive interactions produce various combinations of bottom-up, top-down, and parallel impacts depending on the dominant competing species and whether the landscapes of fear are shared or distinctive among competing species. We contend that these predictions could successfully explain many of the complex and contradictory results of current research. We test some of these predictions based on long-term data for small mammals from the Chihuahuan Desert in the United States, and Mexico. We conclude that the landscape-of-fear model does provide reasonable explanations for many of the reported studies and should be tested further to better understand the effects of bottom-up, top-down, and parallel factors on population dynamics.

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Mahalanobis Distance (D 2 ) Statistic is a multivariate statistical method that has been used to model habitats occupied by wildlife and plant species. The output, whether standardized squared distance or probability values, represents the similarity of a given set of values with those of an optimum habitat configuration defined exclusively by sites where the species of interest is known to occur. Typically, all principal components with nonzero eigenvalues are used to calculate D 2 values. We partitioned D 2 into contributions from individual principal components (PCs) and selected PCs corresponding to relatively invariant aspects of the environment across all use sites to formulate D 2 (k). Partitioned Mahalanobis D 2 (k) represents the similarity of a given set of values with those of "minimum" habitat requirements of the species as defined by occupied sites using the k subset of principal components. We created a GIS-based model of the habitat surrounding 39 confirmed timber rattlesnake hibernacula on the Madison County Wildlife Management Area in northwest Arkansas, USA, using slope, aspect, elevation, and 11 physical soil attributes. We retained 4 of 15 principal components in D 2 (k = 4) calculations, and minimum habitat requirements corresponded to a combination of moderate slope, south to southwest facing slopes, and medium to high elevations. We used bootstrap and cross-validation techniques to examine the stability of the correlation matrix and the effect of each site on overall D 2 (k) values. The D 2 (k = 4) model specifically highlighted habitats similar to known rattlesnake hibernacula. We present a method to translate the probability surface into a qualitative data layer useful in making management decisions by examining the cumulative distributions of the percentages of (1) hibernacula correctly classified and (2) the study area predicted. We selected the probability threshold that maximized the predictive gain by including the greatest number of hibernacula in the smallest area. The probability threshold 0.2 captured 82% of the known hibernacula in 25% of the 5,666-ha study area. Partitioned Mahalanobis D 2 (k) requires only data for where species are known to occur, thus circumventing costly errors in misclassifying habitat use with commonly used classification algorithms that require dichotomous data sets. JOURNAL OF WILDLIFE MANAGEMENT 69(1):33-44; 2005