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Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

AIM: Maps of species ranges are among the most frequently used distribution data in biodiversity studies. As with any biological data, range maps have some level of measurement error, but this error is rarely quantified. We assessed the error associated with amphibian range maps by comparing them with point locality data. LOCATION: Global. METHODS: The maps published by the Global Amphibian Assessment were assessed against two data sets of species point localities: the Global Biodiversity Information Facility (GBIF), and a refined data set including recently published, high‐quality presence data from both GBIF and other sources. Range fit was measured as the proportion of presence records falling within the range polygon(s) for each species. RESULTS: Using the high‐quality point data provided better fit measures than using the raw GBIF data. Range fit was highly variable among continents, being highest for North American and European species (a fit of 84–94%), and lowest for Asian and South American species (a fit of 57–64%). At the global scale, 95% of amphibian point records were inside the ranges published in maps, or within 31 km of the range edge. However, differences among continents were striking, and more points were found far from range edges for South American and Asian species. MAIN CONCLUSIONS: The Global Amphibian Assessment range maps represent the known distribution of most amphibians well; this study provides measures of accuracy that can be useful for future research using amphibian maps as baseline data. Nevertheless, there is a need for greater investment in the continuous updating and improvement of maps, particularly in the megadiverse areas of tropical Asia and South America.

In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ~20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure.Astatistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

A new method of obtaining geometric features of three-dimensional range data for human and tree recognition in an off-road environment is described. The learning algorithm AdaBoost is used to select a set of discriminative features from a very large set of potential geometric features. The proposed geometric feature can be considered as a generalisation of the geometric feature used in previous studies. The experimental results for human and tree recognition show that the proposed method outperforms the other methods.

For more than 50 y ecologists have believed that spatial heterogeneity in habitat conditions promotes species richness by increasing opportunities for niche partitioning. However, a recent stochastic model combining the main elements of niche theory and island biogeography theory suggests that environmental heterogeneity has a general unimodal rather than a positive effect on species richness. This result was explained by an inherent tradeoff between environmental heterogeneity and the amount of suitable area available for individual species: for a given area, as heterogeneity increases, the amount of effective area available for individual species decreases, thereby reducing population sizes and increasing the likelihood of stochastic extinctions. Here we provide a comprehensive evaluation of this hypothesis. First we analyze an extensive database of breeding bird distribution in Catalonia and show that patterns of species richness, species abundance, and extinction rates are consistent with the predictions of the area–heterogeneity tradeoff and its proposed mechanisms. We then perform a metaanalysis of heterogeneity–diversity relationships in 54 published datasets and show that empirical data better fit the unimodal pattern predicted by the area–heterogeneity tradeoff than the positive pattern predicted by classic niche theory. Simulations in which species may have variable niche widths along a continuous environmental gradient are consistent with all empirical findings. The area–heterogeneity tradeoff brings a unique perspective to current theories of species diversity and has important implications for biodiversity conservation.

A major issue in ecology, biogeography, conservation biology and invasion biology is the extent to which climate, and hence climate change, contributes to the positions of species' range boundaries. Thirty years of rapid climate warming provides an excellent opportunity to test the hypothesis that climate acts as a major constraint on range boundaries, treating anthropogenic climate change as a large-scale experiment. UK and global data, and literature. This article analyses the frequencies with which species have responded to climate change by shifting their range boundaries. It does not consider abundance or other changes. For the majority of species, boundaries shifted in a direction that is concordant with being a response to climate change; 84% of all species have expanded in a polewards direction as the climate has warmed (for the best data available), which represents an excess of 68% of species after taking account of the fact that some species may shift in this direction for non-climatic reasons. Other data sets also show an excess of animal range boundaries expanding in the expected direction. Climate is likely to contribute to the majority of terrestrial and freshwater range boundaries. This generalization excludes species that are endemic to specific islands, lakes, rivers and geological outcrops, although these local endemics are not immune from the effects of climate change. The observed shifts associated with recent climate change are likely to have been brought about through both direct and indirect (changes to species' interactions) effects of climate; indirect effects are discussed in relation to laboratory experiments and invasive species. Recent observations of range boundary shifts are consistent with the hypothesis that climate contributes to, but is not the sole determinant of, the position of the range boundaries of the majority of terrestrial animal species.

Most studies examining continental-to-global patterns of species richness rely on the overlaying of extent-of-occurrence range maps. Because a species does not occur at all locations within its geographic range, range-map-derived data represent actual distributional patterns only at some relatively coarse and undefined resolution. With the increasing availability of high-resolution climate and land-cover data, broad-scale studies are increasingly likely to estimate richness at high resolutions. Because of the scale dependence of most ecological phenomena, a significant mismatch between the presumed and actual scale of ecological data may arise. This may affect conclusions regarding basic drivers of diversity and may lead to errors in the identification of diversity hotspots. Here, we examine avian range maps of 834 bird species in conjunction with geographically extensive survey data sets on two continents to determine the spatial resolutions at which range-map data actually characterize species occurrences and patterns of species richness. At resolutions less than 2° ([almost equal to]200 km), range maps overestimate the area of occupancy of individual species and mischaracterize spatial patterns of species richness, resulting in up to two-thirds of biodiversity hotspots being misidentified. The scale dependence of range-map accuracy poses clear limitations on broad-scale ecological analyses and conservation assessments. We suggest that range-map data contain less information than is generally assumed and provide guidance about the appropriate scale of their use.

In historical biogeography, model-based inference methods for reconstructing the evolution of geographic ranges on phylogenetic trees are poorly developed relative to the diversity of analogous methods available for inferring character evolution. We attempt to rectify this deficiency by constructing a dispersal-extinction-cladogenesis (DEC) model for geographic range evolution that specifies instantaneous transition rates between discrete states (ranges) along phylogenetic branches and apply it to estimating likelihoods of ancestral states (range inheritance scenarios) at cladogenesis events. Unlike an earlier version of this approach, the present model allows for an analytical solution to probabilities of range transitions as a function of time, enabling free parameters in the model, rates of dispersal, and local extinction to be estimated by maximum likelihood. Simulation results indicate that accurate parameter estimates may be difficult to obtain in practice but also show that ancestral range inheritance scenarios nevertheless can be correctly recovered with high success if rates of range evolution are low relative to the rate of cladogenesis. We apply the DEC model to a previously published, exemplary case study of island biogeography involving Hawaiian endemic angiosperms in Psychotria (Rubiaceae), showing how the DEC model can be iteratively refined from inspecting inferences of range evolution and also how geological constraints involving times of island origin may be imposed on the likelihood function. The DEC model is sufficiently similar to character models that it might serve as a gateway through which many existing comparative methods for characters could be imported into the realm of historical biogeography; moreover, it might also inspire the conceptual expansion of character models toward inclusion of evolutionary change as directly coincident, either as cause or consequence, with cladogenesis events. The DEC model is thus an incremental advance that highlights considerable potential in the nascent field of model-based historical biogeographic inference.

The Mantel correlogram is an elegant way to compute a correlogram for multivariate data. However, recent papers raised concerns about the power of the Mantel test itself. Hence the question: Is the Mantel correlogram powerful enough to be useful? To explore this issue, we compared the performances of the Mantel correlogram to those of other methods, using numerical simulations based on random, normally distributed data. For a single response variable, we compared it to the Moran and Geary correlograms. Type I error rates of the three methods were correct. Power of the Mantel correlogram was nearly as high as that of the univariate methods. For the multivariate case, the test of the multivariate variogram developed in the context of multiscale ordination is in fact a Mantel test, so that the power of the two methods is the same by definition. We devised an alternative permutation test based on the variance, which yielded similar results. Overall, the power of the Mantel test was high, the method successfully detecting spatial correlation at rates similar to the permutation test of the variance statistic in multivariate variograms. We conclude that the Mantel correlogram deserves its place in the ecologist's toolbox.