Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [AKDE], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation (normalNfalse^area) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing normalNfalse^area. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small normalNfalse^area. While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an normalNfalse^area >1,000, where 30% had an normalNfalse^area <30. In this frequently encountered scenario of small normalNfalse^area, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.

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Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.

ABSTRACT. We updated the checklist of mammals from Mato Grosso do Sul, Brazil based on primary records only. One hundred and sixty-six mammal species were listed as occurring in the state, 47 of them being medium to large, 47 small mammal and 73 bat species. The listed species are distributed in 31 families: Didelphidae (17 spp.), Dasypodidae (7 spp.), Myrmecophagidae (2 spp.), Cebidae (1 sp.), Callithrichidae (2 spp.), Aotidae (1 sp.), Pitheciidae (1 sp.), Atelidae (1 sp.), Leporidae (1 sp.), Felidae (7 spp.), Canidae (4 spp.), Mustelidae (5 spp.), Mephitidae (2 spp.), Procyonidae (2 spp.), Tapiridae (1 sp.), Tayassuidae (2 spp.), Cervidae (4 spp.), Sciuridae (1 sp.), Cricetidae (22 spp.), Erethizontidae (1 sp.), Caviidae (3 spp.), Dasyproctidae (1 sp.), Cuniculidae (1 sp.), Echimyidae (4 spp.), Phyllostomidae (41 spp.), Emballonuridae (2 spp.), Molossidae (16 spp.), Vespertilionidae (9 spp.), Mormoopidae (1 sp.), Noctilionidae (2 spp.), and Natalidade (1 sp.). These numbers represent an increase of fourteen species with primary records for the state in comparison with the previously published checklist. However, it is evident the scarcity of information at several regions of the state, and the need of implementation of regional zoological collections. The state of Mato Grosso do Sul represent only 4.19% of the Brazilian territory, but the number of mammal species reach 24.13% of the known species occurring in the country.

The concept of sustainability is that present use of a resource would not prevent it being available for future generations. For exploited biological populations, sustainability is a demographic question. Herein we reviewed studies (released 1987-2010) evaluating presumably sustainable use of timber, hunting and non-timber forest products (NTFP) in Brazil. The studies analysed 239 cases (each case being one species evaluated in one study). Sustainability could be evaluated only in 126 cases studied with a demographic approach, 48% of which (61/126) showed unsustainable exploitations. The situation was worst for timber (24/39), intermediate for hunting (35/78) and best for NTFP (2/9). Cascading effects on other species were detected in 11/66 cases. Our results show that many presumed sustainable natural resource exploitations in Brazil are actually not sustainable. Clearly, sustainability needs more testing; the concept must be used more carefully.