Mammals are metagenomic in that they are composed not only of their own gene complements but also those of all of their associated microbes. To understand the co-evolution of the mammals and their indigenous microbial communities, we conducted a network-based analysis of bacterial 16S rRNA gene sequences from the fecal microbiota of humans and 59 other mammalian species living in two zoos and the wild. The results indicate that host diet and phylogeny both influence bacterial diversity, which increases from carnivory to omnivory to herbivory, that bacterial communities codiversified with their hosts, and that the gut microbiota of humans living a modern lifestyle is typical of omnivorous primates.Our 'metagenome' is a composite of Homo sapiens genes and genes present in the genomes of the trillions of microbes that colonize our adult bodies (1). The vast majority of these microbes live in our distal guts. 'Our' microbial genomes (microbiome) encode metabolic functions that we have not had to evolve wholly on our own, including the ability to extract energy and nutrients from our diet. It is unclear how distinctively human our gut microbiota is, or how modern H. sapiens' ability to construct a wide range of diets has affected our gut microbial ecology. In this study we address two general questions concerning the evolution of mammals: how do diet and host phylogeny shape mammalian microbiota? When a mammalian species acquires a new dietary niche, how does its gut microbiota relate to the microbiota of its close relatives?The acquisition of a new diet is a fundamental driver for the evolution of new species. Coevolution, the reciprocal adaptations occurring between interacting species (2), produces dramatic physiological changes that are often recorded in fossil remains. For instance, although mammals made their first appearance on the world stage in the Jurassic (~160 Ma), most modern species arose during the Quaternary (1.8 Ma to present (5)), when C4-grasslands expanded in response to a fall in atmospheric CO 2 levels and/or climate changes (6-8). The switch to a C4 plant-dominated diet selected for herbivores with high-crowned teeth (3) and longer gut retention times necessary for the digestion of lower-quality forage (9). However, these adaptations may not suffice for the exploitation of a new dietary niche. The community

The rapid expansion of road networks has reduced connectivity among populations of flora and fauna. The resulting isolation is assumed to increase population extinction rates, in part because of the loss of genetic diversity. However, there are few cases where loss of genetic diversity has been linked directly to roads or other barriers. We analysed the effects of such barriers on connectivity and genetic diversity of 27 populations of Ovis canadensis nelsoni (desert bighorn sheep). We used partial Mantel tests, multiple linear regression and coalescent simulations to infer changes in gene flow and diversity of nuclear and mitochondrial DNA markers. Our findings link a rapid reduction in genetic diversity (up to 15%) to as few as 40 years of anthropogenic isolation. Interstate highways, canals and developed areas, where present, have apparently eliminated gene flow. These results suggest that anthropogenic barriers constitute a severe threat to the persistence of naturally fragmented populations.

The United States Fish and Wildlife Service's (USFWS) designation of critical habitat for the endangered Nelson's bighorn sheep (Ovis canadensis nelsoni) in the Peninsular Ranges of southern California has been controversial because of an absence of a quantitative, repeatable scientific approach to the designation of critical habitat. We used 12,411 locations of Nelson's bighorn sheep collected from 1984–1998 to evaluate habitat use within 398 km2 of the USFWS‐designated critical habitat in the northern Santa Rosa Mountains, Riverside County, California. We developed a multiple logistic regression model to evaluate and predict the probability of bighorn use versus non‐use of native landscapes. Habitat predictor variables included elevation, slope, ruggedness, slope aspect, proximity to water, and distance from minimum expanses of escape habitat. We used Earth Resources Data Analysis System Geographic Information System (ERDAS‐GIS) software to view, retrieve, and format predictor values for input to the Statistical Analysis Systems (SAS) software. To adequately account for habitat landscape diversity, we carried out an unsupervised classification at the outset of data inquiry using a maximum‐likelihood clustering scheme implemented in ERDAS. We used the strata resulting from the unsupervised classification in a stratified random sampling scheme to minimize data loads required for model development. Based on 5 predictor variables, the habitat model correctly classified >96% of observed bighorn sheep locations. Proximity to perennial water was the best predictor variable. Ninety‐seven percent of the observations were within 3 km of perennial water. Exercising the model over the northern Santa Rosa Mountain study area provided probabilities of bighorn use at a 30 times 30‐m2 pixel level. Within the 398 km2 of USFWS‐designated critical habitat, only 34% had a graded probability of bighorn use to non‐use ranging from ≥1:1 to 6,044:1. The remaining 66% of the study area had odds of having bighorn use <1:1 or it was more likely not to be used by bighorn sheep. The USFWS designation of critical habitat included areas (45 km2) of importance (2.5 to ≥40 observations per km2 per year) to Nelson's bighorn sheep and large landscapes (353 km2) that do not appear to be used (<1 observation per km2 per year).