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      Advances in population ecology and species interactions in mammals

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          Abstract

          The study of mammals has promoted the development and testing of many ideas in contemporary ecology. Here we address recent developments in foraging and habitat selection, source–sink dynamics, competition (both within and between species), population cycles, predation (including apparent competition), mutualism, and biological invasions. Because mammals are appealing to the public, ecological insight gleaned from the study of mammals has disproportionate potential in educating the public about ecological principles and their application to wise management. Mammals have been central to many computational and statistical developments in recent years, including refinements to traditional approaches and metrics (e.g., capture-recapture) as well as advancements of novel and developing fields (e.g., spatial capture-recapture, occupancy modeling, integrated population models). The study of mammals also poses challenges in terms of fully characterizing dynamics in natural conditions. Ongoing climate change threatens to affect global ecosystems, and mammals provide visible and charismatic subjects for research on local and regional effects of such change as well as predictive modeling of the long-term effects on ecosystem function and stability. Although much remains to be done, the population ecology of mammals continues to be a vibrant and rapidly developing field. We anticipate that the next quarter century will prove as exciting and productive for the study of mammals as has the recent one.

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          Maximum entropy modeling of species geographic distributions

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            ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE

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              Host-gut microbiota metabolic interactions.

              The composition and activity of the gut microbiota codevelop with the host from birth and is subject to a complex interplay that depends on the host genome, nutrition, and life-style. The gut microbiota is involved in the regulation of multiple host metabolic pathways, giving rise to interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, muscle, and brain. A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to manipulate the gut microbiota to combat disease and improve health.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Mammalogy
                Oxford University Press (OUP)
                0022-2372
                1545-1542
                May 23 2019
                May 23 2019
                May 23 2019
                May 23 2019
                May 23 2019
                May 23 2019
                : 100
                : 3
                : 965-1007
                Affiliations
                [1 ]Department of Wildlife, Fish, & Conservation Biology, University of California, Davis, CA, USA
                [2 ]Museum of Southwestern Biology, University of New Mexico, Albuquerque, NM, USA
                [3 ]School of Biological Sciences, University of Aberdeen, Aberdeen, United Kingdom
                [4 ]Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
                [5 ]Department of Integrative Biology, University of Wisconsin, Madison, WI, USA
                [6 ]Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
                [7 ]Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI, USA
                [8 ]School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
                Article
                10.1093/jmammal/gyz017
                04687a56-a44a-4a53-89cf-96fe60a3b4bc
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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