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      Analysing ecological networks of species interactions : Analyzing ecological networks

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          Most cited references 148

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          Statistical mechanics of complex networks

          Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.
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            Rebuilding community ecology from functional traits.

            There is considerable debate about whether community ecology will ever produce general principles. We suggest here that this can be achieved but that community ecology has lost its way by focusing on pairwise species interactions independent of the environment. We assert that community ecology should return to an emphasis on four themes that are tied together by a two-step process: how the fundamental niche is governed by functional traits within the context of abiotic environmental gradients; and how the interaction between traits and fundamental niches maps onto the realized niche in the context of a biotic interaction milieu. We suggest this approach can create a more quantitative and predictive science that can more readily address issues of global change.
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              Fast unfolding of communities in large networks

              We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .

                Author and article information

                Biological Reviews
                Biol Rev
                June 20 2018
                [1 ]Département de Sciences Biologiques; Université de Montréal; Montréal H2V 2J7 Canada
                [2 ]Québec Centre for Biodiversity Sciences; McGill University; Montréal H3A 1B1 Canada
                [3 ]Department of Ecology; Montana State University; Bozeman MT 59715 U.S.A.
                [4 ]Beaty Biodiversity Research Centre; University of British Columbia; Vancouver V6T 1Z4 Canada
                [5 ]Department of Ecology and Evolutionary Biology; University of Toronto; Toronto M5S 3B2 Canada
                [6 ]Département de Biologie; Université de Sherbrooke; Sherbrooke J1K 2R1 Canada
                [7 ]Departamento de Ecologia, Instituto de Biociências; Universidade de São Paulo; São Paulo 05508-090 Brazil
                [8 ]Department of Ecology and Evolutionary Biology; University of Arizona; Tucson AZ 85721 U.S.A.
                [9 ]School of Natural Resources and Environment; University of Arizona; Tucson AZ 85721 U.S.A.
                [10 ]Pacific Wildland Fire Sciences Laboratory; USDA Forest Service; Seattle WA 98103 U.S.A.
                [11 ]Department of Bioscience; Aarhus University; Aarhus 8000 Denmark
                [12 ]Departamento de Biologia Animal, Instituto de Biologia; Universidade Estadual de Campinas (UNICAMP); Campinas 13083-862 Brazil
                [13 ]Life & Environmental Sciences; University of California Merced; Merced CA 95343 U.S.A.
                [14 ]Santa Fe Institute; Santa Fe NM 87501 U.S.A.
                © 2018


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