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      Co-extinction in a host-parasite network: identifying key hosts for network stability

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      a , 1 , 2
      Scientific Reports
      Nature Publishing Group

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          Abstract

          Parasites comprise a substantial portion of total biodiversity. Ultimately, this means that host extinction could result in many secondary extinctions of obligate parasites and potentially alter host-parasite network structure. Here, we examined a highly resolved fish-parasite network to determine key hosts responsible for maintaining parasite diversity and network structure (quantified here as nestedness and modularity). We evaluated four possible host extinction orders and compared the resulting co-extinction dynamics to random extinction simulations; including host removal based on estimated extinction risk, parasite species richness and host level contributions to nestedness and modularity. We found that all extinction orders, except the one based on realistic extinction risk, resulted in faster declines in parasite diversity and network structure relative to random biodiversity loss. Further, we determined species-level contributions to network structure were best predicted by parasite species richness and host family. Taken together, we demonstrate that a small proportion of hosts contribute substantially to network structure and that removal of these hosts results in rapid declines in parasite diversity and network structure. As network stability can potentially be inferred through measures of network structure, our findings may provide insight into species traits that confer stability.

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            Random forests for classification in ecology.

            Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
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              Is a healthy ecosystem one that is rich in parasites?

              Historically, the role of parasites in ecosystem functioning has been considered trivial because a cursory examination reveals that their relative biomass is low compared with that of other trophic groups. However there is increasing evidence that parasite-mediated effects could be significant: they shape host population dynamics, alter interspecific competition, influence energy flow and appear to be important drivers of biodiversity. Indeed they influence a range of ecosystem functions and have a major effect on the structure of some food webs. Here, we consider the bottom-up and top-down processes of how parasitism influences ecosystem functioning and show that there is evidence that parasites are important for biodiversity and production; thus, we consider a healthy system to be one that is rich in parasite species.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                17 August 2015
                2015
                : 5
                : 13185
                Affiliations
                [1 ]Odum School of Ecology, University of Georgia , Athens, GA 30606 – 4288
                [2 ]Forest and Wildlife Ecology, University of Wisconsin , Madison, WI 53706-1799.
                Author notes
                Article
                srep13185
                10.1038/srep13185
                4538395
                26278333
                7e79612a-5c8a-438a-bd6a-7ecc3863d41e
                Copyright © 2015, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 22 September 2014
                : 21 July 2015
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