11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Comparison of the Effects of Random and Selective Mass Extinctions on Erosion of Evolutionary History in Communities of Digital Organisms

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The effect of mass extinctions on phylogenetic diversity and branching history of clades remains poorly understood in paleobiology. We examined the phylogenies of communities of digital organisms undergoing open-ended evolution as we subjected them to instantaneous “pulse” extinctions, choosing survivors at random, and to prolonged “press” extinctions involving a period of low resource availability. We measured age of the phylogenetic root and tree stemminess, and evaluated how branching history of the phylogenetic trees was affected by the extinction treatments. We found that strong random (pulse) and strong selective extinction (press) both left clear long-term signatures in root age distribution and tree stemminess, and eroded deep branching history to a greater degree than did weak extinction and control treatments. The widely-used Pybus-Harvey gamma statistic showed a clear short-term response to extinction and recovery, but differences between treatments diminished over time and did not show a long-term signature. The characteristics of post-extinction phylogenies were often affected as much by the recovery interval as by the extinction episode itself.

          Related collections

          Most cited references78

          • Record: found
          • Abstract: found
          • Article: not found

          The future of biodiversity.

          Recent extinction rates are 100 to 1000 times their pre-human levels in well-known, but taxonomically diverse groups from widely different environments. If all species currently deemed "threatened" become extinct in the next century, then future extinction rates will be 10 times recent rates. Some threatened species will survive the century, but many species not now threatened will succumb. Regions rich in species found only within them (endemics) dominate the global patterns of extinction. Although new technology provides details of habitat losses, estimates of future extinctions are hampered by our limited knowledge of which areas are rich in endemics.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found

            EXTINCTION VULNERABILITY AND SELECTIVITY:Combining Ecological and Paleontological Views

            Extinction is rarely random across ecological and geological time scales. Traits that make some species more extinction-prone include individual traits, such as body size, and abundance. Substantial consistency appears across ecological and geological time scales in such traits. Evolutionary branching produces phylogenetic (as often measured by taxonomic) nesting of extinction-biasing traits at many scales. An example is the tendency, seen in both fossil and modern data, for higher taxa living in marine habitats to have generally lower species extinction rates. At lower taxononomic levels, recent bird and mammal extinctions are concentrated in certain genera and families. A fundamental result of such selectivity is that it can accelerate net loss of biodiversity compared to random loss of species among taxa. Replacement of vulnerable taxa by rapidly spreading taxa that thrive in human-altered environments will ultimately produce a spatially more homogenized biosphere with much lower net diversity.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Evolution of digital organisms at high mutation rates leads to survival of the flattest.

              Darwinian evolution favours genotypes with high replication rates, a process called 'survival of the fittest'. However, knowing the replication rate of each individual genotype may not suffice to predict the eventual survivor, even in an asexual population. According to quasi-species theory, selection favours the cloud of genotypes, interconnected by mutation, whose average replication rate is highest. Here we confirm this prediction using digital organisms that self-replicate, mutate and evolve. Forty pairs of populations were derived from 40 different ancestors in identical selective environments, except that one of each pair experienced a 4-fold higher mutation rate. In 12 cases, the dominant genotype that evolved at the lower mutation rate achieved a replication rate >1.5-fold faster than its counterpart. We allowed each of these disparate pairs to compete across a range of mutation rates. In each case, as mutation rate was increased, the outcome of competition switched to favour the genotype with the lower replication rate. These genotypes, although they occupied lower fitness peaks, were located in flatter regions of the fitness surface and were therefore more robust with respect to mutations.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                31 May 2012
                13 June 2012
                : 7
                : 5
                : e37233
                Affiliations
                [1 ]Department of Biology, Centre for Ecological and Evolutionary Synthesis, University of Oslo, Oslo, Norway
                [2 ]uTest, Southborough, Massachusetts, United States of America
                [3 ]Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
                [3 ]Centre for Infections, Health Protection Agency, London, United Kingdom
                British Columbia Centre for Excellence in HIV/AIDS, Canada
                Author notes

                Designed code for conversion of Avida population data into phylogenetic trees, and for calculation of tree shape metrics NCS and PHG: JS KGN. Helped to refine experimental methods: CAO. Helped to refine the final manuscript: PMA CAO. Conceived and designed the experiments: GY. Performed the experiments: GY. Analyzed the data: GY.

                Article
                PONE-D-11-07447
                10.1371/journal.pone.0037233
                3365035
                22693570
                58e928ea-be12-4242-abb8-ae067322e48e
                Yedid et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 28 April 2011
                : 18 April 2012
                Page count
                Pages: 13
                Categories
                Research Article
                Biology
                Computational Biology
                Evolutionary Modeling
                Population Modeling
                Ecology
                Ecological Metrics
                Species Diversity
                Ecosystems
                Artificial Ecosystems
                Ecosystem Modeling
                Evolutionary Biology
                Evolutionary Processes
                Species Extinction
                Evolutionary Systematics
                Phyletic Patterns
                Phylogenetics
                Paleontology
                Paleobiology
                Paleontology
                Paleobiology
                Population Biology
                Population Modeling
                Theoretical Biology
                Computer Science
                Computer Modeling
                Computerized Simulations
                Earth Sciences
                Paleontology
                Paleobiology

                Uncategorized
                Uncategorized

                Comments

                Comment on this article