Registering the evolutionary history in individual-based models of speciation

Understanding the emergence of biodiversity patterns in nature is a central problem in biology. Theoretical models of speciation have addressed this question in the macroecological scale, but little has been done to connect microevolutionary processes with macroevolutionary patterns. Knowledge of the evolutionary history allows the study of patterns underlying the processes being modeled, revealing their signatures and the role of speciation and extinction in shaping macroevolutionary patterns. In this paper we introduce two algorithms to record the evolutionary history of populations and species in individual-based models of speciation, from which genealogies and phylogenies can be constructed. The first algorithm relies on saving ancestor–descendant relationships, generating a matrix that contains the times to the most recent common ancestor between all pairs of individuals at every generation (the Most Recent Common Ancestor Time matrix, MRCAT). The second algorithm directly records all speciation and extinction events throughout the evolutionary process, generating a matrix with the true phylogeny of species (the Sequential Speciation and Extinction Events, SSEE). We illustrate the use of these algorithms in a spatially explicit individual-based model of speciation. We compare the trees generated via MRCAT and SSEE algorithms with trees inferred by methods that use only genetic distance between individuals of extant species, commonly used in empirical studies and applied here to simulated genetic data. Comparisons between trees are performed with metrics describing the overall topology, branch length distribution and imbalance degree. We observe that both MRCAT and distance-based trees differ from the true phylogeny, with the first being closer to the true tree than the second.Facultad de Ciencias Naturales y Muse

Nestedness across biological scales

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general.Facultad de Ciencias Naturales y Muse

Nestedness across biological scales

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general.Facultad de Ciencias Naturales y Muse

Nestedness across biological scales

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general.Facultad de Ciencias Naturales y Muse

Vulnerabilidade das microrregiões da Região Sul do Brasil à pandemia do novo coronavírus (SARS-CoV-2)

This is the first report of the ‘Observatório COVID191 - Grupo: Redes de Contágio – Laboratório de Estudos de Defesa’ for the South region of Brazil. We have combined data of confirmed cases of the new coronavirus (SARS-CoV-2) for the South available up to 17/04/2020, with structural analyses of road networks, from within and between states, to estimate the vulnerability and potential influence of the South micro-regions to propagate the disease.Este é o primeiro relatório do Observatório COVID19 - Grupo: Redes de Contágio – Laboratório de Estudos de Defesa para a região Sul do Brasil. Combinamos dados de casos confirmados do novo coronavírus (SARS-CoV-2) para o Sul, disponíveis até o dia 17/04/2020, com análises estruturais da rede de rotas rodoviárias intra e interestaduais para estimarmos a vulnerabilidade e potencial influência das microrregiões sulinas na propagação da doença

Appendix B. Detailed mathematical procedure used to determine the ants that interact more than expected by their natural abundances.

Detailed mathematical procedure used to determine the ants that interact more than expected by their natural abundances

Appendix C. Quantitative and qualitative ordinations of ant communities foraging on plants with and without extrafloral nectaries.

Quantitative and qualitative ordinations of ant communities foraging on plants with and without extrafloral nectaries

Appendix A. Additional sampling procedures of ants and plants in a mature rainforest, São Nicolau Farm, municipality of Cotriguaçu, northern Mato Grosso, Brazil.

Additional sampling procedures of ants and plants in a mature rainforest, São Nicolau Farm, municipality of Cotriguaçu, northern Mato Grosso, Brazil

Registering the evolutionary history in individual-based models of speciation

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORUnderstanding the emergence of biodiversity patterns in nature is a central problem in biology. Theoretical models of speciation have addressed this question in the macroecological scale, but little has been done to connect microevolutionary processes with macroevolutionary patterns. Knowledge of the evolutionary history allows the study of patterns underlying the processes being modeled, revealing their signatures and the role of speciation and extinction in shaping macroevolutionary patterns. In this paper we introduce two algorithms to record the evolutionary history of populations and species in individual-based models of speciation, from which genealogies and phylogenies can be constructed. The first algorithm relies on saving ancestor-descendant relationships, generating a matrix that contains the times to the most recent common ancestor between all pairs of individuals at every generation (the Most Recent Common Ancestor Time matrix, MRCAT). The second algorithm directly records all speciation and extinction events throughout the evolutionary process, generating a matrix with the true phylogeny of species (the Sequential Speciation and Extinction Events, SSEE). We illustrate the use of these algorithms in a spatially explicit individual-based model of speciation. We compare the trees generated via MRCAT and SSEE algorithms with trees inferred by methods that use only genetic distance between individuals of extant species, commonly used in empirical studies and applied here to simulated genetic data. Comparisons between trees are performed with metrics describing the overall topology, branch length distribution and imbalance degree. We observe that both MRCAT and distance-based trees differ from the true phylogeny, with the first being closer to the true tree than the second.510114FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL E NÍVEL SUPERIOR2015/11985-32016/01343-72016/06054-3302049/2015-0152885/2016-1sem informaçã

Registering the evolutionary history in individual-based models of speciation

Understanding the emergence of biodiversity patterns in nature is a central problem in biology. Theoretical models of speciation have addressed this question in the macroecological scale, but little has been done to connect microevolutionary processes with macroevolutionary patterns. Knowledge of the evolutionary history allows the study of patterns underlying the processes being modeled, revealing their signatures and the role of speciation and extinction in shaping macroevolutionary patterns. In this paper we introduce two algorithms to record the evolutionary history of populations and species in individual-based models of speciation, from which genealogies and phylogenies can be constructed. The first algorithm relies on saving ancestor–descendant relationships, generating a matrix that contains the times to the most recent common ancestor between all pairs of individuals at every generation (the Most Recent Common Ancestor Time matrix, MRCAT). The second algorithm directly records all speciation and extinction events throughout the evolutionary process, generating a matrix with the true phylogeny of species (the Sequential Speciation and Extinction Events, SSEE). We illustrate the use of these algorithms in a spatially explicit individual-based model of speciation. We compare the trees generated via MRCAT and SSEE algorithms with trees inferred by methods that use only genetic distance between individuals of extant species, commonly used in empirical studies and applied here to simulated genetic data. Comparisons between trees are performed with metrics describing the overall topology, branch length distribution and imbalance degree. We observe that both MRCAT and distance-based trees differ from the true phylogeny, with the first being closer to the true tree than the second.Fil: Costa, Carolina L. N.. Universidade Estadual de Campinas. Instituto de Biología; BrasilFil: Marquitti, Flavia M. D.. Universidade Estadual de Campinas; BrasilFil: Perez, Sergio Ivan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidade Estadual de Campinas; Brasil. Universidad Nacional de La Plata. Facultad de Cienicas Naturales y Museo. División Antropología; ArgentinaFil: Schneider, David Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidade Estadual de Campinas; BrasilFil: Ramos, Marlon F.. Universidade Estadual de Campinas; BrasilFil: Aguiar, Marcus A. M. de. Universidade Estadual de Campinas; Brasi