2017/09/30 by Carolina Lemes Nascimento Costa, Carolina L. N. Costa, Flavia M. D. Marquitti +7 · 8 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · #Biology #Context (archaeology) #Evolution and Genetic Dynamics #Evolution and Paleontology Studies #Evolutionary biology #Extant taxon #Extinction (optical mineralogy) #Genetic algorithm #Genetic diversity and population structure #Paleontology #Phylogenetic tree #Phylogenetics #q-bio.PE
paper · pdf · doi:10.1016/j.physa.2018.05.150
published in Physica A Statistical Mechanics and its Applications 510, 1-14 (Elsevier BV) · This is a revised version, with a new title and 2 new co-authors. 24 pages, 7 figures
arxiv created 2017/12/19 · openalex publication_date 2018/06/21 · arxiv updated 2018/10/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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 investigated in the macroevolutionary context. Knowledge of the evolutionary history allows the study of patterns underlying the processes considered in these models, 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 in individual-based models of speciation, from which genealogies and phylogenies can be constructed. The first algorithm relies on saving ancestral-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 among extant species, commonly used in empirical studies and applied here to simulated genetic data. Comparisons between tress are performed with metrics describing the overall topology, branch length distribution and imbalance of trees. 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.