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Historical Inference (historical + inference)
Selected AbstractsWHY DOES A METHOD THAT FAILS CONTINUE TO BE USED?EVOLUTION, Issue 11 2008L. Lacey Knowles As a critical framework for addressing a diversity of evolutionary and ecological questions, any method that provides accurate and detailed phylogeographic inference would be embraced. What is difficult to understand is the continued use of a method that not only fails, but also has never been shown to work,nested clade analysis is applied widely even though the conditions under which the method will provide reliable results have not yet been demonstrated. This contradiction between performance and popularity is even more perplexing given the recent methodological and computational advances for making historical inferences, which include estimating population genetic parameters and testing different biogeographic scenarios. Here I briefly review the history of criticisms and rebuttals that focus specifically on the high rate of incorrect phylogeographic inference of nested-clade analysis, with the goal of understanding what drives its unfettered popularity. In this case, the appeal of what nested-clade analysis claims to do,not what the method actually achieves,appears to explain its paradoxical status as a favorite method that fails. What a method promises, as opposed to how it performs, must be considered separately when evaluating whether the method represents a valuable tool for historical inference. [source] The burgeoning field of statistical phylogeographyJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 1 2004L. L. Knowles Abstract In the newly emerging field of statistical phylogeography, consideration of the stochastic nature of genetic processes and explicit reference to theoretical expectations under various models has dramatically transformed how historical processes are studied. Rather than being restricted to ad hoc explanations for observed patterns of genetic variation, assessments about the underlying evolutionary processes are now based on statistical tests of various hypotheses, as well as estimates of the parameters specified by the models. A wide range of demographical and biogeographical processes can be accommodated by these new analytical approaches, providing biologically more realistic models. Because of these advances, statistical phylogeography can provide unprecedented insights about a species' history, including decisive information about the factors that shape patterns of genetic variation, species distributions, and speciation. However, to improve our understanding of such processes, a critical examination and appreciation of the inherent difficulties of historical inference and challenges specific to testing phylogeographical hypotheses are essential. As the field of statistical phylogeography continues to take shape many difficulties have been resolved. Nonetheless, careful attention to the complexities of testing historical hypotheses and further theoretical developments are essential to improving the accuracy of our conclusions about a species' history. [source] Phylogenetic algorithms and the evolution of species communities in forest fragmentsCLADISTICS, Issue 1 2005Roseli Pellens In forest fragmentation studies, low specific richness in small fragments and community nestedness are usually considered to result from species loss. However, except in the case of fragmentation experiments, these studies cannot distinguish between original low richness and secondary species loss, or between original high richness and secondary colonizations in fragments. To distinguish between these possibilities is a matter of historical inference for which phylogenetic algorithms are designed. The methods of phylogenetic analysis, and especially parsimony analysis, can be used to find a tree of relationships between communities from different forest fragments, taking the presence or absence of species among different communities as characters. Parsimony analysis searches if species subsets can be classified in a nested hierarchy, and also establishes how the communities evolved, polarizing species changes into either extinctions or colonizations. By re-analyzing two classical studies in this new and powerful way, we demonstrate that the differences between fragments and large continuous forests cannot be attributed to species loss in all cases, contrary to expectations from models. © The Willi Hennig Society 2005. [source] Reconstructing ancestral ecologies: challenges and possible solutionsDIVERSITY AND DISTRIBUTIONS, Issue 1 2006Christopher R. Hardy ABSTRACT There are several ways to extract information about the evolutionary ecology of clades from their phylogenies. Of these, character state optimization and ,ancestor reconstruction' are perhaps the most widely used despite their being fraught with assumptions and potential pitfalls. Requirements for robust inferences of ancestral traits in general (i.e. those applicable to all types of characters) include accurate and robust phylogenetic hypotheses, complete species-level sampling and the appropriate choice of optimality criterion. Ecological characters, however, also require careful consideration of methods for accounting for intraspecific variability. Such methods include ,Presence Coding' and ,Polymorphism Coding' for discrete ecological characters, and ,Range Coding' and ,MaxMin Coding' for continuously variable characters. Ultimately, however, historical inferences such as these are, as with phylogenetic inference itself, associated with a degree of uncertainty. Statistically based uncertainty estimates are available within the context of model-based inference (e.g. maximum likelihood and Bayesian); however, these measures are only as reliable as the chosen model is appropriate. Although generally thought to preclude the possibility of measuring relative uncertainty or support for alternative possible reconstructions, certain useful non-statistical support measures (i.e. ,Sharkey support' and ,Parsimony support') are applicable to parsimony reconstructions. [source] WHY DOES A METHOD THAT FAILS CONTINUE TO BE USED?EVOLUTION, Issue 11 2008L. Lacey Knowles As a critical framework for addressing a diversity of evolutionary and ecological questions, any method that provides accurate and detailed phylogeographic inference would be embraced. What is difficult to understand is the continued use of a method that not only fails, but also has never been shown to work,nested clade analysis is applied widely even though the conditions under which the method will provide reliable results have not yet been demonstrated. This contradiction between performance and popularity is even more perplexing given the recent methodological and computational advances for making historical inferences, which include estimating population genetic parameters and testing different biogeographic scenarios. Here I briefly review the history of criticisms and rebuttals that focus specifically on the high rate of incorrect phylogeographic inference of nested-clade analysis, with the goal of understanding what drives its unfettered popularity. In this case, the appeal of what nested-clade analysis claims to do,not what the method actually achieves,appears to explain its paradoxical status as a favorite method that fails. What a method promises, as opposed to how it performs, must be considered separately when evaluating whether the method represents a valuable tool for historical inference. [source] DETECTING THE HISTORICAL SIGNATURE OF KEY INNOVATIONS USING STOCHASTIC MODELS OF CHARACTER EVOLUTION AND CLADOGENESISEVOLUTION, Issue 2 2005Richard H. Ree Abstract Phylogenetic evidence for biological traits that increase the net diversification rate of lineages (key innovations) is most commonly drawn from comparisons of clade size. This can work well for ancient, unreversed traits and for correlating multiple trait origins with higher diversification rates, but it is less suitable for unique events, recently evolved innovations, and that exhibit homoplasy. Here I present a new method for detecting the phylogenetic signature of key innovations that tests whethere the evolutionary history of the candidate trait is associated with shorter waiting times between cladogenesis events. The method employs stochastic models of character evolution and cladogenesis and integrates well into a Bayesian framework in which uncertainty in historical inferences (such as phylogenetic relationships) is allowed. Applied to a well-known example in plants, nectar spurs in columbines, the method gives much stronger support to the key innovation hypothesis than previous tests. [source] | ||||||||||