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Biological Assumptions (biological + assumption)
Selected AbstractsDynamic models allowing for flexibility in complex life histories accurately predict timing of metamorphosis and antipredator strategies of preyFUNCTIONAL ECOLOGY, Issue 6 2009Andrew D. Higginson Summary 1.,The development of antipredator defences in the larval stage of animals with complex life cycles is likely to be affected by costs associated with creating and maintaining such defences because of their impact on the timing of maturation or metamorphosis. 2.,Various theoretical treatments have suggested that investment in defence should both increase or decrease with increasing resource availability, but a recent model predicts investment in defences should be highest at intermediate resource level and predator density. 3.,Previous models of investment in defence and timing of metamorphosis provide a poor match to empirical data. Here we develop a dynamic state-dependent model of investment in behavioural and morphological defences that enables us to allow flexibility in investment in defences over development, the timing of metamorphosis and the size of the organism at metamorphosis that were absent from previous theory. 4.,We show that the inclusion of this flexibility results in different predictions to those of the fixed investment approach used previously, especially when we allow metamorphosis to occur at the optimal time and state for the organism. 5.,Under these more flexible conditions, we predict that morphological defences should be insensitive to resource level whilst behavioural defences should either increase or decrease with increasing resources depending on the predation risk and the magnitude of the fitness benefits of large size at metamorphosis. 6.,Our work provides a formal framework in which we might progress in the study of how the use of antipredator defences is affected by their costs. Most of the predictions of our model in are in good accord with empirical results, and can be understood in terms of the underlying biological assumptions. The reasons why simpler models failed to match empirical observations can be explained, and our predictions that are a poor match help to target the circumstances which warrant future study. [source] Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysisMOLECULAR ECOLOGY, Issue 17 2010STEPHEN F. SPEAR Abstract Measures of genetic structure among individuals or populations collected at different spatial locations across a landscape are commonly used as surrogate measures of functional (i.e. demographic or genetic) connectivity. In order to understand how landscape characteristics influence functional connectivity, resistance surfaces are typically created in a raster GIS environment. These resistance surfaces represent hypothesized relationships between landscape features and gene flow, and are based on underlying biological functions such as relative abundance or movement probabilities in different land cover types. The biggest challenge for calculating resistance surfaces is assignment of resistance values to different landscape features. Here, we first identify study objectives that are consistent with the use of resistance surfaces and critically review the various approaches that have been used to parameterize resistance surfaces and select optimal models in landscape genetics. We then discuss the biological assumptions and considerations that influence analyses using resistance surfaces, such as the relationship between gene flow and dispersal, how habitat suitability may influence animal movement, and how resistance surfaces can be translated into estimates of functional landscape connectivity. Finally, we outline novel approaches for creating optimal resistance surfaces using either simulation or computational methods, as well as alternatives to resistance surfaces (e.g. network and buffered paths). These approaches have the potential to improve landscape genetic analyses, but they also create new challenges. We conclude that no single way of using resistance surfaces is appropriate for every situation. We suggest that researchers carefully consider objectives, important biological assumptions and available parameterization and validation techniques when planning landscape genetic studies. [source] Simple evolutionary pathways to complex proteinsPROTEIN SCIENCE, Issue 9 2005Michael Lynch Abstract A recent paper in this journal has challenged the idea that complex adaptive features of proteins can be explained by known molecular, genetic, and evolutionary mechanisms. It is shown here that the conclusions of this prior work are an artifact of unwarranted biological assumptions, inappropriate mathematical modeling, and faulty logic. Numerous simple pathways exist by which adaptive multi-residue functions can evolve on time scales of a million years (or much less) in populations of only moderate size. Thus, the classical evolutionary trajectory of descent with modification is adequate to explain the diversification of protein functions. [source] | ||||||||||