Home About us Contact
|
Home About us Contact | ||
|
|
||
Evolutionary Inferences (evolutionary + inference)
Selected AbstractsEVOLUTION AND STABILITY OF THE G-MATRIX ON A LANDSCAPE WITH A MOVING OPTIMUMEVOLUTION, Issue 8 2004Adam G. Jones Abstract In quantitative genetics, the genetic architecture of traits, described in terms of variances and covariances, plays a major role in determining the trajectory of evolutionary change. Hence, the genetic variance-covariance matrix (G-matrix) is a critical component of modern quantitative genetics theory. Considerable debate has surrounded the issue of G-matrix constancy because unstable G-matrices provide major difficulties for evolutionary inference. Empirical studies and analytical theory have not resolved the debate. Here we present the results of stochastic models of G-matrix evolution in a population responding to an adaptive landscape with an optimum that moves at a constant rate. This study builds on the previous results of stochastic simulations of G-matrix stability under stabilizing selection arising from a stationary optimum. The addition of a moving optimum leads to several important new insights. First, evolution along genetic lines of least resistance increases stability of the orientation of the G-matrix relative to stabilizing selection alone. Evolution across genetic lines of least resistance decreases G-matrix stability. Second, evolution in response to a continuously changing optimum can produce persistent maladaptation for a correlated trait, even if its optimum does not change. Third, the retrospective analysis of selection performs very well when the mean G-matrix (,) is known with certainty, indicating that covariance between G and the directional selection gradient (3 is usually small enough in magnitude that it introduces only a small bias in estimates of the net selection gradient. Our results also show, however, that the contemporary ,-matrix only serves as a rough guide to ,. The most promising approach for the estimation of G is probably through comparative phylogenetic analysis. Overall, our results show that directional selection actually can increase stability of the G-matrix and that retrospective analysis of selection is inherently feasible. One ?riajor remaining challenge is to gain a sufficient understanding of the G-matrix to allow the confident estimation of ,. [source] Predicting the past distribution of species climatic nichesGLOBAL ECOLOGY, Issue 5 2009David Nogués-Bravo ABSTRACT Predicting past distributions of species climatic niches, hindcasting, by using climate envelope models (CEMs) is emerging as an exciting research area. CEMs are used to examine veiled evolutionary questions about extinctions, locations of past refugia and migration pathways, or to propose hypotheses concerning the past population structure of species in phylogeographical studies. CEMs are sensitive to theoretical assumptions, to model classes and to projections in non-analogous climates, among other issues. Studies hindcasting the climatic niches of species often make reference to these limitations. However, to obtain strong scientific inferences, we must not only be aware of these potential limitations but we must also overcome them. Here, I review the literature on hindcasting CEMs. I discuss the theoretical assumptions behind niche modelling, i.e. the stability of climatic niches through time and the equilibrium of species with climate. I also summarize a set of ,recommended practices' to improve hindcasting. The studies reviewed: (1) rarely test the theoretical assumptions behind niche modelling such as the stability of species climatic niches through time and the equilibrium of species with climate; (2) they only use one model class (72% of the studies) and one palaeoclimatic reconstruction (62.5%) to calibrate their models; (3) they do not check for the occurrence of non-analogous climates (97%); and (4) they do not use independent data to validate the models (72%). Ignoring the theoretical assumptions behind niche modelling and using inadequate methods for hindcasting CEMs may well entail a cascade of errors and naïve ecological and evolutionary inferences. We should also push integrative research lines linking macroecology, physiology, population biology, palaeontology, evolutionary biology and CEMs for a better understanding of niche dynamics across space and time. [source] Small worms, big ideas: evolutionary inferences from nematode DNAJOURNAL OF BIOGEOGRAPHY, Issue 1 2010Holly M. Bik No abstract is available for this article. [source] The ontogeny of cross-sex genetic correlations: an analysis of patternsJOURNAL OF EVOLUTIONARY BIOLOGY, Issue 12 2009J. POISSANT Abstract The independent evolution of males and females is typically constrained by shared genetic variance. Despite substantial research, we still know little about the evolution of cross-sex genetic covariance and its standardized measure, the cross-sex genetic correlation (rMF). In particular, it is unclear if rMF tend to vary with age. We compiled 28 traits for which ontogenetic trends in rMF were documented. Decreases in rMF with age were observed significantly more often than increases and the mean effect size for the relationship between rMF and age was large and negative. This suggests that sexual dimorphism (SD) may typically evolve more readily for phenotypes expressed later in ontogeny and that evolutionary inferences related to the evolution of SD should be limited to the ontogenetic stage at which rMF was estimated. Knowledge about ontogenetic variation in rMF should help improving our understanding of evolutionary patterns related to SD and the resolution of intralocus sexual conflicts. [source] | ||||||||||