Niche dynamics in space and time. Trends Ecol Evol

University of Lausanne, Department of Ecology and Evolution, Lausanne, Switzerland.
Trends in Ecology & Evolution (Impact Factor: 16.2). 04/2008; 23(3):149-58. DOI: 10.1016/j.tree.2007.11.005
Source: PubMed

ABSTRACT Niche conservatism, the tendency of a species niche to remain unchanged over time, is often assumed when discussing, explaining or predicting biogeographical patterns. Unfortunately, there has been no basis for predicting niche dynamics over relevant timescales, from tens to a few hundreds of years. The recent application of species distribution models (SDMs) and phylogenetic methods to analysis of niche characteristics has provided insight to niche dynamics. Niche shifts and conservatism have both occurred within the last 100 years, with recent speciation events, and deep within clades of species. There is increasing evidence that coordinated application of these methods can help to identify species which likely fulfill one key assumption in the predictive application of SDMs: an unchanging niche. This will improve confidence in SDM-based predictions of the impacts of climate change and species invasions on species distributions and biodiversity.

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Available from: Peter B Pearman, Sep 25, 2015
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    • "Manos et al., 2007) or niche occupancy (e.g. Pearman et al., 2008). This has led to increasing consensus among evolutionary biologists that fossil taxa should be included in macroevolutionary studies (Jackson and Erwin, 2006; Quental and Marshall, 2010; Dietl and Flessa, 2011; Slater and Harmon, 2013; Fritz et al., 2013). "
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    ABSTRACT: To fully understand macroevolutionary patterns and processes, we need to include both extant and extinct species in our models. This requires phylogenetic trees with both living and fossil taxa at the tips. One way to infer such phylogenies is the Total Evidence approach which uses molecular data from living taxa and morphological data from living and fossil taxa. Although the Total Evidence approach is very promising, it requires a great deal of data that can be hard to collect. Therefore this method is likely to suffer from missing data issues that may affect its ability to infer correct phylogenies. Here we use simulations to assess the effects of missing data on tree topologies inferred from Total Evidence matrices. We investigate three major factors that directly affect the completeness and the size of the morphological part of the matrix: the proportion of living taxa with no morphological data, the amount of missing data in the fossil record, and the overall number of morphological characters in the matrix. We infer phylogenies from complete matrices and from matrices with various amounts of missing data, and then compare missing data topologies to the "best" tree topology inferred using the complete matrix. We find that the number of living taxa with morphological characters and the overall number of morphological characters in the matrix, are more important than the amount of missing data in the fossil record for recovering the "best" tree topology. Therefore, we suggest that sampling effort should be focused on morphological data collection for living species to increase the accuracy of topological inference in a Total Evidence framework. Additionally, we find that Bayesian methods consistently outperform other tree inference methods. We therefore recommend using Bayesian consensus trees to fix the tree topology prior to further analyses. Copyright © 2015. Published by Elsevier Inc.
    Molecular Phylogenetics and Evolution 08/2015; DOI:10.1016/j.ympev.2015.08.023 · 3.92 Impact Factor
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    • "All rights reserved. importance of environmental variables, or evolutionary changes (from either genetic drift or different selection pressures in the invaded range) (Ackerly, 2003; Lavergne & Molofsky, 2007; Pearman et al., 2008; Duncan et al., 2009). "
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    ABSTRACT: Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our "best" model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Global Change Biology 07/2015; DOI:10.1111/gcb.13038 · 8.04 Impact Factor
    • "This study underscores the potential of recently developed phylogenetic comparative methods in uncovering complex dynamics in the evolution of climatic niches. Many studies, particularly in the context of environmental niche modeling, tend to assume that climatic niches are static, yet little is known about how niches evolve when that assumption is not met (Pearman et al. 2008). In this study we demonstrate extensive heterotachy in evolutionary rates in primate climatic niches, revealing the location, number and direction of changes in rates in lineages and the differences among clades. "
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    ABSTRACT: Climatic niches have increasingly become a nexus in our understanding of a variety of ecological and evolutionary phenomena, from species distributions to latitudinal diversity gradients. Despite the increasing availability of comprehensive datasets on species ranges, phylogenetic histories, and georeferenced environmental conditions, studies on the evolution of climate niches have only begun to understand how niches evolve over evolutionary timescales. Here, using primates as a model system, we integrate recently developed phylogenetic comparative methods, species distribution patterns and climatic data to explore primate climatic niche evolution, both among clades and over time. In general, we found that simple, constant-rate models provide a poor representation of how climatic niches evolve. For instance, there have been shifts in the rate of climatic niche evolution in several independent clades, particularly in response to the increasingly cooler climates of the past 10 My. Interestingly, rate accelerations greatly outnumbered rate decelerations. These results highlight the importance of considering more realistic evolutionary models that allow for the detection of heterogeneity in the tempo and mode of climatic niche evolution, as well as to infer possible constraining factors for species distributions in geographical space. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Evolution 07/2015; 69(9). DOI:10.1111/evo.12730 · 4.61 Impact Factor
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