Phylogenetic Comparative Methods and the Evolution of Multivariate Phenotypes

被引:88
作者
Adams, Dean C. [1 ]
Collyer, Michael L. [2 ]
机构
[1] Iowa State Univ, Dept Ecol Evolut & Organismal Biol, Ames, IA 50011 USA
[2] Chatham Univ, Dept Sci, Pittsburgh, PA 15232 USA
来源
ANNUAL REVIEW OF ECOLOGY, EVOLUTION, AND SYSTEMATICS, VOL 50 | 2019年 / 50卷
基金
美国国家科学基金会;
关键词
multivariate; phylogenetic comparative methods; macroevolution; shape analysis; high-dimensional data; MORPHOLOGICAL DIVERSIFICATION; ECOMORPHOLOGICAL CONVERGENCE; PRINCIPAL COMPONENTS; ECOLOGICAL OPPORTUNITY; STABILIZING SELECTION; SHELL SHAPE; R PACKAGE; RATES; INTEGRATION; MODELS;
D O I
10.1146/annurev-ecolsys-110218-024555
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Evolutionary biology is multivariate, and advances in phylogenetic comparative methods for multivariate phenotypes have surged to accommodate this fact. Evolutionary trends in multivariate phenotypes are derived from distances and directions between species in a multivariate phenotype space. For these patterns to be interpretable, phenotypes should be characterized by traits in commensurate units and scale. Visualizing such trends, as is achieved with phylomorphospaces, should continue to play a prominent role in macroevolutionary analyses. Evaluating phylogenetic generalized least squares (PGLS) models (e.g., phylogenetic analysis of variance and regression) is valuable, but using parametric procedures is limited to only a few phenotypic variables. In contrast, nonparametric, permutation-based PGLS methods provide a flexible alternative and are thus preferred for high-dimensional multivariate phenotypes. Permutation-based methods for evaluating covariation within multivariate phenotypes are also well established and can test evolutionary trends in phenotypic integration. However, comparing evolutionary rates and modes in multivariate phenotypes remains an important area of future development.
引用
收藏
页码:405 / 425
页数:21
相关论文
共 107 条
[1]   Phylogenetic ANOVA: Group-clade aggregation, biological challenges, and a refined permutation procedure [J].
Adams, Dean C. ;
Collyer, Michael L. .
EVOLUTION, 2018, 72 (06) :1204-1215
[2]   Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations [J].
Adams, Dean C. ;
Collyer, Michael L. .
SYSTEMATIC BIOLOGY, 2018, 67 (01) :14-31
[3]   On the comparison of the strength of morphological integration across morphometric datasets [J].
Adams, Dean C. ;
Collyer, Michael L. .
EVOLUTION, 2016, 70 (11) :2623-2631
[4]   Permutation tests for phylogenetic comparative analyses of high-dimensional shape data: What you shuffle matters [J].
Adams, Dean C. ;
Collyer, Michael L. .
EVOLUTION, 2015, 69 (03) :823-829
[5]   A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGH-DIMENSIONAL MULTIVARIATE DATA [J].
Adams, Dean C. .
EVOLUTION, 2014, 68 (09) :2675-2688
[6]   A Generalized K Statistic for Estimating Phylogenetic Signal from Shape and Other High-Dimensional Multivariate Data [J].
Adams, Dean C. .
SYSTEMATIC BIOLOGY, 2014, 63 (05) :685-697
[7]   Assessing Trait Covariation and Morphological Integration on Phylogenies Using Evolutionary Covariance Matrices [J].
Adams, Dean C. ;
Felice, Ryan N. .
PLOS ONE, 2014, 9 (04)
[8]   Quantifying and Comparing Phylogenetic Evolutionary Rates for Shape and Other High-Dimensional Phenotypic Data [J].
Adams, Dean C. .
SYSTEMATIC BIOLOGY, 2014, 63 (02) :166-177
[9]   A field comes of age: geometric morphometrics in the 21st century [J].
Adams, Dean C. ;
Rohlf, F. James ;
Slice, Dennis E. .
HYSTRIX-ITALIAN JOURNAL OF MAMMALOGY, 2013, 24 (01) :7-14
[10]   Comparing Evolutionary Rates for Different Phenotypic Traits on a Phylogeny Using Likelihood [J].
Adams, Dean C. .
SYSTEMATIC BIOLOGY, 2013, 62 (02) :181-192