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Phylogenetic Comparative Methods (PCM) for Geometric Morphometrics

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Phylogenetic Comparative Methods (PCM) for Geometric Morphometrics Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Phylogenetic comparative methods (PCM) for geometric morphometrics 0.6 A Node 1 A 0.4 B Node 0 C 0.2 Node 3 Node 1 D D F Node 0 0.0 Node 3 Node 2 PC 2 Node 2 E CNode 4 Node 4 -0.2 E F B -0.4 0 10 20 30 40 50 -0.6 P. David Polly -0.4 -0.2 0.0 0.2 0.4 0.6 Department of Earth and Atmospheric Sciences PC 1 Adjunct in Biology and Anthropology Indiana University Bloomington, Indiana 47405 USA [email protected] Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Outline • The reason for phylogenetic comparative methods • Brownian motion as a null model for comparative methods • Measuring phylogenetic “signal” o Pagel’s lambda (λ) o Blomberg’s kappa (K) • Phylomorphospace: projecting a tree into shape space o Ancestor state reconstruction o Confidence intervals and probabilities • Types of PCM for GMM o Independent contrasts o PGLS “regression” o Phylogenetic MANCOVA • Adams vs. Klingenberg on analyzing multivariate data • Why visualizing results of PCMs is difficult o PCMs measure joint change in shape, not shape per se The pitfalls of phylogenetic principal components analysis Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly What are phylogenetic comparative methods (PCMs)? Modified statistical tests that take into account non-independence in data that come from different species. PCMs are applied to regression, MANOVA, and other standard tests, and they can be used to estimate rates of evolution and to reconstruct ancestral trait states on a phylogeny. A Node 0 B Node 1 C 0 5 10 15 20 25 30 Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Phylogenetic correlation in random traits C 15 A Node 1 D B 10 F Node 0 C Node 3 D Node 2 5 E Node 4 F Randomly evolved trait 2 0 E 0 10 20 30 40 50 A B -10 -5 0 5 10 Randomly evolved trait 1 Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Additional tips multiplies the effect A B 5 L Node 1 C I D K E Node 0 0 F H G Node 3 F J G E H Node 2 -5 I J Node 4 K C Randomly evolved trait 2 BA L -10 D -15 -10 -5 0 5 10 15 0 10 20 30 40 50 Randomly evolved trait 1 Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly How do PCMs work? • PCMs estimate how much covariance a trait should have between taxa (and between traits) is expected from the phylogenetic topology • The expected phylogenetic covariance is removed, leaving the residual between-trait covariance • Statistical tests are carried out on the residual component A strong Node 1 B weak strong Node 0 C Node 3 medium D Node 2 strong E Node 4 F 0 10 20 30 40 50 expected covariance Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly How do we know the expected covariance? Depends on how the traits evolve: Brownian motion (purely random evolution) is usually used for PCMs. Evolution is change in the mean phenotype (=trait value) from generation to generation... Evolution = Mean(selection) + Mean(drift) + Mean(nongenetic variation) Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Definition of Brownian motion • Brownian motion is equivalent to an unbiased random walk (at type of Markov process) • change at each step is random with respect to other steps • change at each step has an equal chance of moving in positive or negative direction • typical implementation specifies steps as coming from a normal distribution with mean=0 and variance=rate of evolution mean = 0 50% negative 50% positive -3 -2 -1 0 1 2 3 change at each step variance determines the rate Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Statistical properties of Brownian motion 1 random walk 100 random walks Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Statistical properties of Brownian motion 1. Probability of endpoints is a normal distribution (central limit theorem ensures that outcome of series of random events is normally distributed) Probability of end points 2. The most likely endpoint is the starting point 3. The standard deviation of endpoints increases with the square root of time 4. The variance of the endpoints increases linearly with time 5. The variance of the endpoints equals average squared change per step (rate) x number of steps (time) Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Expected covariance among tips is proportional to shared branch lengths Because variance increases linearly with time, covariance between tips is linear with respect to shared branch time C D A B E F A B C D E F 50 50 Node 1 Node 3 Node 4 40 40 30 30 Node 2 20 20 10 10 Node 0 0 0 -5 0 5 10 15 Earth andAtmosphericSciences (c) 2018, P. DavidPolly (c) 2018,P. proportional to shared branchlengths to shared proportional Expected covarianceamongtipsis Actual expectedvarianceandcovarianceisCxrate (as explainedabove) branches. of shared total branchlengthbetweentipandbaseo Phylogenetic covariancematrix(“C”ofmanyauthors) hasdiagonalequalto A Node 1 B | Indiana University Node 0 C Node 3 D Node 2 E Node 4 F 0 10 20 30 40 50 ff diagonalsequaltolength Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Expected vs. actual Covariance expected under Brownian motion Covariance of 50 simulated traits Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Summary of Brownian motion • Brownian motion makes a good statistical null because it is a purely random model • Outcomes of Brownian motion processes are statistically predictable • Brownian motion can occur in nature through genetic drift or selective drift (selection that changes randomly in direction and magnitude); therefore Brownian motion does not necessarily equate with “neutral evolution”. Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Measuring phylogenetic “signal” The phylogenetic component of data can only be assessed relative to some expectation, such as Brownian motion. Blomberg’s Κ (kappa) = observed cov / expected cov (Blomberg et al., 2003) see also Adams’ (2014) Κmulti especially for multivariate data K can be thought of as the proportion of the covariance that is due to phylogeny. Usually ranges 0 to 1, but can be greater than 1 if phylogenetic covariances are stronger than under BM. Pagel’s λ (lambda) = scaling factor so that tree fits BM model (Pagel, 1999) λ can be thought of as how you would have to scale the branch lengths so that the data would be obtained under a BM model. Also usually ranges 0 to 1, where 0 is equivalent to no phylogenetic structure and 1 is equal to actual phylogenetic structure. Can also be greater than 1 if phylogenetic covariances are stronger than under BM. Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Pagel’s lambda illustrated 50 40 30 20 10 0 λ = 0(ish) λ = 0.2 λ = 0.5 λ = 1.0 no phylogenetic full phylogenetic “signal” “signal” (BM) G562 Geometric Morphometrics Reconstructing evolution of shape Brownian motion in reverse Most likely ancestral phenotype is same as descendant, variance in Descendant likelihood is proportional time since the ancestor lived 100 80 60 40 20 -20 -10 0 10 20 Ancestor? Department of Geological Sciences | Indiana University (c) 2012, P. David Polly G562 Geometric Morphometrics Ancestor of two branches on phylogenetic tree If likelihood of ancestor of one descendant is normal distribution with variance Descendant 1 Descendant 2 proportional to time, then likelihood of two ancestors is the product of their probabilities. This is the maximum likelihood method for estimating phylogeny, and for reconstructing ancestral phenotypes. (Felsenstein, Common ancestor? Department of Geological Sciences | Indiana University (c) 2012, P. David Polly G562 Geometric Morphometrics Phylogenetic tree projected into morphospace • Ancestral shape scores reconstructed assuming Brownian motion • Ancestors plotted in morphospace • Tree branches drawn to connect ancestors and nodes 0.15 DOG 0.10 OTTER Node 4 0.05 2 FOSSA Node 2 0.00 PC Node 3 Node 1 Node 0 -0.05 HUMAN LEOPARD -0.10 WALLABY -0.15 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 PC 1 Department of Geological Sciences | Indiana University (c) 2012, P. David Polly Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Phylogenetic tree with 95% confidence intervals 0.2 HUMAN 0.1 DOG WALLABY LEOPARD Node 0 Node 1 Node 3 0.0 Node 2 FOSSA PC 2 Node 4 -0.1 -0.2 OTTER -0.2 -0.1 0.0 0.1 0.2 PC 1 Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Gómez-Robles et a.. 2013. PNAS, 110: 18196-18201. Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly What relationship do we expect between phylogeny and PCA space? how will these taxa be arranged in a PCA? A Node 1 B Node 0 C Node 2 D 0 10 20 30 40 50 60 Earth and Atmospheric Sciences | Indiana University (c) 2018, P. David Polly Two clades separated by PC 1 A Node 1 B Node 0 C Node 2 D 0 10 20 30 40 50 60 0.15 0.10 D 0.05 Node 2 0.00 Node 0 NodeA 1 PC 2 B -0.05 C -0.10 -0.15 -0.4 -0.2 0.0 0.2 0.4 PC 1 Earth and Atmospheric Sciences | Indiana University (c) 2018, P.
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