From Micro to Macro: The Evolution of Phenotypic Diversity in Plethodon Salamanders Dean C. Adams Department of Ecology, Evolution and Organismal Biology Department of Statistics Iowa State University

Understanding Phenotypic Diversification How do evolutionary processes at one temporal scale affect patterns of diversity at other scales? Species interactions often drive diversity at contemporary timescales Such patterns are not always seen at the macroevolutionary level (Jablonski 2008) Theoretical approaches linking micro- and macroevolution also remain underdeveloped Therefore, how do we study the link between the two?

From Micro- and Macroevolution: I How do taxa and populations respond to similar selective pressures? Sometimes similar responses are found to common selection pressures (e.g., Schluter and McPhail, 1992; Reznick et al. 1996; Losos et al. 1998) Strong evidence of adaptation, and suggests a link between microevolutionary change and macroevolutionary diversification This begs the question: To what extent is the evolutionary process ‘repeatable’? To assess this question requires two things: 1: A method to quantify patterns of phenotypic evolution 2: Statistically comparing patterns across replicated evolutionary units

Quantifying Phenotypic Evolution Phenotypic evolution is a trajectory in morphospace Evolutionary trajectories completely defined by attributes: Magnitude Orientation Shape V3 V1 V2 Collyer and Adams Ecology. 88: Adams and Collyer Evolution. 61: Adams and Collyer Evolution. 63: p3 p1 p2 Y2Y2

Comparing Evolutionary Trajectories Quantify trajectory attributes Statistically evaluate using residual randomization V3 V1 V2 Phenotypic Evolution Vector Magnitude Direction Collyer and Adams Ecology. 88: Adams and Collyer Evolution. 61: Adams and Collyer Evolution. 63: Shape Note: Trajectory shape only used when trajectories have 3+ levels Summary Stat

Phenotypic Variation in Plethodon Natural, replicated evolutionary experiment 55 species; 105 different community combinations (1-5 spp) Different geographic attributes (narrow/broad, stable/shifting) and competitive interactions Geography, genetics, phylogenetics, behavior, and ecological requirements well documented Many studies reveal phenotypic changes associated with competition Salamander images from Petranka, See: Adams in Biol. Pleth. Salamanders Adams and Rohlf PNAS. 97: Adams Ecology. 85: Maerz, Myers, and Adams Evol. Ecol. Res. 8: Adams et al J. Anim. Ecol. 76: Arif, Adams, Wicknick Evol. Ecol. Res. 9: Myers and Adams Herpetologica. 64:

Example 1: P. jordani & P. teyahalee Extensive ecological work demonstrates competition prevalent Character displacement in head shape observed (Adams 2004) Head shape associated with aggressive behavior (Adams 2004) Species come into contact in several distinct locations Question: Are microevolutionary patterns repeatable across the distributions of these species? Adams Am. Nat. (In Review).

Example 1: P. jordani & P. teyahalee 336 specimens from 3 independent geographic transects Head shape quantified using GMM Evolutionary vectors (allopatry  sympatry) quantified and compared Adams Am. Nat. (In Review).

Example 1: P. jordani & P. teyahalee Patterns suggest phenotypic evolution resulting from competition Adams Am. Nat. (In Review). FactorDf Factor Pillai’s TraceApprox. FdfP Species , 307< Locality Type , 307< Geographic Transect , 616< Species × Locality , 307< Species × Transect , 616< Locality × Transect , 616< Species×Locality×Transect ,

Example 1: P. jordani & P. teyahalee NO difference in magnitude or direction of evolutionary changes among transects within species (i.e. common patterns found) Conclusion: Evolutionary response to competition repeatable in each species: parallel evolution of character displacement Adams Am. Nat. (In Review).

From Micro- and Macroevolution: II Competition among Plethodon species prevalent Competition frequently associated with phenotypic evolution Do microevolutionary changes from competition generate adaptive diversification across lineages? If competitive adaptive diversification, we expect: Association of phenotypic variation and community type Phylogenetic association of phenotype and community Early dispersion of phenotypic evolution in morphospace Greater disparity through time relative to chance Requires a phylogenetic perspective

Example 2: P. cinereus Clade 465 specimens from 52 populations of 8 species (P. sherando & P.serratus not included) Chronogram from tree of Highton (1999: Herpetologica): calibrated branch points from Wiens et al. (2006: Evolution) Adams & Collyer (unpublished). GLS: Morphological variation vs. community composition Within-species morphological disparity examined PGLS: Morphological variation vs. community composition (phylogeny constant) Disparity through time (vs. expectation via Brownian motion simulations)

Example 2: P. cinereus Clade Phenotypic diversity affected by phylogeny Community effect stronger once phylogeny is accounted for 0L,1S 0L,2S 1L,1S 1L,2S 2L,1S 2L,2S 0L,1S 0L,2S 1L,1S 1L,2S 2L,1S 2L,2S LS parameter estimates PGLS parameter estimates PC I PC II PC I Adams & Collyer (unpublished). Phylogenetically naïve Phylogenetically informed

Example 2: P. cinereus Clade Early nodes show significant disparity (adaptive signal) ‘Repulse’ one another in morphospace (indicative of competition) Adams & Collyer (unpublished) PC I PC II Present 2 MYA 4 MYA 6 MYA 8 MYA

Example 2: P. cinereus Clade Significantly greater within-group disparity than expected by chance Microevolutionary changes do not result in phenotypic novelties Suggests recurrent evolution and morphological homoplasy through time Time Relative Disparity (within subclades) Random (Brownian) Observed Disparity Adams & Collyer (unpublished).

Conclusions Some links between micro- and macroevolution can be assessed Replicated patterns (example 1) Phylogenetic trends (example 2) Rates of evolution (e.g., Adams et al. 2009: Proc. Roy. Soc.) Phylogenetic convergence/parallelism (e.g., Revell et al. 2007: Evol.) Models of evolution (e.g., Butler and King 2004: Am. Nat.) Additional analytical methods need to be developed

Previous Lab MembersCurrent Lab Members Dr. Michael Collyer (Postdoc ) Chelsea Berns (PhD student) Saad Arif (MS: 2005) Jim Church (PhD student) Kara Butterworth (MS: 2003) Andrew Kraemer (PhD student) Dr. Melinda Cerney (PhD: 2005) Dr. Jennifer Deitloff (PhD: 2008) Jennifer Donnelly (MS: 2003) Aspen Garry (MS: 2003) Dr. Erin Myers (PhD: 2008) Ashley Connor (undergraduate) Julie Perrett (undergraduate) USNM (esp. A. Wynn) Nicole Seda (undergraduate) Audri Weaver (undergraduate) Mary West (undergraduate) Kate Weigert (undergraduate) Meredith Zipse (undergraduate) Funding NSF DEB NSF CAREER DEB & Supplements Acknowledgments