DOI: 10.5061/dryad.mpg4f4rdw
title: Data from: Coincident transitions across elevation and origins of functional innovations drove the phenotypic and ecological diversity of lungless salamanders

description: Ecological opportunity (EO) is an important catalyst for evolution.
 Whereas theory often centers around a lineage encountering a source of EO
 in isolation, in practice, they experience numerous sources of
 opportunity, either concurrently or sequentially. Such multiplicity can
 obscure the macroevolutionary signature of EO. Here, we test the effects
 of elevation (a proxy of the “mountain effect”) and an array of functional
 innovations on the evolutionary history of plethodontid salamanders, a
 diverse and charismatic radiation of lungless amphibians. Functional
 innovations unlock access to novel microhabitats, ultimately enabling
 sub-lineages to occupy a diverse range of ecological niches, particularly
 in lowland areas where those niches are more abundant. Consistent with
 expanded ecological opportunity, such transitions to lower elevation
 result in rapid phenotypic evolution. At high elevation, by contrast,
 rates of phenotypic evolution and phenotypic disparity decline, reflecting
 a loss of phenotypically extreme ecological specialists. Transitions in
 elevation and the origin of innovations appear largely coincident among
 lungless salamanders, suggesting myriad sources of EO. The magnitude of
 the “mountain effect” on evolutionary rates (~10-fold) is on par with or
 greatly exceeds that of islands, lakes, and coral reefs on other iconic
 vertebrate radiations. Therefore, we find that elevation acts as a major
 ecological moderator and, in concert with functional innovations, shapes
 the ecological and phenotypic diversity of lungless salamanders.
description: # Data from: Coincident transitions across elevation and origins of
 functional innovations drove the phenotypic and ecological diversity of
 lungless salamanders Authors: Edward D. Burress, Meaghan Gade, Eric A.
 Riddell, and Martha M. Munoz correspondence: Edward D. Burress;
 [edburress@ua.edu](mailto:edburress@ua.edu) **The associated Dryad
 repository contains five files (one on Dryad; four on Zenodo)** (1) A .csv
 file (Burress_et_al_supporting_data.csv) that contains the supporting data
 used for the analyses in the manuscript This file contains 13 columns.
 Column abbreviations are as follows: species names (x), snout-vent length
 (SVL), snout length (SnL), head length (HL), body width (BW), forelimb
 length (FLL), hindlimb length (HLL), tail length (TL), mean elevation
 (ln_elevation_mean), median elevation (ln_elevation_midpoint),
 microhabitat designation (microhabitat), alternative microhabitat
 designation (alternative_microhabitat), and notes (notes). All numerical
 values are species means. Column 1 contains species names. Columns 2-8 are
 reported in ln-transformed millimeters. Columns 9-10 are reported in
 ln-transformed meters. Columns 11-12 are categorical microhabitat
 categories (note that not all species have an alternative microhabitat
 designation) Column 13 contains any relevant notes pertaining to
 microhabitat designations (note that not all species have notes) ####
 Supplementary File (2) a .pdf file (Supplementary_Materials_salamander)
 that contains the supplementary materials referenced in the manuscript.
 Table S1. Model fitting among transition models that describe microhabitat
 evolution in lungless salamanders. The row in bold is the best-fitting
 model according to AICc. Abbreviations: Equal rates (ER), symmetrical
 rates (SYM), all rates different (ARD), sample size corrected Akaike
 information criterion (AICc). Figure S1. Species richness (left panel) and
 elevation (right panel) across the distribution of plethodontid
 salamanders. Illustrations depict a species from the indicated geographic
 region. The inset in the right panel depicts species richness across the
 elevational gradient. Figure S2. Evolutionary history of the elevational
 (midpoint) distribution of plethodontid salamanders (A). The ancestral
 states were estimated with Maximum likelihood in the contMap function in
 phytools (Revell 2012). Distribution of elevation (midpoint) among species
 (B). Bars reflect the three discretization schemes used for elevation
 described in the text: the lower and upper 50th percentiles, lowland
 (&lt;1068.5m) and highland (&gt;1068.5m); the lower, mid, and upper 33rd
 percentiles, lowland (&lt;796.3m), midland (7.96.3-1465.6m), and highland
 (&gt;1465.6m); and four quartiles, lowland (&lt;640m), midlow (641-1069m),
 midhigh (1070m-1689m), and highland (&gt;1690m). Compare with the results
 shown in Figures S6-8. Figure S3. Diagram depicting the assessment of
 coincident and delayed rate shifts. Hypothetical scenario in which a
 functional innovation (shown in red) is a synapomorphy of a clade
 (highlighted in gray). An ancestral state reconstruction would highlight
 the most recent common ancestor of the clade as the origin of the
 innovation (delineating the “focal clade”); however, the true origin would
 naturally lie along the rootward branch (bc). Therefore, we consider rate
 shifts along bc as coincident with the origination of the functional
 innovation. To also account for evolutionary lag, we consider delayed rate
 shifts along up to two tipward branches (bd). The same assessment was
 applied to microhabitat transitions. Figure S4. Statistical independence
 of rate and state using limb length as an example (reduced limbs). We
 interrogated the non-independence of phenotype and rate directly following
 the same workflow used in the main text (i.e., the rate-by-state analyses
 depicted in Figure 2). The multivariate evolutionary rate of forelimb
 length is not correlated with limb length (residuals) using phylogenetic
 contrasts or tip rates (all P&gt;0.446). Therefore, although limb length
 is a trait used to calculate the multivariate evolutionary rate in the
 main text, its evolutionary rate is independent of its state (i.e., it is
 statistically unproblematic as an innovation; ‘reduced limbs’). Figure S5.
 Trait correlations for all pairs of traits, as estimated with MuSSCRat
 (May & Moore, 2020). These correlations were used to inform the
 simulated datasets (see main text). During this procedure, we arbitrarily
 used the values from the model with a prior of 100 rate shifts; trait
 correlations from the model with the lowest and highest prior on the
 number of rate shifts are shown to demonstrate that the estimates are
 equivalent. The dashed line depicts a 1-to-1 relationship. Figure S6.
 Rates of salamander phenotypic evolution across elevation quartiles,
 estimated with a random local clock model in MuSSCRat (May and Moore 2020)
 across different prior numbers of rate shifts. Colors depict the elevation
 quartile: lowland (blue), midlow (yellow), midhigh (orange), and highland
 (red). All models have posterior probability = 1.0. Figure S7. Rates of
 salamander phenotypic evolution across elevation quartiles, estimated with
 an uncorrelated lognormal model in MuSSCRat (May and Moore 2020). Colors
 depict the elevation quartile: lowland (blue), midlow (yellow), midhigh
 (orange), and highland (red). Posterior probability = 1.0. Compare with
 the results using a Random Local Clock model depicted in Figure 2a in the
 main text. Figure S8. Sensitivity of the MuSSCRat analyses to different
 numbers of states in the discrete character (i.e., different solutions
 when discretizing elevation). Using 2, 3, or 4 discrete states to
 represent elevation resulted in unanimous support for faster rates of
 phenotypic evolution in lowlands. Posterior probability (PP) that the
 rates are state-dependent is inset for each model. Figure S9.
 Branch-specific background rates of phenotypic evolution across the
 plethodontid phylogeny. Background rates reflect rate heterogeneity not
 attributable to elevation (compare with Figure 1 in the main text). Figure
 S10. Trait-specific rates of evolution in lungless salamanders, as
 estimated with MuSSCRat. Distributions reflect the range of estimates
 across 100,000 iterations. Rates are summarized from a single model, but
 the trait-specific rates were visually identical across all models. Figure
 S11. The origins of functional innovations across the plethodontid
 phylogeny. Posterior probability (PP) of innovation-associated rate shifts
 is labeled with text. Reference Figure 1 for tip names. Note that the rate
 shift associated with miniaturization in Thorius (light blue; PP=0.86) is
 not labeled because the shift occurred in rates of background variation
 (not rates as they respond to elevation as shown here). Figure S12.
 Posterior probability (PP) of rate shifts across models with different
 priors on the number of shifts (A). The support for rate shifts waned with
 priors of fewer rate shifts, but rate shifts along innovation-associated
 branches were concentrated in the 99th and 95th percentiles across models
 with different priors (A). Posterior support for most branches followed
 this pattern, as they fell below the 1:1 line (dashed lines in B). Compare
 to Figure 3 in the main text. Figure S13. Microhabitat and phenotypic
 diversity across the elevational gradient ent summarized (averaged) across
 six different coding schemes presented by Baken and Adams (2019): the
 number of microhabitat specialists (a) and phenotypic disparity (b). The
 x-axis depicts the four elevation quartiles, from low (Q1 to high (Q4).
 Figure S14. Distribution of traits across microhabitat types (mean±95%
 C.I.). With only one exception (wide bodies in arboreal species), the
 phenotypic extremes of each trait are found in a microhabitat type that is
 lost at high elevation. Figure S15. Ancestral state reconstruction of
 microhabitat use in lungless salamanders (A). Pies at internal nodes
 denote the proportion of character states across 1000 stochastic
 histories. Note that “cave” is painted the same color across the plot, but
 was coded as a polymorphic state (i.e., associated with several
 microhabitats; see Figure 1). Transition matrix showing the relative
 transition rates among character states (B; warmer colors reflect higher
 transition rate). #### Code/Software (3 a .Rev file
 (musscrat_script_multivariate_RLC.Rev) including the script to run the
 multivariate, state-dependent musscrat model with a random local clock.
 (4a.Nex file (musscrat_script_multivariate_UCLN) including the script to
 run the multivariate, state-dependent musscrat model with an uncorrelated
 lognormal clock. (5 a .R file (RevBayes_log_reader_script.R) including the
 script to extract parameters from a RevBayes output log file (for use in
 other programs such as R).