Supplemental methods for: Geographic range dynamics drove hybridization in a lineage of

angiosperms

1 1 1 2 1 R.A. FOLK , C.J. VISGER , P.S. SOLTIS , D.E. SOLTIS , R. GURALNICK

1Florida Museum of Natural History

2Biology, University of Florida

3Author for correspondence: [email protected]

1 Sequencing: Sequencing followed previously developed methods1 with the following modifications: library preparation was performed by RAPiD Genomics (Gainesville, FL; using

TruSeq-like adapters as in Folk et al. 2015), the targeted insert size was > 200 bp, and sequencing used a 300-cyle (150 bp read) kit for a HiSeq 3000 instrument. The overall outgroup sampling (21 taxa total; Supplementary Table S1) was improved > 5 fold.2 This includes several representatives each of all lineages that have been hypothesized to undergo hybridization in the

Heuchera group of genera.

For the transcriptomes, reads were assembled against the low-copy nuclear loci from our targeted enrichment experiment, where the targets stripped of intronic sequence but assembly methods otherwise followed a previously developed BWA-based approach1. Transcriptomic reads were also mapped to a parviflora var. saurensis chloroplast genome reference1 which was stripped of intronic and intergenic sequence. Assembly methods for target-enriched data followed the BWA-based approach1 directly.

In practice, intronic sequence can be recovered from RNAseq data,3 but has consistently lower coverage (pers. obs.); moreover non-coding read dropout can be expected to be high for more divergent outgroups added here. For this reason, only coding reference sequences were used to assemble transcriptomic taxa. For nuclear analyses, reads were assembled with 277 references comprising the gene sequences used for bait design, with intronic sequences stripped.

For chloroplast analyses, the reference plastid genome of Heuchera parviflora var. saurensis1 was stripped of all intronic and intergenic sequence; reads were assembled to 113 references comprising coding sequences in the chloroplast (protein-coding genes, rDNA, tRNAs).

Alignment and concatenation: All contig consensus sequences were aligned using

MAFFT4 using “–auto” and default gap cost options (alignments were completely remade rather

2 than adding new sequences to the existing alignments). Manual editing was deemed unnecessary for the plastid analysis and for enriched taxa in the nuclear analysis. However, for several nuclear genes, exon-intron boundaries were incorrectly aligned for the 5 taxa with transcriptomic data. The errors were identified and manually edited in Geneious (version R9) to improve sequence homology assessment. Due to aberrant placement of transcriptome taxa towards the base of the tree, likely caused by the highly non-random nature of missing data (introns, 2/3 of the dataset, absent from transcriptomes), we excluded all intronic positions (still present in genomic assemblies) from the matrix after alignment.

Species tree estimate: Given the strong similarity of concatenated and coalescent phylogenetic estimates with and without various partitioning schemes for the nuclear dataset,2 we chose to focus on concatenation for this case study, which under these conditions serves as an estimate of species phylogeny. To incorporate phylogenetic uncertainty in our ancestral reconstructions, we performed a RAxML rapid bootstrap analysis (option -f a) with 1,000 bootstraps on the nuclear data, using a matrix where one individual was randomly selected per species, setting RAxML to output branch lengths on the bootstrap trees. Since non-coding sites were excluded, and gene-wise partitioning did not previously have a major impact on topology or support, we did not partition nucleotide positions.

Point record synthesis: We synthesized available point records from the California,

Pacific Northwest, and Intermountain Consortia of Herbaria; GBIF, S-NET (http://science- net.kahaku.go.jp/specimen_en/collection/); OS (https://herbarium.osu.edu/online-data-access);

SEINet; and SERNEC. After assessing weaknesses in point records from these repositories, strategic taxa of Heuchera were identified, imaged, and georeferenced from specimen loans from the following herbaria: ARIZ, ASC, ASU, BRIT, CAS, CS, DUKE, F, GH, MEXU, MICH,

3 MINN, MNA, MO, NCU, NMC, NY, RM, RSA, SIU, TENN, TEX, UNM, US, UTC, UTEP,

VT, WCUH, WIS, XAL. Several species (H. acutifolia, H. glomerulata, H. inconstans, H. longipetala, H. mexicana, H. rosendahlii, H. sanguinea, H. soltisii, and H. wellsiae) consist entirely of records identified by the first author and taken from previously published monographic range maps;5-7 additional unpublished identified records were available for

Mexican H. versicolor. Finally, we used a significant number of occurrences from previous fieldwork that serve as ground-truthed records; for H. longiflora, H. missouriensis, H. parviflora,

H. puberula, and H. soltisii we mostly or entirely used field-collected GPS records (all but H. longiflora published previously7,8).

For this work, species delimitations were conservative and primarily followed current taxonomic works 5-12); an exception was made for Heuchera versicolor, which was found to be only distantly phylogenetically related to H. rubescens,2 yet with which it has been synonymized in recent works. Recognizing a grossly polyphyletic species was seen as problematic, so these have been treated separately. Locality data for Heuchera versicolor in the strict sense was downloaded from GBIF and SEINet, together with new georeferencing; few records are available for H. rubescens explicitly identified in the strict sense, so we took all records for H. rubescens sensu lato from SEINet and GBIF; individuals outside of the approximate range of this entity and in the range of H. versicolor were removed following the approximately allopatric range previously recognized.9 We removed all point records calculated to the nearest degree.

Spurious records were removed manually by reference to known ranges in the literature (cited above); in particular, a large number of European records had to be removed because this group contains common garden subjects; other botanical garden records were found by scanning locality fields and removed.

4 Layer correlation: We calculated layer correlation in R using Pearson’s ρ on layers downsampled to 2.5-minute resolution (Python library GDAL, http://www.gdal.org/; using nearest-neighbor sampling which is equivalent to subsampling 1/5th of the untransformed original data) and clipped to the combined range of sampled extant taxa using the training region method noted below on pooled point records; convex hulls were calculated separately for disjunct range portions in Asia and North America and merged for the correlation calculation.

Among highly correlated (ρ ≥ 0.75) environmental layer pairs, we deleted one of the layers using a random-number generator. This resulted in 22 layers, which was still excessive given the limited locality data we had for many species, so we retained from this set 12 layers chosen to capture multiple climatic, edaphic, and topographic aspects relevant to Heuchera12,17: mean annual temperature, temperature mean annual range, annual mean precipitation, mean precipitation of driest quarter (i.e., BIOCLIM 1, 7, 12, and 17), elevation, slope, mean coarse fragment percent, mean pH, mean sand percent, mean organic carbon content, needle-leaf land cover percent, and herbaceous land cover percent.

Predicted niche occupancy profiles: While outgroup occurrence samples were generally sufficient, the genus Heuchera contains a number of extremely narrow endemics

(documented range as small as tens of kilometers), many of which have been described in the last decade. For ten included taxa, we could not obtain more training points than the number of layers (i.e., n < 15, = 12 + 25%); given the extensive loans undertaken from critical collections for western North America and Mexico, this likely represents the limits of our knowledge of occurrences for these taxa. Under these conditions complex multivariate methods such as

Maxent are suspect, yet simply excluding taxa with insufficient data may ultimately be misguided in small trees where the effect of taxon sampling may be large. We addressed this by

5 sampling directly from layer values at occurrence coordinates (somewhat similar to 13), excluding pixel-wise duplicates and creating a PNO (predicted niche occupancy) profile solely of these observed values to be sampled under a uniform distribution (next section).

Ancestral suitability overlap: We projected nodal variable distributions into geographical space using (1) a binary approach, and (2) a binned-probabilities approach.

For the binary map (1), intended as representing a literal Hutchinsonian niche (an n- dimensional hypercube in E-space) and close analog of the BIOCLIM method14 (cf. 15), we calculated 95% credibility intervals (2.5th and 97.5th percentiles) from pooled MCMC chains for both ancestral taxa, thinned as described above. We then calculated a binary map in qGIS for each LGM raster by scoring as present only pixels that were in the credibility interval. We then combined these rasters by taking the intersection (in this case the product), returning all pixels that are within 95% of the distribution of all four BIOCLIM variables in the LGM projection.

We also calculated a binned-probabilities map (2), intended to correctly incorporate distributions of suitability rather than ranges, and hence closer to recent modeling approaches, to quantify joint posterior probability of niche suitability. We calculating probability density histograms for the two ancestral taxa of interest using 50 evenly spaced bins from the minimum to the maximum observed MCMC value (lower limit inclusive). The four BIOCLIM rasters were then classified by the corresponding bin probability; the rasters were multiplied to yield joint probabilities. Viewing the result as representative of a posterior probability density function in geographic space, we calculated niche suitability overlap as the intersection IS of their probability densities16:

.

!" = min ()*, ,*) */0

6 where A and B are raster layers with values corresponding to ancestral posterior probability and n is the number of pixels.

Histogram and credibility interval calculations were performed in R (base package); all raster operations were performed in GDAL. For all following analyses, the input data comprised a pool of all 1,000 MCMC chains, thinned to include every 50th sample (100 samples per chain, required for memory management).

Phylogenetic dating: Suitable fossil records do not exist for Heuchera or any relatives; the nearest useful fossils represent lineages that diverged from the focal group ~90 mya17 which does not compare well with the time frame of interest. Nevertheless, we performed dating in

BEAST v. 1.8.318 using the concatenated low-copy nuclear matrix to confirm the compatibility of an approximate Pleistocene timeframe for the lineages of interest with present knowledge.

Hence we used a secondary date19: 7.27 mya for the Heuchera group of genera (i.e., all taxa but

Peltoboykinia and Telesonix. This was treated under two priors: longnormal and truncated normal (1 mya – 28.55 mya, the latter being the estimated age of the Heuchera group; calibration priors with significant probability density at zero are problematic); both prior treatments were set with 7.27 mya and st. dev 1.645. Node age prior standard deviation was from posterior density data,19 namely ([95% interval upper limit] – mean)/2, to impose the approximate uncertainty of the source date. Otherwise, the tree topology was fixed (the same topology used to plot ancestral reconstructions, above), the nucleotide substitution model was GTR-GAMMA (4 gamma categories, estimated base frequencies, exponential prior on alpha with mean 0.5), lognormal uncorrelated relaxed clock (exponential st. dev. prior with mean 0.33 and approximate reference mean prior), Yule speciation (priors on Yule birthrate and root height uniform [0, ]). MCMC was run for 50 million generations, sampling every 1,000 generations, with 20% of this as

7 burnin; 6 independent chains for nuclear data, and 8 for chloroplast data, were run with these settings were combined for a summary tree (i.e., 240-300 million generations retained, or

240,000-300,000 samples). All ESS values (calculated with Tracer v. 1.618) were >100 for chloroplast analyses and >200 for nuclear analyses. Under an introgression scenario, the MRCA of the source and introgressant in a gene tree is expected to date to the hybridization event for any locus that was transferred. Therefore, as a complementary dating approach, we used the chloroplast genome alignment to estimate the age of the MRCA of and California

Heuchera. All settings and MCMC lengths were the same as for the nuclear analysis.

Benchmarking of new methods: Using a 40-core workstation (Intel Xeon, 2.3 GHz) and the extensive MCMC simulations described above, the analyses took approximately five days per run. These resources needs can be manipulated by reducing the thoroughness of PNO sampling or MCMC chains. Resource usage for geographic predictions is fairly trivial; using historical climate data with 2.5-minute resolution, producing both binary and binned- probabilities maps on a single core (Intel Core i5, 2.9 GHz) takes less than an hour.

Phylogenetic estimate: Our nuclear phylogenetic estimates (Supplemental Figures S3) were consistent with previous results while greatly increasing the robustness of taxon sampling9,26; the addition of outgroup taxa resulted mostly in confident placement for both the nuclear and chloroplast trees. Critically, the chloroplast phylogeny (Supplemental Fig. S12) resolved the California clade of Heuchera as sister to a clade comprising Mitella diphylla and M. nuda, confirming that the ancestor of these Mitella species (rather than only Mitella diphylla9) donated its chloroplast genome to the ancestor of the California Heuchera clade.

Phylogenetic dating: In the nuclear gene tree, 95% credibility intervals for crown

MRCA nodes were 2.1784 mya [0.433-4.5639] and 0.7266 [0.1696-1.5062], respectively, for

8 Mitella and California Heuchera under the lognormal calibration prior, and 2.8 mya [1.0915-

4.5766] and 0.9166 [0.3791-1.4773] under the truncated normal prior. Chloroplast estimates for the date of genome transfer were somewhat younger than nuclear estimates: 1.8792 mya [0.334-

4.0491] under the lognormal calibration prior, and 2.3031 mya [0.7142-4.0726] under the truncated normal prior. Hence, dating was uncertain but compatible with a Pleistocene timeframe for introgression and for co-occurrence of ancestors of these two taxa, motivating further analyses to identify narrower time intervals that facilitate shared geographic range.

Niche modeling metrics: The mean of pixel-wise deduplicated occurrence records available per taxon (Supplemental Table S2) was 140. The mean and minimum AUC (areas under the curve) for the narrow-buffer models were 0.9263 and 0.843, respectively; for wide- buffer models, these were 0.9471 and 0.882. As expected, including more unsuitable habitat in training regions drives fit statistics up; the narrow-buffer training regions were fairly conservative given that no more than ~40 km was included beyond observations. PNO profiles

(graphs given in Supplemental Fig. S7) for extant descendants of the ancestors of interest) were typically somewhat nonnormal.

Niche trajectories: While not our primary goal, our ancestral reconstructions demonstrated apparent conservatism of niche in the Heuchera group of genera. In particular, the

California Heuchera clade and its sister H. brevistaminea are dramatically different from other

Heuchera species for a range of variables examined, particularly for soil sand percent and mean precipitation of the driest quarter. Eastern species (H. americana clade, H. villosa clade) showed strong parallel divergences from their western sisters (respectively: H. parvifolia clade, H. micrantha clade) for most variables examined. The apparent phylogenetic structure in extant niche space for all variables tested suggests the importance of ecological shifts in the

9 diversification of Heuchera (already suspected given the near lack of post-zygotic reproductive barriers among extant taxa11,12). Our results also reveal the benefit of including non-climatic variables, such as soil and land cover, which clearly showed different but complementary patterns, in modeling niche occupancy.

A comment on alternative approaches: The geographic projections inferred in 15,20 are superficially similar; briefly, this method sought to reconstruct a niche envelope defined by minima and maxima across environmental variables; the distribution of suitability is uniform within this outlined space, as with literal Hutchinsonian niche, and hence resultant maps are binary. The niche envelope inferred in this method and used for projection is not correctly formulated; rather than using distributions on ancestral trait values, minima and maxima are found for environmental variables of extant taxa, ancestral reconstruction is performed independently for each, treating them as independent characters, and ancestral minima and maxima are taken as the basis for projecting a binary map. It is unreasonable to conclude that the minimum and maximum of a single physiological limit of an organism evolve independently.

Likewise, minima are not constrained to be lesser than maxima, and may well not become so when modeled independently under Brownian motion, creating impossible E-spaces. The methods presented here instead seek to infer ancestral distribution first, and project using binary or binned-probability methods (the former most directly analogous to the previous method15).

Our binary method, while producing qualitatively similar maps with a similar interpretation to the BIOCLIM modeling method14, also uses percentiles (credibility intervals) to reduce outlier- induced bias.

At times, researchers have attempted to build ecological niche models for higher-level taxa in order to ask questions about deep-level niche evolution (e.g., 21), to the exclusion of

10 attempting to reconstruct past niche evolution. A typical workflow might involve downloading a set of occurrence records for a taxon, and massing these records as representative of a single biological unit for which a correlative niche model (sensu 22) is inferred; the result could be termed a “higher-level ENM.” This approach is theoretically flawed because from the beginning ecological niche has been formulated as a property of species and not of higher-level taxa (23, p.

433; 24, p. 63; 25, p. 416); in certain situations, there is a justification for building models of divergent population-systems within species, which could be viewed as incipient or undetected species 26. While there is nothing methodologically preventing researchers from inferring environmental envelopes from arbitrarily large assemblages of species, monophyletic or otherwise, the biological interpretation of these is suspect, and contained extant species and biological individuals will share only subsets of the E-space in the resultant model. Such an approach would essentially assume that the ancestral niche is equivalent to the weighted sum

(weighted by number of occurrences) of descendant niche space, an assumption guaranteed to be false as long as niche divergence has happened even once without reversal in the clade of interest. The physiological tolerances and biotic interactions implied by this model will likewise have no basis in species or individuals that actually existed or exist in the present. Beyond theoretical concerns, there are practical concerns that suggest biased inference compared to ancestral reconstruction. For examining past environmental suitability, the object of inference is the species that occurred in a time slice of interest; where this slice is deeper than cladogenic events, hypothetical ancestors are necessarily meant. Empirically, a MRCA will almost always be narrower in environmental tolerance than the union or sum of E-space of all descendants. We suspect that higher-level clade ENMs will consistently, and perhaps vastly, over-predict suitability as compared to ancestral niche models, even considering the possibility that the latter

11 estimates could contain over-prediction due to reconstruction error; this may be most acute for clades with high levels of niche dispersion. If researchers are in a position to use paleoclimate information inferred directly from fossil occurrences, in our opinion these are best incorporated in an ancestral reconstruction paradigm as node constraints (which can be set in ambitus using control file templates) or with fossil taxa explicitly incorporated as tip taxa (possible with current phylogenetic methods given a phenotypic data partition). Attempts to directly infer the niche space of extinct species from paleo-occurrence records are typically limited by numbers of occurrences, even considering only the few taxa for which an extensive historical record is available; while perhaps tempting, we aver that shifting the object of inference to a higher taxonomic level to manipulate sample numbers reduces the biological meaning of resultant niche models.

From the Bayesian point of view, if a PNO profile is accepted as a reasonable estimate of the probability of suitability with respect to each environmental predictor, we argue that under certain implementations of Bayesian reconstruction sampling from this distribution is a reasonable way to incorporate ecological breadth. Contra 27, we have not seen results compatible with the assertion that Brownian variance alone drives the variance of nodal estimates, since training region treatments did impact results quantitatively if not qualitatively (cf. Tables 1, 2).

12 REFERENCES

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13 angiosperm family Saxifragaceae. Molecular Phylogenetics and Evolution 83, 86–98 (2015). 20. Yesson, C. & Culham, A. A phyloclimatic study of Cyclamen. BMC Evolutionary Biology 6, 72 (2006). 21. Meseguer, A. S., Lobo, J. M., Ree, R., Beerling, D. J. & Sanmartín, I. Integrating Fossils, Phylogenies, and Niche Models into Biogeography to Reveal Ancient Evolutionary History: The Case of Hypericum (Hypericaceae). Systematic Biology 64, 215–232 (2015). 22. Soberón, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species' distributional areas. Biodiversity Informatics 2, 1–10 (2005). 23. Grinnell, J. The Niche-Relationships of the California Thrasher. The Auk 34, 427–433 (1917). 24. Elton, C. S. Animal ecology. 1–207 (The Macmillan Company, 1927). 25. Hutchinson, G. E. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 22, 415–427 (1957). 26. Pelletier, T. A., Crisafulli, C., Wagner, S., Zellmer, A. J. & Carstens, B. C. Historical Species Distribution Models Predict Species Limits in Western Plethodon Salamanders. Systematic Biology (2014). doi:10.1093/sysbio/syu090 27. Lawing, A. M. & Matzke, N. J. Conservation paleobiology needs phylogenetic methods. Ecography 37, 001–014 (2014).

14 Supplemental figures for: Historical range dynamics drove hybridization in a lineage of

angiosperms

1 1 1 2 1 R.A. FOLK , C.J. VISGER , P.S. SOLTIS , D.E. SOLTIS , R. GURALNICK

1Florida Museum of Natural History

2Biology, University of Florida

3Author for correspondence: [email protected]

1 Fig. S1. Conceptual summary of the training region development method. Red dots represent occurrence records; blue regions represent ecoregion data; green regions represent regions calculated directly from the records.

2 Fig. S2. Nuclear ML tree used for ancestral reconstructions; branch labels represent bootstrap support (plotted where ≥ 50). The two distant outgroups Peltoboykinia and Telesonix were not plotted in trees in the main text; otherwise the sequence of taxa is exactly identical.

3 Fig. S3. Histograms summarizing pooled MCMC distributions across all 12 variables examined for ancestors of Mitella (red) and California Heuchera (blue). The y-axis represents absolute sample number. Wide-buffer models were used for these histograms.

Permilles

4 Fig. S4. BioGeoBEARS result under the DEC+J model, which was favored by AIC and LRT. Pie slices are proportional to probability. Principal extant and ancestral ranges are given in the legend. Legend abbreviations: P = Pacific, S = Sierra Madre, B = Basin-and-Range, R = Rockies, E = Eastern North America and Plains, A = East Asia; multiple-letter abbreviations are combinations of these.

BioGeoBEARS: DEC + J Legend Telesonix jamesii Mitella stauropetala Conimitella williamsii Mitella caulescens Elmera racemosa P Tiarella polyphylla Mitella diphylla Mitella nuda parviflorum S Bensoniella oregona Mitella ovalis Mitella breweri Mitella japonica Mitella pauciflora B Mitella furusei Mitella stylosa Mitella pentandra Tellima grandiflora diplomenziesii R H. cylindrica H. chlorantha H. parviflora H. missouriensis E H. puberula H. villosa H. glabra H. micrantha H. maxima A H. pilosissima H. grossulariifolia H. merriamii H. rubescens var. rubescens H. pulchella PE H. rubescens var. versicolor H. sanguinea H. wellsiae H. rosendahlii H. brevistaminea PA H. parishii H. elegans H. abramsii H. caespitosa H. hirsutissima PR H. acutifolia H. longipetala H. mexicana H. hallii H. bracteata PB H. woodsiaphila H. eastwoodiae H. glomerulata H. novomexicana SB H. soltisii H. inconstans H. wootonii H. parvifolia H. americana BR H. longiflora H. alba H. pubescens H. caroliniana H. richardsonii RA Peltoboykinia watanabei PRA PEA 20 15 10 50 Millions of years ago PREA

5 Fig. S5. Guide to the coding scheme used for coding the six biogeographic regions, using the Nature Conservancy layer on which training schemes were based. Coloration follows the legend for Fig. S4.

6 Fig. S6. Ancestral geographic projections of estimated niche space for ancestors of Mitella (red) and California Heuchera (blue), at all three time slices with both projection methods. These projections show wide-buffer models; narrow-buffer model projections are given in Fig. 3. Last interglacial = 120-140 kya, LGM = 22 kya, Holocene Optimum = 6 kya.

7 Fig. S7. Histograms representing predicted niche occupancy profiles for the extant members of the California Heuchera and Mitella clades that were inferred to have hybridized. Niche occupancy for H. hirsutissima and H. caespitosa was directly sampled from empirical data; hence modeling data for these are not shown. Results are for the narrow-buffer set of models.

California California Mitella group Mitella group Heuchera group Heuchera group

Mean annual temperature Mean annual temperature Elevation Elevation

0.15 0.06 0.09 0.09

0.04 0.10 0.06 0.06 Probability Probability Probability Probability 0.03 0.02 0.03 0.05

0.00 0.00 0.00 0.00 -100 0 100 200 -100 0 100 200 0 1000 2000 3000 0 1000 2000 3000 Degrees * 10 Degrees * 10 m m

Temperature annual range Temperature annual range Herbaceous landcover percent Herbaceous landcover percent 0.8 0.15

0.075 0.75 0.6

0.10 0.050 0.50 0.4 Probability Probability Probability Probability 0.05 0.25 0.025 0.2

0.00 0.000 0.00 0.0 300 400 500 300 400 500 0 5 10 15 20 25 0 5 10 15 20 25 Degrees * 10 Degrees * 10

Mean annual precipitation Mean annual precipitation Needle-leaf landcover percent Needle-leaf landcover percent

0.15 0.6 0.15 0.10

0.10 0.4 0.10 Probability Probability Probability Probability 0.05 0.05 0.05 0.2

0.00 0.00 0.00 0.0 500 1000 1500 500 1000 1500 0 25 50 75 100 0 25 50 75 100 mm mm

Precipitation of driest quarter Precipitation of driest quarter pH pH

0.08 0.15 0.20

0.10 0.06 0.15 0.10

0.04 0.10 Probability Probability

0.05 Probability Probability 0.05 0.05 0.02

0.00 0.00 0.00 0.00 40 50 60 70 80 40 50 60 70 80 0 100 200 300 0 100 200 300 pH * 10 pH * 10 mm mm

Carbon content 0.12 Carbon content Soil sand percent Soil sand percent 0.20 0.3 0.09 0.09 0.15

0.06 0.2 0.06 0.10 Probability Probability Probability 0.03 Probability 0.1 0.05 0.03

0.00 0.0 0 50 100 150 200 250 0.00 0.00 Tons per hectare 0 50 100 150 200 250 20 40 60 80 20 40 60 80 Tons per hectare

Coarse fragment percent Coarse fragment percent Slope Slope

0.09 0.10 0.075 0.3

0.06 0.050 0.2

0.05 Probability Probability Probability Probability 0.03 0.025 0.1

0.00 0.00 0.000 0.0 0 20 40 60 0 20 40 60 0 10 20 30 0 10 20 30 degrees degrees

Heuchera Heuchera Heuchera Mitella Mitella elegans abramsii parishii nuda diphylla

8 Fig. S8. Time calibrated phylogeny from BEAST, using chloroplast data under a truncated normal prior. Branch labels and scale bar represent node heights in millions of years; blue bars represent 95% credibility.

0.217 Heuchera villosa var. macrorhiza 0.4062 H. missouriensis 0.3543 H. americana 0.5567 H. longiflora var. longiflora 0.5877 0.3036H. villosa var. villosa 0.6352 H. pubescens H. americana x H. richardsonii 0.4816 H. americana x H. pubescens 0.6649 H. parviflora var. parviflora 0.391 H. alba 0.8635 0.63850 H. longiflora var. aceroides 0 H. caroliniana 1.03340.4666 H. puberula H. villosa var. arkansana 1.0898 0.7702 H. micrantha var. micrantha H. cylindrica var. alpina 1.0004 H. wootonii 0.2499 H. chlorantha 1.11560.4063 H. grossulariifolia var. grossulariifolia 2x 0.90730.6741 H. cylindrica (H. saxicola) H. cylindrica var. glabra 1.1472 H. rubescens x H. cylindrica H. grossulariifolia var. grossulariifolia 4x 0.9522 H. cylindrica var. cylindrica 1.1846 H. micrantha var. hartwegii 0.1379 H. micrantha var. diversifolia 0.3363 H. glabra x H. micrantha 0.3549 0.5383 H. glabra 0.8361 H. micrantha var. macropetala 1.2343 H. parvifolia var. utahensis 0.7479 Mitella stauropetala 1.1573 Conimitella williamsii 1.30150.5897 Tellima grandiflora Tiarella polyphylla 1.2043 H. merriamii 2.1471 H. micrantha var. erubescens 0.4626 H. maxima 3.3104 H. pilosissima Elmera racemosa 1.6179 Mitella pentandra 3.4697 Mitella caulescens 0.5235 Mitella pauciflora 1.3839 Mitella furusei 2.638 Mitella stylosa 0.21 Mitella japonica 0.2802 H. parvifolia var. nivalis 0.4516 H. inconstans 0.4877 H. glomerulata 0.4363 H. woodsiaphila 0.5892 H. hallii 0.7164 H. parvifolia var. major H. bracteata S H. parvifolia (H. flabellifolia) 4.0783 0.1088 H. wellsiae 0.85160.2094 H. rosendahlii 0.5313 0.401 H. sanguinea 0.4211 H. novomexicana 1.14440.4419 h. soltisii H. eastwoodiae 0.8085 H. eastwoodiae x H. glomerulata 1.2414 H. rubescens var. rubescens H. rubescens var. versicolor 1.4868 0.7344 H. rubescens var. alpicola 5.5239 H. pulchella 0.1699 H. richardsonii E 1.948 0.5875 H. richardsonii W H. bracteata N 0.5955 H. longipetala var. longipetala 1.1099 0 H. acutifolia 1.1643 0 H. mexicana var. mexicana H. brevistaminea 0.9704 6.3688 2.9513 Tolmiea menziesii 3.4872 Lithophragma parviflorum Bensoniella oregona 0.166 H. elegans N 0.3251 H. abramsii 0.4131 H. parishii 9.3667 0.4338 H. elegans S 0.5331 H. hirsutissima 2.3013 H. caespitosa 4.2818 2.0068 Mitella nuda 10.2306 Mitella diphylla 0.4801 Mitella breweri Mitella ovalis Peltoboykinia watanabei Telesonix jamesii

17.5 15 12.5 10 7.5 5 2.5 0

9 Fig. S9. Time calibrated phylogeny from BEAST, using chloroplast data under a lognormal prior. Branch labels and scale bar represent node heights in millions of years; blue bars represent 95% credibility.

0.1796 Heuchera villosa var. macrorhiza 0.3354 H. missouriensis 0.2924 H. americana 0.4627 H. longiflora var. longiflora 0.4893 0.2522 H. villosa var. villosa H. pubescens 0.5299 H. americana x H. richardsonii 0.4019 H. americana x H. pubescens 0.5548 H. parviflora var. parviflora 0.3264 H. alba 0.7213 0.5328 H. longiflora var. aceroides H. caroliniana 0.8661 0.3892 H. puberula H. villosa var. arkansana 0.9137 0.6457 H. micrantha var. micrantha H. cylindrica var. alpina H. wootonii 0.8375 H. chlorantha 0.9356 0.2076 H. grossulariifolia var. grossulariifolia 2x 0.3371 H. cylindrica (H. saxicola) 0.7573 0.5607 H. cylindrica var. glabra 0.9627 H. rubescens x H. cylindrica 0.7947 H. grossulariifolia var. grossulariifolia 4x H. cylindrica var. cylindrica H. micrantha var. hartwegii 0.9954 0.1144 H. micrantha var. diversifolia 0.2798 H. glabra x H. micrantha 0.4476 H. glabra 0.2946 H. micrantha var. macropetala 0.6944 1.038 H. parvifolia var. utahensis 0.6197 Mitella stauropetala Conimitella williamsii 0.9731 0.4922 Tellima grandiflora 1.0971 Tiarella polyphylla 1.0128 H. merriamii 1.7489 H. micrantha var. erubescens 0.3885 H. maxima 2.7 H. pilosissima Elmera racemosa 1.3175 Mitella pentandra 2.833 Mitella caulescens 0.4308 Mitella pauciflora 1.1227 Mitella furusei 2.1324 Mitella stylosa Mitella japonica 0.1727 H. parvifolia var. nivalis 0.2306 H. inconstans 0.3693 H. glomerulata 0.3989 H. woodsiaphila 0.4825 H. hallii 0.5862 0.357 H. parvifolia var. major H. bracteata S 3.3216 H. parvifolia (H. flabellifolia) 0.0893 H. wellsiae 0.1724 H. rosendahlii 0.6977 0.3291 H. sanguinea 0.3457 H. novomexicana 0.3629 h. soltisii 0.9386 0.4364 H. eastwoodiae 0.6617 H. eastwoodiae x H. glomerulata 1.0193 H. rubescens var. rubescens H. rubescens var. versicolor H. rubescens var. alpicola 4.4606 1.2249 0.6052 H. pulchella 0.141 H. richardsonii E 0.4886 H. richardsonii W 1.611 H. bracteata N 0.4966 H. longipetala var. longipetala 0.9371 H. acutifolia 0.983 H. mexicana var. mexicana H. brevistaminea 0.7811 Tolmiea diplomenziesii 5.1302 2.3403 Tolmiea menziesii 2.7871 Lithophragma parviflorum Bensoniella oregona 0.138 H. elegans N 0.2693 H. abramsii 0.3436 H. parishii 7.7472 0.3609 H. elegans S 0.4436 H. hirsutissima 1.8792 H. caespitosa 3.4564 1.6341 Mitella nuda 8.4794 Mitella diphylla 0.393 Mitella breweri Mitella ovalis Peltoboykinia watanabei Telesonix jamesii

20 17.5 15 12.5 10 7.5 5 2.5 0

10 Fig. S10. Time calibrated phylogeny from BEAST, using nuclear data under a truncated normal prior. Branch labels and scale bar represent node heights in millions of years; blue bars represent 95% credibility.

0.5051 H. caespitosa 0.7192 H. abramsii 0.5814 0.9166 H. elegans 1.0686 H. parishii H. hirsutissima 1.3029 H. brevistaminea 0.9278 0.4501 H. rosendahlii 1.1065 H. wellsiae 1.4696 H. sanguinea H. rubescens var. versicolor 1.0222 0.6891 H. longipetala 1.6808 H. acutifolia 1.2149 H. mexicana 1.9196 H. pulchella H. rubescens var. rubescens 2.2773 1.143 H. bracteata 2.7497 H. hallii H. woodsiaphila 2.4054 H. merriamii H. grossulariifolia 0.379 H. soltisii 0.4913 H. novomexicana 3.2378 0.6923 H. glomerulata 0.8177 H. eastwoodiae 1.0495 H. inconstans 1.5948 H. wootonii H. parvifolia 0.619 H. pubescens 2.0643 0.9241 H. alba 0.6974 1.2653 H. longiflora H. americana 3.8475 1.6114 H. caroliniana H. richardsonii 1.0083 H. villosa 1.5077 H. puberula H. missouriensis 2.6856 0.9665 H. parviflora 3.2341 1.7786 H. chlorantha H. cylindrica 3.5498 H. glabra 0.4933 H. pilosissima 6.0907 1.0577 H. maxima H. micrantha 0.3603 Mitella furusei 0.6582 Mitella pauciflora 1.2393 Mitella stylosa 4.1107 Mitella japonica 4.642 Mitella pentandra Tellima grandiflora 5.3568 4.5173 1.5931 Mitella breweri 6.3914 Mitella ovalis 5.0129 Bensoniella oregona 5.7275 2.8 Mitella nuda 4.4467 Mitella diphylla Lithophragma parviflorum 1.9134 Tolmiea menziesii 17.9827 Tolmiea diplomenziesii 3.3109 Elmera racemosa 4.5206 Mitella caulescens 5.9158 3.0475 Conimitella williamsii Mitella stauropetala Tiarella polyphylla Peltoboykinia watanabei Telesonix jamesii

35 30 25 20 15 10 5 0

11 Fig. S11. Time calibrated phylogeny from BEAST, using nuclear data under a lognormal prior. Branch labels and scale bar represent node heights in millions of years; blue bars represent 95% credibility.

0.3983 H. caespitosa 0.4594 0.5691 H. abramsii 0.7266 H. elegans 0.848 H. parishii H. hirsutissima 0.3574 H. brevistaminea 1.034 0.7366 H. rosendahlii 0.878 H. wellsiae 1.1661 H. sanguinea H. rubescens var. versicolor 0.5455 0.8107 H. longipetala 1.3335 H. acutifolia H. mexicana 1.5226 0.9632 H. pulchella H. rubescens var. rubescens 1.8062 0.9036 H. bracteata 2.1802 H. hallii H. woodsiaphila 1.9043 H. merriamii H. grossulariifolia 0.299 H. soltisii 0.389 H. novomexicana 2.5719 0.5499 H. glomerulata 0.6496 H. eastwoodiae 0.8331 H. inconstans 1.2641 H. wootonii H. parvifolia 0.4912 1.6401 H. pubescens 0.7355 H. alba 0.5548 1.0072 H. longiflora 3.0592 1.2811 H. americana H. caroliniana H. richardsonii 0.7933 H. villosa 1.1943 H. puberula 0.7715 H. missouriensis 2.145 H. parviflora 2.5799 1.429 H. chlorantha H. cylindrica 2.8271 H. glabra 0.3901 H. pilosissima 4.8687 0.8431 H. maxima H. micrantha 0.2858 Mitella furusei 0.5246 Mitella pauciflora 0.9866 Mitella stylosa 3.256 Mitella japonica 3.6835 Mitella pentandra Tellima grandiflora 4.2639 1.2622 Mitella breweri 5.116 3.5885 Mitella ovalis 3.9865 Bensoniella oregona 4.5684 2.1784 Mitella nuda 3.5332 Mitella diphylla Lithophragma parviflorum 1.5464 Tolmiea menziesii 13.2847 Tolmiea diplomenziesii 2.6514 Elmera racemosa 3.6204 Mitella caulescens 4.737 2.4669 Conimitella williamsii Mitella stauropetala Tiarella polyphylla Peltoboykinia watanabei Telesonix jamesii

35 30 25 20 15 10 5 0

12 Fig. S12. Chloroplast ML tree; branch labels represent bootstrap support.

99 H. villosa var. macrorhiza H. missouriensis H. americana var. americana H. longiflora var. longiflora 98 H. villosa var. villosa H. pubescens H. americana x H. richardsonii 59 H. americana x H. pubescens 100 H. parviflora var. parviflora 86 H. alba 99 H. longiflora var. aceroides H. caroliniana 54 100 H. puberula H. villosa var. arkansana 95 H. micrantha var. typica H. cylindrica var. alpina H. wootonii H. chlorantha 90 H. grossulariifolia var. grossulariifolia 2x 97 H. cylindrica (H. saxicola) 94 H. cylindrica var. glabella 52 88 H. rubescens x H. cylindrica H. grossulariifolia var. grossulariifolia 4x H. cylindrica var. cylindrica H. micrantha var. hartwegii 100 H. micrantha var. diversifolia 97 H. micrantha x H. glabra 93 H. glabra

91 H. micrantha var. macropetala H. parvifolia var. utahensis 86 Mitella stauropetala Conimitella williamsii 85 Tellima grandiflora 83 81 Tiarella polyphylla 50 H. merriamii 99 H. micrantha var. erubescens 100 H. maxima 94 H. pilosissima Elmera racemosa 100 Mitella pentandra 99 Mitella caulescens 100 Mitella pauciflora 100 Mitella furusei 99 Mitella stylosa Mitella japonica 98 H. parvifolia var. nivalis 100 H. inconstans 62 H. glomerulata 95 H. woodsiaphila 97 H. hallii 9590 H. parvifolia var. major H. bracteata S H. parvifolia (H. flabellifolia) 99 100 H. wellsiae 100 100 H. rosendahlii H. sanguinea 64 92 H. novomexicana 80 100 H. soltisii 71 H. eastwoodiae H. eastwoodiae x H. glomerulata 99 H. rubescens var. rubescens H. rubescens var. versicolor 100 100 H. pulchella 97 H. rubescens var. alpicola 100 H. richardsonii E 100 100 H. richardsonii W H. bracteata N 100 H. longipetala var. longipetala H. acutifolia 100 H. mexicana var. mexicana H. brevistaminea 100 Tolmiea diplomenziesii 94 99 Tolmiea menziesii 97 Lithophragma parviflorum Bensoniella oregona 98 H. elegans N 95 H. abramsii 19 H. parishii 86 100 H. elegans S H. hirsutissima 100 H. caespitosa 69 100 Mitella nuda Mitella diphylla 100 Mitella breweri Mitella ovalis Peltoboykinia watanabei Telesonix jamesii

13 Supplemental tables for: Historical range dynamics drove hybridization in a lineage of

angiosperms

1 1 1 2 1 R.A. FOLK , C.J. VISGER , P.S. SOLTIS , D.E. SOLTIS , R. GURALNICK

1Florida Museum of Natural History

2Biology, University of Florida

3Author for correspondence: [email protected]

1 Table S1. Augmented outgroup sampling over (Folk et al. 2016).

Species Group Source Bensoniella oregona Unplaced Folk et al. 2016 Lithophragma parviflorum Unplaced This study Mitella stauropetala Ozomelis group Folk et al. 2016 Mitella caulescens Ozomelis group This study Conimitella williamsii Ozomelis group This study Elmera racemosa Ozomelis group This study Tiarella polyphylla Ozomelis group 1KP Mitella pentandra Pectiantia group Folk et al. 2016 Mitella furusei Pectiantia group This study Mitella japonica Pectiantia group This study Mitella pauciflora Pectiantia group This study Mitella stylosa Pectiantia group This study Mitella ovalis Pectiantia group This study Mitella breweri Pectiantia group This study Tellima grandiflora Pectiantia group 1KP Tolmiea menziesii Pectiantia group This study Tolmiea diplomenziesii Pectiantia group This study Mitella diphylla Mitella group s.s. Folk et al. 2016 Mitella nuda Mitella group s.s. This study Telesonix heucheriformis Boykinia group 1KP Peltoboykinia watanabei Peltoboykinia group 1KP

2 Table S2. Summary of means and standard deviations of AUC for models (10 bootstraps), as well as pixel-wise de-duplicated occurrence numbers. Taxa omitted (H. brevistaminea, n = 12; H. caespitosa, n = 10; H. hirsutissima, n = 9; H. inconstans, n = 10; H. pulchella, n = 7; H. rosendahlii, n = 2; H. soltisii, n = 12; H. wellsiae, n =4; H. woodsiaphila, n = 3) were narrow endemics for which a uniform distribution on observed pixel-wise deduplicated records was used for the PNO.

NARROW-BUFFER MODELS WIDE-BUFFER MODELS

AUC AUC AUC AUC Occur. mean st. dev. mean st. dev. n 24 Bensoniella oregona 0.972 0.015 0.986 0.004 Conimitella williamsii 0.936 0.018 0.963 0.016 40 Elmera racemosa 0.963 0.008 0.977 0.008 57 Heuchera abramsii 0.999 0.001 1.000 0.000 18 Heuchera acutifolia 0.905 0.028 0.977 0.010 18 Heuchera alba 0.914 0.019 0.965 0.017 19 Heuchera americana 0.857 0.007 0.883 0.005 404 Heuchera bracteata 0.868 0.018 0.959 0.004 67 28 Heuchera caroliniana 0.890 0.043 0.940 0.019 Heuchera chlorantha 0.889 0.022 0.928 0.020 31 Heuchera cylindrica 0.865 0.008 0.897 0.004 426 Heuchera eastwoodiae 0.957 0.016 0.983 0.007 25 Heuchera elegans 0.975 0.007 0.988 0.003 83 Heuchera glabra 0.923 0.012 0.939 0.009 163 Heuchera glomerulata 0.924 0.012 0.954 0.014 48 Heuchera grossulariifolia 0.920 0.017 0.939 0.018 68 Heuchera hallii 0.919 0.024 0.960 0.008 32 Heuchera longiflora 0.956 0.015 0.955 0.012 60 Heuchera longipetala 0.915 0.017 0.943 0.009 82 Heuchera maxima 0.988 0.006 0.997 0.001 20 Heuchera merriamii 0.958 0.007 0.963 0.014 41 Heuchera mexicana 0.923 0.012 0.942 0.015 84 Heuchera micrantha 0.922 0.003 0.938 0.004 503 Heuchera missouriensis 0.944 0.021 0.940 0.013 22 Heuchera novomexicana 0.953 0.012 0.979 0.005 37 Heuchera parishii 0.973 0.002 0.994 0.001 69 Heuchera parviflora 0.909 0.025 0.953 0.019 25 Heuchera parvifolia 0.866 0.005 0.884 0.003 1237 Heuchera pilosissima 0.989 0.004 0.994 0.002 44 Heuchera puberula 0.965 0.006 0.977 0.005 50 Heuchera pubescens 0.921 0.023 0.944 0.015 35

3 Heuchera richardsonii 0.926 0.006 0.924 0.008 261 Heuchera rubescens 0.957 0.005 0.957 0.003 360 Heuchera sanguinea 0.935 0.014 0.954 0.007 73 Heuchera versicolor 0.956 0.005 0.962 0.005 208 Heuchera villosa 0.876 0.009 0.917 0.011 94 Heuchera wootonii 0.963 0.015 0.990 0.003 22 Lithophragma parviflorum 0.926 0.006 0.933 0.008 266 Mitella acerina 0.917 0.018 0.958 0.011 24 Mitella breweri 0.950 0.003 0.957 0.006 269 Mitella caulescens 0.919 0.013 0.923 0.012 105 Mitella diphylla 0.914 0.007 0.927 0.006 153 Mitella furusei 0.883 0.011 0.882 0.014 44 Mitella japonica 0.910 0.016 0.895 0.022 55 Mitella nuda 0.898 0.007 0.895 0.010 248 Mitella ovalis 0.928 0.012 0.948 0.008 109 Mitella pauciflora 0.939 0.012 0.940 0.007 155 Mitella pentandra 0.890 0.005 0.906 0.004 445 Mitella stauropetala 0.942 0.006 0.955 0.005 131 Mitella stylosa 0.918 0.016 0.922 0.015 75 Peltoboykinia tellimoides 0.843 0.027 0.910 0.028 15 Telesonix jamesii 0.974 0.013 0.990 0.005 25 Tellima grandiflora 0.929 0.005 0.939 0.003 468 Tiarella polyphylla 0.942 0.007 0.948 0.004 156 Tolmiea diplomenziesii 0.849 0.017 0.910 0.011 56 Tolmiea menziesii 0.931 0.007 0.955 0.004 155

4 Database*: Collection Species accession Locality Latitude Longitude Collector number Whitman Co., Thomspon Lithophragma SRA: Washington, and parviflorum SRR5253505 USA Pellmyr s.n.

Federation Forest State Park, King Co., Mitella SRA: Washington, 121°42'03.5" caulescens SRR5253504 USA 47°09'12.9" N W Okuyama MC-FDR3 Hellroaring Trail, Gallatin Conimitella SRA: Co., Montana, williamsii SRR5253503 USA 45°26'13" N 111°14'01" W Folk 109 Kittitas Co., Elmera SRA: Washington, Soltis and racemosa SRR5253502 USA Soltis 2179 Jorakuji, Koryo-cho, Mitella furusei SRA: Shimane var. subramosa SRR5253501 Pref., JAPAN Okuyama MSU-Ko4 Takachiho- kyo, Takachiho- Mitella SRA: cho, Miyazaki japonica SRR5253500 Pref., JAPAN Okuyama MJ-T5 Kibune- okumiya, Sakyo, Kyoto Mitella SRA: City, Kyoto pauciflora SRR5253499 Pref., JAPAN 35°08'08.3" N 135°45'55.1"E Okuyama MP−K1004 Kosaka, Ibigawa-cho, SRA: Gifu Pref., MSS- Mitella stylosa SRR5253498 JAPAN Okuyama KZ1106 Fletcher Canyon, Jefferson Co., SRA: Washington, 123°42'25.5" Mitella ovalis SRR5253486 USA 47°31'38.4" N W Okuyama MO-FC8 Rainy Lake Pass, Chelan Co., SRA: Washington, 120°44'07.1" Mitella breweri SRR5253485 USA 48°30'59.9" N W Okuyama MB-RL9 Murii Trail, Engaru City, SRA: Hokkaido Mitella nuda SRR5253484 Pref., JAPAN Okuyama MN01 Tolmiea Lane Co., diplomenziesii Oregon, USA 43.786° N 122.550° W C. Visger 13-9 Tolmiea Linn Co., menziesii Oregon, USA 44.404° N 122.086° W C. Visger 13-12

5 Tiarella polyphylla 1KP: SLOI Chase 38786 Tellima grandiflora 1KP: CTSS Chase 38789 Telesonix jamesii 1KP: OOVX Chase 38782 Peltoboykinia watanabei 1KP: YKFU Chase 38787

6