bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
1 RESEARCH ARTICLE
2 Short Title: Marx et al.—Sawtooth Community Phylogenetics
3
4 Increasing phylogenetic stochasticity at high elevations on summits across a
5 remote North American wilderness
6
7 Hannah E. Marx1,2,3,4, Melissa Richards1, Grahm M. Johnson1, David C. Tank1,2
8
9 1Department of Biological Sciences, University of Idaho, 875 Perimeter Dr. MS 3051, Moscow,
10 ID 83844-3051, USA
11 2Institute for Bioinformatics and Evolutionary Studies, University of Idaho, 875 Perimeter Dr.
12 MS 3051, Moscow, ID 83844-3051, USA
13 3Current address: Department of Ecology and Evolutionary Biology, University of Michigan,
14 Ann Arbor, MI 48109-1048, USA
15 4Author for correspondence: Hannah E. Marx, [email protected], Department of Ecology and
16 Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109-1048, USA
17
18
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19 ABSTRACT
20 PREMISE OF THE STUDY: At the intersection of ecology and evolutionary biology,
21 community phylogenetics can provide insights into overarching biodiversity patterns,
22 particularly in remote and understudied ecosystems. To understand community assembly of the
23 high-alpine flora of the Sawtooth National Forest, USA, we analyzed phylogenetic structure
24 within and between nine summit communities.
25
26 METHODS: We used high-throughput sequencing to supplement existing data and infer a nearly
27 completely sampled community phylogeny of the alpine vascular flora. We calculated mean
28 nearest taxon distance (MNTD) and mean pairwise distance (MPD) to quantify phylogenetic
29 divergence within summits, and assed how maximum elevation explains phylogenetic structure.
30 To evaluate similarities between summits we quantified phylogenetic turnover, taking into
31 consideration micro-habitats (talus vs. meadows).
32
33 KEY RESULTS: We found different patterns of community phylogenetic structure within the six
34 most species-rich orders, but across all vascular plants phylogenetic structure was largely no
35 different from random. There was a significant negative correlation between elevation and tree-
36 wide phylogenetic diversity (MPD) within summits: significant overdispersion degraded as
37 elevation increased. Between summits we found high phylogenetic turnover, which was driven
38 by greater niche heterogeneity on summits with alpine meadows.
39
40 CONCLUSIONS: This study provides further evidence that stochastic processes shape the
41 assembly of vascular plant communities in the high-alpine at regional scales. However, order-
2 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
42 specific patterns suggest adaptations may be important for assembly of specific sectors of the
43 plant tree of life. Further studies quantifying functional diversity will be important to disentangle
44 the interplay of eco-evolutionary processes that likely shape broad community phylogenetic
45 patterns in extreme environments.
46
47 KEY WORDS: Alpine; community phylogenetics; elevation; high-throughput sequencing;
48 Idaho; mean nearest taxon distance; mean pairwise distance; mega-phylogeny; vascular plants
49
50 INTRODUCTION
51 In an ecological context, evolutionary history provides a useful tool for quantifying
52 overall diversity (Pavoine and Bonsall, 2010; Winter et al., 2013; Jarzyna and Jetz, 2016), and a
53 framework to address potential eco-evolutionary drivers of diversity patterns (Webb et al., 2002).
54 On time-scaled phylogenies branch lengths quantify evolutionary time separating species, thus,
55 more closely related species are expected to share ecologically relevant functional traits
56 assuming such traits and niches are phylogenetically conserved (Webb et al., 2002; Cavender-
57 Bares et al., 2009). Generally, this community phylogenetic approach is used to assess the
58 importance of environmental filtering (“clustering” of closely related species within communities
59 in a species pool) or competition defined by limiting similarity (“overdispersion” of distantly
60 related species assemblages) for community assembly (Webb, 2000; Webb et al., 2002).
61 Alternatively, communities could be shaped by assembly processes that are species-neutral, such
62 as colonization and local extinction (MacArthur and Wilson, 1967; Hubbell, 2001).
63 Importantly, many complex ecological and evolutionary processes influence community
64 assembly (Vellend, 2010) requiring careful consideration of system-specific a priori hypotheses
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65 (Gerhold et al., 2015) and cautious interpretations of the resulting community phylogenetic
66 patterns (Mayfield and Levine, 2010). The assumption of phylogenetic niche conservation (PNC)
67 is generally debated (reviewed in Munkemüller et al., 2015), and even with PNC, coexistence
68 theory predicts competition can produce clustering if interspecific competitive hierarchy fitness
69 differences dominate the assembly process (Mayfield and Levine, 2010; HilleRisLambers et al.,
70 2012). Ideally, to interpret processes governing species coexistence, additional information about
71 species functional traits would be used in conjunction with phylogenetic relationships
72 (Cavender-Bares and Wilczek, 2003; Cavender-Bares et al., 2009; Cadotte et al., 2013),
73 especially since different traits may have different levels of conservatism or convergence
74 depending on the community (Cavender-Bares et al., 2006). Detailed, environmentally defined
75 regional species surveys can be used to define environmental filtering in relation to dispersal
76 limitation or competitive exclusion (Kraft et al., 2015), and explicitly test specific environmental,
77 historical, biotic and neutral hypotheses to explain coexistence (Gerhold et al., 2015). However,
78 in remote and understudied ecosystems, such as the high-alpine, these data are challenging to
79 acquire. In such cases, the community phylogenetic approach can be particularly useful for
80 providing insights into macro-ecological and evolutionary processes driving diversity (Marx et
81 al., 2017).
82 With steep environmental gradients over increasing elevation, mountains provide ideal
83 ‘natural experiments’ for understanding general patterns of biodiversity (Körner, 2000; Graham
84 et al., 2014) and adaptive evolution (Körner, 2007; Körner et al., 2011). Alpine regions are the
85 only terrestrial biome with a global distribution (Körner, 2003), yet they represent some of the
86 highest gaps in floristic knowledge (Kier et al., 2005). This is especially concerning because
87 ranges of alpine plants are anticipated to shift with a changing climate (Körner, 2000; Dullinger
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88 et al., 2012; Pauli et al., 2012; Morueta-Holme et al., 2015), so documenting the present floristic
89 diversity in alpine regions is a priority. Previous studies have therefore used the “phylogenetic-
90 patterns-as-a-proxy” for ecological similarity framework (Gerhold et al., 2015) to test the
91 hypothesis that physiologically harsh environments in the high-alpine should filter for closely
92 related species sharing similar traits adapted to abiotic pressures, including low temperatures,
93 extended periods of drought, and extreme ultra-violet radiation (Körner, 1995, 2011). In the
94 Hengduan Mountains Region of China, Li et al. (2014) investigated the community phylogenetic
95 structure of alpine flora across 27 elevation belts ranging from 3000 to 5700 meters. Within sites,
96 they found phylogenetic overdispersion at lower elevations and phylogenetic clustering at higher
97 elevations, and the decrease in pairwise divergence was positively correlated with temperature
98 and precipitation (more significant overdispersion with higher temperatures and precipitation; Li
99 et al., 2014). However, at the highest elevations (above 5500 meters) phylogenetic structure
100 became random (Li et al., 2014), possibly indicating relaxed environmental filtering between
101 treeline and summit belts. In the Rocky Mountain National Park, Colorado, USA, Jin et al.
102 (2015) assessed phylogenetic turnover between 569 plots ranging in elevation from 2195 to 3872
103 meters, and found that plant species were more closely related than expected (high phylogenetic
104 clustering) overall within plots, and had a higher than expected turnover within than among plant
105 clades between plots (Jin et al., 2015). Abiotic environment defined by the elevation of
106 individual plots explained turnover across alpine communities more than spatial distance
107 between sites, implying a regional environmental filter and niche conservatism within clades,
108 which was particularly strong for communities sampled East of the Continental Divide.
109 More recently, community phylogenetic studies in the high-alpine are challenging the
110 ubiquity of abiotic constraints and environmental filtering for shaping communities. In the Écrins
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111 National Park, France, Marx et al. (2017) estimated phylogenetic community structure within
112 and between 15 high-alpine summits. Species were more closely related than by chance on a few
113 summits, but turnover between summits was lower than expected, and neutral models of
114 colonization to and extinction within summits explained community phylogenetic structure for
115 dominant plant orders (Marx et al., 2017). In the Trans-Himalaya, Le Bagousse-Pinguet et al.
116 (2017) found random phylogenetic diversity across phylogenetic and spatial scales, but a
117 tendency towards overdispersion with increasing elevation, contrary to predictions of
118 environmental filtering.
119 These contrasting patterns of community phylogenetic structure likely emerge from
120 complex ecological and evolutionary processes that shape biodiversity in high-alpine ecosystems
121 (Graham et al., 2014), and we are far from a general characterization of elevational diversity
122 patterns across mountain ranges for plants (but see Quintero and Jetz, 2018, for birds). Mountain
123 summits are often inhabited by globally rare or locally endemic lineages (Smith and Cleef, 1988;
124 Kier et al., 2009), so inferring phylogenetic relationships among species is challenging because
125 taxa are either not represented in supertrees, or molecular sequence data is not readily available
126 in repositories such as GenBank for mega-phylogenetic approaches. Targeted-PCR enrichment
127 (Cronn et al., 2012), combined with high-throughput sequencing technologies, provides a
128 solution to retrieving genetic sequence data for entire community assemblages (reviewed in
129 Godden et al., 2012; Grover et al., 2012). These methods are proving useful for resolving
130 diversity patterns within specific lineages (e.g., Uribe-Convers et al., 2016), but are not yet being
131 applied in macro-ecological contexts. Importantly, high-throughput sequencing technologies
132 could potentially capture intra-specific variation between communities, which has been largely
133 unexplored in previous studies of alpine community assembly which either use supertree (Li et
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134 al., 2014) or mega-phylogenetic (Jin et al., 2015; Le Bagousse-Pinguet et al., 2017; Marx et al.,
135 2017) approaches.
136 In this study, we set out to fill a gap in our body of knowledge on high-alpine community
137 assembly by describing the phylogenetic structure of flora across summits within a remote North
138 American wilderness. The Sawtooth National Forest (SNF) located in south-central Idaho, USA
139 (Fig. 1) is known for its immense mountainous terrain (Reid, 1963) and encompasses over
140 200,000 acres of federally designated wilderness area. Lying within the Rocky Mountain chain,
141 this region was formed by the tectonic uplift of the Idaho and Sawtooth batholith (Kiilsgaard et
142 al., 1970). Recent geologic episodes, including the Laramide orogeny in the late Mesozoic and
143 extensive glaciations in the quaternary, resulted in the sharp topography and surface rock
144 formation we currently observe (Borgert et al., 1999), giving the area its namesake (Kiilsgaard et
145 al., 1970). The mountain ranges within the forest boundary include some of the most remote
146 alpine biomes in the contiguous United States, and its alpine flora has been drastically
147 understudied. Besides management focused efforts (Schlatterer, 1972; Harper et al., 1978), no
148 systematic surveys of this region have been conducted.
149 Here, we present the first detailed floristic survey of nine high-alpine summits across
150 three mountain ranges within the SNF (Fig. 1). From these collections, we used targeted high-
151 throughput sequencing to supplement publicly available sequences mined from GenBank and
152 compile a detailed molecular dataset. We used this total combined dataset to infer relationships
153 among all species collected with a mega-phylogenetic approach (Smith et al., 2009; Roquet et
154 al., 2012; Marx et al., 2016), and quantified community phylogenetic structure within and
155 between alpine summits. To test the hypothesis that intense environmental conditions in the
156 high-alpine filter for closely related species that are physiologically able to inhabit the high-
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157 alpine in the SNF, we correlated patterns of community phylogenetic structure with maximum
158 elevation on each summit. Many climatic and geologic factors constitute the local environment,
159 but elevation (a.s.l.) has been used as a proxy for increasing environmental severity of
160 temperature and precipitation in the alpine in general (Korner, 2007), and has been examined in
161 previous studies of high-alpine community phylogenetic structure (Machac et al., 2011; Jin et al.,
162 2015). In central Idaho, corresponding gradients of temperature and precipitation over elevation
163 have been shown to delimit ranges of certain endemic species (Steele et al., 1981; Ertter and
164 Mosely, 1992).
165 If environmental filtering is structuring alpine communities, we expect a negative
166 relationship between phylogenetic distance and maximum elevation – closely related species
167 should occur together more often than by chance (low pairwise phylogenetic distance) at higher
168 elevations (assuming trait conservation for physiologically relevant traits). On the other hand, if
169 traits are convergent or competition is strong, we expect increasing elevation to promote
170 phylogenetic overdispersion (assuming niche conservation). Alternatively, diversity patterns
171 might instead be explained by dispersal limitation in this island-like system (MacArthur &
172 Wilson, 1967), in which case phylogenetic structure within summits is expected to be no
173 different from random, while turnover between summits should be correlated with geographic
174 proximity (Marx et al., 2017). To address how micro-habitats impact community phylogenetic
175 structure above treeline, we separated species collected in alpine meadows from those occurring
176 only on talus slopes. Finally, taxonomic scale is known to impact community phylogenetic
177 structure (Cavender-Bares et al., 2006; Graham et al. 2016), and distinct clades have experienced
178 adaptive radiations into alpine ecosystems (reviewed in Hughes and Atchison, 2015). To assess
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179 how clade-specific strategies may drive community diversity, we investigated patterns across all
180 vascular plants as well as within the six most species-rich taxonomic orders separately.
181
182 MATERIALS & METHODS
183 Study Area and Species Collections
184 For this study, nine alpine summits were sampled from the Sawtooth, White Cloud, and
185 Pioneer mountain ranges within the SNF (Fig. 1). Collections focused on sampling alpine
186 species, here defined as plants occurring in areas above treeline (Billings and Mooney, 1968;
187 Körner, 2003), as this represents a major shift in climate (Richardson and Friedland, 2009).
188 Briefly, starting at the top of each summit, all aspects (as terrain allowed) were traversed down to
189 treeline, and an individual representing each species was collected to sample the diversity, which
190 included herbaceous plants, shrubs, and small trees, and ranged from lycophytes through
191 angiosperms (Johnson et al., in review). Specimens were pressed in the field, and leaf tissues
192 were preserved in silica for molecular analyses. Imaging and processing of the collections were
193 conducted at the University of Idaho Stillinger Herbarium (ID), where all voucher specimens
194 were deposited. Identifications were made using Hitchcock and Cronquist (1973), with
195 nomenclature following the updated taxonomy in the Consortium of Pacific Northwest Herbaria
196 data portal. The combined list of identified species that were collected constitutes the “alpine
197 species pool” considered. Spatial Euclidean distances between summits were calculated from
198 GPS coordinates.
199
200 High-Throughput Sequencing
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201 Total genomic DNA was extracted from silica-dried leaf tissue for all collections
202 following a modified 2x-CTAB extraction protocol (Doyle and Doyle, 1987). Six gene regions
203 with varying rates of molecular evolution that are frequently employed to resolve both recent and
204 distant phylogenetic relationships (Soltis et al., 2011) were chosen for this study, and included
205 representatives of the nuclear (ITS) and chloroplast (atpB, matK, ndhF, rbcL, and trnTLF)
206 genomes. For all vascular plants that were collected on each alpine summit, we used targeted
207 polymerase chain reaction (PCR) to amplify the six gene regions.
208 “Universal” primers for plant systematics were used for amplification and sequencing of
209 all gene regions (sequences and references can be found in Appendix S1). Amplification
210 followed a two-round PCR strategy in overlapping ~400-600 base pair (bp) amplicons to merge
211 across the 300 bp paired-end reads generated with Illumina MiSeq sequencing, so some gene
212 regions (atpB, matK, ndhF, and trnTLF) were amplified in multiple segments (Appendix S1).
213 Following Uribe-Convers et al. (2016), each target-specific primer sequence contained a
214 conserved sequence tag that was added to the 5' end at the time of oligonucleotide synthesis
215 (CS1 for forward primers and CS2 for reverse primers). The purpose of the added CS1 and CS2
216 tails is to provide an annealing site for the second pair of primers. After an initial round of PCR
217 using the CS-tagged, target specific primers (PCR1), a second round of PCR was used to add 8
218 bp sample-specific barcodes and high-throughput sequencing adapters to both the 5' and 3' ends
219 of each PCR amplicon (PCR2). From 5' to 3', the PCR2 primers included the reverse
220 complement of the conserved sequence tags, sample-specific 8 bp barcodes, and either Illumina
221 P5 (CS1-tagged forward primers) or P7 (CS2-tagged reverse primers) sequencing adapters.
222 Sequences for the CS1 and CS2 conserved sequence tags, barcodes, and sequencing adapters
223 were taken from Uribe-Convers et al. (2016). PCR conditions were as follows: PCR1 – 25 ul
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224 reactions included 2.5 ul of 10x PCR buffer, 3 ul of 25 mM MgCl2, 0.30 ul of 20 mg/ml BSA, 1
225 ul of 10 mM dNTP mix, 0.125 ul of 10 uM CS1-tagged target specific forward primer, 0.125 ul
226 of 10 uM CS2-tagged target specific reverse primer, 0.125 ul of 5000 U/ml Taq DNA
227 polymerase, 1 ul template of DNA, and PCR-grade H2O to volume; PCR1 cycling conditions -
228 95°C for 2 min. followed by 20 cycles of 95°C for 2 min., 50-60°C for 1 min. (depending on Tm
229 of target specific primers), 68°C for 1 min., followed by a final extension of 68°C for 10 min.;
230 PCR2 – 20 ul reactions included 2 ul of 10x PCR buffer, 3.6 ul of 25 mM MgCl2, 0.60 ul of 20
231 mg/ml BSA, 0.40 ul of 10 mM dNTP mix, 0.75 ul of 2 uM barcoded primer mix, 0.125 ul of
232 5000 U/ml Taq DNA polymerase, 1 ul of PCR1 product as template, and PCR-grade H2O to
233 volume; PCR2 cycling conditions - 95°C for 1 min. followed by 15 cycles of 95°C for 30 sec.,
234 60°C for 30 sec., 68°C for 1 min., followed by a final extension of 68°C for 5 min. Following
235 PCR2, the resulting amplicons were pooled together and sequenced on an Illumina MiSeq
236 platform using 300 bp paired end reads and 1% of a sequencing lane.
237 Pooled reads from the Illumina MiSeq runs were demultiplexed using the dbcAmplicons
238 pipeline, and consensus sequences were generated using the R script reduce_amplicons.R
239 (https://github.com/msettles/dbcAmplicons) following the workflow detailed in Uribe-Covers et
240 al. (2016). Briefly, for each sample, read-pairs were identified, sample-specific dual-barcodes
241 and target specific primers were identified and removed (allowing the default matching error of 4
242 bases), and each read was annotated to include the species name and read number for the gene
243 region. To eliminate fungal contamination that may have been amplified with ITS, and non-
244 specific amplification of poor PCR products for all gene regions, each read was screened against
245 a user-defined reference file of annotated sequences retrieved from GenBank (using the “-
246 screen” option in dbcAmplicons). Reads that mapped with default sensitivity settings were kept.
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247 Each read was reduced to the most frequent length variant, paired reads that overlapped by at
248 least 10 bp (default) were merged into a single continuous sequence, and a consensus sequence
249 without ambiguities was produced (“-p consensus” in the R script reduce_amplicons.R). Paired
250 reads that did not overlap were concatenated using the program Phyutility version 2.2.4 (Smith
251 and Dunn, 2008), and any merged segments were added to the concatenated reads.
252 Processed MiSeq sequences for each gene region were aligned using MAFFT version
253 7.273 (Katoh and Standley, 2013) with default settings, and segments that were divided for PCR
254 amplification were aligned separately. All alignments were loaded into Geneious version 7.1.9
255 (http://www.geneious.com; Kearse et al., 2012), where visual inspection in addition to a batch
256 blast to the NCBI nucleotide database helped to identify incorrect sequences that escaped our
257 primary screening (e.g. resulting from fungal contamination, non-specific amplification, or
258 contaminated samples). Incorrect sequences (those whose BLAST hit did not match with the
259 species and/or gene region identification) were discarded and the segment was realigned. Each
260 gene segment was then concatenated using Phyutility version 2.2.4 (Smith and Dunn, 2008),
261 resulting in a final alignment for each gene region. To avoid replication for gene regions that
262 were amplified in multiple segments, the overlapping region was removed from one segment
263 prior to concatenation.
264
265 Available Sequence Retrieval from GenBank
266 We used the PHLAWD pipeline (Smith et al., 2009) to retrieve published sequences from
267 GenBank for all species in the alpine pool. Using the combined list of species that were
268 identified across all summits, we searched for the same six gene regions that were amplified with
269 PCR for direct comparison. The PHLAWD pipeline incorporates GenBank taxonomy to
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270 sequentially profile align increasingly higher taxonomic groups together with MAFFT (Katoh
271 and Standley, 2013), and outputs a single alignment file for each query. Infraspecific taxa were
272 collapsed to the species-level to avoid pseudoreplication, and if there was more than one
273 sequence for a species available in GenBank the longest was kept. The resulting super-matrix
274 gene alignments were cleaned using Phyutility version 2.2.4 (Smith and Dunn, 2008) to remove
275 sites that were missing 50% or more data.
276
277 Total Dataset Construction
278 Pre-cleaned alignments from the high-throughput sequencing and GenBank output from
279 PHLAWD were combined, gaps were removed, and sequences were re-aligned using MAFFT
280 (Katoh and Standley, 2013). The longest sequence of duplicates was retained for the total
281 dataset, and the gene alignments were cleaned as described for the GenBank dataset above.
282
283 Community Phylogenetic Inference
284 The species-level total combined high-throughput and GenBank dataset was used to
285 estimate phylogenetic relationships between all alpine species that were collected in the SNF. To
286 initially assess taxonomic concordance, gene trees were estimated for each region under
287 maximum likelihood (ML) criterion using the GTR-CAT model of nucleotide substitution and
288 100 bootstrap replicates in RAxML version 8.2.8 (Stamatakis, 2006). All genes regions were then
289 concatenated into an alignment using Phyutility version 2.2.4 (Smith and Dunn, 2008). This
290 concatenated alignment was used to infer a ML estimate for the community phylogeny in
291 RAxML version 8.2.8 (Stamatakis, 2006) with a GTR-CAT model partitioned by gene region and
292 using the auto MRE bootstrap convergence option to determine the number of bootstrap
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293 replicates for stable support values (Pattengale et al., 2009); all analyses were run on the CIPRES
294 cyberinfrastructure for phylogenetic research (Miller et al., 2010 last accessed 29 July 2017).
295 Following Marx et al. (2016), we used the ‘congruification’ approach (Eastman et al., 2013) in
296 the R package geiger version 2.0 (Pennell et al., 2014) to match node calibrations from the
297 detailed time-tree estimate of Zanne et al. (2014), and penalized likelihood to scale molecular
298 branch lengths to time as implemented in treePL version 1.0 (Smith and O’Meara, 2012).
299
300 Community Phylogenetic Structure
301 Evolutionary relationships estimated from the SNF community phylogeny were used to
302 summarize phylogenetic patterns within (α-diversity) the nine alpine summit communities
303 sampled for all vascular plants (Tracheophyta), as well as the six most species-rich orders. We
304 calculated the mean nearest taxon distance (MNTD) and the mean pairwise distance (MPD;
305 Webb, 2000), to quantify divergence at fine and broad phylogenetic scales, respectively (Mazel
306 et al., 2016; Tucker et al., 2016). We assessed if observed phylogenetic patterns were different
307 from a random expectation by calculating standardized effect sizes (SES) for each metric in the
308 R package picante 1.6-2 (Kembel et al., 2010) by randomly resampling each community
309 phylogeny 10,000 times (random draw null model). Large SES values indicate greater than
310 expected observed phylogenetic divergence from the species pool of alpine flora within the SNF
311 (phylogenetic overdispersion), while small values indicated observed divergence is less than
312 expected (phylogenetic clustering).
313 Changes in community phylogenetic structure between summits (β-diversity) were
314 summarized with two different metrics. First, we calculated the unique branch length
315 contribution relative to the total branch lengths shared between each community with the
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316 UniFrac index (Lozupone and Knight, 2005), which has been used to quantify turnover in other
317 studies of alpine phylogenetic structure (Jin et al., 2015). This broad measure of phylogenetic
318 divergence between sites (Baselga, 2009) does not discern between richness gradients of species-
319 poor communities nested within species-rich communities (Wright and Reeves, 1992) or spatial
320 turnover, whereby environmental filtering or historical processes cause distinct lineages to
321 replace others between sites (Qian et al., 2005). Following Leprieur et al. (2012), we
322 decomposed UniFrac to separate the phylogenetic divergence between summits attributed to
323 accumulation of species richness (UniFrac PD) from divergences that represent true gain or loss
324 of species due to replacement (UniFrac Turn). SES of UniFrac indices were quantified by
325 comparing observed values to a null distribution of indices from tips shuffled across the
326 community phylogeny using the R code provided in Leprieur et al. (2012). Second, we identified
327 nodes within the community phylogeny where species or clades of species were contributing to
328 turnover with PIst, which measures changes in mean phylogenetic distances between sites
329 compared to within sites (Hardy and Senterre, 2007). We used a randomization that shuffles
330 species across the community phylogeny (‘1s’) to test significance of the phylogenetic structure
331 (Hardy, 2008) with the R package spacodiR version 0.13.0115 (Eastman et al., 2011). PIst > 0
332 indicates spatial phylogenetic clustering (species within plots are more closely related than
333 between plots), while PIst < 0 indicates spatial phylogenetic overdispersion (species within plots
334 are less closely related than between plots; Hardy and Senterre, 2007).
335
336 Statistical Analyses
337 To test for environmental filtering of closely related species, we assessed the relationship
338 between patterns of phylogenetic divergence and elevation. Within summits, we used simple
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339 linear regression with SES MNTD and SES MPD as the dependent variables, and maximum
340 elevation as the independent variable. For turnover between sites, elevation and geographic
341 coordinates were expressed as pairwise Euclidean distances between sites using the vegdist
342 fuction R package ‘vegan’ version 2.4-3 (Oksanen et al., 2017). Each compositional pairwise β-
343 diversity matrix was correlated with environmental difference (elevation) and spatial distance
344 using multiple regression on matrices (MRM; nperm = 999) (Lichstein, 2007) as implemented in
345 the R package ‘ecodist’ version 1.2.9 (Goslee and Urban, 2007). On summits with high-alpine
346 meadows, we assessed if niche heterogeneity was driving patterns of phylogenetic divergence by
347 removing species collected in meadows and comparing SES MNTD and SES MPD of species
348 collected only on talus slopes (“Talus”) with all species collected above treeline together (“All
349 Alpine”) using a paired t-test. Statistical analyses were conducted in R (version 3.2.3, R Core
350 Team, 2015).
351
352 RESULTS
353 Species Collections
354 A total of 481 specimens (162 unique species) were collected, and between 28 (Braxon
355 Peak) and 78 (Hyndmann Peak) species were sampled on each summit. A few summits
356 (Thompson Peak, D.O. Lee Peak, Salzburger Spitzl, and Hyndman Peak) had alpine meadows.
357 The six plant orders with the greatest species-richness were Asterales (N = 40), Caryophyllales
358 (N = 21), Poales (N = 18), Lamiales (N = 15), the Brassicales (N = 11), and Ericales (N = 8)
359 (Fig. 2). Vouchers and images can be viewed online at the Consortium of the Pacific Northwest
360 Herbaria data portal (www.pnwherbaria.org; Consortium of Pacific Northwest Herbaria, 2013).
361 Voucher information for each collection and the community matrix showing the presence or
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362 absence of species across summits are provided in the Supporting Information (Appendix S2 and
363 Appendix S3).
364
365 High-Throughput Sequencing
366 Because we used universal primers that were designed primarily for angiosperms and/or
367 seed plant systematics, there was taxonomic variation (and biases) in the efficacy of
368 amplification and/or sequencing of each gene region, and none of the ferns or lycophytes
369 amplified (Fig. 2). MiSeq read pairs only merged across matK_1 and trn_cf (primers in Table
370 S1). Of the regions that were amplified in multiple segments, only atpB1 and atpB2 overlapped,
371 and 130bp were removed from the second segment before concatenation to the first.
372 Amplification of certain gene regions (and segments) was more successful in certain clades than
373 others. Rates of amplification in graminoids were particularly low, especially for matK. The
374 segments ndhF2 and ndhF3 worked better for graminoids than ndhF1, and trn_cf worked better
375 than trn_ab for graminoids and gymnosperms. atpB primers amplified well for graminoids and
376 gymnosperms (especially atpB1). ITS amplified well across a broad range of taxonomic lineages,
377 but there was significant non-specific amplification or fungal contamination, which had to be
378 removed prior to compiling this dataset. rbcL amplified well overall—across all taxonomic
379 groups, in one entire segment, and had few reads with non-specific amplification to remove.
380 Summary statistics from Illumina read processing, including the number of raw reads, and the
381 number of reads remaining after screening and reduction, are listed in Appendix S4.
382
383 Community Phylogenies for High-throughput, GenBank and Total Datasets
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384 After MiSeq reads were processed, screened, and reduced, the concatenated high-
385 throughput alignment included 422 individuals that were collected on different summits and 152
386 unique species (93% of species collected), and a concatenated alignment of 8254 bp for the six
387 gene regions (Table S4). Fewer sequences were available on GenBank, with only 121 species
388 (74% of species collected) represented. Combined, the total dataset included 156 species (96% of
389 species collected). The concatenated total combined dataset had the lowest percent of missing
390 data (40%), while the high-throughput and GenBank datasets had similarly larger proportions
391 missing (71% and 70%, respectively). No major taxonomic conflicts with phylogenetic
392 expectations following The Angiosperm Phylogeny Group IV classification (2016) were found
393 in gene trees estimated from the GenBank, high-throughput, or combined total dataset
394 alignments (Appendix S5), so the gene regions were concatenated into a final alignment that was
395 used for the ML estimate of phylogenetic relationships for each dataset.
396
397 Community Phylogenetic Structure and Statistical Analyses
398 Within summits, observed mean nearest taxon phylogenetic distance (MNTD) was no
399 different than expected from a random assemblage of alpine flora across vascular plants (Fig. 3
400 top panel). However, there was statistically significant overdispersion of mean pairwise distance
401 (MPD) on one summit (Horstmann Peak; Fig. 3 bottom panel). Within specific clades
402 phylogenetic structure was also largely no different than random. The few summits that did have
403 statistically significant order-level phylogenetic structure were mostly clustered, except for
404 Caryophyllalles on Hyndman Peak, which were significantly overdispersed for MPD. Summits
405 with alpine meadows did not have a higher (or lower) phylogenetic divergence than those
406 without (Appendix S6). With increasing maximum elevation, there was a slight but non-
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407 significant increase in phylogenetic distance between closely related species (MNTD; Fig. 4a),
408 and a significant decrease in pairwise phylogenetic divergence across Tracheophyta (MPD; Fig.
409 4b; adjusted R2 = 0.4356; P-value = 0.03161).
410 The decomposed UniFrac index revealed higher than expected true turnover of distinct
411 plant lineages between six of the 36 pairwise summit comparisons, and only one was lower than
412 expected (Fig. 5a, above diagonal). When species collected in high-alpine meadows were
413 removed (from Thompson Peak, D.O. Lee Peak, Salzburger Spitzl, and Hyndman Peak),
414 turnover between summits was no different from random overall (Fig. 5a, below diagonal).
415 However, neither maximum elevation (R2 = 0.0147; P-value = 0.4566), range in elevation (R2 =
416 0.0303; P-value = 0.2698), nor spatial distance explained phylogenetic β-diversity (R2 = 0.0228;
417 P-value = 0.4221). Clades with species less widespread among summits than expected by chance
418 (higher than expected PIst) included the Fabids and the family Caryophyllaceae (Fig. 5b). Spatial
419 phylogenetic overdispersion of closely related species occurring more often on different summits
420 (lower than expected PIst) was found in the genus Eriogonum (Polygonaceae) and the Eurosids.
421 A complete table of results for phylogenetic α-diversity and β-diversity are included as
422 supplementary materials (Appendix S7 and Appendix S8, respectively).
423
424 DISCUSSION
425 High-alpine ecosystems across the remote Sawtooth National Forest in North America
426 are comprised of a diverse array of vascular plants (Fig. 2), dominated by species in the orders
427 Asterales, Caryophyllales, Poales, Lamiales, Brassicales, and Ericales. Over-representation of
428 species from these specific clades contributes to the only significant pattern in community
429 phylogenetic structure across vascular plants that we found: tree-wide overdispersion on one
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430 summit (MPD; Fig. 3). Otherwise, tip-wise phylogenetic structure was no different from random
431 across all vascular plants (MNTD; Fig. 3). The influence of taxonomic scale on patterns of
432 community phylogenetic structure has been well documented in the literature (Emerson and
433 Gillespie, 2008; Vamosi et al., 2009), and when source pools are defined more broadly,
434 communities tend to be more phylogenetically clustered than expected (Cavender-Bares et al.,
435 2006). This pattern was confirmed here, as significant phylogenetic clustering was only ever
436 found within specific clades on a few summits (Fig. 3). Still overall, significant order-specific
437 phylogenetic structure was idiosyncratic and sparse, suggesting clade-specific community
438 assembly mechanisms.
439 To test the hypothesis that extreme environments filter for closely related species in the
440 high-alpine, we investigated the relationship between these community phylogenetic patterns and
441 maximum elevation. We predicted that the physiologically extreme environment is filtering for
442 closely related species driving observed diversity patterns (Billings and Mooney 1968), as has
443 been found in previous studies of community phylogenetic structure in the high-alpine (Li et al.,
444 2014; Jin et al., 2015). Results did show that elevation was significantly correlated with MPD
445 across vascular plants and within the order Poales (Fig. 4). While the summits at lower
446 elevations were comprised of plant assemblages that were more distantly related than expected,
447 phylogenetic structure of summits at higher elevations was not significantly different than a
448 random sample of the alpine species pool (Fig. 4b). This significant negative relationship
449 between MPD and elevation suggests that the environment may be shaping community-wide
450 assembly in the high-alpine of the SNF, but not towards significant clustering of close relative
451 with shared derived traits adapted to extreme alpine conditions, as expected. Instead,
452 phylogenetic structure shifted from significantly overdispersed on summits with lower maximum
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453 elevation to each species having an equal probability of co-occurring at higher maximum
454 elevations (Fig. 3). While not significant, this trend also held for the orders Lamiales and
455 Brassicales.
456 On the other hand, a positive trend between maximum elevation and MNTD was found
457 overall (across vascular plants and within most orders), but was not significant (Fig. 4a). As
458 elevation increased, tip-wise phylogenetic distances increased. If traits are conserved,
459 overdispersion of distantly related species is sometimes interpreted to result from competition in
460 the community phylogenetics framework (Webb, 2000). However, if ecologically relevant traits
461 are convergent, habitat filtering is instead expected to produce phylogenetic overdispersion.
462 Significant phylogenetic overdispersion was also found within the order Caryophyllales for tree-
463 wide distances, and MPD was positively related with maximum elevation for the Caryophyllales,
464 Asterales, and Ericales, though not significantly (Fig. 4b). Globally these orders, and the
465 Caryophyllales in particular, are known to contain many species with a cushion life form,
466 suggesting frequent evolutionary convergence toward this trait (Boucher et al., 2016), and could
467 explain the overdispersion found here. Niche or habitat heterogeneity could also allow distantly
468 related species to fill space (Stein et al., 2014), and promote phylogenetic overdispersion.
469 Despite higher species richness on summits with alpine meadows, habitat heterogeneity did not
470 drive patterns of community phylogenetic structure within summits (Appendix S6). Between
471 summits, high turnover of phylogenetic diversity does appear to be attributable to plants found in
472 high-alpine meadows (Fig. 5a). Species within the genus Eriogonum and across the Eurosids
473 were found uniquely distributed across space (spatial overdispersion; Fig. 5b), which might
474 indicate specific niche preferences in these lineages. Taken together, patterns of phylogenetic
475 structure within and between summit communities suggest functional trait convergence and
21 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
476 niche differentiation promote the co-occurrence of distant lineages at high elevations in the SNF,
477 but further work detailing traits and environmental conditions will be necessary to support this.
478 Besides adaptation, species-neutral processes are expected to shape biodiversity in island-
479 like systems, such as high-alpine summits (MacArthur & Wilson, 1967; reviewed in Marx et al.,
480 2017). A recent study simulated communities under different assembly processes, and revealed
481 how overdispersion can also be caused by stochastic processes, such as local extinction or
482 limited dispersal following allopatric speciation (Pigot and Etienne, 2015). In fact, since
483 allopatric speciation has been shown to drive communities towards overdispersion, clustering
484 should be difficult to detect at all under the random-draw null model (implemented here), unless
485 rates of extinction are high enough to decouple from allopatric speciation, the source pool itself
486 was completely formed by colonization, or the source pool was poorly sampled (Pigot and
487 Etienne, 2015). The lack of significant phylogenetic clustering across vascular plants coupled
488 with largely random phylogenetic structure at the highest maximum elevations (Fig. 3) could
489 signify the importance of such stochastic assembly processes in central Idaho.
490 Further south in the Rocky Mountains of Colorado, phylogenetic clustering of closely
491 related species within alpine summits was found (for MPD), and environment explained high
492 turnover within clades (Jin et al., 2015). While part of the same greater mountain range, it is
493 possible that scale-effects explain the differences in phylogenetic patterns (Cavender-Bares et al.,
494 2006). In this study, we defined communities at the summit-level (everything occurring about
495 tree-line), while in Colorado, communities were defined at the plot-level (approximately 400m2
496 in area). At a similar spatial scale (summit-level) in in the French Alps, however, few summits
497 were significantly clustered (for either MNTD or MPD), and phylogenetic structure was not
498 explained by a series of environmental variables that were tested (including elevation; Marx et
22 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
499 al., 2017). Instead, models explicitly accounting for species-neutral assembly processes such as
500 colonization and local extinction were able to explain phylogenetic patterns, providing further
501 support that these processes play an important role in shaping diversity at this regional scale. But
502 clade-specific patterns differ between Idaho and France—phylogenetic patterns within the Poales
503 mirrored the negative relationship found for MPD across vascular plants in the SNF (Fig. 4b),
504 while environmental conditions were mostly found to drive clustering within the Caryophyllales
505 in the French Alps. The architecture of these alpine ranges is incredibly complex (Körner at al.,
506 2011; Elsen and Tingley, 2015), and factors such as the age of mountain orogeny, bioclimatic
507 belts or the extent of dynamic glacial histories should be considered in greater detail to compare
508 cross-continental community phylogenetic relationships, and more rigorously test biogeographic
509 hypotheses of how historic biogeographic processes shape the evolution and ecology of alpine
510 biodiversity globally (Graham et al., 2014).
511 While not a primary goal of this study, we were also able to compare how community
512 phylogenetic structure estimated from the total combined dataset differs from estimates of
513 community phylogenetic structure from molecular sequence data obtained from 1) GenBank
514 alone, which are openly available but have sampling gaps for species and gene regions, and 2)
515 high-throughput sequencing alone, which can introduce sampling biases depending upon the
516 gene regions targeted for amplification. Mining GenBank for names of high-alpine species
517 collected from our survey, we found only 74% were represented by publicly available molecular
518 sequence data. While these archives have proven useful for comparisons of alpine community
519 phylogenetic structure at macro-ecological scales (e.g. Jin et al., 2015; Marx et al., 2017), the
520 impact of taxon sampling on quantification and interpretations of community phylogenetic
521 patterns is of growing concern (Park et al., 2018). Therefore, we leveraged a targeted high-
23 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
522 throughput sequencing approach recently developed for plant systematics (Cronn et al., 2012;
523 Godden et al., 2012; Grover et al., 2012; Uribe-Convers et al., 2016) to directly sample the
524 genetic diversity of all individuals collected across the nine summits sampled. These novel
525 molecular sequences captured phylogenetic relationships for 93% of the alpine plant species that
526 were collected throughout the region. Importantly, molecular sequences for 422 individuals
527 captured some intraspecific genetic structure between summits, which is not possible when a
528 single sample is used to represent species diversity (as in the GenBank and total combined
529 datasets). However, the taxonomic specificity of the primer pairs used for amplification was
530 biased towards seed-producing vascular plants (i.e., excluded ferns and lycophytes). Including
531 primers that are optimized for these groups would be more effective for documenting the
532 complete flora, and has great potential for exploring infra-specific genetic structure in future
533 studies. By combining the datasets, we were able to supplement taxonomic gaps in the high-
534 throughput dataset with publicly available molecular sequence data from GenBank, resulting in a
535 nearly complete (96%) species-level phylogenetic representation of the alpine flora.
536 Using a paired t-test to compare SES MNTD and SES MPD calculated from each dataset,
537 greater differences of community phylogenetic structure were found between the total combined
538 dataset and the high-throughput dataset than between the total and GenBank datasets (Appendix
539 S9). Compared with the total dataset, community phylogenetic structure in the high-throughput
540 dataset (when the ferns and lycophytes were excluded) moved from neutral towards
541 overdispersion for MNTD (Tracheophyta P-value = 0.0285; Appendix S9a), and from
542 overdispersion towards neutral for MPD (Appendix S9b). Because the high-throughput and
543 GenBank datasets had similar proportions of missing data (71% and 70%, respectively;
544 Appendix S4), this suggests that the similarity in results between the GenBank and total datasets
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545 (Appendix S9) are driven by the inclusion of ferns and allies, rather than missing data. The
546 contribution of long branches introduced by these groups could also inflate community-wide
547 patterns (MPD), especially considering the relatively high occurrence of ferns and gymnosperms
548 on the peak with significant overdispersion (Horstmann Peak; Fig. 2).
549 This and other studies (e.g. Li et al., 2014; Jin et al., 2015; Marx et al., 2017; Le
550 Bagousse-Pingueta et al., 2017) demonstrate the potential for patterns of phylogenetic diversity
551 to elucidate dominant processes driving species co-occurrence in extreme regions, however
552 many assumptions about functional trait evolution and community assembly processes have the
553 potential to be violated when evolutionary relationships are used as a proxy for ecological niche
554 similarity (reviewed in Gerhold et al., 2015). Rather than viewing phylogenetic patterns as a
555 proxy for ecological similarity and accepting the myriad of underlying assumptions, community
556 phylogenetic diversity has strong potential to inform how macroevolutionary processes shape the
557 diversity of multi-species assemblages we observe across space (Gerhold et al., 2015). Because
558 alpine ecosystems are found on every continent, patterns of phylogenetic community structure
559 can be compared globally to assess how rates of diversification constrain (or promote) alpine
560 diversity. However, a central challenge for moving towards investigating macroevolutionary
561 drivers of community phylogenetic patterns (the “phylogenetic-patterns-as-a-cause” approach;
562 Gerhold et al., 2015) is that more studies across lineage-pools are necessary to compare across
563 alpine regions. In this work, we demonstrate how supplementing available molecular sequence
564 data with novel sequences generated using a high-throughput approach efficiently resolved
565 phylogenetic community structure across remote summits, which could be tractable to other
566 high-elevation ecosystems facing a similar deficit in molecular sequence data for community
567 phylogeny inference. The ability to effectively sequence multiple gene regions from hundreds of
25 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
568 plant species at a time also presents an opportunity to capture intraspecific genetic variation of
569 multi-species assemblages across regions. Investigating signatures of selection at the population
570 level could provide deeper insights into the mechanistic basis underlying patterns of community
571 phylogenetic structure, such as the evolution of key traits or life forms that are important for
572 survival at these extremes (e.g. Boucher et al., 2016). Additionally, this high-throughput
573 sequencing approach is extendable across taxonomic lineages, presenting exciting avenues for
574 community phylogenetic networks of plants with associated pollinators (Pellissier et al., 2013) or
575 microbes (Bryant et al., 2008). Furthermore, the power to detect environmental filtering
576 increases with the size of the species pool (Kraft et al., 2007). The targeted high-throughput
577 sequencing approach presented here could be used to supplement available sequence data and
578 sample larger source pools to more explicitly test stochastic models of species-neutral
579 colonization and local extinction (Pigot and Etienne, 2015) and the relative importance of
580 adaptive and species-neutral processes for generating and maintaining biodiversity in the high-
581 alpine.
582
583 CONCLUSIONS
584 Mountains are ideal for testing how ecological and evolutionary mechanisms shape the diversity
585 patterns we observe across space (Graham et al., 2014), but the extreme environmental
586 conditions that define high-alpine areas and open many questions about how this diversity has
587 assembled also pose a challenge to research efforts, so comparisons among regions remain
588 limited. Collections from the first detailed floristic survey of nine summits across the Sawtooth
589 National Forest in central Idaho, USA, contribute to our global synthesis of montane
590 biodiversity, and community phylogenetic relationships from combined novel and publicly
26 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
591 available molecular sequence data show patterns of increasing phylogenetic stochasticity over an
592 elevation gradient. While we interpret these results as an indication that the environment may not
593 be a broad selective force across the vascular plant community as a whole at high elevations, we
594 recognize that elevation gradients are comprised of complex geographic effects (Körner, 2007),
595 and regional distinctions of specific climatic and topological properties will be important for
596 global comparisons in the future. Clade-specific signatures of phylogenetic clustering indicate
597 that environmental filtering may be more important for certain branches of the tree of life than
598 others, and trends toward phylogenetic overdispersion over increasing elevation suggest that
599 traits important for functioning in the high-alpine may have converged in different lineages.
600 Aggregating functional and phylogenetic distances (Cadotte et al., 2013) will be useful in future
601 studies to assess convergence (Cavender-Bares et al., 2006), and differentiate between the
602 complex drivers of diversity across taxonomic levels.
603
604 AUTHOR CONTRIBUTIONS
605 H.E.M. and D.C.T. conceptualized this research. H.E.M. organized collecting expeditions to the
606 Sawtooth National Forest. M.R. conducted laboratory work (total DNA extractions and PCR).
607 H.E.M. performed analyses and wrote the manuscript, with input from G.M.J. and D.C.T.
608
609 ACKNOLEDGEMENTS: This work was supported by a NSF Graduate Research Fellowship
610 grant no. DGE-1144254, and NSF grants no. EF-1550838 and FESD 1338694. Collections were
611 possible with support from an Expedition Fund from the University of Idaho Stillinger Trust, and
612 molecular was supported by a Graduate Student Research Award from the Society of Systematic
613 Biologists and a Rosemary Grant Research Award from the Society for the Study of Evolution.
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614 The Stillinger herbarium processed and stores this collection. The Sawtooth National Forest is
615 thanked for granting collection permits. This floristic survey would not have been achievable
616 without the aid of the Sawtooth Mountain Guides (including Drew Daly, Ryan Jung, Matt
617 Scrivner, and Chris Lundy), the Sawtooth National Forest Ranger Station in Stanley, Idaho, and
618 field assistants Basya R. Clevenger, Chloe M. Stenkamp-Strahm, and Shauna K. Thomas. I also
619 thank Bill Rember for his geological perspective, and providing welcome breaks to reminisce
620 about the Sawtooths and Stanley, Idaho, a reminder of the beautiful diversity of this place. This
621 work is dedicated in memory of Samuel W. Marx, whose encouragement inspired this
622 expedition.
623
624 DATA ACCESSIBILITY
625 A detailed workflow including read processing scripts, known sequences used for MiSeq read
626 screening, reference sequence files for PHLAWD searches, and R scripts are available on GitHub
627 (https://github.com/hmarx/Sawtooth-Alpine-PD). Raw MiSeq data are deposited in the Sequence
628 Read Archive (Project: XXXXX) and processed gene regions are in GenBank (XXXXX). The
629 alpine community matrix, cleaned sequence alignments for each dataset, and treefiles are
630 available on Dryad (XXXXX).
631
632
633
634
635
636
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39 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
890 SUPPORTING INFORMATION
891 Appendix S1: Table with target-specific primer pair sequences and citations.
892 Appendix S2: Voucher numbers and collection information for each accession.
893 Appendix S3: Accession number for the high-throughput (miseq) or GenBank (ncbi) sequence
894 used for each gene region and community matrix.
895 Appendix S4: Table summarizing high-throughput sequencing read statistics and aligned
896 sequence length for each gene region.
897 Appendix S5: Maximum likelihood phylograms estimated from concatenated gene regions of
898 each dataset (total combined, GenBank, and high-throughput).
899 Appendix S6: Comparison of phylogenetic community structure between microhabitats within
900 each summit.
901 Appendix S7: Table summarizing phylogenetic α-diversity.
902 Appendix S8: Table summarizing phylogenetic β-diversity.
903 Appendix S9: Comparison of the datasets used to infer phylogenetic relationships among alpine
904 plant species.
905
906
907
908
909
910
911
912
40 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
913 FIGURES
914 Figure 1: Map of the Sawtooth National Forest, Idaho, USA (grey area on map inlay) showing
915 the locations of the nine high-alpine summits sampled. Colored triangles correspond to different
916 summits, and triangle size is proportional to maximum elevation.
917
918 Figure 2: Community phylogeny of the alpine flora of the Sawtooth National Forest. Color bars
919 on tips match colors of summits on the map, and indicate the presence of each species on each
920 summit. Grey bar closest to tip names indicate species that were collected from alpine meadows.
921 An asterisk marks species with molecular sequence data available in GenBank, and a diamond
922 indicates species with sequence data generated from high-throughput sequencing. Nodes that
923 were congruent with the reference timetree (‘congruified’) are indicated by black circles. Nodes
924 with a light grey dot have bootstrap support (BS) between 75 and 95, and those with a black dot
925 have BS support greater than or equal to 95. Summits with high-alpine meadows include
926 Thompson Peak, D.O. Lee Peak, Salzburger Spitzl, and Hyndman Peak. Representative species
927 within the six most species rich orders are highlighted: (A) Carex sp. on the summit of
928 Snowyside Peak; (B) Draba oligosperma on Thompson Peak; (C) Eriogonum ovalifolium
929 clinging to Snowyside Peak; (D) Phyllodoce glanduliflora at the base of Thompson Peak
930 summit; (E) Castilleja miniata covering a high-alpine meadow on Hyndman Peak; (F) Hulsea
931 algida scattered across a talus slope on Salzburger.
932
933 Figure 3: Phylogenetic α-diversity of alpine flora on summits across the Sawtooth National
934 Forest. Standardized effect size (SES) for mean nearest taxon distance (MNTD) and mean
935 pairwise distance (MPD) estimated from the alpine community phylogeny. Tile rows show
41 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
936 community phylogenetic structure within each summit (ordered by increasing elevation across
937 the x-axis) for all vascular plants (Tracheophyta) and each of the six most species-rich orders
938 (Asterales, Poales, Caryophyllales, Lamiales, Brassicales, and Ericales). Warm tones (positive
939 SES values) indicate phylogenetic overdispersion (high phylogenetic divergence), cool tones
940 (negative SES) indicate phylogenetic clustering (low phylogenetic divergence). Tiles with dots
941 denote higher (or lower) observed divergence than expected by chance (from random resampling
942 the community phylogeny of all alpine plants; P-values <0.05). Cells filled with an “x” had too
943 few species for comparison (only one species was present).
944
945 Figure 4: Statistical analysis of the relationship between environment and phylogenetic
946 community structure. Linear regression of maximum elevation (independent variable) on
947 standardized effect sizes (SES) for (a) mean nearest taxon phylogenetic distance (MNTD) and
948 (b) mean pairwise phylogenetic distance (MPD). Separate models were performed for all
949 vascular plants (Tracheophyta) and each of the six most species-rich orders (Asterales, Poales,
950 Caryophyllales, Lamiales, Brassicales, and Ericales).
951
952 Figure 5: Phylogenetic β-diversity of alpine flora on summits across the Sawtooth National
953 Forest. (a) Pairwise matrices showing species turnover between summits, measured by
954 standardized effect sizes (SES) of UniFrac distances, decomposed into the portion corresponding
955 to true turnover for all alpine species (top half) and those collected only on talus slopes
956 (excluding species collected from alpine meadows; bottom half). Tiles with warm tones indicate
957 high turnover between summit pairs (summits have unique species); cool tones indicate low
958 turnover between summit pairs (summits share the same species). Tiles with dots show pair of
42 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
959 summits with higher or lower turnover than expected (from random resampling the phylogeny;
960 P-values <0.05). (b) Phylogenetic turnover between clades on the community phylogeny of
961 summit species measured by PIst for all alpine species. Species subtending nodes with grey dots
962 have a higher than expected turnover between summits (appear only on certain summits), species
963 with black dots have a lower than expected turnover between summits (appear across all
964 summits), and nodes with white dots have turnover no different than random.
965
966
43 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
967 Figure 1
N
0km 500km 1000km
44.4 N 0km 10km 20km 89:9(;**()*+,( summits !"#$%&#'()*+,( -#.&/$+''()*+,( 44.1 0.+1#'()*+,( 7.+$*.()*+,( 7+&/<*()*+,( 2'#34&56*()*+,(
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43.2 !115.0 !114.5 !114.0 Longitudelong
968
44 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
969 Figure 2
E
F
D
ES IAL M Penstemon montanus var idahoensis
A Bupleurum americanum
L Musineon divaricatum Lomatium idahoense
Penstemon procerus var formosus Pedicularis groenlandica
Castilleja cusickii Castilleja rhexiifolia Castilleja miniata
Crepis nana A Synthyris pinnatifida var cenescens Erythranthe tilingii S TE Gentiana calycosa var obtusiloba Chionophila tweedyi R Penstemon sp Veronica wormskjoldii Agoseris aurantiaca Cirsium scariosum A Cirsium subniveum Arnica mollis Stenotus acaulis Tonestus lyallii L Solidago multiradiata Haplopappus suffruticosus E Ericameria discoidea Haplopappus macronema S S Aster occidentalis var occidentalis E Gentiana calycosa Aster foliaceus var apricus L Aster alpinus Phacelia hastata Erigeron lonchophyllus A Eritrichium nanum Erigeron grandiflorus C Erigeron asperugineus I Erigeron evermannii R Phlox diffusa Erigeron sp ● ● E Phlox pulvinata Erigeron compositus ● ● ● ● PolemoniumCollomia viscosum debilis Artemisia dracunculus ● Phyllodoce glanduliflora ● ● Achillea millefolium var alpicola ● ● ● Cassiope mertensianaPhyllodoce varempetriformis gracilis ● ● Antennaria lanata ● ● ● ● Antennaria rosea ● ● ●● Antennaria microphylla
Androsace septentrionalis ●● Antennaria corymbosa ●● ● Antennaria alpina Horstmann Peak (3155 m) Eremogone aculeata ● Antennaria umbrinella ●● ● Eremogone congesta ● Senecio fremontii ● ● Cerastium beeringianum ●● ● Senecio residifolius ● Braxon Peak (3156 m) ● Senecio werneriifolius Cerastium arvense ● ● Packera subnuda Minuartia obtusiloba ● Hieracium triste ● ● ● Thompson Peak (3203 m) ● Minuartia nuttallii Congruified Taraxacum lyratum
● Hulsea algida ● ● S Silene acaulis ● ● ● Chaenactis alpina E Claytonia megarhiza BS ≥ 95 Snowyside Peak (3246 m) L Claytonia megarhiza var bellidifolia Arnica parryi
A ●● ● Arnica chamissonis Lewisia pygmaea var pygmara ● 75 ≤ BS < 95 L ● ● Arnica latifolia var gracilis L Mount Cramer (3266 m) ● Cistanthe umbellata ● Arnica latifolia Y
Eriogonum ovalifolium var depressum ● H ● ●● ● ● P ● Selaginella densa Eriogonum ovalifolium var purpureum ● ● D.O. Lee Peak (3457 m) ● ● O ● Selaginella watsonii Eriogonum flavum var piperi
Y ● ● Cystopteris fragilis R Eriogonum pyrolaefolium var coryphaeum ● Cryptogramma crispa Salzburger Spitzl (3536 m)
C A ● Eriogonum ovalifolium ●
● ● Juniperus communis
C ●
Eriogonum crosbyae Abies lasiocarpa ●
●
● ● Bistorta bistortoides ● Picea engelmannii Castle Peak (3601 m) Pinus albicaulis Polygonum viviparum ● ● ● ● ● Juncus drummondii ● ●●
Oxyria digyna ● Juncus paryii Aconogonon phytolaccaefolium ● ● Hyndman Peak (3660 m) ● Luzula spicata ● Potentilla brevifolia ● ● Luzula piperi ● ● ● ● ● ● ● ● ● Carex nardina
●
Ivesia gordonii ● ●
Carex straminiformis Collected in an alpine meadow
● ●
●
● Carex microptera
Potentilla glaucophylla var glaucophylla
● Potentilla glandulosa
● ● Carex luzulina
●●
● Dasiphora fruticosa ● Carex scopulorum
● GenBank Data
Trisetum spicatum ● ● Sibbaldia procumbens ● ● ● ● Calamagrostis purpurescens * ● Astragalus eucosmus ● Elymus elymoides ● ● ● Astragalus alpinus ● ● Elymus scribneri
● ● High-Throughput Data
Poa secunda P
Oxytropis parryi
Astragalus kentrophyta Poa fendleriana subsp fendleriana
Phleum alpinum O
Oxytropis deflexa Festuca brachyphylla subsp brachyphylla A
Salix arctica Deschampsia cespitosa subsp cespitosa
Salix nivalis Aquilegia flavescens L
Anemone drummondii E
Anemone drummondii var drummondii Viola adunca S
Ranunculus eschscholtzii var eschscholtzii
Ranunculus eschscholtzii
Sedum debile
Unknown
Sedum lanceolatum var lanceolatum
Lupinus argenteus var depressus Ribes lacustre
Heuchera parvifolia var dissecta
Saxifraga mertensiana
Arabis lemmonii Saxifraga oppositifolia Saxifraga bronchialis
Parnassia fimbriata Epilobium obcordatum
Arabis microphylla
Draba densifolia
Smelowskia calycina
Erysimum asperum
Draba oreibata Draba oligosperma Draba stenoloba
Draba lonchocarpa Draba crassifolia
Epilobium alpinum
Epilobium alpinum var clavatum
S
E
L
A
I
S
S
A
R
B
A B
970
971
972
45 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
973 Figure 3
Tracheophyta
Asterales ●
Poales MNTD
Caryophyllales ● ●
Lamiales
SES Brassicales ● ● 4
Ericales ● 2
0
Tracheophyta ●
−2
Asterales ● ● −4
Poales MPD
Caryophyllales ● ●
Lamiales
Brassicales
Ericales ●
Horstmann PeakBraxon PeakThompson PeakSnowyside PeakMount CramerD.O. Lee PeakSalzburger SpitzlCastle Peak Hyndman Peak
974
46 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
975 Figure 4
(a)
Tracheophyta Asterales Poales Caryophyllales Lamiales Ericales Brassicales 4
2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● SES MNTD −2 ● ● ● ● ● ●
y = 0.001736x - 6.162223 y = 0.002452x - 8.697901 y = 0.001617x - 5.160078 y = 0.002891x - 10.160805 y = 0.0008533x - 3.2594796 y = 0.000745x - 2.760388 y = -0.002520x + 8.035282 adj R2 = -0.01226 ; p-value = 0.3736 adj R2 = 0.07921 ; p-value = 0.235 adj R2 = 0.06986 ; p-value = 0.2463 adj R2 = 0.2346 ; p-value = 0.1055 adj R2 = -0.2079 ; p-value = 0.7278 adj R2 = -0.151 ; p-value = 0.7849 adj R2 = 0.02968 ; p-value = 0.3128 −4 Maximum Elevation (m)
(b)
Tracheophyta Asterales Poales Caryophyllales Lamiales Ericales Brassicales 4
● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●
SES MPD ● ● ● −2 ● ● ● ● y = -0.004627x + 15.957871 y = 0.002810x - 10.410699 y = -0.003159x + 10.599382 y = 0.003539x - 11.888615 y = -3.689e-05x - 6.173e-01 y = 0.001607x - 5.718234 y = -0.0006926x + 2.0217972 adj R2 = 0.4356 ; p-value = 0.03161* adj R2 = 0.3007 ; p-value = 0.07309 adj R2 = 0.4932 ; p-value = 0.02099* adj R2 = 0.2804 ; p-value = 0.08202 adj R2 = -0.2499 ; p-value = 0.9885 adj R2 = -0.09406 ; p-value = 0.5513 adj R2 = -0.147 ; p-value = 0.7591 −4 Maximum Elevation (m)
976
47 bioRxiv preprint doi: https://doi.org/10.1101/454330; this version posted October 26, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.
977 Figure 5
Values of PIst
Penstemon montanus var idahoensis
Bupleurum americanum
Musineon divaricatum
Lomatium idahoense Penstemon procerus var formosus Pedicularis groenlandica Castilleja applegatei
Castilleja cusickii Castilleja rhexiifolia (a) (b) Castilleja miniata Synthyris pinnatifida var cenescens
Erythranthe tilingii
Gentiana calycosa var obtusiloba
Chionophila tweedyi Veronica wormskjoldii Penstemon sp Crepis nana Agoseris aurantiaca Cirsium scariosum SES UniFrac (Total Dataset) Cirsium subniveum Arnica mollis Stenotus acaulis Tonestus lyallii Solidago multiradiata Haplopappus suffruticosus SES UniFrac Gentiana calycosa Ericameria discoidea SES UniFrac Haplopappus macronema Aster occidentalis var occidentalis Phacelia hastata Aster foliaceus var apricus 6 6 Eritrichium nanum Aster alpinus 6 Erigeron lonchophyllus Erigeron grandiflorus Braxon Peak Erigeron asperugineus Braxon Peak Phlox diffusa Phlox pulvinata Erigeron evermannii 4 Erigeron sp 4 PolemoniumCollomia viscosum debilis 3 Phyllodoce glanduliflora Erigeron compositus Cassiope mertensianaPhyllodoce empetriformisvar gracilis Artemisia dracunculus Castle Peak 2 Castle Peak 2 Achillea millefolium var alpicola * * Antennaria lanata 0 Androsace septentrionalis Antennaria rosea 0 Antennaria microphylla 0 Antennaria corymbosa Eremogone aculeata Antennaria alpina * * D.O. Lee Peak D.O. Lee Peak Eremogone congesta
* * All Alpine Antennaria umbrinella −2 −2 Cerastium beeringianum -3 Senecio fremontii Cerastium arvense Senecio residifolius Senecio werneriifolius Horstmann Peak −4 Horstmann Peak −4 Minuartia obtusiloba PIst as expected * * Packera subnuda -6 Minuartia nuttallii Hieracium triste −6 PIst larger than expected Taraxacum lyratum −6 Silene acaulis Hyndman Peak Claytonia megarhiza Hulsea algida * * * * Hyndman Peak Claytonia megarhiza var bellidifolia PIst smaller than expected Chaenactis alpina Arnica parryi Lewisia pygmaea var pygmara Arnica chamissonis Mount Cramer Mount Cramer Cistanthe umbellata Arnica latifolia var gracilis Eriogonum ovalifolium var depressum Arnica latifolia Selaginella densa Eriogonum ovalifolium var purpureum Selaginella watsonii Eriogonum flavum var piperi * Salzburger Spitzl Salzburger Spitzl Cystopteris fragilis * Cryptogramma crispa Eriogonum pyrolaefolium var coryphaeum Eriogonum ovalifolium Juniperus communis Eriogonum crosbyae Abies lasiocarpa Picea engelmannii Snowyside Peak Snowyside Peak Bistorta bistortoides Pinus albicaulis Polygonum viviparum Juncus drummondii Juncus paryii Oxyria digyna Thompson Peak Thompson Peak Aconogonon phytolaccaefolium Luzula spicata Potentilla brevifolia Luzula piperi Carex nardina Braxon Peak Castle Peak D.O. Lee Peak Horstmann Peak Hyndman Peak Mount Cramer Salzburger Spitzl Snowyside Peak Thompson Peak Braxon Peak Castle Peak D.O. Lee Peak Horstmann Peak Hyndman Peak Mount Cramer Salzburger Spitzl Snowyside Peak Thompson Peak Ivesia gordonii Carex straminiformis Carex microptera Carex luzulina Potentilla glaucophylla varPotentilla glaucophylla glandulosa Carex scopulorum Dasiphora fruticosa Trisetum spicatum Calamagrostis purpurescens Sibbaldia procumbens Elymus elymoides Astragalus eucosmus Elymus scribneri Astragalus alpinus Poa secunda Poa fendleriana subsp fendleriana Oxytropis parryi Astragalus kentrophyta Phleum alpinum Festuca brachyphylla subsp brachyphylla Oxytropis deflexa Deschampsia cespitosa subsp cespitosa Salix arctica Aquilegia flavescens Salix nivalis Anemone drummondii Anemone drummondii var drummondii Viola adunca Ranunculus eschscholtzii var eschscholtzii Ranunculus eschscholtzii Sedum debile Sedum lanceolatum var lanceolatum Unknown Ribes lacustre Heuchera parvifolia var dissecta Saxifraga mertensiana Saxifraga oppositifolia Saxifraga bronchialis
Lupinus argenteus var depressus Parnassia Arabisfimbriata lemmonii
Arabis microphylla
Draba densifolia Smelowskia calycina Erysimum asperum Talus Draba oreibata Draba stenoloba Draba oligosperma
Draba lonchocarpa Draba crassifolia
Epilobium alpinum Epilobium obcordatum
PIst: as expected Epilobium alpinum var clavatum PIst: larger than expected PIst: smaller than expected
978
48