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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 1 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 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 3 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. 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 4 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. 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