# Plant phylogenetic history explains in-stream decomposition at a global scale

Carri Leroy, Andrew Hipp, Kate Lueders, Jennifer Follstad Shah, John Kominoski, Marcelo Ardón, Walter Dodds, Mark Gessner, Natalie Griffiths, Antoine Lecerf, et al.

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## To cite this version:

Carri Leroy, Andrew Hipp, Kate Lueders, Jennifer Follstad Shah, John Kominoski, et al. Plant phylogenetic history explains in-stream decomposition at a global scale. Journal of Ecology, 2019, 108 (1), pp.17-35. 10.1111/1365-2745.13262. hal-04957918

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Journal of Ecology

DR CARRI J. LEROY (Orcid ID : 0000-0002-1185-4437)

DR ANTOINE LECERF (Orcid ID : 0000-0002-7802-9773)

Article type : Research Article

Editor : David Wardle

Running head: Phylogeny explains in-stream decomposition

Title: Plant phylogenetic history explains in-stream decomposition at a global scale

Author Details: C.J. LeRoy<sup>1‡*</sup>, A.L. Hipp<sup>2,3‡</sup>, K. Lueders<sup>2</sup>, J.J. Follstad Shah<sup>4</sup>, J.S.

Kominoski<sup>5</sup>, M. Ardón<sup>6§</sup>, W. K. Dodds<sup>7</sup>, M.O. Gessner<sup>8,9</sup>, N.A. Griffiths<sup>10</sup>, A. Lecerf<sup>11</sup>, D.W.P.

Manning<sup>12</sup>, R.L Sinsabaugh<sup>13</sup>, J.R. Webster<sup>14</sup>

<sup>1</sup>Environmental Studies Program, The Evergreen State College, Olympia, WA 98505, <sup>2</sup>The Morton Arboretum, Lisle, IL 60532, <sup>3</sup>The Field Museum, Chicago, IL 60605, <sup>4</sup>Environmental & Sustainability Studies and Dept. of Geography, University of Utah, Salt Lake City, UT 84112, <sup>5</sup>Dept. of Biological Sciences, Florida International University, Miami, FL 33199, <sup>6</sup>Dept. of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27605, <sup>7</sup>Division of Biology, Kansas State University, Manhattan, KS 66506, <sup>8</sup>Dept. of Experimental Limnology, Leibniz-Institute of Freshwater Ecology and Inland Fisheries,

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/1365-2745.13262

This article is protected by copyright. All rights reserved.

Accepted Article
16775 Stechlin, Germany, ⁹Dept. of Ecology, Berlin Institute of Technology (TU Berlin),
10587 Berlin, Germany, ¹⁰Climate Change Science Institute and Env. Sci. Division, Oak
Ridge National Laboratory, Oak Ridge TN 37831, ¹¹Université de Toulouse, UPS, INP,
NCRS, EcoLab (Laboratoire d'Écologie Fonctionelle), 31062 Toulouse, France, ¹²Dept. of
Biology, University of Nebraska at Omaha, Omaha, NE 68182, ¹³Dept. of Biology,
University of New Mexico, Albuquerque, NM 87131, ¹⁴Dept. of Biological Sciences,
Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

*Corresponding Author; Tel: 360-867-5483 ¹LeRoyC@evergreen.edu; ‡These authors
contributed equally to this work; §Authors in alphabetical order beyond this point.

Copyright Statement: This manuscript has been co-authored by UT-Battelle, LLC under
Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States
Government retains and the publisher, by accepting the article for publication, acknowledges
that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide
license to publish or reproduce the published form of this manuscript, or allow others to do
so, for United States Government purposes. The Department of Energy will provide public
access to these results of federally sponsored research in accordance with the DOE Public
Access Plan (http://energy.gov/downloads/doi-public-access-plan).

ABSTRACT

1. Evolutionary history and adaptation to climate shape plant traits. Some include leaf traits that influence litter quality. Thus, evolutionary history should affect litter decomposition, a crucial ecosystem process. In addition, litter decomposition is directly influenced by climate. We consequently expect plant phylogeny, adaptation,
and climate to jointly influence litter decomposition. These effects and their
interactions have yet to be untangled at a global scale.

2. Here we present an analysis of variation in litter decomposition rates in rivers and streams across 285 published studies for 239 species (from ferns to angiosperms) distributed at 494 locations worldwide. We estimated the relative contributions of climatic conditions and phylogenetic heritage on litter decomposition rates, partitioning phylogenetic from climatic effects at the site and species levels using phylogenetic eigenvector analysis and phylogenetic linear mixed models. In addition, we modelled transitions in decomposition rates under a suite of multiple adaptive-regime Ornstein-Uhlenbeck models to test the hypothesis that natural selection has shaped clade-level litter decomposition rates.

3. Leaf litter decomposition rate exhibited a significant phylogenetic signal. Modelling decomposition rate as a function of location, climatic niche, and phylogeny consistently recovered phylogeny alone as one of the top models in species-level analyses. Since many previous studies have focused on the same species across many locations, we also conducted analyses at the species × site level. Both phylogenetic and climatic factors were favoured in models of this analysis, but the single most important predictor for angiosperms and for all taxa analysed together was phylogeny alone.

4. Synthesis. For plant species distributed globally at nearly 500 locations we found that plant phylogenetic history is a critically important predictor of litter decomposition rate in rivers and streams, explaining more of the variance in decomposition than site or climatic regime. Thus, our study demonstrates the influence of evolutionary history on suites of plant traits that shape a key ecosystem process.

Accepted Article
Résumé

1. L'histoire évolutive et l'adaptation au climat façonnent les caractéristiques des plantes. Le contrôle des traits foliaires sur la qualité des litières végétales pourrait sous-tendre l'existence de contraintes évolutives sur la décomposition des litières. Puisque ce processus écologique majeur est également directement modulé par le climat, il est important de mieux comprendre comment la phylogénie des plantes, leurs réponses adaptatives et le climat influencent ensemble la décomposition des litières à une échelle globale.

2. Nous présentons ici les résultats d'une analyses quantitatives des données de décomposition de litières dans des rivières et des ruisseaux, issues de 285 publications couvrant 239 espèces de plantes (des fougères aux angiospermes) dans 494 lieux à travers le monde. Nous avons quantifié la contribution des conditions climatiques et de l'héritage phylogénétique aux variations du taux de décomposition des litières entre les espèces et entre les sites, à l'aide de régressions sur les vecteurs propres phylogénétiques et de modèles phylogénétiques linéaires à effets mixtes. Le modèle de Ornstein-Uhlenbeck a également été utilisé pour tenter de comprendre si et comment la sélection naturelle s'exerce sur la qualité des litières.

3. Un signal phylogénétique cohérent a été détecté au sein des données analysées dans notre étude. La construction de modèles utilisant les informations géographiques, climatiques et phylogénétiques disponibles a permis d'établir la phylogénie comme un des meilleurs prédicteurs des variations interspécifiques du taux de décomposition des litières. Les variations spatiales de la décomposition étaient expliquées par les contraintes climatiques et phylogénétiques. Néanmoins, seule la phylogénie était nécessaire pour prédire les variations du taux de décomposition chez les angiospermes seulement ou bien sur l'ensemble des espèces de litière.
4. *Synthèses*. En analysant les résultats d'études englobant un grand nombre d'espèces de plantes au sein de 500 sites environ, nous avons découvert que l'histoire évolutive des plantes prédisait mieux le taux de décomposition des litières dans les rivières et les ruisseaux que les données géographiques ou climatiques. Notre étude démontre comment les contraintes évolutives s'exerçant sur un ensemble de traits peuvent, de manière ultime, influencer un processus écologique majeur dans les écosystèmes.

**Keywords:** climate, decomposition rate, ecosystem process, evolutionary ecology, global ecology, leaf litter, phylogenetic comparative methods

## INTRODUCTION

The past 20 years have seen a dramatic increase in our understanding of the influence of biodiversity on ecosystem functioning (Hooper et al., 2005; Gessner et al., 2010; Cardinale et al., 2012; Weisser et al., 2017). Expanding beyond the early emphasis of species richness effects on plant productivity, phylogenetic and genetic diversity have also been shown to influence ecosystem processes (Schweitzer et al., 2004; LeRoy et al., 2007; Hughes et al., 2008; Sundqvist et al., 2011; Latzel et al., 2013). The exploration of the role of genetic patterns across species has generally taken one of two routes: 1) a diversity approach that uses phylogenetic diversity as a predictor of biodiversity-ecosystem function relationships (Flynn et al., 2011; Srivastava et al., 2012), or 2) a phylogenetic approach that investigates how the evolution of traits and species relationships and may influence ecosystem functioning (Edwards, Still, & Donoghue, 2007; Donovan et al., 2014; Cadotte et al., 2017). Studies taking the first approach have demonstrated that plant phylogenetic diversity influences plant productivity (Cadotte, Cardinale, & Oakley, 2008; Gravel et al., 2012), nutrient cycling (Cornelissen & Cornwell, 2014), and numerous species traits that
subsequently affect ecosystem functioning (e.g., Cavender-Bares et al., 2009; Matthews et al., 2011; Díaz et al., 2013; Senior et al., 2016). Despite a rapidly growing body of literature addressing these relationships, there have only been a few studies examining the influence of plant phylogenetic diversity on litter decomposition (Cornwell et al., 2008; Makkonen et al., 2012; Liu et al., 2014; Pan et al., 2014; Boyero et al., 2015). Even fewer studies have taken the second approach.

Only two previous studies in terrestrial ecosystems have explicitly investigated how plant phylogeny (and thus evolutionary history) shapes litter decomposition (Liu et al., 2014; Pan et al., 2014). Liu et al. (2014) applied a Brownian motion model to analyse data collected at a single site and found that mass loss was slower in basal angiosperms than in eudicot trees. Pan et al. (2014) compared three phylogenetic models for decomposition data also collected at a single site and found evidence that a constrained single-optimum Ornstein-Uhlenbeck (OU) model best fit their data, suggesting that decomposition rate is under stabilizing selection. No phylogenetic approaches have attempted to address the question of how important evolutionary history is in explaining litter decomposition rates across multiple sites. Understanding the relative contributions of contemporary environment and evolutionary heritage to decomposition rates globally has the potential improve modelling carbon cycling on geological time scales, by deriving the decomposition rates characteristic of lineages that have variously dominated different habitats over deep time.

Plant litter decomposition is a crucial ecosystem process that drives carbon cycling in many terrestrial and aquatic environments. Shaded headwater streams make up the vast majority of river lengths globally (73.2%, Leopold et al. 1964) and strongly rely on plant litter inputs as a resource (Wallace et al. 1997), relative to all lotic habitats. Plant litter decomposition at a global scale has been explained by both extrinsic (climate, latitude, altitude) and intrinsic (litter quality) factors (Aerts, 1997), but most previous studies have
focused on terrestrial decomposition (Cornwell et al., 2008; Weedon et al., 2009; Pietsch et al., 2014). Results of these previous meta-analyses have generally shown that intrinsic species-level traits explain as much variation as extrinsic factors, if not more. Based on a review of 16 studies, Cornwell et al. (2008) found that species differences resulted in larger variation in decomposition rates than climatic differences. Specifically, litter traits like nitrogen and phosphorus concentrations led to faster decomposition and traits like lignin content, leaf mass area, and the concentrations of water- and acid-soluble polysaccharides were associated with slower decomposition (Cornwell et al., 2008). Weedon et al. (2009) examined wood decomposition globally and found a phylogenetic distinction between angiosperms and gymnosperms with lower lignin, and higher N and P in the former, and a higher lignin-to-N ratio in the latter. Pietsch et al. (2014) explored the relationships between litter and wood traits and decomposition published in the two previous studies (Cornwell et al., 2008, Weedon et al., 2009) and found that wood and leaf decomposition rates were decoupled.

Despite decades of investigations into leaf litter decomposition in freshwaters, resulting in a wealth of information (Kaushik & Hynes, 1971; Petersen & Cummins, 1974; Webster & Benfield, 1986; Tank et al., 2010), previous global decomposition syntheses (Cornwell et al., 2008; Weedon et al., 2009; Pietsch et al., 2014) have until recently excluded aquatic studies. Recent global analyses of aquatic litter decomposition (Boyero et al., 2011; 2015; Handa et al., 2014; Follstad Shah et al., 2017, Tiegs et al., 2019) provide a strong framework for new comparative studies. The oldest of these (Boyero et al., 2011) compared breakdown rates at 26 globally distributed sites and found increased decomposition rates with increasing temperature and, when adjusted for temperature effects, with increasing latitude as a result of higher detritivore abundance. A follow-up study (Boyero et al., 2015) found an influence of phylogenetic diversity on breakdown rates of litter mixtures across 24 sites and 70 species.
Follstad Shah et al. (2017) found that aquatic decomposition rates for hundreds of species across the globe tend to increase with latitude when adjusted for temperature effects using a large meta-analysis. Finally, using cotton strips as a standardized substrate at 514 stream and adjacent riparian sites worldwide, Tiegs et al. (2019) found wide variation in in-stream breakdown rates and decreased decomposition rates with increasing latitude.

However, none of these large-scale studies of aquatic leaf litter decomposition explored how the evolutionary history of individual species has shaped leaf litter decomposition. Phylogenetic diversity of plant communities has been addressed, but phylogenetic history of the constituent species integrates leaf trait evolution—indeed, the evolution of all plant traits—and as such may provide improved predictions of decomposition rates globally in linked terrestrial-aquatic ecosystems, where connections are mitigated by traits we may not predict a priori. Phylogenetic diversity of communities alone tells only part of the story of how evolutionary history shapes ecosystem function.

Here we explore how both plant phylogenetic history and climatic variation among sites influence rates of leaf litter decomposition in streams distributed across the globe. Since climatic variables (mean, maximum and minimum temperature; precipitation; seasonality; isothermality) can influence both plant biogeography and the decomposition environment directly, we detangle these two influences (Dodds et al., 2015), testing the predictions that:

(1) across broad spatial scales, decomposition rates will be strongly influenced by site × species interactions, with both factors influenced by climate, and (2) phylogenetic history will affect decomposition rates through its influences on species traits but also on the site × species interaction. As these predictions are not mutually exclusive, we tested them in a modelling framework that addresses the joint influences of phylogeny and climate at both levels of analysis, providing novel insights into how plant evolutionary history shapes stream ecosystem processes globally.
MATERIALS AND METHODS

Plant litter decomposition database compilation

Data compiled for this study were extracted from values reported in the scientific literature as detailed in Follstad Shah et al. (2017). Briefly, we used search terms ‘(leaf OR litter) AND (breakdown OR decomposition OR processing) AND (stream OR river)’ to identify studies. This provided us with an initial list of articles published by May 13, 2011, which was updated by articles cited in review papers on leaf litter decomposition in streams by Follstad Shah et al., (2017). Our final list included 636 papers published between 1966-2011, of which 285 met our criteria that: 1) rates of leaf litter decomposition were measured in natural streams and rivers with perennial flow; and 2) stream temperature and decomposition rate constants were reported or could be calculated. We discarded studies including factors beyond local species differences (e.g. nutrient addition, predator exclusion, exotic species, long-distance reciprocal transplantations). However, we included in our data set a variety of leaf litter decomposition methods used across studies, particularly litter bags of various size and mesh size, deployment periods, and processing methods, to ensure a large sample size. Studies may have also included measurements of water chemistry, velocity, shredder abundance, and microbial measures, but the inconsistency of these measurements made it impossible to comprehensively address these other factors.

For each article, we recorded the scientific name of the leaf litter and the corresponding decomposition rate constants as reported or calculated (see Follstad Shah et al., 2017 and methods therein), where missing, as $m_t = m_0e^{-k_D t}$, where $m_t$ is leaf litter mass remaining at time $t$ and $m_0$ is the initial leaf litter mass. Data compilation resulted in 3189 records of leaf litter decomposition from 494 sites (Fig. 1) for 239 plant taxa from 124 genera, 70 families, and 34 orders. Decomposition rates ranged from 0.0002 to 0.7890 day$^{-1}$ and resulted in a
global, average in-stream decomposition rate of 0.0240 day⁻¹ (Follstad Shah et al., 2017) with a 95% confidence interval of 0.0227-0.0253, and a median rate of 0.0127 day⁻¹.

Latitude and longitude data were extracted from each paper and mapped to identify potential errors which were subsequently rectified. Many of the studies lacked detailed data on environmental conditions (stream discharge, water chemistry, etc.), and a previous study exploring patterns in this dataset examined stream temperature (Follstad Shah et al., 2017), so we focused on broad climatic variables in this analysis. Climatic data were inferred for each site from the 10 min (~340km²) resolution WorldClim v 2.0 dataset (Fick & Hijmans, 2017). All 19 of the WorldClim bioclimatic ("bioclim") variables (https://www.worldclim.org/bioclim) were downloaded and used individually or compiled as ordination axes (see methods for *Comparing environmental and phylogenetic drivers* below). R scripts for executing analyses and downloading data have been archived at (https://github.com/andrew-hipp/decomposition-phylogeny-2019).

## Plant phylogenetic data organization

We used Phylomatic (Webb & Donoghue, 2005; Beaulieu et al., 2012) to assemble a base phylogenetic tree for all species for which we had litter decomposition rate data (Appendix S1), using Phylomatic base tree R20120829 as the starting megatree, based on Angiosperm Phylogeny Group III (Haston et al., 2009; Qian & Jin, 2016). We normalized scientific names using the Taxonomic Name Resolution Service (Boyle et al., 2013) and corrected as needed with names used in published phylogenies (https://github.com/andrew-hipp/decomposition-phylogeny-2019). Mesquite (Maddison & Maddison, 2018) was used to manually resolve branches within families, as the supertree is not fully resolved for all families, based on those same phylogenetic studies (Appendix S1). Node ages of the resulting tree were then calibrated using more recent angiosperm phylogenies (Bell, Soltis, & Soltis, 2010) and the
simple branch length adjuster tool (BLADJ) to even out node spacing between calibration
points. Initial analyses conducted on the tree with unmodified ages did not suggest different
interpretations than analyses conducted on the Phylomatic tree with updated ages, suggesting
that our results are robust to a range of branch length assumptions. Throughout the paper, we
report analyses conducted on the tree with revised clade ages and results for the entire dataset
as well as a dataset including just the angiosperms. The tree was visualized and exported for
publication in R using the ggplot2 and ggtree packages (Yu et al., 2016) and custom scripts
(https://github.com/andrew-hipp/decomposition-phylogeny-2019). Site-level phylogenetic
diversity was calculated for all sites where more than one taxon occurred using mean
pairwise distance (MPD) and mean nearest taxon distance (MNTD) in picante v. 1.7 for R
(Kembel et al., 2010).

For analyses at the site level using generalized linear mixed models, we created a
composite tree with one tip per species per site, creating a polytomy for each species with a
depth of 1.5 Ma to ensure that all species polytomies were younger than the youngest node in
the phylogeny. Thus, each species x site combination had one tip in the tree, allowing us to
partition the site and phylogenetic contributions to variance in litter decomposition rates.

Phylogenetic signal and phylogenetic transitions on decomposition rate

We estimated phylogenetic signal, defined as the “tendency for related species to resemble
each other more than they resemble species drawn at random from the tree,” (Blomberg &
Garland, 2002) in litter decomposition rates using Blomberg’s *K* (Blomberg, Garland, & Ives,
2003). Blomberg’s *K* typically scales from 0 for a character with no phylogenetic
autocorrelation to 1 for a character that has phylogenetic autocorrelation (i.e., similarity in
trait value between close relatives) equivalent to expectations for a trait evolving according to
a Brownian motion (random walk) process on the tree being evaluated (Fig. 2). *K* may range
to greater than 1 for traits that exhibit greater phylogenetic autocorrelation than expected under the assumption of Brownian motion. Stated another way, *K* > 1 indicates that close relatives are more similar than expected if the trait being studied evolves according to a random walk. Significance was assessed by permuting tip states 4999 times, calculating *K* for each permutation, then calculating *P* as 2× the rank position of *K*<sub>observed</sub>; thus yielding a two-tailed *P*-value, reflecting the fact that both clustering (relatives more similar than expected) and overdispersion (close relatives less similar than expected) are possible evolutionary outcomes. These analyses were conducted with the package picante 1.7 (Kembel et al., 2010) in R 3.4.4 (R Core Team 2018). For comparison with previous studies, we also calculated Pagel's lambda (λ; Pagel, 1999; Münkemüller et al., 2012), a scalar of the off-diagonal cells of the covariance matrix estimated from the tree, which scales from 0 to 1 (or a bit higher, depending on the tree structure). At λ = 0, the covariance is equivalent to a star-shaped phylogeny, where all species are equally related, reflecting the case where phylogeny has no effect on litter decomposition rates. At λ = 1, the covariance between any pair of tips is predicted directly by the relative distance from the root of the tree to the most recent common ancestor of those tips; this models a Brownian motion process in which the branch lengths of the tree are known without error. The λ model was fitted using the fitContinuous function in the package geiger 2.0.6 (Harmon et al., 2008) in R 3.4.4.

We identified phylogenetic shifts in litter decomposition rate by evaluating the relative
support for alternative Ornstein-Uhlenbeck (O-U) models, which model transitions in trait
values as responses to shifting selective regimes (Hansen, 1997; Martins & Hansen, 1997;
Butler & King, 2004). Analysis was performed using the l1ou + IC method (Khabbazian et
al., 2016), a stepwise model-selection and evaluation approach implemented using the least
absolute shrinkage and selection operator ('lasso') (Tibshirani, 1996; 2011) method and a
modified information criterion that accounts for clade sizes entailed by shifts in litter
decomposition rate, modelled as shifting optima in trait space (where the trait is litter
decomposition rate). We compared results with an Expectation Maximization (EM) search
algorithm (Bastide et al., 2018), which generally searches parameter space more rapidly.

Analyses were conducted in the 11ou (Khabbazian et al., 2016) and PhylogeneticEM
(Bastide, Mariadassou, & Robin, 2017) packages of R 3.4.4.

We reconstructed phylogenetic transitions in the rate of evolution of litter decomposition
rate (i.e., the rate of evolution of log(k)) using reversible-jump Markov-chain Monte Carlo
(rjMCMC; Green, 1995) as implemented in the rjmcmc.bm function of geiger 2.0.6 (Harmon
et al., 2008; Eastman et al., 2011). In this method, every branch on the phylogeny was
assigned to a class of rates, corresponding to presumed shifts in the rate of evolution on the
phylogenetic tree. The relative rates of all branches within a taxonomic class were allowed to
vary. The assignments of branches to classes were also allowed to vary by allowing
transitions in rate to appear or disappear from the tree.

Comparing environmental and phylogenetic drivers

We used two methods to compare phylogenetic and climatic models to predict global patterns
of litter decomposition rates. First, we used an approach based on phylogenetic eigenvector
regression (PVR; Diniz-Filho, Sant'Ana, & Bini, 1998) to partition variance in litter
decomposition rate into 1) an environmental component based on the four strongest bioclim
variables (Bioclim1: annual mean temperature; Bioclim4: temperature seasonality;
Bioclim12: annual precipitation; and Bioclim14: precipitation of the driest month); 2) four
non-metric multidimensional scaling (NMDS) ordination axes created using a Euclidean
distance measure from all 19 bioclim variables (https://www.worldclim.org/bioclim) (and for
comparison, four principle component analysis (PCA) axes created using Euclidean distance
measures with all 19 variables); 3) a phylogenetic component based on the first four
eigenvectors from principal coordinate analysis (PCoA) of a pairwise phylogenetic distance matrix; and 4) site identity for analyses conducted at the site level. Site identity was added as a random effect to account for any aspects of site that are not directly measured; in this way, site identity in our model is analogous to plot or block in a designed experiment. A stepwise model selection process is generally employed to select phylogenetic eigenvectors for analysis (Diniz-Filho et al., 2012; Gonçlaves-Souza, Diniz-Filho, & Romero, 2014).

However, the lack of an explicit evolutionary model underlying phylogenetic eigenvector analysis (Freckleton, Cooper, & Jetz, 2011) combined with the relative arbitrariness of eigenvector selection—selecting all 238 eigenvectors would overfit the data, while selecting anything less than all fails to capture all phylogenetic data—makes phylogenetic eigenvector regression a heuristic approach. Consequently, we used only four phylogenetic eigenvectors, which cumulatively explain 55% of the total variance in the phylogeny when the relative eigenvalues are used as estimates of the variance explained by each PCoA axis. Axes 2 and 3 of the angiosperm ordination recovered differences among broadly overlapping clades; consequently, we used PCoA axes 1, 2, 4, and 5 for model evaluation in the angiosperms by species models, but this had a negligible effect on model ranking and support. These four eigenvectors were included in models with either four bioclim variables or four bioclim NMDS axes for a comparison of the relative predictive value of both climate and phylogeny.

We selected the same number of axes, four, from both the phylogeny PCoA and the climatic NMDS to avoid biasing our linear regressions toward detecting either climate or phylogeny as a better predictor of decomposition rate. In both cases, the axes explain a fair amount of variation in the data. For the phylogenetic PCoA, the first four axes explain 33.0, 8.6, 8.0 and 5.4% of variation, respectively. For NMDS, variance partitioning is not the target of each axis, but ordination stress—a measure of the mismatch between the K-dimensional ordination and the distance matrix used to generate it. Ordination stress drops from 0.214 at K = 1 to

Accepted Article
0.035 at K = 4; all dimensions above K = 4 reduce stress by less than 0.02 per increase in dimension. To assess the effect of ordination method on model support and parameter estimates, we replicated the analyses with the first four axes of a PCA using the 19 bioclim climatic predictors and found that there is no difference in model support and negligible difference in the estimated regression parameters (Supplemental Table S1). Code to do all analyses is in the repository for this paper (https://github.com/andrew-hipp/decomposition-phylogeny-2019). Linear regression models were compared based on Akaike's Information Criterion (AIC) (Akaike, 1974) and coefficients of determination ($R^2$), and model confidence intervals were constructed using cumulative AIC weights (Burnham & Anderson, 2002; Burnham, Anderson, & Huyvaert, 2011). All analyses were conducted using the ape (Paradis, Claude, & Strimmer, 2004) and vegan (Oksanen et al., 2016) packages of R and custom scripts available at https://github.com/andrew-hipp/decomposition-phylogeny-2019.

Our second approach used phylogenetic generalized linear mixed models (PGLMM) (Hadfield & Nakagawa, 2010) in a Bayesian framework to quantify the effects of phylogeny, climate, and site on litter decomposition rate more precisely. This approach uses the phylogenetic covariance matrix as a predictor of covariance in trait value among tips, and it scales the importance of the phylogeny by rescaling the covariance matrix. For a species-level analysis, this is analogous to fitting a phylogenetic generalized least squares model while simultaneously estimating a scalar of the off-diagonal elements of the covariance matrix (i.e., Pagel's $\lambda$) (Revell, 2010; Housworth, Martins, & Lynch, 2004; Pagel, 1997; Pagel, 1999). In species-level analyses, we treat phylogeny as a random effect and bioclim variables as fixed effects. For a site-level analysis, PGLMM offers the flexibility of treating both phylogeny and site as random effects and bioclim variables as fixed effects. We compared PGLMM models based on Deviance Information Criterion (DIC) (Spiegelhalter et al., 2002; van der Linde, 2005) and residual model variance, and model intervals were
constructed using DIC weights. Analyses were conducted in R using the MCMCglmm package (Hadfield, 2010) and custom scripts available at https://github.com/andrew-hipp/decomposition-phylogeny-2019.

## RESULTS

Litter decomposition rates (k day⁻¹) in stream environments showed a wide range of variation across plant families (min-max: 0.0036-0.1522) and plant orders globally (Table 1). Our results (averaged by plant family) are compared to an early synthesis of decomposition rates (Webster & Benfield, 1986) (Fig. 3) to provide a more complete picture of global decomposition across plant families.

### Phylogenetic signal and phylogenetic transitions in litter decomposition rate

The litter decomposition rate data exhibited a significant phylogenetic signal in both the all-taxa tree (Fig. 4; K = 0.177, P = 0.01; λ = 0.977) and the angiosperm tree (K = 0.376, P = 0.004; λ = 0.961) (Table 2). Because plants are distributed non-randomly across the globe, climatic variables averaged for each species all exhibited some phylogenetic signal. Mean annual temperature (BIO1) and temperature seasonality (BIO4) each exhibited significant phylogenetic signal (on the angiosperm tree, K = 0.230-0.266, P ≤ 0.002, λ = 0.59-0.70; on the all-taxa tree, K = 0.097-0.117, P ≤ 0.006, λ = 0.74-0.83), and precipitation of the driest month (BIO14) exhibited significant phylogenetic signal on the angiosperm tree (K = 0.227, P = 0.038, λ = 0.23), but a nonsignificant phylogenetic signal on the all-taxa tree (K = 0.1, P = 0.054, λ = 0.0), while mean annual precipitation (BIO12) exhibited weak and, in our dataset, non-significant phylogenetic signal (K = 0.094-0.208, P = 0.078-0.112, λ = 0.51-0.64; Table 2). Ordination of these data in four dimensions (Fig. S1) was well-supported (stress = 0.035), and axes 1 and 3 exhibited weak but significant phylogenetic signal on the
angiosperm tree (K = 0.200–0.255, P < 0.042, λ = 0.35–0.70) and the all-taxa tree (K = 0.093–0.111, P < 0.032, λ = 0.52–0.83).

Analyses of alternative multiple-regime Ornstein-Uhlenbeck models identified a significant increase in litter decomposition rate at the base of the eudicots (Fig. S2). Both inferred a relatively high rate of adaptation, corresponding to 5.0–5.5% of the total tree length (for the phylogenetic lasso, α = 14.36 [t₁/₂ = 0.0483] on a tree arbitrarily rescaled to height 1.0; for the E-M algorithm, α = 0.0305 [t₁/₂ = 22.73] on a tree spanning 432 million years, t₁/₂ = 5.26% of total tree depth). While interpreting the rate of adaptation (α) is not perfectly straightforward (Bastide et al., 2018), this rate of adaptation suggests that the transition to a higher litter decomposition rate at the base of the eudicots occurred too rapidly to be explained by a purely random walk model, i.e., by the variance we expected as lineages diverge from one another (Felsenstein, 1985). This in turn suggests that natural selection on leaf traits shaped this transition, driving accelerated rates of litter decomposition in the angiosperms. By contrast, analysis of evolutionary rates using rjMCMC suggests that the evolution of litter decomposition rates is relatively homogeneous: no lineages were recovered as evolving at a significantly higher or lower rate than other clades (Fig. S3).

### Comparing environmental and phylogenetic drivers

Top-ranked linear models for both the species-level and site-level analyses included phylogenetic PCoA axes, irrespective of whether the all-taxa phylogeny or angiosperms-only phylogeny was analysed (Table 3). Climatic predictors, whether included directly in the model as bioclim variables, or indirectly as the first four NMDS axes, appeared in the 95% model confidence set (based on cumulative AIC weight) only in combination with phylogeny, whereas phylogeny alone had the lowest AIC of any of the individual models in the species-level analysis of the angiosperm dataset. The first axis of the phylogenetic PCoA
had a stronger effect than climatic predictors in multiple regression as estimated using regression coefficients on rescaled data for the all-taxa analyses and the angiosperm dataset analysed by species. For the angiosperm dataset analysed by site, climatic predictors had a stronger effect (Table 3). Among all models in the 95% confidence set, mean temperature (BIO1) had the strongest climatic effect on litter decomposition rate as estimated by normalized partial regression coefficients, followed by either mean annual precipitation (BIO12) or temperature seasonality (BIO4) (Table 3).

Including phylogeny as a random effect in analyses by species reduced the residual variance by approximately 40% in the linear mixed models (Table 4). Including just site as a random effect reduced residual variance from 1.0 to 0.58–0.59 in analyses conducted by site; including just phylogeny (also as a random effect) reduced residual variance from 1.0 to 0.62 or 0.65 (Table 4). The single most important predictor in both sets of taxa and both analysis levels was phylogeny, which was for all sets of analyses the lowest-DIC single-predictor model (Table 4). Including climate as a suite of fixed effects did not improve model fit over that of the model including only phylogeny as a random effect in analyses conducted by species. In analyses conducted by site, the model including site and phylogeny as random effects and climate as a suite of fixed effects was by far the best fit model, with phylogeny explaining ca. 6× the variance explained by site in the all taxa dataset, ca. 2× the variance explained by site in the angiosperm dataset (Table 4, site var. and phylo var. columns). In both datasets, mean annual temperature (BIO1) had approximately twice the effect size of mean annual rainfall (BIO12), but neither effect was significant (Table 4). Using ordination to determine the influence of climatic factors on species-specific decomposition rates (239 species plotted in climatic space, Fig. 5), demonstrated that climate is a relatively poor predictor of decomposition rates at a global scale.
Average phylogenetic mean nearest taxon distance (MNTD) for the sites where more than one taxon occurred (N = 179) was 210.4 Ma, and average phylogenetic mean pairwise distance (MPD) was 232.5 Ma. Eight sites showed significant MPD and 10 showed significant MNTD (two-tailed test, P < 0.01, **Fig. S4**). Forty-one sites sampled had at least one eudicot and one species from a different major clade.

DISCUSSION

The analyses presented here demonstrate that phylogenetic history—the evolutionary history that led to the plants we observe across the globe today—is a more powerful predictor of global variation in rates of in-stream leaf litter decomposition than site or the range of climate variables considered in this study. While this is perhaps not surprising at the species level, where we might expect intrinsic properties of species to swamp climatic conditions at the sites where individual plants are collected, it is remarkable that even data analysed at the site level suggests that phylogeny explains 2.2–5.8 times the variation explained by site (Table 4). This is only possible because sites are phylogenetically diverse: of the 179 sites in our study where two or more taxa were measured, the average phylogenetic mean nearest taxon distance (MNTD) and average phylogenetic mean pairwise distance (MPD) corresponded to divergence times of 105 and 116 Ma respectively, demonstrating that sites captured large swaths of phylogenetic history. In fact, 41 sites sampled had at least one eudicot and one species from a different major clade. As the major evolutionary transition in decomposition rates appears to have been on the branch leading to the eudicots, this co-occurrence of eudicots with non-eudicot lineages is key to the fact that site conditions as well as climatic conditions predicted less variance than even the relatively small proportion of phylogenetic variance captured by four phylogenetic eigenvectors (Table 3). Evolutionary history explains variation in traits that are highly relevant to explaining global litter decomposition rates.
Phylogenetic effects observed in this study may be related to variation in whole suites of plant traits that influence microbial decomposers and invertebrate litter consumers and that act across ecosystems, from the terrestrial environments where the plants evolved to the aquatic ecosystems where their leaves decompose. Leaf litter decomposability is influenced by a plant's position on the leaf economics spectrum (Wright et al., 2004; Díaz et al., 2016). In terrestrial systems, plant traits have been found to explain the decomposition rate of leaves (Cornwell et al., 2008) and wood (Hu et al., 2018) more than climatic factors. We were unable to include specific leaf traits as explanatory variables in our study due to inconsistent reporting of leaf traits in the published studies. However, plant traits are phylogenetically heritable (Schmerler et al., 2012; Pearse & Hipp, 2012; Flores et al., 2014; Zanne et al., 2014; Hao et al., 2015; Cavender-Bares et al., 2018), and plant phylogeny is thus expected to integrate across the entire suite of litter quality traits. Hence, our study implies that the traits that predict leaf decomposability track phylogenetic history more closely than climatic transitions among species. Our results demonstrate that phylogeny explains an important fraction of variance in plant traits similar to other studies where they were either correlated with a subset of climatic predictors (e.g., Willis et al., 2008) or incompletely explained by the full set of climatic predictors expected to explain them (Pearse & Hipp, 2009; Li, Ives, & Waller, 2017).

The phylogenetic effect we find in this study is among the strongest reported to date for decomposition rates. Phylogenetic heritability of leaf decomposition in streams analysed in this study (Pagel's *λ* = 0.977 for 239 species globally) is higher than *λ* values found for the decomposability of angiosperm species studied in terrestrial environments in the UK (45 species; *λ*=0.32) and central Argentina (24 species; *λ*=0.70) (Díaz et al., 2013), the eastern US (*λ*=0.39 for 78 leaf species, Jo, Fridley, & Frank, 2016), and Ecuador (17 species; *λ*=0.80, for undamaged leaves; Cárdenas et al., 2015). These smaller studies had lower power to
detect divergence from Brownian motion processes of trait evolution (Boettiger, Coop, &
Ralph, 2012) and are biased toward reporting high λ values by the long average length of
their terminal branches. Therefore, our results are particularly remarkable in light of the fact
that the high species density of our phylogenetic tree and resultant relative shortening of
terminal branches increases statistical power to detect divergence from Brownian motion
(compare, for example, **Fig. 4** of the present study with Fig. 4 of Díaz et al., 2013). In fact,
the phylogenetic signal we observed for in-stream decomposition is greater than the
phylogenetic signals for all leaf traits (λ = 0.15–0.70 for chlorophyll, P, Ca, tannins, phenols,
cellulose, N, and lignin, in increasing order of λ) in a tropical forest for a recent analysis of
184 species (McManus et al., 2016) and a number of leaf traits (λ = 0.27 to 0.76 for leaf area,
chlorophyll and leaf thickness) across 229 species in tropical and subtropical China (Yang et
al., 2014).

Our large-scale synthesis demonstrates that phylogenetic variation shapes a fundamental
ecosystem process across terrestrial-aquatic ecosystem boundaries, despite the fact that the
species being studied evolved in one ecosystem while the process of interest—in-stream
decomposition rate—is measured in another. This phylogenetic effect is detectable even at a
significant distance from the source of the leaf litter and despite a broad set of environmental
factors that also influence decomposition rates – water velocity, water temperature, microbial
colonization, and macroinvertebrate shredders. The patterns we observe here are thus not a
consequence of phylogenetic niche conservatism; rather, the entire suite of phylogenetically
heritable plant traits (e.g., leaf chemistry, toughness) that shape in-stream litter
decomposition rates.
Our study further illustrates that the tree of life is not simply a product of adaptation to changing environments, but that evolutionary history of species and, presumably, their traits drive ecosystem processes (Cornelissen & Cornwell, 2014). The analyses presented here highlight the central role of phylogenetic history's influence on litter decomposition at a global scale and this work represents an important merger of ecosystem science with evolutionary history (Mouquet et al., 2012; Narwani et al., 2015). The result that phylogenetic signals can be observed across terrestrial-aquatic boundaries is novel and strengthens evidence for the organizing power of genes on communities and ecosystems (Whitham et al., 2006; LeRoy et al. 2006; 2007; LeRoy, Wooley, & Lindroth, 2012; Jackrel & Wootton, 2014; LeRoy & Fischer, 2019).

This study also provides further context for our continued exploration of how climate change and global warming are predicted to increase rates of aquatic decomposition through increased stream temperatures (Boyero et al., 2011; Follstad Shah et al., 2017; Tiegs et al., 2019). There are both direct and indirect influences of temperature on this key ecosystem process. Temperature can directly increase rates of decomposition through accelerated microbial growth and litter consumption; however, elevated CO₂ concentrations driving temperature increases could also lower leaf litter quality via higher C:nutrient ratios and thus suppress decomposition rates (Tuchman et al., 2002; Boyero et al., 2017). Temperature can also indirectly affect decomposition through plant species' plastic responses to climate change in terms of expressed phenotypes, adaptation *in situ*, and shifts in species ranges (Hooper et al., 2012; Kominoski et al., 2013; Christmas, Breed, & Lowe, 2016). How these indirect changes may influence stream ecosystems is not well understood. Finally, there may be feedbacks between climate change and in-stream decomposition through carbon evasion from streams. Riverine ecosystems are important contributors to the breakdown of
terrestrially-derived carbon (Cole et al., 2007), and as a result, streams and rivers have been identified as major sources of carbon dioxide to the atmosphere (Raymond et al., 2013).

Streams are influenced by the biomes they are embedded in (Dodds et al., 2015; Tiegs et al., 2019), but biomes and the species assemblages within them will both shift under predicted climate change models. The selective pressures on plants that might influence litter decomposition rates (e.g. predation, defences against microbial attack, desiccation resistance) are decoupled from the physical, chemical, and biological processes occurring in streams. In spite of these complexities, our study highlights the importance of riparian plant community composition on stream litter decomposition. Thus, the interactive changes in phenotypic expression, phenology, and species range shifts could directly and indirectly influence global litter decomposition rates. Taken as a whole, this work highlights the potential of quantifying phylogenetic influences to predict rates of key ecosystem processes.

## ACKNOWLEDGEMENTS

We thank the LTER Network for funding an initial collaboration in 2011. NAG was supported by the Department of Energy's Office of Science, Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. We thank Chuck Hawkins, Sherri Johnson, Chris Swan, Amy Rosemond, and Lydia Zeglin for insightful conversations about this research and for participating in the initial data compilation. We thank Bruce McCune for advice on statistical analyses. We thank Editor Dr. David Wardle and two anonymous reviewers for their comprehensive reviews of this work. All authors have confirmed that they have no relationships, financial or otherwise, that might be perceived as influencing their objectivity or that could be considered a potential source of conflict of interest.
DATA AVAILABILITY STATEMENT

All raw and processed data, R-scripts, and figures are available at https://github.com/andrew-hipp/decomposition-phylogeny-2019.

AUTHORS’ CONTRIBUTIONS

CJL and ALH contributed equally to writing this paper and are joint first authors. CJL
conceived the study idea and coordinated collaborators. ALH and KL compiled the
phylogenetic data, and ALH performed the phylogenetic comparative analyses and statistical
analyses. JJFS led the initial data compilation on litter decomposition in streams, which also
involved JSK, MA, WKD, MOG, NAG, AL, CJL, DWPM, and JRW. CJL, ALH, and KL
wrote the initial draft manuscript and all authors (CJL, ALH, KL, JJFS, JSK, MA, WKD,
MOG, NAG, AL, DWPM, RLS, and JRW) contributed critically to subsequent drafts and
gave final approval for publication. CJL coordinated inputs and edits by all co-authors and
reviewers.
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Accepted Article
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Accepted Article
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Accepted Article
<figure><img src="image_5.png" /><figcaption>**Fig. 1. Map of sampling locations and sample sizes,** includes all 494 sites included in our study. The numbers of decomposition rate values (samples) for each site ranges from 1 to 156.</figcaption></figure>

<figure><img src="image_6.png" /></figure>
<caption>**Fig. 2. Conceptual diagram showing phylogenetic signal strength compared to ecosystem function.** Two phylogenetic trees are shown with hypothetical decomposition rates plotted along the tips of the tree, ranging from slow (white squares) to fast (black squares) decomposition rates (k day⁻¹). If decomposition rates did not vary across the phylogenetic tree, all tips would be the same colour (null model – not shown). If decomposition rates were distributed randomly across the phylogenetic tree, there would be a low phylogenetic signal as in the panel on the right. The panel on the left shows an example with a high phylogenetic signal for decomposition. Representative Pagel's lambda and Blomberg's K values for each scenario are included.</caption>

High phylogenetic signal
Pagel's lambda = 0.9945,
Blomberg's K = 1.5183

<figure><img src="image_7.png" /></figure>

Low phylogenetic signal
Pagel's lambda = 0.2615,
Blomberg's K = 0.0434

<figure><img src="image_8.png" /></figure>

Accepted Article
<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Comparison of decomposition rates by plant family between Webster & Benfield (1986) and this study. Symbols represent mean decomposition rates (k day⁻¹) for both Webster & Benfield (1986; orange circles) and this study (green circles). Error bars represent standard error of the mean calculated by sample for Webster & Benfield (1986) and by species for this study.</figcaption></figure>

This article is protected by copyright. All rights reserved.

Accepted Article
**Fig. 4. Phylogeny of species included in our analyses**, with natural-log transformed global in-stream leaf litter decomposition rates ($k$ day$^{-1}$= kd) and bioclimatic and phylogenetic predictors included in models. Tip states are scaled to range from 0.0 to 1.0. Transitions among clade-level leaf litter decomposition rates were modelled using a multiple-regime Ornstein-Uhlenbeck model with a phylogenetic lasso approach and expectation maximization (EM) algorithm for model fitting. The two approaches converged on all transitions represented here except the decrease in ln(kd) observed in *Rhododendron*, which was recovered using the phylogenetic lasso but not the EM algorithm (see **Fig. S2**). Variables plotted along the tips include: decomposition rate, ln(kd); mean annual temperature (BIO1), mean precipitation (BIO12), temperature seasonality (BIO4), precipitation seasonality (BIO14), and four phylogenetic PCoA axis scores.
<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 5. Ordination of 239 species in climatic space.** Point size corresponds to natural log decomposition rate (k day⁻¹). Isoclines represent mean annual air temperature in degrees centigrade (BIO1 / 10). The first two axes of climatic niche space (using all 19 bioclim climatic variables from WorldClim) demonstrate that climate is a relatively poor predictor of decomposition rates, at least in our study.</figcaption></figure>
**Table 1.** Average decomposition rate constants (k day⁻¹) for global plant families.

Standard deviation (sd) and standard error (SEM) are provided for families with N > 1.

Submitted as Excel

**Table 2. Phylogenetic signal** (Blomberg's *K* and Pagel's *λ*) for key decomposition variables (decomposition rate *k* day⁻¹ = kd; natural log of decomposition rate *k* day⁻¹ = ln kd), and bioclimatic variables: Bio1 (mean annual air temperature), Bio12 (mean annual precipitation), Bio4 (temperature seasonality), Bio14 (precipitation of the driest month), NMDS axes 1-4 (from ordination of all 19 bioclim variables).

<table><thead><tr><th></th><th>Angiosperm tree</th><th>All taxa tree</th></tr></thead><tbody><tr><td>k (day<sup>-1</sup>)</td><td><i>K</i> = 0.376, <i>p</i> = 0.004, <i>λ</i> = 0.961</td><td><i>K</i> = 0.177, <i>p</i> = 0.01, <i>λ</i> = 0.977</td></tr><tr><td>ln k (day<sup>-1</sup>)</td><td><i>K</i> = 0.226, <i>p</i> = 0.01, <i>λ</i> = 0.536</td><td><i>K</i> = 0.122, <i>p</i> = 0.004, <i>λ</i> = 0.716</td></tr><tr><td>Bio1</td><td><i>K</i> = 0.23, <i>p</i> = 0.002, <i>λ</i> = 0.587</td><td><i>K</i> = 0.097, <i>p</i> = 0.006, <i>λ</i> = 0.743</td></tr><tr><td>Bio12</td><td><i>K</i> = 0.208, <i>p</i> = 0.078, <i>λ</i> = 0.505</td><td><i>K</i> = 0.094, <i>p</i> = 0.122, <i>λ</i> = 0.638</td></tr><tr><td>Bio4</td><td><i>K</i> = 0.266, <i>p</i> = 0, <i>λ</i> = 0.703</td><td><i>K</i> = 0.117, <i>p</i> = 0, <i>λ</i> = 0.833</td></tr><tr><td>Bio14</td><td><i>K</i> = 0.227, <i>p</i> = 0.038, <i>λ</i> = 0.225</td><td><i>K</i> = 0.1, <i>p</i> = 0.054, <i>λ</i> = 0</td></tr><tr><td>NMDS1</td><td><i>K</i> = 0.255, <i>p</i> = 0.002, <i>λ</i> = 0.697</td><td><i>K</i> = 0.111, <i>p</i> = 0, <i>λ</i> = 0.826</td></tr><tr><td>NMDS2</td><td><i>K</i> = 0.186, <i>p</i> = 0.204, <i>λ</i> = 0.106</td><td><i>K</i> = 0.08, <i>p</i> = 0.262, <i>λ</i> = 0</td></tr><tr><td>NMDS3</td><td><i>K</i> = 0.2, <i>p</i> = 0.042, <i>λ</i> = 0.343</td><td><i>K</i> = 0.093, <i>p</i> = 0.032, <i>λ</i> = 0.51</td></tr><tr><td>NMDS4</td><td><i>K</i> = 0.192, <i>p</i> = 0.058, <i>λ</i> = 0</td><td><i>K</i> = 0.09, <i>p</i> = 0.072, <i>λ</i> = 0</td></tr></tbody></table>
**Table 3. Phylogenetic Eigenvector Regression Model results** including both phylogenetic and bioclimatic predictors of decomposition rates. All predictors used in the models are indicated by the presence of a regression coefficient; parameters not included in each model are indicated by blanks in the table. Models were run using two sets of data: 1) all taxa in the dataset, and 2) just the angiosperms in the dataset. Analyses were conducted by species and by species × site. Results include coefficients of determination (R²), Akaike's Information Criterion (AIC), differences between the top-ranked model and others (delta AIC), and the cumulative AIC weight (Sum AICw), with lowest AIC scores reported at the top. Regression coefficients are all estimated on data rescaled to unit variance and mean of 0 and are highlighted in bold when p-values are less than 0.05.

*Submitted as Excel*

**Table 4. Phylogenetic Generalized Linear Mixed Model results** including phylogeny as a random effect along with bioclimatic predictors of decomposition rates. All predictors used in the models are indicated by presence of regression coefficient; parameters not included in each model are indicated by blanks in the table. Models were run using two sets of data: 1) all taxa in the dataset, and 2) just the angiosperms in the dataset. Analyses were conducted by species and by site × species. Results include Deviance Information Criterion (DIC), differences between the top-ranked model and others (delta DIC), and the cumulative DIC weight (Sum DICw).

*Submitted as Excel*

Accepted Article
<table><caption>Table 1.</caption><thead><tr><th>Clade</th><th>Order</th><th>Family</th><th>N</th><th>mean k day<sup>-1</sup></th><th>SD</th><th>SEM</th></tr></thead><tbody><tr><td>Fern/fern allies</td><td>eupolypod II</td><td>Blechnaceae</td><td>1</td><td>0.0066</td><td>--</td><td>--</td></tr><tr><td>Fern/fern allies</td><td>Marattiales</td><td>Marattiaceae</td><td>1</td><td>0.013</td><td>--</td><td>--</td></tr><tr><td>Gymnosperm</td><td>Cupressales</td><td>Cupressaceae</td><td>1</td><td>0.008</td><td>--</td><td>--</td></tr><tr><td>Gymnosperm</td><td>Cupressales</td><td>Podocarpaceae</td><td>1</td><td>0.016</td><td>--</td><td>--</td></tr><tr><td>Gymnosperm</td><td>Pinales</td><td>Pinaceae</td><td>8</td><td>0.0058</td><td>0.0054</td><td>0.0019</td></tr><tr><td>Monocot</td><td>Alismatales</td><td>Potamogetonaceae</td><td>2</td><td>0.0091</td><td>0.0077</td><td>0.0054</td></tr><tr><td>Monocot</td><td>Arecales</td><td>Arecaceae</td><td>1</td><td>0.008</td><td>--</td><td>--</td></tr><tr><td>Monocot</td><td>Commelinales</td><td>Pontederiaceae</td><td>1</td><td>0.0122</td><td>--</td><td>--</td></tr><tr><td>Monocot</td><td>Pandanales</td><td>Pandanaceae</td><td>1</td><td>0.0144</td><td>--</td><td>--</td></tr><tr><td>Monocot</td><td>Poales</td><td>Cyperaceae</td><td>3</td><td>0.0127</td><td>0.011</td><td>0.0063</td></tr><tr><td>Monocot</td><td>Poales</td><td>Poaceae</td><td>12</td><td>0.0108</td><td>0.0086</td><td>0.0025</td></tr><tr><td>Monocot</td><td>Poales</td><td>Typhaceae</td><td>1</td><td>0.0252</td><td>--</td><td>--</td></tr><tr><td>Magnoliid</td><td>Laurales</td><td>Atherospermataceae</td><td>1</td><td>0.0072</td><td>--</td><td>--</td></tr><tr><td>Magnoliid</td><td>Laurales</td><td>Lauraceae</td><td>4</td><td>0.006</td><td>0.0043</td><td>0.0022</td></tr><tr><td>Magnoliid</td><td>Magnoliales</td><td>Magnoliaceae</td><td>1</td><td>0.0155</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Apiales</td><td>Araliaceae</td><td>2</td><td>0.0291</td><td>0.01</td><td>0.0071</td></tr><tr><td>Eudicot</td><td>Apiales</td><td>Pittosporaceae</td><td>2</td><td>0.0296</td><td>0.0195</td><td>0.0138</td></tr><tr><td>Eudicot</td><td>Aquifoliales</td><td>Aquifoliaceae</td><td>2</td><td>0.0303</td><td>0.0293</td><td>0.0207</td></tr><tr><td>Eudicot</td><td>Asterales</td><td>Asteraceae</td><td>1</td><td>0.0216</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Brassicales</td><td>Brassicaceae</td><td>1</td><td>0.0845</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Caryophyllales</td><td>Polygonaceae</td><td>2</td><td>0.0078</td><td>0.0032</td><td>0.0023</td></tr><tr><td>Eudicot</td><td>Caryophyllales</td><td>Tamaricaceae</td><td>2</td><td>0.0064</td><td>0.0022</td><td>0.0016</td></tr><tr><td>Eudicot</td><td>Cornales</td><td>Cornaceae</td><td>3</td><td>0.04</td><td>0.0274</td><td>0.0158</td></tr><tr><td>Eudicot</td><td>Cornales</td><td>Nyssaceae</td><td>1</td><td>0.0313</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Dipsacales</td><td>Caprifoliaceae</td><td>1</td><td>0.1018</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Ericales</td><td>Ericaceae</td><td>3</td><td>0.004</td><td>0.0032</td><td>0.0019</td></tr><tr><td>Eudicot</td><td>Ericales</td><td>Primulaceae</td><td>1</td><td>0.007</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Fabales</td><td>Fabaceae</td><td>6</td><td>0.0311</td><td>0.035</td><td>0.0143</td></tr><tr><td>Eudicot</td><td>Fagales</td><td>Betulaceae</td><td>17</td><td>0.0229</td><td>0.0233</td><td>0.0057</td></tr><tr><td>Eudicot</td><td>Fagales</td><td>Casuarinaceae</td><td>1</td><td>0.0209</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Fagales</td><td>Fagaceae</td><td>18</td><td>0.0114</td><td>0.0055</td><td>0.0013</td></tr><tr><td>Eudicot</td><td>Fagales</td><td>Juglandaceae</td><td>3</td><td>0.0095</td><td>0.0039</td><td>0.0022</td></tr><tr><td>Eudicot</td><td>Fagales</td><td>Myricaceae</td><td>1</td><td>0.039</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Fagales</td><td>Nothofagaceae</td><td>4</td><td>0.0106</td><td>0.0071</td><td>0.0035</td></tr><tr><td>Eudicot</td><td>Gentianales</td><td>Apocynaceae</td><td>2</td><td>0.0237</td><td>0.0181</td><td>0.0128</td></tr><tr><td>Eudicot</td><td>Gentianales</td><td>Rubiaceae</td><td>2</td><td>0.0476</td><td>6.00E-04</td><td>4.00E-04</td></tr><tr><td>Eudicot</td><td>Icacinales</td><td>Icacinaceae</td><td>1</td><td>0.006</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Lamiales</td><td>Oleaceae</td><td>4</td><td>0.0182</td><td>0.0085</td><td>0.0042</td></tr><tr><td>Eudicot</td><td>Lamiales</td><td>Scrophulariaceae</td><td>1</td><td>0.1522</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Lamiales</td><td>Verbenaceae</td><td>1</td><td>0.0182</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Malpighiales</td><td>Euphorbiaceae</td><td>5</td><td>0.0411</td><td>0.023</td><td>0.0103</td></tr><tr><td>Eudicot</td><td>Malpighiales</td><td>Salicaceae</td><td>28</td><td>0.0158</td><td>0.0133</td><td>0.0025</td></tr><tr><td>Eudicot</td><td>Malpighiales</td><td>Violaceae</td><td>1</td><td>0.0477</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Malvales</td><td>Malvaceae</td><td>5</td><td>0.0858</td><td>0.1407</td><td>0.0629</td></tr><tr><td>Eudicot</td><td>Myrtales</td><td>Combretaceae</td><td>2</td><td>0.025</td><td>0.0198</td><td>0.014</td></tr><tr><td>Eudicot</td><td>Myrtales</td><td>Lythraceae</td><td>1</td><td>0.0333</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Myrtales</td><td>Melastomataceae</td><td>2</td><td>0.0064</td><td>0.0027</td><td>0.0019</td></tr><tr><td>Eudicot</td><td>Myrtales</td><td>Myrtaceae</td><td>20</td><td>0.0115</td><td>0.0091</td><td>0.002</td></tr><tr><td>Eudicot</td><td>Myrtales</td><td>Onagraceae</td><td>1</td><td>0.0165</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Oxalidales</td><td>Cunoniaceae</td><td>2</td><td>0.0363</td><td>0.0471</td><td>0.0333</td></tr><tr><td>Eudicot</td><td>Oxalidales</td><td>Elaeocarpaceae</td><td>2</td><td>0.0286</td><td>0.0369</td><td>0.0261</td></tr><tr><td>Eudicot</td><td>Proteales</td><td>Platanaceae</td><td>6</td><td>0.0057</td><td>0.0019</td><td>8.00E-04</td></tr><tr><td>Eudicot</td><td>Proteales</td><td>Proteaceae</td><td>2</td><td>0.009</td><td>0.0022</td><td>0.0016</td></tr><tr><td>Eudicot</td><td>Ranunculales</td><td>Eupteleaceae</td><td>1</td><td>0.0133</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Rosales</td><td>Elaeagnaceae</td><td>1</td><td>0.024</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Rosales</td><td>Moraceae</td><td>6</td><td>0.0388</td><td>0.0308</td><td>0.0126</td></tr><tr><td>Eudicot</td><td>Rosales</td><td>Rhamnaceae</td><td>1</td><td>0.0286</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Rosales</td><td>Rosaceae</td><td>4</td><td>0.0394</td><td>0.0443</td><td>0.0222</td></tr><tr><td>Eudicot</td><td>Rosales</td><td>Ulmaceae</td><td>7</td><td>0.0705</td><td>0.0727</td><td>0.0275</td></tr><tr><td>Eudicot</td><td>Rosales</td><td>Urticaceae</td><td>1</td><td>0.02</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Anacardiaceae</td><td>2</td><td>0.0202</td><td>0.0078</td><td>0.0056</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Burseraceae</td><td>2</td><td>0.0036</td><td>4.00E-04</td><td>2.00E-04</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Hippocastanaceae</td><td>1</td><td>0.0066</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Meliaceae</td><td>1</td><td>0.023</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Rutaceae</td><td>1</td><td>0.019</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Sapindaceae</td><td>8</td><td>0.0232</td><td>0.0146</td><td>0.0052</td></tr><tr><td>Eudicot</td><td>Sapindales</td><td>Simaroubaceae</td><td>1</td><td>0.0945</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Saxifragales</td><td>Haloragaceae</td><td>1</td><td>0.025</td><td>--</td><td>--</td></tr><tr><td>Eudicot</td><td>Saxifragales</td><td>Hamamelidaceae</td><td>2</td><td>0.0172</td><td>0.0029</td><td>0.002</td></tr><tr><td>Eudicot</td><td>Vitales</td><td>Vitaceae</td><td>1</td><td>0.0142</td><td>--</td><td>--</td></tr></tbody></table>
<table><caption>Table 3.</caption><thead><tr><th>Taxon set</th><th>Analysis level</th><th>Model</th><th>DIC</th><th>delta DIC</th><th>Sum DICw</th><th>Residual var</th><th>Site var</th><th>Phylo var</th><th>bio1</th><th>bio12</th><th>bio4</th><th>bio14</th></tr></thead><tbody><tr><td>allTaxa</td><td>bySp</td><td>phyloPCOA</td><td>587.107</td><td>0</td><td>0.977</td><td>0.524</td><td>-</td><td>1.42</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySp</td><td>NMDS & phyloPCOA</td><td>594.644</td><td>7.537</td><td>1</td><td>0.542</td><td>-</td><td>1.31</td><td>0.002 (p = 0.238)</td><td>0.000 (p = 0.972)</td><td>0.000 (p = 0.471)</td><td>0.001 (p = 0.591)</td></tr><tr><td>allTaxa</td><td>bySp</td><td>NMDS</td><td>659.939</td><td>72.832</td><td>1</td><td>0.907</td><td>-</td><td>-</td><td>0.002 (p = 0.228)</td><td>0.000 (p = 0.323)</td><td>0.000 (p = 0.258)</td><td>0.001 (p = 0.774)</td></tr><tr><td>allTaxa</td><td>bySp</td><td>no Predictors</td><td>660.457</td><td>73.35</td><td>1</td><td>0.922</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>phyloPCOA & NMDS & site</td><td>1612</td><td>0</td><td>1</td><td>0.203</td><td>0.394</td><td>2.263</td><td><strong>0.122 (p = 0.045)</strong></td><td>-0.053 (p = 0.436)</td><td>-0.125 (p = 0.050)</td><td>-0.014 (p = 0.822)</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>phyloPCOA & site</td><td>1627.434</td><td>15.434</td><td>1</td><td>0.206</td><td>0.414</td><td>2.182</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>phyloPCOA & NMDS</td><td>2193.091</td><td>581.091</td><td>1</td><td>0.563</td><td>-</td><td>2.37</td><td><strong>0.182 (p = 0.000)</strong></td><td>-0.016 (p = 0.786)</td><td><strong>-0.121 (p = 0.026)</strong></td><td>0.008 (p = 0.868)</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>phyloPCOA</td><td>2274.177</td><td>662.177</td><td>1</td><td>0.627</td><td>-</td><td>1.92</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>NMDS & site</td><td>2341.481</td><td>729.481</td><td>1</td><td>0.587</td><td>0.354</td><td>-</td><td><strong>0.121 (p = 0.036)</strong></td><td>-0.009 (p = 0.905)</td><td>-0.109 (p = 0.078)</td><td>-0.012 (p = 0.832)</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>site</td><td>2354.479</td><td>742.479</td><td>1</td><td>0.591</td><td>0.38</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>NMDS</td><td>2562.968</td><td>950.968</td><td>1</td><td>0.956</td><td>-</td><td>-</td><td><strong>0.120 (p = 0.018)</strong></td><td>0.050 (p = 0.409)</td><td>-0.083 (p = 0.084)</td><td>-0.013 (p = 0.792)</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>no Predictors</td><td>2599.644</td><td>987.644</td><td>1</td><td>1.002</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Angiosperms</td><td>bySp</td><td>phyloPCOA</td><td>574.868</td><td>0</td><td>0.939</td><td>0.559</td><td>-</td><td>0.681</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Angiosperms</td><td>bySp</td><td>NMDS & phyloPCOA</td><td>580.337</td><td>5.469</td><td>1</td><td>0.569</td><td>-</td><td>0.68</td><td>0.102 (p = 0.324)</td><td>-0.022 (p = 0.854)</td><td>0.038 (p = 0.683)</td><td>0.068 (p = 0.516)</td></tr><tr><td>Angiosperms</td><td>bySp</td><td>no Predictors</td><td>640.431</td><td>65.563</td><td>1</td><td>0.978</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Angiosperms</td><td>bySp</td><td>NMDS</td><td>641.865</td><td>66.997</td><td>1</td><td>0.966</td><td>-</td><td>-</td><td>0.073 (p = 0.488)</td><td>0.129 (p = 0.346)</td><td>0.071 (p = 0.462)</td><td>0.035 (p = 0.780)</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>phyloPCOA & NMDS & site</td><td>1604.314</td><td>0</td><td>0.999</td><td>0.209</td><td>0.41</td><td>0.98</td><td>0.111 (p = 0.075)</td><td>-0.050 (p = 0.478)</td><td><strong>-0.139 (p = 0.041)</strong></td><td>-0.019 (p = 0.739)</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>phyloPCOA & site</td><td>1619.028</td><td>14.714</td><td>1</td><td>0.213</td><td>0.431</td><td>0.934</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>phyloPCOA & NMDS</td><td>2172.502</td><td>568.188</td><td>1</td><td>0.583</td><td>-</td><td>1.04</td><td><strong>0.181 (p = 0.000)</strong></td><td>-0.019 (p = 0.750)</td><td><strong>-0.141 (p = 0.012)</strong></td><td>0.004 (p = 0.962)</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>phyloPCOA</td><td>2255.31</td><td>650.996</td><td>1</td><td>0.653</td><td>-</td><td>0.816</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>NMDS & site</td><td>2279.589</td><td>675.275</td><td>1</td><td>0.578</td><td>0.361</td><td>-</td><td>0.089 (p = 0.147)</td><td>-0.014 (p = 0.839)</td><td>-0.141 (p = 0.026)</td><td>-0.002 (p = 0.968)</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>site</td><td>2290.683</td><td>686.369</td><td>1</td><td>0.581</td><td>0.389</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>NMDS</td><td>2504.622</td><td>900.308</td><td>1</td><td>0.958</td><td>-</td><td>-</td><td>0.089 (p = 0.085)</td><td>0.040 (p = 0.471)</td><td><strong>-0.117 (p = 0.022)</strong></td><td>0.002 (p = 0.960)</td></tr><tr><td>Angiosperms</td><td>bySpxSite</td><td>no Predictors</td><td>2540.015</td><td>935.701</td><td>1</td><td>1.002</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
<table><caption>Table 4.</caption><thead><tr><th>Taxon set</th><th>Analysis level</th><th>Model</th><th>R<sup>2</sup></th><th>AIC</th><th>delta AIC</th><th>Sum AICw</th><th>bioclim1</th><th>bioclim12</th><th>bioclim14</th><th>bioclim4</th><th>NMDS1</th><th>NMDS2</th><th>NMDS3</th><th>NMDS4</th><th>phylo Axis1</th><th>phylo Axis2</th><th>phylo Axis3</th><th>phylo Axis4</th><th>phylo Axis5</th></tr></thead><tbody><tr><td>allTaxa</td><td>bySp</td><td>NMDS & phyloPCOA</td><td>0.114</td><td>668.205</td><td>0</td><td>0.394</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>0.137</b> (p = 0.033)</td><td>0.006 (p = 0.929)</td><td>0.075 (p = 0.238)</td><td>-0.105 (p = 0.093)</td><td><b>-0.213</b> (p = 0.001)</td><td><b>0.13</b> (p = 0.042)</td><td>0.113 (p = 0.074)</td><td>0.022 (p = 0.727)</td><td>-</td></tr><tr><td>allTaxa</td><td>bySp</td><td>bioclim & phyloPCOA</td><td>0.114</td><td>668.302</td><td>0.097</td><td>0.77</td><td>0.129 (p = 0.224)</td><td>0.164 (p = 0.205)</td><td>-0.008 (p = 0.939)</td><td>0.122 (p = 0.198)</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>-0.22</b> (p = 0.001)</td><td><b>0.13</b> (p = 0.043)</td><td>0.118 (p = 0.062)</td><td>0.019 (p = 0.771)</td><td>-</td></tr><tr><td>allTaxa</td><td>bySp</td><td>phyloPCOA</td><td>0.08</td><td>669.303</td><td>1.098</td><td>0.998</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>-0.236</b> (p &lt; 0.001)</td><td>0.112 (p = 0.075)</td><td>0.101 (p = 0.109)</td><td>0.038 (p = 0.544)</td><td>-</td></tr><tr><td>allTaxa</td><td>bySp</td><td>NMDS</td><td>0.04</td><td>679.394</td><td>11.189</td><td>0.999</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.143 (p = 0.026)</td><td>-0.005 (p = 0.937)</td><td>0.051 (p = 0.428)</td><td><b>-0.131</b> (p = 0.041)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySp</td><td>bioclim</td><td>0.035</td><td>680.678</td><td>12.473</td><td>1</td><td>0.13 (p = 0.222)</td><td>0.131 (p = 0.325)</td><td>0.032 (p = 0.775)</td><td>0.11 (p = 0.256)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>bioclim & phyloPCOA</td><td>0.096</td><td>2523.736</td><td>0</td><td>1</td><td><b>0.131</b> (p = 0.012)</td><td>0.047 (p = 0.41)</td><td>0.002 (p = 0.958)</td><td>-0.077 (p = 0.111)</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>-0.197</b> (p &lt; 0.001)</td><td>0.032 (p = 0.39)</td><td>0.046 (p = 0.206)</td><td>-0.039 (p = 0.23)</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>bioclim</td><td>0.048</td><td>2562.93</td><td>39.194</td><td>1</td><td><b>0.12</b> (p = 0.019)</td><td>0.05 (p = 0.387)</td><td>-0.015 (p = 0.757)</td><td>-0.084 (p = 0.085)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>phyloPCOA</td><td>0.047</td><td>2563.876</td><td>40.14</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>-0.202</b> (p &lt; 0.001)</td><td>0.008 (p = 0.839)</td><td>0.045 (p = 0.225)</td><td>0.001 (p = 0.978)</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>NMDS & phyloPCOA</td><td>0.052</td><td>2566.611</td><td>42.875</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>0.074</b> (p = 0.031)</td><td>-0.029 (p = 0.395)</td><td>0.011 (p = 0.733)</td><td>-0.014 (p = 0.664)</td><td><b>-0.203</b> (p &lt; 0.001)</td><td>0.012 (p = 0.748)</td><td>0.048 (p = 0.203)</td><td>-0.015 (p = 0.66)</td><td>-</td></tr><tr><td>allTaxa</td><td>bySpxSite</td><td>NMDS</td><td>0.005</td><td>2603.493</td><td>79.757</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>0.068</b> (p = 0.044)</td><td>-0.019 (p = 0.578)</td><td>-0.001 (p = 0.984)</td><td>-0.016 (p = 0.635)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>angiosperms</td><td>bySp</td><td>NMDS & phyloPCOA</td><td>0.082</td><td>643.89</td><td>0</td><td>0.356</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>0.149</b> (p = 0.027)</td><td>0.003 (p = 0.963)</td><td>0.055 (p = 0.421)</td><td>-0.103 (p = 0.116)</td><td>-0.249 (p = 0.088)</td><td>-0.03 (p = 0.837)</td><td>-</td><td>0.023 (p = 0.729)</td><td>-0.04 (p = 0.555)</td></tr><tr><td>angiosperms</td><td>bySp</td><td>phyloPCOA</td><td>0.047</td><td>644.313</td><td>0.423</td><td>0.643</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-0.249 (p = 0.09)</td><td>-0.047 (p = 0.747)</td><td>-</td><td>0.044 (p = 0.506)</td><td>-0.035 (p = 0.591)</td></tr><tr><td>angiosperms</td><td>bySp</td><td>bioclim & phyloPCOA</td><td>0.08</td><td>644.384</td><td>0.494</td><td>0.921</td><td>0.13 (p = 0.245)</td><td>0.122 (p = 0.387)</td><td>0.023 (p = 0.842)</td><td>0.087 (p = 0.396)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-0.252 (p = 0.085)</td><td>-0.03 (p = 0.837)</td><td>-</td><td>0.018 (p = 0.786)</td><td>-0.033 (p = 0.623)</td></tr><tr><td>angiosperms</td><td>bySp</td><td>NMDS</td><td>0.031</td><td>648.027</td><td>4.137</td><td>0.966</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.13 (p = 0.051)</td><td>-0.031 (p = 0.639)</td><td>0.04 (p = 0.545)</td><td>-0.108 (p = 0.102)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>angiosperms</td><td>bySp</td><td>bioclim</td><td>0.029</td><td>648.583</td><td>4.693</td><td>1</td><td>0.074 (p = 0.505)</td><td>0.132 (p = 0.341)</td><td>0.036 (p = 0.757)</td><td>0.072 (p = 0.476)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>angiosperms</td><td>bySpxSite</td><td>bioclim & phyloPCOA</td><td>0.076</td><td>2484.951</td><td>0</td><td>1</td><td><b>0.156</b> (p = 0.004)</td><td>0.032 (p = 0.576)</td><td>0.024 (p = 0.618)</td><td>-0.076 (p = 0.129)</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>0.068</b> (p = 0.045)</td><td>-0.061 (p = 0.071)</td><td><b>0.123</b> (p &lt; 0.001)</td><td><b>0.085</b> (p = 0.009)</td><td>-</td></tr><tr><td>angiosperms</td><td>bySpxSite</td><td>bioclim</td><td>0.047</td><td>2504.595</td><td>19.644</td><td>1</td><td>0.089 (p = 0.089)</td><td>0.04 (p = 0.499)</td><td>0.002 (p = 0.959)</td><td><b>-0.117</b> (p = 0.018)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>angiosperms</td><td>bySpxSite</td><td>phyloPCOA</td><td>0.02</td><td>2529.733</td><td>44.782</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.046 (p = 0.175)</td><td>-0.011 (p = 0.745)</td><td><b>0.087</b> (p = 0.011)</td><td><b>0.095</b> (p = 0.004)</td><td>-</td></tr><tr><td>angiosperms</td><td>bySpxSite</td><td>NMDS & phyloPCOA</td><td>0.027</td><td>2531.992</td><td>47.041</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td><b>0.083</b> (p = 0.022)</td><td>-0.022 (p = 0.523)</td><td>0.002 (p = 0.945)</td><td>-0.031 (p = 0.382)</td><td>0.054 (p = 0.132)</td><td>-0.032 (p = 0.359)</td><td><b>0.103</b> (p = 0.004)</td><td><b>0.087</b> (p = 0.01)</td><td>-</td></tr><tr><td>angiosperms</td><td>bySpxSite</td><td>NMDS</td><td>0.005</td><td>2543.133</td><td>58.182</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.064 (p = 0.061)</td><td>-0.05 (p = 0.145)</td><td>0.0 (p = 0.994)</td><td>0.001 (p = 0.975)</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>