Relative roles of local disturbance , current climate and palaeoclimate in determining phylogenetic and functional diversity in Chinese forests

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Full are increasingly also considered (Dynesius and Jansson, 2000;Sandel et al., 2011;Kissling et al., 2012).Many studies have considered the roles of local factors in determining diversity and structure in tropical, subtropical, and temperate forests (Svenning et al., 2004;Chen et al., 2010;Zhu et al., 2010;Wang et al., 2011).For instance, in one tropical forest, spatial location, environmental factors and past land use were all found to have important impacts on species composition (Svenning et al., 2004).Moreover, strong effects of current climate have been found in studies spanning larger geographic extents (Wang et al., 2009a, b).At such scales important imprints of historical-regional factors have also been found (Latham and Ricklefs, 1993;Ricklefs et al., 1999;Greve et al., 2011;Kissling et al., 2012;Rakotoarinivo et al., 2013).
However, these studies mainly focused on species richness, which ignores ecological and evolutionary differences among species.During the past decade, forest ecologists have increasingly used phylogenetic and functional information to provide novel insights into studies on community ecology (Webb et al., 2002;Kunstler et al., 2012;Mi et al., 2012;Swenson, 2013).Overall, phylogenetic patterns should reflect the imprints of evolutionary and biogeographic history on community structure (Kellermann et al., 2012a, b;Kissling et al., 2012).In contrast, functional traits should be directly linked to ongoing ecological processes (Swenson, 2013), while phylogenetic patterns would only indirectly (via niche conservatism) or not all (Purschke et al., 2013) relate to these.
China is a global hotspot for plant diversity (López-Pujol et al., 2006) and harbors abroad range of forest types (Qian and Ricklefs, 1999); its temperate forests are the most diverse on Earth (Qian and Ricklefs, 2000).The high species richness has been attributed to high levels of topographic heterogeneity, strong connectrivity to the tropics, and more stable palaeoclimate with less influence of the Pleistocene glaciations than in other temperate areas such as much of North America (Qian and Ricklefs, 1999;Qian and Ricklefs, 2000).Previous studies have investigated woody plant species richness patterns in forests across China and did find important roles of both current and historical factors (Qian and Ricklefs, 1999;Wang et al., 2009b).However, large-scale vari-Figures

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Full ations in phylogenetic and functional woody plant community structure across China have hitherto remained been unstudied, but is the focus of the present study.Overall, one may predict that phylogenetic structure should primarily reflect current or palaeoclimate via climatic niche conservatism (Wiens and Graham, 2005;Losos, 2008) or palaeoclimatic effects on diversification (Dynesius and Jansson, 2000).Climatic niche conservatism may also result in palaeoclimatic effects via dispersal-lagged climatechange-induced range dynamics (Hortal et al., 2011).In contrast, functional community structure may be more strongly related to contemporary ecological processes, e.g., succession (Purschke et al., 2013).Additionally, assembly mechanisms may differ among organism groups due to differences in dispersal ability or other fucntional traits (Sandel et al., 2011;Lindström and Langenheder, 2012).In northeastern China, tree species richness was found to be mainly limited by climate, while shrub richness was more correlated to local factors (Wang et al., 2009a).Therefore, trees and shrubs may also exhibit different phylogenetic and functional patterns for across Chinese forests, but this has so far not been studied.
Here, we linked data on woody plant communities in Chinese forests to data on local disturbance (representing successional dynamics), current climate, and palaeoclimate (climate shifts between the LGM and now) to assess their relative roles in determining functional and phylogenetic diversity in these ecosystems.We also tested if these relations differed between the two main growth forms, trees and shrubs.A1, Fig. 1).The plots were located in forest types ranging from temperate forests to subtropical and tropical forest, spanning tree diversities from 8 to 342 woody species.All woody individuals with diameter at breast height (DBH) ≥ 1 cm were measured and identified to species when possible.Here, we used the species list and species abundance data from each plot, representing a total of 1102 species and 495 815 individuals, among which gymnosperms constitute 16 species and 23 940 individuals.

Phylogenetic tree
A phylogenetic tree (Fig. A1) including the 1102 species was constructed by the plant phylogeny database Phylomatic (Webb and Donoghue, 2005), which uses the Angiosperm Phylogeny Group III (2009) classification.The BLADJ algorithm was used to estimate phylogenetic branch lengths using Phylocom 4.1 (Webb et al., 2008).To investigate the difference between trees and shrubs, we also built two phylogenetic trees using the same method, with one including 570 species of trees and the other including 167 species of shrubs.Species-level information on growth form (shrub or tree) came from Flora of China (http://www.efloras.org/).Introduction

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Functional trait data
Functional community structure was computed based on a single functional trait of key importance in plant communities, maximum canopy height (H max ).This was the only trait broadly available for the study species; H max of each species was recorded from Flora of China, and, in total, we found H max information for 958 species (n = 559 tree spp.and 161 shrub spp.).A similarity distance matrix for H max of all species was then constructed, comparing the H max values for all pairs of species using Euclidian distance.Subsequently, we conducted a cluster analysis (complete linkage method which finds similar clusters) of this distance matrix and constructed a dendrogram (Fig. A2) based on the results of cluster analysis.Finally, two similar functional trees were computed just for trees and shrubs.
Local disturbance was presented by the proportion of light-demanding species (Molino and Sabatier, 2001).Species were scored as light-demanding (n = 346 spp.) primarily based on their habitat descriptions (occurrence in open forest or in disturbed, anthropogenic habitats) in Flora of China (Feng et al., 2013).Finally, to account for the varying plot areas (Table A1), plot area was also included as an explanatory variable in the modelling.

Methods
The Net Relatedness Index (NRI) (Webb et al., 2002) was used to quantify phylogenetic community structure (NRI PHY ).The formula is: where MPD obs is the observed mean phylogenetic distance (MPD) of a plot, meanMPD rnd is the mean MPD of the null models (shuffle distance matrix labels), and sdMPD rnd is the standard deviation (SD) of MPD of the null models.Functional diversity was calculated in the same way as NRI (Swenson and Enquist, 2009), but using the functional H max -based dendrogram described above (NRI FUN ).Both abundance weighted and presence-absence based NRI PHY and NRI FUN were computed (indicated by _ab and _pr subscripts).As phylogenetic outliers may strongly influence phylogenetic community structure (Letcher et al., 2012;Feng et al., 2013), NRI PHY was furthermore computed including both gymnosperms and angiosperms or only angiosperms (indicated by _gym and _ang subscripts).
To avoid including large number of correlated variables in the later analyses, we conducted a correlation analysis and removed redundant climate variables.Spearman correlation between each diversity index and explanatory variable was used to assess pairwise relationships.Generalized Linear Models (GLMs) with Gaussian error was Introduction

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Full applied to fit the relationship between each diversity index and all combinations of 1 or 2 explanatory variables.More complex models could not be fit with the available sample size.Model explanatory power was represented by pseudo-R 2 , while the small-samplesize corrected version of the Akaike Information Criterion (AIC c ) was used to select the best model for each diversity index.The overall multi-model support for a given explanatory variable was estimated as the summed Akaike weights (w) for all GLMs including this variable.We used Moran's I test to check for spatial auto-correlation (SAC) in the residuals of all GLMs.The statistical calculations were performed in the R 2.15.1 (R Development Core Team, 2010) using the packages picante (Kembel et al., 2010), spdep (Bivand, 2007), pscl (Jackman, 2010) and MuMIn (Barton, 2010).

Results
There were high correlations with groups of the environmental variables representing palaeoclimate and representing current climate (Table A2).In the following, we therefore used MAT to represent current temperature (correlations between MAT and MTWM and MTCM were 0.69 and 0.98, respectively) and MAP to represent current precipitation (correlations between MAP and PWM and PDM were 0.96 and 0.93, respectively).The residuals of most of the GLMs had no spatial autocorrelation, except for a few weakly supported models, so models accounting for spatial dependence were not implemented.
Considering bivariate rank correlations, functional community structure (NRI FUN ) was only significantly correlated with proportion of light-demanding species (LDSR), no matter if species abundances were considered or not, and always so that functional clustering increased with increasing proportion of LDSR (Table 1, Figure 2).In contrast, phylogenetic structure (NRI PHY ) was significantly and positively correlated with the LGM-present MAT anomaly in most cases, i.e., phylogenetic clustering tended to increase with increasing Late Quaternary temperature change.NRI PHY was also sig-Introduction

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Full nificantly correlated with current MAT and LGM-present MAT velocity in some cases, except for NRI PHY_gym_pr which had no significant relations with any variables (Table 1, Fig. 2).
Considering single-predictor GLMs, NRI FUN was also best explained by LDSR, while NRI PHY considering angiosperms was best explained by LGM-present MAT anomaly and current MAT.Again, functional clustering increased with LDSR, while phylogenetic clustering increased with increasing Late Quaternary temperature shift.No significant relations were found for NRI PHY with gymnosperms included (Table 2).
Considering both one-and two-predictor GLMs, LDSR had the strongest support for functional community structure, while LGM-present MAT anomaly was the most important factor for NRI PHY considering only angiosperms (Table 3), and again with similar relations as indicated by the bivariate correlations and single-predictor GLMs.No particular variable was strongly supported for NRI PHY with gymnosperms included, although LGM-present MAT anomaly was among the most supported (Table 3).
Tree and shrub communities showed different functional and phylogenetic patterns (Table 4).Generally, for NRI FUN the best and only important predictor was LDSR for trees and current MAP and MAT for shrubs.Although no significant relationships for growth-form-specific phylogenetic structure to any of the explanatory variables were found, LGM-present MAT anomaly again tended to be among the more important for phylogenetic structure of shrubs, but not for trees (Table 4).

Discussion
For woody plant communities in forests across China, phylogenetic and functional community structure show highly divergent relations to potential driving factors, with the former most strongly constrained by palaeoclimate and the latter most strongly determined by contemporary local ecological dynamics.Hence, these two key aspects of woody plant diversity appear to be predominantly shaped by divergent assembly mechanisms acting on very different spatiotemporal scales.Growth-form specific relations Introduction

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Full were generally weaker and less consistent, perhaps reflecting reduced species-wise sample sizes as well as growth-form specific dynamics.Functional structure in this study was only represented by maximum plant height (H max ).Plant height is a key component of plant ecological strategies (Westoby, 1998), because it reflects the ability to compete for light, especially in dense, tall vegetation such as forests, and may also be correlated with other traits such as canopy area, leaf mass per area, leaf area ratio, metabolic rates, and dispersal ability (Enquist et al., 1998;Falster and Westoby, 2003;Moles et al., 2009;Thomson et al., 2011).Nevertheless, it is important to note that other aspects of functional diversity may very well show other geographic patterns and ecological relationships (Wright et al., 2007;Swenson and Weiser, 2010).In the present case, functional NRI was most correlated with the proportion of light-demanding species (LDSR), an indicator of local-scale disturbance dynamics (Molino and Sabatier, 2001;Feng et al., 2013).In a smaller study area (8107 hectares), Feng et al. (2013) also found LDSR to be well linked with functional diversity in a subtropical forest landscape in southern China and concluded that this might reflect the important role of competition for light in determining forest structure.Across Chinese forests, there was increasing functional clustering and thus decreasing variability in H max with increasing LDSR.Notably, the standard deviation of H max of the 30 % most abundant species in the four plots with the highest LDSR was 7.6, while it was 10.8 in the four plots with the lowest LDSR, even after excluding the TS plot (with the lowest LDSR and the highest H max standard deviation, 40.3) due to its low number of species (n = 8 in total).Studies from other areas and vegetation types have also found functional clustering to increase with disturbance (Cianciaruso et al., 2012;Purschke et al., 2013).
A global study of plant height patterns showed that precipitation in the wettest month was the best predictor of height (Moles et al., 2009), which is partly consistent with our finding as the annual precipitation (highly correlated with the precipitation in the wettest month) was the second best predictor for H max -defined functional NRI.Furthermore, the trend towards increasing clustering with increasing temperature and precipitation Introduction

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Full (Table 1) also agreed with Moles et al. (2009) who found that cold, dry, low productivity systems tended to have species with a wide range of height strategies, perhaps reflecting more shady conditions and increased competition for light under warm, wet conditions.Long-term historical factors have been reported to be well correlated with various aspects of diversity at large geographic scales (Jansson, 2003;Kissling et al., 2012;Sandel et al., 2011;Rakotoarinivo et al., 2013).Notably, several studies have found phylogenetic community structure to be strongly shaped by historical factors (Kissling et al., 2012).As a key example, Kissling et al. (2012) find that global patterns in phylogenetic structure for country-level palm assemblages is strongly linked to deep-time Cenozoic climate change, while there are also imprints of Quaternary climate change within some biogeographic regions.Notably, a strong increase in clustering with increasing LGM-present MAT anomaly is found within South America.Hortal et al. ( 2011) found a similar relation for scarab beetles within Europe, and a consistent pattern emerged for Chinese forest woody-plant communities in the present study.Considering only angiosperms, we also found an increase phylogenetic clustering with LGMpresent MAT anomaly.Such a pattern may reflect strong filtering on phylogenetically conserved traits by the Pleistocene glaciations (Hortal et al., 2011).While eastern Asia was less affected by the Pleistocene glaciations than Europe or North America, these nevertheless caused massive vegetation changes and retraction of forests (Yu et al., 2000;Liu et al., 2003Liu et al., , 2013)).The much weaker and often inconsistent patterns that resulted when gymnosperms were included probably reflect the potentially strong effects of such phylogenetic outlier groups on phylogenetic community structure (Letcher et al., 2012;Feng et al., 2013).
Trees and shrubs communities showed substantially different patterns in terms of functional structure, which was in line with previous studies about species diversity (Wang et al., 2009a;Sandel et al., 2011) and further emphasized the importance of considering different taxonomic and functional groups regarding assembly mechanisms (Lindström and Langenheder, 2012).High correlation between LDSR and H max -Introduction

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Full defined functional NRI was only found in tree communities, while MAP was the best predictor of this measure for shrub communities.Wright (1992) argued that understory shrub diversity is critically affected by dry season rainfall, which may explain the particular importance of MAP for shrub functional NRI.For growth-form-specific phylogenetic structure, we did not find significant relations with any of the explanatory variables.
The partially inconsistent and often weak relations for the growth-form-specific diversity measures may simply reflect the lower species-wise sample size for these subsets and thus greater sensitivity to stochastic processes.
Our study shows that functional and phylogenetic structure of woody-plant communities in Chinese forests are shaped by divergent processes acting on very different spatiotemporal scales.Functional structure was more determined by contemporary local dynamics, while palaeoclimate was more important for phylogenetic structure.These differences are consistent with theoretical expectations and the few existing empirical studies (Kissling et al., 2012;Purschke et al., 2013), pointing to their potential wider generality.Many further studies will be needed for assessing this.Further, functional diversity was in the present study represent by a single -albeit very important -functional trait, namely maximum stem height.Other aspects of functional diversity may well reflect different determining factors (cf.e.g., Wright et al., 2007;Swenson and Weiser, 2010).As both phylogenetic structure and functional traits may affect forest ecosystem functioning and ecosystem services, our findings illustrate that these may be shaped by both local contemporary and long-term regional processes.Introduction

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Full  Full  Full  Full  Full  Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . Both were computed based on the mean of the Community Climate System Model version 3 (CCSM3) and Model for Interdisciplinary Research on Climate version 3.2 (MIROC3.2) palaeoclimatic simulations for the Last Glacial Maximum (LGM, 0.021 Mya).CCSM3 is a coupled climate model, which has components representing the atmosphere, ocean, sea ice and land surface connected by a flux coupler (Collins et al., 2006).MIROC3.2includes a T42 global atmospheric spectral model and a medium-resolution ocean model (K-1 Model Developers, 2004).Both of these models have been widely used in Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Table 1 .
Spearman correlations (r s ) between each diversity index and each explanatory variable.NRI FUN : functional (H max ) NRI, and NRI PHY : phylogenetic NRI, based on either presenceabsence ( pr ) or abundances ( ab ) with gymnosperms ( gym ) or only considering angiosperms ( ang ).Significant values are given in bold; * , significant at p < 0.05; * * , significant at p < 0.01.

Table 2 .
Pseudo-R 2 for single predictor GLMs of each diversity index against each explanatory variable.Significant values are given in bold; * , significant at p < 0.05; * * , significant at p < 0.01.

Table 3 .
Summed Akaike weights (w) for each predictor variable based on all possible singleand two-predictor GLMs.Variables with w ≥ 0.50 are given in bold.

Table 4 .
Pseudo-R 2 for single-predictor GLMs of each diversity index for the two functional groups trees and shrubs against each explanatory variable.* , Significant values are given in bold; * significant level at 0.05.