Accepted Article
Functional Ecology
DR HUI
ZHANG (Orcid ID : 0000-0002-2180-4855)
DR HAN
CHEN (Orcid ID : 0000-0001-9477-5541)
DR QING
Article type
YE (Orcid ID : 0000-0001-5445-0996)
: Research Article
Editor: C. E. Timothy Paine Section: Community Ecology
Using functional trait diversity patterns to disentangle the scale-dependent ecological processes in a subtropical forest Hui Zhang1,2#, Han Y.H. Chen3#, Juyu Lian1,2#, Robert John4, Ronghua Li1,5, Hui Liu1,2, Wanhui Ye1,2, Frank Berninger6,7 and Qing Ye1,2* 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: 10.1111/1365-2435.13079 This article is protected by copyright. All rights reserved.
Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South
Accepted Article
1
China Botanical Garden, Chinese Academy of Sciences, 723 Xingke Road, Tianhe District, Guangzhou 510650, PR China 2
Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden,
Chinese Academy of Sciences, Xingke Road 723, Tianhe District, Guangzhou 510650, Guangdong, China 3
Faculty of Natural Resources Management, Lake head University, 955 Oliver Road, Thunder
Bay, ON P7B 5E1, Canada 4
Department of Biological Sciences, Indian Institute of Science Education and Research,
Kolkata, Mohanpur Campus, West Bengal 741252, India 5
University of Chinese Academy of Sciences, 19A Yuquan road, Beijing 100049, PR China
6
Department of Forest Sciences, POBOX 27, 00014 University of Helsinki, Finland
＃
These authors contributed equally to this work
Running Head: Ecological processes and community assembly *
Author for Correspondence:
Qing Ye Tel: +86-20-37083320 Fax: +86-20-37252615 E-mail:
[email protected]
Summary 1. Disentangling ecological processes that influence community assembly and species diversity across spatial scales remains a major goal of community ecology. Community assembly
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processes influence spatial patterns of species diversity through their interactions with key functional traits. Hence, quantifying spatial patterns of functional trait diversity (FD) represents a useful tool for disentangling the relative importance of abiotic filtering, biotic interactions, random assembly, and dispersal limitation across spatial scales. 2. Here we measured 12 traits of 112 study species in a 20-ha fully-mapped subtropical forest plot. The individuals of the 112 study species account for 99% of all living stems with diameter at breast height (DBH) ≥ 1 cm. We studied important functional traits related to physiological processes of plants including resource acquisition (e.g., CO2 assimilation rate and leaf nutrient concentration) and drought tolerance (e.g., stem hydraulic conductivity and leaf turgor loss point). Additionally, species abundance, spatial locations (x- and y- coordinate for each individual of the 112 study species), as well as topographic and soil variables that represent potentially important attributes of the physical environment of the plot, were also included in our dataset. 3. We employed two FD-based tests (comparing FD within communities to those from random communities, distance- based Moran’s eigenvector maps (MEM) and redundancy analysis based variance partitioning), and one spatial analysis (inhomogeneous bivariate pair correlation analysis) to quantify the spatial patterns of FD of the plot at multiple spatial scales (400, 900, 1600, 2500, and 10000 m2). 4. We demonstrate that abiotic filtering is the major determinant responsible for trait convergence at relatively small scales (400, 900 and1600 m2), whereas dispersal limitation becomes dominant, causing the weakening of trait convergence at relatively large scales (2500 and 10000 m2). This article is protected by copyright. All rights reserved.
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5. Our results highlight the relative contributions of different ecological processes to community assembly at different spatial scales, which can be distinguished using the diversity patterns of key functional traits. Also, our integrated approaches constitute a useful study design to disentangle variable ecological processes in shaping community assembly across spatial scales.
Key-words: abiotic filtering, biotic interactions, dispersal limitation, functional trait diversity based tests, null models, random assembly, spatial structure
Introduction Disentangling ecological processes in shaping community assembly and species diversity and distributions is a central goal in ecological research (Condit et al. 2002). For more than a century, community ecologists have been embroiled in arguments on whether deterministic or stochastic processes play the primary role in structuring ecological communities (Hubbell 2001; Chase & Myers 2011; Trisos et al. 2014). This debate has led to conceptual ideas that invoke multiple processes ranging from niche-based habitat filtering and interspecific competition (niche differentiation) to random dispersal and demographic stochasticity (Weiher et al. 2011). However, despite decades of research, no consistent empirical evidence on the relative importance of deterministic and stochastic processes for community assembly has emerged (Chase 2014). Now community ecologists argue that the answer to the question of the relative This article is protected by copyright. All rights reserved.
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importance of stochastic (e.g., dispersal limitation) vs. deterministic (e.g., habitat filtering and niche partitioning) processes depends on the spatial scale of investigation (McIntire & Fajardo 2009; Chase 2014; Carmona et al. 2016; Aiello-Lammens et al. 2017). Microclimatic parameters, topographical attributes, soil properties, biotic interactions, all vary with spatial scale (John et al. 2007; Portmann, Solomon & Hegerl 2009), and species responses to these factors should vary in strength accordingly (Chave 2013; Thuiller et al. 2015). For example, habitat filtering is expected to be strongest at spatial scales larger than those for competition (Kraft & Ackerly 2010; Trisos, Petchey & Tobias 2014) but small enough to exclude habitat heterogeneity (Weiher et al. 2011). Similarly, biotic interactions among neighbors may vary over multiple scales, with the strength of biotic effects decreasing with increasing spatial scale (Condit et al. 2000; Wiegand, Gunatilleke & Gunatilleke 2007). Dispersal, a critical mechanism in structuring communities, depends on the spatial scale, particularly in diverse forest communities where most species show dispersal limitation with increased spatial scales and are unlikely to reach all suitable sites (Condit et al. 2000; Gilbert & Lechowicz 2004). Hence, assessment of the relative importance of processes involved in community assembly should account explicitly for variation with spatial scale (Trisos, Petchey & Tobias 2014). Using data from stem-mapped forest plots established worldwide, a number of studies have
investigated ecological processes that influence community assembly through testing the spatial patterns of species diversity (i.e., the relative abundance of species) (Legendre et al. 2009; De Caceres et al. 2012; Punchi-Manage et al. 2013). Since functional traits capture plant attributes This article is protected by copyright. All rights reserved.
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that have a direct influence on survival, growth and reproduction (McGill et al. 2006), functional trait-based approaches may be more powerful in testing the mechanisms and ecological processes that influence community assembly (Lavorel & Garnier 2002; Cadotte, Carscadden & Mirotchnick 2011; Paine et al. 2011). In this sense, they complement traditional taxonomy based methods, and collectively providing greater power in analyzing the determinants of community structure. Indeed, it has been shown that community assembly processes influence spatial patterns of species diversity through their interactions with species traits (Adler et al. 2013). Thus spatial patterns in trait variation can provide evidence for the drivers of community assembly at different spatial scales (Bartlett et al. 2015). Functional trait diversity (FD) represents the value and range of functional traits, which are components of an organism’s phenotype that in turn influence ecosystem-level processes (Petchey & Gaston 2006). It has been shown that different ecological processes can cause different spatial patterns of FD and therefore represent a key tool in understanding the spatial-scale dependence of community assembly (Siefert 2012; Smith et al. 2013). When FD patterns indicate random assembly, stochastic processes have the dominant influence on community assembly and allow the coexistence of large numbers of functionally equivalent species. Hence random assembly will give rise to neither trait convergence (observed FD is lower than expected by chance) nor trait divergence (observed FD is more than expected by chance) (Fig. 1A) (Hubbell 2001). Also, random assembly patterns of traits may result from dispersal limitation independent of spatial scale (Kraft, Valencia & Ackerly 2008; de Bello et al. 2012; Götzenberger et al. 2012). In contrast, This article is protected by copyright. All rights reserved.
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abiotic filtering should cause strong trait convergence at small scales (i.e., 100 m2 in Siefert (2012)), while a patchy environment would generate a positive but saturating relationship, with trait convergence leveling off at larger spatial scales (Weiher, Clarke & Keddy 1998; Siefert 2012). With increasing spatial scales, trait convergence would decrease due to increased environmental heterogeneity, which should increase the range of trait values (Weiher, Clarke & Keddy 1998; Grime 2006). Biotic interactions such as niche-based exclusion (niche differentiation) may lead to
significant trait divergence at relatively small scales (Siefert 2012). However, the exclusion of, weaker competitors may cause strong trait convergence at relatively small scales where only strong competitive traits are retained (Mayfield & Levine 2010). These opposing influences of niche differentiation and competitive exclusion on trait dispersions are theoretically expected to decrease with spatial scale (Condit et al. 2000; Wiegand, Gunatilleke & Gunatilleke 2007) (Fig. 1A). Given that multiple ecological processes influence trait variation within communities in a confounding manner, simple trait dispersion analyses may provide only limited insight on the importance of abiotic and biotic effects on shaping community structure. Therefore trait-dispersion methods should be coupled with additional analyses (e.g., variance partitioning provided in Legendre et al. (2009) ) to detect interactions between environmental heterogeneity and trait variations, in order to disentangle these effects (de Bello et al. 2012; Götzenberger et al. 2012; Adler et al. 2013). In a recent paper, we found that plant functional traits related to resource acquisition (e.g., This article is protected by copyright. All rights reserved.
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CO2 assimilation rate and leaf nutrient concentration) and drought tolerance (e.g., hydraulic conductivity and leaf turgor loss point) are good predictors of the compositional shift in a subtropical forest under global change (Li et al. 2015). Therefore, detecting FD patterns of functional traits that are closely associated with key physiological processes of plant life history strategies (e.g., resource acquisition and drought tolerance) should be pertinent in detecting scale-dependent community assembly processes. Moreover, a 20-ha fully-mapped species-rich forest plot was installed in 2005 in this subtropical forest, which provided a good background to test community assembly across spatial scales. Therefore we chose this site to collect an extensive dataset consisting of: i) 12 functional traits that are closely associated with resource acquisition and drought tolerance, including CO2 assimilation rate per unit area (Aarea; μmol s-1), instantaneous water use efficiency (WUEi; μmol mol-1), stomatal conductance per unit area (gsa; mmol m-2 s-1), phosphorus concentration per leaf mass (Pmass; mg kg-1), sapwood specific hydraulic conductivity (Ks; kg m-1 s-1 MPa-1), leaf water potential at turgor loss point (ψtlp; MPa), leaf chlorophyll concentration (Chl; g/m2), leaf size (Ls; cm2), leaf lamina thickness (Thk; cm), specific leaf area (SLA; cm2/g), leaf dry matter content (LDMC; %), and wood density (WD; g/m3) of 112 study species, which accounted for 99% of all living individuals with diameter at breast height (DBH) ≥ 1 cm in the plot, ii) species abundance data of the 112 tree species, iii) topographic and soil variables that represented potentially important attributes of the physical environments, and iv) spatial locations (x- and y- coordinate) for all individuals of the 112 tree species. This article is protected by copyright. All rights reserved.
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Using these data and integrated approaches with complementary functions (Fig. 1B), we
designed a study to evaluate the relative contributions of abiotic filtering, biotic interactions, random assembly, and dispersal limitation, to community assembly across spatial scales. We first used these data to compare the observed FD for each of the 12 functional traits within communities (FDobserved) to those simulated by random sampling within a subplot (FDrandom), to test whether the observed FD was the result of random distribution, trait convergence, or trait divergence. The method proposed by Legendre et al. (2009) employs distance based Moran’s eigenvector maps (MEM) and redundancy analysis based variance partitioning (RDABVP) to partition FD into variations related to abiotic filtering (due to measured environmental variables) and dispersal limitation (due to pure spatial variables) (Myers et al. 2013). We, therefore, used MEM and RDABVP to quantify the relative contribution of abiotic filtering represented by spatial soil and topographic heterogeneity and dispersal limitation manifest in MEM to FD. On the other hand, the combination of MEM and RDABVP allowed for disentangling the coupling influences of abiotic filtering and weaker competitors exclusion on trait convergence, as well as the effects of random assembly and dispersal limitation on random distribution. Since weaker competitors exclusion and niche-based exclusion can lead to opposing bivariate spatial point patterns (inter-specific attraction and inter-specific repulsion, respectively) (Wiegand, Gunatilleke & Gunatilleke 2007; Bartlett et al. 2015), we finally performed inhomogeneous bivariate spatial point patterns to test which of these effects dominate community assembly at different spatial scales. We hypothesized that abiotic filtering is the major determinant This article is protected by copyright. All rights reserved.
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responsible for trait convergence at relatively small scales (400, 900 and1600 m2), whereas dispersal limitation becomes dominant, causing the weakening (toward random distribution) of trait convergence at relatively large scales (2500 and 10000 m2).
Materials and Methods Study site The study site is located in the national reserve of Dinghu Mountain (DHM; 112°30′39′′ -112°33′41′′E, 23°09′21′′-23°11′30′′N) in Guangdong Province, Southern China. Owing to Tibetan plateau uplift and southern subtropical monsoon climate, DHM has the unique subtropical monsoon broad-leaf forest with high species diversity. It comprises low mountains and hilly landscapes (total area 1155 ha), with an altitudinal range from 14 to 1000 m. The site has mean annual temperature (1978-2010) of 20.9 °C, and the lowest mean monthly temperature is 13.9 °C in January and the highest is 28.9 °C in August. Average annual precipitation is 1929 mm, with most of the precipitation occurring between April and September (Zhou et al. 2013). A 20-ha (400 × 500 m2) permanent forest plot was established in the core area of DHM in 2005. This forest is over 400 years old and viewed as climax vegetation in southern China. The plot features rough terrain with a steep hillside in the southeast corner. Topography varies with ridge and valley in the plot and the elevation ranges from 240 to 470 m. All living tree stems with diameter at breast height (DBH) ≥ 1 cm were identified to species and labeled during the plot census in 2010. The spatial locations (x- and y- coordinate) for all labeled individuals were This article is protected by copyright. All rights reserved.
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recorded following a standard field protocol of the Forest Dynamic Plots of the Center for Tropical Forest Science (CTFS) (Condit et al. 2013). Functional trait data collection We measured functional traits for a total of 112 woody species, by sampling the same three to five individuals for each study species in a growing season, with DBH values that are comparable to the mean DBH value of that species. The plot has a total of 208 woody species, with 96 species being rare (less than three individuals), preventing us from having enough sampling replications for functional trait measurements. Nevertheless, the 112 study species accounted for 99% of all individuals with DBH ≥ 1cm in the plot. The 12 functional traits were determined as described in previous studies (Li et al. 2015; Shen et al. 2016), and the specific details are given in the Supplementary Material. Environmental heterogeneity sampling To quantify environmental heterogeneity, we measured both topography and soil properties across the plot. The plot was divided into subplots by 20-m grids. The topography of each subplot was quantified by measuring elevation at the four corners of each cell of the 20-m grids. Elevation at the 5-m cell size was interpolated by ordinary kriging following John et al. (2007) from 20-m data, while the values for larger cell sizes (i.e., 20-, 30-, 40-, 50-, and 100-m) were based on averages of the 5-m cells. For each cell size, we calculated the mean elevation (ME; m), slope (S; degrees), convexity (C; degrees) and aspect (A; degrees from north) of each grid cell following Harms et al. (2001). To quantify soil properties at different scales, we selected 208 This article is protected by copyright. All rights reserved.
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quadrats (20 × 20 m2) in the 20-ha plot (regularly distributed at 30 m intervals) and collected three random soil samples at a depth of 0-20 cm as one composite sample within each quadrat. Following Burt (2009), we analyzed samples individually to determine soil bulk density (BD; g cm-3), soil carbon (SC; mg kg-1), soil total nitrogen (TN; mg kg-1), soil total phosphorus (TP; mg kg-1), soil total potassium (TK; mg kg-1), soil available nitrogen (AN; mg kg-1), soil available phosphorus (AP; mg kg-1), soil available potassium (AK; mg kg-1), soil water content (WC; mg kg-1), and pH. Detailed measurements of all abovementioned soil variables were provided in the Supplementary Material. We then used geostatistical methods (ordinary kriging), to obtain estimates of environmental variables at each spatial scale (400, 900, 1600, 2500, and 10000 m2), following John et al. (2007). Geostatistical analyses were carried out by using the package “gstat” in R (Pebesm 2004). Statistical methods Comparing the observed functional trait diversity to those from null communities Among several available methods (Botta-Dukát & Czúcz 2015), we chose Rao’s quadratic entropy (RaoQ), which is an efficient functional diversity index, because it is an intuitive generalization of the Simpson’s index of diversity, and is easily understandable (Botta-Dukát & Czúcz 2015), across multiple spatial scales. To test whether any observed FD pattern is a random distribution, or shows convergence or divergence at each spatial scale, we first simulated null communities in which species and trait values were randomly assigned. Randomization procedures were applied to calculate “null” distributions for both species composition (i.e., on This article is protected by copyright. All rights reserved.
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the species × quadrat matrix) and FD of all species (Götzenberger et al. 2012). Reshuffling the species × quadrat matrices in each spatial scale was done with three constraints simultaneously, following the method of Zhang et al. (2015), i.e., keeping: i) the same number of species (species richness) per plot in the simulated and observed data; ii) the same number of total species occurrences per region (i.e., number of plots where the species occur in each group of the five spatial scales); and iii) the total abundance of species in a region constant (i.e., the sum of the number of quadrats occupied in all plots). We implemented this using the function “randomizeMatrix” in the “picante” package in R (Kembel et al. 2010). We then compared the observed FD to the FD simulated in 1000 randomly assembled communities. The Standard Effect Size index (SES) following Gotelli & McCabe (2002) was used as a measure for FD patterns:
SES
FDobserved - FDrandom FDsd
(2)
where FDobserved and FDrandom represent observed FD and mean FD values of the simulated null community, respectively. FDsd represents the standard deviation of FD values generated from the 1000 simulations. We used the Wilcoxon signed-rank tests to examine whether SES is significantly more than, less than or approximately equal to zero, which indicates the prevalence of significant trait divergence, trait convergence, and random distribution, respectively. Partitioning the spatial patterns of functional trait diversity We used Moran’s eigenvector maps (MEM) to quantify spatial autocorrelations in FD represented by RaoQ at each spatial scale. MEM was based on the principal coordinates of neighbor matrix (PCNM) axes (Legendre & Legendre 2012) and could be used to describe the This article is protected by copyright. All rights reserved.
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spatial autocorrelations in FD (Zhang et al. 2015). We first used the function of “cmdscale” in “SpacemakerR” package in R (Stephane 2013) to calculate MEM. We then used the R function “poly” to quantify the polynomial terms for each variable including both linear and non-linear relationships between abiotic variables and FD. During performing redundancy analysis (Rda) based variance partitioning, we first used the method developed by Blanchet, Legendre & Borcard (2008), to forward-select significant abiotic variables among measured four topographic variables and ten soil variables and purely spatial variables represented by MEM associated with FD across spatial scales. For significant abiotic variables, we used box plots to examine their spatial heterogeneities by quantifying their trends in means and variances with an increase in spatial scales. Then, we used variance partitioning to allocate FD variation as arising from the into four complementary components: (a) “purely abiotic variables” (explained by abiotic factors only), (b) “spatially structured abiotic variables” (spatial autocorrelation in FD and merely induced by abiotic variables), (c) “purely spatial variables” (spatial autocorrelation in FD independent of abiotic variables and induced by dispersal limitation and biotic interactions), and (d) “undetermined variables” (Legendre et al. 2009; Siefert 2012). At each spatial scale, variance partitioning was done using the function of “varpart” in “vegan” package in R (Oksanen et al. 2016). Inhomogeneous bivariate spatial point patterns analysis For the 112 study species, there are a total of 112×111 = 12432 possible bivariate spatial point patterns. We first used the “pcfcross.inhom” function in R package “spatstat” (Adrian & Rolf This article is protected by copyright. All rights reserved.
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2005) and the spatial locations (x and y coordinates) for all individuals of the 112 tree species to perform inhomogeneous bivariate spatial point pattern analysis (inhomogeneous g12(r)) to test all 12432 possible bivariate spatial point patterns at each spatial scale. We then used 999 Monte Carlo simulations of point processes generated by a inhomogeneous Poisson process to test whether inter-specific spatial distribution between each of the 12432 pairs was significant attraction (inhomogeneous g12(r) >1), random (inhomogeneous g12(r) = 1), or repulsion (inhomogeneous g12(r)
