Succession of a broad-leaved Korean pine mixed forest: Functional plant trait composition

Journal Pre-proof Succession of a broad-leaved Korean pine mixed forest: Functional plant trait composition Hede Gong, Fenggui Yao, Jie Gao PII:

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https://doi.org/10.1016/j.gecco.2020.e00950

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GECCO 950

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Global Ecology and Conservation

Received Date: 1 January 2020 Revised Date:

31 January 2020

Accepted Date: 31 January 2020

Please cite this article as: Gong, H., Yao, F., Gao, J., Succession of a broad-leaved Korean pine mixed forest: Functional plant trait composition, Global Ecology and Conservation (2020), doi: https:// doi.org/10.1016/j.gecco.2020.e00950. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

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Succession of a broad-leaved Korean pine mixed forest: functional

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plant trait composition Hede Gong1, Fenggui Yao1, Jie Gao2

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School of Geography and Ecotourism, Southwest Forestry University, Kunming, Yunnan, China

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2

Beijing Key Laboratory for Forest Resources and Ecosystem Processes,

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university, Beijing 100083, China;

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Corresponding authors: Gao Jie

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E-mail:[email protected].

Beijing

forestry

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Abstracts: A longstanding goal of ecology and conservation biology is to understand the climatic

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and biological controls of forest succession. However, the patterns and mechanisms that guide

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forest succession, especially within broad-leaved forests, remain unclear. We collected leaf traits

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and abiotic data across a 200-year chronosequence within a broad-leaved Korean pine forest in

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northeastern China. We focused on five key leaf traits related to resource acquisition and

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competition by quantifying the community-weighted trait distributions for specific leaf area (SLA),

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leaf dry matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf phosphorus

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content (Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (Nmass / Pmass). We

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also studied how these traits respond to changing environmental variables (climatic, soil and

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topographical) during succession. Redundancy analysis was used to examine the importance of the

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environmental variables in shaping the successional variation in plant traits. Our results revealed

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the following. Older forests differed significantly from younger forests in species composition and

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trait distribution. For example, SLA, leaf N and P content and the N/P ratio tended to increase and

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then decrease throughout the community during forest succession (p < 0.01). LDMC was also

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reduced over the course of succession. Mean annual temperature (MAT) and mean annual

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precipitation (MAP) correlated significantly with the succession of SLA and LDMC. However,

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temperature and rainfall were not significantly related to the temporal pattern of leaf nutrient (N

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and P) contents. The successional variation in leaf nutrients was instead mainly affected by soil

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and terrain factors (slope). Climatic factors independently accounted for 21.5% of the latitudinal

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difference in leaf function, which was slightly higher than soil factors (13.2%) and topographical

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factors (8.7%).

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Keywords: Functional traits; Forest succession; Climate change; Soil; Topographical factors

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1. Introduction

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The broad-leaved Korean pine mixed forest (BKF) is a typical variety of forest vegetation in

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northeast China. Compared with other forests of the world at the same latitude, it is famous for its

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unique species and rich species diversity (Hao et al., 2007). However, BKF biomes have been

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rarely studied, so there is no clear understanding of the mechanisms that drive forest succession

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here. This makes it more difficult to correctly predict their successional trajectories so as to assist

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communities and ecosystems in recovering from major disturbances (Prach & Walker 2011;

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Buzzard et al., 2016). Studying the species composition alone is not enough (Zhou et al. 2014;

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Norden et al. 2015; Buzzard et al., 2016), but studying the composition of functional groups may

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offer a stronger basis on which to build these predictions (Fukami et al. 2005, McGill et al. 2006).

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Buzzard (2016) observed that increased community productivity during forest succession is

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mainly reflected in some key functional traits of plants. Such key plant traits may ramify, so as to

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influence the structure and dynamics of the community and the functioning of the ecosystem. For

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example, in the early stage of succession, plants have a relatively higher growth rate and resource

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acquisition ability, which is manifested as high specific leaf area (SLA) (Muscarella et al., 2016).

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SLA reflects the ability of light interception and self-protection of leaves under strong light.

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However, Reich (2014) pointed out that SLA is actually at its largest in the later stage of

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succession. N / P is an important index of plant growth rate. The lower N / P ratio reflects the

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higher plant growth rate (Gao et al., 2019). The changes in resource availability that occur during

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succession are expected to select for traits associated with different physiological and life-history

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strategies at different stages. For example, increased niche differentiation among various species is

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understood to lead to over-dispersion of traits in later stages of succession (Enquist et al., 2015;

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Buzzard et al., 2016). In contrast however, many studies have found that increased competition

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can lead to a convergence of trait composition in the absence of disturbance late in succession

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(Bell 2001). Community-wide functional traits can reflect the traits of the community as a whole,

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and can solve the wrong estimation of traits caused by species differences (Gong et al., 2019).

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Leaf traits were affected by climate, soil and topography (Wright et al., 2005). However, the

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specific effects of environmental factors on plant functional traits are still unclear. To assess the

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various potential drivers of BKF assembly during succession, we measured environmental factors

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(climatic, soil and topographical), taxonomic composition and functional trait distributions in

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eight BKF communities within a broad-leaved Korean pine mixed forest that differed in stand age.

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Our analyses allowed us to uniquely: (i) characterize how community-wide functional traits

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change during BKF succession; (ii) assess how different potential drivers of functional trait

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dispersion change during BKF succession and (iii) quantify the relative contribution of climatic,

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soil and topographical factors to the variation in leaf traits throughout succession.

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2 Materials and Methods

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2.1 Study Sites

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We sampled forests across a chronosequence in Changbai mountain range, where BKF is the

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typical top community of ecological succession. Changbai mountain range is warm and rainy in

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the summer, and cold and dry in the winter. Using a ‘space-for-time’ substitution, we sampled

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forests with 10,20,30,40,50,70,130 and 200 years. Among them, the forests of 10-year, 20-year,

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30-year, 40-year are secondary birch forests, 50-year and 70-year are secondary broad-leaved

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forests, 130-year is coniferous forests and 200-year is broad-leaved Korean pine forests. Species

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composition of forests with different ages were listed in Table S1.

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We randomly selected four (20 m × 20 m) plots in each forest of different ages as our research

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objects. Thirty-two plots (20 m × 20 m) with eight different stages of succession were selected as

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our study objects. We measured the DBH (diameter at breast height) and height of all living trees

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that were at least 1 cm in DBH. All trees in the plot were identified, with their scientific names

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confirmed using herbarium specimens housed in different regions of China. Each of the 36 forest

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plots was divided into 16 subplots (5m × 5m). We recorded the species, number, height and

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coverage of shrubs in each subplot. We also selected small samples (1m × 1m) from the four

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corners of each subplot to investigate herbaceous species. Species of different life forms in

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different forest ages are listed in Table S1.

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2.2

Environmental variables

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Two climatic variables related to vegetative and successional gradients were quantified and

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obtained for the longitude, latitude and altitude of each plot from the WorldClim database (Fick,

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S.E. & R.J. Hijmans, 2017). The two factors were mean annual temperature (MAT) and mean

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annual precipitation (MAP). Two soil variables related to vegetative and successional gradients

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were also quantified, soil N content (g / kg) and soil P content (g / kg). We dug three soil sections

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at two corners and the center of the sample plot, and took soil according to the depth of 0-20cm

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soil layer. At the same time, we took about 500g soil samples and put them into sample bags to

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determine the content of nitrogen and phosphorus in the soil. Remove the gravel and plant roots

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from the soil sample in the ring knife, and dry them in the oven to constant weight. The total

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nitrogen content of soil was detected by Kjeldahl nitrogen analyzer, and the total P content of soil

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was detected by sodium bicarbonate extraction method. The average values of soil total nitrogen

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and total phosphorus were selected for each succession stage (Powers et al. 2009). The only

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topographical variable studied was slope of every plot. We took the average value of all

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environmental factor data as the overall environmental factor characteristics of a succession stage.

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The change of environmental factors with forest age were shown in Figure S1.

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2.3

Leaf traits

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More than 10 mature and well-developed individuals (tree,shrub and herb) were selected for

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each plant species in each plot, and the leaves without pests and diseases were collected. For trees,

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we selected the leaves on the outer branches of the crown. For trees and shrubs, we first cut the

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branches and then cut the leaves with scissors. For herbs, we cut the leaves directly. We put the

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leaves between two pieces of wet filter paper, put them into self sealing bags, took them back to

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the laboratory, and then put them into the refrigerator fresh-keeping layer for storage. Specific leaf

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area (SLA), leaf nitrogen content (Nmass (g / kg)), leaf phosphorus content (Pmass (g / kg)), Nmass /

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Pmass and leaf dry matter content (LDMC) were selected as key plant traits. We put the leaves in

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the dark environment of 5

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with filter paper, and then weigh them on the electronic balance (saturated fresh weight). We put

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the blade into 80 ℃ oven and dry it for 24 hours, then take it out and weigh it (dry weight).

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LDMC = dry weight of leaves (mg) / saturated fresh weight of leaves (g). We used a scanner to

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scan the leaf area, and then calculate the leaf area. SLA = area of blade (cm2) / dry weight of blade

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(g). For conifer species, we regard each bunch of needles as a cylinder, and calculate the specific

for 12h, then quickly absorb the water on the surface of the leaves

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leaf area by using half of the total surface area. The results of the specific leaf area of broadleaf

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plants and other flat plants calculated by this method are consistent with those of single leaf. For

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broadleaf species, we use a scanner to scan and calculate the specific leaf area. Kjeldahl method

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was used for the determination of total nitrogen in leaves, and Mo sb colorimetry was used for the

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determination of total phosphorus in leaves (Huang et al., 2019). The community leaf functional

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traits (CWM) is the value of leaf traits measured at the species level. Tree、shrub and herb are

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weighted average based on important values to get the average value of each trait at the

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community level. Importance value of tree= (relative density + relative dominance + relative

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frequency) / 300. Importance value of shrub = (relative density + relative coverage + relative

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frequency) / 300. Importance value of herb = (relative height + relative coverage + relative

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frequency) / 300.

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CWM = ∑ pi * traiti

n

i =1

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CWM represents the community trait of species i, pi represents important value of species i and

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traiti represents the mean trait valye of species i.

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2.4 Data analysis

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We logarithmically transformed all the soil and climatic factor data to give it a more normal

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distribution (Gong et al., 2019). A generalized linear mixed model was used to examine the

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relationships between leaf traits, forest age and climatic, soil and topographical variables. R2 was

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used to evaluate the explanatory power of the regression models. To estimate the relative

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importance of climate and soil factors for each functional trait, we used boosted regression trees

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(BRTs), which is a machine learning-based approach to regression (Gong et al., 2019). The

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advantage of using BRTs is that the relationship between predicted (leaf traits) and predictor

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variables (climatic, soil and topographical variables) is independent, and BRTs can also deal with

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nonlinear relationships and with multicollinearity between predictors. The relative influence is

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scaled so it adds up to 100% in each model.

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Redundancy analysis (RDA) was used to analyze the relationship between trait variation and

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climatic and soil variables. In our analysis, variance partitioning led to four individual fractions:

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(i) unique climate; (ii) unique soil; (iii) unique topography as well as shared variation due to

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redundant explained variation for (iiii) climate, soil and topography; These analyses were

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performed using the vegan package in R (Gong et al., 2019).

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3 Results

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We observed wide variation in leaf traits during succession. Across all life-forms, as stand age

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increased, SLA, leaf N and P content increased significantly at first, and then they declined (P <

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0.05). Stand age alone explained more than 82% of the variation in most leaf traits throughout

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succession (SLA, leaf N and P content) across all life forms (Fig.1). For LDMC and leaf N/P

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though, successional trends in trees were opposite those in shrubs and herbs.

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The successional variation in leaf traits that we observed appears to be driven largely by variation

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in climate (Fig.2; Fig.3). As temperature increased, SLA also increased significantly across all

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life-forms (p <0.05). LDMC and N/P trends also correlated similarly with temperature and

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rainfall.

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The role of soil factors and topography cannot be ignore when explaining the successional

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variation in leaf traits. SLA responded to soil N and P content differently (Fig.4; Fig.5). As soil P

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content increased, leaf N content and N/P decreased significantly. Slope had a significantly greater

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effect on N than P content in leaves (Fig.6).

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Among all environmental factors, Soil N content and MAP best explained leaf P content and SLA

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in all three life forms. Soil P content also explained the successional variation in LDMC, leaf N

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and N/P well across all life forms (Fig.7). RDA analysis revealed that climatic variables accounted

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for the largest proportion of variation in leaf traits, explaining 27.1 % of variation. Independently,

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climatic factors contributed 21.5% of the latitudinal difference in leaf function, which was slightly

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higher than soil factors (13.2%) and topographical factors (8.7%) (Fig.8).

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4 Discussion

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We observed that successional variation in leaf functional traits underlies a coordinated

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trade-off between resource acquisition. Leaf traits determine where on a growth vs. survival

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trade-off a species is located, given a set of environmental conditions (Buzzard et al., 2016). Reich

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(2014) believed that successional variation in leaf traits reflects a fundamental trade-off between

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competitive aptitude and the ability to avoid mortality under stressful conditions. The goal of our

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study was to determine the drivers of BKF succession by focusing on traits associated with

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resource acquisition and competitive ecological and life-history strategies.

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Most of the leaf traits showed significant change characteristics with the succession stage.

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With the succession stage, the SLA, N content, P content and N / P of leaves showed a significant

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trend of increasing first and then decreasing (P < 0.05). The results of this study are consistent

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with those of Buzzard (2016). In the early and middle stages of succession, the canopy density is

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low and the light transmittance is high. Therefore, the light resources are sufficient and the SLA is

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very small. With the progress of succession, the transmittance of forest decreased, and the SLA of

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plants began to increase. When the middle and later stage of succession was reached, due to the

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existence of "self thinning" phenomenon of forest, the transmittance of forest increased to a

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certain extent, and then the SLA decreased (Kraft et al., 2009; Díaz et al., 2010). Generally

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speaking, low SLA is accompanied by high LDMC. Although LDMC in this study is higher in the

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early stage of succession, this trend is not significant with the progression of succession. With the

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development of succession stage, N / P increased first and then decreased, which reflected the

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change from N limitation in the early stage of succession to P limitation in the middle and late

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stage of succession (Gao et al., 2019). N / P measurement is an important index of plant growth

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rate. The lower N / P ratio reflects the higher plant growth rate (Wang et al., 2016). Therefore,

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with the development of succession, the growth rate of the whole community showed a significant

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upward trend and then a significant downward trend. These leaf traits are all associated with

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longer-lived leaves, lower photosynthetic rates and slower growth rates (Wright et al. 2005).

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Together, the observed trait shifts appeared to be primarily determined by a concomitant shift in

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resource availability, e.g. water availability.

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Because we used the commonly methods in ecology-‘space-for-time’, in addition to the

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differences in forest age, there are also some differences(climate, soil and topography) in these

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forests. We found that climatic and soil variables were important factors affecting change in leaf

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functional traits during forest succession by changing the species composition and community

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structure under different environmental conditions. According to the theory of community

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construction, species composition in the community varies along the environmental gradient

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according to the differences among species in their adaptability to the external environment

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(Cornwell et al., 2009; Andersen et al., 2012). Under the suitable temperature range, with the

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increase of temperature, the photosynthetic capacity of plants increases, and the plants have higher

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SLA (Wright et al., 2010) and the aging of leaves increases, the photosynthetic capacity of plants

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decreases, and the accumulation of organic matter decreases. Therefore, LDMC decreases

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significantly with the increase of temperature (Niinemets 2001).

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On the whole, the ability of soil factors in shaping the change pattern of plant functional

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characters is lower than that of climate factors. We were surprised to find that there was no

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significant correlation between leaf nutrients and soil nutrients. There is no direct relationship

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between leaf nutrients and soil nutrients (Furtado et al., 2009). Nutrients absorbed by plants from

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the soil enter the roots, leaves and branches of plants. The survival strategies of different plant

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species are different, so the distribution strategies of nutrients obtained from the soil are different,

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which explains that the correlation between soil nutrients and leaf nutrients is not significant with

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the succession (Gao et al., 2019). Slope can affect the growth of plants by affecting the light,

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water and heat conditions. In the degree of soil water absorption, the larger the slope, the easier

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the water loss, the worse the soil water and nutrient conservation capacity, the more unfavorable

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to plant growth (Buzzard et al., 2016). Therefore, SLA will decrease with the increase of slope.

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Although climatic, soil and topographical factors barely explained temporal patterns of leaf

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traits, they did have direct and indirect effects on the spatial distribution of leaf traits in a different

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way. On one hand, environmental factors can directly affect the morphological construction of

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plant leaves, as well as the carbon distribution related to metabolic activities (Moles et al., 2014).

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On the other hand, climatic conditions naturally shape the large-scale geographical pattern of

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vegetation types (Han et al., 2011) and also regulate the availability of soil resources (Buzzard et

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al., 2016).

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Acknowledgements

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This work was supported by the Key Project of National Key Research and Development Plans

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(Grant No. 2017YFC0504004). We would like to thank Elizabeth Tokarz at Yale University for

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her assistance with English language and grammatical editing.

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Figures

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Fig.1. Community-weighted mean (CWM) for functional traits: specific leaf area (SLA), leaf dry

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matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf phosphorus content

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(Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (N/P) as a function of stand

300

age. A solid regression line indicates statistical significance for the best fit regression model (as

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determined by the lowest AIC score).

302 303

Fig.2. Relationship between community-weighted mean (CWM) for functional traits: specific leaf

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area (SLA), leaf dry matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf

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phosphorus content (Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (N/P)

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and annual average temperature (MAT). A solid regression line indicates statistical significance

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for the best fit regression model (as determined by the lowest AIC score).

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Fig.3. Relationship between community-weighted mean (CWM) for functional traits: specific leaf

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area (SLA), leaf dry matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf

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phosphorus content (Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (N/P)

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and annual average precipitation (MAP). A solid regression line indicates statistical significance

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for the best fit regression model (as determined by the lowest AIC score).

314 315

Fig.4. Relationship between community-weighted mean (CWM) for functional traits: specific leaf

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area (SLA), leaf dry matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf

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phosphorus content (Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (N/P)

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and soil N content. A solid regression line indicates statistical significance for the best fit

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regression model (as determined by the lowest AIC score).

320 321

Fig.5. Relationship between community-weighted mean (CWM) for functional traits: specific leaf

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area (SLA), leaf dry matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf

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phosphorus content (Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (N/P)

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and soil P content. A solid regression line indicates statistical significance for the best fit

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regression model (as determined by the lowest AIC score).

326 327

Fig.6. Relationship between community-weighted mean (CWM) for functional traits: specific leaf

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area (SLA), leaf dry matter concentration (LDMC), leaf nitrogen content (Nmass (g/kg)), leaf

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phosphorus content (Pmass (g/kg)) and the ratio of nitrogen content to phosphorus content (N/P)

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and slope of samples. A solid regression line indicates statistical significance for the best fit

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regression model (as determined by the lowest AIC score).

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Fig.7. Effects of climate (MAT and MAP), soil (Soil N and P) and topographical (Slope) factors on

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each functional trait (specific leaf area (SLA), leaf dry matter concentration (LDMC), leaf nitrogen

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content (Nmass (g/kg)), leaf phosphorus content (Pmass (g/kg)) and the ratio of nitrogen content to

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phosphorus content (N/P)) by BRTs analysis.

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Fig.8. Results of variation partitioning for life traits in terms of the fractions of variation explained.

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A: Climate factors B: Topographical factors C: Soil factors. The variation in leaf traits is

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explained by the three groups of explanatory variables (A, B, C), and unexplained is the

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undetermined variation. a, b and c are the unique effects of climate, topographical and soil factors.

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d, e, f and g are fractions indicating their joint effects.

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Table S1

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Table S1 The dominant species composition of different life forms in different succession stages. Stand Age Life forms Tree layer 10a

Dominant Species Betula platyphylla Suk

Shrub layer Syringa oblata Lindl + Ribes komarovii A.Pojark Herb layer Cardamine leucantha + Enemion raddeanum Tree layer

20a

Betula platyphylla Suk + Populus davidiana

Shrub layer Syringa oblata Lindl. + Philadelphus incanus Koehne + Ribes komarovii A.Pojark Herb layer Athyrium filix-femina + Cardamine leucantha+ Enemion raddeanum Tree layer

30a

Shrub layer

Betula platyphylla Suk + Salix matsuda +Populus davidiana Syringa oblata Lindl. + Philadelphus incanus Koehne + Acer barbinerve + Ribes komarovii A.Pojark + Euonymus alatus

Herb layer Athyrium filix-femina + Cardamine leucantha + Enemion raddeanum + Ephedra equisetina Tree layer 40a

Betula platyphylla Suk + Populus davidiana + Acer mono Maxim

Shrub layer Acer barbinerve + Ribes komarovii A.Pojark + Euonymus alatus + Lonicera Japonica Herb layer Athyrium filix-femina + Lonicera caerules Tree layer

50a

Quercus mongolica + Phellodendron amurense + Tilia amurensis

Shrub layer Acer barbinerve + Euonymus alatus + Lonicera Japonica+ Corylus mandshurica Herb layer Athyrium filix-femina + Meehania fargesii + Farges Meehania + Lonicera caerules Tree layer

70a

Quercus mongolica + Phellodendron amurense + Acer mono Maxim + Fraxinus mandshurica + Tilia amurensis

Shrub layer A.pseudo-sieboldiarum + Acer barbinerve + Euonymus alatus + Lonicera Japonica + Corylus mandshurica Herb layer Meehania fargesii + Lonicera caerules Tree layer

130a

Abies nephrolepis + Pinus koraiensis + Picea jezoensis + Phellodendron amurense

Shrub layer Acer triflorum + Actinidia kolomikta + Ribes komarovii A.Pojark + Acer barbinerve Herb layer Flosseu Radix Impatientis nolitangeris + Lonicera caerules + Ephedra equisetina Tree layer

200a Shrub layer

Pinus koraiensis + Quercus mongolica + Tilia amurensis + Fraxinus mandshurica Corylus mandshurica + Ribes komarovii A.Pojark + Deutzia scabra Thunb + Philadelphus incanus + Syringa oblata Lindl. + Acer barbinerve

Herb layer Flosseu Radix Impatientis nolitangeris + Meehania fargesii + Athyrium filix-femina + Cardamine leucantha

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Fig S1

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Fig S1 The change pattern of environmental factors with succession. MAT represents mean annual temperature,

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MAP represents annual average rainfall. Solid line represents fitting curve, R2 represents goodness of fit, and P

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represents significance; the optimal model is selected according to the minimum AIC value standard.

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Conflict of interest statement We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work.