Next Article in Journal
Inhibition of Soil Wind-Erosion and Dust by Shelterbelts in the Hilly Area of Loess Plateau and Its Influencing Factors
Previous Article in Journal
Does Forest Contemplation Provide Greater Psychological Benefits than Passive Exposure to the Urban Forest? A Pilot Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 8
10.3390/f15081412
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Can CSR Strategy Classes Determined by StrateFy Explain the Species Dominance and Diversity of a Forest Community?

by
Ye Peng
1,†,
Gansha Cui
1,†,
Hengyi Li
1,
Ningjie Wang
1,
Xiao Zheng
2,
Hui Ding
2,
Ting Lv
3 and
Yanming Fang
1,*
1
Co-Innovation Center for Sustainable Forestry in Southern China, College of Life Sciences, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, Nanjing Forestry University, Nanjing 210037, China
2
State Environmental Protection Scientific Observation and Research Station for Ecological Environment of Wuyi Mountains and Fujian Wuyishan State Integrated Monitoring Station for Ecological Quality of Forest Ecosystem, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, China
3
Shaanxi Key Laboratory of Sericulture, Ankang University, Ankang 725099, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(8), 1412; https://doi.org/10.3390/f15081412
Submission received: 23 July 2024 / Revised: 9 August 2024 / Accepted: 11 August 2024 / Published: 12 August 2024
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
Plant ecological strategies are essential for assessing habitat stress and disturbance and evaluating community productivity. These strategies provide theoretical frameworks for maintaining the natural state of vegetation and enhancing productivity. The functional traits of leaves reflect a plant’s responses to environmental changes and contribute to understanding ecosystem stability, providing a basis for species diversity maintenance and effective conservation efforts. The Wuyishan National Park, a biodiversity hotspot in China, is a focal point for ecological research. Its evergreen, broad-leaved forest, the zonal vegetation of Mt. Wuyi, underpins plant diversity protection in the region. This study investigates the CSR (competitor, stress-tolerator, ruderal) strategy of 126 species on Wuyi Mountain to elucidate prevalent ecological strategies. The main ecological strategy of plants in the study area is the CS (competitor, stress-tolerator) strategy. The species exhibit nine categories. The most abundant ecological strategy is S/CS (plants from Fagaceae), accounting for 38%, followed by S/CSR at 23% (plants from Theaceae), CS at 20% (plants from Fagaceae and Theaceae), and the remaining strategies collectively at 19%. The different growth habit categories showed variations in the CSR strategies. The trees clustered around a CS median strategy, with no R-selected trees observed. Shrubs and lianas centered around an S/CSR strategy, while grasses and understory shrubs clustered around CS/CSR. Redundancy analysis results indicate that leaf functional traits are primarily influenced by temperature, suggesting that temperature is the key environmental factor driving the differentiation of plant functional traits. This study provides insights into the ecological strategies of plant species in the Mt. Wuyi region, highlighting the importance of considering both biotic and abiotic factors in maintaining biodiversity and ecosystem stability.

1. Introduction

Plants have evolved diverse life history strategies to thrive in Earth’s varied environments, with functional traits explaining variations in these strategies [1]. Two pivotal theories in ecology and evolution address life history strategies: the r-/K-selection theory and the competitor, stress-tolerator, ruderal (CSR) theory. MacArthur and Wilson [2] introduced the terms “r-selection” and “K-selection” in their seminal work. Several naturalists deepened the r-/K-selection theory [3,4,5,6], explored and compared the meanings of the two selection regimes, including mortality, survivorship, population size, intra- and inter-specific competition, relative abundance and selection pressures. The theoretical research of r-/K-selection is still very active [7,8]. Grime [9,10] designed three kinds of plant strategists (competitors, stress-tolerators and ruderals) and established the “C-, S-, and R-Selection” theory, e.g., the so-called CSR triangle. Various methods or models have been successively developed for CSR functional classification (CSR signature) [11,12,13,14,15]. Recently, a new method called “StrateFy” has been applied globally to calculate plants’ CSR strategies across different biomes [16]. In addition, hyperspectral remote sensing was used to assess the CSR functional signature of heathland landscapes [17]. Using imaging spectroscopy, CSR strategy classes were identified for an alpine grassland [18]. Based on plant traits derived from a radiative transfer model on airborne hyperspectral imagery, CSR scores can be generated to characterize community traits [19,20]. Due to the innovation of methods, the CSR theory can be tested and has been widely used in the study of vegetation and communities.
Calculating plant CSR strategies based on functional traits has been a major focus in studies of plant community assembly [21]. There are two crucial dimensions for a plant community: spatial structure patterns and temporal dynamic processes. Spatially, it is manifested as vertical complexity and horizontal heterogeneity [22], whereas temporally, it is characterized by seasonal change and successional dynamics [23]. The CSR theory provides a mechanistic explanation for studies on plant communities and vegetation, allowing for the prediction, quantification, and comparison of community structures and ecosystem processes [24]. Functional diversity is more significant than species richness in revealing the successional process of a community, which can be effectively described by its CSR signature [25]. The CSR signature is considered as a simple but powerful approach to express the state of vegetation that would be comparable spatially and temporally [17]. It has been widely used in investigations of community spatial structure and temporal dynamics for various vegetation types such as lowland grassland [26,27,28,29], alpine grassland [18], wetlands [30], coastal dune [31], riverbank vegetation [32], and forests [33,34]. Specifically, the changing trend in CSR signature has achieved consistent results for several systems. The CSR classification supported a functional shift from ruderal pioneers towards stress tolerators in the late successional stage along a primary succession of a glacier foreland [11] and coastal sand dunes [35]. In sandplain forests, species are highly stress-tolerant despite a diversity of trait combinations [36]. In the Brazilian subtropics, Araucaria and Pampean forests in the cool environment were strongly associated with the stress-tolerance strategy, whereas rainforests in a warm environment mostly presented a competitive strategy [37].
As an active research field, other aspects of CSR theory have also been reported. Plant strategies may be ontogeny-dependent and mating system-dependent. A shift from juvenile R strategy toward adult C/S strategy were reported in an investigation of two main mountain top vegetation types. Ruderals and competitors tended to be selfers and outcrossers, respectively, while stress tolerators were mostly associated with mixed mating systems; thus, CSR strategies are significantly related to mating systems [38]. CSR signature may respond to natural and anthropogenic disturbance. Based on a difference in the triangular CSR space between snow patches and snow-free sites, the timing of snowmelt was identified as a key filter of plant strategies [39]. Logging affects the CSR signature in a forest, and a shift in dominant species from pre-logging stress-tolerators towards post-logging ruderal counterparts was found in temperate fir-beech forests [40]. In addition to the plant community, shifts in CSR functional signature was reported to be a good approach to predict the impact of environmental and anthropogenic stress on species assemblages in coral reef communities [41]. It was reported that plant ecological strategists may have a certain genetic basis in Arabidopsis thaliana [42]. In Arabidopsis thaliana accessions with different geographical origins, substantial intra-specific variation was found along the S–R axis, and genome-wide association studies were recommended to determine the genetic basis of functional strategies [43]. Another intra-specific variation study demonstrated that two hemp genotypes with different mating systems presented substantial differences in the C:S:R ratios (57:26:17% vs. 69:15:16%) [44]. In general, the CSR signature of plant individuals or communities is affected by genetic, environmental and ontogenetic factors.
The CSR signature was demonstrated to link with some ecological topics. Firstly, CSR scores can be used as functional indicators for biodiversity and biogeography [26,45]. According to studies on the arbuscular mycorrhizal symbiosis between plants and fungi, non-ruderal plants sustain significantly higher fungal richness than ruderal species, while putatively ruderal AM fungi trend to grow on ruderal plants [46]. Interestingly, positive associations were found between species’ native range sizes and the R score, as well as between naturalized range size and the C score, whereas all range-size measurements were positively associated with the S score, indicating an application prospect for CSR in biogeography [45]. Secondly, the CSR score can be applied to study invasion biology. The probability of naturalization is positively related with C/R scores, but negatively with S scores [47], and CSR classification can be applied to explain the invasion success of alien plant species in Southern Europe [48]. Compared with stress tolerators, competitors and ruderals exhibit a higher naturalization degree and stronger invasiveness [47]. Third, Grime’s CSR framework was used to understand the life history of mycorrhizal fungi, and competitive, stress-tolerant, and ruderal species were documented for AM fungi [49]. Competitors hosted more than four times as many pathogens as did stress tolerators [50].
In short, the CSR signature has mainly been used in grassland-plant-community research. The general applicability of the CSR classification was not tested outside Britain until 2010 [24]. Some ecologists doubted and challenged the CSR theory due to its limited support [51,52,53]. There are few reports about the application of CSR theory in forest communities [33,34,36,37,40]. The CSR signature in forest communities in China was rarely reported to the author’s knowledge. We do not fully understand the trade-off strategies of plants facing different competition levels under various environmental gradients of stress and disturbance. Therefore, we investigated a subtropical evergreen broad-leaved forest with the main objectives of (i) to identify the CSR strategies of woody plants, and (ii) to evaluate whether CSR strategies can explain plant community assembly.

2. Materials and Methods

2.1. Study Site and Sample Collection

The study was carried out in the “Wuyishan National Park”, Xingcun town, Wuyishan City, Fujian Province, China (27°35′24.23″ N and 117°45′55.43″ E) (Figure S1). The current study was conducted in a permanent plot within the evergreen broad-leaved forest. It is a forest dynamic plot, rectangular in shape, measuring 400 × 240 m, with a projection area of 96,000 m2, extending from the ridge to the foot of the mountain. On the whole, the plot is low in the south and high in the north, with a slope range of 10–50°. The area of the northwest slope is about 1/3 of the total area, while the area of southeast slope is about 2/3 of the total area. The main ridge in the plot is in the northeast–northwest orientation; the long side is basically parallel to the main ridge, and the short side extends from the main ridge to the foot of the mountain on both sides. In addition to the main ridge, there are many small ridges and gullies in the plot, which generally pass through the plot from north to south. The region possesses a typical subtropical monsoon climate, with an annual average temperature of 18.3 °C, relative humidity of 78–84%, and annual precipitation of 1888.1 mm. Precipitation mainly occurs in April to June. Annual average sunshine hours are up to 1910.2 h [54]. The species in the plot are dominated by evergreen broad-leaved plants from Fagaceae, Lauraceae, Ericaceae, Aquifoliaceae and Theaceae. The top 10 species with top importance values are Castanopsis fordii, Castanopsis. eyrei, Castanopsis. carlesii, Castanopsis. faberi, Syzygium buxifolium, Schima superba, Eurya muricata, Itea oblonga, Engelhardia roxburghiana and Rhododendron henryi [55]. The importance value (IV) was calculated as follows: IV (%) = (relative abundance + relative frequency + relative dominance)/3. Specifically, relative abundance (%) = 100 × the number of individuals of a given species/the total number of individuals of all species; relative frequency (%) = 100 × the number of plots in which a given species occurs/the total number of occurrences of all species; relative dominance (%) = 100 × the basal area of a given species/the total basal area of all species.
The total number of species in the Wuyishan forest dynamic plot was 168, and we sampled 126 species (75% of the total species) in the plot. The sampled species belong to 69 genera in 45 families (Table S1). Species selection was mostly based on greater importance values. The 126 species were sorted by life forms, e.g., trees (61), shrubs (51), lianas (9) and herbs (5) in the forest dynamics plot. Three healthy and disease-free individuals with uniform growth were randomly sampled for each species. About 15–20 healthy and complete leaves were collected outside the canopy in four directions of each individual. Anonymous tree-climbing volunteers, together with the application of high branch shears, sampled the materials from big, tall trees. Sample leaves were tagged in the field and kept in self-sealed plastic bags to minimize dehydration, then brought back to the laboratory.

2.2. Trait Measurements

In the laboratory, 10 intact leaves were selected from the samples of each individual, 3 replicates with a total of 30 leaves per species. The leaf samples were treated as follows: the selected leaves were put into self-sealing bags, immersed in distilled water, stored in the dark at 5–8 °C for 12 h. Then the leaf was taken out, the dirt on the leaf surface wiped off with paper towel, and the water was absorbed onto the surface. We took measurements for the following three leaf indices: leaf fresh weight, leaf area, and leaf dry weight. (i) For leaf fresh weight (LFW/mg), the data were obtained by using a thousandth electronic analytical balance. (ii) For leaf area (LA/mm2), the leaves were tiled and scanned with the Epson Perfection V 39 scanner, the images were saved in JPEG format, and the data of leaf area were output by ImageJ8 software. (iii) For leaf dry weight (LDW/mg), after measuring the leaf area, the leaves were put into the oven at 60 °C to dry for about 24–48 h, then taken out and weighed after drying to a constant weight, and the dry weight data of each leaf were obtained. Based on the above data, specific leaf area (SLA/mm2 mg−1) and leaf dry matter content (LDMC/mg g−1) were obtained separately [56].

2.3. Data Analysis

The SLA (mm2·mg−1) was calculated based on the ratio between the LA and the leaf dry weight; the LDMC (mg·g−1) was the leaf dry weight by the saturated leaf fresh weight ratio. Microsoft Excel 2016 software was used to input and process the data. The data of leaf area, specific leaf area and leaf dry mass content were imported into StrateFy to automatically obtain the relative contribution (%) of C, S, and R parameters to the tertiary CSR strategy for each tree species. The steps refer to [16]. In order to identify the main functional traits that may affect the 126 species, we performed principal component analysis (PCA) to reduce the number of functional traits that needed to be analyzed while trying to retain as much information as possible that was contained in the original metrics. Data were normalized before we ran the PCA. In order to explore the relationship between leaf functional traits and climatic characteristics, leaf morphological traits, site climatic and latitudinal/longitudinal coordinates of six sample plots were obtained from the Chinese Plant Trait Database (Table S2) [57]. To reduce the presence of covariance among the explanatory variables, we first removed some variables with high correlation and standardized the selected variables (Figure S2). Finally, redundancy analyses (RDA) were used to discern relationships between functional traits and climatic characteristics. All statistical analyses were performed in R 4.2.3 (https://www.r-project.org/).

3. Results

The relative proportion of C, S and R selection for 126 species was highly variable, 10%–64% for C, 18%–80% for S, and 0%–37% for R, indicating significant functional differentiation. The CSR scores were automatically calculated by StrateFy, and the ternary diagram of CSR output by R package “ggtern” 3.5.0 is shown in Figure 1. We identified nine types of strategies (Figure 1), including C/CS (n = 1), C/CSR (n = 2), CS (n = 25), CS/CSR (n = 14), CSR (n = 5), S/CS (n = 48), S/CSR (n = 29), S/SR (n = 1), SR/CSR (n = 1) (Figure 2). Therefore, the highlight of the CSR signature for this evergreen broad-leaved forest is the dominance of stress-tolerators and lack of ruderals. The S/CS type is typical for the dominant species (Table 1), such as C. eyrei, C. carlesii, C. faberi, and C. faberi belonging to Fagaceae. According to our previous data, the dominant families of the community include Fagaceae (27.40, 14, importance value and species number, the same below), Theaceae (13.56, 18), Elaeocarpaceae (6.23, 5), Ericaceae (5.76, 10), Lauraceae (5.40, 17), Aquifoliaceae (5.01, 14), Magnoliaceae (4.51, 5), Juglandaceae (4.43, 1), Myrtaceae (3.69, 1) and Symplocaceae (3.38, 19). Based on the CSR-score width of the species, CSR combinations of dominant families were expressed in three patterns: broad, median and narrow (Table 2). Therefore, the species diversity of the dominant families is consistent with their CSR-score width (or strategy diversity), in spite of a narrow width of CSR score in Theaceae with 18 species. In particular, Fagaceae with high species diversity and dominance showed a high diversity of CSR strategies.
PCA analysis of LA, LDMC, SLA, LFW and LDW of 126 species was carried out (Figure 3) and the first two axes of PCA together accounted for 90.19% of the variation in the data. The first principal component was primarily explained by LA, LFW, and LDW, effectively distinguishing CS strategy species from other species. The second principal component was mainly explained by LDMC and SLA, separating CS/CSR and CSR strategy species from S/CS species (Figure 3). Tropical rainforest plant functional traits were very different from those of several other vegetation types, mainly influenced by Bio 1, Bio 2, Bio 3. The plant functional traits of Wuyi Mountain were similar to those of YGS and BYS, and differed greatly from those of ZJHR and BRJ due to the influence of Bio 10 and Bio 12 on the two sites (Figure 4). SLA, LA, LFW, LDW were positively correlated with Bio 1, Bio 2, Bio 8, and altitude, and negatively correlated with Bio 10 and Bio 12. LDMC was positively correlated with Bio 10 and Bio 12, yet negatively correlated with Bio 1, Bio 2, Bio 8, and altitude (Figure 4).

4. Discussion

The adaptive specialization of species due to selective processes such as response to environmental stresses and disturbances will determine their ecological niche in the community and ultimately influence community assembly. Therefore, CSR can provide an indirect link to community assembly. Species with similar functional traits within a community have similar resource requirements, which may lead to inter- and intraspecific competition. There are few reports on the CSR strategies of forest communities. In a deciduous oak woodland in Turkey, the less disturbed plot (61 species) hosted 10 different functional types with most abundant type of competitor-ruderals (40%), while the less disturbed plot (50 species) sustained 9 different functional types of which the most abundant was CR (46%) [58]. Based on compiled data from 112 sites of subtropical forests (rainforests, seasonal forests, Araucaria forests and Pampean forests) in southern Brazil, a strong CS component but a weak R component were found for all forests studied [37]. Survey results from 25 species sampled from 14 permanent plots (1 ha in size) in a Brazilian coastal sandplain forest showed a higher proportion of S% (59–80.7) and a lower proportion of C% (19.3–41), and most species presented the S/CS strategy [36]. For the first time, we provide a relatively large data set of CSR strategies for subtropical forest communities in China.

4.1. Functional Traits and CSR Strategy

In our study, we found that the differences in plant responses to habitat in Mt. Wuyi were mainly characterized on the C–S axis in the CSR system (Figure 1), suggesting that plants trade-off between resource competition and stress tolerance. Seven of the top ten species in terms of importance value showed stress tolerance (Table 1). This indicates that the stress tolerant ones dominate at the local scale. They all have larger LDMCs along with lower SLAs and resist stressful environments by accumulating and retaining resources. In addition to these, we found nine ecological responses (Figure 2). One possible explanation is that vegetation under multiple environmental stresses in subtropical forests will be composed of species with diverse ecological strategies [36]. Although there was no absolute R strategy exhibited by plants across the Wuyi forest dynamics plot, the SR/CSR- strategy species Photinia beauverdiana, along with Antidesma japonicum (S/CSR) and Ilex pubescens (S/CSR) exhibited greater ruderal characteristics, and their SLAs were larger (Table S1).
Plant functional traits can be applied to quantify CSR coordinates for various species in a real community [11]. The CSR classification approach is established on the solid basis of quantification for various functional traits in situ [59]. Therefore, functional traits determined in the field are the premise of CSR classification. The internal relationship between leaf functional traits (chemical, structural and physiological properties) is called leaf economic spectrum (LES) [56]. Both LES and CSR plant strategies are two well-established schemes for the investigation of Earth’s plant functional diversity [19]. Six typical leaf functional traits, e.g., photosynthetic capacity (Amass), dark respiration rate (Rmass), leaf mass per area (LMA), leaf lifespan (LL), leaf nitrogen (Nmass) and leaf phosphorus (Pmass), were incorporated into LES analysis [56], whereas only three standard leaf traits, e.g., leaf area (LA), leaf dry matter content (LDMC) and specific leaf area (SLA), were included in CSR coordination [16]. Here, we do not want to compare and evaluate the advantages and disadvantages of the two theoretical models, but try to explain that CSR theory has a real leaf functional basis. Many variables of vegetative and reproductive traits can interpret plant-life history strategies [1,36,60]. However, the relationship between traits is not always independent, but related, which is often expressed as trait–trait co-variation [61,62], coordination [63,64,65], or trade-off [66]. Effective theories avoid too many assumptions and free parameters [21,67] and are easy to verify. Among 14 functional traits involving vegetative, reproductive and chemical properties, three traits, e.g., leaf area (LA), leaf dry matter content (LDMC) and specific leaf area (SLA), were proven to be able to represent multidimensional variables with relatively less information loss [16]. With this availability of robust trait measures, the CSR-strategy approach can be used to investigate plant functional ecology across a wide range of species worldwide [47]. Here, we found that the first axis of PCA was correlated with SLA and LDMC (Figure 3), indicating the acquisitive and conservative types of the plant economic spectrum, respectively, and the second axis of PCA was highly correlated with LA, indicating the plant size spectrum (Figure 3). This suggests that plants with high LA have moderate leaf economy and do not necessarily have high LDMC and SLA, while plants exhibiting high SLA have low LDMC.

4.2. CSR Strategy Differentiation and Community Assembly

Why do multiple related species coexist in one forest community? Several hypotheses have been put forward to explain this pattern. According to the competitive network theory, when the environment is highly heterogeneous and species are limited by multiple factors, the interaction network strengthens diversity and weakens dominance [68]. Species pool size can affect community composition, and an increasing size in the species pool increases the competition for available environmental niches [69]. The deterministic process during community succession, such as environmental filtering and inter-specific competition, may explain species dominance. During a process of community succession, the motivation of species dominance may have different mechanisms; it can be driven by both environmental filtration in early succession and inter-specific competition in late succession [70]. The so-called available resource hypothesis holds that resource availability controlled the dominance of a community [71]. For the case of the forest dynamic plot at Wuyishan, substantial functional differentiation among total species, among families or among dominant species revealed by the CSR strategy signature may explain that there is no prominent dominant species in this community to a certain extent (Table 1 and Table 2; Figure 2). Wuyishan includes a huge species pool across elevations or sites, and 1987 and 2212 species of seed plants were recorded on the south and north slopes, respectively. The high heterogeneity of habitat at Wuyishan is especially reflected in the abundant rainfall and complex terrain [55,72]. A spatial pattern characterized by strongly mingling (mixing) and slightly clustering implied that the evergreen broad-leaved forest at Wuyishan may have reached the middle and late stage of succession [72]. We believe that for the evergreen, broad-leaved forest at Wuyishan, high resource availability, high habitat heterogeneity and large species pool provide an important background to promote diversity and weaken dominance, while the diversity of functional traits is the main factor leading to the lack of dominant species.
CS (competitive stress tolerator) is characterized by both competition and stress tolerance and is found in undisturbed but moderately stressed habitats (e.g., open forests) [73]. At the local scale, the average CSR strategy of a plant community can be used to provide a functional summary of the vegetation and allow comparisons between environments [11]. Most of the 126 plants in this study were woody plants, all of which were intermediate species of secondary or tertiary strategy, and both CS and S/CS were the most common strategies, while the average strategy of the sample site was CS (Figure 1), which is consistent with the results of Pierce’s study [59]. This indicates that plants in this site are mostly affected by more than one environmental factor; competition and stress are the greatest stressors in the study site, and plants suffer some degree of stress but are hardly affected by anthropogenic stemming. Ref. [16], in their study of CSR response allocation in woody vascular plants, found that competitive plants with high light-trapping capacity and high water content and stress-tolerant plants were adapted to highly stressful habitats. In the present study, there may be a large number of taller plants with a large canopy area, which subjects the plants in the forest to a certain degree of light stress, and further increases the competitiveness by increasing the leaf area. At the same time, there are many rocky ridges and ravines in the sample site, and the plants in their vicinity may lack the needed nutrients and show some stress tolerance. The results of the RDA analysis also indicate that the areas where CS are clustered are correlated with stable environments (Figure 4), and that the CS are adapted to warm and humid environmental conditions.

5. Conclusions

In this study, by calculating the CSR strategies of evergreen, broadleaf forest tree species in Mt. Wuyi, we identified a total of nine distinct ecological strategies. The presence of multiple strategies indicates significant interspecific variation in functional traits, reflecting different adaptive responses to the environment among the tree species. Although the community as a whole exhibited a convergence towards the C strategy, the niche differentiation driven by environmental filtering is likely to promote subtropical plant coexistence. The observed differences in the CSR strategies across families and life forms further suggest that CSR classification effectively explains species assemblage and diversity in this community.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15081412/s1, Figure S1: Illustrates the geographic location of the Wuyishan Forest Dynamics Plot (WYS). Figure S2: Assessing multicollinearity among climate and topography variables; Table S1: Functional trait, strategy, and life forms of 126 species in the sample plots; Table S2: Information about the six sites of leaf trait sampling in China.

Author Contributions

Conceptualization, Y.P. and Y.F.; methodology, H.L.; software, H.L.; validation, N.W., T.L. and X.Z.; formal analysis, Y.P.; investigation, Y.P., X.Z. and H.L.; resources, H.D.; data curation, Y.P. and G.C.; writing—original draft preparation, G.C. and Y.P.; writing—review and editing, Y.F.; project administration, X.Z., Y.F. and H.D.; funding acquisition, Y.F. and H.D. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by The Construction and Operation Project of State Environmental Protection Scientific Observation and Research Station for Ecological Environment of Wuyi Mountains (ZX2023QT033).

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

We thank Lu Pan and Xiaoxu Guo for their help in the sampling process.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Adler, P.B.; Salguero-Gómez, R.; Compagnoni, A.; Hsu, J.S.; Ray-Mukherjee, J.; Mbeau-Ache, C.; Franco, M. Functional Traits Explain Variation in Plant Life History Strategies. Proc. Natl. Acad. Sci. USA 2014, 111, 740–745. [Google Scholar] [CrossRef] [PubMed]
  2. MacArthur, R.H.; Wilson, E.O. The Theory of Island Biogeography; Princeton University Press: Princeton, NJ, USA, 2001; ISBN 978-0-691-08836-5. [Google Scholar]
  3. Gadgil, M.; Solbrig, O.T. The Concept of R- and K-Selection: Evidence from Wild Flowers and Some Theoretical Considerations. Am. Nat. 1972, 106, 14–31. [Google Scholar] [CrossRef]
  4. Parry, G.D. The Meanings of R- and K-Selection. Oecologia 1981, 48, 260–264. [Google Scholar] [CrossRef] [PubMed]
  5. Pianka, E.R. On R- and K-Selection. Am. Nat. 1970, 104, 592–597. [Google Scholar] [CrossRef]
  6. Stearns, S.C. The Evolution of Life History Traits: A Critique of the Theory and a Review of the Data. Annu. Rev. Ecol. Syst. 1977, 8, 145–171. [Google Scholar] [CrossRef]
  7. Engen, S.; Sæther, B.-E. Optimal Age of Maturity in Fluctuating Environments under R- and K-Selection. Oikos 2016, 125, 1577–1585. [Google Scholar] [CrossRef]
  8. Oizumi, R.; Kuniya, T.; Enatsu, Y. Reconsideration of r/K Selection Theory Using Stochastic Control Theory and Nonlinear Structured Population Models. PLoS ONE 2016, 11, e0157715. [Google Scholar] [CrossRef]
  9. Grime, J.P. Primary Strategies in Plants. Trans. Bot. Soc. Edinb. 1979, 43, 151–160. [Google Scholar] [CrossRef]
  10. Grime, J.P. Evidence for the Existence of Three Primary Strategies in Plants and Its Relevance to Ecological and Evolutionary Theory. Am. Nat. 1977, 111, 1169–1194. [Google Scholar] [CrossRef]
  11. Caccianiga, M.; Luzzaro, A.; Pierce, S.; Ceriani, R.M.; Cerabolini, B. The Functional Basis of a Primary Succession Resolved by CSR Classification. Oikos 2006, 112, 10–20. [Google Scholar] [CrossRef]
  12. Hills, J.M.; Murphy, K.J.; Pulford, I.D.; Flowers, T.H. A Method for Classifying European Riverine Wetland Ecosystems Using Functional Vegetation Groups. Funct. Ecol. 1994, 8, 242–252. [Google Scholar] [CrossRef]
  13. Hodgson, J.G.; Wilson, P.J.; Hunt, R.; Grime, J.P.; Thompson, K. Allocating C-S-R Plant Functional Types: A Soft Approach to a Hard Problem. Oikos 1999, 85, 282–294. [Google Scholar] [CrossRef]
  14. Li, Y.; Shipley, B. An Experimental Test of CSR Theory Using a Globally Calibrated Ordination Method. PLoS ONE 2017, 12, e0175404. [Google Scholar] [CrossRef]
  15. Novakovskiy, A.B.; Maslova, S.P.; Dalke, I.V.; Dubrovskiy, Y.A. Patterns of Allocation CSR Plant Functional Types in Northern Europe. Int. J. Ecol. 2016, 2016, e1323614. [Google Scholar] [CrossRef]
  16. Pierce, S.; Negreiros, D.; Cerabolini, B.E.L.; Kattge, J.; Díaz, S.; Kleyer, M.; Shipley, B.; Wright, S.J.; Soudzilovskaia, N.A.; Onipchenko, V.G.; et al. A Global Method for Calculating Plant CSR Ecological Strategies Applied across Biomes World-Wide. Funct. Ecol. 2017, 31, 444–457. [Google Scholar] [CrossRef]
  17. Schmidt, J.; Fassnacht, F.E.; Lausch, A.; Schmidtlein, S. Assessing the Functional Signature of Heathland Landscapes via Hyperspectral Remote Sensing. Ecol. Indic. 2017, 73, 505–512. [Google Scholar] [CrossRef]
  18. Schweiger, A.K.; Schütz, M.; Risch, A.C.; Kneubühler, M.; Haller, R.; Schaepman, M.E. How to Predict Plant Functional Types Using Imaging Spectroscopy: Linking Vegetation Community Traits, Plant Functional Types and Spectral Response. Methods Ecol. Evol. 2017, 8, 86–95. [Google Scholar] [CrossRef]
  19. Kattenborn, T.; Fassnacht, F.E.; Schmidtlein, S. Differentiating Plant Functional Types Using Reflectance: Which Traits Make the Difference? Remote Sens. Ecol. Conserv. 2019, 5, 5–19. [Google Scholar] [CrossRef]
  20. Kattenborn, T.; Fassnacht, F.E.; Pierce, S.; Lopatin, J.; Grime, J.P.; Schmidtlein, S. Linking Plant Strategies and Plant Traits Derived by Radiative Transfer Modelling. J. Veg. Sci. 2017, 28, 717–727. [Google Scholar] [CrossRef]
  21. Rosado, B.H.P.; de Mattos, E.A. On the Relative Importance of CSR Ecological Strategies and Integrative Traits to Explain Species Dominance at Local Scales. Funct. Ecol. 2017, 31, 1969–1974. [Google Scholar] [CrossRef]
  22. Cho, Y.; Lee, D.; Bae, S. Effects of Vegetation Structure and Human Impact on Understory Honey Plant Richness: Implications for Pollinator Visitation. J. Ecol. Environ. 2017, 41, 2. [Google Scholar] [CrossRef]
  23. Saenz-Pedroza, I.; Feldman, R.; Reyes-García, C.; Meave, J.A.; Calvo-Irabien, L.M.; May-Pat, F.; Dupuy, J.M. Seasonal and Successional Dynamics of Size-Dependent Plant Demographic Rates in a Tropical Dry Forest. PeerJ 2020, 8, e9636. [Google Scholar] [CrossRef]
  24. Cerabolini, B.E.L.; Brusa, G.; Ceriani, R.M.; De Andreis, R.; Luzzaro, A.; Pierce, S. Can CSR Classification Be Generally Applied Outside Britain? Plant Ecol. 2010, 210, 253–261. [Google Scholar] [CrossRef]
  25. Barba-Escoto, L.; Ponce-Mendoza, A.; García-Romero, A.; Calvillo-Medina, R.P. Plant Community Strategies Responses to Recent Eruptions of Popocatépetl Volcano, Mexico. J. Veg. Sci. 2019, 30, 375–385. [Google Scholar] [CrossRef]
  26. Lewis, R.J.; Pakeman, R.J.; Angus, S.; Marrs, R.H. Using Compositional and Functional Indicators for Biodiversity Conservation Monitoring of Semi-Natural Grasslands in Scotland. Biol. Conserv. 2014, 175, 82–93. [Google Scholar] [CrossRef]
  27. Negreiros, D.; Le Stradic, S.; Fernandes, G.W.; Rennó, H.C. CSR Analysis of Plant Functional Types in Highly Diverse Tropical Grasslands of Harsh Environments. Plant Ecol. 2014, 215, 379–388. [Google Scholar] [CrossRef]
  28. Smith, B.M.; Diaz, A.; Winder, L. Grassland Habitat Restoration: Lessons Learnt from Long Term Monitoring of Swanworth Quarry, UK, 1997–2014. PeerJ 2017, 5, e3942. [Google Scholar] [CrossRef]
  29. Stevens, C.J.; Thompson, K.; Grime, J.P.; Long, C.J.; Gowing, D.J.G. Contribution of Acidification and Eutrophication to Declines in Species Richness of Calcifuge Grasslands along a Gradient of Atmospheric Nitrogen Deposition. Funct. Ecol. 2010, 24, 478–484. [Google Scholar] [CrossRef]
  30. Teuber, L.M.; Hölzel, N.; Fraser, L.H. Livestock Grazing in Intermountain Depressional Wetlands—Effects on Plant Strategies, Soil Characteristics and Biomass. Agric. Ecosyst. Environ. 2013, 175, 21–28. [Google Scholar] [CrossRef]
  31. Lee, J.-S.; Son, D.-H.; Lee, S.-H.; Myeong, H.-H.; Cho, J.-S.; Lee, J.-C.; Lee, J.-Y.; Park, C.-S.; Kim, J.-W. Canonical Correspondence Analysis Ordinations and Competitor, Stress Tolerator, and Ruderal Strategies of Coastal Dune Plants in South Korea. J. Coast. Res. 2020, 36, 528–535. [Google Scholar] [CrossRef]
  32. Wollny, J.T.; Bergmann, W.; Otte, A.; Harvolk-Schöning, S. River Regulation Intensity Matters: Riverbank Vegetation Is Characterized by More Typical Riverbank Plant Species with Increasing Distance from Weirs. Ecol. Eng. 2021, 159, 106082. [Google Scholar] [CrossRef]
  33. De Keersmaeker, L.; Vandekerkhove, K.; Verstraeten, A.; Baeten, L.; Verschelde, P.; Thomaes, A.; Hermy, M.; Verheyen, K. Clear-Felling Effects on Colonization Rates of Shade-Tolerant Forest Herbs into a Post-Agricultural Forest Adjacent to Ancient Forest. Appl. Veg. Sci. 2011, 14, 75–83. [Google Scholar] [CrossRef]
  34. Verstraeten, G.; Baeten, L.; Van den Broeck, T.; De Frenne, P.; Demey, A.; Tack, W.; Muys, B.; Verheyen, K. Temporal Changes in Forest Plant Communities at Different Site Types. Appl. Veg. Sci. 2013, 16, 237–247. [Google Scholar] [CrossRef]
  35. Ciccarelli, D. Mediterranean Coastal Dune Vegetation: Are Disturbance and Stress the Key Selective Forces That Drive the Psammophilous Succession? Estuar. Coast. Shelf Sci. 2015, 165, 247–253. [Google Scholar] [CrossRef]
  36. da Costa, H.d.J.A.; Gurgel, E.S.C.; do Amaral, D.D.; Vasconcelos, L.V.; Rebelo, L.G.B.; Teodoro, G.S. CSR Ecological Strategies, Functional Traits and Trade-Offs of Woody Species in Amazon Sandplain Forest. Flora 2020, 273, 151710. [Google Scholar] [CrossRef]
  37. Rosenfield, M.F.; Müller, S.C.; Overbeck, G.E. Short Gradient, but Distinct Plant Strategies: The CSR Scheme Applied to Subtropical Forests. J. Veg. Sci. 2019, 30, 984–993. [Google Scholar] [CrossRef]
  38. Munoz, F.; Violle, C.; Cheptou, P.-O. CSR Ecological Strategies and Plant Mating Systems: Outcrossing Increases with Competitiveness but Stress-Tolerance Is Related to Mixed Mating. Oikos 2016, 125, 1296–1303. [Google Scholar] [CrossRef]
  39. Good, M.; Morgan, J.W.; Venn, S.; Green, P. Timing of Snowmelt Affects Species Composition via Plant Strategy Filtering. Basic Appl. Ecol. 2019, 35, 54–62. [Google Scholar] [CrossRef]
  40. Eler, K.; Kermavnar, J.; Marinšek, A.; Kutnar, L. Short-Term Changes in Plant Functional Traits and Understory Functional Diversity after Logging of Different Intensities: A Temperate Fir-Beech Forest Experiment. Ann. For. Res. 2018, 50, 223–241. [Google Scholar] [CrossRef]
  41. Darling, E.S.; Alvarez-Filip, L.; Oliver, T.A.; McClanahan, T.R.; Côté, I.M. Evaluating Life-History Strategies of Reef Corals from Species Traits. Ecol. Lett. 2012, 15, 1378–1386. [Google Scholar] [CrossRef]
  42. Takou, M.; Wieters, B.; Kopriva, S.; Coupland, G.; Linstädter, A.; De Meaux, J. Linking Genes with Ecological Strategies in Arabidopsis Thaliana. J. Exp. Bot. 2019, 70, 1141–1151. [Google Scholar] [CrossRef] [PubMed]
  43. May, R.-L.; Warner, S.; Wingler, A. Classification of Intra-Specific Variation in Plant Functional Strategies Reveals Adaptation to Climate. Ann. Bot. 2017, 119, 1343–1352. [Google Scholar] [CrossRef] [PubMed]
  44. Pavlovic, R.; Panseri, S.; Giupponi, L.; Leoni, V.; Citti, C.; Cattaneo, C.; Cavaletto, M.; Giorgi, A. Phytochemical and Ecological Analysis of Two Varieties of Hemp (Cannabis Sativa L.) Grown in a Mountain Environment of Italian Alps. Front. Plant Sci. 2019, 10, 1265. [Google Scholar] [CrossRef] [PubMed]
  45. Liao, H.; Li, D.; Zhou, T.; Huang, B.; Zhang, H.; Chen, B.; Peng, S. The Role of Functional Strategies in Global Plant Distribution. Ecography 2021, 44, 493–503. [Google Scholar] [CrossRef]
  46. Davison, J.; García de León, D.; Zobel, M.; Moora, M.; Bueno, C.G.; Barceló, M.; Gerz, M.; León, D.; Meng, Y.; Pillar, V.D.; et al. Plant Functional Groups Associate with Distinct Arbuscular Mycorrhizal Fungal Communities. New Phytol. 2020, 226, 1117–1128. [Google Scholar] [CrossRef] [PubMed]
  47. Guo, W.-Y.; van Kleunen, M.; Winter, M.; Weigelt, P.; Stein, A.; Pierce, S.; Pergl, J.; Moser, D.; Maurel, N.; Lenzner, B.; et al. The Role of Adaptive Strategies in Plant Naturalization. Ecol. Lett. 2018, 21, 1380–1389. [Google Scholar] [CrossRef] [PubMed]
  48. Dalle Fratte, M.; Bolpagni, R.; Brusa, G.; Caccianiga, M.; Pierce, S.; Zanzottera, M.; Cerabolini, B.E.L. Alien Plant Species Invade by Occupying Similar Functional Spaces to Native Species. Flora 2019, 257, 151419. [Google Scholar] [CrossRef]
  49. Chagnon, P.-L.; Bradley, R.L.; Maherali, H.; Klironomos, J.N. A Trait-Based Framework to Understand Life History of Mycorrhizal Fungi. Trends Plant Sci. 2013, 18, 484–491. [Google Scholar] [CrossRef]
  50. Blumenthal, D.; Mitchell, C.E.; Pyšek, P.; Jarošík, V. Synergy between Pathogen Release and Resource Availability in Plant Invasion. Proc. Natl. Acad. Sci. USA 2009, 106, 7899–7904. [Google Scholar] [CrossRef]
  51. Craine, J.M. Reconciling Plant Strategy Theories of Grime and Tilman. J. Ecol. 2005, 93, 1041–1052. [Google Scholar] [CrossRef]
  52. Herben, T.; Klimešová, J.; Chytrý, M. Philip Grime’s Fourth Corner: Are There Plant Species Adapted to High Disturbance and Low Productivity? Oikos 2018, 127, 1125–1131. [Google Scholar] [CrossRef]
  53. Wilson, J.B.; Lee, W.G. C-S-R Triangle Theory: Community-Level Predictions, Tests, Evaluation of Criticisms, and Relation to Other Theories. Oikos 2000, 91, 77–96. [Google Scholar] [CrossRef]
  54. Xie, L.; Feng, Y.; Zhao, R.; Lv, T.; Wang, N.; Li, Y.; Zheng, X.; Chen, S.; Ding, H.; Fang, Y. Positive Relationships between Species Diversity and Genetic Diversity on a Local Scale at Mt. Wu Yi, China. Biodivers. Conserv. 2023, 32, 4295–4311. [Google Scholar] [CrossRef]
  55. Ding, H.; Fang, Y.; Yang, Q.; Chen, X.; Yuan, F.; Xu, H.; He, L.; Yan, J.; Chen, T.; Yu, C.; et al. Community Characteristics of a Mid-Subtropical Evergreen Broad-Leaved Forest Plot in the Wuyi Mountains, Fujian Province, Southeastern China. Biodivers. Sci. 2016, 23, 479–492. [Google Scholar] [CrossRef]
  56. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Cavender-Bares, J.; Chapin, T.; Cornelissen, J.H.C.; Diemer, M.; et al. The Worldwide Leaf Economics Spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef] [PubMed]
  57. Wang, H.; Harrison, S.P.; Prentice, I.C.; Yang, Y.; Bai, F.; Togashi, H.F.; Wang, M.; Zhou, S.; Ni, J. The China Plant Trait Database: Toward a Comprehensive Regional Compilation of Functional Traits for Land Plants. Ecology 2018, 99, 500. [Google Scholar] [CrossRef] [PubMed]
  58. Kilinç, M.; Karavin, N.; Kutbay, H. Classification of Some Plant Species According to Grime’s Strategies in a Quercus Cerris L. Var. Cerris Woodland in Samsun, Northern Turkey. Turk. J. Bot. 2010, 34, 521–529. [Google Scholar] [CrossRef]
  59. Pierce, S.; Brusa, G.; Vagge, I.; Cerabolini, B.E.L. Allocating CSR Plant Functional Types: The Use of Leaf Economics and Size Traits to Classify Woody and Herbaceous Vascular Plants. Funct. Ecol. 2013, 27, 1002–1010. [Google Scholar] [CrossRef]
  60. Hodgson, J.G.; Montserrat Marti, G.; Šerá, B.; Jones, G.; Bogaard, A.; Charles, M.; Font, X.; Ater, M.; Taleb, A.; Santini, B.A.; et al. Seed Size, Number and Strategies in Annual Plants: A Comparative Functional Analysis and Synthesis. Ann. Bot. 2020, 126, 1109–1128. [Google Scholar] [CrossRef]
  61. de Paula Oliveira, R.; Zotz, G.; Wanek, W.; Franco, A.C. Leaf Trait Co-Variation and Trade-Offs in Gallery Forest C3 and CAM Epiphytes. Biotropica 2021, 53, 520–535. [Google Scholar] [CrossRef]
  62. Yang, Y.; Wang, H.; Harrison, S.P.; Prentice, I.C.; Wright, I.J.; Peng, C.; Lin, G. Quantifying Leaf-Trait Covariation and Its Controls across Climates and Biomes. New Phytol. 2019, 221, 155–168. [Google Scholar] [CrossRef] [PubMed]
  63. Candeias, M.; Fraterrigo, J. Trait Coordination and Environmental Filters Shape Functional Trait Distributions of Forest Understory Herbs. Ecol. Evol. 2020, 10, 14098–14112. [Google Scholar] [CrossRef] [PubMed]
  64. Rosas, T.; Mencuccini, M.; Barba, J.; Cochard, H.; Saura-Mas, S.; Martínez-Vilalta, J. Adjustments and Coordination of Hydraulic, Leaf and Stem Traits along a Water Availability Gradient. New Phytol. 2019, 223, 632–646. [Google Scholar] [CrossRef] [PubMed]
  65. Shen, Y.; Umaña, M.N.; Li, W.; Fang, M.; Chen, Y.; Lu, H.; Yu, S. Coordination of Leaf, Stem and Root Traits in Determining Seedling Mortality in a Subtropical Forest. For. Ecol. Manag. 2019, 446, 285–292. [Google Scholar] [CrossRef]
  66. Onoda, Y.; Wright, I.J.; Evans, J.R.; Hikosaka, K.; Kitajima, K.; Niinemets, Ü.; Poorter, H.; Tosens, T.; Westoby, M. Physiological and Structural Tradeoffs Underlying the Leaf Economics Spectrum. New Phytol. 2017, 214, 1447–1463. [Google Scholar] [CrossRef] [PubMed]
  67. Marquet, P.A.; Allen, A.P.; Brown, J.H.; Dunne, J.A.; Enquist, B.J.; Gillooly, J.F.; Gowaty, P.A.; Green, J.L.; Harte, J.; Hubbell, S.P.; et al. On Theory in Ecology. BioScience 2014, 64, 701–710. [Google Scholar] [CrossRef]
  68. Allesina, S.; Levine, J.M. A Competitive Network Theory of Species Diversity. Proc. Natl. Acad. Sci. USA 2011, 108, 5638–5642. [Google Scholar] [CrossRef] [PubMed]
  69. Karger, D.N.; Tuomisto, H.; Amoroso, V.B.; Darnaedi, D.; Hidayat, A.; Abrahamczyk, S.; Kluge, J.; Lehnert, M.; Kessler, M. The Importance of Species Pool Size for Community Composition. Ecography 2015, 38, 1243–1253. [Google Scholar] [CrossRef]
  70. Lohbeck, M.; Poorter, L.; Martínez-Ramos, M.; Rodriguez-Velázquez, J.; van Breugel, M.; Bongers, F. Changing Drivers of Species Dominance during Tropical Forest Succession. Funct. Ecol. 2014, 28, 1052–1058. [Google Scholar] [CrossRef]
  71. He, X.; Lin, Y.; Han, G.; Tian, X. What Determines the Number of Dominant Species in Forests? J. For. Res. 2010, 21, 287–292. [Google Scholar] [CrossRef]
  72. Chen, T.; Xu, F.; Yang, Q.; Chen, S.; Ge, X.; Wu, J.; Cui, P.; Fang, Y.; Ding, H. Spatial Distribution Characteristics of an Evergreen Broad-Leaved Forest in the Wuyi Mountains, Fujian Province, Southeastern China. J. Ecol. 2018, 38, 1817–1825. [Google Scholar]
  73. Grime, J.P. Vegetation Classification by Reference to Strategies. Nature 1974, 250, 26–31. [Google Scholar] [CrossRef]
Forests 15 01412 g001
Figure 1. Relative proportion (%) of C, S and R selection for 126 angiosperm species with tree, shrub, liana and undershrub life forms from the Wuyishan forest dynamic plot in Fujian Province, China, using the CSR analysis tool “StrateFy” (CSR GVP v1.0).
Figure 1. Relative proportion (%) of C, S and R selection for 126 angiosperm species with tree, shrub, liana and undershrub life forms from the Wuyishan forest dynamic plot in Fujian Province, China, using the CSR analysis tool “StrateFy” (CSR GVP v1.0).
Forests 15 01412 g001
Forests 15 01412 g002
Figure 2. Relative proportion (%) of nine CSR strategy types in 126 angiosperm species with tree, shrub, liana and undershrub life forms from the Wuyishan forest dynamic plot in Fujian Province, China. C, S and R refer to competitor, stress-tolerator and ruderal, respectively. The nine secondary CSR strategy classes (competitor strategy CS, C/CS, C/CSR; stress-tolerator strategy S/CS, S/SR, S/CSR; intermediate type CSR, CS/CSR, SR/CSR) are named according to [15].
Figure 2. Relative proportion (%) of nine CSR strategy types in 126 angiosperm species with tree, shrub, liana and undershrub life forms from the Wuyishan forest dynamic plot in Fujian Province, China. C, S and R refer to competitor, stress-tolerator and ruderal, respectively. The nine secondary CSR strategy classes (competitor strategy CS, C/CS, C/CSR; stress-tolerator strategy S/CS, S/SR, S/CSR; intermediate type CSR, CS/CSR, SR/CSR) are named according to [15].
Forests 15 01412 g002
Forests 15 01412 g003
Figure 3. A principal components analysis (PCA) showing the two main axes of variability in leaf traits among 126 angiosperm species with tree, shrub, liana and undershrub life forms from the Wuyishan forest dynamics plot.
Figure 3. A principal components analysis (PCA) showing the two main axes of variability in leaf traits among 126 angiosperm species with tree, shrub, liana and undershrub life forms from the Wuyishan forest dynamics plot.
Forests 15 01412 g003
Forests 15 01412 g004
Figure 4. Redundancy analysis (RDA) ordination plot for multivariate effects of geographical and climatic variables on leaf traits. Bio 1: Annual mean temperature (°C), Bio 2: Mean diurnal temperature range, Bio 8: Mean temperature of wettest quarter (°C), Bio 10: Mean temperature of warmest quarter (°C), Bio 12: Annual precipitation (mm).
Figure 4. Redundancy analysis (RDA) ordination plot for multivariate effects of geographical and climatic variables on leaf traits. Bio 1: Annual mean temperature (°C), Bio 2: Mean diurnal temperature range, Bio 8: Mean temperature of wettest quarter (°C), Bio 10: Mean temperature of warmest quarter (°C), Bio 12: Annual precipitation (mm).
Forests 15 01412 g004
Table 1. Importance value (IV), relative proportion of C, S and R selection, and secondary CSR strategy classes of 10 dominant species from the Wuyishan forest dynamic plot in Fujian Province, China.
Table 1. Importance value (IV), relative proportion of C, S and R selection, and secondary CSR strategy classes of 10 dominant species from the Wuyishan forest dynamic plot in Fujian Province, China.
SpeciesIV%C%S%R%CSR Strategy Class
Castanopsis carlesii7.21187111S/CS
Castanopsis fordii5.2842553CS
Castanopsis eyrei4.5819810S/CS
Engelhardia roxburghiana4.4330637S/CS
Syzygium buxifolium3.69207011S/CS
Schima superba3.2037595CS
Rhododendron henryi2.9634642S/CS
Itea oblonga2.9041536CS
Eurya muricata2.79226117S/CSR
Castanopsis faberi2.7934642S/CS
Table 2. Relative proportion of C, S and R selection, and secondary CSR strategy classes of eight dominant families from the Wuyishan forest dynamic plot. “-” indicates that this family is absent in the CSR strategy classes.
Table 2. Relative proportion of C, S and R selection, and secondary CSR strategy classes of eight dominant families from the Wuyishan forest dynamic plot. “-” indicates that this family is absent in the CSR strategy classes.
FamiliesC%S%R%CSR-Score WidthCSR Strategy Classes
CSS/CSS/CSRC/CSCSRS/SR
Fagaceae18–5050–810–11broad√√√---
Lauraceae19–4341–740–32broad√√--√-
Aquifoliaceae10–6040–700–33broad√√√--√
Magnoliaceae31–6422–590–26broad√√-√√-
Ericaceae17–3550–708–26median√√√---
Symplocaceae21–5050–630–17median√√----
Theaceae19–3856–690–17narrow√√√---
Elaeocarpaceae24–3456–664–12narrow√√----
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Peng, Y.; Cui, G.; Li, H.; Wang, N.; Zheng, X.; Ding, H.; Lv, T.; Fang, Y. Can CSR Strategy Classes Determined by StrateFy Explain the Species Dominance and Diversity of a Forest Community? Forests 2024, 15, 1412. https://doi.org/10.3390/f15081412

AMA Style

Peng Y, Cui G, Li H, Wang N, Zheng X, Ding H, Lv T, Fang Y. Can CSR Strategy Classes Determined by StrateFy Explain the Species Dominance and Diversity of a Forest Community? Forests. 2024; 15(8):1412. https://doi.org/10.3390/f15081412

Chicago/Turabian Style

Peng, Ye, Gansha Cui, Hengyi Li, Ningjie Wang, Xiao Zheng, Hui Ding, Ting Lv, and Yanming Fang. 2024. "Can CSR Strategy Classes Determined by StrateFy Explain the Species Dominance and Diversity of a Forest Community?" Forests 15, no. 8: 1412. https://doi.org/10.3390/f15081412

APA Style

Peng, Y., Cui, G., Li, H., Wang, N., Zheng, X., Ding, H., Lv, T., & Fang, Y. (2024). Can CSR Strategy Classes Determined by StrateFy Explain the Species Dominance and Diversity of a Forest Community? Forests, 15(8), 1412. https://doi.org/10.3390/f15081412

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop