DOI: 10.5061/dryad.w3r2280wv
title: Data from: Interspecific association and environmental interpretation of dominant species in shrub layer of Pinus massoniana community on Chinese islands

description: Understanding the factors driving species coexistence and competition in
 the shrub layer of semi-natural forests is crucial for effective forest
 management and conservation. However, there is limited knowledge
 about the interspecific associations of the main species in the shrub
 layer of Pinus massoniana communities in the
 semi-natural forest of Sandu Gulf, Ningde, Fujian Province, China.
 Therefore, this study aimed to investigate the influence of the abiotic
 environment on plant communities within the semi-natural forest of P.
 massoniana on the islands of Sandu Gulf. By exploring these
 interspecific associations, we sought to provide a more accurate
 understanding of the causes and processes of species coexistence and
 competition. The ultimate goal of this project was to offer a reference
 basis for optimizing the shrub layer structure in P.
 massoniana (plantation) forests. We found that (1)
 Heptapleurum heptaphyllum was the most dominant species in the
 shrub layer, while Smilax china demonstrated the broadest range of
 environmental adaptability and correspondingly broader niche than other
 species. (2) Our analysis revealed a predominance of positive associations
 among the dominant species in the shrub layer. However, the overall
 association was not significant, with relatively small positive and
 negative associations between species pairs. The significant test rate was
 low, and the NRI exhibited a non-significant
 aggregation. These findings suggest that the plant community in the shrub
 layer has not yet reached its most stable stage. (3) We also observed that
 the distribution of dominant species in the shrub layer was primarily
 affected by factors such as total potassium, pH, available potassium, and
 light (canopy density). (4) Soil pH value decreased gradually as
 sampling points moved inward away from the coastline, and island
 isolation, temperature, and precipitation indirectly affected the
 species' importance in the shrub layer. Considering the
 intense competition among the understory species, it is crucial for
 conservation efforts to prioritize species pairs with reduced ecological
 niche overlap or significant positive associations. This approach will
 effectively reduce competition and contribute to the maintenance of
 stability in the shrub layer. This study provides a theoretical
 basis for environmental and vegetation restoration, optimizing the species
 composition of island plantation forests, rationalizing plant composition,
 and implementing effective operation and management practices for local P.
 massoniana forests.
description: Primarily, we wanted to determine the dominant position of each
 species in the shrub layer. For this, the average importance value
 (<em>IV</em>%) of all species in each of the 40 sample plots
 was used to screen the dominant species in the shrub layer. The
 calculation formula used is as follows (Chen et al., 2021):
 <em>IV</em>% = (relative abundance + relative
 frequency + relative coverage) / 3. Relative
 abundance = (the abundance of a certain species in the quadrat / the sum
 of the abundance of all species) × 100% Relative
 frequency = (frequency of a certain species in the sample / total
 frequency of all species) × 100% Relative
 coverage = (the coverage of a certain species in the sample/the total
 coverage of all species) × 100% Niche and interspecific
 association analyses were conducted for dominant species with
 <em>IV</em>% &gt;1 in the shrub layer, using each site (or
 each environmental gradient) and each species importance value as
 different resource levels and species resource utilization states,
 respectively (Chen et al., 2021; Zhang et al., 2018; Zheng et al., 2015).
 Niche width was calculated using the Shannon index
 (<em>B</em><sub>S</sub>), and niche overlap was
 determined using the Pianka niche overlap index
 (<em>O</em><em><sub>ik</sub></em>)
 (Liu et al., 2020).  We used the species, genus and
 family lists of dominant species and the “V. PhyloMaker 2” package of
 R 4. 4. 1 to draw the phylogenetic tree, and the dominant species
 multiplicity and the “picante” package to calculate the net relatedness
 index (<em>NRI</em>) (Jin and Qian, 2023). This indexe
 represent the standardized effect sizes of the mean phylogenetic distance
 (<em>MPD</em>) (Webb et al., 2002). The
 overall association was determined using variance ratio method
 (<em>VR</em>) (Schluter, 1984) and test its significance by
 statistic <em>W</em>. Because this study
 was based on non-continuous sampling, the interspecific association was
 corrected by Yates continuous correction formula
 <em>c</em><sup>2</sup> statistics for qualitative
 study (Zhang, 2011). Before the
 <em>c</em><sup>2</sup> test, data conversion was
 performed according to the selected dominant species, 0 means the species
 does not exist in the sample, and a value of 1 means the species exists.
 Conversion data are included in a table of 2 × 2 columns, and the values
 of <em>a</em>, <em>b</em>, <em>c</em>,
 and <em>d</em> were calculated. The
 interspecific correlation
 <em>c</em><sup>2</sup> test only qualitatively
 describes whether a relationship between species are significant, whereas
 Pearson’s correlation coefficient test and Spearman’s rank correlation
 coefficient tests reflect clearly reflect the linear relationship between
 species (Jian et al., 2009). The 2 correlation coefficient tests can
 effectively complement and refine the correlation of other unrelated
 species pairs, the strength of connection of individual species pairs, and
 the differences in interspecific associations that were not accurately
 detected by the <em>c</em><sup>2</sup> test,
 through quantitative data. Therefore, in this study, quantitative
 correlations among dominant species were analyzed using the abundance of
 dominant species in the shrub layer as a quantitative indicator for
 Pearson's correlation coefficient test and Spearman's rank
 correlation coefficient test (Xu et al., 2016). To
 better explain the environmental factors that lead to species coexistence
 and competition. We used the Canoco 5.0 software to conduct detrending
 correspondence analysis (DCA) on the relative importance values of
 dominant species. Afterword, we used a forward selection method of
 redundancy analysis (RDA). And Monte Carlo test (499 cycles) were used to
 screen the environmental factors with significant impact
 (<em>P</em> &lt; 0.05), then studied the impact of
 environmental factors on species distribution (Xiao et
 al, 2023b). We used regression analysis to further
 explore the effects of environmental factors on species distributions and
 the correlations between environmental factors. We chose the following 11
 equations to fit for the regression analysis: linear function, quadratic
 function, cubic function, composite function
 <em>y</em>=<em>ab</em><em><sup>x</sup></em>, power function <em>y</em>=<em>ax</em><em><sup>b</sup></em>, S-curve <em>y</em>=e<em><sup>a</sup></em><sup>+</sup><em><sup>b</sup></em><sup>/</sup><em><sup>x</sup></em>, growth function <em>y</em>=e<em><sup>a</sup></em><sup>+</sup><em><sup>bx</sup></em>, exponential function <em>y</em>=<em>a</em>e<em><sup>bx</sup></em>, logarithmic function <em>y</em>=<em>a</em>+<em>b</em>ln(<em>x</em>), inverse function <em>y</em>=<em>a</em>+<em>b</em>/<em>x</em>, and logistic function <em>y</em>=1/(1/<em>c</em>+<em>ab</em><em><sup>x</sup></em>) (a, b, and c are parameters to be estimated). For regression analysis, we used box plots, Probability-Probability Plot (P-P Plots), and Quantile-Quantile Plot (Q-Q Plot) to test the data for anomalies, and then the outliers are either replaced (by averaging all the y-values corresponding to the same level of x-values) or eliminated before proceeding with the regression analysis (Rahm and Hong, 2000). In order to explore whether there are environmental differences between islands, we used principal component analysis (PCA) (Greenacre et al., 2022; Ringnér, 2008) to rank the soil factors of the 40 sample plots and used the PC (principal components) axis values with the island isolation factor (the distance to the nearest island and the distance to the mainland) and climate factor to make regression analysis. If the explanation rate of the first three main eigenvectors is higher than 40%, it indicates that the ranking result is acceptable (Gauch, 1989).
description: SPSS，R，Canoco，Origin，Excel were used for analysis.
description: # Data from: Interspecific association and environmental interpretation of
 dominant species in shrub layer of Pinus massoniana community on Chinese
 islands
 [https://doi.org/10.5061/dryad.w3r2280wv](https://doi.org/10.5061/dryad.w3r2280wv) ## Description of the data and file structure The dataset contains multiple CSV files. There are a total of seven files, which are: * Degree_Table.csv, - 24.11.26_Importance_values_for_all_shrub_species.csv, * Soil_Info.csv, - Species_importance_values_at_available_potassium_gradients.csv, * Importance_values_of_dominant_species.csv, - Species_importance_values_at_pH_gradients.csv, and * Species_importance_values_at_total_potassium_gradients.csv. Among them, Degree_Table.csv is primarily used for phylogenetic analysis. The files 24.11.26_Importance_values_for_all_shrub_species.csv and Importance_values_of_dominant_species.csv are the most critical data, especially the latter, which is used for analyzing niche width, redundancy analysis (RDA), regression analysis, path analysis, and almost all analyses rely on it. For specific details, please refer to the methods section of the article. Soil_Info.csv contains soil information and is used for redundancy analysis, path analysis, and regression analysis. The files Species_importance_values_at_available_potassium_gradients.csv, Species_importance_values_at_pH_gradients.csv, and Species_importance_values_at_total_potassium_gradients.csv are primarily used to analyze niche width. Abbreviations：*Hh：Heptapleurum heptaphyllum，Lr: Litsea rotundifolia *var. *oblongifolia, Ss: Symplocos sumuntia, Rt: Rhodomyrtus tomentosa, Mp1: Mussaenda pubescens, Sc: Smilax china, Gj: Gardenia jasminoides, Am: Adinandra millettii, Pa1: Psychotria asiatica, Mm: Melastoma malabathricum, Sh: Syzygium hancei, Lc: Loropetalum chinense, Ip: Ilex pubescens, Mp2: Melicope pteleifolia, Ia: Ilex asprella, Ts: Toxicodendron succedaneum, Pa2: Pseudosasa amabilis, En: Eurya nitida, Sl: Smilax lanceifolia, Rc: Rubus corchorifolius, Sg: Smilax glabra, It: Ilex triflora, As: Alyxia sinensis. *S: Sandu Island; Q: Qingshan Island; C: Changyao Island; J: Jigongshan Island; B: Baipao Island; D: Doumao Island. A: Adret; SA: Semi-adret; SU: Semi-ubac; U: Ubac. K: Total potassium; A-K: Available potassium; T-P: Total phosphorus; A-P: Available phosphorus; T-N: Total nitrogen; A-N: Alkali-hydrolyzed nitrogen;