Data from: Interspecific association and environmental interpretation of dominant species in shrub layer of Pinus massoniana community on Chinese islands
Data files
Nov 28, 2024 version files 40.28 KB
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24.11.26_Importance_values_for_all_shrub_species.csv
22.71 KB
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Degree_Table.csv
2.90 KB
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Importance_values_of_dominant_species.csv
4.12 KB
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README.md
2.41 KB
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Soil_Info.csv
2.21 KB
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Species_importance_values_at_avaiable_potassium_gradients.csv
2 KB
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Species_importance_values_at_pH_gradients.csv
2 KB
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Species_importance_values_at_total_potassium_gradients.csv
1.93 KB
Abstract
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.
README: 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
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;
Methods
Primarily, we wanted to determine the dominant position of each species in the shrub layer. For this, the average importance value (IV%) 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):
IV% = (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 IV% >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 (BS), and niche overlap was determined using the Pianka niche overlap index (Oik) (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 (NRI) (Jin and Qian, 2023). This indexe represent the standardized effect sizes of the mean phylogenetic distance (MPD) (Webb et al., 2002).
The overall association was determined using variance ratio method (VR) (Schluter, 1984) and test its significance by statistic W.
Because this study was based on non-continuous sampling, the interspecific association was corrected by Yates continuous correction formula c2 statistics for qualitative study (Zhang, 2011). Before the c2 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 a, b, c, and d were calculated.
The interspecific correlation c2 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 c2 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 (P < 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 y=abx, power function y=axb, S-curve y=ea+b/x, growth function y=ea+bx, exponential function y=aebx, logarithmic function y=a+bln(x), inverse function y=a+b/x, and logistic function y=1/(1/c+abx) (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).
Usage notes
SPSS,R,Canoco,Origin,Excel were used for analysis.