Patterns and drivers of plant species richness in Phragmites australis marshes in China

doi:10.17520/biods.2023194

Abstract

Abstract:

Aims Determining the distribution pattern and drivers of broad-scale species richness is significant for predicting the response to biodiversity and formulating conservation programs to reduce biodiversity loss. This paper discusses the distribution pattern and driving mechanism of plant species richness obtained from nationwide field survey of Phragmites australis marsh data, combined with climate, geography, soil, and other environmental factors.

Method Initially, we used correlation analysis and general linear model to determine the relationship between species richness and annual mean temperature (MAT), annual precipitation, minimum temperature of coldest month (MTCM), altitude, latitude, longitude, soil pH, soil organic carbon (SOC), soil total nitrogen (TN), and topographic wetness index (TWI). Then, we used hierarchical partitioning to determine the most important drivers of species richness utilizing the following nine variables: soil factors (soil pH, SOC, TN), climate factors (MAT, annual precipitation, MTCM), geography factors (latitude, altitude), and water regime. Finally, piecewise structural equation modeling was used to assess the direct and indirect effects of these nine variables on plant species richness.

Results (1) The overall richness of plant species in Chinese P. australis marshes was (a) higher in the subtropical humid zone, temperate humid and semi-humid zone, and (b) lower in the Tibetan Plateau region, temperate arid and semi-arid zone, and coastal region. (2) The richness of plant species in P. australis marshes was significantly positively correlated with annual precipitation, SOC, TN, and latitude, but negatively correlated with the MAT, MTCM, soil pH, and altitude. (3) Soil factors, especially soil pH, were the most important factors affecting the richness of plant species in P. australis marshes, followed by water regime, climate factors, and geography factors. (4) Piecewise structural equation modeling showed that soil factors, water regime, and climate factors directly affected the richness of plant species, while geography factors indirectly affected the species richness of plants by regulating soil factors, water regime, and climate factors.

Conclusion Species richness of plants in Chinese P. australis marshes is spatially heterogeneous between the different regions. The species richness distribution pattern is affected by a combination of factors, where soil factors are the key environmental factors affecting the species richness pattern of plants. This study provides a new understanding of the broad-scale distribution pattern of plant diversity and the pattern’s conservation in P. australis marshes.

Key words: species richness, Phragmites australis marshes, distribution pattern, soil factors

Jingci Meng, Guodong Wang, Guanglan Cao, Nanlin Hu, Meiling Zhao, Yantong Zhao, Zhenshan Xue, Bo Liu, Wenhua Piao, Ming Jiang. Patterns and drivers of plant species richness in Phragmites australis marshes in China[J]. Biodiv Sci, 2024, 32(2): 23194.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2023194

https://www.biodiversity-science.net/EN/Y2024/V32/I2/23194

Figures/Tables 5

Fig. 1 Distribution of plant species richness in Phragmites australis marshes in China (a) and the differences in plant species richness in P. australis marshes between different distribution areas (b) (mean ± SE). One-way ANOVA was used to analyze the differences in plant species richness in different distribution areas, different letters indicate significant difference (P < 0.05).

Fig. 2 Correlation analysis of plant species richness and environmental factors in Phragmites australis marshes in China. * P < 0.05; ** P < 0.01; *** P < 0.001.

Fig. 3 Linear relationships between plant species richness and environmental factors in Phragmites australis marshes in China

Fig. 4 Hierarchical partitioning analysis between species richness and environmental factors

Fig. 5 Piecewise structural equation modeling accounting for the direct and indirect effects of geography factors, climate factors, soil factors, and water regime on the species richness of Phragmites australis marshes. Dividing the geography, climate, and soil factors into composite variables, with the numbers adjacent to measured variables being their coefficients with composite variables, and the numbers adjacent being arrows are path coefficients, which is the relationship between the magnitude of the direct normalization effect. * P < 0.05; ** P < 0.01; *** P < 0.001. R2 marginal is explained by fixed effects, and R2 condition is explained by both fixed effects and random effects.

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