Introduction
Microbial organisms inhabit all biomes of the Earth (Koskella, 2020), and provide a number of life-support functions for their host (Cordovez et al., 2019). Therefore, we must develop a better understanding of the distribution and ecological drivers of aboveground and belowground microbial communities. Recent studies have demonstrated the immense role of plant compartment and environmental factors in driving the assembly of microbiomes (Lan et al., 2023; Wei et al., 2022a; Xu et al., 2024). Geographic location and seasonal change have been demonstrated to influence community composition. For example, it was suggested that the assembly of the phyllosphere bacterial and fungal communities is predominantly determined by host compartment (epiphytic and endophytic) and site location (Wei et al., 2022a). As for soil and rhizosphere, microbiomes are influenced by environmental factors (e.g., site, soil properties, and climate) (Grady et al., 2019; Lundberg et al., 2012; Thiergart et al., 2020; Wei et al., 2022b; Xiong et al., 2021; Xu et al., 2024). However, these studies mainly focused on single niche or compartment, and a significant knowledge gap exists on how spatial heterogeneity versus time shape the diversity and structure of microbial communities along the soil–plant continuum. Different scales have varying impact on plant microorganisms (Jumpponen and Jones, 2009; Laforest-Lapointe et al., 2016; Qian et al., 2018; Wang et al., 2023). Moreover, these examples have shown that soil microbial communities are influenced by spatial or temporal change, but, understanding of how seasonal changes (e.g., dry and rainy seasons) affect the compositions and diversity of soil–plant continuum microbial communities at the regional scale is still limited.
Rubber plantation is the most economically important agro-ecosystem in tropical China, particularly in Hainan Island and Xishuanbanna (abbreviated as Banna below) (Lan et al., 2017), where accounting for more than 90% of the total rubber plantation area of China (Xu et al., 2014). It is reported rubber plantation have multiplied quickly throughout Southeast Asia over the last two decades (Chen et al., 2023; Li et al., 2015). As far as we know, rubber forests account for almost 25% and 40% of the total vegetation area in Hainan Island and Banna (Lan et al., 2020), respectively. Previous work in Hainan has shown that seasonal change or site location were the dominant factors resulting in shifts in soil microbial composition at the local and geographic scales, respectively (Lan et al., 2018; Lan et al., 2020; Lan et al., 2019; Wei et al., 2022b). However, these studies were limited, particularly in the sample sizes scales used. Given the importance of microbes in functional roles in tropical forests ecosystem, such as nutrient acquisition, disease resistance, and stress tolerance (Trivedi et al., 2020), and the central part of rubber plantation of terrestrial ecosystems both in Hainan and Banna. Moreover, little attention has been paid to the plant-associated microbial communities in the same scale, as a result, it is difficult to describe the overall pattern of the soil–plant continuum. Therefore, it is necessary to study the spatiotemporal pattern and ecological drivers of the rubber tree soil–plant continuum microbial communities of these two locations.
In this study, we examine fungal communities across multiple compartments (bulk soils, rhizosphere, rhizoplane, root endosphere, phylloplane, and leaf endosphere) based on field samples of rubber tree during both dry and rainy seasons in two major areas of China ( Figure S1). We aimed to (1) determine the distribution pattern of fungal communities along the soil–plant continuum of rubber tree; (2) identify the relative importance of spatial heterogeneity versus season and dominant drivers for driving fungal community at the regional scale. We hypothesized that (1) both geographic and seasonal factors will influence the assembly of rubber-associated fungal communities; (2) the spatiotemporal distribution pattern is mediated by the spatiotemporal variation of the driving factors.