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.