Data from: Amount or pattern? Grassland responses to the heterogeneity and availability of two key resources

Patterns of resource availability and heterogeneity shape the composition,
productivity, and dynamics of plant assemblages in a wide variety of terrestrial ecosystems. Despite this, the responses of plant assemblages to simultaneous changes in the availability
and heterogeneity of more than a single resource are virtually unknown. To fill this gap, microcosms consisting of assemblages formed by Lolium perenne, Plantago lanceolata, Anthoxantum odoratum, Holcus lanatus, and Trifolium repens were grown in a factorial experiment with the following treatments: nutrient availability (NA), water availability (WA),
spatial nutrient heterogeneity (NH), and temporal water heterogeneity (WH). Assemblages exhibited precise root foraging patterns in response to nutrient heterogeneity, which were modified by NA and WA. A series of two- and three-way interactions involving the four factors evaluated determined biomass production, the belowground : aboveground biomass
ratio, the patterns of root biomass allocation with depth, and the relative contribution to aboveground biomass of Lolium and Anthoxanthum. In all cases, these interactions explained significant amounts of the variation found in the data. Our study demonstrates that
considering the interactions between resource availability and heterogeneity allows for a refinement of predictions that can detectably reduce the error associated with extrapolating from single factor analyses.

资源可获得性与异质性模式可塑造各类陆地生态系统中植物群落的组成、生产力及动态过程。尽管已有诸多研究证实了这一规律，但目前学界几乎尚未探明植物群落在多种资源的可获得性与异质性同步改变时的响应特征。为填补这一研究空白，本研究采用析因实验设计，以由多年生黑麦草（Lolium perenne）、长叶车前（Plantago lanceolata）、黄花茅（Anthoxantum odoratum）、绒毛茅（Holcus lanatus）以及白三叶（Trifolium repens）构建的植物群落微宇宙为实验材料，设置了4类处理因子：养分可获得性（nutrient availability, NA）、水分可获得性（water availability, WA）、空间养分异质性（spatial nutrient heterogeneity, NH）以及时间水分异质性（temporal water heterogeneity, WH）。实验群落展现出针对养分异质性的精准根系觅食策略，且该策略会受到养分可获得性与水分可获得性的调控。由上述4类因子构成的一系列二因子、三因子交互作用，共同决定了群落的生物量生产水平、地下-地上生物量比、根系生物量的深度分配模式，以及黑麦草与黄花茅对地上生物量的相对贡献占比。在所有观测场景中，这些交互作用均能解释实验数据中可观比例的变异量。本研究证实，纳入资源可获得性与异质性间的交互作用，可优化预测框架，从而显著降低单因子分析外推时所伴随的误差。