Modelling the relationships and feedbacks between spatial patterns of anecic earthworm populations, related preferential flow pathways and agrochemical transport and degradation in rural catchments

蚯蚓通过调节土壤结构在农业生态系统功能中发挥关键作用，显著影响土壤水力特性、有机质动态和植物生长。这个项目的重点是蚯蚓，如蚓地，它们创造了垂直的半永久性洞穴，作为优先流动通道。大孔中的优先流动是一个关键过程，它强烈影响渗透，并可能导致农药快速运输到80至150厘米的深度，在那里它们经历了更慢的降解。因此，优先运输是一个环境问题，因为表土被绕过，而表土最初被认为是保护底土和浅层地下水的过滤器。评估杀虫剂在蚯蚓洞中的环境风险以及人类管理如何对这种风险进行反馈，需要开发一个综合生态水文模型。该模型可以预测i)蚯蚓的时空分布和种群动态，ii)连接优先流动途径（即蚯蚓洞穴）的相关模式，以及iii)溶质渗透和移动深度分布的时空模式。这使我们能够理解小规模模式如何调节农村景观中的大规模过程，以及蚯蚓工程和运输特征之间的反馈如何影响（农业）生态系统的功能。我们期望我们的最终模型适用于流域规模的风险评估，这可能有助于农用化学品的登记。

Earthworms play a pivotal role in agro-ecosystem functioning by modulating soil structure that significantly influences soil hydraulic properties, organic matter dynamics, and plant growth. This project focuses on anecic earthworms like Lumbricus terrestris which create vertical semi-permanent burrows that function as preferential flow pathways. Preferential flow in macropores is a key process which strongly affects infiltration and may cause rapid transport of pesticides into depths of 80 to 150 cm where they experience a much slower degradation. Therefore, preferential transport is an environmental problem because the topsoil is bypassed, which has been originally thought to act as a filter to protect the subsoil and shallow groundwater. Assessing the environmental risk of pesticides in earthworm burrows and how human management practise feedbacks on that risk requires the development of an integrated eco-hydrological model. This model allows predictions of i) the spatiotemporal distribution and population dynamics of anecic earthworms, ii) the related pattern of connective preferential flow pathways (i.e., earthworm burrows), and iii) the space-time pattern of infiltration and travel depth distribution of solutes. This enables the understanding of how small-scale patterns regulate large-scale processes in rural landscapes and how feedbacks between earthworm engineering and transport characteristics affect the functioning of (agro-)ecosystems. We expect our final model to be applicable for catchment-scale risk assessment that may assist agrochemical registration.