A novel approach to disentangle climate change and arable land management change as drivers of weed flora shifts - Combining species distribution modelling and dynamic population modelling

耕地杂草分布受环境因素和作物管理的影响。气候变化和强化耕地管理是近年来杂草区系变化的重要驱动因素。预计农业适应气候变化措施也会产生间接影响。虽然这两种驱动因素已经得到了深入的研究，但它们的相互作用和综合效应到目前为止几乎没有得到解决。一个因素可能增加或减少另一个因素的影响，相互作用可能具有空间或时间模式。分析的一个主要障碍是，两者对不同时间和空间尺度的驱动因素起作用，因此需要用不同的技术来分析它们的影响。例如，物种分布模型（SDM）研究预测了未来气候下东北方向的范围变化。SDM可以在一定程度上适应耕地环境，因为它经常受到作物措施的强烈干扰。然而，由于SDM具有相对粗糙的分辨率，它们不能纳入作物管理变化的局部规模效应，例如更简化的作物轮作。因此，该项目提出了一种新的、结合SDM和动态作物：杂草模型的方法，以分析气候变化和土地管理变化对可耕地杂草区系变化的相对贡献。据推测，在气候变化的影响下，不同的种植制度（包括不同的轮作和不同的作物管理）将通过减少向单一物种高丰度的更均匀的杂草植物群组成的转变，促进杂草管理。该项目将重点放在梅克伦堡-前波莫瑞，一个典型的中欧温带可耕地地区，但该方法将可转移到其他地区。将研究2030-2060年和2070-2100年期间的变化。该项目在三个部分解决了一些研究问题和假设。首先，针对对估计未来区域分布和物种库具有重要意义或预期具有重要意义的杂草物种建立SDM。第二部分使用了FlorSys，这是一个非常详细的作物：杂草模型，用于多物种杂草关联的种群动态。该模式是否适用于德国北部的条件将受到考验。然后，在不同的土地管理和气候变化情景下，模拟了SDM导致的物种库的发展。随后，将两种建模方法的结果结合起来进行排序、聚类和回归分析，以研究每种驱动因素及其相互作用对杂草群落的影响。除了深入了解气候变化、土地管理变化和杂草区系之间的关系外，该方法还将在适当的区域尺度上为农民提供有关未来可能出现的杂草问题及其原因的信息。因此，它有助于制定气候变化适应战略。

Arable weed distributions are influenced by environmental factors and crop management. Climate change and intensified arable land management have been important drivers of recent shifts in weed flora. Indirect impacts are also expected from on-farm adaptation measures to climate change. Although both drivers have been intensively studied, their interaction and combined effects have until now hardly been addressed. One factor may increase or decrease the impact of the other and the interactions may have spatial or temporal patterns. A major obstacle for analysis is that both act on drivers differing temporal and spatial scales, so their impacts need to be analysed with different techniques. Species distribution modelling (SDM) studies have for example predicted range shifts under future climate with a North Eastern direction. SDM can be adapted to some extent to the arable context with its regular, strong disturbances by crop measures. However, because SDM have relatively coarse resolutions they cannot incorporate local scale effects of changes in crop management such as a more simplified crop rotation.This project therefore proposes a novel, combined approach of SDM and dynamic crop:weed modelling to analyse the relative contribution of climate change and land management change on arable weed flora changes. It is hypothesised that diverse cropping systems (with diverse rotations and diverse crop management) would facilitate weed management under the impact of climate change by reducing shifts to a more homogenous weed flora composition with high abundance of single species. The project will focus on Mecklenburg-Vorpommern, a typical temperate arable region of Central Europe, but the approach will be transferable to other regions. Changes will be investigated for the time periods 2030-2060 and 2070-2100.The project addresses a number of research questions and hypotheses in three parts. First, SDM are built for weed species that are important or are expected to become important to estimate future regional distributions and species pools. The second part FlorSys is used, a very detailed crop:weed model for population dynamics of multi-species weed associations. The transferability of the model to the conditions of Northern Germany will be tested. Then the development of the species pools resulting from SDM is modelled in different scenarios of land management and climate change. Subsequently, the results of both modelling approaches are combined in ordination, clustering and regression analyses to investigate the impact of each driver and their interactions on weed floras.Besides the insight into the relation between climate change, land management change and weed floras, the approach will provide farmers with information on possible future weed problems and their causes on an appropriate, regional scale. It therefore contributes to the preparation of adaptation strategies for climate change.