Using weighted expert judgement and nonlinear data analysis to improve Bayesian belief network models for riverine ecosystem services.
Using weighted expert judgement and nonlinear data analysis to improve Bayesian belief network models for riverine ecosystem services.
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DOI：
10.1016/j.scitotenv.2022.158065
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Marcin Rafał Penk;M. Bruen;C. Feld;J. Piggott;M. Christie;C. Bullock;M. Kelly-Quinn
中科院分区：
文献类型：
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作者：
Marcin Rafał Penk;M. Bruen;C. Feld;J. Piggott;M. Christie;C. Bullock;M. Kelly-Quinn
河流是水文循环的一个关键部分，也是水资源的重要通道，但受到人类压力的威胁越来越大。将压力与生态系统服务联系起来是一项挑战，因为通常使用不同的方法来捕捉物理化学、生物和社会经济要素之间的相互联系过程。我们的目标是，首先，推进现有的贝叶斯信念网络（BBN）模型的原理证明，生态系统服务的考虑纳入河流管理。我们因果联系流域压力与生态系统服务使用加权证据从专家研讨会（捕获专家组之间的信心），立法和出版的文献。通过对国家监测数据（包括非线性关系和具有生态意义的断点）的分析和专家判断，对生物基本平衡进行了校准。我们使用了一种新的期望指数的愿望量化模型的输出。其次，我们应用BBN的三个案例研究集水区在爱尔兰，以证明在不同的环境中的生态系统服务的压力水平的变化的影响。数据分析中的七个重要关系中有四个是非线性的，这突出表明非线性在生态系统中很常见，但在环境建模中很少考虑。河岸遮阳不足被认为是一种普遍而强烈的影响，应加以解决，以提高广泛的社会效益，特别是在集水区的河岸遮阳稀缺。泥沙负荷对河流生物的影响较小，在闪光的河流，它有较小的潜力解决。沉积物与有机物和磷酸盐协同作用，这些压力是活跃的;同时处理这些压力对可以产生额外的社会效益相比，其单独的影响，这突出了综合管理的价值。我们的BBN模型可以参数化为其他爱尔兰集水区，而我们的方法的元素，包括预期的可取性指数，可以在全球范围内适应。
Rivers are a key part of the hydrological cycle and a vital conduit of water resources, but are under increasing threat from anthropogenic pressures. Linking pressures with ecosystem services is challenging because the processes interconnecting the physico-chemical, biological and socio-economic elements are usually captured using heterogenous methods. Our objectives were, firstly, to advance an existing proof-of-principle Bayesian belief network (BBN) model for integration of ecosystem services considerations into river management. We causally linked catchment stressors with ecosystem services using weighted evidence from an expert workshop (capturing confidence among expert groups), legislation and published literature. The BBN was calibrated with analyses of national monitoring data (including non-linear relationships and ecologically meaningful breakpoints) and expert judgement. We used a novel expected index of desirability to quantify the model outputs. Secondly, we applied the BBN to three case study catchments in Ireland to demonstrate the implications of changes in stressor levels for ecosystem services in different settings. Four out of the seven significant relationships in data analyses were non-linear, highlighting that non-linearity is common in ecosystems, but rarely considered in environmental modelling. Deficiency of riparian shading was identified as a prevalent and strong influence, which should be addressed to improve a broad range of societal benefits, particularly in the catchments where riparian shading is scarce. Sediment load had a lower influence on river biology in flashy rivers where it has less potential to settle out. Sediment interacted synergistically with organic matter and phosphate where these stressors were active; tackling these stressor pairs simultaneously can yield additional societal benefits compared to the sum of their individual influences, which highlights the value of integrated management. Our BBN model can be parametrised for other Irish catchments whereas elements of our approach, including the expected index of desirability, can be adapted globally.