ABSTRACT

Interactions between stressors in freshwater ecosystems, including those associated with climate change and nutrient enrichment, are currently difficult to detect and manage. Our understanding of the forms and frequency of occurrence of such interactions is limited; assessments using field data have been constrained as a result of varying data forms and quality. To address this issue, we demonstrate a statistical approach capable of assessing multiple stressor interactions using contrasting data forms in 3 European catchments (Loch Leven Catchment, UK: assessment of phytoplankton response in a single lake with time series data; Pinios Catchment, Greece: macroinvertebrate response across multiple rivers using spatial data; and Lepsämänjoki Catchment, Finland: phytoplankton response across multiple rivers using spatiotemporal data). Statistical models were developed to predict the relative and interactive effects of climate change and nutrient enrichment sensitive indicators (stressors) on indicators of ecological quality (ecological responses) within the framework of linear mixed effects models. In all catchments, indicators of nutrient enrichment were identified as the primary stressor, with climate change-sensitive indicators causing secondary effects (Loch Leven: additive, total phosphorus [TP] × precipitation; Pinios: additive, nitrate × dissolved oxygen; Lepsämänjoki: synergistic, TP × summer water temperature), the intensity of which varied between catchments and along the nutrient stressor gradient. Simple stressor change scenarios were constructed for each catchment and used in combination with mechanistic models to explore potential management responses. This approach can be used to explore the need for multiple stressor management in freshwaters, helping practitioners navigate a complex world of environmental change.

摘要

淡水生态系统中压力源之间的相互作用，包括与气候变化和营养丰富相关的压力，目前难以检测和管理。我们对此类互动发生的形式和频率的理解是有限的；由于不同的数据形式和质量，使用实地数据的评估受到限制。为了解决这个问题，我们展示了一种统计方法，能够使用 3 个欧洲流域中的对比数据形式评估多种压力源相互作用（Loch Leven 流域，英国：使用时间序列数据评估单个湖泊中的浮游植物响应；希腊 Pinios 流域：大型无脊椎动物使用空间数据跨多条河流的响应；和芬兰 Lepsämänjoki 集水区：使用时空数据跨多条河流的浮游植物响应）。在线性混合效应模型的框架内，开发了统计模型来预测气候变化和养分富集敏感指标（压力源）对生态质量指标（生态响应）的相对和交互影响。在所有流域，养分富集指标被确定为主要压力源，气候变化敏感指标造成次要影响（Loch Leven：添加剂，总磷 [TP] × 降水；Pinios：添加剂，硝酸盐 × 溶解氧；Lepsämänjoki：协同效应, TP × 夏季水温), 其强度在集水区之间和沿养分压力梯度变化。为每个流域构建了简单的压力源变化情景，并与机械模型结合使用，以探索潜在的管理响应。