A Bayesian network to simulate macroinvertebrate responses to multiple stressors in lowland streams

Aquatic ecosystems are affected by multiple environmental stressors across spatial and temporal scales. Yet the nature of stressor interactions stressor-response relationships is still poorly understood. This hampers selection appropriate restoration measures. Hence, there a need to understand how respond unravel combined effects individual on ecological status waterbodies. Models may be used relate responses changes as well measures thus provide valuable tools for water management. Therefore, we aimed develop test Bayesian Network (BN) simulating stream macroinvertebrates stressors. Although predictive performance further improved, developed model was shown suitable scenario analyses. For selected lowland streams, an increase in macroinvertebrate-based quality (EQR) predicted scenarios where streams were relieved from single Especially combination increasing flow velocity enhancing cover coarse particulate organic matter showed significant EQR compared current conditions. The use BNs promising avenue analyses have capacity clear visual communication dependencies uncertainty associated with input data results allow types knowledge about stressor-effect relations. Still, make predictions more robust, deeper understanding required parametrize Also, sufficient training should available type interest. Yet, application now already help contribution effect bodies, which turn aid that lead desired improvements quality.