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The approach of applying stressor load limits or thresholds to aid estuarine management is being explored in many global case studies. However, there is growing concern regarding the influence of multiple stressors and their cumulative effects on the functioning of estuarine ecosystems due to the considerable uncertainty around stressor interactions. Recognising that empirical data limitations hinder parameterisation of detailed models of estuarine ecosystem responses to multiple stressors (suspended sediment, sediment mud and metal content, and nitrogen inputs), an expert driven Bayesian network (BN) was developed and validated. Overall, trends in estuarine condition predicted by the BN model were well supported by field observations, including results that were markedly higher than random (71-84% concordance), providing confidence in the overall model dynamics. The general BN framework was then applied to a case study estuary to demonstrate the model's utility for informing management decisions. Results indicated that reductions in suspended sediment loading were likely to result in improvements in estuarine condition, which was further improved by reductions in sediment mud and metal content, with an increased likelihood of high abundance of ecological communities relative to baseline conditions. Notably, reductions in suspended sediment were also associated with an increased probability of high nuisance macroalgae and phytoplankton if nutrient loading was not also reduced (associated with increased water column light penetration). Our results highlight that if stressor limit setting is to be implemented, limits must incorporate ecosystem responses to cumulative stressors, consider the present and desired future condition of the estuary of interest, and account for the likelihood of unexpected ecological outcomes regardless of whether the experts (or empirical data) suggest a threshold has or has not been triggered., (Copyright © 2021. Published by Elsevier Ltd.)

Coastal nutrient pollution is an ever-present threat to estuaries worldwide. Benthic denitrification has been identified as a crucial ecosystem service to help mitigate increasing N loads to the coast. However, the controls on denitrification in low-nutrient systems are not well constrained and are likely different to those in more widely studied eutrophic systems. This study aims to identify the specific controls on denitrification in low-nutrient estuaries, including the contribution of the macrofaunal community to denitrification rates, and to understand how this important service fits into the network of ecogeochemical processes in these systems. Results show that porewater ammonium concentrations and mud content are good predictors of net N2 flux in the dark. Additionally, models predict N2 flux rates much more effectively in the dark than in the light, but the macrofaunal community data, specifically species richness, is a key factor in both increasing the explanatory power of both models by nearly 20%. Additionally, interaction networks reveal that increasing mud content results in a shift in the macrofaunal community and a reduction in the N removal capacity of these intertidal systems. [ABSTRACT FROM AUTHOR]