Bayesian inference for predicting ecological water quality under different climate change scenarios

Abstract

The aim of this paper is to assess the separate and interactive effects of eutrophication and climate variables on the sea water quality in Pärnu Bay (the Gulf of Riga, Baltic Sea) using multivariate statistical analyses and the Bayesian Belief Network (BBN) methodology. The assessment was based on the following biological quality elements: phytoplankton, submerged aquatic vegetation and benthic invertebrates. The multivariate statistical analyses suggest that zoobenthos communities are largely driven by weather conditions (i.e. climate variables), phytoplankton by nutrient loads while the dynamics of macrophyte communities was due to the combined effect of weather and nutrient loads. The BBN constructed for this study represents uncertainty in ecological water quality assessment. Probabilistic modeling shows that phytoplankton and zoobenthos are not sensitive to climate change impacts while phytobenthos would suffer from decrease in sea water salinity. Under climate change, therefore, phytobenthos is one of the key variables in determining the water quality in the study area.

Forest Ecosystem Services: An Analysis of Worldwide Research

“…All this can generate a wide range of large-scale and difficult impacts, which can be costly or irreversible for ecosystems and biodiversity [20,21]. This would reduce the ability to mitigate adverse effects and, consequently, reduce human wellbeing, particularly for the poorest populations of low-income countries and arid climate regions [14,22].…”

Classifying the Baltic Sea Shallow Water Habitats Using Image-Based and Spectral Library Methods

“…Eutrophication enhances the growth of opportunistic filamentous algae, which are known to out-compete slow-growing perennial macrophytes, such as Fucus vesiculosus [4]. Due to the direct relationships between submerged aquatic vegetation and water quality, trends in the changes of vegetation cover indicate the state of water in the coastal areas [5]. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats enables us to adequately estimate the status of coastal marine habitats, provide better evidence for environmental changes and describe processes that are behind the changes.…”

Bayesian inference-based environmental decision support systems for oil spill response strategy selection

“…Using expert knowledge to estimate rivers' carrying capacity for Atlantic salmon smolt (Uusitalo, 2007). Predicting the effects of eutrophication and climate change on sea water quality (Kotta et al, 2010). Assessing threats to public health arising from sanitary sewer overflows following rainstorms (Goulding et al, 2012).…”