A study of anthropogenic and climatic disturbance of the New River Estuary using a Bayesian belief network

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

“…A high proportion of these studies have sought to understand climatic and/or non-climatic drivers and their impacts on water-related systems and to evaluate the performance of management options under these changing conditions. Effects of numerous climatic drivers have been considered, including sea-level rise on water supply management (e.g., [2]), and on water quality management (e.g., [49]); precipitation and temperature on groundwater management (e.g., [63,64,67]), on reservoir management (e.g., [135]), on water supply management (e.g., [3,132]), on water quality management (e.g., [27,43,45] and on nutrient management (e.g., [119,126]) and precipitation on water supply and demand management (e.g., [18,127,130,131]). Similarly, the non-climatic drivers that have been considered, have included effects of population growth on water supply and demand management (e.g., [128]), and on water quality management (e.g., [41]); crop production changes in irrigation system management (e.g., [96,102]); population growth and agricultural production on water supply and demand management (e.g., [128]); agricultural production on irrigation water management (e.g., [93][94][95]100]), on water supply management (e.g., [85]), and on groundwater management (e.g., [67]); changes in domestic use and in agricultural and industrial production on water supplies and demand management (e.g., [127]).…”

Integrating Computational Methods to Investigate the Macroecology of Microbiomes

“…Bayesian networks show several advantages that support their recent application in complex fields, such as: 1) network modularity, being able to integrate multiple ecosystem components (Chen and Pollino, 2012;Nojavan et al, 2014;Nojavan et al, 2017;Uusitalo, 2007), such as in management decisions field, where it is possible to integrate several sub-models as social, ecological and economic aspects (Chen and Pollino, 2012); 2) the capability of dealing with complex and nonlinear systems (Uusitalo, 2007;Aguilera et al, 2011;Phan et al, 2016;Beuzen et al, 2018); 3) possibility of incorporating expert knowledge (Uusitalo, 2007;Aguilera et al, 2011;Alameddine et al, 2011;Death et al, 2015;Phan et al, 2016), through blacklists (i.e., unrealistic relationships that are not allowed in the model) and whitelist (i.e., relationships already known in the literature); 4) being able to use a small number of samples (Uusitalo, 2007;Phan et al, 2016) 5) simplicity and little difficulty in interpreting outputs, even for non-modelers (Aguilera et al, 2011;Death et al, 2015); 6) being a rather "open" approach, different from other methods, which can be considered complicated "black-box" approaches (Chen and Pollino, 2012); 7) being able to handle high dimensional systems with the proper number of samples (Aguilera et al, 2011); 8) dealing with missing data through conditional probabilities or Bayes theorem (Uusitalo, 2007;Aguilera et al, 2011;Death et al, 2015), and finally 9) presenting less computational cost to analyze and compare different scenarios, such as climatic changes, by setting variables states in the model (Chen and Pollino, 2012;Death et al, 2015).…”

“…This can be bypassed by integrating models. The most critical drawback pointed in most studies is the discretization of continuous variables (Uusitalo, 2007;Aguilera et al, 2011;Nojavan A. et al, 2014;Death et al, 2015;Phan et al, 2016). The principal argument is that it causes an inevitable loss of information from data, linear relationships and consequently model performance (Uusitalo, 2007;Nojavan A. et al, 2017;Beuzen et al, 2018).…”

Integrated network models for predicting ecological thresholds: Microbial – carbon interactions in coastal marine systems

“…Although BNs are limited by the inability to model feedbacks that are important in aquatic ecosystem processes unless a computationally demanding dynamic network is developed, they have some benefits that in particular circumstances, such as data limited conditions, can outweigh this limitation (McDonald et al, 2015). A benefit of the BN approach is the ability to iteratively evolve based on the successive incorporation of available and new emerging knowledge of the investigated system into a scientifically informed framework that can be used to investigate probabilistic relationships between variables, make predictions and test scenarios (Lowe et al, 2014;Nojavan et al, 2014). Additionally, the fact that probabilistic dependencies between variables in BNs are explicitly shown supports the communication of the model across disciplines such as management and science, and microbiology and computer science (Fletcher et al, 2014;Levontin et al, 2011).…”