Reviewing the use of Bayesian and life cycle assessment of inland artisanal fisheries in Lake Victoria, Kenya

This study is based on the need to enhance management decision making by encouraging the use of participatory research methodologies in situations where data is missing, uncertain or primarily based on expert judgment with the added advantage of having scenario predictive outcomes which can be of use in the current situations involving climate change and invasive species as well as provide direction towards the enhancement of ecosystem restoration after disturbance based on informal settlement and industrial growth among others. Using the probabilistic Bayesian neural network (BNN) and deterministic life cycle impact analysis (LCIA) concept we evaluate use and provide insights for future value use. LCIA handles fixed values while BNN represents parameters as probabilistic distributions providing a more realistic assessment of environmental risks. This review evaluation aims to quantify the use of varied ecosystem goods and services with the aim of prioritizing policies based on sustainable development of ecosystem benefits by evaluating, assessing and quantifying previous work done while attempting to promote and quantify relationships among risk factors, ecological structure and function and ecosystem goods and services all within the framework of supporting small-scale fisheries livelihoods in an area where resource use conflict management and resolution is of paramount importance.