Simple, policy friendly, ecological interaction models from uncertain data and expert opinion

In the marine environment, humans exploit natural ecosystems for food and economic benefit. Challenging policy goals have been set to protect resources, species, communities and habitats, yet ecologists often have sparse data on interactions occurring in the system to assess policy outcomes. This paper presents a technique, loosely based on Bayesian Belief Networks, to create simple models which 1) predict whether individual species within a community will decline or increase in population size, 2) encapsulate uncertainty in the predictions in an intuitive manner and 3) require limited knowledge of the ecosystem and functional parameters required to model it. We develop our model for a UK rocky shore community, to utilise existing knowledge of species interactions for model validation purposes. However, we also test the role of expert opinion, without full scientific knowledge of species interactions, by asking non-UK based marine scientists to derive parameters for the model (non-UK scientists are not familiar with the exact communities being described and will need to extrapolate from existing knowledge in a similar manner to model a poorly studied system). We find these differ little from the parameters derived by ourselves and make little difference to the final model predictions. We also test our model against simple experimental manipulations, and find that the most important changes in community structure as a result of manipulations correspond well to the model predictions with both our, and non-UK expert parameterisation. The simplicity of the model, nature of the outputs, and the user-friendly interface makes it potentially suitable for policy, conservation and management work on multispecies interactions in a wide range of marine ecosystems

Climate adaptation for rural water and sanitation systems in the Solomon Islands: A community scale systems model for decision support

Delivering water and sanitation services are challenging in data poor rural settings in developing countries. In this paper we develop a Bayesian Belief Network model that supports decision making to increase the availability of safe drinking water in five flood-prone rural communities in the Solomon Islands. We collected quantitative household survey data and qualitative cultural and environmental knowledge through community focus group discussions. We combined these data to develop our model, which simulates the state of eight water sources and ten sanitation types and how they are affected by season and extreme events. We identify how climate and current practices can threaten the availability of drinking water for remote communities. Modelling of climate and intervention scenarios indicate that water security could be best enhanced through increased rainwater harvesting (assuming proper installation and maintenance). These findings highlight how a systems model can identify links between and improve understanding of water and sanitation, community behaviour, and the impacts of extreme events. The resultant BBN provides a tool for decision support to enhance opportunities for climate resilient water and sanitation service provision

Probabilistic evaluation of solar photovoltaic systems using Bayesian networks: a discounted cash flow assessment

This paper was published by John Wiley & Sons Ltd as Open Access with a CC BY 4.0 licence.Solar PV technology (PV) is now a key contributor worldwide in the transition towards low carbon electricity systems. To date, PV commonly receives subsidies in order to accelerate adoption rates by increasing investor returns. However, many aleatory and epistemic uncertainties exist with regards these potential returns. In order to manage these uncertainties, a probabilistic approach using Bayesian networks has been applied to the techno-economic analysis of domestic solar PV. Using the UK as a representative case study, empirical datasets from over 400 domestic PV systems, together with national domestic electricity usage datasets, have been used to generate and calibrate prior probability distributions for PV yield and domestic electricity consumption respectively for typical urban housing stock. Subsequently, conditional dependencies of PV self-use with regards PV generation and household electricity consumption have been simulated via stochastic modelling using high temporal resolution demand and PV generation data. A Bayesian network model is subsequently applied to deliver posterior probability distributions of key parameters as part of a discounted cash flow analysis. The results indicate the sensitivity of investment returns to specific parameters (including PV self-consumption, PV degradation rates and geographical location), and quantify inherent uncertainties when using economic indicators for the promotion of PV adoption. The results’ implications for potential rates of sector-specific adoption are discussed, and implications for policy makers globally are presented with regards energy policy imperatives, as well as fiscal imperatives of meeting investors’ requirements in terms of returns on investment in a post-subsidy context