Effects of nitrogen inputs on freshwater wetland ecosystem services - a Bayesian network analysis

An introduction to Bayesian networks as assessment and decision support tools for managing coral reef ecosystem services

Quantifying ecosystem goods and services can help evaluate policies aimed at protecting present and future generations from losing ecosystem benefits. Explicating and quantifying the relationships among risk factors, ecological structure and function, and delivery of ecosystem goods and services requires analytical methodologies that propagate uncertainties. The capabilities of Bayesian networks in generating predictions and accounting for uncertainty are explored with a focus on coral reef ecosystem service assessments. The qualitative aspects of Bayesian networks can be applied to conceptual frameworks developed for coral reef ecosystem service assessments. This is demonstrated using qualitative graphs that describe the relationships between coral reef condition endpoints and benefits from ecosystem services including property protection, recreational opportunities, fish for fisheries, and biochemical metabolites for commercial products developed from reef organisms. Bayesian networks help weigh uncertainties between management decision impacts on stressors and the corresponding delivery of ecosystem services. Quantitative capabilities for inferences are examined in hypothetical scenarios evaluating how decisions affect coral reef ecosystem services and economic benefits and resilience to episodic stress. The described methods facilitate visualizing the potential impacts on ecosystem services from alternative scenarios.

Bayesian network for point and diffuse source phosphorus transfer from dairy pastures in South otago, new zealand

Many factors affect the magnitude of nutrient losses from dairy farm systems. Bayesian Networks (BNs) are an alternative to conventional modeling that can evaluate complex multifactor problems using forward and backward reasoning. A BN of annual total phosphorus (TP) exports was developed for a hypothetical dairy farm in the south Otago region of New Zealand and was used to investigate and integrate the effects of different management options under contrasting rainfall and drainage regimes. Published literature was consulted to quantify the relationships that underpin the BN, with preference given to data and relationships derived from the Otago region. In its default state, the BN estimated loads of 0.34 ± 0.42 kg TP ha for overland flow and 0.30 ± 0.19 kg TP ha for subsurface flow, which are in line with reported TP losses in overland flow (0-1.1 kg TP ha) and in drainage (0.15-2.2 kg TP ha). Site attributes that cannot be managed, like annual rainfall and the average slope of the farm, were found to affect the loads of TP lost from dairy farms. The greatest loads (13.4 kg TP ha) were predicted to occur with above-average annual rainfall (970 mm), where irrigation of farm dairy effluent was managed poorly, and where Olsen P concentrations were above pasture requirements (60 mg kg). Most of this loading was attributed to contributions from overland flow. This study demonstrates the value of using a BN to understand the complex interactions between site variables affecting P loss and their relative importance.