A Bayesian approach to ecosystem service trade-off analysis utilizing expert knowledge

Abstract

The concept of ecosystem services is gaining attention in the context of sustainable resource management. However, it is inherently difficult to account for tangible and intangible services in a combined model. The aim of this study is to extend the definition of ecosystem service trade-offs by using Bayesian Networks to capture the relationship between tangible and intangible ecosystem services. Tested is the potential of creating such a network based on existing literature and enhancement via expert elicitation. This study discusses the significance of expert elicitation to enhance the value of a Bayesian Network in data-restricted case studies, underlines the importance of inclusion of experts’ certainty, and demonstrates how multiple sources of knowledge can be combined into one model accounting for both tangible and intangible ecosystem services. Bayesian Networks appear to be a promising tool in this context, nevertheless, this approach is still in need of further refinement in structure and applicable guidelines for expert involvement and elicitation for a more unified methodology.

What is the relationship between ecosystem services and farmers’ livelihoods? Based on measuring the contribution of ecosystem services to farmers’ livelihoods

“…Previous research has found that traditional ES valuation is unable to combine tangible and intangible ESs (Fisher et al, 2009), and therefore is even less able to confirm the impact of ESs on farmers' livelihoods. With the development of machine learning, related researches have confirmed under the DPSIR framework, various qualitative and quantitative indicators of ESs and farmers' livelihoods may be coupled into one model by using BN, thus making the model more realistic and sufficiently permeable (van Dam et al, 2013;Barton et al, 2016;Höfer et al, 2020). The research framework presented in this study is generalizable to other regions as long as the driving forces, pressures, states, influences and response factors in the study area are identified.…”

An application of Bayesian Belief Networks to assess management scenarios for aquaculture in a complex tropical lake system in Indonesia

“…Increasing applications in environmental risk assessment is also documented [50][51][52]. Other applications as a tool for understanding PLOS ONE complex (socio-ecological) systems, habitat suitability, management evaluation, and modelling ES are well reported [23,25,[53][54][55][56][57]. Further, BBN are also increasingly applied in environmental studies to connect management actions and environmental impacts in complex environments using interdisciplinary knowledge (i.e., [57][58][59]).…”

“…Second, this paper contributes to the growing literature on the use of BBN to assess ecosystem service (ES) trade-offs; this paper specifically investigates trade-offs between two provisioning services: fish provided by aquaculture and a native species capture fishery. There are few BBN case studies that aim to simulate ES trade-offs especially in complex-spatial systems [23][24][25]. More importantly, this study rigorously applies a variable correlation table to build the direct acyclic diagram (DAG), which is rarely conducted by previous BBN studies [26].…”

A prescriptive framework for recommending decision attributes of infrastructure disaster recovery problems

“…To overcome this challenge, the model-based approach has been introduced to systematically select attributes that cover the concerns and values of the problem under consideration. Accordingly, a few studies provide model-driven attributes by formulating the selection process of attributes as a choice problem (Cinelli et al 2020;Höfer et al 2020;Otto et al 2018;Rossberg et al 2017;Dale et al 2015;. Regardless of the source of the alternative pool, these studies evaluate candidate attributes based on properties of the desired attribute and apply a systematic process to select the attribute set.…”