S2.2 Bayesian Interface Techniques

27 Sep 2018
15:00 - 16:15
Lecture Hall 4

S2.2 Bayesian Interface Techniques

S2.2 _ Why Bayesian? Integrating environmental modelling with Bayesian inference techniques. (Session Chairs: George Arhonditsis, Alex Neumann and Yuko Shimoda)

The credibility of the scientific methodology of environmental models and their adequacy to form the basis of public policy decisions have been frequently challenged. The current challenges make compelling the development of more realistic modeling platforms (i) to elucidate causal mechanisms, complex interrelationships, direct and indirect ecological paths; (ii) to examine the interactions among the various stressors (e.g., climate change, urbanization/land‐use changes, alternative management practices, invasion of exotic organisms); and (iii) to assess their potential consequences on ecosystem functioning. The proposed session aims to provide insights into the current state of the field, and also highlight the major challenges and future directions of research. Special emphasis will be placed on studies that address topics, such as novel uncertainty analysis techniques, Bayesian inference methods (including Bayesian networks), development of new model formulations and proper representation of biotic functional types, emerging techniques of data assimilation and model optimization, effective integration of physics with biology, and strategies to improve the contribution of complex models to ecological theories. The proposed session encourages contributions from both mathematical and statistical ecosystem modelers.

Keywords: bayesian inference, uncertainty analysis, environmental management, policy analysis

Talk 01-05

Predicting Ecological Responses to Climate Variability with a Dynamic Bayesian Network Model
Neda Trifonova, Mandy Karnauskas, and Chris Kelble

Uncertainty assessment of scenarios on climate and land use changes for the Millbrook catchment – reservoir system simulated by the model ensemble SWAT-SALMO
Hanh Hong Nguyen, Friedrich Recknagel, and Wayne Meyer

What is a Prior and How to Find One
Song Qian

Uncertainty Analysis by Bayesian Inference
George Arhonditsis

Beyond Allopatric Speciation: Testing for Genetic Homogeneity in Duttaphrynus melanostictus in Relation to Human-induced Dispersal
Siti N. Othman, Yi-Huey Chen, Desiree Andersen, Ming-Feng Chuang, Yikweon Jang and Amaël Borzée