An approximate Bayesian algorithm for training fuzzy cognitive map models of forest responses to deer control in a New Zealand adaptive management experiment

Keyphrases

• Model Selection 100%
• New Zealand 100%
• Deer 100%
• Adaptive Management 100%
• Approximate Bayesian Computation 100%
• Fuzzy Cognitive Maps 100%
• Bayesian Algorithm 100%
• Management Experiment 100%
• Forest Response 100%
• MAP Model 100%
• Management Decisions 66%
• Ecological Processes 33%
• Soil nutrients 33%
• Seedling Growth 33%
• Modeling Approach 33%
• Growth Rate 33%
• Soil Moisture 33%
• Forest Management 33%
• Tree Seedlings 33%
• Expert Knowledge 33%
• Management Paradigms 33%
• Light Availability 33%
• Forest Understory 33%
• Forest Ecosystem 33%
• Management Scale 33%
• Simulated Dataset 33%
• Reliable Model 33%
• Scaled Experiment 33%
• Uncertainty Levels 33%
• Situation Model 33%
• Model Inference 33%
• Structural Uncertainty 33%
• System Behavior 33%
• Models of Computation 33%
• Impact on Management 33%
• Deer Density 33%
• Seedling Biomass 33%
• Parameter Inference 33%
• Markov Chain Monte Carlo Algorithm 33%
• Plant Competition 33%

Earth and Planetary Sciences

• New Zealand 100%
• Adaptive Management 100%
• Soil Nutrient 50%
• Soil Moisture 50%
• Forest Management 50%
• Forest Ecosystem 50%
• Markov Chain Monte Carlo 50%
• Understory 50%
• Light Availability 50%

Agricultural and Biological Sciences

• Adaptive Management 100%
• Soil Nutrient 50%
• Soil Moisture 50%
• Understory 50%
• Forest Management 50%
• Forest Ecosystem 50%
• Seedling Growth 50%
• Markov Chain 50%
• Plant Competition 50%