Applying ecological model evaludation: Lessons learned with the forest dynamics model Samsara2
Ecological models are increasingly used as decision-making tools and their reliability is becoming a key issue. At the same time, the sophistication of techniques for model development and analysis has given rise to a relative compartmentalization of model building and evaluation tasks. Several guidelines invite ecological modelers to follow an organized sequence of development and analysis steps and have coined the term “evaludation” for this process. The objective of this paper is to assess the feasibility and the value of a structured evaludation process, based on the working example of the Samsara2 model, a spatially explicit individual-based forest dynamics model. We implemented the six steps of model design, process level calibration, qualitative evaluation, quantitative evaluation, global sensitivity analysis, and partial recalibration using approximate Bayesian computing. We then evaluated how the evaludation process revealed model strengths and weaknesses, specified the model's conditions of use, clarified how the model works, and provided insights into forest ecosystem functioning. Finally, the efficiency/cost ratio of the process and future improvements are discussed.
Summary: | Ecological models are increasingly used as decision-making tools and their reliability is becoming a key issue. At the same time, the sophistication of techniques for model development and analysis has given rise to a relative compartmentalization of model building and evaluation tasks. Several guidelines invite ecological modelers to follow an organized sequence of development and analysis steps and have coined the term “evaludation” for this process. The objective of this paper is to assess the feasibility and the value of a structured evaludation process, based on the working example of the Samsara2 model, a spatially explicit individual-based forest dynamics model. We implemented the six steps of model design, process level calibration, qualitative evaluation, quantitative evaluation, global sensitivity analysis, and partial recalibration using approximate Bayesian computing. We then evaluated how the evaludation process revealed model strengths and weaknesses, specified the model's conditions of use, clarified how the model works, and provided insights into forest ecosystem functioning. Finally, the efficiency/cost ratio of the process and future improvements are discussed. |
---|