{"id":3279,"date":"2018-03-22T11:54:02","date_gmt":"2018-03-22T11:54:02","guid":{"rendered":"http:\/\/ar17.iiasa.ac.at\/?p=3279"},"modified":"2018-05-24T08:01:26","modified_gmt":"2018-05-24T07:01:26","slug":"dynamic-vegetation","status":"publish","type":"post","link":"https:\/\/ar17.iiasa.ac.at\/dynamic-vegetation\/","title":{"rendered":"Laying the foundation for a new generation of models"},"content":{"rendered":"
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Dynamic global vegetation models (DGVMs) were first conceived at IIASA twenty-five years ago and have since become indispensable for understanding the biosphere and estimating ecosystem services. However, while their versatility is increasing as new processes and variables continue to be added, their accuracy suffers from the accumulation of uncertainty, especially in the absence of overarching principles controlling their concerted behavior.<\/p>\n
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The crosscutting IIASA project Dynamic vegetation models: The next generation (DVM) aims to address this problem by developing the foundations of a new generation of models centered on a \u2018missing law\u2019 \u2013 adaptation and optimization principles rooted in natural selection. Even though this missing law constrains relationships between traits, and can therefore vastly reduce the number of uncertain parameters in ecosystem models [1], it has rarely been applied to DGVMs. The project is working towards operationalizing this concept through scientific discussion and research collaboration in an international working group of leading experts from multiple disciplines coordinated by IIASA.<\/p>\n