Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 404624
Title Gaining local accuracy while not losing generality – extending the range of gap model applications
Author(s) Didion, M.P.; Kupferschmid, A.D.; Zingg, A.; Fahse, L.; Bugmann, H.
Source Canadian Journal of Forest Research 39 (2009)6. - ISSN 0045-5067 - p. 1092 - 1107.
Department(s) CE - Forest Ecosystems
Publication type Refereed Article in a scientific journal
Publication year 2009
Keyword(s) forest patch model - species composition - sensitivity-analysis - population biology - mountain forests - growth - stand - dynamics - simulation - succession
Abstract For the study of long-term processes in forests, gap models generally sacrifice accuracy (i.e., simulating system behavior in a quantitatively accurate manner) for generality (i.e., representing a broad range of systems' behaviors with the same model). We selected the gap model ForClim to evaluate whether the local accuracy of forest succession models can be increased based on a parsimonious modeling approach that avoids the additional complexity of a 3D crown model, thus keeping parameter requirements low. We improved the representation of tree crowns by introducing feedbacks between (i) light availability and leaf area per tree and (ii) leaf area per tree and diameter growth rate to account for the self-pruning in real stands. The local accuracy of the new model, ForClim v2.9.5, was considerably improved in simulations at three long-term forest research sites in the Swiss Alps, while its generality was maintained as shown in simulations of potential natural vegetation along a broad environmental gradient in Central Europe. We conclude that the predictive ability of a model does not depend on its complexity, but on the reproduction of patterns. Most importantly, model complexity should be consistent with the objectives of the study and the level of system understanding.
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