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Comparison of Distance-Dependent and Distance-Independent Stand Growth Models—Is Perfect Aggregation Possible?

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Selecting a suitable level of accuracy in stand growth estimations is a question of practical importance. A detailed model is more complicated and demands more initial information from the user to produce reliable results. Despite being correct and more precise, it may well be that the more detailed results give no practical bonus value. The essential part of the estimations could also have been received with coarser means. We have used three levels of stand description accuracy. The first is a mean tree model (MTM), where all trees are assumed identical. The second is a distribution-based model (DBM), where size differences are allowed. Both versions are distance-independent and the spatial configuration of the stand is considered random. The third is a spatially explicit model (SEM), where all trees are described individually and their spatial locations are taken into account. The core dynamics in all models are kept the same, so a previous model is a straightforward aggregation of the latter one. The possibility of a perfect aggregation was examined both theoretically and by practical simulations. A process-based stand growth model ACROBAS was used to simulate the different levels of aggregation. The results demonstrate that under the Poisson assumption of stand spatial configuration there are hardly any differences among model aggregations. As mortality is not totally random in the SEM, the Poisson assumption does not hold through the whole simulation period. As the spatial configuration eventually tends toward regularity, the total growth in the stand is somewhat increased, which is evident in variables such as total stem volume and foliage mass. However, mean height and stocking density developments are almost identical in all models. This suggests that the mean height is “an almost perfect” aggregation variable.
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Keywords: Process-based model; perfect aggregation

Document Type: Research Article

Publication date: 2006-12-01

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

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    June 1, 2016 to Feb. 28, 2017

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