Evaluating structural indices by reversing forest structural analysis

It is widely acknowledged that spatial forest structure is a driving factor behind growth processes and that forest growth, in return, influences the structural composition of woodlands. Also any impact on forests is primarily a change of spatial forest structure. In the last few decades an impressive number of structural indices have been developed to quantify spatial forest structure and it has also been suggested that they can be used as surrogate measures for quantifying biodiversity [Pommerening, A., 2002. Approaches to quantifying forest structures. Forestry 75, 305–324]. Of particular interest in this regard is the development of a family of individual tree neighbourhood-based indices, which are measures of small-scale variations in tree positions, species and dimensions, developed by Gadow and Hui [Gadow, K.v., Hui, G., 2002. Characterising forest spatial structure and diversity. In: Bjoerk, L. (Ed.), Proceedings of the IUFRO International workshop ‘Sustainable forestry in temperate regions’, Lund, Sweden, pp 20–30]. Especially when expressed as frequency distributions these indices offer valuable information on spatial woodland structure. An important element of appraising the merits of such indices is a detailed evaluation of their performance for a specified purpose. One possible evaluation path is based on the idea that a successful quantification of spatial forest structure should allow the analysis to be reversed and enable the synthesis of forest structure from the indices derived. This idea is investigated here with a simulation model that uses the concept of cellular automata combined with further development of an approach by Lewandowski and Gadow [Lewandowski, A., Gadow, K.v., 1997. Ein heuristischer Ansatz zur Reproduktion von Waldbeständen (A heuristic method for reproducing forest stands). Allg. Forst- u. J.-Zeitung 168, 170–174]. The rules according to which the spatial pattern of tree positions “grows” in the stand matrix are deduced directly from the distributions of the structural indices of the input data. Different combinations of indices are used to assess and simulate the structure of four sample stands. The results show that simulations using species specific distributions of indices and a limit to the number of neighbours used for index calculation to three or four neighbours are most successful at reconstructing the original stand structure. The specific sequence of simultaneous distributions of structural indices was not s