## 3 Materials and Methods
### Validation of the Modified Miami Model
The most challenging task during the modeling and validation of the model was to find verified data. When data are found, they are generally presented in a more or less wide range, which gives a subjective perspective to the dataset used within the model, even if it was intended to be objective. Most critical for this concept is the use of MAT values from the native distribution area (MAT\({}_{\rm nat}\)) of a species. For some species, it is relatively easy to find plausible values, since they are well documented or native in a relatively limited area, but for others it is quite difficult and this means that mean values estimated over an extended area have to be used. Besides the inclusion of MAT\({}_{\rm nat}\), the inclusion of evapotranspiration (E) in the MAP function is considered as critical. For this study, the linear correlation between MAT and E according to the findings of [PERSON], [PERSON] and [PERSON] [25] was used. However, since E was not allowed to exceed MAP, because this would result in negative biomass production, a logical function had to be included, setting any negative value to zero. Optimization might eliminate this problem. Furthermore, the model has not been verified for higher temperatures, where the MAP function becomes more important. According to the findings of [PERSON], [PERSON] and [PERSON], verification in the range of higher temperatures will show whether it is possible to use the function of E originally developed for mountain forest areas in Japan or whether further adaptation is necessary. However, the best option would be a global formulation for the correlation between MAT and E.  
If optimization is carried out for water availability, attempts should be made to manage this via the distribution of precipitation over the year rather than soil water capacity; this approach would further exclude soil data. In general, soil texture and soil structure are critical impact factors, for instance because of water storage capacity and nutrient supply. In Sweden, soil data are quite similar over the entire country, and the factor soil could therefore be treated as a constant in the present study, but this would not be valid for other regions in Europe. On the other hand, topography, even on a small scale, also has a very strong influence on water and nutrient supply, and this would make more general modeling on a larger scale impossible. Furthermore, the exclusion of soil as a separate parameter increases the importance of the choice of the BM\({}_{\text{max}}\) parameter. Even if soil with poorer conditions would be a reduction factor for the biomass production of the species, this fact should not influence their ranking. An overestimation of above-ground biomass production can be avoided by using average values for BM\({}_{\text{max}}\). The advantage of this model is that the formulation is very easy. The two climate variables (MAT and MAP) necessary for estimating the biomass production for a site can be found quite easily in weather homepages. The challenge in this formulation is to find valid species-specific parameters (BM\({}_{\text{max}}\) and MAT\({}_{\text{nat}}\)). Apart from their effect on ABM, it is simple to demonstrate the influence of climate by changing the values for MAT and MAP. The model provides some interesting results. Although some of the species listed in Table 2 have an enormous capacity for biomass production, it is not possible to use this potential in Sweden. _Paulownia_ shows a relatively high ABM in southern Sweden, but this must be weighed against high risks, as this species grows on the border of its survival. The situation in northern Sweden seems to be quite different. If the foreign and hybrid species, in particular hybrid aspen, are excluded, only Norway spruce and reed canary grass have a biomass production higher than 4.5 Mg ha\({}^{-1}\)a\({}^{-1}\), which explains and emphasizes the importance of Norway spruce for the forest industry in that region. Considering foreign species, Douglas fir, silver fir and larch (_Larix_ spp.)