## 1 Introduction
### Model Runs
#### 2.5.1 Distribution Modeling  
To simulate the distribution of the species the 3-PG model was run for 20 years using the current, climate, and a second and third run completed using the modeled climate normals for the 2020s (2011-2040) using the A1 and A1B climate projections to reach maximum LAI and canopy closure. The simulations were then stopped and the four monthly modifiers were extracted. The degree that available soil water, suboptimal temperature, frost, and VPD restricted photosynthesis was then determined for winter (December-February), spring (March-May), summer (June-August) and fall (September-November), as well as annually.  
A decision-tree analysis was then applied to assess the extent to which the 3-PG modifiers could predict the distribution of Chinese fir. This type of analysis is increasingly common for ecological research, as decision tree approaches are not dependent on the assumption of normally distributed data and are well-suited to dealing with collinear and categorical datasets [28, 35, 36, 37]. Decision tree approaches automatically separate the dependent variables (presence or absence of the species) into a series of choices that not only identifies the importance of each constraining variable, but also establishes thresholds that best separate one species from another [28]. The decision-tree analysis was undertaken with a 10-fold cross validation technique, similar to a \"jackknifing\" procedure, which starts by using all available data (the reference tree). The total dataset is partitioned randomly into 10 equally sized groups (or folds). One set is held in reserve, while the other nine are pooled and a model is generated. The accuracy of the model is assessed using the remaining 10% of the data not used in model development. This process is then repeated ten times, resulting in ten different test trees and ten different accuracy assessments. The decision rules of the ten models are then merged to produce a final decision tree with an overall accuracy assessed by averaging the independent results of the ten simulations [38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Meaning/Comments** & **Name** & **Unit** & **Value** & **Source** \\ \hline \multicolumn{4}{c}{Production and respiration} \\ \hline Canopy quantum efficiency & alpha & molC/ molPAR & 0.033 & [32] \\ \hline Ratio NPP/GPP & Y & & 0.5 & [32] \\ \hline \multicolumn{4}{c}{Conductance} \\ \hline Maximum canopy conductance & MaxCond & m/s & 0.02 & [32] \\ LAI for maximum canopy conductance & LAIgcx & & 3 & This study \\ Defines stomatal response to VPD & CoeffCond & 1/mBar & 0.05 & Default \\ Canopy boundary layer conductance & BLcond & m/s & 0.2 & This study \\ \hline \multicolumn{4}{c}{Basic Density} \\ \hline Minimum basic density—For young trees & rhoMin & t/m\({}^{3}\) & 0.3 & This study \\ Maximum basic density—For older trees & rhoMax & t/m\({}^{3}\) & 0.37 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Presence-only data were used to train the decision tree model using an approach adapted from [39], who undertook a detailed review of methods to predict species distributions from presence only data and proposed approaches which were then assessed against actual presence/absence data. Following this approach we first generated a number of random \"pseudo\" absences matching the number of presences. These points were randomly located through the region however they had to be at least 1 km away from existing presences. An initial decision tree was then used to predict likely absences using the modifiers generated by the 3-PG model. Random \"pseudo\" absences which had a less than or equal to 30% probability of being a \"true\" absence were removed from the absence list. A second and final decision model was then developed using the filtered \"pseudo\" absences along with the presences to predict the current Chinese fir distribution. To predict the future distribution under climate projections A1B and A2 we applied to the same decision rules.