## 2 Materials and Methods
### Features of the DSS  
Spatial visualization emerged as the field that combines the abilities of human perception through data picturing and computer simulation using analytics at the landscape level. Spatial visualization was successfully applied in various studies, and the current platform is inspired by these concepts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Model Name** & **Model Main Mathematical Expression** & **Comment** & **Reference** \\ \hline \multirow{2}{*}{1} & **[PERSON] and** & & 12 sub-models & [PERSON] and \\  & **[PERSON]** & & \(m(T)=\frac{p\frac{T}{T_{0}}}{1+\frac{p\frac{T}{T_{0}}}{1+\frac{p\  
The main features of the platform and how they are linked is described in the unified modeling language (UML) use case diagram shown in Figure 1.  
The model fitting and mapping process are done through an interactive process that involves the user in all steps to ensure good and reliable decisions are taken, based on graphical outputs and statistical criteria available in the DSS. The process is summarized in Figure 2.  
The fitting procedure begins with a preliminary visual display of input data using statistical aggregation functions to provide the average value for all replicates of the experiment. Then, among the 82 nonlinear mathematical expressions, those which can better fit the data are selected. The database of mathematical expressions is obtained from the literature, and each equation is selected based on its ability to capture the relationship that exists between the dependent and the independent variable. The functions used during this process are implemented in R computer language. The implementation of the Levenberg-Marquardt algorithm in R-package minpack.lm is used for fitting the mathematical expressions to data and estimating the parameters [21; 22; 23]. \(R\)-squared, \(R\)-squared adjusted, Akaike information criterion (AIC), root-mean-squared error (RMSE), and the sum of squared residual are proposed to help to choose the best-fitted model. Once the fitting process is completed, the information is saved and transferred to the mapping perspective.  
Figure 1: Use case diagram presenting the interactions among the platform components and their functionalities. Each circle represents a feature implemented within the decision support system (DSS). The links from the user to a feature represent the direct interactions of the user with the DSS, while the arrows with labels “include” characterize the relationships and level of dependency between features.  
The mapping exercise requires spatial data as input, which are organized by grids that allow applying the model at the landscape level. From the graphical user interface (GUI), a request is made to a specific studied region, and the values of temperature in the selected locations are extracted from the database in the form of monthly and annual averages. A point object is used to pick the model mathematical expression, and it is consecutively applied in each geographical coordinate of the region of interest to estimate the value of the dependent variable. This allows the value of the dependent variable at each location to be estimated and stored in an ASCII file format (.asc). Additionally, the mapping perspective includes some Udig features for map editing and viewing. It further offers the possibility to transfer the ASCII file obtained to any geographic information system (GIS) software for processing (see Figure 11, Appendix A, for a summary of the algorithm).