## 4 Pre-Selection of Predictive Variables
### Predictive Variables for Environmental Sciences  
For terrestrial environmental modeling, many predictive variables are available. Many previous applications provided examples of variables used for terrestrial environmental modeling [13; 42; 49; 50; 51; 53; 56; 71; 72].  
In contrast, the information of predictive variables is often scarce for marine environmental modeling. In many cases, proxy variables are usually used for predictive modeling [69; 73]. For example, to predict the spatial distribution of seabed sediments for Australian Exclusive Economic Zone (AEEZ) at a resolution of 0.01 or 0.0025 degrees, only a few predictive variables were available for the whole AEEZ [12; 41; 74] (Table 2). For spatial predictions over smaller areas, quite often more predictive variables became available at desired resolution such as for seabed sediment [4; 75; 76; 77], seabed hardness [78; 79; 80], and sponge species richness [6; 81] (Table 2). Bathymetry and backscatter were also used to predict seabed sediment at local scale [82]. Some derived information may be used as predictive variables. For example, in Table 2, predictive variables 5-13 were derived from bathymetry (bathy), while predictive variables 15-19 were derived from backscatter (bs). Some other variables were used for seabed grain size at small scale [48]. In addition, many variables were reviewed [69; 83] and could be used for marine environmental modeling. Fuzzy geographic features were also used for spatial predictive modeling at local scales [77; 84].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**No** & **Predictive Variables** & **Seabed Sediment/Grain Size** & **Seabed Hardness** & **Sponge Species Richness** & **Window/Kernel Size(s)** \\ \hline
1 & Longitude (long) & yes & yes & yes & \\
2 & Latitude (lat) & yes & yes & yes & \\
3 & Distance to cost (dist) & yes & & yes & \\
4 & Bathymetry (bathy) & yes & yes & yes & \\
5 & Local Moran’s 1 from bathymetry & yes & yes & yes & yes \\
6 & Mean curvature & yes & yes & yes & yes \\
7 & Planar curvature & yes & yes & yes & yes \\
8 & Profile curvature & yes & yes & yes & yes \\
9 & Relief & yes & yes & yes & yes \\
10 & Ruggov (or surface, surface complexity) & yes & yes & yes & yes \\
11 & Slope & yes & yes & yes & yes \\
12 & Topographic or bathymetric position index (pi or typi) & yes & yes & yes & yes \\
13 & Fuzzy morphometric features & yes & & yes & yes \\
14 & Backscatter (bs) 10-36 & yes & yes & yes & \\
15 & Entropy from bs & & yes & yes & yes \\
16 & Homogeneity from bs & & yes & yes & yes \\
17 & Local Moran’s 1 from bs & & yes & yes & yes \\
18 & Prock from bs & & yes & yes & yes \\
19 & Variance from bs & & yes & yes & yes \\
20 & Suspended particulate matter & yes & & & \\
21 & Mean tidal current velocity & yes & & & \\
22 & Peak orbital velocity of waves at seabed & yes & & & \\
23 & Roughness from bathy * & yes & & & \\
24 & Roughness from bs * & yes & & \\
25 & Sobel filter from bathy * & yes & & \\ \hline \hline \end{tabular}  
* The difference between the minimum and maximum of cell and is eight neighbors [48].
* A directional filter that emphasizes areas of large spatial frequency (edges) running horizontally (O) or vertically (Y) across the image [48].  
\end{table}
Table 2: A list of predictive variables used for some marine environmental variables.