2.7.4.3.1 Qualitative mathematical model
A qualitative model was developed for wet sclerophyll forests based on the model structure for forested wetlands, with the overstorey (WSOS in Figure 25 and Figure 26) providing the same ecosystem functions as for forested wetlands. An added feature of this model was the effect of the overstorey in creating a microclimate (MC) that supported ground-layer and mid-storey vegetation (WSGL and WSMS, respectively). Overstorey vegetation was described as also being an important habitat for grey-headed flying foxes (GHFF), which utilise wet sclerophyll forest, dry sclerophyll forest and rainforest canopies as camp areas (Lunney and Moon, 1997). Deep groundwater (DGW) is suggested as being important to the survival of the trees, which access stores of water through deep roots, but it was recognised that this is likely only happening in the alluvial deposits of river floodplains or on lower slopes within the area of interest. There was even greater uncertainty as to whether stores of shallow groundwater (SGW) benefited mid-storey vegetation, which led to two alternative models: one with (model 1 in Figure 25) and one without (model 2 in Figure 26) a positive link from shallow groundwater to mid-storey vegetation.
Variables are: arboreal mammals (AM), aggressive native honeyeaters (ANHE), deep groundwater (DGW), diurnal raptor (DR), forest fragmentation (FF), forest habitats (FH), flowers & nectar (FN), grey-headed flying fox (GHFF), koala (Koa), microclimate (MC), nocturnal raptor (NR), precipitation (Ppt), recruitment (Rec), regent honeyeater (RHE), shallow groundwater (SGW), swift parrot (SP), wet sclerophyll ground layer (vegetation) (WSGL), wet sclerophyll mid-storey (vegetation) (WSMS), and wet sclerophyll overstorey (vegetation) (WSOS).
Data: Bioregional Assessment Programme (Dataset 1)
Variables are: arboreal mammals (AM), aggressive native honeyeaters (ANHE), deep groundwater (DGW), diurnal raptor (DR), forest fragmentation (FF), forest habitats (FH), flowers & nectar (FN), grey-headed flying fox (GHFF), koala (Koa), microclimate (MC), nocturnal raptor (NR), precipitation (Ppt), recruitment (Rec), regent honeyeater (RHE), shallow groundwater (SGW), swift parrot (SP), wet sclerophyll ground layer (vegetation) (WSGL), wet sclerophyll mid-storey (vegetation) (WSMS), and wet sclerophyll overstorey (vegetation) (WSOS).
Data: Bioregional Assessment Programme (Dataset 1)
Surface water and groundwater modelling predict potential impacts of coal mining to both deep and shallow groundwater stores, and a single cumulative impact scenario (C1) was developed based on a simultaneous decrease in both these sources of groundwater in models 1 and 2 (Table 26).
Table 26 Summary of the cumulative impact scenarios for the ‘Wet sclerophyll forests’ landscape class in the Gloucester subregion
|
Cumulative impact scenario |
Deep groundwater |
Shallow groundwater |
|
C1 model 1 |
– |
– |
|
C1 model 2 |
– |
– |
Cumulative impact scenarios are determined by combinations of no change (0) or a decrease (–) in the following signed digraph variables: deep groundwater (DGW), shallow groundwater (SGW). Scenario C1 shows the changes to these variables under the coal resource development pathway (CRDP).
Data: Bioregional Assessment Programme (Dataset 1)
Qualitative response predictions were the same for both models depicting wet sclerophyll forest communities (Table 27). Response predictions for these two models also matched those for forested wetlands (Table 25), with the same dynamics of the effects for release from competitive dominance and release from predation contributing to ambiguous response predictions for diurnal raptors, swift parrots, regent honeyeaters and grey-headed flying foxes.
Table 27 Predicted response of the signed digraph variables of the ‘Wet sclerophyll forests’ landscape class to cumulative changes in hydrological response variables for the Gloucester subregion
Qualitative model predictions that are completely determined are shown without parentheses. Predictions that are ambiguous but with a high probability (0.80 or greater) of sign determinancy are shown with parentheses. Predictions with a low probability (less than 0.80) of sign determinancy are denoted by a question mark. Zero denotes completely determined predictions of no change.
Data: Bioregional Assessment Programme (Dataset 1)
