2.7.4.4.1 Qualitative mathematical model
A model for dry sclerophyll forests was developed based on the model structure of wet sclerophyll forests. All features of this model were the same as for wet sclerophyll forests, but with added uncertainty in the role of microclimate (MC) in supporting understorey vegetation components. This uncertainty was whether the microclimate created by the dry sclerophyll forest overstorey (DSOS) was beneficial to mid-storey (DSMS) and ground-layer (DSGL) vegetation, which led to two alternative models, one with (model 1 in Figure 27) and one without (model 2 in Figure 28) positive links from microclimate to mid-storey and ground-layer vegetation. There was also uncertainty between two experts as to whether or not dry sclerophyll trees could effectively access stores of deep groundwater (DGW) outside of alluvial deposits, which involves the positive link from deep groundwater to the dry sclerophyll overstorey variable. Omitting this link would essentially mean that the system was not a groundwater-dependent ecosystem. It would also remove any possible impact from coal mining on groundwater stores. This uncertainty in model structure was therefore not propagated through the qualitative analysis of perturbation response.
Variables are: arboreal mammals (AM), aggressive native honeyeaters (ANHE), deep groundwater (DGW), diurnal raptor (DR), dry sclerophyll ground layer (vegetation) (DSGL), dry sclerophyll mid-storey (vegetation) (DSMS), dry sclerophyll overstorey (vegetation) (DSOS), 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), and swift parrot (SP).
Data: Bioregional Assessment Programme (Dataset 1)
Variables are: arboreal mammals (AM), aggressive native honeyeaters (ANHE), deep groundwater (DGW), diurnal raptor (DR), dry sclerophyll ground layer (vegetation) (DSGL), dry sclerophyll mid-storey (vegetation) (DSMS), dry sclerophyll overstorey (vegetation) (DSOS), 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), and swift parrot (SP).
Data: Bioregional Assessment Programme (Dataset 1)
As for the other GDEs in this region, surface water and groundwater modelling predict potential impacts of coal mining to both deep and shallow groundwater stores. A single cumulative impact scenario (C1) was developed based on a simultaneous decrease in both sources of groundwater in models 1 and 2 (Table 28).
Table 28 Summary of the cumulative impact scenarios for the ‘Dry sclerophyll forests’ landscape class for 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 dry sclerophyll forest communities (Table 29), except for the predicted response of ground-layer and mid-storey vegetation, which in model 2 had a predicted response of zero (no change). Other than these zero-response predictions, the response predictions for these two models matched those for forested wetlands (Table 25) and wet sclerophyll forests (Table 27), with the same dynamics contributing to ambiguous response predictions noted above.
Table 29 Predicted response of the signed digraph variables of the ‘Dry 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)
