Modelling waterbird abundance We found the most contributing predictor variables from 2048 possible models were local wetland area and current and lagged LEB total wetlands area (Fig. 77). The highest ranking model (AICc score) included local wetland area and current and lagged LEB total wetlands area (Table 35 and 36). The model (Eq. 11) was highly significant (t=15.99, p<0.001), explaining 96.1% of the variance of the intercept (adjusted R2 = 0.95). L_Lake.Yamma.Yamma ~ LEB_WTA + Lag1_LEB_WTA+ Lake.Yamma.Yamma.WTA (Eq. 11) Table 35. Performance and coefficients of best model used to predict waterbird abundance, across the surveyed area of the Lake Yamma Yamma. Variable Estimate Std.Error t/F valuea Pr(>|t|) Intercept 1.05654 0.06608 15.99 9.65e-14 LEB_WTA 3.991 0.0329 Lag1_LEB_WTA 4.116 0.0303 Lake.Yamma.Yamma.WTA 200.358 <2e-16 at value estimated for intercept and F value for other variables
Modelling waterbird abundance We found the most contributing predictor variables from 2048 possible models were local wetland area and current and lagged LEB total wetlands area (Fig. 77). The highest ranking model (AICc score) included local wetland area and current and lagged LEB total wetlands area (Table 35 and 36). The model (Eq. 11) was highly significant (t=15.99, p<0.001), explaining 96.1% of the variance of the intercept (adjusted R2 = 0.95). L_Lake.Yamma.Yamma ~ LEB_WTA + Lag1_LEB_WTA+ Lake.Yamma.Yamma.WTA (Eq. 11) Table 35. Performance and coefficients of best model used to predict waterbird abundance, across the surveyed area of the Lake Yamma Yamma. Variable Estimate Std.Error t/F valuea Pr(>|t|) Intercept 1.05654 0.06608 15.99 9.65e-14 LEB_WTA 3.991 0.0329 Lag1_LEB_WTA 4.116 0.0303 Lake.Yamma.Yamma.WTA 200.358 <2e-16 at value estimated for intercept and F value for other variables
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