Table 12. Ranking of models that performed best (within 2 AICs of best fit) in terms of explaining waterbird abundance across Lake Dunn, 1983-2012, in relation to each of the explanatory variables tested (see Table 1b for acronyms). Models were ranked from the best performing (highest AIC) to worst performing from 2048 candidate models. Rain Rain Model# weight Rain AICc df delta Local Lag1_Rain LEB_WTA Lag1_Local Lag1_Lake.Dunn.WTA Lag1_LEB_WTA Lake.Dunn.WTA Local.Flow SOI Intercept logLik 129 2.43 + 3.93 -19.81 49.03 0.00 0.34 130 2.43 + + 5.69 -17.28 49.23 0.20 0.31 161 2.41 + + 4.93 -18.91 50.24 1.21 0.19 193 2.41 + + 4.93 -19.10 50.61 1.58 0.16
Table 12. Ranking of models that performed best (within 2 AICs of best fit) in terms of explaining waterbird abundance across Lake Dunn, 1983-2012, in relation to each of the explanatory variables tested (see Table 1b for acronyms). Models were ranked from the best performing (highest AIC) to worst performing from 2048 candidate models. Model# Intercept Local Rain Lag1_Rain LEB_WTA Rain Lag1_Local Rain Lag1_Lake.Dunn.WTA Lag1_LEB_WTA Lake.Dunn.WTA Local.Flow SOI df logLik AICc delta weight 129 2.43 + 3.93 -19.81 49.03 0.00 0.34 130 2.43 + + 5.69 -17.28 49.23 0.20 0.31 161 2.41 + + 4.93 -18.91 50.24 1.21 0.19 193 2.41 + + 4.93 -19.10 50.61 1.58 0.16
waterbirds-lake-eyre-basin-condition-assessment-report.docx