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Table 4 Models best describing Plasmodium prevalence (n = 20)

From: Avian malaria prevalence and mosquito abundance in the Western Cape, South Africa

Factor Coefficient estimate (± S.E.) P d.f. Residual deviance AIC ΔAIC
  Plasmodium (best-fit model)    
Intercept - 0.86 ± 0.17 <0.001 34 69.37 179.54 -
Winter - 0.78 ± 0.18 <0.001     
Salinity 0.20 ± 0.05 <0.001     
Mosquito prevalence - 5.85 ± 2.00 <0.005     
Rainfall (at 4 months) - 0.01 ± 0.008 <0.05     
  Plasmodium model 2    
Intercept - 1.13 ± 0.11 <0.001 36 75.53 181.70 2.16
Winter - 0.58 ± 0.16 <0.001     
Salinity 0.25 ± 0.05 <0.001     
Mosquito prevalence - 6.80 ± 1.99 <0.001     
  Plasmodium model 3    
Intercept - 1.10 ± 0.11 <0.001 35 72.80 182.97 3.43
Winter - 0.41 ± 0.25 <0.001     
Salinity 0.25 ± 0.05 <0.001     
Mosquito prevalence - 6.55 ± 2.01 <0.001     
Rainfall (sampling month) - 0.007 ± 0.009 0.40     
  Plasmodium model 4    
Intercept - 1.01 + 0.16 <0.001 36 74.17 184.89 5.35
Salinity 0.22 + 0.06 <0.001     
Mosquito prevalence - 5.46 + 1.97 0.006     
Rainfall (sampling month) - 0.02 + 0.01 <0.005     
Rainfall (at 4 months) - 0.01 + 0.01 0.32     
  Plasmodium model 5    
Intercept - 8.50 ± 0.17 <0.001 36 80.72 188.90 9.36
Winter - 0.86 ± 0.18 <0.001     
Salinity 0.21 ± 0.06 <0.001     
Rainfall (at 4 months) - 0.02 ± 0.01 <0.005     
  1. Models are ranked using ΔAIC and range between ΔAIC ≤ 10.