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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.