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Table 4 An overview of different models to ensure consistency in parameter estimates of temperature effects

From: Temporal pattern of questing tick Ixodes ricinus density at differing elevations in the coastal region of western Norway

Year

Temp

(Temp)2

Elevation (high vs. low)

ns(Date, df = 2)

Elevation* ns(Date, df = 2)

Estimate Temp

Estimate (Temp)2

AIC

Resid trend

X

X

X

X

X

X

0.23

0.0066

5838.6

No

X

X

X

X

X

 

0.22

0.0062

5872.42

No

X

X

X

 

X

 

0.22

0.0062

5882.58

No

X

X

X

X

  

0.31

0.0092

5893.2

Yes

X

X

X

   

0.31

0.009

5593.62

Yes

X

X

 

X

X

X

0.040

 

5847.86

No

X

X

 

X

X

 

0.049

 

5881.1

No

X

X

  

X

 

0.050

 

5891.34

No

X

X

 

X

  

0.056

 

6003.92

Yes

X

X

    

0.056

 

6014.36

Yes

X

X

X

X

X

X

0.24

0.0069

5847.18

No

 

X

X

X

X

 

0.22

0.0062

5872.42

No

X

X

X

 

X

 

0.24

0.0068

5893.62

No

 

X

X

X

  

0.31

0.0094

5996.04

Yes

 

X

X

   

0.31

0.0094

6006.56

Yes

 

X

 

X

X

X

0.048

 

5857.68

No

X

X

 

X

X

 

0.048

 

5894.48

No

X

X

  

X

 

0.048

 

5904.76

No

 

X

 

X

  

0.049

 

6017.82

Yes

 

X

    

0.049

 

6028.36

Yes

  1. Inclusion of a 2nd degree term for temperature improves the AIC-value. ns = natural cubic spline, * = interaction, resid trend = “Yes” means that there is a time trend in the residuals. X = term included in the model. Bold face indicates the chosen prevailing weather model. Note that this model is the same as the best model from Table 2 with parameter estimates presented in Table 3.
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