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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.