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Table 3 Various negative binomial regression models validated during model selection

From: A spatially-explicit count data regression for modeling the density of forest cockchafer (Melolontha hippocastani) larvae in the Hessian Ried (Germany)

 

Model

AIC

Explained deviance (%)

Dispersion parameter

g 1(μ i ) = β 01

8.1

5335.4

0

g 2(1/ϕ) = β 02;

ϕ = 0.255

g 1(μ i ) = β 01 + f 11(DWT i )

8.2

5207.1

12

g 2(1/ϕ) = β 02;

ϕ = 0.308

g 1(μ i ) = β 01 + f 11(DWT i ) + f 21(CTH i )

8.3

5171.9

15.7

g 2(1/ϕ) = β 02;

ϕ = 0.323

g 1(μ i ) = β 01 + f 11(DWT i ) + f 21(CTH i ) + f 31(east i , north i ), edf for f 31(east i , north i ) = 25.96

8.4

4343.8

63.4

g 2(1/ϕ) = β 02;

ϕ = 1.051

g 1(μ i ) = β 01 + f 11(DWT i ) + f 21(CTH i ) + f 31(east i , north i ), edf for f 31(east i , north i ) = 117.35

8.5

4161.3

75.1

g 2(1/ϕ) = β 02;

ϕ = 1.664

g 1(μ i ) = β 01 + f 11(DWT i ) + f 21(CTH i ) + f 31(east i , north i ), edf for f 31(east i , north i ) = 117.35

8.6

4154.5

75.4

g 2(1/ϕ ι ) =

β 02 + f 12(DWT i )

  1. Complexity increases from model 8.1 to 8.6. The flexibility of the spatial model component increases with increasing estimated degrees of freedom (edf) of the 2-dimensional smoothing function f 3.
  2. with DWT i : simulated distance to water table in October 2007 at plot i (m); CTH i : modeled clay thickness at plot i (%); (east i , north i ): Gauß-Krüger east and north coordinates of plot i defined in relation to the 3rd meridian.