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Table 2 Fit of Bayesian models for exponential, time-dependent and density-dependent gyrodactylid population growth to experimental data-sets

From: Population regulation in Gyrodactylus salaris – Atlantic salmon (Salmo salar L.) interactions: testing the paradigm

Dataset Fish stock and replicate Exponential model Time- dependent model Density- dependent model Reference
A Loneelva single worm SQ 1.2882 SQ 0.2542 SQ 3.6544 2
DIC 29.61 DIC -20.70 DIC -8.667
Bi Batnfjordselva SQ 72.825 SQ 0.345 SQ 1.665 Unpublished
DIC 104.882 DIC – 4.452 DIC -3.380
Bii Lierelva SQ 1.57 SQ 0.2135 Failed to converge Unpublished
DIC 15.464 DIC -38.464
C Imsa SQ 2.4703 SQ 0.3912 SQ 2.4374 Unpublished
DIC 25.839 DIC -80.125 DIC -36.162
D Altaelva SQ 14.461 SQ 1.6153 Failed to converge 20
DIC 157.55 DIC -135.64
Ei Akerselva SQ 9.4279 SQ 3.2636 Failed to converge Unpublished
DIC 307.242 DIC 277.149
Eii Akerselva SQ 1.453579 SQ 0.862444 Failed to converge Unpublished
DIC 169.796 DIC 80.931
Eiii Akerselva SQ 9.767961 SQ 3.461779 Failed to converge Unpublished
DIC -157.044 DIC -154.38
F Conon SQ 18.9395 SQ 5.2759 SQ 9.39509 19
DIC 207.660 DIC 74.986 DIC 138.759
G Shin SQ 68.0709 SQ 11.3739 Failed to converge 19
DIC 394.026 DIC 139.259
H Numedalslågen SQ 12.04599 SQ 6.240825 Failed to converge 10
DIC 203.285 DIC 197.68
I Lierelva SQ 28.8556 SQ 9.526 Failed to converge 19
DIC 361.378 DIC 96.384
J Indalsälv SQ 6.59083 SQ 4.396193 SQ 3.541148 21
DIC 137.99 DIC 77.159 DIC 40.235
K Neva SQ 80.1418 SQ 8.46197 Failed to converge 20
DIC 198.842 DIC 120.456
L Neva single worm SQ 17.6825 SQ 7.54459 Failed to converge 2
DIC 86.573 DIC 57.064
M Neva SQ 28.3822 SQ 8.2056 Failed to converge Unpublished
DIC 198.84 DIC 120.456
N Namsen SQ 0.4047 SQ 0.1839 Failed to converge Unpublished
DIC 6.074 DIC 11.345
O Altaelva SQ 4.0926 SQ 0.58324 Failed to converge Unpublished
DIC 123.98 DIC 11.626
  1. The Sum of Squares (SQ) and the Bayesian Deviance Information Criterion (DIC) are provided for each model