Model Intercept es o lo p li es × o es × lo es × p es × li df logLik AICc ΔAICc wi
i 0.99±0.01 0.14±0.01 -0.33±0.05 0.08 0.03 8 -1700.47 3416.97 0 0.76
ii 0.99±0.01 0.14±0.01 -0.28±0.04 -0.12±0.04 7 -1703.19 3420.40 3.43 0.14
iii 0.99±0.01 0.14±0.01 -0.33±0.05 0.08±0.03 -0.07±0.03 8 -1702.66 3421.36 4.39 0.08
iv 0.99±0.01 0.14±0.01 -0.13±0.02 -0.06±0.02 7 -1705.3 3424.64 7.67 0.02
v 0.99±0.01 0.14±0.01 -0.13±0.02 -0.01±0.03 -0.05±0.02 8 -1705.2 3426.44 9.47 0.01
vi 0.99±0.01 0.14±0.01 -0.13±0.02 -0.01±0.03 -0.07±0.03 8 -1707.34 3430.71 13.74 0
vii 0.99±0.01 0.14±0.01 -0.12±0.03 0.18±0.07 -0.07±0.03 8 -1719.17 3454.37 37.4 0
viii 0.99±0.02 0.14±0.01 -0.12±0.03 0.18±0.07 -0.03±0.01 8 -1719.96 3455.95 38.98 0
ix 0.99±0.01 0.14±0.01 -0.04±0.01 -0.03±0.01 7 -1723.81 3461.66 44.69 0
x 0.99±0.01 0.14±0.01 -0.03±0.03 -0.07±0.03 7 -1726.13 3466.28 49.32 0
xi 0.99±0.14 0.14±0.01 5 -1729.77 3469.56 52.59 0
xii 0.99±0.14 -0.28±0.05 5 -1757.55 3525.12 108.15 0
xiii 0.99±0.14 -0.13±0.02 5 -1759 3528.01 111.05 0
xiv 0.99±0.14 -0.04±0.02 5 -1770.03 3550.08 133.11 0
xv 0.99±0.14 -0.03±0.04 5 -1772.25 3554.51 137.54 0
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%. N.d.f: Numerator degree of freedom, D.d.f: Denominator degree of freedom, es: Exposure Salinity, o: Origin, lo: Location, p: Population, li: Lineage. AICc is an inverse indicator of model parsimony, considering fit (logLik = log-Likelihood) and complexity (df = number of parameters to be estimated in the candidate model). The ΔAIC < 6 cut-off rule was used to define the top-model set (Richards, 2005, 2008). The top model set with a ΔAICc < 6 (AICc difference with the best candidate model) comprises 2 concurrent models of i, ii, and iii (in bold) with a weight of evidence wi ranging from about 8 to 76%.