. 2019 Sep 3;10:1060. doi: 10.3389/fpls.2019.01060

Table 5.

General linear models (GLMs) showing relationships of allelic richness (A _R), genetic diversity within populations (H _S), and genetic admixture index (A _D) with elevation and climate for each population of Quercus chenii. The best combination of variables was selected by a backward elimination algorithm based on Akaike information criterion (AIC) scores.

	Estimate	SE	t	P	VIF	AIC
	Model: H_S ∼ climate + elevation
(intercept)	0.665	0.004	156.317	0.000***	–	−88.091
elevation	−0.038	0.005	−8.418	0.000***	1.061
bio9	−0.010	0.005	−2.025	0.061*	1.061
	Model: H_S ∼ climate
(Intercept)	0.664	0.005	120.850	0.000***	–	−78.337
bio1	0.034	0.007	4.861	0.000***	1.438
bio2	0.009	0.006	1.367	0.193	1.112
bio4	0.033	0.006	5.365	0.000***	1.393
	Model: A_R ∼ climate + elevation
(Intercept)	5.122	0.062	82.456	0.000***	–	9.001
elevation	−0.473	0.069	−6.889	0.000***	1.142
bio2	0.133	0.075	1.781	0.097*	1.158
bio4	0.260	0.063	4.106	0.001***	1.160
	Model: A_R ∼ climate
(Intercept)	5.102	0.077	66.068	0.000***	–	17.252
bio1	0.484	0.097	4.979	0.000***	1.444
bio2	0.194	0.091	2.143	0.052*	1.121
bio4	0.558	0.091	6.113	0.000***	1.575
bio8	0.112	0.089	1.255	0.231	1.176
	Model: A_D ∼ climate + elevation
(Intercept)	0.398	0.055	7.191	0.000***	–	3.504
elevation	−0.198	0.058	−3.432	0.003***	–
	Model: A_D ∼ climate
(Intercept)	0.398	0.063	6.280	0.000***	–	9.091
bio1	0.174	0.079	2.215	0.043**	1.376
bio4	0.154	0.071	2.177	0.046**	1.376

SE, standard error; VIF, variance inflation factor, VIF < 2 indicates that multicollinearity does not confound the interpretation of individual predictors in the models; bio1, annual mean temperature; bio2, mean diurnal temperature range; bio4, temperature seasonality; bio8, mean temperature of wettest quarter; bio9, mean temperature of driest quarter; ***, P < 0.01; **, P < 0.05; *, P < 0.10