Table 3 Variance partitioning analysis of prokaryotic community in Bohai Bay according to seawater environmental factors and geospatial factors. The spatial factors including linear trend and PCNM variables. Forward selection procedures were used to select the best subset of environmental, trend, and PCNM variables explaining community variation, respectively. The community variation was calculated on the weighted and unweighted UniFrac distance matrix, respectively. Monte Carlo permutation test was performed on each set without the effect of the other by permuting samples freely (999 permutations).

	Env	Trend	PCNM	Trend*Env	Env*PCNM	Trend*PCNM	TrendEnvPCNM
May
Weighted	❌	❌	❌	❌	❌	❌	❌
Unweighted	❌	❌	❌	❌	❌	❌	❌
Aug
Weighted	0.114 (0.051)	0.019 (0.277)	0 (0.411)	0.082	0.067	0.078	0.095
Unweighted	0.082 (0.073)	0 (0.752)	0.053 (0.095)	0.032	0.189	0.112	0.162
Oct
Weighted	0.388 (0.001)	0.028 (0.051)	0.105 (0.002)	0	0.016	0.021	0.245
Unweighted	0.077 (0.051)	0.06 (0.038)	0.007 (0.286)	0.137	0.08	0.033	0.037
All
Weighted	0.995 (0.001)	0 (0.453)	0 (0.749)	0	0	0	0
Unweighted	1.042* (0.001)	0.001 (0.28)	0 (0.861)	0	0	0.001	0

Values in the table represent how many percent could the variables explain the PCC variation and values in parentheses represent the P value based on Monte Carlo permutation test. The asterisk (*) which the individual fractions of Env accounted more than 1 since there were negative R²_adj value during the redundancy analysis, negative R²_adj means that the explanatory variable can account for a smaller proportion of the total variation than that of the randomly generated variable. Therefore, usually in practical application, negative R²_adj is usually treated as 0, but it must be labeled as true R²_adj in order for all R²_adj components to add up to 1 (Referenced from Legendre and Legendre, 2012).

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