Table 2.

Explanatory contributions of EV and SV variables in canonical correspondence analyses (CCAs), determined by permutation test (999 Monte Carlo permutations and α≤0.05). (Full model CCA with all selected EV and SV variables; EV-CCA (pure EV- and spatially-structured EV-fraction); SV-CCA (pure SV- and spatially-structured EV-fraction), and covariable CCA (pure EV-fraction). Abbreviations: λ, explained variance; %, explained variance in percentages; LA, latitude; VO, lake volume; SU, lake surface; TE, temperature; PT, total phosphorus load. Marginal effects explain the variation in the species data singly, whereas conditional effects show the amount of extra variation each variable contributed when it was added to the models. For details see text.)

model type	variable	marginal (independent) effects				conditional (partial) effects
		λ	p	F	%	λ	p	F	%
full model CCA	LA	0.30	0.001	6.53	13.3	0.30	0.001	6.35	13.3
	PT	0.14	0.022	2.80	6.2	0.15	0.008	3.28	6.6
	SU	0.11	0.075	2.16	4.9	0.09	0.059	2.24	4.0
	TE	0.27	0.002	5.52	12.0	0.12	0.026	2.69	5.3
	VO	0.13	0.051	2.44	5.8	0.13	0.011	3.13	5.8
EV-CCA	PT	0.14	0.023	2.80	6.2	0.12	0.024	2.85	5.3
	SU	0.11	0.073	2.16	4.9	0.18	0.002	4.14	8.0
	TE	0.27	0.001	5.52	12.0	0.27	0.001	5.52	12.0
	VO	0.13	0.038	2.44	5.8	0.12	0.017	2.68	5.3
SV-CCA	LA	0.30	0.001	6.35	13.3	0.30	0.001	6.35	13.3
covariable CCA	PT	0.15	0.012	3.24	6.6	0.15	0.012	3.28	6.6
	SU	0.09	0.111	1.84	4.0	0.10	0.053	2.24	4.4
	TE	0.10	0.085	2.11	4.4	0.11	0.035	2.69	4.9
	VO	0.08	0.143	1.74	3.5	0.12	0.009	3.13	5.3