Table 4.

Relationships between leaf functional traits and reproduction in Glycine max

Variable	Trait	Simple linear models					Mixed model diagnostics
		Intercept	Trait	Trait2	Model P	r 2	Marginal r2	Conditional r2
Pod count	A mass	–1.2	11.8	–14.1	0.087	0.11	0.071	0.757
	A area	1.1	–0.1	0.002	<0.001	0.466	0.204	0.751
	Log LMA	60.9	–73.2	22.3	<0.001	0.685	0.643	0.75
	Leaf N	–2.3	1.8	–0.2	0.084	0.112	0.127	0.79
	Leaf C	–16.2	0.64	–0.01	0.003	0.239	0.043	0.715
	Log leaf C:N	–14.7	30.5	–14.6	0.164	0.083	0.122	0.799
	Log WUE	1.5	–0.4	–0.4	0.232	0.067	0.019	0.743
	g s	1.7	–1.4	0.7	0.12	0.096	0.022	0.766
	Log leaf area	–11.0	14.3	–4.1	<0.001	0.442	0.282	0.792
Seed count	A mass	–1.0	12.5	–14.9	0.048	0.135	0.088	0.772
	A area	1.4	–0.03	0.002	<0.001	0.453	0.199	0.77
	Log LMA	71.0	–84.0	25.4	<0.001	0.625	0.545	0.714
	Leaf N	–2.5	2.2	–0.3	0.03	0.153	0.128	0.766
	Leaf C	–40.1	1.8	–0.02	0.001	0.294	0.067	0.717
	Log Leaf C:N	–18.2	37.8	–18.0	0.068	0.12	0.119	0.774
	Log WUE	2.3	–1.8	0.8	0.291	0.057	0.023	0.782
	g s	2.1	–1.4	0.8	0.11	0.1	0.008	0.76
	Log leaf area	–8.7	11.6	–3.2	<0.001	0.478	0.34	0.839

Simple linear regression models including a second-order polynomial term were used to assess relationships across the entire data set, whereas linear mixed-effects models were employed to describe the relationship between traits and nodules while accounting for the nested structure of data. Marginal r² values therefore correspond to the variance in pod and seed counts explained by traits alone, while the conditional r² value corresponds to the variance in reproduction explained by traits as well as the nested random effects (namely sample blocks nested within management conditions).

Sample sizes for all models are n = 134 plants, except for WUE and g_s analyses where n = 133 plants. Pod and seed count data were log transformed prior to analyses.