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Table 3 Results of tests of the association of symmetric component, asymmetric component and leaf size (all samples) with three sets of predictor variables using distance-based redundancy analysis (dbRDA): marginal tests on the left and partial (conditional) tests in which we tested the significance of each variable set, controlling the other two variable sets, on the right

From: Genetic, geographic, and climatic factors jointly shape leaf morphology of an alpine oak, Quercus aquifolioides Rehder & E.H. Wilson

Models

Marginal tests

Models

Conditional tests

Variable set

F

P

%VAR

Variable set

F

P

%VAR

Symmetric component

Marginal (all variables)

Geography

3.842

0.001***

3.8

Conditional (climate + genetics)

Geography

2.085

0.002**

2

Genetics

1.166

0.02*

19

Conditional (geography + climate)

Genetics

1.166

0.012*

19

Climate

5.436

0.001***

7.1

Conditional (geography + genetics)

Climate

2.7

0.001***

3.5

Asymmetric component

Marginal (all variables)

Geography

2.006

0.002**

2.2

Conditional (climate + genetics)

Geography

1.67

0.012*

1.8

Genetics

0.966

0.683

17.7

Conditional (geography + climate)

Genetics

0.966

0.686

17.7

Climate

1.004

0.469

1.5

Conditional (geography + genetics)

Climate

1.21

0.162

1.8

Leaf size

Marginal (all variables)

Geography

2.145

0.102

1.6

Conditional (climate + genetics)

Geography

0.786

0.494

0.6

Genetics

1.834

0.002**

22.5

Conditional (geography + climate)

Genetics

1.834

0.001***

22.5

Climate

23.763

0.001***

23.3

Conditional (geography + genetics)

Climate

1.522

0.198

1.5

  1. The marginal test included all variables, while the conditional tests accounted for variation in the selected variables
  2. %VAR, percentage of variance explained by each variable; F, F values; ***P < 0.001; **P < 0.01; *P < 0.05