Table 2 Seasonality as a driver of microbial physiology.

From: Microbial growth and carbon use efficiency show seasonal responses in a multifactorial climate change experiment

 

MBC

Gm

Rm

CUE

 

df

F

p

df

F

p

df

F

p

df

F

p

Date

1

4.24

0.0432

1

18.40

0.0001

1

51.88

<0.0001

1

1.19

0.2794

eCO2

1

0.26

0.6134

1

4.53

0.037

1

4.69

0.034

1

1.15

0.2867

eT

1

0.03

0.8711

1

0.46

0.4979

1

1.55

0.2171

1

0.27

0.6034

eCO22

   

1

8.41

0.0051

1

1.45

0.2326

   

eT2

date:eCO2

1

1.08

0.3018

1

0.13

0.7238

1

0.26

0.6101

1

0.67

0.4163

date:eT

1

0.07

0.7865

1

1.57

0.2145

1

3.92

0.0106

1

0.04

0.8381

eCO2:eT

1

2.32

0.1321

1

6.72

0.0117

1

0.80

0.3746

1

0.02

0.8796

date:eCO22

   

1

0.37

0.5416

1

0.11

0.7442

   

date:eT2

date:eCO2:eT

1

0.07

0.7937

1

1.62

0.2079

1

0.04

0.8388

1

0.11

0.7394

  1. Statistical significances of the effect of seasonality (date), elevated CO2 (eCO2) and elevated temperature (eT) on microbial biomass carbon (MBC, µgC g−1 DM), biomass-specific growth rate (Gm, mgC h−1 g−1 MBC), microbial biomass-specific respiration (Rm, mgC h−1 g−1 MBC) and microbial carbon use efficiency (CUE). Values are derived from GLS models. eCO22 & eT2 represent quadratic functions, “:” indicates the interaction of two or three predictors. df Degree of freedom, p value < 0.05 are given in bold. (n = 26 in each month, for specific replicate number of each treatment see Methods section, Fig. 5).
Back to article page