Ecological Archives E096-130-A2

Carly J. Stevens, Eric M. Lind, Yann Hautier, W. Stanley Harpole, Elizabeth T. Borer, Sarah Hobbie, Eric W. Seabloom, Laura Ladwig, Jonathan D. Bakker, Chengjin Chu, Scott Collins, Kendi F. Davies, Jennifer Firn, Helmut Hillebrand, Kimberly J. La Pierre, Andrew MacDougall, Brett Melbourne, Rebecca L. McCulley, John Morgan, John L. Orrock, Suzanne M. Prober, Anita C. Risch, Martin Schuetz, and Peter D. Wragg. 2015. Anthropogenic nitrogen deposition predicts local grassland primary production worldwide. Ecology 96:14591465. http://dx.doi.org/10.1890/14-1902.1

Appendix B. Anthropogenic nitrogen deposition predicts local grassland primary production worldwide: Site information and expanded results.

Contents:

Table B1. List of site locations, along with mean climatic and plot variables.

Table B2. Coefficient of variation of variables used to predict ANPP (variables were standardized in analysis). CV is defied as 100*SD/Mean where SD is the standard deviation.

Fig. B1. Posterior predictive model checking and Bayesian p value of best hierarchical regression model.

Fig. B2. Parameter estimates for model explaining ANPP without N deposition.

Fig. B3. Modeled relationship of N deposition and ANPP with and without the highest N deposition site

Fig. B4. Interactions between N deposition and plot-level soil-ANPP relationships.

Figure B5: Relationship of ANPP to MAP by region

Figure B6: Relationship of ANPP to N deposition by region.

 

Table B1. Site location, climate, and production data.

site_code

latitude

longitude

elevation (m)

MAT (C)

MAP (mm)

PET (mm)

Atmospheric N (kg/ha/y)

mean soil pH

mean soil C (%)

mean soil N (%)

mean soil P (ppm)

mean production (g/m²)

amcamp.us

48.465

-123.014

41

9.8

557

740

3.68

5.56

7.31

0.48

64.80

441.01

azi.cn

33.670

101.870

3500

2

667

852

9.58

5.85

6.19

0.54

68.97

369.18

barta.us

42.245

-99.652

767

8.7

597

1086

6.99

5.99

0.64

0.06

17.37

205.79

bldr.us

39.972

-105.234

1633

9.7

425

1151

1.94

6.82

1.37

0.10

16.50

167.00

bnch.us

44.277

-121.968

1318

5.5

1647

860

2.84

5.55

8.87

0.61

13.83

141.99

bogong.au

-36.874

147.254

1760

5.7

1592

833

5.15

4.47

8.78

0.58

44.37

416.19

bttr.us

44.280

-121.957

1500

5

1718

852

2.84

5.58

11.98

0.96

22.47

231.33

burrawan.au

-27.735

151.140

425

18.4

683

1573

2.25

5.61

1.30

0.08

17.07

272.37

cbgb.us

41.785

-93.385

275

9

855

1007

18.00

6.09

0.63

0.06

62.89

243.25

cdcr.us

45.401

-93.201

270

6.3

750

898

6.98

5.67

0.80

0.06

59.22

196.92

cdpt.us

41.200

-101.630

965

9.5

445

1127

3.12

6.61

1.40

0.12

32.67

120.39

cowi.ca

48.460

-123.380

50

9.8

764

713

3.68

5.62

5.17

0.39

40.57

469.45

derr.au

-37.807

144.791

38

14.5

574

1100

2.15

5.92

2.36

0.17

8.08

123.17

fnly.us

44.410

-123.280

68

11.3

1104

1015

2.84

5.23

3.53

0.23

13.68

257.51

frue.ch

47.113

8.542

995

6.5

1355

650

18.63

5.49

3.76

0.37

71.10

616.86

gilb.za

-29.284

30.292

1748

13.1

926

1194

5.03

5.14

20.57

1.16

17.70

280.53

glac.us

46.869

-123.034

33

10.5

1311

945

3.68

5.11

15.67

1.15

23.77

164.67

hall.us

36.872

-86.702

194

13.6

1282

1218

14.29

5.16

1.45

0.13

33.17

441.10

hart.us

42.724

-119.498

1508

7.4

272

1087

2.02

7.22

1.21

0.10

66.63

175.71

hnvr.us

43.419

-72.138

271

6.4

1033

912

19.35

5.05

4.43

0.38

63.93

442.25

kiny.au

-36.200

143.750

90

15.5

426

1321

2.15

6.05

1.24

0.11

10.13

151.50

lancaster.uk

53.986

-2.628

180

8

1322

599

12.96

4.77

20.13

1.07

32.69

156.48

lead.us

46.615

-124.048

2

9.9

2072

766

3.68

5.10

12.39

0.83

145.20

467.39

look.us

44.205

-122.128

1500

4.8

1898

820

2.84

5.07

16.66

1.20

51.73

98.13

mtca.au

-31.782

117.611

285

17.3

330

1505

1.07

5.26

1.38

0.09

9.13

104.15

pape.de

53.086

7.473

1

8.9

783

666

35.32

4.02

16.50

0.66

62.30

917.76

sage.us

39.430

-120.240

1920

5.7

882

1028

3.36

6.06

8.91

0.68

35.83

121.12

sava.us

33.344

-81.651

71

17.3

1194

1432

10.32

5.00

0.78

0.03

47.60

58.14

sedg.us

34.700

-120.017

550

14.9

521

1278

1.14

6.83

2.27

0.19

66.13

197.35

sereng.tz

-2.255

34.513

1536

22.1

854

1662

7.37

6.76

1.64

0.13

67.00

308.05

sevi.us

34.359

-106.691

1600

12.6

252

1473

1.96

8.34

0.32

0.03

33.53

95.38

sgs.us

40.817

-104.767

1650

8.4

365

1122

3.12

6.12

0.84

0.09

64.87

129.56

shps.us

44.243

-112.198

910

5.5

262

978

1.85

7.88

2.40

0.21

37.55

99.37

sier.us

39.236

-121.284

197

15.6

935

1340

3.36

5.97

2.27

0.18

17.30

180.86

smith.us

48.207

-122.625

62

9.8

597

767

3.68

6.06

7.23

0.54

74.43

392.68

spin.us

38.136

-84.501

271

12.5

1140

1139

13.88

6.39

2.64

0.26

229.17

468.42

summ.za

-29.812

30.716

679

18.2

939

1282

5.03

5.14

6.50

0.33

12.13

313.08

temple.us

31.044

-97.349

184

19.1

871

1463

7.25

7.68

9.82

0.37

19.85

317.32

tyso.us

38.519

-90.565

169

12.5

997

1178

11.13

5.88

3.05

0.27

23.88

440.66

ukul.za

-29.670

30.400

843

18.1

880

1393

5.03

5.70

5.13

0.32

9.27

468.98

unc.us

36.008

-79.020

141

14.6

1163

1275

13.12

5.28

2.17

0.16

21.57

304.18

valm.ch

46.631

10.372

2320

0.3

1098

442

18.91

5.64

7.86

0.61

45.83

321.98

 

Table B2. Coefficient of variation of variables used to predict ANPP (variables were standardized in analysis). CV is defied as 100*SD/Mean where SD is the standard deviation.

Predictor

N

Coefficient of Variation

MAP

42

49.46

PET

42

25.27

MAT

42

45.30

MAP_VAR

42

48.02

TEMP_VAR

42

39.87

N_deposition

42

83.59

pH

1310

15.08

SoilN

1310

93.92

SoilP

1310

87.88

 

a)

FigB1a

b)

FigB1bb)

Fig. B1. Posterior model preictions and model checking.

(a) Observed (x-axis) vs. predicted mean ANPP by site. Error bars are 95% Credible intervals on predictions.

(b) Posterior-predictive check, performed by drawing one random observation from the model for each observed data point, creating “perfect” residuals. These are plotted against actual residuals from the model. A Bayesian pvalue (the mean fraction of perfect residuals greater than the actual residuals) is extremely close to 0.5, indicating appropriate model construction.


 

FigB2

Fig. B2. Results of multi-level model excluding N deposition. The multi-level model was run as described in the main text but without N deposition included as a site-level predictor. Despite mild correlation between PET and N deposition values across sites, PET is not a good predictor of ANPP either with or without N deposition in the model. The model without N deposition (below) had a Deviance Information Criterion (DIC, a Bayesian generalization of Akaike’s Information Criterion) of 1689.3, whereas the model including N deposition (main text) had a much lower DIC of 1626.2.


 

FigB3

Fig. B3. Results of model using dataset excluding highest N deposition site. The multi-level model was run exactly as in the main text but with the omission of the Papenburg, Germany (pape.de) site, which had extraordinarily high N deposition values. N deposition remained a strong and significant predictor of site production. Solid line and dark shading: full model from main text; dashed line and light shading: N deposition influence on site intercept excluding the highest N deposition site.


 

FigB4

Fig. B4. Interactions between site- and plot-level predictors in the multi-level model. Here the slope estimate of primary production (ANPP) to edaphic conditions (soil pH, N, C, and P) within each site is related to the site-level N deposition. A significant ‘interaction’ would be revealed by predictability of within-site slopes by across-site variance in N deposition. No such interactions appeared in the model.


 

FigB5

Fig. B5. Relationship of ANPP to MAP by region. Shown are raw data points (plot-level ANPP measurements) vs. site-level MAP. Lines are best fit linear slopes by region. Regions represent coherent geographic land masses, although sites are not equally replicated within regions.


 

FigB6

Fig. B6. Relationship of ANNP to N deposition by region. Shown are raw data points (plot-level ANPP measurements) vs. site-level N deposition. Lines are best fit linear slopes by region. Regions represent coherent geographic land masses, although sites are not equally replicated within regions.


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