SUPPLEMENTARY INFORMATION FOR WRIGHT ET AL “THE WORLDWIDE LEAF ECONOMICS SPECTRUM”
1. GLOPNET DATASET
(see additional file)
2. DATA SOURCES
Dataset code from leaf economics dataset
MAT Rain VPD RAD PET Refs
Ackerly_Jasper
14.6
652
0.44
170 1004 Unp
Baruch&Goldstein_Hawaii_High_Old
12.0 3000
0.59
152
852 1
Baruch&Goldstein_Hawaii_High_Rec
12.0 3000
0.59
152
852 “
Baruch&Goldstein_Hawaii_Low_Old
22.4 2182
0.64
153 1552 “
Baruch&Goldstein_Hawaii_Med_Old
18.6 3300
0.95
152 1087 “
Baruch&Goldstein_Hawaii_Med_Rec
18.6 3300
0.95
152 1087 “
7.3 1101
0.27
127
Bongers_et_al_Los_Tuxtlas
24.6 4725
0.85
163 1303 3
Cavender_Bares_Florida
20.5 1358
0.60
159 1405 Unp
Chapin_etc_Toolik_Lake
-8.8
0.14
Bassow&Bazzaz_Petersham_Ma
318
69
775 2
461 4,5,
Unp
Christodoulakis_Malakasa
16.7
635
0.72
175 1529 6,7
Chua_et_al_Malaysia
25.2 2688
0.42
146 1283 8
Coley_BCI
26.2 2606
0.51
179 1121 9,10
Cornelissen_UK_Sheffield
8.7
773
0.18
DeLucia91Ecol_Reno_Nevada
7.3
536
0.66
160 1194 11,12
DeLucia95_Okefenokee_Swamp
19.5 1303
0.55
157 1334 13
Diemer_Korner_Austria_high
-2.0 2165
0.12
118
775 14
Diemer_Korner_Austria_low
7.6 1024
0.27
119
744 “
Diemer-Ecuador_high
6.7 1040
0.58
137
800 15,16
Diemer-Ecuador_highest
5.2 1040
0.42
137
744 “
Diemer-Ecuador_low
8.1 1040
0.51
137
852 “
Diemer-Ecuador_lowest
8.7 1040
0.37
137
875 “
0.44
170 1004 17
Field_et_al_83_Jasper_Ridge
14.6
652
91
647 Unp
Franco&Luttge_Brasilia
21.9 1535
0.81
155 1431 18
Garnier_etal_F/CR
12.9
0.52
149 1233 19-21,
772
Unp
Garnier_etal_Les_Agros
14.3 1010
0.48
164 1259 “
Garnier_etal_SM/C
11.7 1148
0.45
144 1239 “
Gulias_Binifaldó
13.6 1265
0.30
154 1244 22,
Unp
Gulias_Puigpunyent
16.3
751
0.54
153 1244 “
Gulias_Sóller
16.8
857
0.62
153 1244 “
Gulias_UIB
16.8
514
0.70
153 1244 “
Hikosaka-Japan_Chiba_Japan
13.9 2288
0.33
139 1111 23
Hikosaka-Malaysia_Mt_Kinabalu_high
10.8 2842
0.45
156
815 24
7.5 2842
0.39
156
692 “
Hikosaka-Malaysia_Mt_Kinabalu_low
23.4 2842
0.23
156 1511 “
Hikosaka-Malaysia_Mt_Kinabalu_med
18.5 2842
0.57
156 1102 “
Hogan_etal_PNM_crane
26.3 1657
0.60
183 1146 25
Jayasekara_Sri_Lanka
15.2 2234
0.30
168 1012 26
Jose_Gillespie_Indiana
11.3 1049
0.41
137
943 27,28
811
0.24
124
542 29,30
26.3 1657
0.60
183 1146 31
Koike_SAPPORO__JAPAN
6.8 1216
0.23
119
667 32-34
Korner_et_al_86_Haast_Valley_NZ
9.5 5302
0.30
123
982 35
0.14
76
339 36,
Hikosaka-Malaysia_Mt_Kinabalu_highest
Jurik86_Pellston_MI
Kitajima_Panama
Kudo_Cornelissen_Abisko
5.2
-2.3
446
Unp
Kudo_Cornelissen_Latnjajaure
-3.1
978
0.18
70
331 “
Kudo_Cornelissen_Svalbard
-6.8
505
0.13
64
261 “
Kudo96_high
-2.4 1113
0.16
114
453 37
Kudo96_low
4.3 1362
0.18
114
677 “
Kuppers_Bayreuth
7.2
652
0.26
106
814 38
Lal_etal_Inceptisol
25.9 1011
1.42
192 1314 39,40
Lal_etal_Ultisol
25.6 1081
1.36
187 1286 “
Lamont_S_Africa_1_Citrusdal
15.5
0.71
182 1369 41
396
Lamont_S_Africa_10_Kylemore
15.6
714
0.43
175 1179 “
Lamont_S_Africa_11_Soetanysberg
17.9
437
0.79
192 1240 “
Lamont_S_Africa_12_Salmonsdam
15.0
828
0.74
193 1311 “
Lamont_S_Africa_13_Salmonsdam
16.1
558
0.56
171 1285 “
Lamont_S_Africa_14_Herrmanus
16.1
699
0.52
175 1285 “
Lamont_S_Africa_2_Stellenbosch
17.0
596
0.64
188 1241 “
Lamont_S_Africa_3_Opdieberg
16.6
369
0.90
202 1303 “
Lamont_S_Africa_4_Matjies_River
13.7
240
0.82
199 1509 “
Lamont_S_Africa_5_Algeria
11.3
720
0.65
202 1022 “
Lamont_S_Africa_6_Scarborough
17.0
596
0.64
188 1241 “
Lamont_S_Africa_7_Hopefield
18.0
385
0.85
195 1182 “
Lamont_S_Africa_9_Jonkershonk
12.9 1200
0.40
175 1012 “
Lamont_WA_Darling_Scarp
17.2 1100
0.93
184 1378 “
Lamont_WA_Eneabba
18.5
479
0.97
195 1607 “
Lamont_WA_Esperance
16.5
555
0.63
166 1143 “
Lamont_WA_Fitzgerald_River
16.0
574
0.65
159 1208 “
Lamont_WA_Kalbarri
20.6
369
1.05
204 1626 “
Lamont_WA_Lake_King
16.3
365
0.82
169 1174 “
Lamont_WA_Merridin_etc
17.5
304
1.03
181 1316 “
Lamont_WA_Millbrook
15.3
831
0.46
153 1265 “
Lamont_WA_Stirling_Ranges
15.5
681
0.58
153 1177 “
Lamont_WA_Walpole
14.5
794
0.61
159 1399 “
Lamont_WA_Watheroo
18.1
497
1.01
193 1604 “
Lee_Cedar_Creek2
6.3
730
0.42
127
940 Unp
Lee_NZ_Murchison_Mtns
5.5 2225
0.32
123
882 Unp
Lusk_saplings_Cordillera_Pelada
10.6 2795
0.49
167
811 42
Lusk-adults_Concepción
12.9 1308
0.35
183 1244 43,44,
Unp
765 “
6.6 1308
0.66
180
Lusk-adults_Puyehue
10.6 3200
0.26
167 1002 “
Marin_Medina_Piritu_Venezuela
25.7
506
0.70
176 1612 45
Martin_etal_Guanacaste
23.8 2220
0.55
189 1433 46
Lusk-adults_Los Lleuques
McAllister_Konza
12.6
854
0.60
148 1194 47
Mediavilla_et_al_Salamanca
11.8
513
0.58
162 1314 48
MidgelySA_Alexandria
18.4
694
0.51
161 1254 49
MidgelySA_Amatolas
12.9 1024
0.56
165 1033 “
MidgelySA_Dukuduku
19.0 1250
0.54
160 1606 “
MidgelySA_Jonkershoek_Mtn
14.9 2500
0.76
186
MidgelySA_Jonkershoek_Rip
14.9 1600
0.86
186 1046 “
MidgelySA_Knysna
15.0
870
0.60
179 1412 “
MidgelySA_Mapelane
19.0
989
0.54
160 1606 “
MidgelySA_Sand_Forest
17.7
767
0.61
162 1447 “
MidgelySA_Umtiza
18.9
790
0.59
161 1447 “
MitchellNC_Coweeta
11.6 1740
0.31
146 1035 50
Miyazawa_Chiba_M
14.7 1790
0.38
136 1125 51
Mooney_etal_81_desert
18.6
133
1.52
179 1416 52
Mooney_etal_81_old-field
10.7
982
0.45
137
Mooney_etal83_Jonk_Mtn
17.0 2500
0.93
188 1062 53
Mulkey9193_BCI_Panama
25.9 2893
0.51
179 1121 54,55
Nelson_etal_Texas
22.1
733
0.71
160 1152 56
Niinemets_Kull94_Estonia
4.8
589
0.20
98
588 57
Niinemets_Kull98_Tartu
5.3
653
0.20
99
581 58
13.9 2288
0.33
139 1111 59
565
0.29
114
23.1 1027
0.63
165 1541 60,
Nitta_Chiba_N
OleksynPol_Siemanice
Olivares_Caracas_Venezuela
8.4
985 “
953 “
657 Unp
Unp
Osada_Thomas_Pasoh
Poorter_de_Jong_Along_ditch
25.5 1875
9.3
802
0.47
0.21
148 1262 61,62
98
746 63,
Unp
Poorter_de_Jong_Chalk_grassland
9.8
804
0.26
101
588 “
Poorter_de_Jong_Dry_heath
9.8
804
0.26
101
588 “
Poorter_de_Jong_Dry_open_grassland
9.8
804
0.26
101
588 “
Poorter_de_Jong_Poor_hay_meadow
9.3
802
0.21
98
746 “
Poorter_de_Jong_Quaking_Fen
9.3
802
0.21
98
746 “
Poorter_de_Jong_Reed_marsh
9.3
802
0.21
98
746 “
Poorter_de_Jong_Wet_heath
9.8
804
0.26
101
588 “
Prado&DeMoraes1997_Sao_Carlos
20.2 1470
0.69
131 1232 64
Prior_dry monsoon forest
27.3 1575
1.18
186 1543 65
Prior_open forest
27.3 1575
1.18
186 1459 “
Prior_swamp
27.3 1575
1.18
186 1459 “
Prior_woodland
27.3 1575
1.18
186 1459 “
Pyankov_Tadjikistan_Tadjikistan_high
-2.2
164
0.61
155
430 66
Pyankov_Tadjikistan_Tadjikistan_higher
-4.4
243
0.52
155
401 “
Pyankov_Tadjikistan_Tadjikistan_highest
-5.3
225
0.49
153
465 “
2.4
470
0.32
108
714 67,68
Reichetal_Colorado
-1.5
959
0.38
142
506 69,70
Reichetal_N_Carolina
11.6 1740
0.31
146 1035 “
Reichetal_New_Mexico
13.5
272
0.96
179 1630 “
Reichetal_S_Carolina
18.2 1295
0.58
152 1020 “
Reichetal_Venezuela
26.0 3171
0.47
154
894 “
Pyankov_Urals_Yekaterinburg
Reichetal_Wisconsin
8.2
909
0.37
134
927 “
Ricklefs_SE_Ontario
6.1
883
0.28
129
740 71
Schulze_Kapalga
27.5 1370
1.24
187 1647 72
Schulze_Katherine
26.7 1079
1.48
194 1764 “
Schulze_Kidman Springs
27.1
720
1.81
199 1796 “
Schulze_Melville Island
27.3 1749
1.09
185 1397 “
Schulze_Mt_Sanford
26.2
494
2.01
206 1977 “
Shipley_Sherbrooke
4.5 1110
0.26
122
731 73
Small1972_Ottawa
5.5
898
0.30
129
763 74
Sobrado&Medina_SanCarlos_bana
26.0 3171
0.47
154
894 75
Sobrado_Charallave
24.6
917
0.67
164 1541 76
Specht_Rundel_Dark_Island_heath
15.0
467
0.64
153 1308 77
Specht_Rundel_Dark_Island_mallee
15.0
467
0.64
153 1308 “
Specht_Rundel_Mt_Lofty
12.0 1193
0.61
165
Tan_et_al_adinandra_trema_belukar
26.7 2146
0.55
146 1394 78
Terashima_Nepal
-2.8 1015
0.54
156
979 “
672 79
Tezara_etal98_Coro
Tjoelker_Cedar_Creek
26.8
495
0.96
176 1305 80
6.3
730
0.42
127
940 81,
Unp
Tuohy_etal_Zimbabwe_CHID
21.8
498
0.72
182 1355 82
Tuohy_etal_Zimbabwe_CRST_MCLW
18.9
840
0.62
179 1514 “
Tuohy_etal_Zimbabwe_MAT
18.9
623
0.78
182 1486 “
Turner_&_Tan_Adinandra_Belukar
26.7 2146
0.55
146 1394 83
Turner_&_Tan_Beach_forest
26.7 2146
0.55
146 1394 “
Turner_&_Tan_Mangroves
26.7 2146
0.55
146 1394 “
Turner_&_Tan_Undegraded_secondary_fore
26.7 2146
0.55
146 1394 “
Veneklaas_W_Australia
18.3
690
0.85
182 1272 Unp
Villar_Andalucía_mesic
17.2
609
0.66
173 1490 84
Villar_Andalucía_xeric
17.2
609
0.66
173 1490 “
Villar_California_chaparral
14.1
636
0.44
170 1004 “
Villar_California_forest
12.3 1020
0.33
172
Villar_Canary_Is_lauriphyll
16.6
394
0.56
166 1244 “
Villar_Canary_Is_xeric
16.6
394
0.56
166 1244 “
Villar_Chihuahua
18.2
349
1.19
174 1577 “
-16.1
168
0.06
Villar_Douala-Edea Forest, Cameroon
26.5 2731
0.60
124 1033 “
Villar_Kibale Forest, Uganda
21.7 1329
0.60
158 1171 “
Villar_N_Carolina_forest
15.9 1206
0.50
151
980 “
st
Villar_Devon_Is_Canada
75
920 “
207 “
Villar_Tierra_del_Fuego
4.3
787
0.26
103
531 “
Villar_Toronto
6.8
792
0.28
132
763 “
Williams et al_LosTuxtlas2
24.6 4725
0.85
163 1303 85,86
Williams_Linera_Mexico
15.8 1837
0.47
156 1231 87
Wright_Oz_syd_hiP
17.5 1148
0.63
162 1166 88-90
Wright_Oz_syd_loP
17.5 1148
0.63
162 1166 “
Wright_Oz_wnsw_hiP
17.1
412
0.95
177 1390 “
Wright_Oz_wnsw_loP
17.1
412
0.95
177 1390 “
Zotz_Fortuna_Panama
22.6 2875
0.40
184 1169 91
Notes. MAT: mean annual tremperature (oC), Rian (mm), VPD (kPa), RAD: solar
radiation (W m-2), PET (mm). Refs: Unp (unpublished).
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3. FURTHER DETAILS OF BIVARIATE TRAIT RELATIONSHIPS
The Standardised Major Axis gives the central slope through a cloud of points (e.g. the
central axis of a bivariate-normal ellipse). SMA analyses are appropriate when the
purpose is to estimate the relationship between two variables, usually when the slope of
the relationship is of primary interest. The fitting of an SMA slope involves
simultaneous minimisation of sums of squares in both Y and X dimensions. By contrast,
standard model I regression involves minimisation of sums of squares in the Y
dimension only. For many purposes this is a desirable property, especially for
calculating predictive regression equations. These equations would be appropriate if
one wanted to apply the results from the source paper to other datasets where one wants
to predict values for one trait based on known values for another trait.
This section of Supplementary Information provides further details relevant to
bivariate trait analyses presented in the source paper (Tables 1 and 3). In the following
tables the grand mean of Y and X variables for each analysis is given (Y variables in
left hand column, X variables in top row). Using these data together with SMA slope
and r2 data from Tables 1 and 3 the following parameters can be calculated:
1. Y-intercept for the Standardised Major Axis equation with Y and X variables as
treated in the text.
2. SMA slope and intercept for the analysis if Y and X variables were swapped.
3. Standard model I regression parameters (slope, intercept) for Y on X, or if Y and X
variables were swapped.
A worked example is given below the data tables. Further information on SMA analysis
and its relationship to model I regression can be found in Sokal, R. R. & Rohlf, F. J.
Biometry: the principles and practice of statistics in biological research (W. H.
Freeman and Company, New York, 1995). A free DOS-based program for running
various SMA routines, including an SMA analogue of ANCOVA, is Falster, D.S.,
Warton, D.I. & Wright, I.J. (2003) (S)MATR - (Standardised) Major Axis Tests &
Routines. Available from http://www.bio.mq.edu.au/ecology/
Mean Y, mean X data for analyses in Table 1 (source paper)
log LMA
log LL
log Amass
log Nmass
log Pmass
log Rdmass
0.942, 2.018 0.945, 1.970 0.959, 0.238 1.061, -1.041 1.068, 0.975
log LMA
2.014, 1.985 2.019, 0.224 2.133, -1.098 2.108, 0.985
log Amass
1.987, 0.246 1.845, -1.073 1.915, 0.982
log Nmass
0.105, -1.10
log Pmass
0.181, 0.983
-1.245, 0.920
Mean Y, mean X data for analyses in Table 3 (source paper)
log LMA
log LL
log Aarea
log Narea
log Parea
log Rdarea
0.942, 2.108 0.942, 0.997 0.947, 0.253 1.056, -0.966 1.068, 0.117
log LMA
2.014, 0.999 2.019, 0.241 2.133, -0.971 2.108, 0.093
log Aarea
1.008, 0.272 0.958, -0.929 1.038, 0.104
log Narea
log Parea
0.240, -0.957 0.292, 0.099
-1.062, 0.147
Worked example
We use SMA data for log LL (Y) on log LMA (X). SMA slope = 1.71; mean Y = 0.942,
mean X = 2.018, r2 = 0.422 (Table 1, source paper).
1. Just as for model I regression, a Standardised Major Axis slope always passes
through the point (mean X, mean Y). Hence, the intercept is given by Y – bX =
0.942 – 1.71 * 2.018 = - 2.509
2. If the Y and X variables were swapped, the SMA slope for log LMA on log LL
would be the reciprocal of that for log LL on log LMA, i.e. 1/1.71 = 0.585. As
before, the intercept is then calculated from the slope and data for mean Y and X, in
this case = 2.018 – 0.585 * 0.942 = 1.467. As before, the r2 value for the relationship
is 0.422.
3. A SMA slope for Y on X is equal to the model I regression slope of Y on X divided
by the correlation r value for the variables. The r2 value and hence correlation r are
the same irrespective of whether one is interested in calculating SMA or model I
regression parameters. Hence, the model I regression slope for log LL on log LMA
= (0.422)0.5 * 1.71 = 1.11. Since the regression slope passes through the point (mean
X, mean Y), the Y-intercept can be calculated as above, i.e. = 0.942 – 1.11 * 2.018 =
- 1.30. Similarly, parameters for the model I regression of log LMA on log LL can
be calculated from the SMA parameters for log LMA on log LL, i.e. regression
slope = (0.422)0.5 * 0.585 = 0.38; intercept = 2.018 – 0.38 * 0.942 = 1.66.
Geometrically, the SMA slope bisects the model I slopes Y on X and 1 / (X on Y),
i.e. it is the geometric mean of the two. Taking the model I slopes for log LL on log
LMA (1.11) and log LMA on log LL (0.38), the SMA slope could be calculated as
1.11 *1 / 0.38 = 1.71 = 1.11 / (0.422)0.5 (the model 1 slope divided by the
correlation r).
4. DETAILS OF MULTIPLE REGRESSION EQUATIONS MENTIONED IN
THE SOURCE PAPER
Standard errors for regression coefficients are given in parentheses for the following
regression equations. All coefficients were highly significant (p < 0.0001) except where
noted. r2 values describe the explanatory power of each model, sample n refers to the
number of species included in each analysis. Units (prior to log10 transformation in
many cases): Amass nmol g-1 s-1; Aarea mol m-2 s-1; Nmass %; Narea g m-2; LMA g m-2;
Rdmass nmol g-1 s-1; rain mm y-1; MAT oC; VPD kPa; PET mm y-1; RAD W m-2; (Ca –
Ci) ppm CO2; gs mmol m-2s-1.
1. Photosynthetic capacity (mass basis, Amass) on leaf N per mass (Nmass) and leaf mass
per area (LMA)
log Amass = 0.74 (0.05) log Nmass – 0.57 (0.04) log LMA + 2.96 (0.09)
r2 = 0.63, n = 706
2. Photosynthetic capacity (area basis, Aarea) on leaf N per area (Narea) and LMA
log Aarea = 0.69 (0.05) log Narea – 0.28 (0.04) log LMA + 1.40 (0.07)
r2 = 0.20, n = 705
3. Dark respiration rate (mass basis, Rdmass) on Nmass and LMA
log Rdmass = 0.75 (0.07) log Nmass – 0.35 (0.05) log LMA + 1.59 (0.12)
r2 = 0.62, n = 267
4. Dark respiration rate (areas basis, Rdarea) on Narea and LMA
log Rdarea = 0.68 (0.08) log Narea – 0.03 (0.06) log LMA – 0.03 (0.11)
r2 = 0.34, n = 267. P-value for LMA = 0.568, P-value for intercept = 0.755
4. LMA on site mean annual temperature (MAT) and rainfall.
log LMA = 0.015 (0.001) MAT – 0.25 (0.02) log rain + 2.56 (0.06)
r2 = 0.15, n = 2370
5. Leaf lifespan (LL) on LMA and rainfall.
log LL = 1.23 (0.05) log LMA + 0.47 (0.04) log rain – 2.95 (0.18)
r2 = 0.51, n = 678
6. LL on LMA and MAT
log LL = 1.70 (0.08) log LMA – 0.048 (0.005) [log LMA * MAT] + 0.106 (0.010)
MAT – 2.59 (0.16)
r2 = 0.54, n = 678
7. LL on LMA and vapour pressure deficit (VPD)
log LL = 0.580 (0.074) log LMA – 1.56 (0.18) [log LMA * log VPD] + 3.37 (0.37)
LAVPD – 0.118 (0.155)
r2 = 0.49, n = 678. P-value for intercept = 0.446.
8. LL on LMA and Penman-Monteith potential evapotranspiration (PET)
log LL = 2.69 (0.16) log LMA – 0.0015 (0.0001) [log LMA * PET] + 0.0031 (0.0003)
PET – 4.63 (0.32)
r2 = 0.52, n = 678
9. LL on LMA and irradiance (RAD)
log LL = 3.01 (0.23) log LMA – 0.013 (0.001) [log LMA x RAD] + 0.030 (0.003) RAD
– 5.56 (0.47)
r2 = 0.52, n = 678
10. CO2 drawdown (Ca – Ci) on Narea and stomatal conductance to water (gs)
(Ca – Ci) = 64.8 (9.98) log Narea – 107.2 (6.56) log gs + 344.3 (15.59)
r2 = 0.47, n = 343
11. Aarea on gs and rainfall
log Aarea = 0.455 (0.023) log gs – 0.333 (0.025) log rain + 0.928 (0.091)
r2 = 0.52, n = 498.
5. PCA (PRINCIPAL COMPONENTS ANALYSIS) LOADINGS FOR AREABASED ANALYSIS OF LEAF TRAITS
Traits: leaf mass per area (LMA), leaf lifespan (LL), photsynthetic capacity (Aarea), dark
respiration rate (Rdarea), leaf N per area (Narea), leaf P per area (Parea). All traits logtransformed prior to analysis. Axis 1 explained 43% of the total trait variation, axis 2
explained a further 29% (total 72 %).
Trait
Loading on axis 1
Loading on axis 2
LL
0.25
0.91
LMA
0.67
0.61
Narea
0.91
0.03
Parea
0.66
-0.23
Aarea
0.44
-0.62
Rdarea
0.80
-0.30