Supplementary information for Wright et al “The world-wide leaf economics spectrum”
1.Glopnet dataset
(see additional file)
2.data sources
Dataset code from leaf economics dataset / MAT / Rain / VPD / RAD / PET / RefsAckerly_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 / “
Bassow&Bazzaz_Petersham_Ma / 7.3 / 1101 / 0.27 / 127 / 775 / 2
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 / 318 / 0.14 / 69 / 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 / 91 / 647 / Unp
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 / “
Field_et_al_83_Jasper_Ridge / 14.6 / 652 / 0.44 / 170 / 1004 / 17
Franco&Luttge_Brasilia / 21.9 / 1535 / 0.81 / 155 / 1431 / 18
Garnier_etal_F/CR / 12.9 / 772 / 0.52 / 149 / 1233 / 19-21, 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
Hikosaka-Malaysia_Mt_Kinabalu_highest / 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
Jurik86_Pellston_MI / 5.2 / 811 / 0.24 / 124 / 542 / 29,30
Kitajima_Panama / 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
Kudo_Cornelissen_Abisko / -2.3 / 446 / 0.14 / 76 / 339 / 36, 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 / 396 / 0.71 / 182 / 1369 / 41
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
Lusk-adults_Los Lleuques / 6.6 / 1308 / 0.66 / 180 / 765 / “
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
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 / 985 / “
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 / 953 / “
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
Nitta_Chiba_N / 13.9 / 2288 / 0.33 / 139 / 1111 / 59
OleksynPol_Siemanice / 8.4 / 565 / 0.29 / 114 / 657 / Unp
Olivares_Caracas_Venezuela / 23.1 / 1027 / 0.63 / 165 / 1541 / 60, Unp
Osada_Thomas_Pasoh / 25.5 / 1875 / 0.47 / 148 / 1262 / 61,62
Poorter_de_Jong_Along_ditch / 9.3 / 802 / 0.21 / 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 / “
Pyankov_Urals_Yekaterinburg / 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 / “
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 / 979 / “
Tan_et_al_adinandra_trema_belukar / 26.7 / 2146 / 0.55 / 146 / 1394 / 78
Terashima_Nepal / -2.8 / 1015 / 0.54 / 156 / 672 / 79
Tezara_etal98_Coro / 26.8 / 495 / 0.96 / 176 / 1305 / 80
Tjoelker_Cedar_Creek / 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_forest / 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 / 920 / “
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 / “
Villar_Devon_Is_Canada / -16.1 / 168 / 0.06 / 75 / 207 / “
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 / “
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:
- Y-intercept for the Standardised Major Axis equation with Y and X variables as treated in the text.
- SMA slope and intercept for the analysis if Y and X variables were swapped.
- 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
Mean Y, mean X data for analyses in Table 1 (source paper)
log LMA / log Amass / log Nmass / log Pmass / log Rdmasslog LL / 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 / 0.181, 0.983
log Pmass / -1.245, 0.920
Mean Y, mean X data for analyses in Table 3 (source paper)
log LMA / log Aarea / log Narea / log Parea / log Rdarealog LL / 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 / 0.240, -0.957 / 0.292, 0.099
log Parea / -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).
- 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
- 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.
- 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 = 1.71 = 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; Aareamol 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)