Front. Plant Sci. Frontiers in Plant Science Front. Plant Sci. 1664-462X Frontiers Media S.A. 10.3389/fpls.2020.00915 Plant Science Correction Corrigendum: Multi-Dimensional Plant Element Stoichiometry—Looking Beyond Carbon, Nitrogen, and Phosphorus Ågren Göran I. 1 * Weih Martin 2 1 Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden 2 Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden

Edited and reviewed by: Sebastian Leuzinger, Auckland University of Technology, New Zealand

*Correspondence: Göran I. Ågren, goran.agren@slu.se

This article was submitted to Functional Plant Ecology, a section of the journal Frontiers in Plant Science

 03 07 2020 2020 11 915 06 05 2020 04 06 2020 Copyright © 2020 Ågren and Weih 2020 Ågren and Weih

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 A Corrigendum on Multi-Dimensional Plant Element Stoichiometry—Looking Beyond Carbon, Nitrogen, and Phosphorus by Ågren GI and Weih M. (2020). Front. Plant Sci. 11:23. doi: 10.3389/fpls.2020.00023 ecological stoichiometry elementome ionome homeostasis mineral nutrients plant growth scaling stoichiometric niche volume

In the original article, the numerical values of the scaling exponents in the text, tables, and figures were incorrectly stated. This was due to a misinterpretation of the algorithm for calculating the scaling exponent. We provide here revised tables and figures ( Figures 3 , 4 , 5 , Tables 1 , 4 , 5 ) and changes in the text, where the value of the scaling exponent is important.

Scaling exponents with 95% confidence intervals as a function of limiting element in the Birch data set. All refers to the scaling exponent when data for all limiting elements are combined.

Scaling exponents as function of site age in the Hawaii data set. The lowest age is the value for the scaling taken over all ages.

Scaling exponent as a function of the number of elements included in VOth. The scaling exponent for n = 1 is the average of the scaling exponents in Table 4 .

Summary of data sets used.

Tomato Birch Ideal Wheat1 Wheat2 CO2-exp Salix ICP IBP Hawaii
# of samples 16 46 20 70 32 40 115 200 29 62
Location Lab Lab Lab Sweden Sweden World Sweden Europe World Hawaii
N, mg/g * * * * * * * * * *
P, mg/g * * * * * * * * * *
K, mg/g * * * * * * * * * *
Ca, mg/g * * * * * * * *
Mg, mg/g * * * * * * * * * *
S, mg/g * * * * * * *
Cu,µg/g * * * (*) *
B, µg/g * (*) *
Zn,µg/g * * * (*) * *
Fe, µg/g * * * * * *
Mn, µg/g * * * * * *
α 1.209 ± 0.341 1.363 ± 0.587 0.541 ± 03772 1,190 ± 0.177 0.746 a ± 0.221 0.207 ± 0.070 -1.262 ± 0.200 1.976 ± 0.125 1.676 ± 0.351 -3.804 ± 0.258
r 2 0.93 0.48 0.97 0.93 0.79 0.67 0.00 0.31 0.23 0.06

Scaling computed for harvests BBCH23 (3 tillers detectable) plus BBCH37 (beginning of stem elongation).Asterisks, i.e. “*” in a column indicate that the element is included in the data set, and. α RMA are the RMA scaling exponents between VNP andVOth with 95% confidence intervals with only K, Ca, and Mg (K and Mg in Ideal) included in Oth. For data sets with more than one subset, the scaling is for the entire set. r is the Ordinary Least-Squares regression coefficient. All r2,except for CO2, Salix and Hawaii, are significant at 1% level.

RMA scaling exponents (α RMA ) with 95% confidence intervals for regressions between ln(VNP ) and ln(VOth) for data sets that can be split into subsets. For Wheat2 All refers to BBCH23 plus BBCH37. All r2, except for CO2 and Salix, are significant at 1% level.

Data set Treatment n α RMA r 2
Tomato All 16 1.136 ± 0.075 0.93
6 8 1.133 ± 0.269 0.97
18 8 1.138 ± 0.117 0.97
Wheat1 All 70 0.984 ± 0.082 0.93
BBCH23 a 14 0.980 ± 0.132 0.87
BBCH37 c 28 1.005 ± 0.093 0.92
BBCH65 e 28 0.949 ± 0.194 0.82
Wheat 2 All 39 1.025 ± 0.080 0.50
BBCH31 b 20 0.964 ± 0.165 0.57
BBCH61 d 19 1.154 ± 0.110 0.53
Salix All 115 1.382 ± 0.025 0.00
C 48 1.144 ± 0.088 0.05
W+F 67 1.148 ± 0.022 0.07
CO2-exp All 40 1.150 ± 0.024 0.02
A 20 1.170 ± 0.060 0.02
E 20 1.127 ± 0.038 0.12

BBCH23 3 tillers detectable.

BBCH31 beginning of stem elongation.

BBCH37 flag leaf visible.

BBCH61 beginning of flowering.

BBCH65 full flowering.

RMA scaling exponents (α RMA ) with 95% confidence intervals for regressions between ln(VNP ) and one single elements calculated from the combined Wheat1 , Wheat2, plus ICP data and the Ideal data sets respectively. Note that Ca is missing in the Ideal data set.

Element Mg S Cu Ca K Zn Fe Mn
Combined 0.524± 0.477± 0.927± 1.030 0.698± 0.611± 1.400± 2.666±
0.120 0115 0.118 0.110 0.101 0.127 0.094 0.122
Ideal 0.297± 0.449± 0.307± 0.2487± 0.742± 0.582± 0.394±
0.480 0306 0.473 0.259 0.349 0.157 0.287

In Abstract the text should read

We show that the scaling exponent is rather insensitive to environmental conditions or species and ranges from -3.804 to 1.976 (average 0.384) in nine out of ten data sets. For single elements, Mg has the smallest scaling exponent (0.524) and Mn the largest (2.666). In two of the ten data sets the scaling exponent is negative but positive in the other eight sets.

In Materials and Methods, sub-section Theory

“…the range cn,max – cn,opt of the response niche…” should read

“…the range cn,min – cn,opt of the response niche…”

In Results

“We note that taken over all data sets the scaling between N and P is P ∝ N 1.04±0.02” should read

“…the scaling between N and P is P ∝ N 0.21±0.05”

The last sentence of Results should be replaced by

Figure 5 shows that the scaling exponent increases linearly with number of elements (n) (α = -0.7687+0.8044n, r2 = 0.8449).

In Discussion (last sentence of first paragraph) the text should be replaced by

We no longer observe a trend with site age in the Hawaii data set. We have no explanation for the negative scaling exponent in this data set. On the other hand, the negative scaling exponent in the Salix data set is a result of dilution of some micronutrients caused by increased growth by heavy NP fertilization.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.