Supplementary Information:
Text
Figures S1 and S2
Tables S1-S15
References
Data
The types of cells comprising multicellular organisms in our database include the
following cell types and tissues: the midgut epithelium, palisade mesophyll, bundle
sheath, mesophyll, apical meristem, cotyledon, hypocotyl, exocrine pancreatic cells,
sporophyte, lymphocyte, upper epidermal, pinealocyte, ventricular muscle, brain,
chondrocyte, unfertilized egg, liver, pancreas, chondrocytes, moss rhizoid cells, root
meristem, moss rhizoid cells, cholangiocytes, pericarp, spongy mesophyll, leidig,
photobiont, mycobiont, sporogenous tissue of anther, tapetum tissue of anther, and
oocyte. In many cases, cells in different parts of an organ or tissue have vastly different
sizes and metabolic specializations, in which case multiple cell types were assigned to
some tissues following the categories employed by the authors of papers from which we
obtained the data. For example, our data include lower and upper proliferative
chondrocytes, lower and upper hypertrophic chondrocytes, and reserve chondrocytes. In
data for a few unicellular species, the organisms had profoundly different life stages
during their cell cycle and were thus separated into different cell types, such ellipsoidal
and promastigote cells types in Leishmania braziliensis. We omitted hydrogenosomes
and mitosomes from our analyses because they have a largely different function than
mitochondria – these reduced organelles do not contribute to cellular respiration. Also,
few data were found that would have allowed us to compare their scaling to mitochondria
and chloroplasts.
Some cells under certain conditions can form reticulated networks of
mitochondria. In fully reticulated networks, there are so many mitochondria in the cell
that individual mitochondria appear physically merged with other mitochondria (either in
appearance or reality). In these cases, accurate stereological counting of mitochondria
becomes impossible. These few data, which represented seven different species, were
1
thus excluded from our analyses (Table S15). In nearly all cases we were able to find
other studies of the same species and cell type that did not have such fully reticulated
networks and that could be used to provide estimates of the number and size of
mitochondria.
We classified each cell according to whether it is a unicellular organism or
comprises a cellular unit of a multicellular organism. We only assigned an organism as
multicellular if it followed the following criteria: (i) cell-cell adhesion, (ii) cell-cell
communication and coordination, and (iii) programmed cell death. Thus, cells that may
grow in colonies or filaments were classified as unicellular. Of course, it might be
reasonably argued that some cells fall along a continuum between unicellularity and
multicellularity. Yet this perspective does not undermine our approach, as under this
perspective the categories of unicellularity and multicellularity can simply be considered
operational variables necessary for analysis.
We also classified cells according to trophic group. We included only
photoautotrophic and chemoheterotrophic cells in our analyses, as we had insufficient
data for exploring scaling in mixotrophic cells. In all cases, cells in the chemoheterotroph
category were cells grown in conditions in which they used organic carbon as both an
energy source and carbon source. Photoautotrophic cells were cells growing in conditions
in which they relied entirely on light as an energy source and inorganic carbon as a
source of carbon (no photoheterotrophically growing cells were used in our analyses).
Note that in a couple species, such as Chlorella vulgaris, the cells could be
photoautotrophic or chemoheterotrophic depending on growth conditions or the strain.
So, when data were available for both trophic growth experiments or strains, these
species were included in both categories.
We are in the process of curating the database to publish it as a data paper. In the
interim, data are available upon request.
Data Analysis
Unless otherwise noted, residuals of regression analyses were approximately normally
distributed and homoscedastic.
2
As noted by others [1,2], the choice of regression method for a particular study in
comparative biology should be based on the objectives of the study and a priori theory
and knowledge concerning the assigning of natural variation between the two variables.
To estimate scaling exponents, we emphasize reliance on Reduced Major Axis regression
(the standard in allometry) rather than OLS regression (type 1 regression) for our study.
Numerous authors have argued that RMA is more appropriate when investigating “lawlike”, functional, and structural relations between variables, especially when substantive
biological error (natural variation not attributable to measurement or sampling error, aka
equation error) is expected in both variables [2–6]. Our use of RMA follows these
authors’ advice, since this is the objective of our regression analysis and evolutionary,
physiological, and ecological factors certainly cause substantive natural variation in both
the ordinate and the abscissa axes (e.g., for a given oordinate value, such as the number
of organelles, there could be variation in log cell volume due to the particular cell type
and its function, which may require allocation of biomass to other organelles such as
vacuoles).
Recently, however, some authors have emphasized a different rationale, suggesting
instead that the symmetry or direction of causality is also a key factor determining which
regression method to use when investigating scaling exponents [1,2]. In our case, the
direction of causation is not clear and in fact it is likely bidirectional: variation in cell size
does not necessarily cause variation in number of organelles; rather, other genetic factors
working in conjunction with the cytoskeleton may regulate both cell size and the number
of organelles, and changes in the number of organelles due to changes in metabolic
demand or environmental conditions could in turn lead to changes in cell size. At longer
timescales that lead to interspecific differences, differences in cell size and organelle
abundance or size between cell types or species still reflect underlying differences in the
niche of the cell and regulation of both of these variables, and changing metabolic
demands on certain cell types may lead to increased number of organelles, which may in
turn necessitate larger cell volumes. The same kind of reason applies to the relationship
between metabolic rate and organelle or cell size. Thus, we report RMA regression
exponents. RMA confidence intervals around exponents were calculated using the OLS
intervals, as suggested by allometry researchers [4,5].
3
For the same reasons as described above, using type 1 regression to estimate the
organelle metabolic scaling slope in Equation 2.2 may be inappropriate for our purposes,
and there is a stronger theoretical and statistical basis for employing type 2 regression.
However, multiple regression is required to estimate the metabolic scaling exponents and
we are not aware of equivalent multiple regression techniques that use a type 2 regression
model. Thus, we developed an approach that accounts for this issue, which we describe
below.
In bivariate linear regression, RMA slopes can be calculated by taking the geometric
mean of the OLS slopes and the inverse-OLS slope [7]. Similarly, in order to estimate
metabolic scaling exponents from Equation 2.2, we used the GLM inverse-estimated
OLS slopes to provide “GLM-adjusted” RMA slopes. These were calculated by taking
the geometric mean of the GLM OLS slope and GLM inverse-estimated OLS slope. For
example, to estimate the slope (  org ) for the scaling of mitochondrion metabolic rate with
cell volume, a GLM was run with log Nmt as the response variable and log Vmt , log Vc ,
unicellularity/multicellurity, and trophic lifestyle as the predictors, thereby allowing for
the estimating of the GLM OLS slopes in Equation 2.2:
log Nmt   mt log Vmt   c logVc  log C (factors omitted here for simplicity of
presentation). Next, we re-performed the GLM analysis but with log Vmt as a response
variable and log Nmt and log Vc as predictors, along with the other aforementioned
predictors, thereby estimating  mt from the slope coefficient for log Nmt , as seen in a
rearrangement of Equation 2.2: log Vmt  ( c /  mt ) log Vc  (1/  mt ) log N mt  (1/  mt ) log C .
This allowed us to determine the GLM inverse-estimated OLS value for  org . The
“GLM-adjusted” RMA slope was then calculated by taking the geometric mean of the
GLM OLS slope and GLM inverse-estimated slope.
We repeated this method to estimate the metabolic scaling slopes for chloroplast
photosynthetic rates, cell respiration rates, and cell photosynthetic rates. For example,
estimation of the cellular photosynthetic rates are performed by estimating  c in the
equation log N cp   cp log Vcp   c log Vc  log C and the inverse-OLS estimation of 
4
using the equation log Vc  ( cp /  c ) log Vcp  (1/  c ) log N cp  (1/  c ) log C . In Tables S7S12 we report the GLM statistics used to calculate the adjusted slopes.
Although it could be interesting and informative, we choose to abstain from
employing phylogenetic comparative methods such as independent contrasts in order to
estimate scaling slopes and their significance for the following reasons. First, it is beyond
the scope of this paper, which presents a first-pass analysis at this large, new database,
which took years to develop – using phylogenetic comparative methods would further
delay communication of results and lead to a much longer paper inappropriate for a broad
audience. Second, there are still many unresolved issues with the Eukaryote tree that
could lead to incorrect conclusions. In particular, it is not clear how trees, which assume
no horizontal gene transfer, should incorporate the numerous horizontal gene transfers
that occurred in different photosynthetic lineages resulting from primary, secondary, and
tertiary endosymbioses, which occurred in many of our species [8,9]. A third related
issues is that there exists even less knowledge about the phylogenetic relationships
between all the different primary, secondary, and tertiary chloroplasts. Since the
chloroplasts’ genomes and phylogenies may affect their intracellular number, size, and
metabolic scaling, a phylogenetically-informed analysis of our data should incorporate
both the phylogenetic histories of the host cells and of the chloroplasts, which would
require the development of new methods and theory beyond the scope of this paper.
Fourth, a likely much stronger effect on the statistical independence of our data points is
the effect of cell type on the scaling relationships, but examining these effects, as well as
phylogenetic effects, is beyond the scope of this paper, requiring more involved analyses
and theory that we are planning for a second follow-up paper. Finally, we mostly have
tight correlations in our data, reducing the probability that phylogenetic independence
contrasts would more than minimally change the estimation of scaling relations [1].
Additional Scaling Theory and Discussion
If the metabolic rate under investigation is meant to represent the total power produced
by oxidation of organic matter in eukaryotes, then Bc  Nmt Bmt / (1  cs ) , where cs is the
proportion of the total metabolic rate that results from substrate-level phosphorylation in
5
the cytosol. cs must be a small fraction of total metabolic rate, since in total only 2 ATP
per glucose are produced by substrate-level phosphorylation in the cytosol compared to
the up to 30 ATP yielded by the mitochondria per glucose molecule [10]. These
differences in ATP production are biochemically constrained [10].Thus it is unlikely that
cs can vary much across aerobic life, and thus a first-order assumption is that cs is
invariant of cell size in aerobic organisms.
borg can be altered through biochemical adjustments such as increasing the
surface density of electron transport chains on inner mitochondrion membranes. Changes
in the ultrastructure and shape of organelles can also influence borg by altering the
surface-area-to-volume ratio and scaling of organelle surfaces with their volumes [11].
However, there are functional tradeoffs and biophysical and geometric limits to the
density of enzymes on membranes [12] and the alteration of organelle ultrastructure [11]
that constrain the degree to which organisms can alter borg . We are not aware of any
evidence suggesting that the density of respiratory machinery in the mitochondria and
shape and fractal geometry of mitochondria may differ between small and large cells.
Thus, it seem unlikely that borg varies very much with cell size. In any case, variation in
borg with cell size would only affect our estimate of  c in Equation 2.2, not our results
regarding  org , r, q, or  , and would have to vary a lot in order to have a strong impact
on our estimate of  c . For example, if borg changed by two-fold across the five orders of
magnitude variation in cell volume of our data, this would only alter the estimates of  c
by 0.06 and would not alter the estimate of  org . The effect of variation in borg can be
formally incorporated into the scaling theory and method for estimating metabolic scaling

exponents: If borg scales with Vc as borg  z0Vcz1 , then Norg  (1  cs )bc z01Vorg org Vcc  z1
should be used in place of Equation 2.2 and  c  r  q org  z should be used in place of
Equation 2.3.
The metabolic scaling exponents of organelles determines the whole-cell
organellar scaling strategy that maximizes mass-specific metabolic rate and minimizes
6
biomass allocation to the organelles. If the organelle’s metabolic scaling is sublinear (
 org  1 ), then the optimal strategy is to contain many small organelle units that are sizeinvariant with respect to cell size. If organelle metabolic scaling is superlinear (  org  1 ),
then the strategy that maximizes mass-specific metabolic rate is to contain an invariant
small number of large organelles. If  org  1 , then the cell can increase both the number
and size of organelles with increasing cell size without affecting the optimality criterion.
Since GLM analysis suggests that r, q, and  org are invariant of cellular
organization, our results suggest that  c is also invariant of cellular organization. Also,
since in protists having only a single chloroplast, 𝑁𝑜𝑟𝑔 ∝ 𝑉𝑐0 , the scaling of individual
chloroplast volume with cell volume is q   c /  org . Thus, assuming that  c   org , as
our analyses suggest, scaling theory predicts 𝑉𝑜𝑟𝑔 ∝ 𝑉𝑐1 in single-chloroplast cells, which
is indeed what we found (Figure S2).
Our theoretical framework quantifies how the scaling of total organellar volume
reflects the metabolic scaling of cells, the organelle scaling strategy employed, and the
metabolic scaling of those organelles. In plants, because chloroplast size is largely
invariant of cell size, our baseline theory predicts that the scaling of the total chloroplast
volume should parallel the cells’ photosynthetic scaling (see Equation 2.3) and so exhibit
sublinear scaling, as observed. In unicellular algae, because we found an increase in
chloroplast size with cell size along with sublinear metabolic scaling in chloroplasts, all
else being equal, the scaling of total chloroplast volume (found to have an exponent of
0.87 with an R-sq of 0.90) should be steeper than cellular photosynthetic scaling, with the
difference equal to q(1   org ) . We had insufficient statistical power to sufficiently
resolve photosynthetic cellular scaling in unicells (separate from plants), but the
photosynthetic and biomass production scaling reported by many researchers (e.g.,[13])
is lower than the 0.87 scaling of total chloroplast volume, as predicted. In contrast, since
we found linear metabolic scaling of mitochondria, the scaling of total mitochondrial
volume should always parallel cellular respiratory scaling and so exhibit linear scaling.
Interestingly, although we do not find a significant difference in the intercepts of
the allometric scaling relations for total organellar volume, graphical analysis suggests
7
that the scaling of total mitochondrion volume may have a higher intercept in unicellular
heterotrophs than multicellular heterotrophs (Fig. 1A). In contrast, in phototrophs there is
no visible difference between unicellular and multicellular organisms in intercepts for
total organellar volume scaling of mitochondria or chloroplasts (Fig. 1B and C). A likely
explanation for this apparent difference between trophic groups is that organism surface
and transportation network constraints in multicellular heterotrophs reduce the overall
supply of resources to the average cell (and their mass-specific metabolic rates), leading
to a reduced need in multicellular heterotrophs for mitochondrial biomass (which
metabolize these resources). In contrast, a large portion of the multicellular plant cell data
originates from leaf tissue specializing in photosynthesis and so would not be expected to
have lower photosynthetic capacities than their unicellular counterparts.
Figures
8
Figure S1. Scaling of the individual size of mitochondria and chloroplasts with cell
volume. The effect of cell volume was only significant in chloroplasts. Although
multicellularity did not significantly affect the slope or intercept of this relationship, the
scaling relationship between chloroplast size and cell volume was largely driven by cells
having a single chloroplast, all of which are unicells (see Figure S2). The black line in the
final panel was determined by RMA regression on the pooled data.
9
Figure S2. Scaling of chloroplast unit volume with cell volume in unicellular organisms
having only one chloroplast. This exceptionally strong relationship provides strikingly
clear evidence for the operation of strategy V in unicellular phototrophs containing a
single unit chloroplast and thus an overall mixed strategy M when considering the entire
assemblage of unicellular phototrophs. Slope determined by RMA regression.
Tables
Table S1. Summary statistics and parameter estimates from a General Linear Model
(GLM) explaining total mitochondrial volume with log cell volume, trophic lifestyle, and
unicellularity/multicellularity as predictor variables. Note that the estimated slopes and
intercepts presented here using ordinary least-squares in the GLM differ from the values
estimated using Reduced Major Axis regression, which were presented in the text and are
considered more appropriate for this allometry study.
Term
Coefficient
-1.1626
0.94004
Constant
log cell vol.
Trophic life
Heterotrophic
-0.0622
Cellularity
Multicellular
0.0182
log cell vol.*Trophic lifestyle
Heterotrophic
0.10613
log cell vol.*Cellularity
Multicellular
-0.03334
R-Square = 87.75% Total DF = 98
SE
Coefficient
0.1185
0.04194
T
-9.81
22.42
P
0.000
0.000
0.1124
-0.55
0.582
0.1131
0.16
0.873
0.03763
2.82
0.006
0.04033
-0.83
0.411
10
Table S2. Summary statistics and parameter estimates from a General Linear Model
explaining log total chloroplastic volume with log cell volume and
unicellularity/multicellularity as predictor variables.
Term
Constant
log cell vol.
Cellularity
Multicellular
log cell vol.*Cellularity
Multicellular
R-Square = 78.33%
Coefficient
-0.1156
0.79242
SE
Coefficient
0.2105
0.06957
T
-0.55
11.39
P
0.585
0.000
0.0331
0.2105
0.16
0.876
-0.04068
0.06957
-0.58
0.560
Total DF = 76
Table S3. Summary statistics and parameter estimates from General Linear Models
explaining log number of mitochondria per cell with log cell volume, trophic lifestyle,
and unicellularity/multicellularity as predictor variables.
Term
Coefficient
Constant
-0.1364
log cell vol.
0.81218
Cellularity
Multicellular
0.0784
log cell vol.*Cellularity
Multicellular
-0.01138
Trophic life
Heterotrophic
-0.1746
log cell vol.*Trophic lifestyle
Heterotrophic
0.12464
R-Square = 78.42%
SE
Coefficient
0.2121
0.07029
T
-0.64
11.55
P
0.522
0.000
0.2155
0.36
0.717
0.07174
-0.16
0.874
0.1701
-1.03
0.309
0.05741
2.17
0.034
Total DF = 69
Table S4. Summary statistics and parameter estimates from General Linear Models
explaining average log mitochondrion volume (size) with log cell volume, trophic
lifestyle, and unicellularity/multicellularity as predictor variables.
Term
Constant
log cell vol.
Cellularity
Multicellular
log cell vol.*Cellularity
Multicellular
Trophic life
Heterotrophic
Coefficient
-1.1368
0.1411
SE Coefficient
0.2535
0.08254
T
-4.49
1.71
P
0
0.092
-0.0279
0.2539
-0.11
0.913
-0.02001
0.08292
-0.24
0.81
0.3001
0.2081
1.44
0.154
11
log cell vol.*Trophic lifestyle
Heterotrophic
R-Square = 6.92%
-0.08535
0.0686
-1.24
0.218
Total DF = 68
Table S5. Summary statistics and parameter estimates from General Linear Models
explaining log number of chloroplasts per cell with log cell volume and
unicellularity/multicellularity as predictor variables.
Term
Constant
log cell vol.
Cellularity
Multicellular
log cell vol.*Cellularity
Multicellular
R-Sq = 65.85%
Coefficient
0.4772
0.15686
SE Coefficient
0.165
0.04278
T
2.89
3.67
P
0.004
< 0.001
0.8346
0.165
5.06
< 0.001
-0.07796
0.04278
-1.82
0.07
Total DF = 163
Table S6. Summary statistics and parameter estimates from General Linear Models
explaining average log chloroplast volume with log cell volume and
unicellularity/multicellularity as predictor variables.
Term
Constant
log cell vol.
Cellularity
Multicellular
log cell vol.*Cellularity
Multicellular
R-Square = 42.42%
Coefficient
-0.6962
0.7049
SE Coefficient
0.3852
0.1171
T
-1.81
6.02
P
0.076
< 0.001
-0.1205
0.3852
-0.31
0.756
-0.1592
0.1171
-1.36
0.18
Total DF = 57
Table S7. Summary statistics for the general regression model explaining log number of
mitochondria per cell with log cell volume, log mitochondrion volume, trophic lifestyle,
and unicellularity/multicellularity as predictor variables. This GLM, in concert with
Table S8 and Table S9, was used to estimate the respiratory scaling exponents presented
in Table 1 of the main text.
Term
Constant
log cell volume
Cellularity
Multicellular
log cell volume*Cellularity
Multicellular
Trophic lifestyle
Coefficient
-1.02141
0.94888
SE
Coefficient
T
0.179769 -5.6818
0.052153 18.1942
P
< 0.001
< 0.001
0.04985
0.173763
0.2869
0.775
-0.01311
0.050737
-0.2584
0.797
12
Heterotrophic
Trophic lifestyle*log cell volume
Heterotrophic
log mitochondrion volume
log mitochondrion volume*Cellularity
Multicellular
Trophic lifestyle*log mitochondrion volume
Heterotrophic
R-Square = 91.00%
Total DF = 64
0.01193
0.156189
0.0764
0.939
0.1016
-0.74817
0.042646
0.081783
2.3824
-9.1483
0.021
< 0.001
0.11984
0.086505
1.3854
0.171
0.12057
0.084554
1.4259
0.159
Table S8. Summary statistics for general regression model with log cell volume as the
response variable and log number of mitochondria per cell, log mitochondrion volume,
trophic lifestyle, and unicellularity/multicellularity as predictor variables. This regression
model in concert with Table S7 was used to estimate the respiratory rate scaling exponent
for cells presented in Table 1 of the main text.
SE
Term
Coefficient Coefficient
T
P
Constant
1.48102
0.11438 12.9483 < 0.001
log mitochondrion volume
0.73316
0.07673 9.5551 < 0.001
Trophic lifestyle
Heterotrophic
-0.31339
0.11228 -2.7911 0.007
Cellularity
Multicellular
0.17893
0.115911 1.5437 0.128
log mitochondrion volume*Trophic lifestyle
Heterotrophic
-0.18615
0.078007 -2.3863 0.020
log mitochondrion volume*Cellularity
Multicellular
-0.19526
0.081208 -2.4044 0.020
log number of mitochondria
0.87212
0.049377 17.6625 < 0.001
log number of mitochondria*Trophic lifestyle
Heterotrophic
-0.00287
0.044279 -0.0647 0.949
log number of mitochondria*Cellularity
Multicellular
-0.07269
0.047883
-1.518 0.135
R-Square = 92.34%
Total DF = 64
Table S9. Summary statistics for General Linear Model with log mitochondrion volume
as the response variable and log number of mitochondria per cell, log cell volume, trophic
lifestyle, and unicellularity/multicellularity as predictor variables. This GLM in concert
with Table S7 was used to estimate the metabolic scaling exponent for mitochondria
presented in Table 1 of the main text.
Term
Constant
Coefficient
1.48102
SE
Coefficient
T
P
0.11438 12.9483 < 0.001
13
log mitochondrion volume
Trophic lifestyle
Heterotrophic
Cellularity
Multicellular
log mitochondrion volume*Trophic lifestyle
Heterotrophic
log mitochondrion volume*Cellularity
Multicellular
log number of mitochondria
log number of mitochondria*Trophic lifestyle
Heterotrophic
log number of mitochondria*Cellularity
Multicellular
R-Square = 66.21%
Total DF = 64
0.73316
0.07673
9.5551 < 0.001
-0.31339
0.11228
-2.7911 0.592
0.17893
0.115911
1.5437 0.958
-0.18615
0.078007
-2.3863 0.723
-0.19526
0.87212
0.081208 -2.4044 0.016
0.049377 17.6625 0.000
-0.00287
0.044279
-0.0647 0.057
-0.07269
0.047883
-1.518 0.994
Table S10. Summary statistics for the general regression model explaining log number of
chloroplast per cell with log cell volume, log chloroplast volume, and
unicellularity/multicellularity as predictor variables. This GLM, in concert with Tables
S11 and S12, was used to estimate the photosynthetic rate scaling exponents presented in
Table 1 of the main text. Note that due to high Variance Inflation Factors for both
covariates (5.5 for log cell volume and 3.5 for log chloroplast volume) and dominance of
single-chloroplast cells in the unicellular data for which we also have chloroplast volume,
we do not believe we have appropriate statistical power to examine scaling differences
between unicellular and multicellular phototrophs and so report only the scaling exponent
common to both in Table 1.
Term
Constant
log cell volume
Cellularity
Multicellular
log chloroplast volume
log cell volume*Cellularity
Multicellular
log chloroplast volume*Cellularity
Multicellular
R-Square = 89.43% Total DF = 56
Coefficient
0.201518
0.611508
SE
Coefficient
T
0.157616 1.27854
0.078157 7.82408
P
0.207
< 0.001
0.658974
-0.74037
0.157616 4.18087
0.076836 9.63575
< 0.001
< 0.001
-0.172176
0.078157 2.20295
0.032
0.076836
0.532
0.048353
0.6293
Table S11. Summary statistics for the general regression model explaining log cell
volume with log number of chloroplasts, log chloroplast volume, and
unicellularity/multicellularity as predictor variables. This GLM, in concert with Tables
14
S10, was used to estimate the photosynthetic rate scaling exponent for cells presented in
Table 1 of the main text.
Term
Constant
log number of chloroplasts
Cellularity
Multicellular
log chloroplast volume
log number of chloroplasts*Cellularity
Multicellular
log chloroplast volume*Cellularity
Multicellular
R-Sq = 84.78%
Total DF = 56
Coefficient
0.49246
1.14541
-0.25511
1.05725
SE
Coefficient
0.247947
0.155159
T
1.9861
7.3821
P
0.052
< 0.001
0.247947 -1.0289
0.084852 12.4599
0.308
< 0.001
0.19055
0.155159
1.2281
0.225
0.09616
0.084852
1.1332
0.262
Table S12. Summary statistics for the general regression model explaining log
chloroplast volume with log number of chloroplasts, log cell volume, and
unicellularity/multicellularity as predictor variables. This model, in concert with Table
S10, was used to estimate the photosynthetic rate scaling exponent for chloroplasts
presented in Table 1 of the main text.
Term
Constant
log number of chloroplasts
Cellularity
Multicellular
log cell volume
log number of chloroplasts*Cellularity
Multicellular
log cell volume*Cellularity
Multicellular
R-Sq = 86.88% Total DF = 56
Coefficient
0.1056
-1.04005
0.68455
0.78753
SE
Coefficient
0.197415
0.107734
T
0.5349
-9.6538
P
0.595
< 0.001
0.197415 3.4676
0.057676 13.6544
0.001
< 0.001
-0.07774
0.107734
-0.7216
0.474
-0.17486
0.057676
-3.0318
0.004
Table S13. Summary statistics and coefficients for the general linear model explaining
the number of organelles with the following predictors: log cell volume, log organelle
unit volume, trophic lifestyle (of cell), cellularity (i.e., multicellular or unicellular),
organelle trophic lifestyle (i.e., mitochondria or chloroplasts), and several interaction
terms between predictors, as listed in the table. This GLM was used to test for differences
in scaling between whole-cell respiration rate and whole-cell photosynthetic rate. Note
that due to the dominance of single-chloroplast cells in the unicellular data for which we
also have chloroplast volumes, we do not believe we have appropriate statistical power to
examine scaling differences between unicellular and multicellular phototrophs and so
report only the scaling exponent common to both in Table 1.
15
Coefficien
t
-0.35865
SE Coefficient
0.120323
0.812446
0.040092
log organelle volume
-0.75351
Trophic lifestyle
Heterotrophic
-0.10237
Cellularity
Multicellular
0.294244
Organelle trophic lifestyle
Heterotrophic
-0.45638
Trophic lifestyle*log cell volume
Heterotrophic
0.112284
Organelle trophic lifestyle*log organelle volume
Heterotrophic
0.018183
Cellularity*log Vc
Multicellular
-0.09367
Cellularity*log Vorg
Multicellular
0.08141
Organelle trophic lifestyle* log cell volume
Heterotrophic
0.08764
R-Sq = 91.12%
Total DF = 117
0.048644
T
-2.9807
20.264
5
15.490
3
0.119432
-0.8571
0.393
0.115358
2.5507
0.012
0.139943
-3.2612
0.001
0.040017
2.8059
0.006
0.046972
0.3871
0.699
0.038578
-2.4281
0.017
0.029402
2.7689
0.007
0.042294
2.0721
0.041
Term
Constant
log cell volume
P
0.004
< 0.001
< 0.001
Table S14. List of species, the trophic mode of the cells in the growth conditions
observed, and the cellular organization of the species. Note that in a few protist species,
different reported strains were categorized as different species, as the biological species
concept often does not clearly apply to these taxa and different strains can be more
genetically or morphologically distinct than two plant or animal cells of sister species
(thereby warranting consideration as sufficiently distinct evolutionary and/or ecomorphological populations).
Species
Acanthamoeba rhysodes
Acanthamoeba castellani
Aedes aegypti
Amphidinium carterae
Angiopteris evecta
Arabidopsis thaliana
Astasia longa
Asterionella japonica
Aureodinium pigmentosum
Beta vulgaris
Trophic lifestyle
Chemoheterotrophic
Chemoheterotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Cellularity
Unicellular
Unicellular
Multicellular
Unicellular
Multicellular
Multicellular
Unicellular
Unicellular
Unicellular
Multicellular
16
Bigelowiella longifila
Candida albicans
Canis lupus familiaris
Chaetoceros debilis
Chaetoceros dichaeta
Chaetoceros simplex
Chlamydomonas reinhardtii
Chlorarachnion globasum
Chlorella autotrophica
Chlorella emersonii
Chlorella fusca
Chlorella pyrenoidosa
Chlorella sp.
Chlorella vulgaris
Chrysochromulina ephippum
Chrysochromulina polylepis
Chrysochromulina pringshemii
Chrysosphaera magna
Citrullus lanatus
Coccomyxa relative
Cryptomonas erosa
Cryptomonas reflexa
Cryptomonas tetrapyrenoidosa
cryptophytic eukaryote
Cucumis sativus
Cucumis melo
Cucurbita pepo
Cyanidioschyzon merolae
Cyanidium caldarium
Cyanophora paradoxa
Cyclotella meneghiniana
Cyclotella sp.
Cylindrotheca closterium
Diatoma tenue
Drimys winteri
Dunaliella salina
Dunaliella tertiolecta
Eckloniopsis radicosa
Emiliana huxleyi
Encephalartos altensteinii
Photoautotrophic
Chemoheterotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic,
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Unicellular
Unicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Unicellular
Unicellular
Multicellular
Unicellular
Multicellular
17
Euglean laciniata
Euglena anabaena var. minor (UTEX
# 373)
Euglena cantabrica
Euglena geniculata
Euglena gracilis
Euglena granulata
Euglena limosa
Euglena mainxi
Euglena mutabilis
Euglena myxocilindraeca
Euglena reticulata
Euglena stellata
Euglena viridis
Eutreptrella marina
Fragilaria capucina
Galeidinium rugatum
Gephyrocapsa oceanica
Ginkgo biloba
Glaucocystis sp.
Gossypium hirsutum
Gymnodinium inversum
Gymnodinium mikimotoi
Gymnodinium sp. 1
Gymnodinium sp. 2
Haptophyte 1
Haptophyte 2
Heterophrys myriapoda
Heterosigma akashiwo
Homo sapiens
Hordeum vulgare
Hymenomonas roseola
Isochrysis galbana
Karenia bidigitata
Karenia papilonacea
Leishmania amazonensis
Leishmania braziliensis
Leishmania donovani
Lepocinclis fusiforme
Lolium perenne
Lotharella vacuolata
Photoautotrophic
Unicellular
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic,
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Chemoheterotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Unicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Unicellular
18
Magnolia grandiflora
Megaceros flagellaris
Megaceros mexicanus
Megaceros vincetianus
Melosira granulata
Meriones unguiculatus
Mesocricetus auratus
Mesostigma viride
Metopus palaeformis
Micromonas pusilla
Micromonas squamata
Monochrysis lutheri
Monodus subterraneus
Monoraphidium contotum
Monoraphidium minutum
Morone saxatilis
Mus musculus
Nephroselmis olivacea
Nephroselmis pyriformis
Nephroselmis rotunda collection sp.
2
Nephroselmis spinosa
Nitzschia closterium
Nitzschia valdestriata
Ochromonas danica
Ochromonas sp.
Panicum decipiens
Panicum hylaeicum
Panicum laxum
Panicum maximum
Panicum millioides
Panicum prionitis
Panicum rivulare
Panicum schenckii
Pavlova lutheri
Peridinium lindemanni
Phaeodactilum tricornatum
Pharbitis nil
Phleum pratense
Physcomitrella patens
Picophagus flagellatus
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Chemoheterotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Multicellular
Multicellular
Multicellular
Multicellular
Unicellular
Multicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
Unicellular
Unicellular
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Chemoheterotrophic
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
Multicellular
Multicellular
Multicellular
Multicellular
Multicellular
Multicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
Multicellular
Unicellular
19
Pinguiochrysis pyriformis
Pityrosporum orbiculare
Platymonas subcordiformis
Polytoma papillatum
Prorocentrum micans
Prorocentrum minimum
Prymnesium parvum
Pseudonitzchia brasiliana
Pycnococcus provasoliI
Pyramimonas parkeae
Rattus norvegicus
Rhodomonas minuta
Rhodomonas salina
Rhodosorus magnei 4016
Rhodosorus marinus 4023
Saccharomyces cerevisiae
Scenedesmus dimorphus
Scenedesmus obliquus
Scenedesmus quadricauda
Scenedesmus sp.
Scnedesmus ecornis
Skeletonema costatum
Solanum lycopersicum
Spinacia oleracea
Stephanodiscus binderanus
Strombidium purpureum
Suaeda maritima
Sus scrofa
Symbiodinium microadriaticum
subsp.
Symbiodinium sp.
Tetrahymena pyriformis
Tetraselmis sp.
Thalassiosira eccentrica
Thalassiosira pseudonana
Trachelomonas grandis
Trachelomonas hispida
Trachelomonas volvocina
Trichosphaerium sp.
Triticum aestivum
Triticum dicoccoides
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
Unicellular
Unicellular
Multicellular
Multicellular
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Unicellular
Multicellular
Multicellular
20
Triticum dicoccum
Triticum monococcum
Triticum tauschii
Triticum urartu
Tritrichomonas foetus
Umbilicaria grisea
Umbilicaria hirsuta
Umbilicaria polyphylla
Umbilicaria vellea
Vigna radiata
Zea mays
Photoautotrophic
Photoautotrophic
Photoautotrophic
Photoautotrophic
Chemoheterotrophic
Photoautotrophic
Chemoheterotrophic
Chemoheterotrophic
Chemoheterotrophic
Photoautotrophic
Photoautotrophic
Multicellular
Multicellular
Multicellular
Multicellular
Unicellular
Multicellular
Multicellular
Multicellular
Multicellular
Multicellular
Multicellular
Table S15. List of studies in which data on the number and size of mitochondria were
omitted from our database and analyses because the cells in the studies had reticulated
networks of mitochondria.
Species
Candida albicans
Chlorella fusca
Reference
[14]
[15]
Leishmania amazonensis
Mus musculus
Polytoma obtusum
[16]
[17]
[18]
Polytoma papillatum
Chlamydomonas reinhardtii
[19,20]
[21]
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