Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities.

TLDR: Evidence is provided that climate and plant management dynamics influence biogeography and ecology of C3 /C4 plants in lawns and their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities.

Abstract: In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13 C, index of C3 /C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4 carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13 C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3 /C4 competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3 /C4 plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities.

Figures

FIG. 1. Observed C4 proportion of lawn cover (a) and theoretical C4 carbon gain (b) vs. mean growing season temperature (GST) across seven U.S. cities; Baltimore, Maryland (BAL); Boston, Massachusetts (BOS); Los Angeles, California (LA); Miami, Florida (MIA); Minneapolis-St. Paul, Minnesota (MSP); Phoenix, Arizona (PHX); and Salt Lake City, Utah (SLC).

FIG. 3. C4 proportion of turf (black circles) and non-turf (white circles) lawn cover vs. mean annual temperature (MAT, °C) and mean annual precipitation (MAP, cm) across seven cities. Regression line shown for turf C4 proportion of lawn cover and MAT, and for non-turf C4 proportion of lawn cover and MAP. For city abbreviations, see Fig. 1.

FIG. 2. C4 proportion of lawn cover in residential lawns vs. mean annual temperature (a) (MAT; °C) and the theoretical C4 carbon gain in lawns (b). For city abbreviations, see Fig. 1.

TABLE 1. The expected dominance of C3 or C4 plants based on each city’s climate.

FIG. 4. Proportion of C3 turf (dark green), C3 non-turf (light green), C4 turf (dark blue), and C4 non-turf (light blue) lawn cover vs. mean annual winter minimum temperature (°C). Regression line shown for turf C3 (dark green) and turf C4 proportion of lawn cover and winter minimum temperature.

FIG. 5. The C3 turf (dark green), C3 non-turf (light green), C4 turf (dark blue), and C4 non-turf (light blue) proportion of lawn cover across the seven cities. Proportion for the C3 and C4 turf (white) and non-turf (black) components of the lawn are shown, except when the proportion is below 0.03. Cities are shown in order of increasing mean annual temperature, from coolest (MSP) to warmest (MIA) city. For city abbreviations, see Fig. 1.

Full text

Climate and lawn management interact to control C
4
plant
distribution in residential lawns across seven U.S. cities
TARA L. E. TRAMMELL ,
1,20
DIANE E. PATAKI,
2
CHRISTOPHER J. STILL ,
3
JAMES R. EHLERINGER,
2
MEGHAN L. AVOLIO ,
4
NEIL BETTEZ,
5
JEANNINE CAVENDER-BARES,
6
PETER M. GROFFMAN,
7
MORGAN GROVE,
8
SHARON J. H ALL ,
9
JAMES HEFFERNAN,
10
SARAH E. HOBBIE,
6
KELLI L. LARSON,
11
JENNIFER L. MORSE,
12
CHRISTOPHER NEILL,
13,19
KRISTEN C. NELSON,
14
JARLATH O’NEIL-DUNNE,
15
WILLIAM D. PEARSE,
6,16
RINKU ROY CHOWDHURY,
17
MEREDITH STEELE,
18
AND MEGAN M. WHEELER
9
1
Department of Plant and Soil Sciences, University of Delaware, 531 S. College Ave, 152 Townsend Hall, Newark, Delaware 19716
USA
2
Department of Biology, University of Utah, Salt Lake City, Utah 84112 USA
3
Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon 97331 USA
4
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland 21218 USA
5
Cary Institute of Ecosystem Studies, Millbrook, New York 12545 USA
6
Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, Minnesota 55108 USA
7
City University of New York, Advanced Science Research Center at the Graduate Center, New York, New York 10031 USA
8
USDA Forest Service, Baltimore Ecosystem Study, University of Maryland, Baltimore County, Baltimore, Maryland 21227 USA
9
School of Life Sciences, Arizona State University, Tempe, Arizona 85287 USA
10
Nicholas School of the Environment, Duke University, Durham, North Carolina 27708 USA
11
School of Geographic Science and Urban Planning, School of Sustainability, Arizona State University, Tempe, Arizona 85287 USA
12
Department of Environmental Science and Management, School of Environment, Portland State University, Portland, Oregon 97207
USA
13
Marine Biological Laboratory, Ecosystems Center, Woods Hole, Massachusetts 02543 USA
14
Department of Forest Resources and Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul,
Minnesota 55108 USA
15
Spatial Analysis Laboratory, Rubenstein School of Environment & Natural Resources, University of Vermont, Burlington, Vermont
05405 USA
16
Department of Biology & Ecology Center, Utah State University, Logan, Utah 84322 USA
17
Department of Geography, Indiana University, Bloomington, Indiana 47405 USA
18
Department of Crop and Soil Environmental Science, Virginia Tech, Blacksburg, Virginia 24061 USA
Citation: Trammell, T. L. E., et al. 2019. Climate and lawn management interact to control C
4
plant distribution in residential lawns across seven U.S. cities. Ecological Applications 29(4):
e01884. 10.1002/eap.1884
Abstract. In natural grasslands, C
4
plant dominance increases with growing season tem-
peratures and reflects distinct differences in plant growth rates and water use efficiencies of C
3
vs. C
4
photosynthetic pathways. However, in lawns, management decisions influence interac-
tions between planted turfgrass and weed species, leading to some uncertainty about the degree
of human vs. climatic controls on lawn species distributions. We measured herbaceous plant
carbon isotope ratios (d
13
C, index of C
3
/C
4
relative abundance) and C
4
cover in residential
lawns across seven U.S. cities to determine how climate, lawn plant management, or interac-
tions between climate and plant management influenced C
4
lawn cover. We also calculated the-
oretical C
4
carbon gain predicted by a plant physiological model as an index of expected C
4
cover due to growing season climatic conditions in each city. Contrary to theoretical predic-
tions, plant d
13
C and C
4
cover in urban lawns were more strongly related to mean annual tem-
perature than to growing season temperature. Wintertime temperatures influenced the
distribution of C
4
lawn turf plants, contrary to natural ecosystems where growing season tem-
peratures primarily drive C
4
distributions. C
4
cover in lawns was greatest in the three warmest
cities, due to an interaction between climate and homeowner plant management (e.g., planting
C
4
turf species) in these cities. The proportion of C
4
lawn species was similar to the proportion
of C
4
species in the regional grass flora. However, the majority of C
4
species were nonnative
turf grasses, and not of regional origin. While temperature was a strong control on lawn species
composition across the United States, cities differed as to whether these patterns were driven
by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy
Manuscript received 9 April 2018; revised 23 January 2019; accepted 20 February 2019. Corresponding Editor: Elisabeth
Huber-Sannwald.
19
Present address: Woods Hole Research Center, 149 Woods Hole Road, Falmouth, Massachusetts 02540 USA
20
E-mail: ttram@udel.edu
Article e01884; page 1
Ecological Applications, 29(4), 2019, e01884
© 2019 by the Ecological Society of America

plants appeared to dominate, while in other cities, C
4
plants were predominantly imported and
cultivated. Elevated CO
2
and temperature in cities can influence C
3
/C
4
competitive outcomes;
however, this study provides evidence that climate and plant management dynamics influence
biogeography and ecology of C
3
/C
4
plants in lawns. Their differing water and nutrient use effi-
ciency may have substantial impacts on carbon, water, energy, and nutrient budgets across
cities.
Key words: C
4
plant distribution; lawns; macroecology; plant d
13
C; residential; urban; yard
management.
INTRODUCTION
Turf grasses across the continental United States
occupy over 160,000 km
2
with important consequences
for air and water quality as well as human health and
well-being (Milesi et al. 2005). Residential land covers
the majority of urban greenspace (62%), and lawns
account for most of this greenspace (52–80%; Richards
et al. 1984). While lawns are a significant component of
residential landscapes, we still know very little about the
ecological structure and function of this widespread
American Residential Macrosystem (Groffman et al.
2009, 2014). In intensively managed lawns, the distribu-
tion of plant functional types is likely to reflect interac-
tions between human decisions (e.g., planting and
maintenance), biophysical factors (e.g., climate), and
biological interactions (e.g., plant dispersal and competi-
tion). However, at present there are insufficient data on
the distribution of urban plant species to understand the
roles of biophysical and human factors in structuring
plant communities in cities.
Throughout the United States, nurseries and sod com-
panies offer different lawn species and cultivars, and
lawn grasses that form an even turf are typically pre-
ferred (Christians and Engelke 1994). Weedy species and
forbs (non-turf species) are also common in lawns and
can vary by region and lawn management practices, such
as fertilizer or herbicide application (Stewart et al. 2009,
Bertoncini et al. 2012). Turf scientists have long investi-
gated turf performance and made recommendations for
which turf grasses to plant based on climate (e.g., Chris-
tians and Engelke 1994, Dionne et al. 2010, Bertrand
et al. 2013). Historically, recommendations were based
on growing season temperatures and wintertime freeze
tolerance (Madison 1971, Beard and Beard 2005). How-
ever, empirical evidence for the prevalence of warm-sea-
son vs. cool-season grass and forb species (i.e., C
4
vs. C
3
photosynthesis) in in situ residential lawns is lacking at
continental scales. Following planting, turf grasses and
weedy species undergo ecological dynamics due to abi-
otic and biotic interactions that are not well studied
in situ (Bell 2011). At regional scales, previous research
demonstrated the importance of elevated urban temper-
ature and atmospheric CO
2
on the competitive dynamics
of C
3
and C
4
plants in lawns (Bijoor et al. 2008, Duffy
and Chown 2016, Hobbie et al. 2017). However, under-
standing the controls on C
3
/C
4
plant distribution in
cities across continental scales is necessary to contribute
to the growing understanding of how human-dominated
and natural ecosystems differ (or do not differ) in eco-
logical dynamics (Pickett and Cadenasso 2017).
Grass species that utilize the C
4
photosynthetic path-
way account for only 3% of land plant species, yet they
have a wide global distribution and contribute about
25% of global terrestrial primary production (Sage
2004). Various metrics of local air temperature are sig-
nificantly correlated with continental and global distri-
butions of C
4
grass abundance and dominance (e.g.,
growing-season minimum temperature; Terri and Stowe
1976, Ehleringer et al. 1997). The theoretical basis for
these patterns in grasslands is the difference between
photosynthetic light-use efficiencies in C
3
vs. C
4
plants,
or the ratio of photosynthetic carbon (C) gain to pho-
tons absorbed (Ehleringer and Bj
€
orkman 1977). At high
temperatures, photosynthetic light-use efficiencies of C
3
plants are low because of increased photorespiration
(Ehleringer et al. 1997, Collatz et al. 1998), favoring C
4
plants. However, C
4
photosynthesis has energetic costs
(Ehleringer 1978, Ehleringer et al. 1991). As a result, C
4
plants are expected to outcompete C
3
species only in
regions with warmer growing-season conditions and
adequate rainfall to support grass growth (Ehleringer
1978, Ehleringer et al. 1997).
While temperature is a dominant control on the distri-
bution of C
4
plants globally, human-mediated changes
in land cover and use, such as agricultural crop produc-
tion and altered fire regimes, also influence natural C
4
grassland and pasture distributions (Still et al. 2003).
Furthermore, in cities across the United States, residen-
tial landowners may plant turf-forming grass species
irrespective of local climatic conditions since local
resource limitations can be overcome by water and fer-
tilizer subsidies and competitive outcomes can be influ-
enced by use of selective herbicides (Ward 1969). While
planting recommendations for warm season vs. cool sea-
son grasses tend to be based on climate (Christians and
Engelke 1994, Bertrand et al. 2013), we do not know
the impacts of planting choices on the continental distri-
bution of turf grasses when multiple species and culti-
vars are available from local commercial sources. In
addition, the ecological dynamics that subsequently take
place, such as the invasion of lawns by weed species, are
not well documented. As a result, the extent to which
the distribution of C
3
vs. C
4
species in lawns follows
similar biogeographical patterns as natural ecosystems
is still a significant gap in our basic understanding of
Article e01884; page 2 TARA L. E. TRAMMELL ET AL.
Ecological Applications
Vol. 29, No. 4

the biogeography and ecology of major plant func-
tional types.
The carbon stable isotope ratio (d
13
C) of plant tissues
can be a valuable tool to measure the relative abundance
of C
3
and C
4
grasses (O’Leary 1981). For all plants, the
natural abundance d
13
C in plants is depleted in
13
C rela-
tive to atmospheric CO
2
because of discrimination
against
13
C during photosynthesis (Farquhar et al.
1989). The greater discrimination against
13
C by Rubisco
compared with PEP (phosphoenolpyruvate) carboxylase
during photosynthesis causes isotopically distinct plant
d
13
C values in C
3
(average d
13
C = 27&) and C
4
(aver-
age d
13
C = 13&) plants (O’Leary 1988, Boutton
1996). Biogenic and anthropogenic factors control plant
d
13
C values in urban lawns through the relative propor-
tion of C
3
vs. C
4
plant composition.
We sought to understand how C
4
plants are dis-
tributed in lawns throughout the United States by (1)
sampling the composition of lawns in seven cities of
varying climate (BOS, Boston, Massachusetts; BAL,
Baltimore, Maryland; LA, Los Angeles, California;
MIA, Miami, Florida; MSP, Minneapolis-St. Paul, Min-
nesota; PHX, Phoenix, Arizona; SLC, Salt Lake City,
Utah), and (2) comparing observed C
4
lawn distribution
with theoretical carbon gain for C
4
plants (i.e., simulated
C
4
carbon assimilation as a function of temperature for
each city; Ehleringer 1978, Sage et al. 1999, Still et al.
2003). We evaluated how direct climate and an interac-
tion between climate and lawn management controls the
distribution of C
4
plants in lawns. Climatic constraints
on large-scale C
3
and C
4
plant distributions have been
commonly evaluated using a mean monthly temperature
threshold of 22°C and a minimum precipitation con-
straint for C
4
competitive advantage (Collatz et al. 1998,
Sage and Kubien 2003, Still et al. 2003). Based solely on
this temperature threshold, we predicted that BAL,
BOS, LA, MSP, and SLC residential lawns would be C
3
dominated, whereas MIA and PHX would be C
4
domi-
nated (Table 1). If there is a direct influence of climate
on C
3
vs. C
4
plant growth, then we expected C
4
lawn
cover to be quantitatively related to growing-season tem-
perature (GST) and to the theoretical carbon gain that
C
4
plants would have in each city. Alternatively, if lawn
management practices (e.g., planting, weeding, irriga-
tion, and fertilization) override climatic constraints on
grass performance and interspecific competition, then
C
4
lawn cover will be unrelated to climate parame-
ters (such as MAT) and to the theoretical C
4
carbon
gain in lawns.
The distribution of spontaneous (i.e., weedy non-turf)
vs. cultivated (i.e., turf) plant species in urban lawns
across these cities should provide insight as to which spe-
cies are most successful under varying climatic condi-
tions. If human management of residential lawns
interacts with climate to determine the availability and/
or selection of seed or sod, then we expected to see a
relationship between temperature (MAT) and turf C
4
lawn cover, whereas non-turf (weed species) C
4
lawn
cover will be related to precipitation (mean annual pre-
cipitation, MAP), suggesting homeowners can select C
4
lawn turf for optimal year-round temperatures and over-
ride any soil moisture constraints (i.e., irrigation). Fur-
thermore, a relationship between winter minimum
temperatures and C
3
/C
4
turf lawn cover, and no relation-
ship with C
3
and C
4
non-turf species supports an
interaction between climate and human management
influence on C
3
/C
4
turf distribution since spontaneous
and cultivated plants are not similarly controlled by low
temperatures. Finally, a high proportion of nonnative C
4
turf species would support the idea that homeowner
planting of C
4
turf species is a dominant control in these
residential lawns. This analysis adds a new dimension to
our understanding of the processes governing biodiver-
sity, composition, and ecological dynamics of urban
plant communities.
M
ETHODS
Study area
Plant samples were collected in residential lawns in
seven major metropolitan areas across the United
States: Baltimore, Maryland; Boston, Massachusetts;
Los Angeles, California; Miami, Florida; Minneapolis-
TABLE 1. The expected dominance of C
3
or C
4
plants based on each city’s climate.
City Temperature (°C) Precipitation (cm) Climate prediction Turfgrass climate zone Dominant lawn community
BAL 12.8 106.4 C
3
humid transitional warm/cool grass mix
BOS 10.8 111.2 C
3
semi-cool humid cool season grasses
LA 17.0 32.6 C
3
cool semiarid Pacific warm/cool grass mix
MIA 25.1 157.2 C
4
warm tropical warm season grasses
MSP 7.9 77.7 C
3
semi-cool humid cool season grasses
PHX 23.9 20.4 C
4
warm arid warm season grasses
SLC 11.6 40.9 C
3
cool semiarid cool season grasses
Notes: Cities are Baltimore, Maryland (BAL); Boston, Massachusetts (BOS); Los Angeles, California (LA); Miami, Florida
(MIA); Minneapolis-St. Paul, Minnesota (MSP); Phoenix, Arizona (PHX); and Salt Lake City, Utah (SLC). Temperature and pre-
cipitation data are shown for mean annual 30-yr norms (National Climatic Data Center 2016), and the climate prediction is based
on whether temperatures are > 22°C. Turfgrass climate zones and potential lawn management practices are incorporated into rec-
ommendations for dominant lawn communities across the United States (Cook and Ervin 2010).
June 2019 C
4
PLANT DISTRIBUTION IN URBAN LAWNS Article e01884; page 3

St. Paul, Minnesota; Phoenix, Arizona; and Salt Lake
City, Utah. These cities represented multiple ecological
biomes and climatic regions across the United States. In
all cities, the experimental design included residential
parcels (n = 17–30 per city) stratified by urban density
classes (i.e., urban, suburban, and exurban [settlements
outside the city, usually a prosperous area beyond the
suburbs]) and socioeconomic status (i.e., high, medium,
or low), which were identified using the PRIZM (Poten-
tial Rating Index for Zipcode Markets) market classifi-
cation system (Claritas 2008). The PRIZM classification
utilizes demographics (based on census data) and con-
sumer behavior to define social groups and life stage
groups. Social groups are defined by urban density (i.e.,
population and housing density) and socioeconomic sta-
tus (i.e., income, education, occupation, and home
value), whereas life stage groups are defined by resident
age, socioeconomic rank, and presence of children at
home. The experimental design varied slightly in each
city to account for local variation in factors controlling
yard structure and function in different regions across
the United States, (i.e., previous land use in BAL, BOS,
and PHX; soil conditions in MIA and MSP; tempera-
ture in LA; and yard landscaping in PHX [i.e., xeriscap-
ing]). For further details about experimental designs, see
Trammell et al. (2016). All yards were randomly selected
from a list of willing participants originally identified
from a telephone survey (9,480 respondents across the
cities). For the purposes of this study, we analyzed data
from yards with lawns, thus only excluding yards with
xeriscaping in PHX.
Plant d
13
C
In each residential yard, bulk plant leaf samples were
collected in two random locations in the lawn during
peak growing season for each city (i.e., summer 2012 for
BAL, BOS, MSP, and MIA, spring 2013 for LA and
PHX, summer 2013 for SLC). In LA and SLC, replicate
bulk plant samples were collected within 30 cm of each
other at each sampling location. Replicate samples were
not collected in BAL, BOS, MIA, and PHX, so each bulk
plant sample was divided prior to sample processing to
create within-sample replicates. In MSP, species-specific
plant leaf samples were collected instead of bulk plant
samples. Thus, the weighted average for each species was
calculated from lawn quadrat abundance data (see C
4
pro-
portion of lawn cover) and applied to d
13
C data. Thus,
MSP data are not included in the analysis of relationships
between plant d
13
CandC
4
lawn cover across the seven
cities (i.e., Appendix S1: Fig. S1). After collection, plant
leaves were dried at 60°C for at least 48 hours.
All leaves were selected from the bulk plant samples in
order to exclude other plant material (i.e., flowers, roots)
prior to C analysis. Plant leaf samples were ground to a
fine powder using a Retsch Ball Mixer Mill (MM200,
Haan, Germany). Natural abundance isotopic C compo-
sition, d
13
C, was measured with a DELTA Plus Isotope
Ratio Mass Spectrometer (Finnigan-MAT, Bremen,
Germany) interfaced with an elemental analyzer (Model
1110, Carlo Erba, Milan, Italy) at the Stable Isotope
Ratio Facility for Environmental Research (SIRFER) at
the University of Utah, Salt Lake City. Two primary
(PLRM) reference materials, calibrated against National
Institute of Standards and Technology and International
Atomic Energy Agency certified reference materials, and
one secondary (SLRM, spinach leaf) reference material
were used as internal standards with d
13
C precision
of  0.1&. The plant d
13
C values were expressed rela-
tive to the international standard (Vienna-PeeDee
Belemnite) in the conventional d notation:
d
13
C ¼½ð
13
C
sample
=
12
C
sample
Þ=
ð
13
C
standard
=
12
C
standard
Þ11000&
:
C
4
proportion of lawn cover
The plant species cover in each lawn was assessed
using three randomly placed 1-m
2
quadrats in the front
and back lawns of each residential yard (6-m
2
total). For
each species identified in the quadrats, percent cover was
estimated and species were assigned a cover category
(<1%, 1–2%, 3–5%, 6–15%, 16–25%, 26–50%, 51–75%,
76–100%). The median of each cover category was used
in data analysis (e.g., <1%, median = 0.5%; 76–100%,
median = 88%). Plant species were identified as having
the C
3
or C
4
photosynthetic pathway according to Wal-
ler and Lewis (1979), Sage and Monson (1999), Smith
and Knapp (1999), Sage (2001), Bruhl and Wilson
(2007), and Sage et al. (2011). The proportion of total
plant cover contributed by plants with C
4
photosynthe-
sis was calculated for each quadrat (C
4
proportion of
total plant cover). We separated the cultivated lawn
grass (“turf”) species (Table 2) from all other species
such as weeds (“non-turf”) according to Wheeler et al.
(2017; Appendix S1: Table S1).
Modeling theoretical C
4
carbon gain
Modeling photosynthesis and photosynthetic carbon iso-
tope fractionation.—Net photosynthetic and transpira-
tion rates for grasses in each pathway (C
3
and C
4
) were
calculated at hourly intervals for a representative day in
each month of the growing season. The growing season
for each city was defined as the warm months with
ample precipitation for grass growth (>25 mm/yr; Col-
latz et al. 1998), which may not coincide with irrigation
inputs alleviating this moisture constraint (e.g., LA
growing season November–April, whereas irrigation
increases growing season through September). This
approach simplifies the calculation of fluxes at sub-
hourly intervals for each day of the month, which
requires comprehensive and gap-free data not easily
Article e01884; page 4 TARA L. E. TRAMMELL ET AL.
Ecological Applications
Vol. 29, No. 4

attainable across all sites. Simulating sub-hourly fluxes
using real weather and radiation data would also require
a comprehensive biosphere model with soil moisture cal-
culations, canopy leaf area and radiation attenuation,
and a host of other processes. Rather, our simplified
approach was meant to capture the dominant photosyn-
thetic physiology differences between C
3
and C
4
grasses,
and compare the modeled predictions against site data
on C
3
and C
4
distributions.
Representative fluxes were predicted using the coupled
C
3
and C
4
leaf photosynthesis and stomatal conductance
models of Collatz et al. (1991, 1992). Parameter values,
such as maximum carboxylation rates (V
max
) and tem-
perature response functions, were taken from Sellers
et al. (1996). V
max
for C
3
grasses was assumed to be 90
lmolm
2
s
1
at 298 K, and 30 lmolm
2
s
1
for C
4
grasses at 298 K. These models, described in Collatz
et al. (1991, 1992) in detail, estimate net leaf photosyn-
thetic rates as a function of temperature, relative humid-
ity, insolation, and the partial pressure of atmospheric
carbon dioxide and dioxygen. The latter quantities were
calculated from fixed concentrations (400 and
20,900 ppm, respectively) and elevation-dependent
atmospheric pressures. The other (diurnally varying)
driving radiation and weather variables were calculated
as described in Diurnal variations in air temperature, rela-
tive humidity, and surface insolation.
Diurnal variations in air temperature, relative humidity,
and surface insolation.— Representative hourly air tem-
perature values (T
air
) were calculated fr om mean monthly
minimum (T
min
)andmaximum(T
max
) air tempera tur es
(Campbell and Norman 2012), and monthly T
min
and
T
max
data for each city’s airport were obtained fr om
NO AA (2015). Mean daily time courses of air temperature
and relativ e humidity (%) were calculated based on the fol-
lowing empirical functions (Campbell and Norman 2012):
T
air
¼ T
max
 c þ T
min
 1  cðÞ
c ¼ 0:44  0:46  sin
p
12

 time þ 0:9

þ 0:11  sin
p
12

 time þ 0:9

where T
max
and T
min
represent the mean daily maximum
and minimum temperatures for a given month, and time
represents hourly values from 1 to 24. T
min
was used as a
proxy for dew point temperature (T
dew
). Daily mean
ambient vapor pressure (e
a
, in mbar; 1 bar = 1 9 10
5
Pa)
and hourly saturation vapor pressure (e
sat
, in mbar) were
estimated using T
dew
and hourly modeled T
air
, respec-
tively, using the following formula (Campbell and Nor-
man 2012):
e
sat
¼ 6:112  exp
17:67  temp
temp þ 243:5ðÞ

where e
sat
is the saturation vapor pressure (mbar) and
temp is air temperature (°C).
Downwelling solar irradiance or shortwave insolation
at hourly time steps was modeled using the method
described in Bonan (2008). In short, surface solar irradi-
ance at a given location depends on latitude, altitude,
and time of year. For each month, the mid-month day of
year (DOY) was used (i.e., 15 May is DOY 135 in a non-
leap year), and the latitude and altitude of each city’s
airport were used. These calculations require an estimate
of cloud-free atmospheric transmittance, and for these
simulations, a value of 0.7 was used in all locations. Total
shortwave insolation (direct and diffuse in W/m
2
) was
converted to the flux of photosynthetically active radia-
tion (PAR, in lmolm
1
s
1
) by assuming that one-half
of shortwave insolation was in the PAR wavelengths
TABLE 2. Residential lawn turf species found in the seven cities.
Latin name Common name Photosynthetic pathway Cities present
Agrostis capillaris L. colonial bentgrass C
3
BAL, BOS, MSP
Agrostis stolonifera L. creeping bentgrass C
3
BAL, BOS, MSP
Cynodon dactylon (L.) Pers. Bermuda grass C
4
BAL, BOS, LA, MIA, PHX, SLC
Festuca filiformis Pourr. fineleaf sheep fescue C
3
BAL, BOS
Festuca ovina L. sheep fescue C
3
BOS
Festuca rubra L. red fescue C
3
BOS, LA, MSP, SLC
Lolium perenne ssp. multiflorum Lam. Italian ryegrass C
3
PHX
Lolium perenne L. perennial ryegrass C
3
BAL, BOS, LA, MSP, PHX, SLC
Paspalum notatum Fluegge Bahia grass C
4
MIA
Pennisetum clandestinum Hochst. ex Chlov. Kikuyu grass C
4
LA
Poa pratensis L. Kentucky bluegrass C
3
BAL, BOS, LA, MSP, SLC
Poa trivialis L. rough bluegrass C
3
MSP
Schedonorus arundinaceus (Schreb.) Dumort. tall fescue C
3
BAL, LA, MSP, SLC
Stenotaphrum secundatum (Walter) Kuntze St. Augustine grass C
4
LA, MIA, PHX
Zoysia tenuifolia Willd. ex Thiele Mascarene grass C
4
MIA
Notes: City codes are identified in Table 1. Cities present represents the cities where turf species were identified in the lawn.
June 2019 C
4
PLANT DISTRIBUTION IN URBAN LAWNS Article e01884; page 5

Frequently asked questions

Q1. What are the contributions in "Climate and lawn management interact to control c4 plant distribution in residential lawns across seven u.s. cities" ?

Huber-Sannwald et al. this paper measured herbaceous plant carbon isotope ratios ( dC, index of C3/C4 relative abundance ) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. 

Q2. What future works have the authors mentioned in the paper "Climate and lawn management interact to control c4 plant distribution in residential lawns across seven u.s. cities" ?

The authors showed that d13C of lawns across seven cities was strongly correlated with the proportion of observed C4 plant cover, providing a simple means of assessing the distribution of C3 vs. C4 species in lawns. In cities with hot, mesic summers ( BAL, BOS ), substantial cover by C4 non-turf species suggests that weedy species may be responding to warm summer temperatures in these cities even though homeowners select C3 turf species. Furthermore, minimal C4 non-turf cover in LA, PHX, and SLC suggests weed species are not thriving in these arid cities, and either are not competitive or are not present in the local seed pool. 

Q3. What is the theoretical basis for the distribution of C4 plants in grasslands?

While temperature is a dominant control on the distribution of C4 plants globally, human-mediated changes in land cover and use, such as agricultural crop production and altered fire regimes, also influence natural C4 grassland and pasture distributions (Still et al. 2003). 

Q4. What is the way to simulate sub-hourly fluxes?

Simulating sub-hourly fluxes using real weather and radiation data would also require a comprehensive biosphere model with soil moisture calculations, canopy leaf area and radiation attenuation, and a host of other processes. 

Q5. What is the effect of the climatic conditions on the plant composition in residential lawns?

The plant composition in residential lawns is a result of dynamics between homeowner plant management and competition between cultivated (turf) and spontaneous (non-turf) plants. 

Q6. What is the effect of climate on the composition of residential lawns?

The species composition of residential lawns is a result of complex relationships between climate controls on the competitive dynamics between C3 and C4 plants and resident lawn management and horticultural practices, such as cultivating desirable turf species and weeding undesirable plants. 

Q7. What is the theoretical basis for the distribution of C4 plants in urban lawns?

Biogenic and anthropogenic factors control plant d13C values in urban lawns through the relative proportion of C3 vs. C4 plant composition. 

Q8. What is the relationship between climate and lawn management?

if lawn management practices (e.g., planting, weeding, irrigation, and fertilization) override climatic constraints on grass performance and interspecific competition, then C4 lawn cover will be unrelated to climate parameters (such as MAT) and to the theoretical C4 carbon gain in lawns. 

Q9. What is the impact of planting choices on the continental distribution of grasses?

While planting recommendations for warm season vs. cool season grasses tend to be based on climate (Christians and Engelke 1994, Bertrand et al. 2013), the authors do not know the impacts of planting choices on the continental distribution of turf grasses when multiple species and cultivars are available from local commercial sources. 

Q10. What are the main factors that determine the net leaf photosynthetic rates?

These models, described in Collatz et al. (1991, 1992) in detail, estimate net leaf photosynthetic rates as a function of temperature, relative humidity, insolation, and the partial pressure of atmospheric carbon dioxide and dioxygen. 

Q11. What is the theoretical basis for these patterns in grasslands?

The theoretical basis for these patterns in grasslands is the difference between photosynthetic light-use efficiencies in C3 vs. C4 plants, or the ratio of photosynthetic carbon (C) gain to photons absorbed (Ehleringer and Bj€orkman 1977). 

Q12. What was the d13C precision of the primary and secondary reference materials?

Two primary (PLRM) reference materials, calibrated against National Institute of Standards and Technology and International Atomic Energy Agency certified reference materials, and one secondary (SLRM, spinach leaf) reference material were used as internal standards with d13C precision of 0.1&.