SUPPLEMENTAL FIGURES
Figure S1. These data are the differences between hourly control and warmed site means for 5
cm soil temperatures over the course of the experiment (n=13076). At the warmest air
temperatures, the infrared (IR) warming lamps were not able to further warm the soil. The
reduced effectiveness of the IR lamps began at around 27 °C. The opposite effect of the lamps on
soil temperature (i.e., IR warmed plots being colder than controls) when air temperatures were
cold is a real effect and is due to the IR lamp-induced melting of snow and the associated loss of
soil insulation. The red line indicates a loess regression with span=0.5.
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Figure S2. Appearance of one of the automated chambers during a snow event on March 8-12,
2006 (photo taken on March 9, 2006). Substantial photosynthesis was observed below the snow
during this event (marked with an asterisk in Figure 1) and several others like it throughout the
study.
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Figure S3. Juxtaposition of NSE from our clear automated chambers with NEE from eddy flux
tower data in a nearby ecosystem showing that the peak in carbon loss in biocrust soils in our
spring season coincides with peak carbon uptake in the ecosystem as a whole. a. The data are the
same as those shown in Figure 1, but are binned by week (both treatment years are averaged)
with units reconciled to match Bowling et al. (2010) b. the Bowling et al. (2010) NEE data show
two drought years (2001 and 2002) and two post-drought years (2003 and 2007). We note the
site described in Bowling et al. (2010) had poorly-developed biocrusts.
3
APPENDIX 1 – GAP FILLING PROCEDURES
Our NSE data set consisted of data from 5 control chambers and 5 warmed chambers each
collecting hourly measurements of net CO2 efflux, for a total of 15,048 hours (January 1, 2006September 20, 2007). In 51.5% of these hourly measurements, all 10 chambers were working,
and in only 9.4% of measurements were all 10 not working. Most of the periods in which all 10
chambers were not working were accounted for by multiday periods during which power was
lost at the field site. The periods where only some of the chambers were working included a mix
of longer periods when one of the two data loggers were not working, and failures in which an
individual chamber experienced a temporary malfunction.
To calculate best estimates of cumulative NSE for annual, seasonal, and daily time periods,
these chamber data were imputed using an iterative imputation algorithm based on ensemblelearning methods (random forests) in the R package missForest (Stekhoven and Buhlmann
2012). The missForest algorithm iteratively fills all missing data in a set of multiple variables
using predictions based on random forest models (Breiman 2001). The algorithm starts with the
variable with the fewest missing data, continues through variables with more missing data, and
then iteratively re-fits new imputation models until a stopping criterion is reached. We fit two
missForest models, one for each of our treatments, control and warmed. Each model included the
NSE fluxes from the five chambers in the treatment, air temperature both from our onsite
weather station and a nearby station in Castle Valley, UT (included to improve predictions
during times when our system was down), time of day, moisture and temperature values from
two of our sensors (one from each of two data loggers to minimize missing values), and 3 days’
worth of lagged NSE data on either side of each measurement from two of the chambers (again,
one from each logger; 12 lag variables total). The lagged variables were particularly helpful in
4
creating reasonable imputations, as they tied imputed values to values from surrounding days
when the chambers were working.
The chambers were strongly correlated with one another (e.g., r2=0.75 for a simple linear
regression of NSE in chambers from blocks 1 and 2), and with the addition of environmental
data, random forest model fits were typically on the order of 80-90% when functioning chambers
were used to predict the values of malfunctioning ones (for example, a model using
environmental variables and control chamber 3 data to predict fluxes in control chamber 1 had
r2=86%). Thus we believe the data from time points during which at least two chambers were
functioning are imputed to a strong degree of accuracy. The data from the 9% of time points
during which none of the chambers were working are the least certain, but were necessary to
estimate cumulative NSE values. During times when the chambers were down, our weather
sensors were typically down as well (often due to power outage); thus the imputed values during
these times were largely interpolated from values on surrounding days, with the one additional
constraint of daily max, min, and average temperatures from a nearby field site in Castle Valley,
UT (WRCC2014). We refer to these data as the fully imputed data sets.
In addition to these two fully imputed data sets, which were used for cumulative estimates of
NSE, we also used a more conservative partially imputed data set for examination of correlations
between NSE and environmental variables such as temperature, moisture, and light. For these
correlations, we first selected only time points at which at least two values were available for
NSE: moisture at 5 and 15 cm and soil temperature at 5 and 15 cm for both control and warmed
treatments. 87% of the time points met these criteria. We then ran a missForest imputation model
with data from each of the 5 IR lamp and control plots along with air temperature,
photosynthetically active radiation (PAR), and time of measurement. There were 53 total
5
variables in this missForest model. The most conservative approach to assess these correlations
would have been to use only time points at which no values required imputation. However,
because that would eliminate an additional 40% of the data, and because NSE and climate data
were well predicted by one another for simultaneous measurements, we felt a better overall
picture of correlations between NSE and environmental data could be attained by looking at 87%
of the time series with a small amount of high-confidence imputation, than by looking at 50% of
the data with no imputation. After imputation, we took site averages of NSE and each of the
environmental variables by treatment. We refer to this data set as the partially imputed data set.
Finally, to examine correlations between NSE and climate variables at the daily time scale in
the absence of diel fluctuations, we summed daily values from the fully imputed data set, but
eliminated any days on which more than 4 hours of data were missing from 3 or more chambers
or climate sensors. In total, 519/628 (82%) of days met this criterion. We refer to these data as
the daily sums data set.
6
APPENDIX 2 – DISCUSSION OF PARTITIONING OF CO2 LOSSES
The source of the CO2 losses from the biocrust soils at our site is a key question that arises from
the data. Here we discuss the processes that may have contributed to these losses. While
partitioning fluxes was beyond the scope of this study, the purpose of this supplemental
discussion is to further contextualize the size of the losses and to inform future work on
partitioning CO2 losses. We consider four possible CO2 sources in turn: biocrust autotrophic
organisms (e.g., lichen, moss, cyanobacteria), soil heterotrophs, pedogenic carbonates, and
vascular plant roots, with the understanding that more than one likely contribute to the overall
patterns observed.
Biocrust organisms
Previous studies of carbon (C) exchange in autotrophic biocrust organisms show that
respiration rates in crust organisms can be high enough to account for the C losses that we
observed (Lange et al. 1997, Lange et al. 1998, Grote et al. 2010, Coe et al. 2012). However,
these crust physiology studies were almost exclusively done in artificially wetted soils under
laboratory conditions and cannot explain the CO2 losses we observed when biocrusts were dry,
which in the field was most of the time. For example, around half of the annual CO2 loss in 2006
occurred during the almost completely dry spring season. Furthermore, the laboratory studies of
biocrust lichens in which measurements were made at low soil moisture showed little
photosynthesis or respiration when the lichens were dry (Lange et al. 1997, Lange et al. 1998).
Overall, the small percentage of days on which net C uptake was driven by crust photosynthesis
(7±1%) and the relatively low net C uptake on those days, compared with the size of net C losses
at other times, suggest that biocrust photosynthesis is unable to fuel the substantial C losses
7
observed in Figure 1. Unless there are years that periodically occur with a level of biocrust C
uptake vastly different from what we observed during the measurement period, such an
imbalance in biocrust C stocks (i.e., respiration>>photosynthesis) could not drive persistent C
losses over longer time periods. Thus, while respiration by autotrophic biocrust organisms is
surely a component of CO2 flux to the atmosphere, some contribution of CO2 from a nonbiocrust source is highly likely.
Soil heterotrophs
Heterotrophic activity within and below the biocrust layer could also be depleting soil
organic C (SOC) stocks and respiring CO2. While we could not find any published studies
reporting respiration of sub-crust soil in the absence of biocrusts, the eddy flux studies cited
above suggest that it is possible for dryland ecosystems to gain or lose substantial quantities of
C, particularly in response to inter-annual moisture availability (Emmerich 2003, Mielnick et al.
2005, Poulter et al. 2014). Furthermore, the larger SOC pool in the sub-crust soil could provide a
more realistic source for the annual losses we observed (62±8 g m-2 yr-1). While the 0-2 cm
biocrust layer has ~300 g C m-2, which would be depleted rapidly if it were the sole C source, the
sub-crust 2-10 cm layer has ~430 g m-2 and soils are on average 50 cm deep at the site,
suggesting that the total sub-crust soil C is >1500 g C m-2 (Roybal, Whitney, and Reed,
unpublished data). With a pool of that magnitude, depletion of SOC stocks could be substantial
contributors to the C losses we observed.
Pedogenic carbonates and other inorganic CO2 sources
8
The contribution of pedogenic carbonates to C losses is the most difficult to evaluate in the
absence of isotopic information on CO2 emissions (Schlesinger et al. 1989). Some studies have
suggested that fluxes into and out of pedogenic carbonate pools (e.g., caliche layers) can be
substantial (Emmerich 2003), but pedogenic carbonates are usually reported as sinks of C instead
of sources (Marion 1989). Furthermore, while inorganic C could contribute to the CO2 losses we
observed, seasonal and daily patterns are more consistent with biotic phenomena, particularly
because CO2 formation from pedogenic carbonates would be expected to occur during wet
conditions when the carbonates can be dissolved (Mielnick et al. 2005). This makes carbonates
an unlikely cause of the C losses during the dry spring of 2006 and the other dry periods in our
study. This supposition is supported by a study that combined flux measurements with isotopic
analyses to show that even exposed calcite produces very little CO2 flux to the atmosphere
(Serna-Pérez et al. 2006).
There are two other possible inorganic CO2 sources: photodegradation and geo-gas from
seismic activity (Rey 2015). The latter is highly unlikely due to lack of nearby seismic activity.
Photodegradation losses would primarily be expected from senesced plant biomass (Austin and
Vivanco 2006), which we have rarely observed deposited within our chambers since they are
kept free of annual plants, are not under the canopy of perennial plants, and are shielded from
blowing litter by the chamber collars.
Vascular plants
The timing of CO2 losses is most consistent with some of the observed soil column CO2 loss
being driven by plant-root-derived C. We observed the highest CO2 losses, including elevated
nighttime respiration values, during the spring seasons in both measurement years. Eddy flux
9
measurements show that CO2 uptake for the whole ecosystem (plants and soils) is highest in the
spring season, even in drought years (Bowling et al. 2010). Measurements of photosynthetic
rates in the dominant plants at our site confirm that photosynthetic rates in the dominant plants
(A. confertifolia, P. jamesii, and A. hymenoides) are high in the spring, with rates rapidly
declining in June (Wertin et al. 2015). Juxtaposing CO2 loss in biocrust soils with CO2 gain in the
ecosystem as a whole (see an illustration of this juxtaposition in Figure S3) shows patterns
consistent with plant root respiration providing a C source outside of their canopies and below
our measurement chambers. The depth of the measurement chambers (27 cm) would exclude
many of the fine roots from the grasses present at the site, but dryland ecosystems often have
substantial shrub root biomass below that depth (Jackson et al. 1996). Deeper lateral roots could
be a source of CO2 that diffuses to the surface following root respiration or soil heterotrophic
respiration stimulated by deeper-root rhizodeposition and turnover. Finally, there is the fact that
although both spring and summer were relatively dry, we observed a hump-shaped relationship
between temperature and NSE (with respiratory losses peaking around 30 °C) in the spring but
not in the summer. This suggests a stronger biotic respiration signal in the spring that could be
driven by vascular plants in our plots, like Atriplex confertifolia, that have access to water
sources deeper than biocrusts (which have no roots) can access and deeper than our soil moisture
probes (Sperry and Hacke 2002). Nevertheless, the size of the plant contribution remains unclear.
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