The reactive trace gases nitric oxide (NO) and nitrous acid (HONO) are
crucial for chemical processes in the atmosphere, including the formation of
ozone and OH radicals, oxidation of pollutants, and atmospheric
self-cleaning. Recently, empirical studies have shown that biological soil
crusts are able to emit large amounts of NO and HONO, and they may therefore
play an important role in the global budget of these trace gases. However,
the upscaling of local estimates to the global scale is subject to large
uncertainties, due to unknown spatial distribution of crust types and their
dynamic metabolic activity. Here, we perform an alternative estimate of
global NO and HONO emissions by biological soil crusts, using a process-based
modelling approach to these organisms, combined with global data
sets of climate and land cover. We thereby consider that NO and HONO are
emitted in strongly different proportions, depending on the type of crust and
their dynamic activity, and we provide a first estimate of the global
distribution of four different crust types. Based on this, we estimate global
total values of 1.04 Tg yr-1 NO–N and 0.69 Tg yr-1 HONO–N
released by biological soil crusts. This corresponds to around 20 % of
global emissions of these trace gases from natural ecosystems. Due to the low
number of observations on NO and HONO emissions suitable to validate the
model, our estimates are still relatively uncertain. However, they are
consistent with the amount estimated by the empirical approach, which
confirms that biological soil crusts are likely to have a strong impact on
global atmospheric chemistry via emissions of NO and HONO.
Introduction
Biological soil crusts (hereafter “biocrusts”) are complex communities of
organisms which cover large areas around the globe, mainly in arid and
semiarid regions . They consist of various different species
of free-living cyanobacteria, green algae, lichens, bryophytes, fungi, and
bacteria. In contrast to vascular plants, biocrust organisms are able to dry
out and then restart their metabolic activity depending on the availability
of water, which explains their abundance in regions of extreme climatic
conditions. Predecessors of today's biocrusts may have formed the first
terrestrial ecosystems in the Proterozoic, 2 billion years ago
. It has been suggested that these early biocrusts were
already highly productive and that they may have influenced global
atmospheric composition and climate .
Also today's biocrusts have been suggested to affect biogeochemical cycles,
both at the regional and the global scale . In
drylands, they provide several essential ecosystem services, such as
protection of the soil surface against erosion and input of carbon, nitrogen, and phosphorus into the soil
. Carbon input into the ecosystem is carried out by lichens,
bryophytes, cyanobacteria, and algae, which constitute the primary producers
of biocrusts . Fixation of atmospheric nitrogen is due to
cyanobacteria which are either free-living or hosted as symbionts by lichens
and bryophytes . Phosphorus is trapped in the form of dust
particles by lichens and bryophytes or may be acquired through enhanced
weathering of surface rocks by the organisms .
Input of carbon and nitrogen into biocrusts is the precondition for microbial
activity, which leads to transformation of organic nitrogen and subsequent
release of nitrogen trace gases,
such as nitric oxide (NO), nitrous acid (HONO), and nitrous oxide (N2O) .
To estimate the global impacts of these biocrust-related biogeochemical
processes, empirical upscaling of field measurements has been performed. By
extrapolating field observations of productivity grouped into ecosystem
classes, estimated a global net primary productivity (NPP)
of 0.6 Gt yr-1 of carbon for biocrusts in desert and steppe
ecosystems. This represents around 6 % of total NPP in the world's arid and
semiarid regions and around 1 % of global terrestrial NPP
. Moreover, used their approach to estimate the
importance of biocrusts for biotic nitrogen fixation by natural terrestrial
ecosystems. They estimated that biocrusts contribute around 25 % to biotic
nitrogen fixation in desert and steppe regions around the world, which
corresponds to approximately 12 % globally .
In addition to inputs of nitrogen by biocrusts into terrestrial ecosystems,
emissions of different nitrogen species by biocrusts at the global scale have
been quantified. estimated global emissions of
N2O by lichens, bryophytes, cyanobacteria, and algae based on
large-scale patterns of their NPP as presented by . The
authors found that these organisms are responsible for 4 %–9 % of global
emissions of N2O from natural terrestrial sources. In drylands,
where they represent main components of biocrusts, they may even contribute
up to 100 % to N2O emissions. This has implications for global
climate, since N2O acts as an efficient greenhouse gas and depletes
stratospheric ozone.
Apart from N2O, biocrusts and soils emit further nitrogen trace
gases, such as NO and HONO . These gases are
crucial for atmospheric chemistry, since they control the formation of OH
radicals, which are in turn necessary for the oxidation of atmospheric
pollutants and which also affect cloud formation .
found that biocrusts emit ∼1.7 Tg yr-1 of
nitrogen in the form of NO and HONO at the global scale,
which corresponds to ∼20 % of global nitrogen oxide emissions from natural ecosystems .
This points at an important role of biocrusts for global atmospheric
processes.
While these studies, which are based on empirical upscaling of field
measurements, suggest significant impacts of biocrusts on global
biogeochemical cycles, they are usually subject to large uncertainties. The
reason for this is the low number and small scale of measurements of
biocrust-related biogeochemical functions in the field, which generally show
high variation.
As an alternative method to the empirical upscaling of field measurements,
process-based models may be applied to quantify global biogeochemical effects
of biocrusts. In general, these models predict the functioning of the
organisms based on climate and other environmental conditions. Since
high-resolution climate data are available at the global scale, the model
estimates do not depend on the upscaling of a low number of field
measurements to global values.
In contrast to vascular vegetation, however, the non-vascular photoautotrophs
which form biocrusts are seldom considered in global process-based vegetation
models. An exception to this is the LiBry model, which is specifically
designed for non-vascular vegetation. LiBry predicts photosynthesis,
respiration, growth and dynamic surface cover of lichens, bryophytes,
terrestrial cyanobacteria, and algae as a function of climate
. An important aspect of the LiBry model is its explicit
representation of functional diversity of vegetation. Instead of aggregating
diversity into a low number of functional types, LiBry simulates multiple
physiological strategies, similar to the JeDi vegetation model
, which increases the realism of the simulated vegetation.
The model has already been used in various studies on the effects of
non-vascular vegetation on global biogeochemical cycles. Global NPP of
lichens and bryophytes was simulated by LiBry and was found
to be consistent with the empirical estimate by . Further
applications of the LiBry model include climate effects of early non-vascular
vegetation in the geological past and also
effects on permafrost soil processes at high latitudes .
Moreover, global N2O emissions by non-vascular vegetation were
estimated by LiBry, based on the simulated respiration of the organisms
.
Global emissions of NO and HONO by biocrusts, however, have not yet been
estimated using the LiBry model or another process-based modelling approach.
The main reason for this is the strong dependence of observed NO and HONO
emissions on the type of the biocrust . The types are
categorized according to the dominant photoautotrophic organisms of the
biocrust, such as cyanobacteria, lichens, and mosses. Consequently, estimating
total NO and HONO emissions by biocrusts for a certain region requires
knowledge of the relative abundance of different local photoautotrophs, which
has to be considered in the modelling approach. Another complicating factor
is the marked nonlinear dependence of NO and HONO emissions on the water
saturation of the biocrust , which therefore has to be
simulated at a high temporal resolution. Given the potential large
contribution of biocrusts to global NO and HONO emissions, however, a refined
estimate in this regard is crucial for assessing the significance of
biocrusts for global atmospheric chemistry.
The objective of this study is to provide an alternative estimate of global
NO and HONO emissions by biocrusts, based on the process-based non-vascular
vegetation model LiBry.
To this end, we extended the LiBry model in three central aspects: first, we
introduced a scheme which categorizes the large number of physiological
strategies simulated by LiBry for drylands into lichens, mosses, and
cyanobacteria. We then defined the different biocrust types considered in the
study by according to these vegetation groups. This enabled
us to take into account the strong differences in NO and HONO emissions
between biocrust types. Secondly, we altered the scheme for dynamic surface
cover of the physiological strategies in LiBry, which enabled us to predict
the relative cover of each biocrust type. Thirdly, we extended LiBry by an
empirical scheme which calculates NO and HONO emissions of different biocrust
types based on their water saturation. Thereby, saturation of the biocrusts
is based on the dynamic water content of the individual physiological
strategies simulated by LiBry. We evaluated our estimates of biocrust surface
cover both at the local and the global scale by comparison to observations,
and we compared simulated NO and HONO emissions to the available estimates
from the literature.
Please note that in the remainder of the article, we will use the term
“moss” instead of “bryophyte”, although the group of bryophytes also
includes liverworts and hornworts in addition to mosses. The reason for this
terminology is that the corresponding biocrust is usually called
“moss-dominated biocrust”.
MethodsDescription of the LiBry model
The Lichen and Bryophyte model (LiBry) is a process-based vegetation model,
which is specifically designed for non-vascular organisms. The original model
version, published in , was developed to quantify global NPP
of lichens and mosses. Since then, LiBry has been extended in various
aspects, which has increased its applicability to questions of global
biogeochemistry .
LiBry computes photosynthesis and respiration of non-vascular vegetation as a
function of climate and other environmental conditions. Photosynthesis is
calculated by the Farquhar scheme and thus depends on
light, CO2, and temperature. To account for the adjustment of
metabolism to water availability, which is characteristic of non-vascular
vegetation, photosynthesis also depends on the water saturation of the
organisms, as simulated by the model. Furthermore, the decreasing effect of
water saturation on diffusion of CO2 into non-vascular organisms is
considered in the model. Respiration is computed as a function of temperature
through a Q10 relationship, and it also depends on water saturation, in
the same way as photosynthesis. The dynamic water saturation of the organisms
is calculated based on the balance of water inputs via rainfall, snowmelt,
and dew, and water losses due to evaporation. Thereby, evaporation and
surface temperature of the organisms are derived from the surface energy
balance, using a modified Penman–Monteith equation
. Hence, the climate data which are necessary to
drive the model comprise short-wave solar radiation, downwelling long-wave
radiation, air temperature, relative humidity, rainfall, snowfall, and
near-surface wind speed.
Several physiological properties regulate the dynamic water saturation of
non-vascular vegetation in the model: first, the uptake of water is limited
not only by rainfall or snowmelt, but also by the water storage capacity of
the organisms, which depends on it height and the porosity of the biomass. At
full saturation, additional water input infiltrates into the soil. The extent
to which dew can be used as a water source in the model depends mostly on
climatic conditions, and to a limited extent on properties of the organisms
which influence the surface temperature, e.g. via evaporation. Secondly, also
water loss is regulated by properties of the organisms. These are the same as
for water uptake, namely the specific water storage capacity, capillary
structure of the biomass, and albedo. Note that non-vascular vegetation does
not possess stomata, so an active reduction of evaporation is not possible.
NPP is calculated as the difference of photosynthesis and respiration, and it
is translated into growth of biomass, thereby accounting for tissue turnover,
which leads to loss of biomass. Growth and loss of biomass lead to a dynamic
surface cover in the model, which further depends on the frequency of
disturbances. Therefore, in addition to climatic fields, data on the
interval of disturbance events, such as fire, for instance, are required to
run the model. Furthermore, leaf and stem area index of vascular vegetation
are needed, since the canopy influences radiation and moisture supply to
ground-based non-vascular organisms, and it may also serve as a habitat for
non-vascular epiphytes. Climate data and other boundary conditions are
provided as time series for a large number of points on a global grid.
In contrast to most global vegetation models, which aggregate diversity into
a few average plant functional types, LiBry simulates a large number of
physiological strategies, similar to the JeDi vegetation model
. In this way, LiBry explicitly represents functional
diversity of non-vascular vegetation. To create the strategies, a Monte Carlo
approach is used, which randomly samples ranges of observed physiological
properties derived from many studies on non-vascular organisms (see
Appendix B in ). For each location on the global grid, only
a fraction of all initial simulated physiological strategies will be able to
survive under the given climatic conditions. This simulated natural selection
leads to global patterns of physiological strategies in the model, which are
driven by climate. An advantage of this approach is that the model selects
the appropriate parameters for each set of climatic conditions itself; it is
thus not necessary to determine the physiological parameters of all species
everywhere.
Representation of biocrust types in LiBry
For this study we extended the LiBry model to make possible the calculation
of NO and HONO emissions by biocrusts. In a first step, we introduced
different types of biocrusts into LiBry. This is motivated by the strong
dependence of NO and HONO emissions on the type of biocrust, which was found
by . They distinguished four biocrust types, named after the
dominating photoautotrophic organisms:
light cyanobacteria-dominated biocrusts, colonized by a thin layer of
cyanobacteria
dark cyanobacteria-dominated biocrusts, colonized by a thicker, more dense
layer of cyanobacteria, which may grow together with cyanolichens
chlorolichen-dominated biocrusts, colonized by green-algal lichens, and
moss-dominated biocrusts, colonized by mosses.
Furthermore, found that NO and HONO emissions show a marked,
nonlinear relation to the water saturation of the biocrusts. Hence, for our
study, we used their full data set to establish average NO and HONO emissions
as a function of water saturation and biocrust type (see
Fig. ). Moreover, we assumed a Q10 value of 2.0 and
a reference temperature of 25 ∘C for the dependence of NO and HONO
emissions on ambient temperature .
Average emissions of (a) NO and (b) HONO as a
function of water saturation of four different biocrust types. The emissions
were measured on biocrust samples in the laboratory at 25 ∘C. See
for further details.
To implement these relations into the LiBry model, we discretized the curves
shown in Fig. and created a look-up table, which assigns
values of NO and HONO emissions for each value of water saturation.
Subsequently, the emissions were scaled according to surface temperature:
ENO,HONO=ENO,HONO(Θ)QTS-TREF10.0,
where ENO,HONO are the emissions of NO and HONO, respectively,
ENO,HONO(Θ) are the emissions at a given water
saturation Θ based on the look-up table, Q is the
Q10 value, and TS-TREF is the difference between
the surface temperature of the simulated organisms and the reference
temperature. In this way, NO and HONO emissions were calculated from the
simulated water saturation at each time step of the model run.
It should be mentioned that this approach does not simulate the complete
nitrogen cycling in biocrusts. The input of nitrogen, either via fixation or
deposition, and losses of nitrogen in the form of leaching or gases other than NO
and HONO, are not represented. The strong observed NO and HONO emissions by
cyanobacteria-dominated biocrusts (Fig. ) indicate that
the nitrogen source may be biotic fixation. However, further quantitative
studies are needed to implement these processes in biocrust models.
To represent different biocrust types in LiBry, each of the many
physiological strategies simulated by LiBry was assigned to one of the four
biocrust types. We want to point out that LiBry simulates only
photoautotrophic organisms, not the whole biocrust continuum which
additionally includes fungi, soil bacteria, and animals, and the mineral soil.
However, the dominating photoautotrophic organisms exert a strong influence
on the microbial composition and the physiological functioning of the whole
biocrust . This justifies the classification of the four
biocrust types listed above according to their dominant photoautotrophs and,
consequently, the utilization of physiological strategies simulated by LiBry as indicators of the biocrust type.
We only considered strategies growing in drylands, since these are the main regions where biocrusts occur at the large scale.
To assign strategies to biocrust types, we first determined to which group of
photoautotrophs (lichens, mosses, or cyanobacteria) each simulated
physiological strategy belonged. This was necessary since the LiBry model
does not categorize the simulated strategies by default. Instead, an
individual strategy is defined only through its unique combination of values
of several physiological parameters, as described above. We used these
physiological parameters to distinguish the strategies into lichens, mosses,
and cyanobacteria. For this purpose, the following parameters were taken into
account: height, CO2 diffusivity in the wet state, and
photosynthetic capacity. The growth height of a strategy has several effects
in the model: for the same amount of cover expansion, the higher a strategy
is, the more biomass is needed, which is a competitive disadvantage. However,
taller strategies have more potential to store water per given area, and they
may also outcompete smaller strategies with regard to light availability. The
CO2 diffusivity at high water saturation is an important
physiological constraint, since organisms with higher diffusivity are able to
grow more than those with low diffusivity in the model. This advantage is,
however, associated with increased loss of water through evaporation for
given climatic conditions, due to the more open structure of the biomass.
Photosynthetic capacity controls the ability of a photoautotroph to use high
light intensities and to capture CO2 from the atmosphere.
Strategies with a high photosynthetic capacity are able to grow more than
those with low capacity under certain climatic conditions, but this advantage
comes at the cost of increased maintenance respiration and turnover. We want
to mention that the categorization of strategies into lichens, mosses, and
cyanobacteria has no impact on the dynamics of the vegetation in the model;
it only affects the simulated NO and HONO emissions.
We used a stepwise scheme to identify the growth form of a physiological
strategy simulated by LiBry (see Fig. ): if the height of
the simulated strategy exceeds 2 mm, it is either a lichen or a moss;
otherwise it is a cyanobacterium. It should be clarified that a physiological
strategy in LiBry does not correspond to an individual organism, but rather
to a layer of biomass with certain physiological properties. The value of
2 mm therefore does not correspond to the size of an individual
cyanobacterium, but it represents the assumed upper limit for the height of a
layer of cyanobacterial biomass.
To distinguish between lichens and mosses, we used the range of
CO2 diffusivity at full water saturation based on all strategies in
a global simulation. If the CO2 diffusivity of a strategy lies in
the upper half of this range, it is a moss; otherwise it is a lichen, since
mosses generally show higher diffusivities at saturation than lichens
. While lichens and mosses in biocrusts may show a
certain amount of overlap in their CO2 diffusivity, we are not
aware of any systematic studies in this regard. Hence, we applied the
general, simple pattern of distinct CO2 diffusivities here, which
may be further refined in future studies. We then assumed that lichens
indicate chlorolichen-dominated biocrusts and mosses indicate moss-dominated
biocrusts.
To differentiate cyanobacteria in the model, we used the photosynthetic
capacity of the strategies, which roughly corresponds to their
rubisco content per area. If the photosynthetic capacity of a less than 2 mm
tall strategy lies in the upper half of the simulated global range, the
strategy is assumed to indicate dark, and, otherwise, light
cyanobacteria-dominated biocrusts. In general, light and dark
cyanobacteria-dominated biocrusts differ by their abundance of cyanobacteria
per area, while there is no reason why the photosynthetic capacities of the
individual organisms should differ. However, the LiBry model represents the
organisms as a layer of biomass with a certain rubisco concentration, which
is proportional to photosynthetic capacity. Thus, LiBry actually captures the
effect of a lower abundance of cyanobacteria in light crusts compared to dark
crusts by simulating a layer with a lower total photosynthetic capacity.
Scheme to assign each simulated strategy in the LiBry model to one
of four biocrust types for dryland regions.
New dynamic surface cover scheme in LiBry
LiBry calculates the dynamic water content of a physiological strategy from
the balance of water uptake by rainfall, snowmelt, or dew, and water loss by
evaporation. Using the biocrust type-specific relations between water
saturation and NO and HONO emissions, it is therefore possible to quantify
the emissions for each strategy at each time step of the simulation. To
obtain emissions per biocrust type, however, it is necessary to weight the
emissions of each physiological strategy which belongs to the respective
biocrust type by their relative abundance at a given location. In a second
step, we therefore introduced a new scheme into LiBry, which determines the
relative abundances of the physiological strategies. A similar scheme was
already developed for an earlier version of the model .
However, the strategies did not interact in the old scheme; they were all
simulated independently for each location of the global grid, and
subsequently their properties were weighted by their productivity at the
given location. While such an approach is appropriate for calculating
large-scale average carbon fluxes per area, it is not ideal for computing the
surface cover of the strategies. Without interaction, the surface cover of a
strategy only depends on climatic conditions and disturbance events, which
may not be realistic enough. In the new scheme, all strategies growing at the
same grid point of the model have to share the available area for growth, and
the weights for computing large-scale fluxes are thus their relative cover
values (Fig. ). Interactions between strategies then take
place during the expansion of the surface cover. New surface cover of a
strategy depends on the growth of new area per area of the organism. To
express this new area per area of the model grid, it is multiplied by the
fraction of surface covered by the strategy at a given location of the grid.
Expansion of surface cover is only possible into free area, which is not
already covered by strategies. Thereby, we assume that the accessibility of
the free area decreases as the fraction of free area becomes smaller at a
given location. Hence, the strategies cannot contribute with their full
surface cover to the formation and expansion of new cover, but only with a
part of their cover (Fig. a). To account for direct
competition between strategies, we redistribute the area which contributes to
new cover among all strategies. Thereby, the weight of a strategy used for
the redistribution corresponds to its height divided by the sum of the
heights of all strategies. The model has reached a steady state with regard
to surface cover, when the expansion of cover of all strategies is
compensated on average by the loss of cover due to disturbance
(Fig. b). The steady-state spatial pattern of the total
surface cover thus depends on spatial differences in growth due to climate,
and also spatially differing disturbance frequencies. Without disturbance,
the strategies would gradually cover the whole area of the model grid. The
relative cover of a strategy, which is the share of a strategy on the total
cover, depends on growth rate per area and height of the strategy. The height
is set as a constant property of each strategy, and the average growth rate
will be constant in steady state. Thus, also the relative cover of each
strategy, and, consequently, the relative cover of each biocrust type, will
be constant in the steady state of a model simulation.
In the new surface cover scheme, the properties of the dominant simulated
strategy, and, consequently, the predicted most abundant biocrust type,
depend on environmental conditions in the following way: under unfavourable
conditions, such as low water availability or frequent disturbances, total
biocrust cover will be low and the success of the individual strategies will
be mainly limited by their potential expansion rate. This favours small
strategies such as cyanobacteria, since they need less growth of biomass per
area for a given expansion rate than taller strategies. Under optimal
conditions, in turn, total biocrust cover will be high and expansion will be
limited by the available area. This favours tall strategies, such as mosses
and lichens, since they have a higher share on the new cover than small
strategies for the same given expansion rate .
Dynamic surface cover scheme of the LiBry model. The surface cover
of a strategy expands due to growth of biomass. The new cover is proportional
to the area fraction covered by the strategy, and also to the growth rate of
the strategy. For simplicity, the three strategies shown have the same growth
rate. (a) Only a part of the cover of a strategy can contribute to
the new cover, indicated by the dotted magenta line, the remainder is not
able to access the free area for growth. This contributing area decreases
proportionally to the available free area. (b) To account for
competition between strategies, the contributing area is redistributed
according to the height of the strategies. Larger strategies have a higher
weight in the redistribution than smaller ones, which represents their
competitive advantage. Disturbance reduces the cover of all strategies by a
certain fraction at a given time interval, leading to a steady state of cover
where expansion is balanced by disturbance.
Simulation setup
We ran the LiBry model for 600 years to achieve a steady state regarding
biomass and surface cover, using an initial number of 3000 physiological
strategies at each point of the model grid. Climate forcing data were based
on the WATCH data set (see , and also
http://www.eu-watch.org/data_availability, last access: 21 February 2012), which span the years 1958 to 2001. These years were
repeated for the total length of the simulation (600 years). The WATCH data
set comprises short-wave solar radiation, downwelling long-wave radiation,
air temperature at 2 m height, rainfall, snowfall, wind speed at 10 m
height, surface pressure, and specific humidity. The latter two variables were
used to determine relative humidity. The data have a temporal resolution of
3 h, and they were interpolated to hourly resolution to match the hourly
time step of the model, except for rainfall. To achieve a more realistic
temporal distribution of rainfall, we used a random generator, which
disaggregates daily sums of rainfall into hourly values, as described in
. Maps of monthly leaf and stem area index and bare soil
area were based on the Community Land Model . They were used to
compute the available area for growth of the organisms, and also the
partitioning of radiation between the canopy and the ground. All data were
spatially remapped to the resolution of 2.8125∘×2.8125∘ (T42) of the rectangular
model grid. In LiBry, intervals of different disturbance events are assigned
to each point of the model grid based on the biome type at the respective
location (see Tables B4 and B5 in ). This means that, for
each of the 16 biomes which are considered in LiBry at the global scale, a
characteristic interval between disturbance events was determined from the
literature. Thereby, LiBry accounts for processes such as fire, windbreak of
trees, which destroys the habitat of epiphytic non-vascular vegetation, and
also trampling by animals. The disturbance interval is then converted into
the fraction of the biocrust surface cover which is destroyed once per month
in the simulation. For example, an interval of 8 years would result in roughly 1 % of the surface cover being disturbed each month.
The biome classification is based on . We constrained our
simulation to regions belonging to the biomes desert, steppe, savanna, and
mediterranean woodlands , where biocrusts are usually
abundant. Furthermore, we excluded regions which exhibit more than 700 mm of
annual rainfall, since biocrusts are usually outcompeted there by grasses and
trees. To calculate global estimates, we averaged the last 20 years of the
simulation for each point of the model grid.
Model validation and sensitivity analysis
We validated our modelling approach at two spatial scales: first, we compared
the total biocrust cover simulated by LiBry for each location of the model
grid to observed values of biocrust cover around the world, which were
derived from the literature (Table ). While our data set of
observed biocrust cover only includes a subset of the available studies, it
is sufficient to obtain characteristic median values of cover for large
regions. We used these median values, since the spatial coverage of field
observations was not sufficient to directly determine the large-scale
biocrust cover for each cell of the model grid. To estimate biocrust cover at
large scale, empirical models have been used which, however, rely on
correlations between cover and climatic conditions or soil properties to
extrapolate field observations . To exclude these factors,
we compared the model estimates to median values, although this means a
spatially less detailed validation. In addition to the total cover, we
validated the relative cover simulated by LiBry for the four biocrust types
described above, also by comparison to median values of observations
(Table ).
Observations of average water saturation of biocrusts for long periods of
time, and also field measurements of NO and HONO emissions, are quite rare.
Hence, it was not possible to compute meaningful median values for these
properties at the large scale. We therefore compared our simulated estimates
to the available observations on an order-of-magnitude basis.
Secondly, we validated the LiBry model at the local scale. For this purpose,
we used observations of total biocrust cover and relative cover of the four
biocrust types from four study sites near Soebatsfontein, South Africa
. We forced the LiBry model by climatic variables measured
directly at the study sites, and we subsequently compared simulated cover to
observations. Moreover, we evaluated our global climate data set, by
selecting the grid point which includes Soebatsfontein and comparing
simulated cover resulting from the global climate data to the cover resulting
from the site climate data. To assess the water balance computed by LiBry, we
compared simulated water content to observations of water content of
biocrusts at Soebatsfontein. Since the maximum water storage capacity of the
biocrusts was not determined in the field, we could only compare absolute
water content, and not water saturation. These two quantities, however, are
proportional to each other. Furthermore, we compared the surface temperature
of the simulated biocrusts to field measurements in Soebatsfontein. We also
assessed the model performance for another local site, located close to
Almería in southern Spain , where metabolic activity of
biocrusts was observed, which is closely connected to their dynamic water
content. Consequently, we compared simulated active time to the field
observations. It should be mentioned that not all input variables needed for
running the LiBry model were available from the two study sites. While the
most important variables solar radiation, rainfall, air temperature, and
relative humidity were available, we used atmospheric downwelling longwave
radiation from our global data set and, for Soebatsfontein, also wind speed.
Moreover, we used leaf and stem area index from the global data, which are
needed to compute shading effects but are very low for the regions of
Soebatsfontein and Almería and thus hardly affect the results. Due to lack
of knowledge on the disturbance regime, we assumed the standard disturbance
interval of 100 years used in LiBry for the desert biome. All data from
Soebatsfontein were obtained between October 2008 and October 2009, and the
data from Almería are from 2013. The generally low availability of data
sets which include biocrust cover together with time series of soil moisture,
temperature, and climate variables limits our validation to two locations.
To assess the effects of uncertain parameter values on the model estimates,
we performed a sensitivity analysis. The original model was already tested in
this regard, and it was found that simulated NPP was not very sensitive to
changes in various model parameters. This means that varying parameter values
by ±50 % resulted in substantially smaller than 50 % variation in
estimated NPP in most cases (see Table 2 in ). For this
reason, we only analysed a subset of model parameters here. We selected
parameters which are supposed to have a significant impact on the main
estimates of this study, namely total biocrust cover, relative cover of the
four different biocrust types, and NO and HONO emissions.
The simulated total surface coverage was previously tested to be most sensitive to the disturbance interval.
Therefore, the disturbance frequencies for the biomes considered here were varied from the doubled value to half the value.
The values of photosynthetic capacity and CO2 diffusivity,
which were used to distinguish between biocrust types, directly affect the
relative cover of the crust types. Hence, these values were increased and
decreased by 20 %, respectively.
The Q10 value, which controls the temperature dependence of NO and
HONO emissions by biocrusts, may markedly affect the simulated global
emissions. It was therefore increased and decreased by a value of 0.5,
respectively.
Additionally, we tested two uncertain parameters which may influence the
dynamic water saturation of the simulated organisms, since this will also
affect estimated NO and HONO emissions.
The maximum amount of dew which can be collected by non-vascular vegetation in the model was varied from the doubled value to half the value.
Limitation of dew formation in LiBry is necessary since the model does not simulate explicitly the dynamic water content of air in the atmosphere.
The default value of 40 mm yr-1 in LiBry is based on observations of annual dew in drylands (e.g. ).
The resistance of the vegetation surface to evaporation of water was
increased from 0 to 100 s m-1. Since non-vascular organisms have no
active means to control water loss, such as stomata, no resistance against
evaporation is assumed by default in LiBry. However, we cannot exclude the
possibility that certain morphological features reduce evaporation, and thus
we tested a resistance value which roughly corresponds to an average stomata
conductance .
Moreover, we accounted for uncertainty resulting from variation in the
measured relations between water saturation and NO and HONO emissions of
different biocrust types.
We calculated the standard deviation of the measurements made by
and subtracted it from the average curves shown in Fig. 1 to
create a lower bound of NO and HONO emissions as a function of biocrust water
content. To create a corresponding upper bound, we added one standard
deviation to the curves.
We replaced our default relationship between water content and NO and HONO emissions of different biocrust types by an alternative one established by .
They used a similar approach, but for a different location, a field site in Cyprus.
ResultsGlobal patterns of biocrust cover and NO and HONO emissions
Emissions of NO and HONO by biocrusts have been shown to strongly depend on
the type of biocrust (see Fig. ). To take this into
account, we introduced a new scheme into the LiBry model which allows for a
representation of different biocrust types and their associated NO and HONO
emissions. Total NO and HONO emissions were then estimated by weighting the
biocrust types by their relative abundances.
Global patterns of biological soil crust cover simulated by the
LiBry model. Relative cover of (a) light cyanobacteria-dominated,
(b) dark cyanobacteria-dominated, (c) chlorolichen-dominated, and
(d) moss-dominated biocrusts is shown, and also (e) total
biocrust surface cover. White areas at the land surface denote regions which
are excluded since they do not belong to the biomes desert, steppe, savanna,
and mediterranean woodlands, or they exhibit more than 700 mm of annual rainfall.
Grey areas denote regions where no simulated strategies are able to survive,
although they match the above criteria for biocrust cover.
Spatial patterns of relative cover of the four types light cyanobacteria-dominated,
dark cyanobacteria-dominated, chlorolichen-dominated, and moss-dominated biocrust simulated by
the LiBry model at the global scale are shown in Fig. a to
d. Light cyanobacteria-dominated biocrusts are abundant throughout all of
the considered biomes desert, savanna, steppe, and mediterranean woodlands,
except for the driest parts of the world's large deserts, such as the Sahara
or the Arabian Desert, for instance. Interestingly, the relative cover of
light cyanobacteria-dominated biocrusts increases with increasing dryness of
regions up to a certain point, beyond which no simulated strategies are able
to survive in the model. Dark cyanobacteria-dominated biocrusts show a
spatial pattern which is similar to light ones, but their relative cover in
areas close to extremely dry regions is smaller compared to light
cyanobacteria-dominated biocrusts. Chlorolichen-dominated biocrusts have a
more constrained global distribution than light and dark
cyanobacteria-dominated biocrusts in the model. They are, for instance,
excluded from the dry inner part of Australia, and they also occupy smaller
areas compared to light and dark cyanobacteria-dominated biocrusts in the
Sahara and the Arabian Desert. The spatial pattern of moss-dominated
biocrusts is similar to chlorolichen-dominated biocrusts. Moss-dominated
biocrusts are slightly less abundant than chlorolichen-dominated ones, except
for a few regions in inner Australia. In general, moss- and
chlorolichen-dominated biocrusts are more abundant in more poleward regions,
and light and dark cyanobacteria-dominated biocrusts are more abundant in
regions at low latitudes.
The global pattern of the total cover of biocrusts in the biomes considered
here is shown in Fig. e. According to our simulation,
biocrusts cover 11 % of the global land surface. Interestingly, biocrust
cover seems to be highest for desert regions, compared to other biomes,
although biocrust cover tends to increase with rainfall within a biome. In
Fig. we show the dependence of the simulated relative
cover of the different biocrust types on the amount of rainfall and the
average temperature. In general, the relative cover fractions of light and
dark cyanobacteria-dominated biocrusts increase with warmer and drier
climatic conditions, while the share of chlorolichen- and moss-dominated biocrusts on the total coverage increases for cooler and wetter conditions.
For regions with the lowest rainfall, only light cyanobacteria-dominated
biocrusts occur in the model. If rainfall slightly increases, the relative
cover of dark cyanobacteria-dominated biocrusts rises, until they are equally
abundant as the light cyanobacteria-dominated crusts. The slope of this
increase, however, depends on temperature (Fig. a, b):
dark cyanobacteria-dominated biocrusts increase in abundance faster under
cooler climatic conditions. The response of the relative cover fractions of
chlorolichen- and moss-dominated biocrusts to temperature and rainfall is
similar: they are both absent under the warmest and driest climatic
conditions, but under cool and wet conditions they have the largest share on
the total cover. Chlorolichen-dominated crusts seem to grow slightly better
under cool and dry conditions compared to moss-dominated crusts
(Fig. a, c). The dependence of biocrust type on amount of
rainfall and average temperature is also reflected in the average relative
cover values per biome simulated by LiBry (see Table ). In
deserts, light cyanobacteria-dominated biocrusts show the highest relative
cover, while in savanna and steppe regions, the four biocrust types are
equally abundant,
and woodlands show higher relative cover of chlorolichen- and moss-dominated biocrusts.
Potential explanations for the simulated patterns of relative cover of the
four biocrust types, the global pattern of total biocrust cover, and the
correlation of cover types with rainfall are discussed below.
Dependence of relative cover of different biocrust types on annual
rainfall and average temperature. (a–d) Only a subset of all grid
cells in the study area are included in the relations, depending on the
climatic conditions specified in the upper left corner of the plots. The
point symbols (boxes, crosses) denote average values of relative cover for
bins of rainfall with a range of 30 mm yr-1. Regions with zero
biocrust cover are excluded from the data shown in
(a)–(d).
NO and HONO emissions not only depend on the type of biocrust, but they
are also strong nonlinear functions of the water saturation of the crust (see
Fig. ). Therefore, it is important that the model
captures the temporal patterns of water saturation in a realistic way. Since
time series of the water saturation of biocrusts are rarely determined in the
field, the fraction of metabolically active time may be used instead as a
measure of the hydrological dynamics of the crust. In
Fig. we show the simulated percentage of time which is
spent in a metabolically active state, averaged over all physiological
strategies at each location. The large-scale spatial pattern of active time
shows highest values in more poleward regions, medium values in large parts
of (sub)tropical regions, and lowest values in desert regions. This may be
explained by a combination of the patterns of rainfall and surface
temperature (see Fig. ): in poleward regions, rainfall is
relatively high and evaporation is moderate, due to lower surface
temperatures, resulting in relatively large water supply, as long as the
water is not frozen. In many tropical regions, rainfall is also high but
evaporation is markedly increased compared to more poleward regions, as
indicated by the higher surface temperatures. This results in less available
water and thus in less active time.
Global pattern of the percentage of time spent in a metabolically
active state per year, simulated by LiBry. White areas denote excluded
regions, while grey areas correspond to regions where no simulated strategies
are able to survive.
Based on simulated relative surface cover of biocrust types and their
dynamical water saturation, we estimate global patterns of NO and HONO
emissions by biocrusts, which are shown in Fig. . Highest
values of NO and HONO emissions occur in East Africa, around the Sahara, in
northern Australia, and in the hot deserts of North and South America. Northern,
cooler regions show less strong NO and HONO emissions, although the
percentage of active time is relatively high there. These patterns may be
explained by the combined effects of ambient temperature and water saturation
on NO and HONO emissions, which lead to high emission rates in warm
conditions at intermediate to low water content. We estimate annual values of
1.04 Tg yr-1 NO–N and 0.69 Tg yr-1 HONO–N released by biocrusts
at the global scale.
Global patterns of (a) NO and (b) HONO emissions
simulated by LiBry, in units of milligrams of nitrogen per metre squared per year. White areas denote excluded regions, while
grey areas correspond to regions where no simulated strategies are able to
survive.
An overview of the results in the form of global total values for different
biomes and the whole study region is provided in Table .
Annual global total values of biological soil crust cover, relative
cover of biocrust types, metabolically active time, and NO and HONO emissions
estimated by LiBry. “Grassland” means steppe and savanna biomes,
“Woodlands” means mediterranean woodland biomes. The abbreviations in the
second row stand for light cyanobacteria-dominated (LC), dark cyanobacteria-dominated (DC),
chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts. Units are shown in
square brackets. Note that NO and HONO emissions are global sums, while
the other properties are average values for the respective regions. For
relative cover and active time, regions where no strategies were able to
survive in the model were excluded. Total biocrust cover in deserts is
relatively low due to large areas where no simulated strategies survive. If
these areas were excluded, biocrust cover in deserts would amount to
45 %.
We validate our modelling approach both at the global scale and the local
scale. In Fig. a simulated biocrust cover for the biomes
desert, savanna/steppe, and mediterranean woodlands, and also for the whole
considered area are shown together with observed biocrust cover from the
literature. Simulated cover matches well to median values of observations,
and also the large range of the observations is reproduced well by the model.
The higher biocrust cover in deserts compared to savanna and steppe regions,
which is predicted by the LiBry model (see also Fig. e), is
also reflected in the median values of the observations. Furthermore, our
simulated value of total biocrust cover of 11 % of the global land surface
area is in good agreement with the value of 12 % estimated by a recent
empirical large-scale study on the global extent of biocrusts
.
Figure b shows simulated average relative cover of light
cyanobacteria-dominated, dark cyanobacteria-dominated, chlorolichen-dominated and moss-dominated
biocrusts at the global scale together with observations from the literature.
The model reproduces well the sequence of the median values of relative
cover: light cyanobacteria-dominated > dark cyanobacteria-dominated and
chlorolichen-dominated > moss-dominated biocrusts. The large range of
observations is also represented by the model. Compared to the median values,
LiBry underestimates relative cover of light cyanobacteria-dominated
biocrusts, while relative cover values of dark cyanobacteria-, chlorolichen-,
and moss-dominated biocrusts are slightly overestimated.
Regarding active time and NO and HONO emissions, only a few studies report
observational data from field experiments. estimate a value
of 24 % of metabolically active time of biocrusts throughout the year in a
savanna ecosystem in northern Australia. The LiBry model predicts between 10 %
and 20 % active time for the region which includes this ecosystem, which is
consistent with the field measurements. estimate average
combined NO–N and HONO–N emissions of 160 mg m-2 yr-1
originating from the land surface in Cyprus, of which 28 %–46 % can be
attributed to biocrusts, depending on climatic conditions. Cyprus is not
represented by the LiBry model due to the relatively coarse resolution of the
model grid. However, for the close-by area of southern Turkey the model predicts
combined NO and HONO emissions of 30–50 mg m-2 yr-1, which
agrees well with the empirical estimate by .
estimate annual NO emissions of 2 to 16 mg m-2 yr-1 by biocrusts
at two fields sites in the Canyonlands National Park, Utah, USA. This
compares well to the large-scale estimate of 10 to
20 mg m-2 yr-1 of NO–N simulated by LiBry for this region.
Comparison of biocrust cover estimated by LiBry to field
observations (a) for three different biomes and the total study region
and (b) for four different biocrust types. The abbreviations on the
x axis stand for light cyanobacteria-dominated (LC), dark cyanobacteria-dominated (DC),
chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts. The grey crosses show field
measurements of biocrust cover from various studies, which are listed in the
Appendix in Tables and . Grey rectangles
correspond to the median values of all field measurements in a column. The
green circles show (a) average simulated biocrust cover for all
points of the model grid belonging to a biome and (b) average
relative cover for all points of the study region, separated into biocrust
types. Green bars denote the range of (a) cover and
(b) relative cover of all considered grid points. Note that regions
where no strategies were able to survive in the model are excluded from the
comparison.
Local validation
In Fig. we compare simulated cover to field observations
from four study sites near Soebatsfontein, South Africa . The
LiBry model, which is forced by climate data measured near Soebatsfontein,
reproduces well the total biocrust cover at the sites and also the sequence
of relative cover values for the four biocrust types. Relative cover of dark
cyanobacteria-dominated biocrusts, however, is underestimated by the model,
while relative cover of chlorolichen-dominated biocrusts is overestimated.
The field observations show a larger spread between the four sites than the
model estimates. Additionally, we compare the observations to relative cover
based on a simulation forced by global climate data from the region which
includes Soebatsfontein (Fig. ). These LiBry estimates
reproduce observed values of cover as well as the LiBry estimates which are
based on the locally measured climate data.
Comparison of biocrust cover estimated by LiBry to observations at
four different field sites at Soebatsfontein, South Africa. Absolute values
of cover of the four different biocrust types and also total cover are shown.
The abbreviations on the x axis stand for light cyanobacteria-dominated (LC),
dark cyanobacteria-dominated (DC), chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts. The
field sites are distinguished by different geometric symbols. Grey symbols on
the left side of each column denote field observations, while green symbols
on the right correspond to model estimates based on climate data measured at
the field sites. Blue crosses show model estimates obtained from global
climate data for the Soebatsfontein region.
Figure a shows observed water content of biocrusts together
with values simulated by LiBry for Soebatsfontein. The model captures well
the timing and magnitude of moisture content, while the durations of the
moist periods are slightly underestimated for larger rainfall events. In
Fig. b we compare surface temperature of biocrusts simulated
by LiBry for the Soebatsfontein area to field measurements. The model
reproduces well the annual cycle of surface temperature and the magnitude of
daily variations in temperature. Simulated surface temperature in the warm
season is, however, slightly underestimated. Figure only
shows one biocrust type for one of the four field sites in Soebatsfontein for
clarity. The complete overview of simulated dynamic water content and surface
temperature compared to field observations can be found in the Appendix
(Figs. to ).
Figure c and d show observed metabolically active time of
biocrusts together with estimates simulated by LiBry for Almería. The
observed monthly pattern of active time is well reproduced by the model
(Fig. c), and also for the daily pattern, the model agrees
with the measurements (Fig. d). While the response to rain
events is captured in general, the model predicts several small to moderate
peaks in activity, which do not occur in the observations. Moreover, the
model does not entirely reproduce observed periods of prolonged activity in
late spring and winter, which leads to a slight underestimation of total
annual activity by the model. These findings are discussed below.
Comparison of (a) biocrust water content and
(b) surface temperature estimated by LiBry to observations for one
field site (number 4) and one biocrust type (dark cyanobacteria-dominated
biocrust) at Soebatsfontein, South Africa. Comparison of (c) monthly
and (d) daily biocrust active time fraction simulated by LiBry to
observations from a field site near Almería, southern
Spain.
Sensitivity analysis
In Table we show the outcome of our sensitivity analysis,
where we tested how the variation of uncertain parameter values affects the
global estimates. We selected parameters which likely affect total biocrust
cover, the relative cover of the four different biocrust types, and NO and
HONO emissions. Total cover slightly decreases for shorter disturbance
intervals and increases for longer intervals, which is expected. Shifting the
values of CO2 diffusivity and photosynthetic capacity, which
determine the biocrust type, leads to changes in the relative cover of the
respective biocrust types in the expected direction. Changes in the
Q10 value of NO and HONO emissions have no effect on biocrust cover,
since the emissions do not feed back on the organisms in the model. However,
NO and HONO emissions seem to be inversely related to their Q10 value,
which is discussed further below.
Biocrust cover is relatively sensitive to changes in the amount of available
dew. While a reduction in dew mostly affects the relative cover fractions of
the different crust types, a doubling of dew increases total simulated cover
from 30 % to 41 %, active time slightly increases, and estimated NO and HONO emissions more than double.
Furthermore, active time and, consequently NO and HONO emissions by biocrusts
are sensitive to increasing the resistance to evaporation. Active time
increases by 10 %, and NO and HONO emissions by 48 %.
In general, our global results show low sensitivity to variation of the
selected parameters, which means that the estimates change substantially less
than the varied parameters on a relative basis. Reducing the disturbance
interval by half, for instance, only leads to a 10 % reduction in total
biocrust cover, and doubling it causes only a 7 % increase. Compared to
this, shifting the threshold values for the assignment of physiological
strategies to certain biocrust types has a larger effect on the relative
cover fractions of the four biocrust types. However, we find a relatively
large sensitivity of estimated NO and HONO emissions to parameters which
control active time and the relationship between water saturation and
emissions. This is discussed below in more detail.
Simulated NO and HONO emissions are also sensitive to variation in the
relationship between water content and emissions. Decreasing the specific
emissions at a given water content by one standard deviation reduces
simulated total global NO emissions by 25 % and HONO emissions by 50 %
compared to the originally estimated emissions
(see Table ).
Increasing specific emissions by one standard deviation raises simulated NO
emissions by 85 % and HONO emissions by 107 %. Moreover, replacing our
default relation between water content and NO and HONO emissions, which is
based on , by an alternative relation derived from
, significantly affects our estimates. Simulated NO emissions
decrease by 73 %, while HONO emissions are only reduced by 17 %.
Impact of varied parameter values on annual global total values of
biocrust cover, relative cover of biocrust types, metabolically active time,
and NO and HONO emissions estimated by LiBry. The abbreviations in the second
row stand for light cyanobacteria-dominated (LC), dark cyanobacteria-dominated (DC),
chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts. “Control” means the control
run, which is also shown in Table . “τD”
denotes the disturbance interval, which is multiplied by 0.5 and 2.0.
“DCO2” and “PSCCO2” stand for the value of
CO2 diffusivity and photosynthetic capacity, respectively, which
are used to distinguish between crust types. Both parameters are increased
and decreased by 20 %. “Q10” represents the temperature dependence
of NO and HONO emissions, and it is increased and decreased by a value of 0.5.
“dewMAX” corresponds to the maximum amount of dew per year,
which is multiplied by 0.5 and 2.0. “rS100” denotes the surface
resistance of the vegetation against evaporate, which is increased from 0 to
100 s m-1. “ELB” and “EUB” stand for the
lower and upper boundaries, respectively, of the relation between water
content and NO and HONO emissions. “ECYP” corresponds to an
alternative relation between water saturation and NO and HONO emissions of
biocrust types.
In this study we estimated NO and HONO emissions by biological soil crusts
(biocrusts) at the global scale using the process-based non-vascular
vegetation model LiBry. Thereby, emission rates of NO and HONO were based on
water saturation of four distinct biocrust types. LiBry quantified spatial
and temporal patterns of relative cover and saturation for each biocrust type
and thus computed large-scale emissions.
Cover and biocrust types
We found a dependence of biocrust type on the amount of rainfall and average
temperature (Fig. ). In the driest regions, light
cyanobacteria-dominated biocrusts are most abundant. With increasing
rainfall, the relative cover of dark cyanobacteria-dominated crusts
increases, followed by chlorolichen- and moss-dominated biocrusts. With
decreasing temperature, chlorolichen- and moss-dominated biocrusts become
increasingly abundant. At cool and wet conditions, they are more abundant
than light and dark cyanobacteria-dominated biocrusts, which has also been
reported from field studies .
To understand why the LiBry model predicts these climate-driven spatial
patterns of different biocrust types, several physiological processes have to
be considered, which are implemented in the model. First, the dynamic cover
scheme requires all strategies to compensate for losses in their surface cover
through growth, in order to survive. These losses result from disturbance and
turnover of biomass, and they are not directly related to climate. Growth,
however, is proportional to the difference between photosynthesis and
respiration, which means that it decreases with drier conditions, which
reduce the amount of active time, and it also decreases with higher temperatures, since respiration increases with temperature in the model.
Consequently, markedly dry and warm conditions are unfavourable in general for
the simulated strategies. Secondly, smaller strategies have the advantage of
a more efficient cover expansion in the model, since they can produce more
surface area for a given amount of biomass growth than taller strategies.
This means that they are more likely to maintain their cover against
disturbance and turnover under unfavourable climatic conditions. Since we
defined small strategies (less than 2 mm) as cyanobacteria, this explains
the high relative cover of light and dark cyanobacteria-dominated biocrusts
under warm and dry conditions. Figure shows that dark
cyanobacteria-dominated crusts do not perform as well as light
cyanobacteria-dominated crusts under the warmest and driest conditions. This
can be explained by another physiological trade-off implemented in the model,
which links photosynthetic capacity to maintenance respiration. We have
defined that small strategies with a high photosynthetic capacity and thus a
high respiration rate belong to dark cyanobacteria-dominated crusts, while
those with low photosynthetic capacity and respiration are assigned to light
cyanobacteria-dominated crusts (Fig. ). Since respiration
increases stronger than photosynthesis at high temperatures, strategies with
a high “baseline” respiration may grow less under warm conditions,
which explains why dark cyanobacteria-dominated crusts are less abundant in the warmest regions.
Moreover dark cyanobacteria-dominated crusts are more negatively affected by
reduced active time due to dry climate compared to light
cyanobacteria-dominated biocrusts, also at moderate temperatures
(Fig. c). A potential reason for this is the increase in
turnover rate with higher photosynthetic capacity, which is not reduced as
strongly as growth during periods with low activity. This means that dark
cyanobacteria-dominated crusts can use their potential for stronger growth
due to their higher photosynthetic capacity only if sufficient active time is
available (and sufficient radiation). For this reason, their relative cover
is maximal under moderate temperatures and sufficiently wet climatic
conditions (Fig. d). Finally, the growth height of a
simulated strategy represents a competitive advantage in the model. If two
strategies compete for the same location of available free area
(Fig. ), the taller strategy will be able to overgrow the
smaller one. Hence, under favourable climatic conditions (moderate
temperatures and sufficient rainfall), simulated lichens and bryophytes may
partly outgrow cyanobacteria, and therefore chlorolichen- and moss-dominated
biocrusts have the largest share on the total cover
(Fig. ). The higher relative cover of
chlorolichen-dominated biocrusts compared to moss-dominated crusts at cooler
and drier conditions (Fig. a, c) may be explained by
another physiological trade-off in the LiBry model: strategies with a high
diffusivity for CO2 during the saturated state can grow more under
favourable conditions than strategies with a low CO2 diffusivity.
However, due to their more open structure, they also evaporate more water,
and thus become limited faster under dry conditions and are less productive.
Since we have defined in the model that mosses have a higher
CO2 diffusivity and higher evaporation than lichens on average,
the simulated moss-dominated biocrusts may be more affected by dry climate and are thus less abundant than chlorolichen-dominated crusts.
This pattern is more pronounced for cool climatic conditions compared to warm
conditions (Fig. a, b). A possible reason for this is that
the higher resistance of lichens against evaporation is not sufficient to
prevent desiccation under warm climatic conditions, which means that they
have no advantage over mosses anymore. Large water compensation and
saturation values of bryophytes have also been experienced during
CO2 gas exchange measurements under controlled conditions
.
Another finding is that simulated biocrust cover in desert regions is
generally higher than in savanna and steppe regions, which is also supported
by observational data (Fig. a). This may be explained by
different disturbance regimes in these biomes, which are also taken into
account in the LiBry model. More frequent disturbances due to fire and
grazing in steppe and savanna biomes seem to have a more negative impact on
biocrust cover than the lower potential for growth in deserts. Furthermore,
competition by grasses reduces the potential cover in steppe and savanna
biomes, which is represented in LiBry by a decreased fraction of area
available for growth in these regions. Negative effects of multiple types of
disturbance and also the increasing competition of vascular plants at
increasing annual precipitation rates have been described in multiple
studies (e.g. ).
The comparison of simulated biocrust cover to field observations shows a good
agreement in general, both at the global scale as well as the local scale of
the Soebatsfontein field sites. At the global scale, the model underestimates
relative cover of light cyanobacteria-dominated biocrusts, while the other
biocrust types are slightly overestimated (Fig. b). At the
local scale, however, relative cover of light cyanobacteria-dominated
biocrusts is well reproduced for three out of four field sites, while dark ones are
underestimated and chlorolichen ones overestimated (Fig. ).
There are several explanations for these findings: first, the scheme we use
to distinguish between different biocrust types may not reflect physiological
properties of real biocrusts with high accuracy. However, we are not aware of
studies which provide representative values for height, photosynthetic
capacity, and CO2 diffusivity of the biocrust types considered here
at the global scale. This uncertainty is taken into account by the simple
scheme used here, which divides simulated ranges of photosynthetic capacity
and CO2 diffusivity in half. We think this method is more
appropriate than calibrating the threshold values for these properties in a
way that relative cover of biocrust types is accurately reproduced by the
model. By selecting a slightly higher threshold value for photosynthetic
capacity, for instance, we could have increased the relative cover of
chlorolichen-dominated biocrusts in inner Australia compared to
moss-dominated biocrusts. However, the relative cover of the different
biocrust types simulated by LiBry not only depends on the two
physiological properties, CO2 diffusivity, and photosynthetic
capacity, which were used to distinguish the crust types, but also on other
factors, such as disturbance interval, for instance. By calibrating the model
with regard to CO2 diffusivity and photosynthetic capacity, we
would implicitly assume that all other uncertain parameters have the correct
values, which is not necessarily true.
Secondly, the number of observations of relative biocrust cover is relatively
low and shows a large variation (Fig. b). Hence, the median
values of relative cover may not reflect real abundances of cover types very
accurately. Finally, regarding the field sites at Soebatsfontein, the
simulated disturbance interval or shading by vascular vegetation may not
represent well the actual conditions at the sites, leading to biased cover
estimates.
We find that the global climate data for the grid cell which includes
Soebatsfontein are a good approximation to the climate data from the local
station (see also Fig. ). Consequently, the estimated
values of cover of biocrust types based on the different climate input data
are similar. However, for the site near Almería, the local data represent
significantly drier conditions than the large-scale data for southern Spain,
which explains the higher activity simulated by LiBry for Spain in general compared to the Almería site.
This illustrates that the global data are not necessarily a good approximation for those local conditions which strongly deviate from the regional climate.
The sensitivity analysis shows that relative cover of light
cyanobacteria-dominated biocrusts increases at the expense of dark ones for
increasing disturbance intervals. This seems counterintuitive, since less
frequent disturbances should lead to a higher relative cover of dark
cyanobacteria-dominated biocrusts. However, this effect is overruled by the
increase in total biocrust area under longer disturbance intervals, which is
populated mainly by light cyanobacteria-dominated biocrusts, thus increasing
their cover relative to dark ones.
Active time and surface temperature
Regarding active time of biocrusts, a global-scale evaluation of the model
estimates is difficult due to lack of suitable field observations. At the
field sites of Soebatsfontein, the pattern of simulated water content matches
well the observations (Fig. ). However, the durations of the
moist periods are slightly underestimated, which means that also
metabolically active time may be underestimated. This may be explained by the
fact that the moisture sensors which were installed at the field sites
measure the water content of the uppermost 5 mm of the whole biocrust, which
also include fungi, soil bacteria, and mineral soil. The LiBry model, however,
only considers the water storage capacity of the photoautotrophic organisms,
which is smaller than the whole biocrust storage capacity. Due to lower water
storage capacity, the photoautotrophs may become desiccated earlier than the
whole soil, leading to a shorter simulated active time compared to the
observations at Soebatsfontein.
Simulated surface temperature of biocrusts is reproduced well by LiBry
compared to observations at Soebatsfontein. For the warm season, however,
surface temperature is underestimated by the model. This may result from the
fact that protection of biocrusts by dark pigments against UV radiation is
not considered in the model. Hence, simulated strategies with higher albedo
than observed in the field may be selected in the model, which exhibit lower
surface temperatures to reduce respiration losses. However, this is not of
great importance for our main results, since the biocrusts are mostly
inactive in the warm season. Thus, underestimated temperature values do not
affect the simulated annual NO and HONO emissions to a large extent.
At the field site of Almería, the simulated monthly and daily patterns of
active time match well the observations in general. However, in spring and
autumn, the model predicts several small and a few larger peaks of activity
which cannot be seen in the measurements. One possible explanation for this
is that the model may overestimate dew input from the atmosphere, possibly
due to the spatially uniform value of maximum dew which is used in LiBry for
all dryland regions. In contrast, the model underestimates longer periods of
high activity in late spring and winter, which are not directly related to
rainfall events (Fig. d). Potential reasons for this may be
the missing water storage capacity of the soil in the model or
underestimation by the model of activation from unsaturated air at relatively
high humidity. Although the latter process is represented in the model, it
contributes little to the total simulated water supply. Hence, same as for
Soebatsfontein, LiBry tends to slightly underestimate active time.
The relatively large sensitivity of our estimated NO and HONO emissions to
parameters which influence the dynamic water content is plausible. If maximum
available dew is doubled, or a resistance to evaporation is introduced, the
potential area where biocrusts may occur in the model is significantly larger
and total biocrust cover and active time significantly increase
(Table ).
The strong increase in NO and HONO emissions can further be explained by the
large increase in biocrust cover in warm regions, where high temperatures
further enhance NO and HONO release (Eq. ). However, the
simulated global patterns of biocrust coverage under doubled dew or increased
surface resistance seem to be inconsistent with observations. In extremely
dry regions of North Africa, Arabia, and Australia, high cover values of more
than 70 % are simulated, which are substantially higher than large-scale
average values which are commonly assumed for these regions
(see Fig. ).
Moreover, although dew of 80 mm yr-1 may occur locally under certain
conditions in drylands, this value is most likely too high for a large-scale
estimate (see also , for typical values).
NO and HONO emissions
The LiBry model estimates global annual total values of 1.04 Tg yr-1
NO–N and 0.69 Tg yr-1 HONO–N released by biocrusts. These values are
in good agreement with the 1.1 Tg yr-1 NO–N and 0.6 Tg yr-1
HONO–N estimated by using an empirical upscaling of field
measurements. However, it should be noted that include a
considerably larger area in their study, since they do not constrain
potential biocrust extent by high rainfall. Hence, our approach predicts
higher NO and HONO emissions per area than the empirical one (see
Fig. for an estimate without the rainfall constraint).
There are several factors that may lead to an underestimation of emissions by
the empirical approach: first, emissions of NO and HONO were not computed
directly from water saturation of biocrusts by . Instead, they
assumed that each rainfall event leads to a full wetting and drying cycle of
the biocrust. By measuring the integrated NO and HONO emissions during one
average wetting and drying cycle in the laboratory, and subsequently
multiplying these emissions by the number of precipitation events, global
emissions were estimated. However, comparison with field observations
suggests that the number of precipitation events derived from global rainfall
data may have been considerably underestimated, due to the coarse 3-hourly
temporal resolution of the data set. Since we disaggregate the 3-hourly
rainfall data to hourly data using a stochastic model , we
consider a higher number of rainfall events, which may partly explain our
higher per-area estimates. Thereby, also small rainfall events, which do not
cause a full wetting and drying cycle, may result in high NO and HONO
emissions, due to the nonlinear dependence of emissions on water saturation
(Fig. ). Secondly, globally uniform values of relative
coverage of the four different biocrust types were assumed in order to
upscale NO and HONO emissions to global values. However, if the relative
cover of the biocrust types correlates with global patterns of rainfall,
which is suggested by LiBry results, the upscaling will be biased, since the
biocrust types differ in their NO and HONO emission rates. It is difficult to
estimate if this would result in an under- or overestimation of NO and HONO
emissions: dark cyanobacteria-dominated biocrusts, which were shown to be the
most effective emitters of NO and HONO, tend to have higher coverage values
at lower annual precipitation amounts, but their peak emissions only require
a water saturation of around 20 %. To summarize, our current approach
allowed us to model biocrust emissions in a much more detailed manner, but,
in the long run, additional field measurements would be helpful to further
corroborate these results.
Alternatively, our modelling approach may overestimate the true NO and HONO
emissions at large scale. A potential reason for this may be overestimation
of the simulated water saturation of dark cyanobacteria-dominated biocrusts
during their periods of metabolic activity. Since NO and HONO emissions of
dark cyanobacteria-dominated biocrusts show a strong peak at a water
saturation of approximately 20 %, emissions would be overestimated if this
value was simulated too frequently by the model (Fig. ).
However, Fig. a suggests that the LiBry model correctly
predicts the number of wetting events. To assess this further, field
measurements of water saturation would be needed, which allow the
reconstruction of
the temporal distribution of the degree of saturation. Another reason for
overestimated NO and HONO emissions may be a potential overestimation of
active time of biocrusts by LiBry. However, the model tends to rather
underestimate active time at the local scale, since it only considers the
water reservoir of the photoautotrophic organisms, which is smaller than that
of the whole biocrust. Hence, we think that a significant overestimation of
active time by LiBry is unlikely. Finally, the relations between water
saturation and NO and HONO emissions which we use in the model may not
reflect the true dependence of emissions on saturation and biocrust type. The
relations are obtained by averaging the saturation-emission curves of four
different samples of each biocrust type, which partly show considerable
variation. The sensitivity analysis demonstrates that varying the relation
between water saturation and NO and HONO emissions of biocrusts by one
standard deviation leads to relatively large changes in the estimated NO and
HONO emissions. Also, replacing the default relation by an alternative one
which is based on measurements from a different field site significantly
affects simulated NO and HONO emissions by biocrusts. Further analyses of the
dependence of NO and HONO emissions on the water saturation state of
biocrusts are needed to determine the causes of this large variation. A
potential first step in this direction would be to determine the dynamic
nitrogen content of biocrusts and also of the underlying soil. Subsequently,
the nitrogen content could be related to variation in the relationship
between NO / HONO emissions and biocrust type. It is likely that
emissions will increase with soil nitrogen content, which means that
biocrusts will contribute relatively more to total emissions in areas with
nitrogen-poor soils and less on nitrogen-rich soils. Including this factor
in our modelling approach would lead to a more differentiated global pattern
of NO and HONO emissions. Furthermore, processes which control the nonlinear
dependence of NO and HONO emissions on water saturation need to be clarified.
One possible explanation for the decrease of emissions at high water
saturation is the limitation of the microbes which produce NO and HONO by
increasingly low oxygen supply. Differences in the structure between biocrust
types and, consequently, their diffusivity for oxygen, may then explain
variation in the shape of the relation between water saturation and
NO / HONO emissions. A fully process-based scheme of NO and HONO
emissions by biocrusts may contribute to further quantifying nitrogen cycling
in dryland soils. Currently, it is still difficult to close the nitrogen
balance for these areas due to uncertainty regarding the amount of both
nitrogen inputs via atmospheric deposition and biotic fixation and also
outputs
through various gaseous losses, leaching, and erosion.
Further analyses of the dependence of NO and HONO emissions on the water saturation state of biocrusts are needed to determine the causes of this large variation.
The sensitivity analysis shows that NO and HONO emissions seem to be
inversely related to their Q10 value. This can be explained by the fact
that emissions which take place below the reference temperature of the
Q10 relationship (25 ∘C) are increased if the Q10 value
decreases. Since large areas covered by biocrusts in the model are located in
relatively cool regions, increased emissions there overrule the decreased
emissions in warm regions at a low Q10 value.
Due to the paucity of studies which report observation-based estimates of NO
and HONO emissions by biocrusts, the validation of our model is relatively
limited in this regard. To obtain annual values for NO and HONO emissions,
usually short-term measurements on wetted biocrust samples are extrapolated
to the whole year based on the number of wetting events at the respective
field site. Hence, for a future, more detailed validation of the LiBry model
it would be useful to create data sets which include both climate data with
high temporal resolution and also NO and HONO emissions from the same field
site. In addition, activity of the biocrusts should be monitored, so
potential mismatches between model and observations can be traced to either
the simulation of the dynamic water content or the specific emissions per
crust type.
Our estimates confirm the main result found by , namely a
considerable impact of biocrusts on global NO and HONO emissions. According
to our simulation, biocrusts in drylands contribute around 20 % to global
terrestrial NO and HONO emissions in natural ecosystems . This
suggests that biocrusts are important contributors to the global nitrogen
cycle and have a considerable effect on chemical processes in the atmosphere.
Given their biogeochemical significance, it would be interesting to assess
impacts of future climate change on biocrusts and their functions. Strong
decreases in NO and HONO emissions by biocrusts, which may result from
reduced cover or active time, may have considerable effects on atmospheric
chemistry. If climate change leads to a shift in relative cover of biocrust
types towards biocrusts which release large amounts of NO and HONO, however,
emissions may further increase. The LiBry model is an appropriate tool for
such an assessment due to its mechanistic design, which allows direct
validation of individual processes and parameters. Another interesting topic
for future work related to NO and HONO emissions by biocrusts would be a more
detailed analysis of the processes which cause the release of NO and HONO.
This may help to explain the large variation in emissions between different
samples of the same biocrust type and further constrain model-based
estimates.
Conclusions
We estimated global patterns and total annual values of NO and HONO emissions
by biological soil crusts (biocrusts) using the process-based model of
non-vascular vegetation LiBry. We found emissions of 1.04 Tg yr-1
NO–N and 0.69 Tg yr-1 HONO–N by biocrusts, which corresponds to a
large contribution of around 20 % of global emissions of these trace gases
from natural ecosystems. This suggests a considerable impact of biocrusts on
global atmospheric chemistry. Our global estimate is consistent with an
earlier, empirical approach, although we estimate higher per-area emissions,
which are compensated for by a smaller predicted global biocrust area. Uncertainty in
the empirical approach mainly resulted from assumptions concerning the
frequency of metabolic activity of biocrusts in the field at the global
scale. In our approach, we found a low sensitivity of NO and HONO emissions
to several uncertain parameters, which are likely to affect the simulated
emissions. We discuss potential approaches to improve the reliability of our
estimates, and we conclude that a detailed analysis of the processes which
relate NO and HONO emissions to water saturation of biocrusts would be
helpful in this regard.
Code availability
The non-vascular vegetation model LiBry used in this study
is integrated in an interface for parallel computing which was developed at
the Max Planck Institute for Biogeochemistry, Jena, Germany. The LiBry model
excluding this interface is freely available provided that the names of the
copyright holders and a disclaimer are distributed along with the code in
source or binary form. The code is available from the corresponding author
upon request. Model output data which are presented as maps in this study are
available as netCDF files from the authors on request.
Complementary model validation tables and figures
Studies reporting values of total biocrust
cover which are used in the model–data comparison (Fig. ).
Studies reporting values of total biocrust cover and relative cover of four biocrust types which are used in the model–data comparison (Fig. ).
The abbreviations stand for light cyanobacteria-dominated (LC), dark cyanobacteria-dominated (DC), chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts.
All cover values are in percent.
Global patterns of (a) rainfall and (b) surface
temperature of biocrusts simulated by LiBry, in units of millimetres per year and
degrees Celsius, respectively. White areas denote excluded regions, while grey
areas correspond to regions where no simulated strategies are able to
survive.
Comparison of measured and simulated moisture content for one field
site and four biocrust types at Soebatsfontein. The abbreviations in the
figure legend stand for light cyanobacteria-dominated (LC), dark cyanobacteria-dominated (DC),
chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts.
Comparison of measured and simulated moisture content for one
biocrust type (dark cyanobacteria-dominated biocrust) and four field sites at
Soebatsfontein.
Comparison of measured and simulated surface temperature for one
field site and four biocrust types at Soebatsfontein. The abbreviations in
the figure legend stand for light cyanobacteria-dominated (LC), dark
cyanobacteria-dominated (DC), chlorolichen-dominated (CC), and moss-dominated (MC) biocrusts.
Comparison of measured and simulated surface temperature for one
biocrust type (dark cyanobacteria-dominated biocrust) and four field sites at
Soebatsfontein.
Comparison of climate data measured at one of the four
Soebatsfontein field sites to global climate data for the region including
Soebatsfontein. Note that global data for the year 2009 were not available
from the standard data set used for LiBry, which spans the years 1958 to
2001. Climate data for the other three field sites at Soebatsfontein are
similar to the site shown here.
Total biocrust surface cover for (a) doubled dew
availability and (b) increased surface resistance to evaporation (from 0
to 100 s m-1).
Global patterns of (a) NO and (b) HONO emissions
simulated by LiBry, in units of units of milligrams of nitrogen per metre squared per year. White areas denote excluded regions, while
grey areas correspond to regions where no simulated strategies are able to
survive. Contrary to the standard model setup, the study region is only
constrained by biome type; no maximum amount of annual rainfall is
considered. Total global NO and HONO emissions by biocrusts amount to
2.48 Tg yr-1 NO–N and 1.69 Tg yr-1 HONO–N.
Author contributions
PP, BW, and UP designed the model
simulations and PP carried them out. AT, JR, and YC provided data for model parametrization and validation. PP
prepared the manuscript with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank all the authors of the initial study on NO and
HONO emissions of biological soil crusts , which provided
input data for our current investigation. Bettina Weber would like to thank Paul Crutzen for awarding
her the Nobel Laureate Fellowship and Philipp Porada gladly appreciates
funding by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation) – 408092731. The Max Planck Institute for Biogeochemistry
provided computational resources.
Review statement
This paper was edited by Akihiko Ito and reviewed by two anonymous referees.
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