<figure><img src="image_19.png" /><figcaption>**Figure 14.** Model output optimised to observational data from the Athabasca Glacier, Canada.</figcaption></figure>

(11.3 µg N g⁻¹); however later increase to a similar relative abundance to nitrogen-fixing heterotrophs when DIN stocks are more plentiful.

The model predicts the observed accumulation of carbon substrate with a substantially lower allochthonous input of substrate ($v_{Sub} = 0.05$) compared to the Damma Glacier ($v_{Sub} = 0.60$). Autotrophic production accounts for > 87.5% of total bacterial production. DIN remains low compared to the Damma Glacier. The majority of the DIN assimilation in young soils (< 15 years) is by nitrogen fixation (up to 96.6%). The seasonal oscillations in microbial biomass and activity at the Athabasca Glacier forefield are considerably smaller than the Damma Glacier forefield, due to increased nutrient scarcity (inhibiting growth and slowing the biotic response to seasonal variability) and lower microbial biomass. A high level of bacterial production is sustained by a continuous pool of labile substrate. Our ability to put these model results into context with field data is limited since there the Athabasca Glacier forefield is considerably less intensively studied than the Damma Glacier. Nevertheless, the Athabasca Glacier site acts as a secondary test and validation of model behaviour against published obser-

vational and experimental data suggesting an increase in microbial biomass and accumulation of organic carbon.

# 6 Model development, application and recommendations

The SHIMMER framework greatly improves our ability to quantify dominant biogeochemical processes and make scenario-based predictions of soil development. An additional result of developing the SHIMMER framework has been an appreciation of the types of data that are necessary to build a mechanistic and fully constrained numerical representation of these systems, and this is presented here as a recommendation to future field and laboratory efforts.

## 6.1 Data availability

Model development and confidence in model evaluations would be improved by higher temporal and spatial sampling resolution along the chronosequence and interdisciplinary (microbial and geochemical) data sets (concentrations and rates). Resolution of autotrophic and heterotrophic biomass would help in validating model predictions of community
composition and net autotrophy/heterotrophy. SHIMMER does not explicitly account for vegetation and thus cannot reproduce the high organic carbon accumulation in vegetated sites (Fig. 11). Therefore, there is a need for test sites where the influence of vegetation is not so great. A full mass budget of inputs (e.g. aerial deposition) and outputs (e.g. leaching) of substrate and nutrients through the soil surface would greatly improve the predictive power of SHIMMER, as demonstrated in sensitivity analysis (Sect. 5.2.1).

## 6.2 Death and dormancy

Many biological processes including microbial growth and death are simplified in order to describe them mathematically. Cell death is not well constrained, with a lack of empirical measurements, and fundamental differences in how these rates are defined in models (German et al., 2012; Grant et al., 1993; Scott et al., 1995; Toal et al., 2000; Boudreau, 1999; Kravchenko et al., 2004; Knapp et al., 1983; Blagodatsky and Richter, 1998; Ingwersen et al., 2008; Zelenev et al., 2000; Lancelot et al., 2005). Additionally, in situ measurements of active and dormant microbes in field studies are rare, thus making validation difficult. Those that are provided in the literature (Toal et al., 2000; Blagodatsky and Richter, 1998; Vandewerf and Verstraete, 1987b) are mostly derived from models and are a snap shot in time. These rates strongly influence both the microbial populations' stability in size, and the available substrate and necromass. Therefore, more focused empirical observations are needed to support and inform model processes, increase confidence in predictions and support future development.

## 6.3 Bacterial growth

Parameters defined as constants in mathematical models are often known to vary in time depending on prevailing environmental and biogeochemical conditions, for example sensitivity to temperature, soil moisture, oxygen availability, C : N ratio and quality of soil organic matter (Manzoni et al., 2012; Erhagen et al., 2015). Variable growth efficiencies and background maintenance, for example, would require additional parameters that currently cannot be defined with sufficient confidence, increasing uncertainty rather than improving the model. Laboratory incubations are useful to estimate parameter values experimentally (Blagodatsky et al., 1998). It is our intention that future laboratory analysis can at least in part inform parameters such that confidence in model output is increased.

## 6.4 Discrete pools vs. continuum representation

In a real soil system, the composition of the microbial community, along with the quality of substrate are continua, rather than discrete categories (as in SHIMMER). Classifying variables into categories (e.g. labile and refractory) ultimately provides a simplistic view of the system, but also

provides tuning parameters, flexibility and a high degree of generality. Other modelling approaches such as individual-based modelling (IBM) would account for population heterogeneity, and the ability to link mechanisms to population dynamics at the individual level (Hellweger and Bucci, 2009). However, this is outside the scope of the current model version.

## 6.5 Deterministic vs. stochastic

The biogeochemical development of a de-glaciated forefield is extremely heterogeneous across a range of spatial scales, which affects the signal of biogeochemical changes in observations (Bernasconi et al., 2011; Bradley et al., 2014). Stochastic variability resulting from disturbances, heterogeneity in biotic and abiotic processes, lateral and vertical particle movement and diffusion, inter-annual variability, and changing environmental conditions affect the biogeochemical development of a chronosequence in such a way that a deterministic model cannot predict. Furthermore, the SHIMMER model does not predict or account for the effect of plant biomass, which is highly abundant in the forefield of the Damma Glacier (Bernasconi et al., 2011). However, a first attempt to gain quantitative insight into glacier forefield dynamics favours a deterministic framework.

## 6.6 0-D vs. multi-D

Currently, nearly all soil system models assume that at the finest level of detail, soil is a well-mixed homogeneous particle with respect to its composition and dynamics, whereas microbial populations and metabolic rates are known to be heterogeneous across a number of different spatial scales and directions. A large amount of additional parameters and equations would need to be incorporated in order to include 1-D or 2-D processes in SHIMMER. It is currently unfeasible to incorporate this level of detail due to limitations in the observational data, and the resulting model would not be useful for the purpose it is intended for, that is to describe, predict and provide insight on the development of initial ecosystems such as those exposed due to glacier retreat.

## 7 Conclusions

Accurate quantitative prediction of the biogeochemical development of de-glaciated forefields is important to understand the primary succession of microbial communities and the formation of organic carbon in extreme oligotrophic environments. The forefield ecosystem reacts rapidly to climate change (Smittenberg et al., 2012), and the fine glacial flour, highly reactive sediments and rapid biological cycling of nutrients typical of forefields may be significant to global biogeochemical cycles in the context of future large-scale ice retreat.
composition and net autotrophy/heterotrophy. SHIMMER does not explicitly account for vegetation and thus cannot reproduce the high organic carbon accumulation in vegetated sites (Fig. 11). Therefore, there is a need for test sites where the influence of vegetation is not so great. A full mass budget of inputs (e.g. aerial deposition) and outputs (e.g. leaching) of substrate and nutrients through the soil surface would greatly improve the predictive power of SHIMMER, as demonstrated in sensitivity analysis (Sect. 5.2.1).

## 6.2 Death and dormancy

Many biological processes including microbial growth and death are simplified in order to describe them mathematically. Cell death is not well constrained, with a lack of empirical measurements, and fundamental differences in how these rates are defined in models (German et al., 2012; Grant et al., 1993; Scott et al., 1995; Toal et al., 2000; Boudreau, 1999; Kravchenko et al., 2004; Knapp et al., 1983; Blagodatsky and Richter, 1998; Ingwersen et al., 2008; Zelenev et al., 2000; Lancelot et al., 2005). Additionally, in situ measurements of active and dormant microbes in field studies are rare, thus making validation difficult. Those that are provided in the literature (Toal et al., 2000; Blagodatsky and Richter, 1998; Vandewerf and Verstraete, 1987b) are mostly derived from models and are a snap shot in time. These rates strongly influence both the microbial populations' stability in size, and the available substrate and necromass. Therefore, more focused empirical observations are needed to support and inform model processes, increase confidence in predictions and support future development.

## 6.3 Bacterial growth

Parameters defined as constants in mathematical models are often known to vary in time depending on prevailing environmental and biogeochemical conditions, for example sensitivity to temperature, soil moisture, oxygen availability, C:N ratio and quality of soil organic matter (Manzoni et al., 2012; Erhagen et al., 2015). Variable growth efficiencies and background maintenance, for example, would require additional parameters that currently cannot be defined with sufficient confidence, increasing uncertainty rather than improving the model. Laboratory incubations are useful to estimate parameter values experimentally (Blagodatsky et al., 1998). It is our intention that future laboratory analysis can at least in part inform parameters such that confidence in model output is increased.

## 6.4 Discrete pools vs. continuum representation

In a real soil system, the composition of the microbial community, along with the quality of substrate are continua, rather than discrete categories (as in SHIMMER). Classifying variables into categories (e.g. labile and refractory) ultimately provides a simplistic view of the system, but also

provides tuning parameters, flexibility and a high degree of generality. Other modelling approaches such as individual-based modelling (IBM) would account for population heterogeneity, and the ability to link mechanisms to population dynamics at the individual level (Hellweger and Bucci, 2009). However, this is outside the scope of the current model version.

## 6.5 Deterministic vs. stochastic

The biogeochemical development of a de-glaciated forefield is extremely heterogeneous across a range of spatial scales, which affects the signal of biogeochemical changes in observations (Bernasconi et al., 2011; Bradley et al., 2014). Stochastic variability resulting from disturbances, heterogeneity in biotic and abiotic processes, lateral and vertical particle movement and diffusion, inter-annual variability, and changing environmental conditions affect the biogeochemical development of a chronosequence in such a way that a deterministic model cannot predict. Furthermore, the SHIMMER model does not predict or account for the effect of plant biomass, which is highly abundant in the forefield of the Damma Glacier (Bernasconi et al., 2011). However, a first attempt to gain quantitative insight into glacier forefield dynamics favours a deterministic framework.

## 6.6 0-D vs. multi-D

Currently, nearly all soil system models assume that at the finest level of detail, soil is a well-mixed homogeneous particle with respect to its composition and dynamics, whereas microbial populations and metabolic rates are known to be heterogeneous across a number of different spatial scales and directions. A large amount of additional parameters and equations would need to be incorporated in order to include 1-D or 2-D processes in SHIMMER. It is currently unfeasible to incorporate this level of detail due to limitations in the observational data, and the resulting model would not be useful for the purpose it is intended for, that is to describe, predict and provide insight on the development of initial ecosystems such as those exposed due to glacier retreat.

## 7 Conclusions

Accurate quantitative prediction of the biogeochemical development of de-glaciated forefields is important to understand the primary succession of microbial communities and the formation of organic carbon in extreme oligotrophic environments. The forefield ecosystem reacts rapidly to climate change (Smittenberg et al., 2012), and the fine glacial flour, highly reactive sediments and rapid biological cycling of nutrients typical of forefields may be significant to global biogeochemical cycles in the context of future large-scale ice retreat.
Here we present SHIMMER, a novel modelling framework designed to predict microbial community development during the initial stages of ecosystem formation. The model accurately predicts the accumulation of microbial biomass and organic carbon during the initial stages of soil development from two glacier forefields (Bernasconi et al., 2011; Goransson et al., 2011; Insam and Haselwandter, 1989), and supports our general understanding of these ecosystems. Autotrophic production and nitrogen fixation are fundamental to the establishment of microbial communities and stable and labile pools of organic substrate and inorganic nutrients, a finding that is supported by field experiments at the Damma Glacier (Zumsteg et al., 2013b; Esperschütz et al., 2011; Frey et al., 2013; Duc et al., 2009) and elsewhere including the Puca Glacier in Peru (Schmidt et al., 2008; Ne mergut et al., 2007), the Mendenhall Glacier in Alaska (Sat tin et al., 2009; Knelman et al., 2012), and Anvers Island in Antarctica (Strauss et al., 2012). Soil respiration is compa rable to field observations (Schulz et al., 2013; Guelland et al., 2013a). The seasonal evolution of glacier forefields is not well understood (Bradley et al., 2014); however modelling work is likely to provide insight into the dynamics of the “non-growing season”. For example, modelled summer pro- duction accounts for roughly 70% of total annual respira- tion, in line with field observations from the Damma Glacier (Guelland et al., 2013b).

The accumulation of microbial biomass is highly sen-
sitive to variation in the Q₁₀ values, active biomass, the
bioavailability of organic matter, bacterial growth efficiency,
and rates of microbial growth and death. These parameters
also bear high uncertainty due to a relatively large range of
plausible values. Many of the well-constrained parameters
(e.g. half-saturation constants and exudation rates) have low
sensitivity and uncertainty and show mostly linear behaviour.
One of the striking outcomes of the sensitivity study is the ap-
parent strong dependence between the heterotrophic and au-
totrophic microbial communities. Heterotrophic production
degrades organic matter and recycles nutrients, in turn sup-
porting autotrophic and heterotrophic growth. Increasing het-
erotrophic growth rates therefore increases the accumulation
in all pools of biomass.

SHIMMER is the first step towards an iterative and in-
terdisciplinary framework (presented in Fig. 2), integrating
fieldwork and laboratory experiments with model develop-
ment, testing and application. The development of SHIM-
MER (1.0) is informed by previous experimental and field
campaigns (Bradley et al., 2014). It is expected that further
quantitative analysis of forefield dynamics will guide and in-
form future studies that will provide new data and insights,
which will inform further model development and so forth.
SHIMMER thus contributes to more accurate quantitative
predictions that enable a deeper understanding of the pro-
cesses which control microbial communities, their role on
global biogeochemical cycles and their response to climate
variations in the future.

Code availability

The source code related to this article is provided as a Sup-
plement package together with a read me file. The code is
written and executed in the free open-source computing en-
vironment and programming language R, which is available
for download on the web (http://www.r-project.org/).

**The Supplement related to this article is available online**
**at doi:10.5194/gmd-8-3441-2015-supplement.**

*Acknowledgements.* We thank Stefano Bernasconi and Anna Laz-
zaro (ETH Zurich, Switzerland), Beat Frey, Thomas Rime and
Tobias Jonas (WSL, Switzerland), and Hans Göransson (BOKU
Vienna, Austria) for helpful and insightful discussions, and for
support in acquiring meteorological data. We also thank the
two anonymous referees who provided valuable comments on
the manuscript. This research was supported by NERC grant
no. NE/J02399X/1 to A. M. Anesio. S. Arndt acknowledges
support from NERC grant no. NE/IO21322/1.

Edited by: C. Sierra

References

Achuff, P. L. and Coen, G. M.: Subalpine Cryosolic Soils in Banff and Jasper National-Parks, Can. J. Soil Sci., 60, 579–581, 1980.

ACIA: Arctic Climate Impacts Assessment, Cambridge University Press, Cambridge, 2005.

Allison, S. D.: Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments, Ecol. Lett., 8, 626–635, 2005.

Anderson, S. P., Drever, J. I., Frost, C. D., and Holden, P.: Chem-
ical weathering in the foreland of a retreating glacier, Geochim.
Cosmochim. Ac., 64, 1173–1189, 2000.

Anderson, T. H. and Domsch, K. H.: Maintenance Carbon Require-
ments of Actively-Metabolizing Microbial-Populations under In-
situ Conditions, Soil Biol. Biochem., 17, 197–203, 1985.

Anesio, A. M., Hodson, A. J., Fritz, A., Psenner, R., and Sattler, B.: High microbial activity on glaciers: importance to the global carbon cycle, Global Change Biol., 15, 955–960, 2009.

Arndt, S., Jorgensen, B. B., Larowe, D. E., Middelburg, J. J., Pan-
cost, R. D., and Regnier, P.: Quantifying the degradation of or-
ganic matter in marine sediments: A review and synthesis, Earth-
Sci. Rev., 123, 53–86, 2013.

Bernasconi, S. M., Bauder, A., Bourdon, B., Brunner, I., Bunemann, E., Christl, I., Derungs, N., Edwards, P., Farinotti, D., Frey, B., Frossard, E., Furrer, G., Gierga, M., Goransson, H., Gulland, K., Hagedorn, F., Hajdas, I., Hindshaw, R., Ivy-Ochs, S., Jansa, J., Jonas, T., Kiczka, M., Kretzschmar, R., Lemarchand, E., Luster, J., Magnusson, J., Mitchell, E. A. D., Venterink, H. O., Plotze, M., Reynolds, B., Smittenberg, R. H., Stahli, M., Tamburini, F., Tipper, E. T., Wacker, L., Welc, M., Wiederhold, J. G., Zeyer,
Here we present SHIMMER, a novel modelling framework designed to predict microbial community development during the initial stages of ecosystem formation. The model accurately predicts the accumulation of microbial biomass and organic carbon during the initial stages of soil development from two glacier forefields (Bernasconi et al., 2011; Goransson et al., 2011; Insam and Haselwandter, 1989), and supports our general understanding of these ecosystems. Autotrophic production and nitrogen fixation are fundamental to the establishment of microbial communities and stable and labile pools of organic substrate and inorganic nutrients, a finding that is supported by field experiments at the Damma Glacier (Zumsteg et al., 2013b; Esperschütz et al., 2011; Frey et al., 2013; Duc et al., 2009) and elsewhere including the Puca Glacier in Peru (Schmidt et al., 2008; Ne mergut et al., 2007), the Mendenhall Glacier in Alaska (Sat tin et al., 2009; Knelman et al., 2012), and Anvers Island in Antarctica (Strauss et al., 2012). Soil respiration is compa rable to field observations (Schulz et al., 2013; Guelland et al., 2013a). The seasonal evolution of glacier forefields is not well understood (Bradley et al., 2014); however modelling work is likely to provide insight into the dynamics of the “non-growing season”. For example, modelled summer pro duction accounts for roughly 70% of total annual respira tion, in line with field observations from the Damma Glacier (Guelland et al., 2013b).

The accumulation of microbial biomass is highly sen-
sitive to variation in the Q₁₀ values, active biomass, the
bioavailability of organic matter, bacterial growth efficiency,
and rates of microbial growth and death. These parameters
also bear high uncertainty due to a relatively large range of
plausible values. Many of the well-constrained parameters
(e.g. half-saturation constants and exudation rates) have low
sensitivity and uncertainty and show mostly linear behaviour.
One of the striking outcomes of the sensitivity study is the ap-
parent strong dependence between the heterotrophic and au-
totrophic microbial communities. Heterotrophic production
degrades organic matter and recycles nutrients, in turn sup-
porting autotrophic and heterotrophic growth. Increasing het-
erotrophic growth rates therefore increases the accumulation
in all pools of biomass.

SHIMMER is the first step towards an iterative and in-
terdisciplinary framework (presented in Fig. 2), integrating
fieldwork and laboratory experiments with model develop-
ment, testing and application. The development of SHIM-
MER (1.0) is informed by previous experimental and field
campaigns (Bradley et al., 2014). It is expected that further
quantitative analysis of forefield dynamics will guide and in-
form future studies that will provide new data and insights,
which will inform further model development and so forth.
SHIMMER thus contributes to more accurate quantitative
predictions that enable a deeper understanding of the pro-
cesses which control microbial communities, their role on
global biogeochemical cycles and their response to climate
variations in the future.

Code availability

The source code related to this article is provided as a Sup-
plement package together with a read me file. The code is
written and executed in the free open-source computing en-
vironment and programming language R, which is available
for download on the web (http://www.r-project.org/).

**The Supplement related to this article is available online**
**at doi:10.5194/gmd-8-3441-2015-supplement.**

*Acknowledgements.* We thank Stefano Bernasconi and Anna Laz-
zaro (ETH Zurich, Switzerland), Beat Frey, Thomas Rime and
Tobias Jonas (WSL, Switzerland), and Hans Göransson (BOKU
Vienna, Austria) for helpful and insightful discussions, and for
support in acquiring meteorological data. We also thank the
two anonymous referees who provided valuable comments on
the manuscript. This research was supported by NERC grant
no. NE/J02399X/1 to A. M. Anesio. S. Arndt acknowledges
support from NERC grant no. NE/IO21322/1.

Edited by: C. Sierra

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Anderson, T. H. and Domsch, K. H.: Maintenance Carbon Require-
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