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Registration form (basic details) 1a. Details of applicant
Lisa Ceelen
Preference for correspondence in English: Yes Mobile phone: 06-46438780
E-mail: Lisa.ceelen@student.uva.nl 1b. Title of research proposal
Secondary succession underground, soil food web structures, stability and nutrient cycling.
1c. Summary of research proposal
Organisms in the soil food web provide important ecosystem services regarding nutrient cycling. As vegetation changes during secondary succession the soil community changes as well. Morriën et al. (2017) provides data on soil communities in different stages of succession. The question remains, what do the differences between these soil
communities mean for structural stability and nutrient cycling? This proposal aims to answer this question with various models and testing mainly in the parameters that, according to literature, are related to secondary succession. First aim is to estimate nutrient flux of carbon and nitrogen between the functional groups of the soil food web with the model by Hunt et al. (1987). Results will be used to parameterize interaction matrices, estimate maximum weight loop and investigate trophic coherence to define whether the late successional soil food webs are more stable. In addition, the soil food webs can be investigated in terms of dynamic stability by utilizing KEYLINK (Deckmyn, unpublished). KEYLINK is a dynamic simulation model incorporating the relations
between plants, organic matter production, the soil food web, nutrient cycling and abiotic factors. Results can be used to investigate what (aspects of) soil food webs lead to organic matter accumulation. Comparing sensitivity analyses from the model by Hunt et al. (1987) to the sensitivity analyses of KEYLINK may provide further insight to
sensitivities inherent to modelling choice. 1d. Keywords (Max. five words)
Soil food web, Nutrient cycles, Secondary succession, modelling 1e. Current institution of employment
Student at University of Amsterdam 1f. Prospective host institution University of Amsterdam
1g. NWO domain (Choose one)
Applied and Engineering Sciences (TTW)
NWO Science Domain (ENW) X
Health Research and Development (ZonMw) Social Sciences and Humanities (SGW) Cross-domain committee (DO)
1h. Main field of research
Code Main field of research
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i. Public summary of your research proposal NL
Secundaire successie ondergronds, voedselweb structuur, stabiliteit en nutriënt cycli
Secundaire successie is een proces dat niet alleen bovengronds verloopt, ook ondergronds verandert de bodemcommunity. Moderne technieken brachten nieuw inzicht over veranderingen in de structuur van het bodemvoedselweb tijdens successie. In dit onderzoek worden bestaande modellen voor nutriënt cycli gebruikt om de
implicaties van deze verschillen in kaart te brengen. ENG
Secondary succession underground, soil food web structures, stability and nutrient cycling.
Secondary succession is not solely an aboveground process, underground soil
communities change as well. Modern techniques brought new insights about structural differences regarding secondary succession. This research uses existing models to investigate the implications of these differences in soil food webs for nutrient cycling.
Research proposal
2a. Description of the proposed research
1. INTRODUCTION
Organisms in the soil food web provide important ecosystem services regarding nutrient cycling. Understanding of the soil food web and its role in nutrient cycling is useful in the light of climate change mitigation (retention and mineralisation of soil carbon - C) and ecosystem restoration (influencing plant growth and succession). Soil organisms return C to the atmosphere as carbon dioxide (CO2) through mineralisation of organic molecules. Multiple steps of the nitrogen (N) cycle are performed by microorganisms. Composition and structure of the soil food web determine how nutrients are recycled and empirical evidence has proved it relates strongly to vegetation type and performance (references in theoretical framework). Shifts in vegetation type during secondary succession are
associated with shifts in the composition of the soil food web (Morriën et al., 2017; Holtkamp et al., 2008). Soil food webs of different secondary stages can be modelled to investigate them in terms of nutrient cycling and stability (Holtkamp et al., 2011; De Ruiter et al., 1995)
2. THEORETICAL FRAMEWORK
2.1 Soil Food Webs
A soil food web describes the organisms in soil and their feeding relationships. The typical figure of a food web is a topological food web (Fig. 1), which is “a map of which
organisms in a community eat which other kinds” (Pimm et al., 1991).
Figure 1. Topological soil food web including a wide range of soil species. Taken from Rutgers et al. (2018)
In soil food webs the numerous species are aggregated into functional groups based on feeding preferences (Moore et al., 1988). In figure 1 this is depicted by the nematodes and springtails occupying multiple positions, while bacteria only occupy a single position
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even though their functions are numerous on the species level. For modelling the definition of functional groups is mainly limited by the groups in which they are
measured, this thesis will parameterize the model with data from Morriën et al. (2017). Food webs can be structured according to (1) trophic level (Hairston and Hairston et al., 1993), and (2) feeding channel (Moore et al., 1988). If quantitative information about nutrient fluxes between species is added the food web, it is called a flow web. Hunt et al. (1987) was first to model flow rates of nutrients in a soil food web.
2.2 Plant soil interaction
Plant-Soil interaction is a two-way relationship, where characteristics of the vegetation influence the composition of organisms in the soil and vice versa. Individual plants and plant community together influence the soil community (Bezemer et al., 2010, Wardle et al., 2004) and leave behind long term legacies that influence growth of subsequent vegetation (Kulmatiski and Beard, 2011). Spatial patterns in soil biota are found to be related to patterns in vegetation (Ettema and Wardle, 2002) and patterns in soil organic matter (SOM) (Fromm et al., 1993). Plants mainly influence the soil organisms indirectly through variations in organic litter quality and quantity (Saetre and Bååth, 2000). Long term changes are studied through chrono sequences of secondary succession (Morriën et al., 2017; Holtkamp et al., 2011; Van de Voorde et al., 2011; Kardol et al., 2006).
Characteristics of the soil community influence plant performance (De Kroon et al., 2012; Kardol et al., 2006), community structure (Wubs et al., 2016) and diversity (De Deyn et al., 2003). Mainly through consuming and recycling plant litter, roots and root-exudates. De Deyn et al. (2003) empirically determined that also higher trophic levels in the soil food web influence grassland succession by regulating the lower trophic levels in the soil food web.
Morriën et al. (2017) concludes that over successional time species in the soil community become more connected and reveal that fungi communities shift over time to a more beneficial and slower-growing species. Ectomychorrhizal fungi and arbuscular
mychorrhizal fungi get a more prominent role in nutrient cycling in late successional stages. Recent work by Wubs et al. (2019) also suggests an increasing connectedness between plants and soil organisms over successional time.
Soil structure is important for soil food web functioning. Organisms in the soil food web actively interact with soil structure. Fungi actively influence and are influenced by soil structure (Ritz and Young, 2004; Rillig and Mummey, 2006) through exerting substances that create or break down aggregates. Micro- and Macro-aggregates in the soil are found to be specific environments for soil species (Mummey et al., 2006). Earthworms, even though not directly part of predatory relationships in the soil food web, have an influence on soil structure and litter quality (Hättenschwiler and Gasser, 2005; Aira et al., 2008; Gómez-Brandón et al., 2010). Soil structure in turn determines the capacity to retain water and thus the amount of water available for plants and soil fauna (Saxton and Rawls, 2006). Brockett et al. (2012) concludes that soil moisture and organic matter were more closely related to microbial communities than to tree species. Also, soil structure creates niches through different particle and pore sizes. Sessitsch et al. (2001) demonstrates the existence of “specific microbe-particle associations that are affected to only a small extent by external factors”, thus reveal that soil structure is one of the most important abiotic influences to the soil community.
2.3 Modeling functions in the soil food web
Nutrient cycles
Models that estimate nutrient cycles through the soil food web such as Hunt et al. (1987) (De Ruiter et al., 1993; Holtkamp et al., 2011; Osler and Sommerkorn, 2007; Bezemer et al., 2010; Neutel et al., 2007) are based on top-down control through feeding
relationships. These models determine nutrient fluxes assuming that the biomass of functional groups remains unchanged. Biomass loss - through death and predation - must be restored by feeding on the lower trophic level. This results in a discrete representation of nutrient fluxes in the measured soil food webs through feeding relationships. The concepts of commensalistic/mutualistic relations (like mycorrhiza; Hannula et al., 2017), impact on soil structure (like macro-fauna and fungi; Coleman and Wall, 2015; Ritz and Young, 2004) and the ability to adapt feeding strategies to changed conditions (Schmitz et al., 2015; Manzoni and Porporato, 2009) are mostly ignored. Mechanistic models to simulate plant performance and nutrient fluxes often assume the activity of the soil community as a whole based on litter type and abiotic factors. (YASSO, Liski et al., 2005; CENTURY, Parton et al., 1988; ROTHC, Coleman and Jenkinson, 1996; Liski et al., 2005). Fang et al. (2005) first added a single functional group of
microorganisms to this type of model. Some models linked plant performance with multiple functional groups in the soil food web and abiotic influences (Hunt et al., 1991; ANAFORE, Deckmyn, 2008; Bortier et al., 2018; KEYLINK, Deckmyn, in review),
simulating the relations between plants, organic matter production, the soil food web, nutrient cycling and abiotic factors in totality (in figure 2). These type of models simulate nutrient cycles over time, adding the opportunity to investigate both top-down and bottom-up controls and feedbacks; as well as incorporation of responses to climatic conditions, such as hydrology and temperature.
Figure 2. Structure of the KEYLINK model, showing how nutrient cycling in the food web can be expanded to incorporate abiotic factors and bottom-up food availability. Taken from presentation by G. Deckmyn about KEYLINK (In review, 2019)
Manzoni and Porporato (2009) describe the evolution of models that simulate soil C and N fluxes. A wide range of aspects can be incorporated in models either through
mechanistic or linear relationships. Incorporation of many aspects might reflect the complexity of real systems, but might not be equally effective for interpretation of observed patterns. The choice which aspects to incorporate in a model and how
(mechanistic or linear), should take into account what is effective for the question asked and on which spatial scale it should operate.
Both Schmitz et al. (2015) and Manzoni and Porporato (2009) make a good case of incorporating changes in functional traits according to both abiotic and biotic factors. For
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example, bacteria may have different C:N ratios for substrates differing in C:N ratio to make their nutrient uptake more efficient. Or, in case of high predation, the prey may change their strategies to counteract the predation pressure. Especially when researching soil food webs within different environmental states these changes in functional traits may underlie the observed patterns.
Network structure
May started the discussion about complexity and stability in ecosystems in 1972. If random models prove to be less stable with increasing complexity, how is it possible that nature harbours so many complex ecosystems without collapsing? The search of the non-random aspect underlying the stability of natural complex systems is still ongoing. Part of this thesis will investigate some of these underlying aspects through network analysis models.
Stability of an ecosystem (in this case a soil food web) can be estimated by analysing the network structure. De Ruiter et al. (1995) did this by investigating sensitivity and
stability of jacobian community matrices of soil food webs, in which the interaction strengths between groups are valued by their feeding rates, as estimated through the model by Hunt et al. (1987). By investigating the eigenvalues of the interaction matrix while shifting parameters, sensitivity of the network can be investigated, thus providing insight to the relative importance of functional groups in the soil food web.
Maximum loop weight is a measure for stability regarding the structure of a food web (Neutel et al., 2002). This measure uses the same values for interaction strengths, but in this case searches for the loop in the network with the maximum weight. Neutel et al. (2007) revealed that with advancing succession the position of the maximum weight loop changes. Maximum weight loops can be used to determine the strongest drivers of (in)stability.
Trophic coherence is another measure that can be computed from network structure only, instead of a weighted network with biomass and interaction strength. Johnson et al., (2014) states that “Although this model does not attempt to replicate other characteristic features of food webs, such as a phylogenetic signal or bodysize effects, it reproduces the empirical stability of the 46 webs analysed quite accurately”. Trophic coherence is determined by taking the trophic distances between interacting species and testing the degree of homogeneity of this network.
3. RESEARCH METHODS
3.1 Research aim
The question this research wishes to address is “What are key differences in the soil food web and nutrient cycling between early and late successional food webs?”. Morriën et al. (2017) shed new light on various aspects of soil food web structure in relation to
secondary succession by measuring a chronosequence after abandonment of agricultural practices. It used innovative techniques to determine individual species (DNA analysis), co-occurrence of species (network analysis) and feeding relationships (measuring C-flux with C13 labelling). Main conclusions are a shift in the active fungal community from a fast-growing pathogenic association to a slow-growing and beneficial association, together with more efficient uptake of C derived from plant exudates throughout the fungal channel.
I want to investigate how these concepts affect nutrient cycling and stability in the soil food web by using existing models with same data used by Morriën et al. (2017).
Starting with repeating the model by Hunt et al. (1987). Morriën et al. (2017) provided data on biomass and C:N ratios of specific groups in these food webs. Specific death rates, assimilation and production efficiencies will be estimated from literature. The parameters can be validated using measurements of actual N-mineralization rates of microbial communities from the same research (Morriën et al., unpublished). Statistical analysis on these results, as done by Holtkamp et al. (2011), reveals the relative influence of functional groups and how this changes over succession. These results can be used to further investigate the structure for stability and coherence of the soil food webs. To estimate stability this research will follow the footsteps of De Ruiter et al. (1995) by investigating sensitivity and stability of jacobian community matrices, Neutel et al. (2002, 2007) by investigating the weight of loops, and Johnson (2014) by
investigating the trophic coherence of the food webs. These models will be parameterized with the same data and outcomes from the food web model from Hunt et al., (1987). Parameters such as C:N ratio and many feeding preferences were assumed from previous literature rather than empirically estimated. The new empirically derived values by
Morriën et al. (2017) give an opportunity to re-evaluate the internal parameters of these models.
Another novel route to follow is the extensive dynamic food web model KEYLINK
(Deckmyn et al., in review), which connects not only feeding levels, but abiotic factors as well. The model takes into account soil structure, soil quality and hydrology while it allows functional groups to grow based on food availability. (connecting a series of Lotka-Volterra type differential equations). Stability in nature is a result of dynamic responses rather than maintaining a rigid steady state, dynamic simulation models such as KEYLINK provide a chance to investigate stability in terms of these dynamic responses. Statistical analysis of the results and comparison to the results from the model by Hunt et al. (1987) can show where choice of model may influence sensitivity of specific groups. This comparison provides insight not only on the secondary succession this data entails, but also how choice of model can influence the results.
3.2 Hypotheses
Hypotheses regarding the model (1) are:
(1.1) The static model based on Hunt et al. (1987) will not show major differences when examining shifts in the fungal channel, as it is modelled from top to bottom.
(1.2) Differences in the static model will be due to differences in biomass and feeding preferences of higher trophic levels.
(1.3) Shifts in the fungal channel will show greater effects to nutrient cycling when using simulation models to estimate nutrient fluxes, as it is both bottom up and top down. Hypotheses on the effects of shifts (2) over secondary succession are:
(2.1) In late successional food webs higher trophic levels contain more nutrients derived from commensalistic sources (as opposed to predatory/pathogenic sources) that passed through the fungal channel.
(2.2) Changing the functioning of the fungal group, as found to be typical over
succession, will cause shifts in biomass for higher trophic levels in the simulation models. (2.3) The structure of the late successional food web will prove to be more stable and more coherent than early successional food webs, as well as conceptual food webs only focussing on a specific aspect of change during succession.
(2.4) A function change in carbon uptake by fungi will have a positive effect on SOM formation.
3.4 Methods
The model by Hunt et al. (1987) is based on top down feeding relationships together with the assumption that biomass of functional groups remains the same. Organic material consumed by an organism is split into three components, (1) Excretion of organic
material - faeces, (2) Excretion of inorganic material - mineralization, (3) Incorporation in biomass (Figure 3). Biomass can be lost from a functional group through natural death
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and predation. Together these parameters calculate the feeding rate of a functional group through equation 1.
𝐹 =
𝐷∗𝐵+𝑃
𝑒
𝑎𝑠𝑠∗𝑒
𝑝𝑟𝑜𝑑 [1]where F is feeding rate (kg C ha-1 year-1), D is specific natural death rate (year-1), B is biomass (kg C ha-1), P is death rate due to predation (k g C ha year-1), e-ass is
assimilated carbon per unit consumed carbon, and e-prod is biomass production per unit assimilated carbon.
Starting from the assumed top predator that only has biomass loss through natural death and cascading down through the soil food web until the level of detritus and roots is reached.
Figure 3. Scheme relating consumption, biomass production, excretion of organic material and excretion of inorganic material
(Hunt et al., 1987)
Feeding groups may have multiple food sources, choice of diet is based on feeding preference as well as availability of the source through equation 2:
𝐹
𝑖
=
𝑤
𝑖∗𝐵
𝑖∑
𝑛𝑖=1𝑤
𝑖𝐵
𝑖𝐹
[2]where Fi is feeding rate on prey i (kg C ha-1 year-1) wi is preference for prey i relative to other prey types, and n is number of prey types. Bi is the biomass of prey i. The amount of C mineralized per interaction is estimated through equation 3:
𝐶
𝑚𝑖𝑛
= 𝑒
𝑎𝑠𝑠
(1 − 𝑒
𝑝𝑟𝑜𝑑
)𝐹
[3]
calculated per trophic interaction and depended on feeding rate, assimilation efficiency, production efficiency and the C:N ratios of food and consumer as in equation 4:
𝑁
𝑚𝑖𝑛
= 𝑒
𝑎𝑠𝑠
(
1
𝑟
𝑝𝑟𝑒𝑦−
𝑒
𝑝𝑟𝑜𝑑𝑟
𝑝𝑟𝑒𝑑) 𝐹
[4]Where Nmin is the nitrogen mineralization rate (kg C ha-1 year-1), r-prey is the C:N ratio of prey and r-pred is the C:N ratio of predator.
This model requires six parameters for each of the functional group and the SOM substrate, which are listed in table 1. The source of these parameters regarding the scope of this research is also listed.
Table 1. Parameters needed for mineralization model by Hunt et al., (1987)
Parameter Symbol Source
Biomass
B
Morriën et al. (2017)Feeding preferences
F
i Morriën et al. (2017)C:N ratio
r
Morriën et al. (2017)Specific death rates
D
Hunt et al. (1987), De Ruiter et al. (1993), Holtkamp et al. (2011) and references thereinAssimilation
efficiencies
e
ass Hunt et al. (1987), De Ruiter et al. (1993), Holtkamp et al. (2011) and references therein Productionefficiencies
e
prod Hunt et al. (1987), De Ruiter et al. (1993), Holtkamp et al. (2011) and references therein Hypotheses 1.1, 1.2 and 2.1 can be investigated using the results the mineralization model. Primary food resources are set to (1) labile organic litter, (2) recalcitrant organic litter, (3) roots and (4) root exudates. Where root exudates are assumed to be acommensalistic route of consumption. Primary consumers are bacteria, fungi, mycorrhiza and herbivorous nematodes. Bacteria feed on litter (1) and root exudates, Fungi feed on litter (1 and 2) and root exudates, mycorrhiza feed on root exudates and litter (1 and 2), and herbivorous nematodes feed on roots.
To test hypotheses 1.1 and 1.2 four topological structures of soil food webs should be passed through the model to disentangle effects of the shift in the fungal channel and the differences in composition of the higher trophic levels. Table 2 is a schematic overview of these differences. Actual soil food webs to be modelled will be created in collaboration with experts in the field, especially dr. W.E. Morriën, who did the measurements used for this modelling exercise.
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Table 2. Matrix of assumptions to test hypotheses 1.1 and 1.2. Trophic structure Early
succession Trophic structure Late succession Results comparison
Early successional feeding preferences Fungi > Exudates > labile litter Bacteria > Exudates > labile litter
Nutrient fluxes and mineralization rates as derived from an early successional soil food web.
Responses in nutrient cycling by higher trophic levels from a late successional food web on nutrient uptake by an early successional Fungi:Bacteria state.
Differences in nutrient cycling due to different
structures in higher trophic levels?
Late successional feeding preferences Fungi < Exudates > recalcitrant litter Bacteria > Exudates > labile litter Responses in nutrient cycling by higher trophic levels from an early successional food web on nutrient uptake by a late successional
Fungi:Bacteria state.
Nutrient fluxes and
mineralization rates as derived from late successional food webs.
Differences in nutrient cycling due to different
structures in higher trophic levels?
Results Comparison Differences in nutrient cycling due to different Fungi:Bacteria state?
Differences in nutrient cycling due to different Fungi:Bacteria state?
Hypothesis 2.1 can be answered by performing the same calculations as done by Holtkamp et al. (2011): “Mineralisation rates of each trophic level and energy channel were calculated as the sum of mineralisation rates of groups belonging to the
corresponding level or channel. Relative direct contributions of functional groups and trophic levels were calculated as its mineralisation rate divided by the total mineralisation rate.” When defining the “root exudates” channel as being a commensalistic channel, while “root- and litter-channels” are defined as feeding channels the relative amount of C obtained through the “root exudates” channel can be calculated in this manner.
Hypothesis 1.3, 2.2 and 2.4 can be investigated through the use of simulation models. Starting with using our data to run the existing model KEYLINK (Deckmyn, in review). For comparison of discrete (Hunt et al., 1987) to dynamic (KEYLINK) models for nutrient cycling - hypothesis 1.3 - the same data - the same topological food web structures as in table 2 - is run through the KEYLINK model. Change ratios can be derived for
comparison. For example: ”when shifting the feeding preferences of bacteria and fungi, feeding rates of omnivorous nematodes increased with x% between early and late
successional soil food webs in the static model, while it increased with y in the simulation model).
Hypothesis 2.3 is investigated by the network modelling activities regarding stability and coherence. If late successional soil food webs are truly more stable, then the results of the models should give higher values for stability and coherence than the early
successional food webs.
For Hypothesis 2.2 and 2.4 first results are obtained with KEYLINK as it is, comparing the model outputs of nutrient cycles and biomass accumulation between the early- to the late successional soil food webs as defined on data from Morriën et al. (2017). Using the four conceptual soil food web structures will help to disentangle where actual effects come from.
(1) individual functional groups, (2) grouped trophic levels and (3) grouped feeding channels. To compare differences in production of organic matter - hypothesis 2.4 - we can compare SOM accumulation for the different food webs.
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4. PLANNING
I wish to conduct this master thesis research at the university of Amsterdam under supervision of dr. W.E. Morriën. This modeling exercise is a follow up on field and lab results from the IBED. Dr. W.E. Morriën mapped soil foodwebs over secondary succession using a chronosequence, in collaboration with her I will use this data to model nutrient cycles in these soil food webs.
4.1 Time schedule
Month 1
Week 1+2: Developing two soil food web structures (early and late successional) based on empirical research by Morriën et al., (2017). As well as the conceptual “in-between” structures to test specific changes related to secondary succession.
Week 3+4: Obtain parameters needed to run the model by Hunt et al. (1978). Month 2
Write and run the model to estimate nutrient fluxes for the soil food web structures defined in first weeks.
Write first results and conclusions on statistical analysis of the results. Month 3
Week 1+2: Study the 3 network analysis models (personal development)
Week 3+4: Perform the jacobian community matrix stability analysis on the soil food web structures developed in week 1 and 2. De Ruiter et al. (1995)
Month 4
Week 1+2: Perform the Weight loops network analysis on the soil food web structures Neutel et al. (2002, 2007)
Week 3+4: Perform the trophic coherence analysis on the soil food web structures Johnson et al. (2014)
Month 5
Week 1+2: Develop and write conclusions regarding network stability of early and Late successional soil food webs.
Week 3+4: Study dynamic models of soil food webs (KEYLINK and others) and obtain additional parameters needed.
Month 6
Run the KEYLINK simulation model with the soil food web structures developed in first weeks Month 7
Develop and write conclusions regarding the different results of the discrete versus the dynamic models.
Finalize thesis discussing the results from different modelling exercises. Concluding
which aspects of the soil food web structures point to phenomena of interest for further research. I do realize that this might be a tight schedule and a choice may have to be made between the network analysis or the KEYLINK model. After the first two months there will be an evaluation on the progress and scope of the available data to take off in either direction. At this moment first results are obtained from the model by Hunt et al. (1987).
2b. Knowledge utilisation
Yes, this proposal has the potential of knowledge utilization
x
No, this proposal has no direct knowledge utilization
If no, please motivate why your proposal has no direct knowledge utilization.
The scope of this research cannot inform decisions on societal issues, nor create revenue for business. Utilization of results from this thesis is mainly in the scope of research and science. Deeper understanding of soil food webs and nutrient cycle may provide a practical use in the future for nature restoration. Actively imposing perturbation to ecosystems to direct them in a certain direction during secondary succession is one of the possibilities once knowledge has accumulated. Actively influencing soil food webs to retain more carbon in the soil can be useful for climate mitigation.
2c. Number of words used
Section 2a: 3970 words (max. 4,000 words) Section 2b: 93 (max. 1,000 words)
2d. Literature references
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Bezemer, T.M., Fountain, M.T., Barea J.M., Christensen, S., Dekker, S.C., Duyts, H., van Hal, R., Harvey, J.A., Hedlund, K., Maraun, M., Mikola, J., Mladenov, A.G., Robin, C. de Ruiter, P.C., Scheu, S., Setälä, H., Šmilauer, P. and van der Putten, W.H. (2010). Divergent composition but similar function of soil food webs of individual plants: plant species and community effects. Ecology, 91(10), 2010, 3027–3036.
Bortier, M., Andivia, E., Genon, J., Grebenc, T., & Deckmyn, G. (2018, 7 3). Towards understand-ing the role of ectomycorrhizal fungi in forest phosphorus cyclunderstand-ing : a modellunderstand-ing approach. Central European Forestry Journal, 64(2), 79-95.
Brockett, B., Prescott, C., & Grayston, S. (2012, 1). Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biology and Biochemistry, 44(1), 9-20.
Coleman K. and Jenkinson, D. (1996). RothC-26.3 - A Model for the turnover of carbon in soil. In P. Powlson David S. and Smith (Red.), Evaluation of Soil Organic Matter Models (pp. 237-246). Berlin, Heidelberg: Springer Berlin Heidelberg.
Coleman, D.C., Wall, D.H. (2015). Soil Fauna: Occurrence, Biodiversity, and Roles in Ecosystem Function. Chapter 5 in: Soil Microbiology, Ecology, and Biochemistry, Fourth edition. By Paul, E.A. (2015). Elsevier.
De Deyn, G., Raaijmakers, C., Zoomer, H., Berg, M., De Ruiter, P., Verhoef, H., . . . Van der Put-ten, W. (2003, 4 17). Soil invertebrate fauna enhances grassland succession and diversity. Nature, 422(6933), 711-713.
de Kroon, H., Hendriks, M., van Ruijven, J., Ravenek, J., Padilla, F., Jongejans, E., . . . Mommer, L. (2012, 1). Root responses to nutrients and soil biota: Drivers of species coexistence and ecosystem productivity. Journal of Ecology, 100(1), 6-15.
De Ruiter, P., Neutel, A.-M., & Moore, J. (1995). Energetics, Patterns of Interaction Strengths, and Stability in Real Ecosystems.
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De Ruiter, P., Van Veen, J., Moore, J., Brussaard, L., & Hunt, H. (1993). Calculation of nitrogen mi-neralization in soil food webs.
Deckmyn, G., Verbeeck, H., Op de Beeck, M., Vansteenkiste, D., Steppe, K., & Ceulemans, R. (2008, 7 24). ANAFORE: A stand-scale process-based forest model that includes wood tissue devel-opment and labile carbon storage in trees. Ecological Modelling, 215(4), 345-368.
Deckmyn, G., KEYLINK model available on Github: https://github.com/Plant-Root-Soil-Interac-tions-Modelling/KEYLINK
Ettema, C.H., Wardle, D.A. (2002). Spatial soil ecology. Ecology & Evolution, 17(4), 177-183. Fang, C., Smith, P., Smith, J., & Moncrieff, J. (2005, 12). Incorporating microorganisms as decom-posers into models to simulate soil organic matter decomposition. Geoderma, 129(3-4), 139-146. Fromm, H., Winter, K., Filser, J., Hantschel, R., & Beese, F. (1993, 12 1). The influence of soil type and cultivation system on the spatial distributions of the soil fauna and microorganisms and their interactions. Geoderma, 60(1-4), 109-118.
Gómez-Brandón, M., Lazcano, C., Lores, M., & Domínguez, J. (2010, 3 1). Detritivorous earth-worms modify microbial community structure and accelerate plant residue decomposition. Applied Soil Ecology, 44(3), 237-244.
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Wubs, E., van der Putten, W., Mortimer, S., Korthals, G., Duyts, H., Wagenaar, R., & Bezemer, T. (2019). Single introductions of soil biota and plants generate long-term legacies in soil and plant community assembly. Ecology Letters. Blackwell Publishing Ltd.
2e. Data management
Scripts, changes to existing scripts, parameters used for running, intermediate results and final results will be stored in organized manner using SURFdrive. In case of publication I will make these (partly) available through trustable online platforms approved by the University of Amsterdam.
Cost estimates 3a. Budget Values in €.
Description
Staff FTE Months Costs/month
WP* Elly Morrien, Professor 0.1 = 1h / week 8 400 NWP* Lisa Ceelen, Student 1 8 4,800 Total Staff 2 1.025 8
Equipment Use of calculating power 100
Costs / month 5,300
Grand total 37.100
3e. Intended starting date Sept 02, 2019
3f. Have you applied for any additional grants for this project either from NWO or from any other institution, and/or has the same idea been submitted elsewhere? No
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Statements by the applicant Declarations
By submitting this form, I endorse the code of conduct for laboratory animals and the code of conduct for biosecurity/possibility for dual use of the expected results and will act accordingly, if applicable.
I have completed this form truthfullyBy submitting this document I declare that I satisfy the nationally and
internationally accepted standards for scientific conduct as stated in the Netherlands Code of Conduct for Scientific Practice (Association of Universities in the
Netherlands)
I have submitted the completed and signed embedding guarantee
I have submitted non-referees.
If applicable: I have included one or more authorised letters from the prospected host institution and/or a third party, guaranteeing to meet part of the costs of this research project.Name: Lisa Ceelen Place: Amsterdam Date: June 14, 2019.
