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Biomass or growth? How to measure soil food webs to understand structure and function
Soil Biology & Biochemistry xxx (2016) 1e3
Contents lists available at ScienceDirect
Soil Biology & Biochemistry journal homepage: www.elsevier.com/locate/soilbio
Biomass or growth? How to measure soil food webs to understand structure and function Johannes Rousk Section of Microbial Ecology, Department of Biology, Lund University, Ecology Building, 22362 Lund, Sweden
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 January 2016 Received in revised form 23 June 2016 Accepted 1 July 2016 Available online xxx
Food web links reflect the feeding rate of organisms and should thus capture the biomass production rate of the consumer. This information is rarely available for detrital food webs and has instead been inferred from biomass estimates to construct quantitative detrital food webs. Published method comparisons between biomass, growth and mineralisation show that microbial biomass is a poor predictor for process rates. This calls into question the current practise of parameterising soil detrital food webs. Emerging methods to estimate microbial growth rates and growth efficiencies are promising new avenues. If quantitative assessments of detrital food webs are revised by incorporating information that reflects the feeding rates of organisms, they could represent a powerful conceptual tool to investigate fundamental ecological theory, including how stability links to complexity and how function depends on the structure of whole food webs. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Food web interactions Soil fauna Biogeochemistry Food chain Trophic interaction Bacteria and fungi
“It is interesting to contemplate a tangled bank” e C.R. Darwin (1859). The food-web concept is central to ecology, and it has a long history. Organisms are interrelated and interact in a complex web, or a “tangled bank” (Darwin, 1859). More recent developments of the concept include landmark contributions (Elton, 1927; Lindeman, 1942) that have added precision and quantification. These gradually moulded the concept to become a means to track energy units as an ecological currency through ecosystems (Odum and Odum, 1976). This led up to ambitious efforts to quantify whole ecosystems including the International Biological Programs (IBPs) (Coleman et al., 1976) and several post-IBP programs (e.g. Bååth et al., 1981; Hunt et al., 1987; Bradford et al., 2002). These enterprises also shifted the focus from pathways that always stemmed from primary producers to include detritus based food webs (Coleman et al., 1988; Moore et al., 1989) and associated plant nutrient supply. This arena, the detrital food web in soil, has served as an active forum for several theoretical debates on subjects including for instance the link between complexity and stability (Neutel et al., 2002) and the role of omnivory in soil systems (Neutel et al., 2007). By providing an interface for integrating
ecological connections and biogeochemical fluxes (de Vries et al., 2013; Neutel and Thorne, 2014) food web research was and remains a hotspot for linking ecosystem structure with function. How have the complex interactions that form detrital food webs been resolved and linked with functions? Qualitative demonstrations have shown that trophic interactions can characterise the detrital food web’s ability to provide plant nutrition (Santos et al., 1981; Crowther et al., 2015). Treatments used to control the presence or absence of microbivores have revealed that microbivore grazing stimulates nutrient mineralisation (Wardle, 1999). For instance, plant-soil microcosm systems in the presence of fauna have been shown to turnover detritus and provide plant nutrition, while systems without fauna accumulate detritus, providing poor €la € and Huhta, 1991). These experimental plant productivity (Seta demonstrations have also been extended to constructed full ecosystems (Bradford et al., 2002), and even natural conditions (Clarholm, 1981, 1985), demonstrating how active grazing on detritus feeding microorganisms impacts nutrient availability in soil, especially in the rhizosphere (Bonkowski, 2004). Examples such as these have highlighted that biotic interactions occurring in food webs have considerable implications for system functioning. However, quantitative representations of full food webs, assessing interactions and elemental energy pathways, have mostly been assembled from more indirect inferences. Such efforts have relied on quantifying the abundance of soil organisms, classifying
E-mail address: [email protected]. http://dx.doi.org/10.1016/j.soilbio.2016.07.001 0038-0717/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Rousk, J., Biomass or growth? How to measure soil food webs to understand structure and function, Soil Biology & Biochemistry (2016), http://dx.doi.org/10.1016/j.soilbio.2016.07.001
2
J. Rousk / Soil Biology & Biochemistry xxx (2016) 1e3
these into functional groups based on their feeding preferences, and causally linking them up based on trophic links inferred from literature (de Ruiter et al., 1994; Hunt et al., 1987). The feeding rate, or the rate of resource consumption, for all included functional groups is subdivided into three e an excretion rate (back to the detrital pool), the biomass production rate of the consumer, and a mineralisation rate of nutrients and carbon (de Ruiter et al., 1994). These three rates are based on population or biomass sizes, feeding preferences, assimilation efficiencies, production efficiencies and nutrient contents of detritus and organisms. Of these variables, normally only the biomass or population sizes are determined for the specific site studied, while other variables are taken from literature values or are inferred (Hunt et al., 1987). The modelled food web estimates mineralisation rates, which are then compared with measurements from the studied sites, and used to verify the explanatory power of the model in the studied system (Hunt et al., 1987; de Ruiter et al., 1994; de Vries et al., 2013). This is where I would like to stop for reflection. These important assumptions by which biomass measures of functional groups are combined with literature values for assimilation and production efficiencies and used to assemble the interactions and material flows of whole food webs e are these variables well constrained and understood? Can biomass be used to estimate growth and mineralisation? Are the central assumptions used to construct food web models met? What do biomass based assessments of soil organisms reveal about growth rates and mineralisation? Let us consider the bottom levels e microbial decomposers of detritus. Assuming that the markers or proxies used to determine microbial biomass are reliable (Joergensen and Wichern, 2008), biomass based assessments certainly can reveal the amount of nutrients and elemental contents held in for instance bacterial and fungal biomass (Cleveland and Liptzin, 2007; Strickland and Rousk, 2010). However, does the biomass size of microorganisms also capture their biomass production rate and contribution to mineralisation? This has been addressed and exhaustively studied in soil (Strickland and Rousk, 2010; Rousk and Bååth, 2011; Blagodatskaya and Kuzyakov, 2013). Although the intuitive expectation could be that growth rates of microorganisms correlate with biomass concentrations, at least during “stable state” conditions, this expectation is not supported. The lack of correspondence between biomass and growth rates holds true during periods of perturbation (Rousk and Bååth, 2007; van Groenigen et al., 2010; Jones et al., 2012), as well as during quasi stable state conditions in soil (Rousk et al., 2009, 2011; Pausch et al., 2016). In fact, the amount of microbial biomass is largely uninformative about the activity of microorganisms in soil, including their growth rate and contribution to mineralisation, unless the status of the microbial biomass is also known, ranging along a continuum from dead, dormant, slowly growing, and growing fast (Rousk and Bååth, 2011; Blagodatskaya and Kuzyakov, 2013). Problematically, this suggests that using biomass estimates to capture the intended targets (i) biomass production rate of the consumer and (ii) mineralisation rate of nutrients and carbon (Hunt et al., 1987; de Ruiter et al., 1994; de Vries et al., 2013) is misled. So what information do we need for microorganisms in food webs? The growth rate of a microorganism is a pivotal life history trait since it is a critical component of fitness in most environments (Kirchman, 2001; Rousk and Bååth, 2011). Life history traits in general are indeed defined as traits that affect a microorganism’s reproduction or survival, such as responses to resource availability or quality, susceptibility to or dependence on environmental factors, etc. The full repertoire of life history traits that together describe an organism’s reproduction and survival in an environment defines its life history (Neidhardt, 1999; Roller and Schmidt, 2015). As such, to understand the ecology of microorganisms, we need a measure of the coordinated operation of whole cells, or
indeed whole communities. Microbial growth rates offer such a metric. They will also define part of the information required to link organisms in a food web (Hunt et al., 1987; de Ruiter et al., 1994), but do not capture the food web’s ability to provide mineralisation of resources, and, as such, the fundamental connection to plant nutrition (Wardle, 1999). The microbial growth efficiency e microorganisms produced per unit of resource consumed e is an equally important life history trait to the growth rate. The growth efficiency depends on environmental and population specific factors, including the resource energy content (Linton and Stephenson, 1978), pathways of resource utilization (Flamholtz et al., 2013), biomass precursor availability (Kirchman, 2001) and the energy fraction devoted to maintenance functions compared with growth (Hoehler and Jørgensen, 2013). The growth efficiency thus integrates an organism’s physiology, ecology and evolutionary history. In addition, microbial growth efficiencies have important consequences for the understanding of how ecosystems operate, by setting the fundamental premises for the microbial impact on C and nutrient cycling, and the mineralisation consequences of detrital food webs. Reality check e why are food webs not parametrised with growth rates and growth efficiency estimates? While the importance of microbial growth efficiencies has been recognised in soil microbial ecology (Manzoni et al., 2012) and biogeochemistry modelling (Allison et al., 2010; Wetterstedt and Ågren, 2011), it has been one of the most elusive microbial parameters in soil (Lee and Schmidt, 2014; Roller and Schmidt, 2015). It is likely that a lack of effective methods to estimate microbial growth rates in soil has impeded development there (Blagodatskaya and Kuzyakov, 2013; Rousk and Bååth, 2011). However, with the emergence and rapid spread of cost effective isotope tracer methods, early reports are now beginning to emerge (Frey et al., 2013; Hagerty et al., 2014; Lee and Schmidt, 2014). Yet, the general understanding of how environmental factors modulate the microbial growth efficiency, how much it varies in natural environments and its dynamics over time, remain rudimentary at best. However, the systematic work to develop conversion factors achieved in aquatic systems to estimate microbial growth rates in units of C (del Giorgio and Cole, 1998) should be prioritized also in soil systems. Growth rates could then be readily compared with respiration and N mineralisation to achieve system-specific estimates of growth, growth efficiencies, and, consequently, repercussions for mineralisation. These estimates could be used to parametrise the first levels of the detrital food web, and subsequently be connected to higher trophic levels, to finally link ecosystem functions to the ecological structure in quantitative food webs. This could lead to a situation where existing theoretical modelling (Neutel et al., 2002, 2007) could be used to re-assess how complexity affects system stability in suitably parametrised datasets, thus exploring central ecological questions in soil study systems. Naturally, even given the anticipated progress, challenges would still remain to be resolved. For instance, how should the immense taxonomic complexity of the soil microbial community be resolved? Will the “mean” growth rate and C-use efficiencies of those complex communities be sufficient, or should major groups, such as the division between fungi and bacteria, be resolved (Rousk and Frey, 2015)? Or do we need to resolve the growth rates of each microbial taxon (Hungate et al., 2015; Lankiewicz et al., 2016)? Will taxa differ significantly in their resource-use efficiency due to differences in traits such as the ability for waste metabolism, thus affecting the link between growth rates and mineralisation rates? Moreover, we have merely scratched the surface of the tangled bank by only considering the first trophic level of the detrital food web. Will higher trophic levels present similarly poor correspondence between consumption rates and biomass estimates, and thus also require reassessment? The
Please cite this article in press as: Rousk, J., Biomass or growth? How to measure soil food webs to understand structure and function, Soil Biology & Biochemistry (2016), http://dx.doi.org/10.1016/j.soilbio.2016.07.001
J. Rousk / Soil Biology & Biochemistry xxx (2016) 1e3
frequently observed match between protozoan population sizes and their feeding rates on bacteria (e.g. Clarholm, 1981; Bonkowski, 2004) anecdotally suggests that this is not the case. As such it seems like the failure of biomass estimate to capture process rates may well be microbial-specific in soil. John Keats worried that Newton’s prismatic reduction of white light, and consequent unweaving of the rainbow, had taken the poetry out of the wonder of nature (Keats, 1820). Will it still be “interesting to contemplate the tangled bank”, once its links have been defined, understood and quantified? Of course it will. It is the only way to bring light into the opaque murkiness of the belowground and reveal its grandeur. Powerful theoretical tools such as food web models can only reveal the natural world when conceptual clarity and parametric precision are reached. Theoretical analyses of central questions in ecology including, e.g., how stability depends on complexity (Neutel et al., 2002, 2007), can then transition from interesting exercises to true insights about how nature works. Acknowledgements I thank Guest Editor Mark A. Bradford for inviting this contribution to the Special Issue, and Kathrin Rousk for thoughtful feedback on earlier drafts. This work was supported by grants from the Swedish Research Council (Vetenskapsrådet; grant no 201504942) and the Swedish Research Council Formas (grant no 9422015-270). References Allison, S.D., Wallenstein, M.D., Bradford, M.A., 2010. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336e340. € derstro €m, B., Sohlenius, B., 1981. Bååth, E., Lohm, U., Lundgren, B., Rosswall, T., So Impact of microbial-feeding animals on total soil activity and nitrogen dynamics e a soil microcosm experiment. Oikos 37, 257e264. Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192e211. Bonkowski, M., 2004. Protozoa and plant growth: the microbial loop in soil revisited. New Phytol. 162, 617e631. Bradford, M.A., Jones, T.H., Bardgett, R.D., Black, H.I.J., Boag, B., Bonkowski, M., Cook, R., Eggers, T., Gange, A.C., Grayston, S.J., Kandeler, E., McCraig, A.E., Newington, J.E., Prosser, J.I., Set€ al€ a, H., Staddon, P.L., Tordoff, G.M., Tscherko, D., Lawton, J.H., 2002. Impacts of soil faunal community composition on model grassland ecosystems. Science 298, 615e618. Clarholm, M., 1981. Protozoan grazing of bacterial in soil e impact and importance. Microb. Ecol. 7, 343e350. Clarholm, M., 1985. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol. Biochem. 17, 181e187. Cleveland, C.C., Liptzin, D., 2007. C: N:P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 85, 235e252. Coleman, D., Crossley, D., Beare, M., Hendrix, P., 1988. Interactions of organisms at root/soil and litter/soil interfaces in terrestrial ecosystems. Agric. Ecosyst. Environ. 24, 117e134. Coleman, D.C., Andrews, R., Ellis, J.E., Singh, J.S., 1976. Energy-flow and partitioning in selected man-managed and natural ecosystems. Agro-Ecosystems 3, 45e54. Crowther, T.W., Thomas, S.M., Maynard, D.S., Baldrian, P., Covey, K., Frey, S.D., van Diepen, L.T.A., Bradford, M.A., 2015. Biotic interactions mediate soil microbial feedbacks to climate change. Proc. Natl. Acad. Sci. U. S. A. 112, 7033e7038. Darwin, C.R., 1859. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. John Murray, London, England. de Ruiter, P.C., Neutel, A.-M., Moore, J.C., 1994. Modelling food webs and nutrient cycling in agro-ecosystems. Trends Ecol. Evol. 9, 378e383. De Vries, F.T., Thebault, E., Liiri, M., Birkhofer, K., Tsiafouli, M.A., Bjørnlund, L., Jørgensen, H.B., Brady, M.V., Christensen, S., de Ruiter, P.C., d’Hertefeldt, T., Frouz, J., Hedlund, K., Hemerik, L., Hol, W.H.G., Hotes, S., Mortimer, S.R., €la €, H., Sgardelis, S.P., Uteseny, K., var der Putten, W.H., Wolters, V., Seta Bardgett, R.D., 2013. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl. Acad. Sci. U. S. A. 110, 14296e14301. del Giorgio, P.A., Cole, J.J., 1998. Bacterial growth efficiency in natural aquatic systems. Annu. Rev. Ecol. Syst. 29, 503e541. Elton, C.S., 1927. Animal Ecology. The MacMillan Company, New York, USA. Flamholtz, A., Noor, E., Bar-Even, A., Biebermeister, W., Milo, R., 2013. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc. Natl. Acad. Sci. U. S. A. 110, 10039e10044.
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Frey, S.D., Lee, J., Melillo, J.M., Six, J., 2013. The temperature response of soil microbial efficiency and its feedback to climate. Nat. Clim. Change 3, 395e398. Hagerty, S.B., van Groenigen, K.J., Allison, S.D., Hungate, B.A., Schwartz, E., Koch, G.W., Kolka, R.K., Dijkstra, P., 2014. Accelerated microbial turnover but constant growth efficiency with warming in soil. Nat. Clim. Change 4, 903e906. Hoehler, T.M., Jørgensen, B.B., 2013. Microbial life under extreme energy limitation. Nat. Rev. Microbiol. 11, 83e94. Hunt, H.W., Coleman, D.C., Ingham, E.R., Ingham, R.E., Elliott, E.T., Moore, J.C., Rose, S.L., Reid, C.P.P., Morley, C.R., 1987. The detrital food web in a short grass prairie. Biol. Fertil. Soils 3, 57e68. Hungate, B.A., Mau, R.L., Schwartz, E., Caporaso, J.G., Dijkstra, P., van Gestel, N., Koch, B.J., Liu, C.M., McHugh, T.A., Marks, J.C., Morrissey, E.M., Price, L.B., 2015. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570e7581. Joergensen, R.G., Wichern, F., 2008. Quantitative assessment of the fungal contribution to microbial tissue in soil. Soil Biol. Biochem. 40, 2977e2991. Jones, D.L., Rousk, J., Edwards-Jones, G., DeLuca, T.H., Murphy, D.V., 2012. Biocharmediated changes in soil quality and plant growth in a three year field trial. Soil Biol. Biochem. 45, 113e124. Keats, J., 1820. Lamia. In: Lamia, Isabella, the Eve of St. Agnes, and Other Poems. Taylor and Hessey, Fleet Street, London, UK. Kirchman, D., 2001. Measuring bacterial biomass production and growth rates from leucine incorporation in natural aquatic environment. Methods Microbiol. 30, 227e237. Lankiewicz, T.S., Cottrell, M.T., Kirchman, D.L., 2016. Growth rates and rRNA content of four marine bacteria in pure cultures and in the Delaware estuary. ISME J. 10, 823e832. Lee, Z.M., Schmidt, T.M., 2014. Bacterial growth efficiency varies in soil under different land management. Soil Biol. Biochem. 69, 282e290. Lindeman, R.L., 1942. The trophic-dynamic aspect of Ecology. Ecology 23, 399e417. Linton, J., Stephenson, R., 1978. A preliminary study on growth yields in relation to the carbon and energy content of various organic growth substrates. FEMS Microbiol. Lett. 3, 95e98. Manzoni, S., Taylor, P., Richter, A., Porporato, A., Ågren, G.I., 2012. Environmental and stoichiometric controls on microbial carbon-use efficiency in soils. New Phytol. 196, 79e91. Moore, J.C., Walter, D.E., Hunt, H.W., 1989. Arthropod regulation of micro- and mesobiota in belowground detrital food webs. Annu. Rev. Entomology 33, 419e439. Neidhardt, F.C., 1999. Bacterial growth: constant obsession with dN/dt. J. Bacteriol. 181, 7405e7408. Neutel, A.-M., Thorne, M.A.S., 2014. Interaction strengths in balanced carbon cycles and the absence of a relation between ecosystem complexity and stability. Ecol. Lett. 17, 651e661. Neutel, A.-M., Heesterbeek, J.A.P., de Ruiter, P.C., 2002. Stability in real food webs: weak links in long loops. Science 296, 1120e1123. Neutel, A.-M., Heesterbeek, J.A.P., van de Koppel, J., Hoenderboom, G., Vos, A., Kaldeway, C., Berendse, F., de Ruiter, P.C., 2007. Reconciling complexity with stability in naturally assembling food webs. Nature 449, 599e602. Odum, H.T., Odum, E.P., 1976. Energy Basis for Man and Nature. McGraw-Hill, New York, USA. Pausch, J., Kramer, S., Scharroba, A., Scheuneman, N., Butenschoen, O., Kandeler, E., Marhan, S., Riederer, M., Scheu, S., Kuzyakov, Y., Ruess, L., 2016. Small but active e pool size does not matter for carbon incorporation in below-ground food webs. Funct. Ecol. http://dx.doi.org/10.1111/1365e2435.12512 (in press). Roller, B.R.K., Schmidt, T.M., 2015. The physiology and ecological implications of efficient growth. ISME J. 9, 1481e1487. Rousk, J., Bååth, E., 2007. Fungal and bacterial rowth in soil with plant materials of different C/N ratios. FEMS Microbiol. Ecol. 62, 258e267. Rousk, J., Bååth, E., 2011. Growth of saprotrophic fungi and bacteria in soil. FEMS Microbiol. Ecol. 78, 17e30. Rousk, J., Brookes, P.C., Bååth, E., 2009. Contrasting soil pH effects on fungal and bacterial growth suggest functional redundancy in carbon mineralisation. Appl. Environ. Microbiol. 75, 1589e1596. Rousk, J., Brookes, P.C., Bååth, E., 2011. Fungal and bacterial growth responses to N fertilization and pH in the 150-year ‘Park Grass’ UK grassland experiment. FEMS Microbiol. Ecol. 76, 89e99. Rousk, J., Frey, S.D., 2015. Revisiting the hypothesis that fungal-to-bacterial dominance characterizes turnover of soil organic matter and nutrients. Ecol. Monogr. 85, 457e472. Santos, P.F., Phillips, J., Whitford, W.G., 1981. The role of mites and nematodes in early stages in buried litter decomposition in a desert. Ecology 62, 664e669. €l€ Seta a, H., Huhta, V., 1991. Soil fauna increase Betula-pendula growth e laboratory experiments with coniferous forest floor. Ecology 72, 665e671. Strickland, M.S., Rousk, J., 2010. Considering fungal:bacterial dominance in soils e methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385e1395. van Groenigen, K.-J., Bloem, J., Bååth, E., Boeckx, P., Rousk, J., Bode, S., Forristal, D., Jones, M.B., 2010. Abundance, production and stabilization of microbial biomass under conventional and reduced tillage. Soil Biol. Biochem. 42, 48e55. Wardle, D., 1999. How soil food webs make plants grow. Trends Ecol. Evol. 14, 418e420. Wetterstedt, J.Å.M., Ågren, G.I., 2011. Quality or decomposer efficiency e which is most important in the temperature response of litter decomposition? A modelling study using the GLUE methodology. Biogeosciences 8, 477e487.
Please cite this article in press as: Rousk, J., Biomass or growth? How to measure soil food webs to understand structure and function, Soil Biology & Biochemistry (2016), http://dx.doi.org/10.1016/j.soilbio.2016.07.001
Contents lists available at ScienceDirect
Soil Biology & Biochemistry journal homepage: www.elsevier.com/locate/soilbio
Biomass or growth? How to measure soil food webs to understand structure and function Johannes Rousk Section of Microbial Ecology, Department of Biology, Lund University, Ecology Building, 22362 Lund, Sweden
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 January 2016 Received in revised form 23 June 2016 Accepted 1 July 2016 Available online xxx
Food web links reflect the feeding rate of organisms and should thus capture the biomass production rate of the consumer. This information is rarely available for detrital food webs and has instead been inferred from biomass estimates to construct quantitative detrital food webs. Published method comparisons between biomass, growth and mineralisation show that microbial biomass is a poor predictor for process rates. This calls into question the current practise of parameterising soil detrital food webs. Emerging methods to estimate microbial growth rates and growth efficiencies are promising new avenues. If quantitative assessments of detrital food webs are revised by incorporating information that reflects the feeding rates of organisms, they could represent a powerful conceptual tool to investigate fundamental ecological theory, including how stability links to complexity and how function depends on the structure of whole food webs. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Food web interactions Soil fauna Biogeochemistry Food chain Trophic interaction Bacteria and fungi
“It is interesting to contemplate a tangled bank” e C.R. Darwin (1859). The food-web concept is central to ecology, and it has a long history. Organisms are interrelated and interact in a complex web, or a “tangled bank” (Darwin, 1859). More recent developments of the concept include landmark contributions (Elton, 1927; Lindeman, 1942) that have added precision and quantification. These gradually moulded the concept to become a means to track energy units as an ecological currency through ecosystems (Odum and Odum, 1976). This led up to ambitious efforts to quantify whole ecosystems including the International Biological Programs (IBPs) (Coleman et al., 1976) and several post-IBP programs (e.g. Bååth et al., 1981; Hunt et al., 1987; Bradford et al., 2002). These enterprises also shifted the focus from pathways that always stemmed from primary producers to include detritus based food webs (Coleman et al., 1988; Moore et al., 1989) and associated plant nutrient supply. This arena, the detrital food web in soil, has served as an active forum for several theoretical debates on subjects including for instance the link between complexity and stability (Neutel et al., 2002) and the role of omnivory in soil systems (Neutel et al., 2007). By providing an interface for integrating
ecological connections and biogeochemical fluxes (de Vries et al., 2013; Neutel and Thorne, 2014) food web research was and remains a hotspot for linking ecosystem structure with function. How have the complex interactions that form detrital food webs been resolved and linked with functions? Qualitative demonstrations have shown that trophic interactions can characterise the detrital food web’s ability to provide plant nutrition (Santos et al., 1981; Crowther et al., 2015). Treatments used to control the presence or absence of microbivores have revealed that microbivore grazing stimulates nutrient mineralisation (Wardle, 1999). For instance, plant-soil microcosm systems in the presence of fauna have been shown to turnover detritus and provide plant nutrition, while systems without fauna accumulate detritus, providing poor €la € and Huhta, 1991). These experimental plant productivity (Seta demonstrations have also been extended to constructed full ecosystems (Bradford et al., 2002), and even natural conditions (Clarholm, 1981, 1985), demonstrating how active grazing on detritus feeding microorganisms impacts nutrient availability in soil, especially in the rhizosphere (Bonkowski, 2004). Examples such as these have highlighted that biotic interactions occurring in food webs have considerable implications for system functioning. However, quantitative representations of full food webs, assessing interactions and elemental energy pathways, have mostly been assembled from more indirect inferences. Such efforts have relied on quantifying the abundance of soil organisms, classifying
E-mail address: [email protected]. http://dx.doi.org/10.1016/j.soilbio.2016.07.001 0038-0717/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article in press as: Rousk, J., Biomass or growth? How to measure soil food webs to understand structure and function, Soil Biology & Biochemistry (2016), http://dx.doi.org/10.1016/j.soilbio.2016.07.001
2
J. Rousk / Soil Biology & Biochemistry xxx (2016) 1e3
these into functional groups based on their feeding preferences, and causally linking them up based on trophic links inferred from literature (de Ruiter et al., 1994; Hunt et al., 1987). The feeding rate, or the rate of resource consumption, for all included functional groups is subdivided into three e an excretion rate (back to the detrital pool), the biomass production rate of the consumer, and a mineralisation rate of nutrients and carbon (de Ruiter et al., 1994). These three rates are based on population or biomass sizes, feeding preferences, assimilation efficiencies, production efficiencies and nutrient contents of detritus and organisms. Of these variables, normally only the biomass or population sizes are determined for the specific site studied, while other variables are taken from literature values or are inferred (Hunt et al., 1987). The modelled food web estimates mineralisation rates, which are then compared with measurements from the studied sites, and used to verify the explanatory power of the model in the studied system (Hunt et al., 1987; de Ruiter et al., 1994; de Vries et al., 2013). This is where I would like to stop for reflection. These important assumptions by which biomass measures of functional groups are combined with literature values for assimilation and production efficiencies and used to assemble the interactions and material flows of whole food webs e are these variables well constrained and understood? Can biomass be used to estimate growth and mineralisation? Are the central assumptions used to construct food web models met? What do biomass based assessments of soil organisms reveal about growth rates and mineralisation? Let us consider the bottom levels e microbial decomposers of detritus. Assuming that the markers or proxies used to determine microbial biomass are reliable (Joergensen and Wichern, 2008), biomass based assessments certainly can reveal the amount of nutrients and elemental contents held in for instance bacterial and fungal biomass (Cleveland and Liptzin, 2007; Strickland and Rousk, 2010). However, does the biomass size of microorganisms also capture their biomass production rate and contribution to mineralisation? This has been addressed and exhaustively studied in soil (Strickland and Rousk, 2010; Rousk and Bååth, 2011; Blagodatskaya and Kuzyakov, 2013). Although the intuitive expectation could be that growth rates of microorganisms correlate with biomass concentrations, at least during “stable state” conditions, this expectation is not supported. The lack of correspondence between biomass and growth rates holds true during periods of perturbation (Rousk and Bååth, 2007; van Groenigen et al., 2010; Jones et al., 2012), as well as during quasi stable state conditions in soil (Rousk et al., 2009, 2011; Pausch et al., 2016). In fact, the amount of microbial biomass is largely uninformative about the activity of microorganisms in soil, including their growth rate and contribution to mineralisation, unless the status of the microbial biomass is also known, ranging along a continuum from dead, dormant, slowly growing, and growing fast (Rousk and Bååth, 2011; Blagodatskaya and Kuzyakov, 2013). Problematically, this suggests that using biomass estimates to capture the intended targets (i) biomass production rate of the consumer and (ii) mineralisation rate of nutrients and carbon (Hunt et al., 1987; de Ruiter et al., 1994; de Vries et al., 2013) is misled. So what information do we need for microorganisms in food webs? The growth rate of a microorganism is a pivotal life history trait since it is a critical component of fitness in most environments (Kirchman, 2001; Rousk and Bååth, 2011). Life history traits in general are indeed defined as traits that affect a microorganism’s reproduction or survival, such as responses to resource availability or quality, susceptibility to or dependence on environmental factors, etc. The full repertoire of life history traits that together describe an organism’s reproduction and survival in an environment defines its life history (Neidhardt, 1999; Roller and Schmidt, 2015). As such, to understand the ecology of microorganisms, we need a measure of the coordinated operation of whole cells, or
indeed whole communities. Microbial growth rates offer such a metric. They will also define part of the information required to link organisms in a food web (Hunt et al., 1987; de Ruiter et al., 1994), but do not capture the food web’s ability to provide mineralisation of resources, and, as such, the fundamental connection to plant nutrition (Wardle, 1999). The microbial growth efficiency e microorganisms produced per unit of resource consumed e is an equally important life history trait to the growth rate. The growth efficiency depends on environmental and population specific factors, including the resource energy content (Linton and Stephenson, 1978), pathways of resource utilization (Flamholtz et al., 2013), biomass precursor availability (Kirchman, 2001) and the energy fraction devoted to maintenance functions compared with growth (Hoehler and Jørgensen, 2013). The growth efficiency thus integrates an organism’s physiology, ecology and evolutionary history. In addition, microbial growth efficiencies have important consequences for the understanding of how ecosystems operate, by setting the fundamental premises for the microbial impact on C and nutrient cycling, and the mineralisation consequences of detrital food webs. Reality check e why are food webs not parametrised with growth rates and growth efficiency estimates? While the importance of microbial growth efficiencies has been recognised in soil microbial ecology (Manzoni et al., 2012) and biogeochemistry modelling (Allison et al., 2010; Wetterstedt and Ågren, 2011), it has been one of the most elusive microbial parameters in soil (Lee and Schmidt, 2014; Roller and Schmidt, 2015). It is likely that a lack of effective methods to estimate microbial growth rates in soil has impeded development there (Blagodatskaya and Kuzyakov, 2013; Rousk and Bååth, 2011). However, with the emergence and rapid spread of cost effective isotope tracer methods, early reports are now beginning to emerge (Frey et al., 2013; Hagerty et al., 2014; Lee and Schmidt, 2014). Yet, the general understanding of how environmental factors modulate the microbial growth efficiency, how much it varies in natural environments and its dynamics over time, remain rudimentary at best. However, the systematic work to develop conversion factors achieved in aquatic systems to estimate microbial growth rates in units of C (del Giorgio and Cole, 1998) should be prioritized also in soil systems. Growth rates could then be readily compared with respiration and N mineralisation to achieve system-specific estimates of growth, growth efficiencies, and, consequently, repercussions for mineralisation. These estimates could be used to parametrise the first levels of the detrital food web, and subsequently be connected to higher trophic levels, to finally link ecosystem functions to the ecological structure in quantitative food webs. This could lead to a situation where existing theoretical modelling (Neutel et al., 2002, 2007) could be used to re-assess how complexity affects system stability in suitably parametrised datasets, thus exploring central ecological questions in soil study systems. Naturally, even given the anticipated progress, challenges would still remain to be resolved. For instance, how should the immense taxonomic complexity of the soil microbial community be resolved? Will the “mean” growth rate and C-use efficiencies of those complex communities be sufficient, or should major groups, such as the division between fungi and bacteria, be resolved (Rousk and Frey, 2015)? Or do we need to resolve the growth rates of each microbial taxon (Hungate et al., 2015; Lankiewicz et al., 2016)? Will taxa differ significantly in their resource-use efficiency due to differences in traits such as the ability for waste metabolism, thus affecting the link between growth rates and mineralisation rates? Moreover, we have merely scratched the surface of the tangled bank by only considering the first trophic level of the detrital food web. Will higher trophic levels present similarly poor correspondence between consumption rates and biomass estimates, and thus also require reassessment? The
Please cite this article in press as: Rousk, J., Biomass or growth? How to measure soil food webs to understand structure and function, Soil Biology & Biochemistry (2016), http://dx.doi.org/10.1016/j.soilbio.2016.07.001
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frequently observed match between protozoan population sizes and their feeding rates on bacteria (e.g. Clarholm, 1981; Bonkowski, 2004) anecdotally suggests that this is not the case. As such it seems like the failure of biomass estimate to capture process rates may well be microbial-specific in soil. John Keats worried that Newton’s prismatic reduction of white light, and consequent unweaving of the rainbow, had taken the poetry out of the wonder of nature (Keats, 1820). Will it still be “interesting to contemplate the tangled bank”, once its links have been defined, understood and quantified? Of course it will. It is the only way to bring light into the opaque murkiness of the belowground and reveal its grandeur. Powerful theoretical tools such as food web models can only reveal the natural world when conceptual clarity and parametric precision are reached. Theoretical analyses of central questions in ecology including, e.g., how stability depends on complexity (Neutel et al., 2002, 2007), can then transition from interesting exercises to true insights about how nature works. Acknowledgements I thank Guest Editor Mark A. Bradford for inviting this contribution to the Special Issue, and Kathrin Rousk for thoughtful feedback on earlier drafts. This work was supported by grants from the Swedish Research Council (Vetenskapsrådet; grant no 201504942) and the Swedish Research Council Formas (grant no 9422015-270). References Allison, S.D., Wallenstein, M.D., Bradford, M.A., 2010. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336e340. € derstro €m, B., Sohlenius, B., 1981. Bååth, E., Lohm, U., Lundgren, B., Rosswall, T., So Impact of microbial-feeding animals on total soil activity and nitrogen dynamics e a soil microcosm experiment. Oikos 37, 257e264. Blagodatskaya, E., Kuzyakov, Y., 2013. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192e211. Bonkowski, M., 2004. Protozoa and plant growth: the microbial loop in soil revisited. New Phytol. 162, 617e631. Bradford, M.A., Jones, T.H., Bardgett, R.D., Black, H.I.J., Boag, B., Bonkowski, M., Cook, R., Eggers, T., Gange, A.C., Grayston, S.J., Kandeler, E., McCraig, A.E., Newington, J.E., Prosser, J.I., Set€ al€ a, H., Staddon, P.L., Tordoff, G.M., Tscherko, D., Lawton, J.H., 2002. Impacts of soil faunal community composition on model grassland ecosystems. Science 298, 615e618. Clarholm, M., 1981. Protozoan grazing of bacterial in soil e impact and importance. Microb. Ecol. 7, 343e350. Clarholm, M., 1985. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol. Biochem. 17, 181e187. Cleveland, C.C., Liptzin, D., 2007. C: N:P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 85, 235e252. Coleman, D., Crossley, D., Beare, M., Hendrix, P., 1988. Interactions of organisms at root/soil and litter/soil interfaces in terrestrial ecosystems. Agric. Ecosyst. Environ. 24, 117e134. Coleman, D.C., Andrews, R., Ellis, J.E., Singh, J.S., 1976. Energy-flow and partitioning in selected man-managed and natural ecosystems. Agro-Ecosystems 3, 45e54. Crowther, T.W., Thomas, S.M., Maynard, D.S., Baldrian, P., Covey, K., Frey, S.D., van Diepen, L.T.A., Bradford, M.A., 2015. Biotic interactions mediate soil microbial feedbacks to climate change. Proc. Natl. Acad. Sci. U. S. A. 112, 7033e7038. Darwin, C.R., 1859. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. John Murray, London, England. de Ruiter, P.C., Neutel, A.-M., Moore, J.C., 1994. Modelling food webs and nutrient cycling in agro-ecosystems. Trends Ecol. Evol. 9, 378e383. De Vries, F.T., Thebault, E., Liiri, M., Birkhofer, K., Tsiafouli, M.A., Bjørnlund, L., Jørgensen, H.B., Brady, M.V., Christensen, S., de Ruiter, P.C., d’Hertefeldt, T., Frouz, J., Hedlund, K., Hemerik, L., Hol, W.H.G., Hotes, S., Mortimer, S.R., €la €, H., Sgardelis, S.P., Uteseny, K., var der Putten, W.H., Wolters, V., Seta Bardgett, R.D., 2013. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl. Acad. Sci. U. S. A. 110, 14296e14301. del Giorgio, P.A., Cole, J.J., 1998. Bacterial growth efficiency in natural aquatic systems. Annu. Rev. Ecol. Syst. 29, 503e541. Elton, C.S., 1927. Animal Ecology. The MacMillan Company, New York, USA. Flamholtz, A., Noor, E., Bar-Even, A., Biebermeister, W., Milo, R., 2013. Glycolytic strategy as a tradeoff between energy yield and protein cost. Proc. Natl. Acad. Sci. U. S. A. 110, 10039e10044.
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Please cite this article in press as: Rousk, J., Biomass or growth? How to measure soil food webs to understand structure and function, Soil Biology & Biochemistry (2016), http://dx.doi.org/10.1016/j.soilbio.2016.07.001