(N) in soils (Schimel and Schaeffer

(N) in soils (Schimel and Schaeffer, 2012) to justify their complexity.
This is likely to occur when biotic interactions modify ecosystem
responses to environmental perturbations in unexpected directions
(Bradford and Fierer, 2012). Fauna-microbe interactions exhibit this
potential for unexpected, non-linear response to environmental
change. For example, the response of fauna to a changing climate
might alter microbial communities in opposite directions to the
direct effects of climate on microbial communities. We know that
abiotic constraints from energy limitation and substrate availability
may broadly limit microbial activity and biogeochemical fluxes
across soil environments (Mikola and Set€al€a, 1998). Accordingly,
current biogeochemical models project changes in microbial activity
with relaxation of these abiotic constraints, resulting in
accelerated soil C turnover with environmental warming. If, however,
changes in temperature, moisture, or nutrient availability
relax these bottom-up constraints on microbial decomposers, one
outcome could be that biotic, or top-down controls from food webs
dampen the magnitude of ecosystem response, providing a stabilizing
effect on ecosystem biogeochemical dynamics (Crowther
et al., 2015). These dynamics may not be projected from simpler
model structures that ignore food webs.
The most straightforward way to begin representing top-down
effects in biogeochemical models would be to implicitly represent
faunal effects on microbial communities and their activity by
modifying static parameters with functions that consider how
abiotic factors affect biotic processes and rates of biogeochemical
transformations. For example, if warming releases bottom-up
limitations on microbial communities, but grazers dampen the
observed biogeochemical effects, we could assume a lower temperature
sensitivity of soil organic matter turnover (e.g., Q10 value)
than would be expected from laboratory incubations or cross-site
observations. Current microbial-explicit models, including
MIMICS, represent microbial biomass pools with defined turnover
and biomass-dependent substrate uptake rates. Fauna could be
represented in such models by increasing biomass turnover rates
under conditions where microbivores are expected to be especially
active, including those with ideal combinations of temperature,
moisture and substrate quality. Increasing turnover rates would
subsequently decrease standing microbial biomass and substrate
uptake rates and potentially alleviate stoichiometric constraints
(e.g. N limitation) in the model. In another scenario, microarthropod
alteration of the chemical quality of plant residues that
microbes ultimately transform to mineral-associated SOM
(Wickings and Grandy, 2011; Wickings et al., 2012) could be represented
by changing the C:N ratio of inputs to soil biogeochemical
models (Soong et al., 2016). Lab and field faunal exclusion experiments
across a wide range of ecosystems would further help to
parameterize the effects of fauna on microbial activity.

ผลลัพธ์ (อังกฤษ) 2:

(N) in soils (Schimel and Schaeffer, 2012) to justify their complexity.
This is likely to occur when biotic interactions Modify Ecosystem
Responses to Environmental perturbations in Unexpected Directions
(Bradford and Fierer, the 2,012th). Fauna-microbe interactions Exhibit this
potential for Unexpected, non-linear response to Environmental
Change. For example, the response to a changing Climate fauna of
microbial communities in Opposite Directions Alter might to the
Direct effects of Climate on microbial communities. We know that
from abiotic constraints and Energy Limitation substrate Availability
May Limit broadly microbial and biogeochemical fluxes Activity
Across soil environments (Mikola and a € Set € al, 1,998th). Accordingly,
current biogeochemical models Project Changes in microbial Activity
with Relaxation of these abiotic constraints, resulting in
accelerated soil C turnover with Environmental warming. If, however,
Changes in Temperature, Moisture, or nutrient Availability
Relax these bottom-up constraints on microbial decomposers, one
outcome could be that biotic, or top-down Controls from Food webs
Dampen the magnitude of Ecosystem response, providing a stabilizing
Effect on. biogeochemical Ecosystem Dynamics (Crowther
et al., 2015). These Dynamics May not be projected from simpler
Model Structures that ignore Food webs.
Most straightforward Way to BEGIN representing the top-down
effects in biogeochemical models would be to implicitly represent
faunal effects on microbial communities and their Activity by
modifying static Parameters with functions that consider How
abiotic factors affect biotic processes and Rates of biogeochemical
transformations. For example, if warming releases bottom-up
limitations on microbial communities, but grazers Dampen the
biogeochemical effects observed, we could assume a Lower Temperature
sensitivity of soil Organic Matter turnover (eg, Q10 VALUE)
than would be expected from Incubations or Cross-Laboratory. Site
observations. Explicit-microbial current models, including
Mimics, microbial biomass represent Pools with defined turnover
and biomass-dependent uptake Rates substrate. Fauna could be
represented in such models by increasing biomass Rates turnover
under conditions where Microbivores are expected to be especially
active, including those with Ideal combinations of Temperature,
Moisture and substrate quality. Rates increasing turnover would
subsequently decrease microbial biomass and standing substrate
uptake Rates and potentially alleviate stoichiometric constraints
(eg N Limitation) in the Model. In another scenario, Microarthropod
Alteration of the Chemical quality of Plant residues that
microbes Ultimately transform to Mineral-associated SOM
(Wickings and Grandy, 2,011; Wickings et al., 2,012th) could be represented
by changing the C: N ratio of inputs to soil. biogeochemical
models (Soong et al., two thousand and sixteen). Lab and Field faunal exclusion experiments
Across a Wide Range of ecosystems would further Help to
parameterize the effects of microbial fauna on Activity.

ผลลัพธ์ (อังกฤษ) 3:

(N) in soils (Schimel and Schaeffer 2012), to justify their complexity.This is likely to occur when biotic interactions modify ecosystem.Responses to environmental perturbations in unexpected directions.(Bradford, and Fierer 2012). Fauna-microbe interactions exhibit this.Potential, for unexpected non - linear response to environmental.Change. For example the response, of fauna to a changing climate.Might alter microbial communities in opposite directions to the.Direct effects of climate on microbial communities. We know that.Abiotic constraints from energy limitation and substrate availability.May broadly limit microbial activity and biogeochemical fluxes.Across soil environments (Mikola and Set euros al euros a 1998). Accordingly,,Current biogeochemical models project changes in microbial activity.With relaxation of these abiotic constraints resulting in,,Accelerated soil C turnover with environmental warming. If however,,Changes in temperature moisture or nutrient availability,,,Relax these bottom-up constraints on, microbial decomposers one.Outcome could be, that biotic or top-down controls from food webs.Dampen the magnitude of ecosystem response providing a, stabilizing.Effect on ecosystem biogeochemical dynamics (Crowther.Et al, 2015). These dynamics may not be projected from simpler.Model structures that ignore food webs.The most straightforward way to begin representing top-down.Effects in biogeochemical models would be to implicitly represent.Faunal effects on microbial communities and their activity by.Modifying static parameters with functions that consider how.Abiotic factors affect biotic processes and rates of biogeochemical.Transformations. For example if warming, releases bottom-up.Limitations on microbial communities but grazers, dampen the.Observed, biogeochemical effects we could assume a lower temperature.Sensitivity of soil organic matter turnover (e.g, Q10 value).Than would be expected from laboratory incubations or cross-site.Observations. Current, microbial-explicit models including.MIMICS represent microbial, biomass pools with defined turnover.And biomass-dependent substrate uptake rates. Fauna could be.Represented in such models by increasing biomass turnover rates.Under conditions where microbivores are expected to be especially.Active including those, with ideal combinations, of temperatureMoisture and substrate quality. Increasing turnover rates would.Subsequently decrease standing microbial biomass and substrate.Uptake rates and potentially alleviate stoichiometric constraints.(e.g. N limitation) in the model. In, another scenario microarthropod.Alteration of the chemical quality of plant residues that.Microbes ultimately transform to mineral-associated SOM.(Wickings, and Grandy 2011; Wickings et al, 2012) could be represented.By changing the C: N ratio of inputs to soil biogeochemical.Models (Soong et al, 2016). Lab and field faunal exclusion experiments.Across a wide range of ecosystems would further help to.Parameterize the effects of fauna on microbial activity.