In both cases, strong competition occurs between plants and microbes so that actual nutrient uptake by individual consumers is often less than their demand due to limited supply and uptake of a nutrient by one consumer suppresses the functioning of other consumers . Furthermore, as CO2 concentrations increase, nutrient competition between plants and microbes is expected to intensify. Because elevated CO2 concentrations fertilize plant carbon productivity, plants will require more soil nutrients to facilitate enhanced photosynthesis and for tissue construction . On the other hand, enhanced carbon assimilation dilutes tissue nutrient concentrations and lowers litter quality . Decomposing lower quality litter implies that soil microbes may need to immobilize nutrients to maintain their stoichiometric balance . In addition, under elevated CO2 conditions, available nutrients will progressively move from fast cycling tissues to slow cycling tissues , which induces progressive nutrient limitation that further exacerbates nutrient limitations. Although increased external nutrient inputs and accelerated nutrient mineralization rates under warming soil conditions may enhance soil nutrient availability and partly ease plant– microbe nutrient competition, these additional nutrients may be insufficient to satisfy the enhanced plant nutrient demands . To investigate nutrient competition and its effects on the terrestrial carbon cycle,maceta 15 litros different theories of plant-soil nutrient competition have been developed and implemented in Earth System Models . However, the oretical justification and observational support for these theories are rarely discussed, which may have resulted in large biases in modeled nutrient and carbon cycling.
To reconcile this inconsistency between theory, observations, and models, we focus on one overarching question in this study: Is there an observationally consistent, theoretically supported, and mathematically robust theory that is simple enough to implement in ESMs while accurately representing plant–microbe competition for nutrients? To answer this question, we first survey four existing nutrient competition theories and their implementation in ESMs . In Results, we discuss in detail these four competition theories: CT1, no direct competition; CT2, microbial decomposers out compete plants; CT3, competition depends on pore-scale soil fertility heterogeneity; and CT4, plant–microbe relative demand controls competition. Then we describe a new theory of nutrient competition based on Equilibrium Chemistry Approximation kinetics . We test our new theory together with other existing competition theories against a unique observational data set of N competition in a grassland ecosystem.To inform the development of ESM land models, observations have to satisfy two criteria. First, observations should capture plant and microbe competition at the whole-soil level, because the significance of microsite heterogeneity diminishes at this spatial scale. Second, measurements should target short-term nutrient uptake, thus enabling relatively clear separation of the instantaneous competitive interactions from other ecosystem dynamics that occur over longer time scales . Among the four existing theories surveyed, the traditional Nutrient Competition Theory assumes that plants and microbes do not compete for nutrients. This theory presumes that plants can assimilate carbon directly from the atmosphere but rely on nutrients released from soil microbial activity, so plants are carbon rich but nutrient limited . Conversely, because soil microbes decompose soil organic matter to obtain carbon and nutrients , they are relatively nutrient abundant but carbon limited.
A second reason ecologists hypothesize that plants and microbes do not compete is that microbes can directly use organic N during decomposition , while plants primarily use inorganic N . How ever, depending on their carbon use efficiency and biomass stoichiometric imbalances against substrates , microbes do immobilize inorganic nutrients and thus directly compete with plants, creating the first contradiction against the CT1 theory. Further, plants may also utilize some low molecular weight amino acids through mycorrhizal fungi associations or direct root uptake , which creates a second contradiction to the theory. However, no existing ESMs apply CT1 to represent nutrient com petition . The second theory posits that microbial decomposers out-compete plants in nutrient acquisition. This theory assumes that microbial nutrient uptake is extremely efficient , and microbes assimilate as much nutrients as they can during decomposition, provided they are not carbon limited. When carbon is limited, mineral nutrients are released as a “waste product” . This concept leads to the classic idea that plants can only use “leftover” nutrients after microbial demands are satisfied , which is why measured net mineralization rates are commonly used as a proxy for plant-available nutrients . However, no evidence exists to support its validity at the whole-soil or ecosystem level. In contrast, 15N labeling studies have demonstrated that plants can continuously acquire inorganic nutrients, even when both plants and microbes are nutrient limited . Other observations indicate that plants may even suppress microbial nutrient uptake . CT2 has been applied in several ESMs. HadGEM2 and GFDL assume that soil microbial decomposers always outcompete plants and have priority for available nutrients . IPSL and BNU-ESM also assume that microbial immobilization has priority, but apply this priority to the estimated gross mineralization flux in the current model time step, as opposed to the nutrient pool. The third competition theory applies the emerging perspective that plant–microbe nutrient competition depends on the spatial heterogeneity of soil nutrient fertility, and therefore plants do not completely lose the competition at the whole-soil or ecosystem level.
In a heterogeneous soil medium, inorganic nutrients move from nutrient-rich microsites toward nutrient-limited microsites , with roots potentially intercepting the nutrients . CT3 has been integrated into very fine-spatial scale models that explicitly consider the role of microsite soil nutrient heterogeneity, nutrient diffusion, root–microbe interactions , and microbe–microbe competition . In these models, plants do not completely lose the competition with microbes because they can take advantage of fine-scale spatial gradients between immobilizing and mineralizing microbes. The emergent responses from these models indicate that nutrient diffusion rates, sink strength ,indoor garden and competitor spatial distributions are the most important factors affecting plant competitiveness. However, these models’ fine spatial resolution is not directly applicable to ESMs. In ESMs, each soil column is assumed to be a well-mixed environment of nutrients and competitors. Such an assumption is currently necessitated, at least, by limited computational power and observations. Although ESM spatial resolutions likely will become finer, simulating microsite-level soil heterogeneity will remain impractical in the near future. In addition, a model based on CT3 may have high explanatory value but low predictive value, because it requires fine resolution observations of soil heterogeneity.In these ESMs, plant nutrient demand is simulated based on potential Net Primary Production in the absence of nutrient constraints and the plant C to N ratio ; an analogous approach is taken for microbial nutrient demand. When soil nutrient supply is insufficient to satisfy these demands, both plant and microbial demands are reduced in proportion to their respective demands . The actual NPP is then calculated by rescaling NPP demand with the reduction factor. This “relative demand” theory implicitly assumes that the consumer with higher demand will be relatively more competitive. While being simple, the CT4 predicted plant nutrient uptake is mechanistically inconsistent with measurements , although Goll et al. argued that the “demand-driven” approach requires fewer model parameters. The ESMs that apply CT4 include CLM-CN and NorESM , CLM-CNP , and JSBACH-CNP .We compared observations from the 15N tracer study with three model structures for competition: CT2 , CT4 , and CT5 . We were unable to build a model based on CT3 for the study site due to a lack of detailed information about soil N heterogeneity, root architecture, and N diffusion and mass flow rates. Further, such a complex model structure would currently be computationally intractable for ESM applications, although below we discuss a possible intermediate-complexity approach based on CT3 concepts that could be integrated with CT5 in an ESM land model. The CT2 model predicts that topsoil plant 15N uptake is very small due to large microbial nutrient demand . In contrast, because of lower microbial nutrient uptake at depth, there are more “left-over” nutrients and plant 15N uptake is relatively higher, although root biomass density decreases with depth.
Therefore, there is an increasing microbial to plant 15N uptake ratio with increasing root biomass for the CT2 model . For relative-demand-based competition , the predicted microbial nutrient uptake declines with depth, because topsoil litter substrates are nutrient depleted and microbial biomass declines sharply with depth . However, in this calculation, the whole plant nutrient demand is fixed. This constraint implies that microbial decomposers are more competitive in the topsoil than they are in subsoil, while plant competitiveness remains constant across the soil profile. Therefore, the predicted ratio of microbial to plant 15N uptake increases with increasing root biomass . The CT2 and CT4 models were unable to match the observed nitrogen partitioning between microbes and plants. Comparing CT2 and CT4 in the topsoil, CT2 predicted a much higher ratio of the microbe to plant 15N uptake, because plants do not completely lose the competition in the relative demand approach . Importantly, in our evaluation, both CT2 and CT4 resulted in nutrient competition profiles qualitatively opposite to those observed. We also confirmed that no combination of parameters for either CT2 or CT4 could reproduce the qualitative shape of the observed competitive relationship because, for both CT2 and CT4 models, the target variable UPmic/UP plant is proportional to microbial biomass . Shaping parameters only affect the steepness of UPmic/UPplant, but not the general trend. The ECA approach explicitly considers the substrates and enzymes competitive interactions throughout the profile. It captures the general competition pattern using literature-derived parameters from other ecosystems , and qualitatively and quantitatively captures the competition pattern using parameters derived for this site .The ECA representation of nutrient competition provides a theoretical and modeling construct that resulted in very good comparison with the nitrogen uptake partitioning. These predictions demonstrate that integrated across the soil profile, plants were less competitive than microbial decomposers; plant competitiveness against microbes is a spatially distinct property and there is no simple coefficient that can scale their “competitiveness”; the ECA framework offers a theoretically consistent approach to continuously update individual competitiveness; plant competitiveness is controlled by functional and structural traits ; and in the topsoil, plants might out-compete microbes and consequently suppress microbial nutrient uptake. Of course, applying the ECA competition to ESMs comes at the cost of introducing new parameters and additional uncertainty associated with those parameters. However, the ECA approach does not necessarily increase overall model uncertainty . In fact, ECA competition largely reduced the uncertainty in global-scale predictions by considering essential processes that govern system dynamics . We argue that an analogous result occurred in this analysis, i.e., that the uncertainty reduction in model structure overwhelmed uncertainty associated with new model parameters. In addition, most of the ECA parameters are kinetic parameters, which can be directly measured or optimized , implying that targeted experiments and model calibration could further reduce parameter uncertainty.Nutrient competition constantly occurs between plants and microbes in natural terrestrial ecosystems and it will likely intensify under climate change . Therefore, two fundamental questions arise: what controls the partitioning of limited nutrient resources between plants and microbes and how should short-term competition be modeled? Regarding the first question, we highlight the very few observations available to quantitatively partition nutrient acquisition by plants and microbes, and contend that such observations are critical to improve carbon-climate feedback predictions. As we showed here, the detailed 15N tracer experiment used in this study allowed us to evaluate the existing and newly developed plant–microbe N competition hypotheses, because the experiment was conducted at the plot scale and 15N was directly injected in the rooting zone . Thus, most of the observed plant N uptake pattern reflected the direct competition between roots and microbes, via nutrient carrier enzymes quantity and quality. Regarding the second question, we show here that plant and microbial nutrient uptake can be mechanistically explained as different nutrient transporter enzymes reacting with soil nutrients in a competitive manner. By linking plant root and microbial biomass density to nutrient transporter enzyme abundances, our new competition theory produces qualitatively correct competition patterns with literature-derived parameters from other ecosystems, and is easy to calibrate for specific ecosystems. Further, the linkage of nutrient competition with plant and microbial traits will allow a model to represent the competitors’ dynamic allocation of resources to acquire necessary nutrients.