Understanding soil phosphorus cycling for sustainable development: A review

Phosphorus (P) limits productivity in many ecosystems and has the potential to constrain the global carbon sink. The magnitude of these effects depends on how climate change and rising CO2 affect P cycling. Some effects are well established. First, P limitation often constrains CO2 fertilization, and rising CO2 often exacerbates P limitation. Second, P limitation and P constraints to CO2 fertilization are more common in warmer and wetter sites. Models that couple P cycling to vegetation generally capture these outcomes. However, due largely to differences between short-term and long-term dynamics, the patterns observed across climatic gradients do not necessarily indicate how climate change over years to decades will modify P limitation. These annual-to-decadal effects are not well understood. Furthermore, even for the well-understood patterns, much remains to be learned about the quantitative details, mechanisms, and drivers of variability. The interface between empirical and modeling work is particularly ripe for development. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Terrestrial Phosphorus Cycling: Responses to Climatic Change

Climate change is altering the planet and threatens humanity. Earth system models simulate the planet's physical, chemical, and biological processes to help scientists understand current environmental changes and make projections for Earth's future, which can inform society's responses to combat and mitigate climate change's negative effects. Climate change will fundamentally change life on Earth, including microorganisms. Microbes will also influence climate change by driving biogeochemical cycles through the consumption and production of greenhouse gasses. Thus, explicitly including microbial processes into Earth system models can improve model projections. However, fully understanding the feedbacks between climate change and microbes, and then including those processes into Earth systems models, is a major challenge. This report is based on the deliberations of experts who participated in a virtual colloquium on 6 and 8 December, 2022, organized by the American Academy of Microbiology, which is the honorific leadership group and think tank within the American Society for Microbiology. At the colloquium, these experts from the climate and microbial sciences attempted to clearly articulate current knowledge gaps of the two fields. As a result, the participants compiled a list of top ten challenges to better incorporate microbial processes into Earth system models. Solving these challenges requires new thinking and approaches. Transdisciplinary efforts have the potential to propel science—and society—towards combating climate change.

Microbes in Models: Integrating Microbes into Earth System Models for Understanding Climate Change

Abstract Mined rock phosphate is expected to become a scarce resource within the next few decades as global phosphorus (P) deposits are declining. As a result, mineral P fertilizer will be less available and more expensive. Therefore, improved knowledge is needed on other P resources, for example, apatite fertilizers derived from the by‐products of iron mining. Forestry is a potential future consumer of apatite‐rich products with the aim of obtaining more wood per hectare. The actual P availability in apatite to plants has so far been barely quantified. We therefore examined tree P uptake using 33 P apatite under chamber‐grown and outdoor conditions. We examined the P uptake for the two main conifer species spruce ( Picea abies ) and pine ( Pinus sylvestris ) used in Fenno‐Scandinavian forestry. We synthesized 33 P‐enriched apatite and applied it to mesocosms with growing seedlings of spruce and pine. The P uptake from 33 P‐labelled hydroxylapatite was subsequently traced by (bio)imaging of radioactivity in the plants and by liquid scintillation counting (LSC) upon destructive harvest in all plant fractions (leaves, stem and roots) and rhizosphere soil. Two climatic conditions were compared, one at natural outdoor conditions and one set as 5°C warmer than the climate record from the previous years. Plant P uptake from 33 P‐labelled hydroxylapatite was enhanced in chamber‐grown compared with outdoor seedlings for both tree species. This uptake was manifested in the clear radioactive images obtained over ca. 1 month after soil apatite application. Furthermore, all aboveground plant fractions of both spruce and pine seedlings showed a higher P uptake in warmer than colder daytime environments. The observed quantities and rates of P uptake from 33 P‐labelled hydroxylapatite by spruce (18 Bq g −1 hour −1 ) and pine (83 Bq g −1 hour −1 ; averages in chamber condition) are as to our knowledge unique observations. Natural forest soils in Sweden are often P‐poor. Our research suggests that apatite‐based P fertilization of spruce and pine forests can increase wood production by overcoming any existing P limitation. 

Spruce and pine utilization of phosphorus in soil amended with <scp>33P</scp>‐labelled hydroxylapatite

Abstract. Describing the coupling of nitrogen (N), phosphorus (P), and carbon (C) cycles of land ecosystems requires understanding microbial element use efficiencies of soil organic matter (SOM) decomposition. These efficiencies are studied by the Soil Enzyme Steady Allocation Model (SESAM) at the decadal scale. The model assumes that the soil microbial communities and their element use efficiencies develop towards an optimum where the growth of the entire community is maximized. Specifically, SESAM approximated this growth optimization by allocating resources to several SOM-degrading enzymes proportional to the revenue of these enzymes, called the Relative approach. However, a rigorous mathematical treatment of this approximation has been lacking so far. Therefore, in this study we derive explicit formulas of enzyme allocation that maximize the total return from enzymatic processing, called the Optimal approach. Further, we derive another heuristic approach that prescribes the change of allocation without the need of deriving a formulation for the optimal allocation, called the Derivative approach. When comparing predictions across these approaches, we found that the Relative approach was a special case of the Optimal approach valid at sufficiently high microbial biomass. However, at low microbial biomass, it overestimated allocation to the enzymes having lower revenues compared to the Optimal approach. The Derivative-based allocation closely tracked the Optimal allocation. These findings increase our confidence in conclusions drawn from SESAM studies. Moreover, the new developments extend the range of conditions at which valid conclusions can be drawn. Further, based on these findings we formulated the constrained enzyme hypothesis. This hypothesis provides a complementary explanation why some substrates in soil are preserved over decades, although they are often decomposed within a few years in incubation experiments. This study shows how optimality considerations lead to simplified models, new insights, and new hypotheses. This is another step in deriving a simple representation of an adaptive microbial community, which is required for coupled stoichiometric C–N–P dynamic models that are aimed to study decadal processes beyond the ecosystem scale.


Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)

Abstract. Phosphorus (P) availability affects the response of
terrestrial ecosystems to environmental and climate change (e.g., elevated
CO2), yet the magnitude of this effect remains uncertain. This
uncertainty arises mainly from a lack of quantitative understanding of the
soil biological and geochemical P cycling processes, particularly the P
exchange with soil mineral surfaces, which is often described by a Langmuir
sorption isotherm. We first conducted a literature review on P sorption experiments and
terrestrial biosphere models (TBMs) using a Langmuir isotherm. We then
developed a new algorithm to describe the inorganic P exchange between soil
solution and soil matrix based on the double-surface Langmuir isotherm and
extracted empirical equations to calculate the sorption capacity and
Langmuir coefficient. We finally tested the conventional and new models of P
sorption at five beech forest sites in Germany along a soil P stock gradient
using the QUINCY (QUantifying Interactions between terrestrial Nutrient
CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach
in most TBMs largely differed from P sorption experiments regarding the
sorption capacities and Langmuir coefficients, and it simulated an overly low soil
P-buffering capacity. Conversely, the double-surface Langmuir isotherm
approach adequately reproduced the observed patterns of soil inorganic P
pools. The better representation of inorganic P cycling using the double-surface
Langmuir approach also improved simulated foliar N and P concentrations as well as
the patterns of gross primary production and vegetation carbon across the
soil P gradient. The novel model generally reduces the estimates of P
limitation compared with the conventional model, particularly at the low-P
site, as the model constraint of slow inorganic P exchange on plant
productivity is reduced.


https://bg.copernicus.org/articles/20/57/2023/bg-20-57-2023.pdf

Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of phosphorus limitation in temperate forest ecosystems

Abstract. Understanding the coupling of nitrogen (N) and carbon (C) cycles of land ecosystems requires understanding microbial element use
efficiencies of soil organic matter (SOM) decomposition. Whereas important controls of those efficiencies by microbial community adaptations have
been shown at the scale of a soil pore, a simplified representation of those controls is needed at the ecosystem scale. However, without abstracting
from the many details, models are not identifiable; i.e. they cannot be fitted without ambiguities to observations. There is a need to find, implement,
and validate abstract simplified formulations of theses processes. Therefore, we developed the Soil Enzyme Allocation Model (SEAM). The model explicitly represents community adaptation strategies of resource
allocation to extracellular enzymes and enzyme limitations on SOM decomposition. They thus provide an abstraction from several microbial functional
groups to a single holistic microbial community. Here we further simplify SEAM using a quasi-steady-state assumption for extracellular
enzyme pools to derive the Soil Enzyme Steady Allocation Model (SESAM) and test whether SESAM can provide the same decadal-term predictions as SEAM. SESAM reproduced the priming effect, the SOM banking mechanism, and the damping of fluctuations in carbon use efficiency with microbial competition
as predicted by SEAM and other more detailed models. This development is an important step towards a more parsimonious representation of soil
microbial effects in global land surface models.


Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)

This paper by Yu et al. describes a new algorithm to better represent soil phosphorus sorption dynamics in a terrestrial biosphere model – QUINCY. The authors proposed the use of a double-surface Langmuir isotherm to better capture the non-linear relationships between solution P and labile P pools in the soil. They performed a review on both published data and model assumptions on P sorption. They then compared their simulation against data at a range of P availability sites, performed sensitivity on

Reply on RC2

. Understanding the coupling of nitrogen (N) and carbon (C) cycles of land ecosystems, requires understanding microbial element use efﬁciencies of soil organic matter (SOM) decomposition. Whereas important controls of those efﬁciencies by microbial community adaptations have been shown at the scale of a soil pore, a simpliﬁed representation of those controls is needed at the ecosystem scale. However, without abstracting from the many details, models are not identiﬁable, i.e. can not be ﬁtted without ambiguities to observations. There is a need to ﬁnd, implement, and validate abstract simpliﬁed formulations 5 of theses processes. Therefore, we developed the soil enzyme allocation model (SEAM) model. The models explicitly represent community adaptation strategies of resource allocation to extracellular enzymes and enzyme limitations on SOM decomposition. They thus provide a abstraction from several microbial functional groups to a single holistic microbial community. Here we further simplify the SEAM model using a quasi-steady-state assumption for extracellular enzyme pools to derive the SESAM model 10 and test, whether SESAM can provide the same decadal-term predictions as SEAM. SESAM reproduced the priming effect, the SOM banking mechanism, and the damping of ﬂuctuations of carbon use efﬁciency with microbial competition as predicted by SEAM and other more detailed models. This development is an important step towards a more parsimonious representation of soil microbial effects in global land surface models.

Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small scale microbial processes: The SESAM Soil Enzyme Steady Allocation Model (v3.0).

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Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of phosphorus limitation in temperate forest ecosystems

Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)

Reply on RC2

Reply on RC2

Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small scale microbial processes: The SESAM Soil Enzyme Steady Allocation Model (v3.0).