Global pattern of soil nitrogen stable isotope composition: Partitioning N loss by natural abundance. By Ning Dong

The natural abundance of δ15N has been widely in research on terrestrial nitrogen cycling. The variations of material’s stable isotopes composition in natural abundance due to the enzymatic preferences for light N (14N), especially during enzymatic kinetic processes, therefore isotope fractionations can be used as integrators of different processed during nitrogen cycling (Robinson, 2002). Also spatial variations of N isotopes natural abundance in soils or plants generally decline with temperatures and increase with precipitation, which reflect the dominant pathways by nitrogen mass balance of a ecosystem in global or local scales. We use simple soil isotope model to simulate the global pattern of soil N stable isotope composition based on first principles, which presents the balance of N losses by runoff-dependent in hydrologic pathways and soil temperature-dependent in gaseous pathway.

Ning Dong

Fig. 1 The relationship of soil N stable isotope against runoff ratios and soil temperature factor

 

Reference:

Robinson, David. δ15N as an integrator of the nitrogen cycle. Trends in Ecology & Evolution 16.3 (2001): 153-162.

 

 

Posted in Uncategorized | Leave a comment

Increasing vegetation light use efficiency in the high latitudes of the northern hemisphere. By Rebecca Thomas

In the high latitudes of the northern hemisphere (north of 45N), we have observed an increase in the amplitude of the seasonal cycle of CO2 of 5.0±0.8ppm over the last 50 years. In other words, vegetation exchanged 57±9.8% more CO2 with the atmosphere in 2009-10 compared to 1958-611. In previous posts, I have shown that current terrestrial biosphere models (from the MsTMIP simulations) underestimate the increase in seasonal cycle amplitude. So what has caused the increase in seasonal cycle amplitude, and why are models doing a poor job?

If we decompose the seasonal cycle of CO2, we can begin to answer this question. In the northern hemisphere, the seasonal cycle of CO2 can almost entirely be attributed to the terrestrial biosphere. Therefore, to a good approximation, the seasonal cycle of net ecosystem production (NEP) is proportional to the seasonal cycle of atmospheric CO2. NEP is the difference between Net Ecosystem Production and Heterotrophic Respiration (NPP-Rh).  Changes in NPP or Rh will then result in changes in NEP, and thus the seasonal cycle of CO2. Since we know that the land biosphere is a net sink for CO2, it is unlikely that changes in Rh are driving the change in NEP. Thus, it is the change in NPP that is driving the change in NEP.

Rebecca Thomas blog

Figure 1: A simple schematic of the terrestrial carbon cycle. Changes to any of these fluxes or pools can alter NEP. (from Bonan et al. 2008).

NPP is sensitive to atmospheric CO2 concentrations and temperature, both of which have increased over the last 50 years. These changes have relaxed some of the limitations that had previously prevented vegetation growth, particularly in the high northern latitudes. For example, the warmer temperatures have reduced the amount of snow cover and duration, allowing vegetation to start growing sooner and continue to grow for longer. This increase in growing season length has resulted in an increase in peak greenness, and vegetation greening of 0.25-0.5%/yr has been observed in high northern latitudes since 1982 (first satellite observations)2. The MsTMIP models are able to reproduce this trend, and agree that it is mostly driven by changes in climate. Greening will therefore be partly causing the increase in seasonal cycle amplitude, but this is not the whole story.

NPP can also increase without leading to greening, as vegetation can become more efficient. In particular, an increase in vegetation light use efficiency (LUE) could explain the observed changes (Where LUE is the NPP per unit area of absorbed photosynthetically active radiation (NPP/aPAR)3). I propose three mechanisms, not adequately represented in current models, which can increase LUE and may be responsible for the increase in seasonal cycle amplitude:

  1. CO2 fertilisation:- Increases in ambient CO2 increases leaf internal CO2 and therefore can increase the biochemical rate of photosynthesis. Additionally, a reduction in stomatal conductance at higher ambient CO2 increases vegetation water use efficiency4.
  2. Shift to above ground carbon allocation:- Less carbon needs to be allocated to roots at higher temperatures because nutrient cycling is faster. Also, depending on the species and environment, more carbon can be allocated to wood (increasing NPP without leading to greening) or leaves (increasing NPP and vegetation greenness)5.
  3. Acclimation of autotrophic respiration to sustained higher temperatures:- Most models calculate NPP as the difference between gross primary production and autotrophic respiration (NPP=GPP-Ra). Ra is sensitive to temperature, but it acclimatises to higher temperatures5. While this may have occurred in the real world, models do not include this acclimatisation, and therefore underestimate NPP at higher temperatures.

 

Improving how these mechanisms are represented in models should increase NPP and therefore increase the seasonal cycle amplitude of NEP and atmospheric CO2.

 

References:

  1. Graven, H. D., R. F. Keeling, S. C. Piper, P. K. Patra, B. B. Stephens, and S. C. a. Wofsy (2013), Enhanced seasonal exchange of co2 by northern ecosys- tems since 1960, Science, 341(6150), 1085–1089.
  2. Murray-Tortarolo, G., A. Anav, P. Friedlingstein, S. Sitch, S. Piao, and Z. a. Zhu (2013), Evaluation of land surface models in reproducing satellite-derived lai over the high-latitude northern hemisphere. part i: Uncoupled dgvms, Remote Sensing, 5(10), 4819–4838.
  3. Medlyn, B. E. (1998), Physiological basis of the light use efficiency model, Tree Physiology, 18(3), 167–176.
  4. Franks, P. J., M. A. Adams, J. S. Amthor, M. M. Barbour, J. A. Berry, and D. S. a. Ellsworth (2013), Sensitivity of plants to changing atmospheric co2 concentration: from the geological past to the next century, New Phytologist, 197(4), 1077–1094.
  5. Way, D. A., and R. Oren (2010), Differential responses to changes in growth temperature between trees from different functional groups and biomes: a review and synthesis of data, Tree Physiology, 30(6), 669–688.NPP is sensitive to atmospheric CO2 concentrations and temperature, both of which have increased over the last 50 years. These changes have relaxed some of the limitations that had previously prevented vegetation growth, particularly in the high northern latitudes. For example, the warmer temperatures have reduced the amount of snow cover and duration, allowing vegetation to start growing sooner and continue to grow for longer. This increase in growing season length has resulted in an increase in peak greenness, and vegetation greening of 0.25-0.5%/yr has been observed in high northern latitudes since 1982 (first satellite observations)2. The MsTMIP models are able to reproduce this trend, and agree that it is mostly driven by changes in climate. Greening will therefore be partly causing the increase in seasonal cycle amplitude, but this is not the whole story.

     

    NPP can also increase without leading to greening, as vegetation can become more efficient. In particular, an increase in vegetation light use efficiency (LUE) could explain the observed changes (Where LUE is the NPP per unit area of absorbed photosynthetically active radiation (NPP/aPAR)3). I propose three mechanisms, not adequately represented in current models, which can increase LUE and may be responsible for the increase in seasonal cycle amplitude:

     

    1. CO2 fertilisation:- Increases in ambient CO2 increases leaf internal CO2 and therefore can increase the biochemical rate of photosynthesis. Additionally, a reduction in stomatal conductance at higher ambient CO2 increases vegetation water use efficiency4.
    2. Shift to above ground carbon allocation:- Less carbon needs to be allocated to roots at higher temperatures because nutrient cycling is faster. Also, depending on the species and environment, more carbon can be allocated to wood (increasing NPP without leading to greening) or leaves (increasing NPP and vegetation greenness)5.
    3. Acclimation of autotrophic respiration to sustained higher temperatures:- Most models calculate NPP as the difference between gross primary production and autotrophic respiration (NPP=GPP-Ra). Ra is sensitive to temperature, but it acclimatises to higher temperatures5. While this may have occurred in the real world, models do not include this acclimatisation, and therefore underestimate NPP at higher temperatures.

     

    Improving how these mechanisms are represented in models should increase NPP and therefore increase the seasonal cycle amplitude of NEP and atmospheric CO2.

     

    References:

    1. Graven, H. D., R. F. Keeling, S. C. Piper, P. K. Patra, B. B. Stephens, and S. C. a. Wofsy (2013), Enhanced seasonal exchange of co2 by northern ecosys- tems since 1960, Science, 341(6150), 1085–1089.
    2. Murray-Tortarolo, G., A. Anav, P. Friedlingstein, S. Sitch, S. Piao, and Z. a. Zhu (2013), Evaluation of land surface models in reproducing satellite-derived lai over the high-latitude northern hemisphere. part i: Uncoupled dgvms, Remote Sensing, 5(10), 4819–4838.
    3. Medlyn, B. E. (1998), Physiological basis of the light use efficiency model, Tree Physiology, 18(3), 167–176.
    4. Franks, P. J., M. A. Adams, J. S. Amthor, M. M. Barbour, J. A. Berry, and D. S. a. Ellsworth (2013), Sensitivity of plants to changing atmospheric co2 concentration: from the geological past to the next century, New Phytologist, 197(4), 1077–1094.
    5. Way, D. A., and R. Oren (2010), Differential responses to changes in growth temperature between trees from different functional groups and biomes: a review and synthesis of data, Tree Physiology, 30(6), 669–688.

 

Posted in Uncategorized | Leave a comment

Optimality explains photosynthetic responses to elevation. By Wang Han

Alpine plants have long fascinated ecologists, and provide a rich variety of adaptations to extremes of low temperature, wind strength, and others stressors. But just one aspect of high-elevation environments is unique, namely the low atmospheric pressure. There has been speculation about how low pressure might influence plant physiology. For example, it has been suggested that low carbon dioxide (CO2) partial pressure at high elevation might make plants more sensitive to changes in the mixing ratio of CO2 to dry air, whether natural lower as in past ice ages, or higher, as we see currently due to human activity. But this view is oversimplified, as the low partial pressure of oxygen (O­2) at high elevation reduces the photorespiratory burden as well. It has also been shown both by field measurements, and indirectly using stable carbon isotope ratios (δ13C), that ratios of leaf-internal to ambient CO2 partial pressures (pi:pa) are consistently lower at high elevations, whereas photosynthetic capacity (Vcmax) tends to be higher at high elevations. This phenomenon has been discussed extensively, but no clear explanation has emerged.

The least-cost theory now provides a coherent explain on this well documented phenomenon. The least-cost theory states that plants minimize the combined unit costs of maintaining water transport and carbon fixation capacities, and has been tested with field observations. With this theory, we argue that alpine plants adapt to the low air pressure (the unique elevation effect) by investing more on photosynthesis capacity and less on water transpiration because the first cost is cheaper (enhanced affinity of Rubisco for CO2 under low O2 environments) while the latter is more expensive (higher leaf to air vapor pressure deficit as actual vapor pressure declines with elevation). So the ecological explanation is quite simple and clear, but can never be achieved unless considering the two competing costs of photosynthesis at the same time, whereas previous hypothesis only focus on minimizing the transpiration cost.

With the help of partial derivative, theoretical quantification of the elevation effects (i.e. air pressure changes) on pi:pa and Vcmax could also be achieved, and shows consistent results with previous observation. A stronger CO2 fertilization for alpine plants compared to the low land plants is also predicted.

Posted in Uncategorized | Leave a comment

Mycorrhizal type as a primary control on the CO2 fertilization effect in nitrogen-limited ecosystems by Cesar Terrer Moreno

Terrestrial ecosystems sequester annually about a quarter of anthropogenic CO2 emissions, slowing climate change appreciably. The terrestrial carbon sink is generally attributed to the effect of increasing atmospheric CO2 concentrations via the CO2 fertilization effect” (CFE) on plant biomass. However, results from CO2 enrichment (eCO2) experiments range from large and persistent to transient or even non-existent CFE (Norby & Zak, 2011), complicating projections of Earth’s future climate. Plant nitrogen (N) availability is thought to be one of the most important constraints for the CFE (Hungate et al. 2003), however, N-availability alone cannot explain why some ecosystems seem to stay productive under eCO2 under N-limitations (e.g. Duke FACE, McCarthy et al. 2010). I hypothesize that the association of plants with certain species of mycorrhizal fungi might play an important role in increasing plant N-availability to take advantage of the CFE. Among the most common mycorrhizal associations, ectomycorrhizal (ECM) fungi have been associated with a larger transfer of N to the host plant than arbuscular mycorrhizal (AM) fungi (Phillips et al. 2013), and their different nutrient economies might explain the magnitude of the CFE. In this presentation I show, by synthesizing data from 88 eCO2 experiments in a meta-analysis, that the enhancement of biomass by CO2 depends largely on N-availability and the type of mycorrhizae, whereas climate, age and functional plant type are far less important predictors. In N-limited ecosystems eCO2 (~640 μmol mol−1) enhanced total plant growth by 30±5% in species dominated by ECM fungi, and non-significantly in species dominated by AM fungi. In N-fertilized environments total biomass was enhanced by ~35% in both ECM and AM-dominated species. These results provide a consistent framework to explain the range of observations in eCO2 experiments and conclude that the projected CFE (IPCC 2013, Chapter 6) is not valid for N-limited AM-dominated ecosystems, unless other longer-term processes (such as potentially enhanced N fixation) come into play.

Cesar Nov 15

References

Norby, R. J. & Zak, D. R. Ecological Lessons from Free-Air CO 2Enrichment (FACE) Experiments. Annu Rev Ecol Evol Syst 42, 181–203 (2011).

Hungate, B. A., Dukes, J. S., Shaw, M. R., Luo, Y. & Field, C. B. Atmospheric science. Nitrogen and climate change. Science 302, 1512–1513 (2003).

McCarthy, H. R. et al. Re‐assessment of plant carbon dynamics at the Duke free‐air CO2 enrichment site: interactions of atmospheric [CO2] with nitrogen and water availability over stand development. New Phytol 185, 514–528 (2010).

Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal‐associated nutrient economy: a new framework for predicting carbon–nutrient couplings in temperate forests. New Phytol 199, 41–51 (2013).

Posted in Uncategorized | Leave a comment

T model – a simple carbon allocation model for tree growth Two case studies for both high and low [CO2] by Li Guangqi (Macquarie University)

Tree ring width is one of the most important materials for past climate reconstruction. However, “divergence” problem (correlation strength between temperature and ring width breaks where temperature was conferred as the limiting factors in high elevation and high latitude regions and ring width data are previously applied for temperatures) challenges the classical dendrochronology principle of the single-limiting factor and uniformitarian, which means the same strength of the most limited factor had the same strength in the past. Physiology research has shown that tree growth is jointly controlled by multiple factors, e.g., photosynthesis active radiation (PAR), temperature, soil moisture (represented by actual to equilibrium evapotranspiration α), air moisture (vapour pressure deficit, VPD), and CO2. Therefore, we build a forward process-based modelling method to simulate realistic ring width and to illustrate the climate control on tree growth. We combined a simple generic light-use-efficiency GPP model (P model, Wang et al., 2014) with a species-specific carbon allocation tree growth model (T mode, Li et al., 2014). The P model provides values for gross primary production (GPP) per unit of absorbed photosynthetically active radiation (PAR). Absorbed PAR is estimated from the current leaf area. GPP is allocated to foliage, transport tissue, and fine- root production and respiration in such a way as to satisfy well-understood dimensional and functional relationships. Our approach thereby integrates two modelling approaches separately developed in the global carbon-cycle and forest- science literature.

Li Fig 1

Figure1 Ageing effect simulated by T model

Figure 2 Climate control analysis for both the observation and simulation (example from Callitris in Great West Woodland (west Australia))

Figure 2 Climate control analysis for both the observation and simulation (example from Callitris in Great West Woodland (west Australia))

T model can represent both the ontogenetic (ageing effect, figure 1) and the effects of multiple environmental variations (climate control, figure2) and trends (simulation VS observation, figure 3). Both the observation and simulation shows the same positive response to PAR and soil moisture (α), and negative to VPD, which is the opposite of moisture availability in the atmosphere. This climate control pattern has been found consistent for at least three sites, e.g. Pinus in Changbai Mountain (northeast China), Callitris in Great West Woodland (west Australia), and Juniperus in Hamilton (west USA).

Li Fig 3

Figure 3 Simulation VS observation (example from Juniperus in Hamilton, west USA)

However, missing [CO2] signal was found in the observation for Callitris in GWW. After the time-dependent tuning for all of the parameter, we found only the ratio of fine root mass to foliage area (ζ) is the only one changed during the past century (Figure 4). There is a ~14% increase of root allocation as the fast increase of [CO2] since 1950. This missing [CO2] signal has been found in the past 150 year research for the tropical forests (van der Sleen et al., 2015), where increasing water use efficiency has been found along with the increase of [CO2], but no increase for the radial growth. The [CO2] signal is also missed in the tree ring research (Kienast and Luxmoore, 1988; Gedalof and Berg, 2010).

Li Fig 4

Figure 4 Time-dependent tuning with real [CO2] (example from Callitris in Great West Woodland (west Australia))

In the cold and low [CO2] glacial period, Juniperus needs to reduces ~25% of root carbon allocation (Figure 5) to get the observed ring width from the fossil data, which is quite similar to today’s growth rate. Besides the changing carbon allocation strategy, the glacial climate change also benefit tree growth by reducing photorespiration (decreasing temperature), wetter moisture for both air (increasing relative humidity and decreasing temperature) and soil (increasing precipitation).

Figure 5 Potential strategies for glacial Juniperus to keep the same stem growth in low [CO2] conditions.

Figure 5 Carbon allocation strategy for glacial Junperus grew at the similar rate as today

Figure 5 Carbon allocation strategy for glacial Junperus grew at the similar rate as today

References:

Wang, H., Prentice, I. C., and Davis, T. W.: Biophsyical constraints on gross primary production by the terrestrial biosphere, Biogeosciences, 11, 5987–6001, doi:10.5194/bg-11-5987-2014, 2014.

Li, G., Harrison, S. P., Prentice, I. C., and Falster, D.: Simulation of tree-ring widths with a model for primary production, carbon allocation, and growth, Biogeosciences, 11, 6711– 6724, doi:10.5194/bg-11-6711-2014, 2014.

van der Sleen, P., Groenendijk, P., Vlam, M., Anten, N. P., Boom, A., Bongers, F., Pons, T. L., Terburg, G., and Zuidema, P. A.: No growth stimulation of tropical trees by 150 years of CO2 fertilization but water-use efficiency increased, Nat. Geosci., 8, 24–28, 2015.

Posted in Uncategorized | Leave a comment

How plants take heat from the environment? By Ning Dong

The leaves interact with the above physical environment in two ways: energy & mass exchange. From the biophysical control aspect, leaves can maintain their temperatures within a narrower range than ambient temperatures, which can be predicted by leaf energy balance theory. We present and predict the existence of crossover temperature by field observation (Fig.1 & fig.2) and approximate Priestley-Taylor. From metabolic processes, the temperature has great impact on plants by enzyme kinetics and CO2 drawdown, but plants can acclimate their plant function traits (fig.3 & fig.4) to responsd to the environment temperature change. The double mass of fundamental biophysics and temperature acclimation from recent vegetation model research, therefore leaf temperature and acclimation of traits should be key variables included in future DGVMs.

Fig 1. Diurnal time course of leaf ΔT during the dry season in tropical savanna woodland

Fig 1. Diurnal time course of leaf ΔT during the dry season in tropical savanna woodland

Fig 2. Differences between temperature fields for January and July 2002.  Calculated canopy temperature minus air temperature (left, TCAN-LPJ-TAIR); MODIS 2m surface temperature minus air temperature (right, TCAN OBS-TAIR). (P. N. Foster et al.2014, BG)

Fig 2. Differences between temperature fields for January and July 2002. Calculated canopy temperature minus air temperature (left, TCAN-LPJ-TAIR); MODIS 2m surface temperature minus air temperature (right, TCAN OBS-TAIR). (P. N. Foster et al.2014, BG)

Fig.3. Bivariate linear regressions of Vcmax, Vcmax25 (mol m-2s-1) versus growing season temperature above 0 (˚C)

Fig.3. Bivariate linear regressions of Vcmax, Vcmax25 (mol m-2s-1) versus growing season temperature above 0 (˚C)

Fig.4. Bivariate linear regressions of nature transform Narea (m-2g-1) versus growing season temperature above 0 (˚C)

Fig.4. Bivariate linear regressions of nature transform Narea (m-2g-1) versus growing season temperature above 0 (˚C)

Posted in Uncategorized | Leave a comment

The Fertility Question: How do soil nutrients influence the carbon cycle? by Beni Stocker

A general notion of “soil fertility” is commonly left unaccounted for in models of the terrestrial biosphere and has received relatively little attention in carbon cycle science. This changed since the presentation of results by Vicca et al. (2012) and Fernandez-Martinez et al. (2014). Using data from forests around the globe where gross primary productivity (GPP) has been measured in parallel to changes in ecosystem biomass storage, these authors showed that forests produce biomass more efficiently on fertile soils.

This efficiency can be quantified as the ratio of biomass production (BP) to GPP. Two factors affect this ratio. First, autotrophic respiration (Ra) – the C cost of maintaining cellular function (maintenance respiration) and synthesising new tissue (growth respiration). This factor has been shown previously to cause relatively large variations in CUE (= NPP/GPP = (GPP-Ra)/GPP) due to effects of forest stand age, climate, and forest type. However, Ra doesn’t appear to vary with soil fertility (Fernandez-Martinez et al., 2014). The second factor that determines the ratio of BP:GPP is the amount of C exported to the soil (Cex). There is also a third factor, the production of volatile organic compounds, VOCs, that has been shown to significantly affect the plant’s C budget, especially under heat stress. Let’s ignore this for now. Traditionally, Cex hasn’t commonly been separated out from NPP, and is often left unquantified in field studies. However, more recent research has shown that up to around 20% of NPP may be consumed by Cex. We can now write:

new:    BP = GPP – Ra – Cex – VOC

old:      NPP = GPP – Ra

In parallel to the observed variations on BP:GPP by Vicca et al. (2012) and Fernandez-Martinez et al. (2014), they also found that the ratio of fine root to total biomass was almost three times higher in nutrient poor than in nutrient rich forest, and that the LAI per unit fine-root biomass is twice as large in nutrient rich forests. This is perfectly in line with earlier studies reporting general and strong variations of these allocation patterns along soil fertility gradients (Poorter et al., 2012). Apparently, on low-fertility soils, plants invest more C below-ground. C allocation thus dynamically adjusts in response to soil fertility – a mechanism ignored by today’s generation of global vegetation and terrestrial carbon cycle models altogether.

But where does Cex go and what is its function? Clearly, growing more roots serves a better soil exploration in the search for nutrients required for plant growth (apart from accessing soil moisture and maybe even ground water). In contrast, the exuded C (Cex) may be consumed by an array of soil organisms with the pleasant effect of improving nutrient availability for the plant – either directly (N2-fixing bacteria in root nodules; mycorrhizal fungi living on the root tips) or indirectly (free-living N2-fixing bacteria in the soil; other heterotrophs that accelerate soil turnover and N mineralization, so called priming). The importance of these effects are only now becoming to be fully appreciated and integrated into models of the terrestrial C budget, and a quantitative and mechanistic link to soil fertility is yet to be established – the goal of my work here in the group of Colin Prentice.

The lesson from all this (relatively new) research described above is that the nutrient availability appears to be under clear biological control, but that their acquisition comes at a cost to the plant: C used below-ground (root production and Cex) is missing for above-ground growth and the competition for light. We can say that this cost is dependent on the soil fertility and implies that there must be a soil fertility-dependent balance of above- versus below-ground allocation that is optimal for the plant with respect to maximizing its “fitness”. Such considerations are the target of theoretical studies that bear great potential in providing quantitative predictions of C allocation (Mäkelä et al., 2008; Franklin et al., 2014).

Now two more points that need clarification: 1. What is soil fertility? and 2. How does the concept of soil fertility relate to the classical view of nutrient limitation?

Soil fertility is often indirectly defined by “where plants grow well”. This may also be expressed as “where nutrients are easily available” a condition affected by the soil chemistry and structure in ways that are difficult to describe and predict mechanistically, especially at a global scale. However, there is a set of parameters often measured, directly or indirectly, in the field that appear to determine “soil fertility”. These include pH, the cation exchange capacity, available N and P, C:N ratio of soil organic matter, C content, and the texture (fractions of sand, clay, loam, silt). The soil type itself is something like a “syndrome” expressed by typical values of these measurable parameters, but may also provide readily accessible information. Soil formation is determined by climate, parent material, and time. This is reflected by large scale gradients of soil fertility across different biomes – from infertile, acidic soils in boreal regions; fertile, C rich soils in seasonally cold grasslands where decomposition is inhibited; N-poor but otherwise relatively fertile young soils in temperate region; to highly weathered, i.e. P-poor, old soils in tropical lowlands. Sorption of P to surfaces in the particular soil matrix in tropical soils, leaching of mobile P under high precipitation, and very small inputs through atmospheric deposition gradually deplete soils of P over time in the tropics, while N is gradually added by abundant N fixing organisms. As opposed to such old tropical lowland soils, soils at high latitudes are younger (previous glaciation!) and P is still more readily available, whereas constraints to N fixation prevent its accumulation.

Figure HWSD Global Soil Quality – constraints on nutrient availability (Fisher et al., 2008). Accessed from http://databasin.org/datasets/20dcb500682c4ec891e2fc881c2ed65c

Figure HWSD Global Soil Quality – constraints on nutrient availability (Fisher et al., 2008). Accessed from http://databasin.org/datasets/20dcb500682c4ec891e2fc881c2ed65c

The dominant pattern of N limitation in boreal and temperate biomes and P limitation in tropical biomes is also revealed by classical fertilization experiments. Added N generally stimulates plant growth at higher latitudes (but not so for added P), and vice versa at low latitudes. Such a nutrient limitation concept is implemented in different ESMs: Either GPP or NPP is capped and further plant growth is halted if nutrients (usually just N, sometimes also P) becomes unavailable. This leads to substantially lower predictions of C uptake under rising CO2 and the important negative feedback to anthropogenic climate change (through CO2 emissions) disappears almost completely when applying such models (Wieder et al., 2015). But doesn’t this disagree with the widely observed, and theoretically plausible biological control on nutrient availability as discussed above? I would argue that yes indeed, these predictions are add odds with them! This Liebig Law-type view of nutrient limitation also fails to explain the clear CO2 stimulation of growth under high-N as well as low-N conditions. So how can the observed variations of BP:GPP with soil fertility and nutrient limitation be reconciled?

In my understanding (following the understanding of others of course), nutrient limitation is not a fixed cap, but under biological control, expressable as a “C cost for nutrient uptake”. On infertile soils, this cost is generally higher. Be it because of low pH (which directly affects soil chemistry, nutrient availability and microbial activity) or because of soil texture (affecting nutrient retention, resistance to root growth, etc.) or other parameters. If an ecosystem is “limited” by, for example, N (i.e., no growth stimulation by adding P or elevating CO2), then the cost for taking up N is infinite. In that respect, the concept of a C cost (reflected in Cex and BP:GPP) is a generalization of the Liebig-type limitation concept, and is also able to integrate other factors that determine soil fertility (cost is as a function of pH, …).

So is nutrient limitation just a question of C cost and thus energy availability? (Radiation energy is converted by photosynthesis to labile C, the fuel for any plant function). Maybe this is indeed the case for limitation by N. Obviously, there is no limit (other than energy) to biological control of N availability through converting N2 into reactive forms. In view of the ever-increasing N inputs through atmospheric deposition, reactive N, previously created by fossil fuel combustion, N limitation even seems to be relieved “for free” in affected ecosystems. This is different for P limitation. P doesn’t have a gaseous phase and is merely rock-derived. Also atmospheric deposition is very small, and the acceleration of anthropogenic inputs of reactive P lags behind the one for N (Penuelas et al., 2013). Once made available to plants, P recycling in ecosystems is highly conservative: only very small losses occur because it’s very costly for plants to accrue new P once it’s lost. The N cycle is much more leaky – N seems to be cheaper. Up to 25% of BP is fuelled by new inputs in the tropics (Cleveland et al., 2013). Taken together, it seems like enhanced demand for N under increasing CO2 should be met. No? On the other hand, P may impose much more of a limitation to future C uptake.

The goal of my research is to investigate the C cost for nutrient uptake. What soil fertility parameters have to be taken into account to accurately predict observed gradients in biomass production efficiency and above- versus below-ground allocation patterns. And what is the implication of biological control – taking into account such C cost effects – on the future of the land C sink? Please stay tuned …

References

Vicca, S.; Luyssaert, S.; Peñuelas, J.; Campioli, M.; Chapin, F. S.; Ciais, P.; Heinemeyer, A.; Högberg, P.; Kutsch, W. L.; Law, B. E.; Malhi, Y.; Papale, D.; Piao, S. L.; Reichstein, M.; Schulze, E. D. & Janssens, I. A.Fertile forests produce biomass more efficiently Ecology Letters, Blackwell Publishing Ltd, 2012, 15, 520-526

Fernandez-Martinez, M.; Vicca, S.; Janssens, I. A.; Sardans, J.; Luyssaert, S.; Campioli, M.; Chapin III, F. S.; Ciais, P.; Malhi, Y.; Obersteiner, M.; Papale, D.; Piao, S. L.; Reichstein, M.; Roda, F. & Penuelas, J. Nutrient availability as the key regulator of global forest carbon balance Nature Climate Change, 2014, 4, 471-476

Poorter, H.; Niklas, K. J.; Reich, P. B.; Oleksyn, J.; Poot, P. & Mommer, L. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control New Phytologist, 2012, 193, 30-50

Mäkelä, A.; Valentine, H. T. & Helmisaari, H.-S. Optimal co-allocation of carbon and nitrogen in a forest stand at steady state New Phytologist, 2008, 180, 114-123

Franklin, O.; Näsholm, T.; Högberg, P. & Högberg, M. N. Forests trapped in nitrogen limitation – an ecological market perspective on ectomycorrhizal symbiosis New Phytologist, 2014, 203, 657-666

Wieder, W. R.; Cleveland, C. C.; Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability  Nature Geosci, 2015, 8, 441-444

Peñuelas, J.; Poulter, B.; Sardans, J.; Ciais, P.; van der Velde, M.; Bopp, L.; Boucher, O.; Godderis, Y.; Hinsinger, P.; Llusia, J.; Nardin, E.; Vicca, S.; Obersteiner, M. & Janssens, I. A. Human-induced nitrogen–phosphorus imbalances alter natural and managed ecosystems across the globe Nat Commun, 2013, 4

Cleveland, C. C.; Houlton, B. Z.; Smith, W. K.; Marklein, A. R.; Reed, S. C.; Parton, W.; Del Grosso, S. J. & Running, S. W. Patterns of new versus recycled primary production in the terrestrial biosphere Proceedings of the National Academy of Sciences, 2013, 110, 12733-12737

Fischer, G., F. Nachtergaele, S. Prieler, H.T. van Velthuizen, L. Verelst, D. Wiberg, 2008. Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria and FAO, Rome, Italy.

Posted in Uncategorized | Leave a comment