6.8: Nitrogen Cycle - Biology

Alfalfa, clover, peas, beans, lentils, lupins, mesquite, carob, soy, and peanuts. What are these?

Legumes. Legume plants have the ability to fix atmospheric nitrogen, due to a mutualistic symbiotic relationship with bacteria found in root nodules of these plants.

The Nitrogen Cycle

Nitrogen makes up 78 percent of Earth’s atmosphere. It’s also an important part of living things. Nitrogen is found in proteins, nucleic acids, and chlorophyll. The nitrogen cycle moves nitrogen through the abiotic and biotic parts of ecosystems. Figure below shows how nitrogen cycles through a terrestrial ecosystem. Nitrogen passes through a similar cycle in aquatic ecosystems.

Nitrogen Cycle in a Terrestrial Ecosystem. Nitrogen cycles between the atmosphere and living things.

Even though nitrogen gas makes up most of Earth's atmosphere, plants cannot use this nitrogen gas to make organic compounds for themselves and other organisms. The two nitrogen atoms in a molecule of nitrogen gas are held together by a very stable triple bond. This bond must be broken for the nitrogen to be used. The nitrogen gas must be changed to a form called nitrates, which plants can absorb through their roots. The process of changing nitrogen gas to nitrates is called nitrogen fixation. It is carried out by nitrogen-fixing bacteria. The bacteria live in soil and roots of legumes, such as peas.

When plants and other organisms die, decomposers break down their remains. In the process, they release nitrogen in the form of ammonium ions. This process is calledammonification. Nitrifying bacteria change the ammonium ions into nitrites and nitrates. Some of the nitrates are used by plants. The process of converting ammonium ions to nitrites or nitrates is called nitrification. Still other bacteria, called denitrifying bacteria, convert some of the nitrates in soil back into nitrogen gas in a process called denitrification. The process is the opposite of nitrogen fixation. Denitrification returns nitrogen gas back to the atmosphere, where it can continue the nitrogen cycle.

Summary

• The nitrogen cycle moves nitrogen back and forth between the atmosphere and organisms.
• Bacteria change nitrogen gas from the atmosphere to nitrogen compounds that plants can absorb.
• Other bacteria change nitrogen compounds back to nitrogen gas, which re-enters the atmosphere.

Review

1. Why can't plants use nitrogen gas directly?
2. What is nitrogen fixation?
3. Explain why bacteria are essential parts of the nitrogen cycle.
4. What is ammonification?

Cycles, phase synchronization, and entrainment in single-species phytoplankton populations

Complex dynamics, such as population cycles, can arise when the individual members of a population become synchronized. However, it is an open question how readily and through which mechanisms synchronization-driven cycles can occur in unstructured microbial populations. In experimental chemostats we studied large populations (>10 9 cells) of unicellular phytoplankton that displayed regular, inducible and reproducible population oscillations. Measurements of cell size distributions revealed that progression through the mitotic cycle was synchronized with the population cycles. A mathematical model that accounts for both the cell cycle and population-level processes suggests that cycles occur because individual cells become synchronized by interacting with one another through their common nutrient pool. An external perturbation by direct manipulation of the nutrient availability resulted in phase resetting, unmasking intrinsic oscillations and producing a transient collective cycle as the individuals gradually drift apart. Our study indicates a strong connection between complex within-cell processes and population dynamics, where synchronized cell cycles of unicellular phytoplankton provide sufficient population structure to cause small-amplitude oscillations at the population level.

Phase synchronization is an adjustment of the rhythms of oscillating objects that can lead to the emergence of complex synchronized behavior (1–3), such as periodic color changes of catalytic microparticles (4), the simultaneous flashing of fireflies (5), or the rhythmic clapping of human audiences (6). Similarly, the densities of many ecological populations oscillate with frequencies that cannot be explained by diurnal, annual, or other seasonal variation (7–9). Often, such regular oscillations are caused by multispecies interactions (10–13). Experiments have shown that also single-species populations can undergo regular sustained or damped oscillations (14, 15). “Single-generation cycles” and “delayed-feedback cycles” (16) are types of single-species oscillations that are known to occur when vital rates are density dependent. Here we are concerned with single-species oscillations that occur when individuals synchronize the progression through their life cycles. Synchronization may be caused by locking of individual life cycles to an external force (entrainment), but it may also arise spontaneously through the internal interactions among the individuals (2–4), and can occur in spatially distant populations (10, 13, 17, 18). Populations with obvious internal structure can easily become synchronized by environmental triggers, for example, an insect population that loses all adults to a cold spell before eggs are produced and needs to restart growth on the basis of the surviving larval fraction of the population. In contrast, little is known about the potential for synchronized cycles in microbial populations, despite their important role in all ecosystems across the globe.

In this study, we experimentally induced regular oscillations in populations of unicellular algae that lack distinct life stages other than defined by their cell cycle. The oscillations could be maintained in the absence of external periodic rhythms and can be explained through collective synchronization among a large population of interacting phase oscillators, in agreement with a generalized version of the Kuramoto model (19). Given the causal link between the cell cycle and the cycling of the population, we provide evidence for synchronization of oscillatory dynamics across biological levels of organization.

We ran chemostat experiments with three different unicellular freshwater phytoplankton species and compared the dynamics with those predicted by a mathematical model that allows for nitrogen availability and the nitrogen-dependent progression of phytoplankton cells through their cell cycle (SI Section 1 and 3). To track phytoplankton dynamics in the chemostats we used an automated light extinction measurement system (20) (SI Section 2). This allowed us to collect measurements with a signal sensitivity and temporal resolution (5-min intervals) that is unusually accurate for ecological time-series experiments. In addition, we used a particle counter to determine cell abundance and size distribution (4- to12-h intervals). We used cell volume as a proxy for the phase of the cell cycle in which a phytoplankton cell is located.

Terrestrial nitrogen�rbon cycle interactions at the global scale

Interactions between the terrestrial nitrogen (N) and carbon (C) cycles shape the response of ecosystems to global change. However, the global distribution of nitrogen availability and its importance in global biogeochemistry and biogeochemical interactions with the climate system remain uncertain. Based on projections of a terrestrial biosphere model scaling ecological understanding of nitrogen–carbon cycle interactions to global scales, anthropogenic nitrogen additions since 1860 are estimated to have enriched the terrestrial biosphere by 1.3 Pg N, supporting the sequestration of 11.2 Pg C. Over the same time period, CO₂ fertilization has increased terrestrial carbon storage by 134.0 Pg C, increasing the terrestrial nitrogen stock by 1.2 Pg N. In 2001–2010, terrestrial ecosystems sequestered an estimated total of 27 Tg N yr −1 (1.9 Pg C yr −1 ), of which 10 Tg N yr −1 (0.2 Pg C yr −1 ) are due to anthropogenic nitrogen deposition. Nitrogen availability already limits terrestrial carbon sequestration in the boreal and temperate zone, and will constrain future carbon sequestration in response to CO₂ fertilization (regionally by up to 70% compared with an estimate without considering nitrogen–carbon interactions). This reduced terrestrial carbon uptake will probably dominate the role of the terrestrial nitrogen cycle in the climate system, as it accelerates the accumulation of anthropogenic CO₂ in the atmosphere. However, increases of N₂O emissions owing to anthropogenic nitrogen and climate change (at a rate of approx. 0.5 Tg N yr −1 per 1°C degree climate warming) will add an important long-term climate forcing.

1. Introduction

Nitrogen is a fundamental component of living organisms. Ecosystem available forms of nitrogen (ammonium, as well as nitrate among other oxidized nitrogen forms), hereafter reactive N (N_r), are scarce in unperturbed ecosystems owing to low atmospheric inputs, the high energetic costs of assimilating elementary N₂ through biological fixation and nitrogen losses to leaching and volatilization, particularly after disturbances [1]. The productivity of plants and soil organisms strongly depends on nitrogen, imposing stoichiometric constraints at the level of an individual organism. These two facts lead to a tight coupling of the terrestrial nitrogen and carbon cycles, as evidenced by the constrained flexibility of ecosystem C : N stoichiometry [2]. N availability thereby plays an important role in controlling the productivity, structure and spatio-temporal dynamics of terrestrial ecosystems: perturbations in the nitrogen cycle will have repercussions in the carbon cycle, and vice versa.

The terrestrial biogeochemical cycles have been disturbed in the past by human actions altering land cover and land-use, by increasing the atmospheric abundance of CO₂, and by doubling the inputs of N_r through the burning of fossil fuel and the creation of agricultural fertilizer since 1860 [3,4]. These anthropogenic changes must have had consequences for the terrestrial store and turnover of nitrogen and carbon. However, because of the uncertainty in (i) the global distribution of nitrogen availability and demand in terrestrial ecosystems, (ii) the capacity of the terrestrial biosphere to retain added nitrogen and (iii) the tightness of the coupling between the terrestrial nitrogen and carbon cycles, these consequences are not well understood. The regional distribution of the anthropogenic perturbation is also important to take into account, as the fertilization by anthropogenic CO₂—even if regionally constrained by nitrogen availability—is ubiquitous, whereas high levels of anthropogenic N_r only affect a small fraction of the global land surface, and land-use changes mainly act locally.

Quantifying the changes in the terrestrial carbon and nitrogen budgets is relevant not only to understand the fate of the anthropogenic N_r, and the cascading effects of this nitrogen, but also because these changes matter for the climate system [4]. Limited natural N availability reduces the carbon storage potential of the terrestrial biosphere. Anthropogenic N_r deposition generally increases terrestrial C sequestration and thus decreases the rate of anthropogenic CO₂ accumulation in the atmosphere, but at the same time enhances nitrogen losses for instance to the greenhouse gas N₂O, which might compensate for the C-cycle-related climate benefit [5,6]. This is important because the long atmospheric lifetime of the N₂O can transform even subtle but long-term changes in terrestrial emission into a significant climate forcing.

The objective of this paper is to provide an assessment of the present and future nitrogen–carbon cycle interactions with a focus on the role of the natural and perturbed terrestrial nitrogen cycles in shaping the terrestrial net carbon and nitrogen balance and terrestrial carbon–climate feedbacks. A suite of new global ecosystem models that integrates current ecological and biogeochemical understanding with process-based descriptions of the terrestrial energy and water balance at a comparatively high spatial resolution is now available for such a task [7]. However, no systematic and comprehensive analyses have been performed so far with several models that would allow for a systematic model synthesis. I therefore present past, present and future nitrogen and carbon budgets based on one model only, the O–CN model [6,8], and discuss the uncertainties related to the application of this model in the light of other modelling studies and independent estimates.

2. Material and methods

(a) The O–CN model

O–CN [6,8] is a terrestrial biosphere model, which has been developed from the land surface model ORCHIDEE [9], and describes the nitrogen and carbon fluxes and stocks of vegetation and soil organic matter for 10 natural plant functional types, as well as C3 and C4 croplands at a half hourly time scale. The biogeochemical fluxes are tightly coupled to the calculations of the terrestrial energy and water balance. Nitrogen availability directly controls photosynthesis and respiration of vegetation through tissue nitrogen concentrations and effects on plant allocation (e.g. the root : shoot ratio), and thus foliage area and root growth. Nitrogen availability also affects the temperature-sensitive rate of organic matter decomposition and the net mineralization of nitrogen. The stoichiometry of plant tissues, litter and soil organic matter varies prognostically within observed limits, depending on the relative availabilities of nitrogen and carbon. The modelled ecosystem receives N_r inputs from biological nitrogen fixation and atmospheric N_r deposition, and simulates losses of nitrogen to leaching and volatilization based on the process-based simulation of nitrification and denitrification. Fertilizer is applied to the cropland fraction of each model grid cell at distinct dates during the growing season, but the treatment of cropland management and biomass removal is very simple, and manure systems are not taken into account. O–CN does not simulate the industrial sources and atmospheric transport of N_r. The model has been evaluated and applied to study the interactions of nitrogen and carbon cycling over the past few decades, and was found to simulate carbon and nitrogen fluxes that are generally commensurate with current understanding [6,8,10,11].

(b) Modelling protocol

O–CN was applied at a 3.75° × 2.5° spatial resolution. The model was brought into steady-state for 1860 conditions and then run transiently in a factorial design to identify the contribution of the individual driving forces. To isolate the effects of nitrogen dynamics, the model has been run twice, once with explicit accounting for nitrogen dynamics (referred to as O–CN), and once with nitrogen concentrations set to global averages of observed values (referred to as O–C), such that plant productivity and soil organic matter decomposition correspond to an ecosystem with average nitrogen availability not taking account of the spatial–temporal patterns of N availability. Two sets of simulations were performed: a ‘historic’ run driven by observed or reconstructed changes in land use, climate, atmospheric CO₂, cropland fertilization and atmospheric deposition (1860–2010), and a ‘future’ run (1860–2100) with a reduced set of forcings (climate, atmospheric CO₂ and N_r deposition) for the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 scenario.

(c) Datasets

(i) Historic run

The climate forcing (1901–2010) was taken from the CRU-NCEP dataset (v. 4 [12]) in the spatially degraded form (at 3.75° × 2.5° spatial resolution) provided by C. Huntingford (2012, personal communication). Gridded time series of cropland fertilization rates [6] and annual land-use changes [13] were used for the period 1860–2005, and assumed constant thereafter. Biological nitrogen fixation in natural vegetation was prescribed based on a climatology developed after Cleveland et al. [11,14]. To assess the uncertainty related to the estimation of nitrogen deposition, decadal time slices of monthly nitrogen deposition fields were obtained from two atmospheric chemistry transport models (CTMs), TM5 [15] and NCAR-CTM [16], and linearly interpolated to arrive at annual values. No estimates beyond 2000 were available for TM5. These were constructed by extrapolating the TM5 estimate for 2000 to 2001–2010 using the grid-cell wise monthly trends of the NCAR-CTM.

(ii) Future run

The future projections are described in detail by Zaehle et al. [10]. The simulations were forced with the SRES A2 climate and atmospheric CO₂ change scenario of the IPSL-CM4 climate model [17], and a nitrogen deposition scenario, which increases the deposition on land from 10 Tg N yr −1 (1860) to 51 Tg N yr −1 (1993) and 106 Tg N yr −1 (2050), after which it was assumed to be constant [3]. This roughly follows the upper boundary of the representative concentration pathway (RCP) nitrogen deposition scenarios [18].

3. Results

(a) Current global terrestrial nitrogen and carbon budget

The contemporary nitrogen and carbon budget displayed in figure 1 (see also table 1) for 2001–2010 is based on the ‘unperturbed’ state of the cycles (1860s) and the historic changes in land cover, climate, atmospheric CO₂ abundance and anthropogenic N_r inputs from atmospheric deposition and fertilizer application between the years 1860 and 2010.

Table 1. Global and continental carbon and nitrogen budgets for the years 2001–2010 derived from the O–CN simulations driven with NCAR (TM5) nitrogen deposition fields. BNF, biological nitrogen fixation GPP, gross primary production NBP, net biome production = net ecosystem production − anthropogenic C losses.

Figure 1. The 2001–2010 global carbon and nitrogen cycles of terrestrial ecosystems. The values in parentheses are the changes from the pre-industrial equilibrium fluxes (1860s) owing to land-use, climate and atmospheric CO₂ change (blue) and anthropogenic nitrogen additions (red). Carbon fluxes: Pg C yr −1 nitrogen fluxes: Tg N yr −1 . NO_x, N₂O and N₂ emissions are from soils only. R_a, R_r and R_h are autotrophic, rhizosphere and heterotrophic respiration, respectively. BNF, biological nitrogen fixation GPP, gross primary production.

(i) Nitrogen budget

During 2001–2010, direct (N_r additions) or indirect (land-use change, climate change and increase in atmospheric CO₂) anthropogenic factors are responsible for 0.5 Pg N of the nitrogen stored in vegetation (15% of the global total), and for 1.6 Pg N (2%) stored in soils and litter (excluding wetlands and permafrost soils figure 2). The most significant cause for vegetation N changes prior to the 1960s has been forest clearance, which has only partly been compensated by increased sequestration owing to CO₂ fertilization. Anthropogenic N_r plays an increasing role after 1960, but remains only a modest cause of additional N stored in vegetation compared with the other drivers. Conversely, anthropogenic N_r substantially increases soil organic N—partly by decreasing the soil C : N—contributing thereby the largest share of the significant increase in soil N, whereas the effects of climatic, atmospheric CO₂ and land-use changes on soil N storage largely cancel out.

Figure 2. Estimated development of the terrestrial nitrogen stores in (a) vegetation and (b) litter and soil organic matter. The blue line marks the changes from land-use change alone, the shaded areas indicate the changes due to individual driving forces and the black line denotes the total change of the respective nitrogen pool.

The average 2001–2010 rate of terrestrial nitrogen sequestration (27 Tg N yr −1 , figure 3) is a very small fraction of annual global terrestrial nitrogen turnover (about 800 Tg N yr −1 ). This estimate is somewhat smaller than the 60 Tg N yr −1 estimate by Galloway et al. [3] for the 1990s. However, Galloway did not separate sequestration from N exports owing to land use and land cover change (15 Tg N yr −1 ), which compares well with the export number simulated by the ISAM-CN terrestrial biosphere model (15.6 Tg N yr −1 [19]). The contribution of global N_r deposition to the 2001–2010 N sequestration is 10 Tg N yr −1 (figure 3), which is very close to the estimate of 9 Tg N yr −1 by Schlesinger [20], based on the assumption that 50 per cent of the deposited N over forests would be sequestered. In O–CN, there is a large spatial gradient with close to 100 per cent retention in nutrient poor boreal systems and nearly no retention in nitrogen-saturated tropical and temperate ecosystems. Nitrogen retention is estimated to have declined globally from about 50 per cent in 1860 to 30 per cent at present. The estimated retention rate peaked in the 1980s with about 16 Tg N yr −1 , and remained high until the early 1990s when N deposition started to decline regionally (e.g. in Central Europe), and highly polluted ecosystems reached saturation. O–CN predicts gradually increasing terrestrial N losses since the 1950s, stagnating at year 2000 levels as a consequence of the estimate of declining global nitrogen deposition in 2001–2010 simulated by NCAR-CTM. This is a significant difference to cropland ecosystems, which show a modest and stable N sequestration rate since the 1980s, but strongly increasing leaching and volatilization losses with increasing fertilizer consumption.

Figure 3. Estimated development of the terrestrial nitrogen balance due to (a) anthropogenic N_r deposition and (b) fertilizer application. The grey line marks the changes in the net balance induced by changes in atmospheric CO₂ abundance and climate.

(ii) Nitrogen–carbon couplings

The spatial pattern of N availability shows a strong latitudinal gradient, which is regionally dominated by the signature of the human N_r perturbation due to deposition and fertilizers. Figure 4 displays the resulting patterns of contemporary N limitation of vegetation growth and carbon storage, which follows closely the pattern of N availability: the naturally high N limitation in the boreal and temperate zone due to low natural N fixation is regionally dominated by anthropogenic N_r inputs. This regional pattern is consistent with current understanding [2,11], but difficult to evaluate quantitatively because of the lack of suitable observations. Hyper-spectral remote sensing might be one way forward, as it provides a direct measure of chlorophyll. However, a range of complicating factors in interpreting these data hamper their application at present [21].

Figure 4. Average estimates and effect of nitrogen dynamics on (a,b) foliar nitrogen concentration, (c,d) net primary production and (e,f) living biomass for the years 2001–2010, as simulated by O–CN. The effect of N dynamics are expressed as per cent deviation between the O–CN and O–C model estimates.

Nitrogen additions to the terrestrial biosphere have increased global productivity by an estimated 2.6 Pg C yr −1 , which corresponds to 2 per cent of the global annual total production and 12 per cent of the increase since pre-industrial times (table 2). About 0.2 Pg C yr −1 of this increased production is sequestered in the terrestrial biosphere, corresponding to 10–20 per cent of the global net land carbon uptake (table 2). Earlier studies based on simple biogeochemical models and upscaling of field-based carbon-sequestration estimates have estimated C sequestration based on N deposition estimates as 0.4–0.7 Pg C yr −1 in 1990 [5,22]. The estimate of the process-based O–CN model applied here is somewhat lower, but within the range of model simulations with the current generation of carbon–nitrogen cycle models (0.2–0.6 Tg N yr −1 [7]). Over the period 1860–2010, 1.3 Pg N of the added anthropogenic nitrogen caused an increase of the terrestrial C stocks by 11.3 Pg C (table 2). The tight stoichiometry of this new material results from the large share of N sequestration in soils with a low C : N ratio as well as increases in tissue and soil N concentrations. The anthropogenic N_r additions thus enrich the biosphere with nitrogen relative to carbon. This is a striking difference to the consequences CO₂ fertilization, which leads to the sequestration of 135 Pg C, but only 1.2 Pg N, predominantly in vegetation.

Table 2. Attribution of the changes in the global nitrogen budget from 1860 to 2010 due to changes in land cover and land-use (‘LUCC’), increased atmospheric CO₂ abundance (‘CO₂’), climatic variability and changes (‘climate’), anthropogenic reactive nitrogen additions (‘deposition’) and industrial fertilizer application (‘fertilizer’). Note that this analysis does not take account of manure additions. Land-use emissions in 2000–2010 are an underestimate, as the dataset for land-use changes stops in 2005 [13]. Values are reported from simulations driven with the NCAR (TM5) nitrogen deposition fields. GPP, Gross primary production NBP, net biome production = net ecosystem production – anthropogenic C losses.

The additional carbon sequestration due to anthropogenic N_r additions has a perceivable but small cooling effect for the climate system, as it reduces the rate of atmospheric CO₂ accumulation due to fossil-fuel burning. The nitrogen–carbon cycle interactions have further climate-relevant consequences, as increased plant N uptake due to CO₂ fertilization reduces nitrogen losses globally, including the terrestrial N₂O emissions from soils (table 2). This counteracts the strong simulated increase in terrestrial N₂O emissions due to recent climate changes (0.8 Tg N yr −1 corresponding to an increase of 13% relative to pre-industrial conditions). Warmer temperatures will enhance nitrogen cycling and probably also N₂O production where N is not limiting [23]. However, there is mixed empirical evidence from ecosystem warming experiments, which show varying responses of soil N₂O emissions, resulting from the concurrent effects of changes in the moisture regime, plant and microbial N demand and biodiversity [24–27]. In agreement with earlier studies [5,6], the dominant cause for the estimated increase in terrestrial N₂O emissions is anthropogenic N_r inputs (table 2), which reduce or even overcompensate the climatic benefits from carbon sequestration in response to anthropogenic N_r inputs. There have also been slight increases in NO_x emissions from natural and fertilized soils (table 2), with as yet unquantified effects on the climate system. However, this anthropogenic soil NO_x source remains small compared with the anthropogenic NO_x from combustion sources, which globally has a strongly negative effect on the climate forcing [28]. While these changes all matter to the climate system, the net effect of anthropogenic N_r on the climate system is still unknown [29].

(b) Future projections of coupled nitrogen–carbon cycle dynamics

Figure 5 illustrates the development of nitrogen limitation on terrestrial plant production and carbon sequestration between 1950 and 2100, based on projections with the SRES A″ scenario. Increasing atmospheric CO₂ enhances plant productivity and, therefore, N demand, which increases nitrogen limitation, as the higher demand cannot be completely met by reduced nitrogen losses, increasing N deposition or biological N fixation. This additional limitation is most pronounced in the boreal zone, where N constraints attenuate the direct CO₂ fertilization effect on plant production by more than 50 per cent and on carbon sequestration by nearly 80 per cent (in the year 2100) relative to a projection not taking N limitation explicitly into account (figure 5e,f). These projections are broadly consistent with the importance of a nitrogen constraint in free-air CO₂ enrichment experiments [30,31], and the simulated geographical distribution of nitrogen limitation (figure 4).

Figure 5. Estimated reduction in (a,c,e) terrestrial net primary production and (b,d,f) terrestrial carbon sequestration due to explicitly accounting for N dynamics under the SRES A2 scenario, expressed as difference between the O–CN and O–C simulations. (a,b) The global total, (c,d) the temperate and (e,f) the boreal latitudinal bands. The percentage numbers in (b,d,f) refer to the relative difference between the model simulations with and without N dynamics. Blue lines, CO₂ red lines, climate yellow lines, N-deposition black lines, all factors.

Consistent with observational evidence from a temperate forest soil warming study [32], warming increases carbon sequestration because of the remineralization of nitrogen from soil, which fertilizes the vegetation and thus increases accumulation of biomass. This climate effect is simulated to have increased productivity during most of the twenty-first century in the boreal and temperate zones, but the global effect is rather small because of opposite trends in tropical regions related to increased respiration costs. The same processes operate in two other global modelling studies [33,34]. However, these two studies suggest a stronger positive effect from climate change, such that in these studies the total carbon balance in the year 2100 is changed from a negative carbon balance to a positive carbon balance owing to the considerations of nitrogen–carbon cycle interactions.

Nitrogen deposition is estimated here to play only a small role in future carbon uptake (figure 5), as also reported by a simulation with the CLM4 terrestrial biosphere model [33]. The C sequestration resulting from anthropogenic N_r deposition (27 Pg C, sequestering also 3.9 Pg N) is slightly less than the sequestration resulting from climate change induced remineralization of soil N and enhanced vegetation growth (44 Pg C, recapturing 1.2 Pg N). A thorough analysis of the effects of future N_r deposition is still lacking. However, given these results, N_r deposition needs to be a component of future global carbon-cycle projections.

N limitation of CO₂ fertilization dominates the estimated long-term trend of terrestrial carbon sequestration at all latitudinal bands (figure 5), consistent with two other independent modelling studies [33,34]. The dominance of the reduced CO₂ fertilization due to N limitation has important consequences for projections of future climate changes with interactive biogeochemical cycling: neglecting an explicit treatment of N dynamics in coupled carbon-cycle climate modelling studies such as the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP) [35] will lead to an underestimation of the build-up of fossil CO₂ in the atmosphere [7]. For the O–CN model and the SRES A2 scenario, nitrogen dynamics reduce global carbon sequestration between 1860 and 2100 by 164 Pg C (358 Pg C for the CO₂ fertilization effect only), because of a regional nitrogen deficit of 5.7 (12.0) Pg N. Depending on whether the radiative forcing in Earth system models is prescribed (RCP-type forcing) or calculated based on the greenhouse gas and aerosol burden of the atmosphere, neglecting nitrogen–carbon cycle interactions will lead to an underestimation of the need for emission reductions of carbon-sequestration efforts to meet a certain radiative forcing pathway, or the rate of climate change, respectively. Remineralization of nitrogen due to accelerated soil organic matter turnover, and deposition of reactive nitrogen deposition are not strong enough to counteract this phenomenon, even though they lead to increased carbon sequestration.

The future changes in the terrestrial nitrogen and carbon balance also induce changes in NO_x and N₂O emissions from soils. O–CN suggests a change of +3.1 (−0.8) Tg N yr −1 from pre-industrial to 2100 soil N₂O emissions due to climate change (CO₂ fertilization), with similar changes occurring also for the terrestrial soil NO_x source. This results implies a positive terrestrial N₂O-climate feedback of 0.54 Tg N yr −1 K −1 , which would be weakened by a smaller negative carbon-concentration–N₂O feedback. However, one should place limited confidence in this estimate from one model and one scenario. A feedback of this magnitude would be important enough to require further consideration in coupled biogeochemistry–climate models, even though the biospheric feedback might, as with anthropogenic CO₂, be small compared with future anthropogenic emissions of N₂O from managed ecosystems [36].

4. Discussion

This study provides an advance over previous assessments [3,20], as it relies on a process-based ecosystem model that integrates the key carbon–nitrogen cycle interactions and their coupling to biogeophysical processes, while considering the impacts of atmospheric (climate, CO₂) and land cover changes. Tables 1 and 2 provide an assessment of the uncertainties related to estimates in nitrogen deposition, and show that the simulated trends and spatial patterns are reasonably robust against these uncertainties. Regionally important ecosystem types (e.g. wetland and peatland ecosystems [37]), land management characteristics (such as nitrogen efficient farming, manure-based agriculture [38]) and effects of N_r-related air pollution (such as tropospheric ozone [39]) have been neglected, because they cannot be simulated by the current version of O–CN, but they might nonetheless be globally significant.

The increased complexity of the analyses introduces new uncertainties. While the simulated trends are considered robust, other carbon–nitrogen cycle models may give notably differing estimates. Key uncertainties in the modelling include: (i) the response of canopy-level photosynthesis to nitrogen additions (ii) changes in the allocation patterns (root : shoot ratio) (iii) the competition of plants and soil microbes for the added (or reduced) amount of nitrogen, and therefore the temporal dynamics of the fate of the added N (iv) the change of ecosystem stoichiometry over time (v) responses of and controls on biological N fixation and (iv) the fraction of N that is exported from ecosystems. Evaluation against ecosystem manipulation experiments, which were part of the O–CN model evaluation [8,10], help to understand whether the model's sensitivities to perturbations are adequate. However, the interpretation of these experiments is complex, and their regional representativity unclear, such that, whereas O–CN's sensitivities appear reasonable, large uncertainties remain in the modelled responses, requiring further assessments.

Another factor omitted in this assessment is the co-limitation of the terrestrial nitrogen and carbon cycles by phosphorus. Plants have evolved strategies to access soil P using phosphatase exudation, such that P limitation mainly occurs on old, deeply weathered and P-deprived soils [40]. The results presented here are consistent with the hypothesis [41,42] that temperate and boreal ecosystems are limited by N, whereas moist tropics are not. Given that most of the anthropogenic perturbation of the nitrogen cycle so far occurred in predominantly N-limited regions, it is unlikely that the analyses of the fate of anthropogenic N and its consequences for the carbon cycle would be dramatically altered when accounting for the phosphorus cycle. However, future projections of the global carbon cycle will be different in regions where P limitation prevails.

5. Concluding remarks

The estimates presented in this study result from a state-of-the-art terrestrial biosphere model integrating biophysical, biogeochemical and ecological process understanding. There is considerable uncertainty in any such model and a systematic assessment of nitrogen–carbon cycle interactions by an ensemble of such models seems the logical next step to take. Nonetheless, some conclusions appear robust:

— anthropogenic N_r additions currently enhance nitrogen and carbon sequestration in the biosphere (figure 1), but cause at the same time increased emissions of NO_x and N₂O from soils. Each of the factors is large enough to matter to the climate system, but the net climatic effect is still uncertain

— nitrogen is limiting terrestrial productivity in many ecosystems, and therefore the capacity of the terrestrial biosphere to sequester carbon in response to increased atmospheric abundance of CO₂

— regional and global strategies for increasing terrestrial carbon storage in either woody biomass or soils need to consider the consequences for nutrient cycling and anticipate the effects of nutrient limitation when discussing the effectiveness of different measures and

— future projections of the global carbon cycle will underestimate the fraction of anthropogenic fossil-fuel-based CO₂ emissions remaining in the atmosphere, unless nitrogen dynamics are taken into account. Because of the tight coupling of the terrestrial nitrogen and carbon cycles and their interactions with climate, nitrogen dynamics need to be accounted for interactively in the next generation of Earth system models designed for long-term studies of biogeochemical–climate interactions.

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Soil conditioning: A step by step guide

Soil pH is an excellent chemical indicator of soil condition (quality and its ability to avail both macro and micro nutrients to the crop) on top of other soil structural quality properties. The soil pH also affects microbial activities in the soil which can impact crop growth and yield.

Why is soil sampling and analysis important?

The objectives of soil sampling and analysis are to:

• determine the average nutrient status in a field
• determine pH and recommend soil conditioning
• determine clay content for herbicide application, determining crop types to be grown and for irrigation purposes
• to obtain a measure of nutrient variability in the field. When the variability is known, fertilizer application can be adjusted to more closely meet the supplemental nutrient needs of a crop for specific field areas. Correct fertilizer nutrient use can result in increased yield, reduced cost, and reduced potential environmental pollution.
• It is important to sample soils for analysis after every 3-4 years depending on soil type.

How to sample soils?

• The most commonly used procedure for soil sampling would be based on soil type.
• Fields are split into sampling blocks that contain similar soils e.g. block A, B, C and so on.
• Hillsides are kept separate from bottoms since the soil types will vary greatly.
• Soil survey maps, if applicable, can help organize the soil types throughout the sampling area. Samples will not necessarily need to be collected for every soil type however, similar soils should be kept together.
• The zig zag, random, the cross diagonal methods are commonly used and recommended where samples are taken in a zig zag or at cross diagonal format from a block. This will result in a sample which scientifically represents the whole block.
• The sampling block will be dependent on the soils and topography. Generally, a block of 10-20 ha is considered the maximum size.
• Smaller sampling blocks may be needed if the soils are quite variable or a production problem is apparent and evident.
• Once the sampling block is determined, a sufficient number of sites/cores should be taken to acquire a representative sample. This is generally 10 to 20 sites. The depth of sample for surface soils would be about 20cm or as deep as the primary tillage or specifically as deep as the root zone of intended crop(s). This is also called the tillage layer.
• The most commonly used tools for taking samples are augers, probes, hoes or sometimes shovels.
• Samples from different sites in a block are then mixed thoroughly and bagged into a khaki pocket and labelled. The samples should be sun dried to remove moisture before bagged. Information on the labels should include farmer name, farm name, contact details, block name, date taken and intended crop before they are submitted for analysis to approved laboratories.

Example: Random sampling method

When to sample soils?

• Winter (just after harvesting a summer crop) is the ideal time for soil sampling except for testing for nitrate-nitrogen in sandy soils.
• Winter sampling allows more time to get the results from the testing laboratory and avoids the busy laboratory schedule in the spring.
• Getting results on time will also allow time for actioning of the recommendations e.g. if lime is to be applied then the best time is 3 – 6 months before crop establishment and concurrently with winter tillage.
• Mid or late summer is the appropriate time to collect soil samples for winter wheat.
• Phosphorus level in the soil should be determined prior to seeding winter wheat.
• Nitrate-nitrogen tests made prior to planting winter wheat help predict nitrogen fertilizer needs for the crop.
• It is recommended to take soil samples after 2 to 4 years of land use to determine pH and other soil quality aspects.

What are Acid Soils?

• These are soils with a pH measure of less than 7 on a Calcium Chloride Scale in Zimbabwe. These soils contain high levels of active hydrogen and or aluminum in relation to calcium and magnesium levels.
• Farmers can improve the soil quality of acid soils by liming to adjust pH to the levels needed by the crop to be grown.
• Soil pH is the measure of the acidity or alkalinity of the soil. The degree of acidity or alkalinity is determined by measuring the concentration of the hydrogen ions in the soil solution. This is expressed in terms of a scale with a range of 0 to 14.
• A soil with a pH of 7 is considered neutral while less than 6 is considered acid and a soil with pH greater than 7 is considered alkaline.

What causes soils to be acidic?

• Soils may become more acid as a result of harvested crops removing bases such calcium and magnesium from the soil. This is a normal and natural process. Different crops remove different amounts of Calcium and Magnesium from the soil.
• Rainfall also affects soil pH, whereby water passing through the soil leaches basic nutrients such as Calcium and Magnesium beyond the root zone into drainage water replacing them with acidic elements such as Hydrogen, Manganese and Aluminum and thereby acidifying the soil.
• Application of nitrogen fertilizers e.g. Ammonium Nitrate or Urea and, to a lesser extent, basal fertilisers, contribute to soil acidity by nitrification of ammonium to nitrate through a process which releases hydrogen ions.
• Organic matter breaks down naturally in the soil and hydrogen ions are released, which causes an increase in soil acidity. Plants release hydrogen ions to the soil which contributes to the soil acidity.

Why does soil acidity matter to crop productivity?

• Toxicity to crop: as the pH decreases below 5.5, the availability of aluminum and manganese increase and may reach a point of toxicity to the plant.
• Excess aluminium ions in the soil solution interfere with root growth and function, as well as restricting plant uptake of certain nutrients.
• Effect on phosphorus availability: Acid soils cause phosphorus to form insoluble compounds with aluminium and iron. Liming of soils with low pH dissolves these insoluble compounds and allows phosphorus to be more available for plant uptake
• Micronutrient availability: Acidic soils affect the availability of micronutrients in the soil and ultimately general crop development and productivity Soil organisms: Some micro-organisms e.g. important bacteria and fungi in the soil associated with nitrification require a certain soil pH level to function efficiently in acidic soils (low pH).
• Soil physical condition: Liming improve soil physical structure by reducing soil crusting/capping and this promotes better emergence of small seeded crops and ultimately result in better crop stands. Remember population stand is key in attaining higher yields generally in all crops
• NPK uptake efficiency can also be affected

The following table illustrates NPK uptake efficiency vs pH levels: This is critical information

4. Experimental Section

4.1. Samples

Reservoir sediment samples were collected from the Zhoucun drinking water reservoir (34끖'38.74''N, 117끁'14.13''E). In June 2011, surface sediments were collected at a deep layer of 0 to 10 cm using a sterilized Petersen stainless steel grab sampler [38,40]. The reservoir source water was sampled. The samples were stored in black plastic bags at 4 ଌ, and transferred to the Key Laboratory of Northwest Water Resource, Environment and Ecology, Xi𠆚n University of Architecture and Technology.

4.2. Enrichment Cultures and Isolation of Aerobic Denitrifiers

The 100 mL sludge sample was added into 700 mL heterotrophic enrichment denitrification broth medium (HEDM) at pH 7.0𠄷.5: CH₃COONa (0.5 g/L) NaNO₃ (0.1 g/L) K₂HPO₄୳H₂O (0.1 g/L) CaCl₂ (0.05 g/L) MgCl₂୶H₂O (0.05 g/L) [38,40]. Every three days we removed the liquid medium, reduced the concentration of the medium by one-tenth, and put the new medium into the sludge sample, until the concentration of the HEDM became one-tenth the first concentration. The enrichment of aerobic denitrifiers lasted almost one month [59]. The temperature and DO of the enrichment cultures were controlled at room temperature and nearly 5 mg/L. The enrichment sludge suspension was sampled via gradient dilution, and the gradient dilutions were carried on as follows: 10 𢄡 dilution (1 mL enrichment sludge suspension added to 9 mL sterile distilled water) 10 𢄢 dilution (1 mL 10 𢄡 dilution suspension added to 9 mL sterile distilled water) 10 𢄣 dilution (1 mL 10 𢄢 dilution suspension added to 9 mL sterile distilled water) 10 𢄤 dilution (1 mL 10 𢄣 dilution suspension added to 9 mL sterile distilled water) 10 𢄥 dilution (1 mL 10 𢄤 dilution suspension added to 9 mL sterile distilled water) 10 𢄦 dilution (1 mL 10 𢄥 dilution suspension added to 9 mL sterile distilled water) 10 𢄧 dilution (1 mL 10 𢄦 dilution suspension added to 9 mL sterile distilled water). The resultant bacterial suspension was streaked on a screening medium plates and incubation temperature of 30 ଌ for 3 days. A screening medium (SM) [36] plate at agar (20 g/L) pH 7.0𠄷.5 CH₃COONa (0.1 g/L) NaNO₃ (0.02 g/L) K₂HPO₄୳H₂O (0.02 g/L) CaCl₂ (0.01 g/L) MgCl₂୶H₂O (0.01 g/L). Separate colonies were picked and purified by repeated streaking on fresh agar plates. The isolates were harvested and cultivated in SM medium with NaNO₃ as the sole nitrogen source in order to detect the aerobic denitrifying bacteria performance. An isolate N299 with high nitrogen removal efficiency was obtained in this study, and SM slant medium at 4 ଌ and on SM Glycerin medium at � ଌ.

4.3. Analysis of 16S rRNA Gene Sequence

The 16S rRNA sequence of Zoogloea sp. N299 was obtained via PCR. The PCR used the primers [60]: 7F 5'-CAGAGTTGATCCTGGCT-3', and 1540R 5'-AGGAGGTGATCCAGCCGCA-3'. The PCR reaction mix consisted of the following reagents, which were extracted and sequenced by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China): 5× Buffer (with Mg 2+ ) (2.5 μL), DNA template (0.5 μL), dNTP (each 2.5 mM), Taq DNA Polymerase (0.2 μL), and sterile nuclease free water to 25 μL. The PCR was carried out as follows: 94 ଌ for 4 min for one cycle and then 30 cycles of denaturation at 94 ଌ for 45 s, annealing at 55 ଌ for 15 s, and extension at 72 ଌ for 1 min. After a final extension at 72 ଌ for 10 min, reactions were stored at 4 ଌ. The Seqman was used to align the sequences. Homology searching of the sequences in GenBank was performed using BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi). A neighbor-joining tree was constructed in MEGA5.0 program using neighbor-joining (NJ) method with the maximum composite likelihood model and 1000 bootstrap replicates [35]. Culture strains highly similar to the genera are listed in Figure 2 . Finally, The sequence of the N299 strain has been submitted into GenBank for its accession number the strain was also deposited to the China General Microbiological Culture Collection Center (CGMCC).

4.4. Amplification of the napA Gene

The napA was amplified with the forward primers NAP1: 5'-TCTGGACCATGGGCTTCAACCA-3' [48], and NAP2: 5'-ACGACGACCGGCCAGCGCAG-3' [48]. The PCR reaction mix (50 μL) consisted of 2× Taq Mastermix (0.1 U Taq Polymerase/μL), 500 μM dNTP, 20 mM Tris-HCl (pH 8.3), 100 mM KCl, 3 mM MgCl₂ (25 μL), NAP1 (1 μL), NAP2 (1 μL), and sterile nuclease-free water to 50 μL. Conditions used for this reaction were as per [48]: initial start at 94 ଌ for five min for one cycle, followed by 30 cycles of denaturation at 94 ଌ for 30 s, annealing at 59 ଌ for 30 s, and extension at 72 ଌ for 1 min. After a final extension at 72 ଌ for 7 min, reactions were stored at 4 ଌ. PCR products were separated using 1.0% agarose gel electrophoresis and then stained with ethidium bromide for visualization.

4.5. Growth Characteristics of the Zoogloea sp. N299

The growth characteristics of the isolated strain N299 were determined through measuring OD₅₁₀ in a shake flask experiment in which 400 mL of the liquid SM medium was put in 1000 mL shake flasks, inoculated with 4 mL of strain pre-culture, and then cultivated at 30 ଌ. During incubation, 3 mL culture was removed periodically for the determination of cell optical density. The aerobic denitrifying bacteria N299 was pre-cultured for 24 h in 50 mL liquid SM medium (without agar) in a 100 mL Erlenmeyer flask at 30 ଌ and 120 rpm in order to be activated [36]. According to the study conducted by Duu-Jong Lee [51], the logistic growth equation describes the cell growth curve:

where t is time (h) y(t) is bacterial cell density at t h (OD) μ is the maximum specific cell growth rate (h 𢄡 ) and a is the maximum bacterial cell density (OD) c is the bacterial cell density (t = 0). Correlation analysis using OriginPro (Ver. 8.0, OriginLab Corporation, Northampton, MA, USA) yielded.

4.6. Nitrogen Removal Performance in Pure Culture Medium System

The pre-cultured N299 strain was inoculated in 10% (v/v) into 150 mL liquid SM, short-SM, and HNM of 250 mL Erlenmeyer flask at 30 ଌ, 120 rpm, respectively. The nitrate, nitrite, TN, TDN, TP, TOC concentrations, and cell optical density (OD) were measured to reflect the denitrification performance of the N299 strain. All parameters were measured in triplicate (n = 3). The SM medium included, at pH 7.0𠄷.5: CH₃COONa (0.1 g/L), NaNO₃ (0.02 g/L), K₂HPO₄୳H₂O (0.02 g/L), CaCl₂(0.01 g/L), and MgCl₂୶H₂O (0.01 g/L). A short SM medium [59], at pH 7.0𠄷.5: CH₃COONa (0.1 g/L), NaNO₂ (0.018 g/L), K₂HPO₄୳H₂O (0.02 g/L), CaCl₂ (0.01 g/L), MgCl₂୶H₂O (0.01 g/L). Heterotrophic nitrification medium (HNM) [38] was also prepared at pH 7.0𠄷.5: CH₃COONa (0.5 g/L), NH₄Cl₄ (0.1 g/L), K₂HPO₄୳H₂O (0.1 g/L), CaCl₂ (0.05 g/L), and MgCl₂୶H₂O (0.05 g/L).

4.7. Nitrogen Removal Performance in Oligotrophic Reservoir Source Water System

In order to investigate whether adding agents could purify the sterilized oligotrophic reservoir source water and to study whether bacteria in source water affected the N299’s denitrification, sterilized reservoir source water and non-sterilized source water experiments were carried out. The pre-cultured N299 was inoculated in 10% (v/v) into 150 mL sterilized oligotrophic reservoir source water and non-sterilized oligotrophic source water of 250 mL Erlenmeyer flask at 30 ଌ at 120 rpm. The TN, TDN, TOC, cell optical density, pH, and DO were measured to reflect the denitrification performance of N299. All parameters were measured in triplicate (n = 3).

4.8. Effect of Different Factors on Nitrate Removal

The heterotrophic aerobic denitrification characteristics of isolated strain were determined under different culturing conditions, including carbon source, Temperature, C/N, inoculums dosage (v/v) and pH. Glucose, sodium succinate, sodium citrate, and sodium acetate were used to explore the effects of carbon source on nitrate removal. To observe the effect of temperature on nitrate removal, the experiment was performed within the range of 10� ଌ. The effect of C/N (sodium acetate as carbon source) on nitrate removal was examined by adjusting the ratio between 1 and 10 with a fixed amount of 3.54 mg/L NO₃ − -N. The influence of inoculums dosage (v/v) on nitrate removal was conducted by changing the inoculums dosage to 2%, 3%, 5% and 10%. The effect of pH on nitrate removal was examined in 6, 7, 8, 9 and 10. All parameters were measured in triplicate (n = 3).

4.9. Analytical Methods

The optical density of the culture broth was measured at 510 nm (OD₅₁₀) using a spectrophotometer (DR6000, HACH Company, Loveland, CO, USA) [61]. Nitrite was determined by N-(1-naphthalene)-diaminoethane photometry method [62]. TN and nitrate were measured by the hydrochloric acid photometry method [62]. TP was measured by ammonium molybdate spectrophotometric method [63]. TOC determined by TOC analyzer (ET1020A, Shanghai, China). SEM analyzed by S-3400N (Hitachi, Tokyo, Japan). The samples of nitrate, nitrite, TDN, TOC and TP were filtered using a 0.45 μm cellulose-acetate filter for removing bacteria. pH was measured by HQ11d (HACH Company) and DO was measured by HQ30d (HACH Company). Surface sediments were collected at a deep layer of 0 to 10 cm using a sterilized Petersen stainless steel grab sampler [38,40]. Phylogenetic analysis was constructed in MEGA5.0 program using a neighbor-joining (NJ) method and the maximum composite likelihood model [38].

4.10. Statistical Analyses

Data are presented as means ± SD (standard deviation of means), and analyzed by one-way ANOVA with Tukey’s HSD test (p < 0.05) using SPSS software (Ver. 20.0, IBM Corporation, Armonk, NY, USA).

Crosscutting Concepts

Crosscutting concepts have application across all domains of science. As such, they are a way of linking the different domains of science. They include patterns cause and effect scale, proportion, and quantity systems and system models energy and matter structure and function and stability and change. The Framework emphasizes that these concepts need to be made explicit for students because they provide an organizational schema for interrelating knowledge from various science fields into a coherent and scientifically based view of the world.

1 . Patterns

Observed patterns in nature guide organization and classification and prompt questions about relationships and causes underlying them.

2 . Cause and Effect

Events have causes, sometimes simple, sometimes multifaceted. Deciphering causal relationships, and the mechanisms by which they are mediated, is a major activity of science and engineering.

3 . Scale, Proportion, and Quantity

In considering phenomena, it is critical to recognize what is relevant at different size, time, and energy scales, and to recognize proportional relationships between different quantities as scales change.

4 . Systems and System Models

A system is an organized group of related objects or components models can be used for understanding and predicting the behavior of systems.

5 . Energy and Matter

Tracking energy and matter flows, into, out of, and within systems helps one understand their system’s behavior.

6 . Structure and Function

The way an object is shaped or structured determines many of its properties and functions.

7 . Stability and Change

For both designed and natural systems, conditions that affect stability and factors that control rates of change are critical elements to consider and understand.

Our Approach

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Sea Ice

[5.1] Nichols, Carol Mancuso, John P. Bowman, and Jean Guezennec. “Effects of Incubation Temperature on Growth and Production of Exopolysaccharides by an Antarctic Sea Ice Bacterium Grown in Batch Culture.” Applied Environmental Microbiology. 2005 July 71(7): 3519–3523.

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[5.4] Morgan-Kiss, Rachael M., et al. “Adaptation and Acclimation of Photosynthetic Microorganisms to Permanently Cold Environments.” MICROBIOLOGY AND MOLECULAR BIOLOGY REVIEWS, Mar. 2006, p. 222–252.

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[5.8] Biuw, M., et al. “Variations in behavior and condition of a Southern Ocean top predator in relation to in situ oceanographic conditions.” Proceedings of the National Academy of Sciences. 2007 August 21 104(34): 13705–13710.

[5.9] Ducklow, Hugh W, et al. “Marine pelagic ecosystems: the West Antarctic Peninsula.” Philosophical Transactions of the Royal Society Biological Sciences. 2007 January 29 362(1477): 67–94.

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Freshwater Ice

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[6.4]: Tanabe, et al. (2007). Phytoplankton blooms under dim and cold conditions in freshwater lakes of East Antarctica. Polar biology, 31(2), 199-208.

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[6.16]: Bratina, B. (1998). Manganese reduction by microbes from oxic regions of the Lake Vanda (Antarctica) water column. Applied and environmental microbiology, 64(10), 3791-3797.

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Edited by [Brenna Riley, Sabrina Koperski, Rebecca Dickerson, Trevor Mickelson,Timbrely Fong, Srdjan Sonjara], students of Rachel Larsen

Watch the video: Ο κύκλος του άνθρακα The carbon cycle subtitles (July 2022).

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