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6.8: Nitrogen Cycle - Biology

6.8: Nitrogen Cycle - Biology


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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 (Nr), 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 Nr 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 Nr 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 Nr, 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 Nr 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 Nr inputs from biological nitrogen fixation and atmospheric Nr 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 Nr. 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 Nr 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 Nr 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 . NOx, N2O and N2 emissions are from soils only. Ra, Rr and Rh are autotrophic, rhizosphere and heterotrophic respiration, respectively. BNF, biological nitrogen fixation GPP, gross primary production.

(i) Nitrogen budget

During 2001–2010, direct (Nr 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 Nr 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 Nr 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 Nr 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 Nr 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 Nr 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 Nr 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 Nr 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 Nr 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 Nr inputs (table 2), which reduce or even overcompensate the climatic benefits from carbon sequestration in response to anthropogenic Nr inputs. There have also been slight increases in NOx emissions from natural and fertilized soils (table 2), with as yet unquantified effects on the climate system. However, this anthropogenic soil NOx source remains small compared with the anthropogenic NOx 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 Nr 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 Nr 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 Nr deposition is still lacking. However, given these results, Nr 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 NOx 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 NOx 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 Nr-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 Nr additions currently enhance nitrogen and carbon sequestration in the biosphere (figure 1), but cause at the same time increased emissions of NOx 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.


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References

Adler JF, Williams Q (2005) A high-pressure X-ray diffraction study of iron nitrides: implications for Earth's core. J Geophys Res 110. https://doi.org/10.1029/2004JB003103

Alexander MOD, Fogel M, Yabuta H, Cody GD (2007) The origin and evolution of chondrites recorded in the elemental and isotopic compositions of their macromolecular organic matter. Geochim Cosmochim Acta 71:4380–4403

Armstrong LS, Hirschmann MM, Stanley BD, Falksen EG, Jacobsen SD (2015) Speciation and solubility of reduced C-O-H-N volatiles in mafic melt: implications for volcanism, atmospheric evolution, and deep volatile cycles in the terrestrial planets. Geochim Cosmochim Acta 171:283–302

Bassett WA, Shen AH, Bucknum M, Chou IM (1994) A new diamond cell for hydrothermal studies to 2.5 GPa and from −190°C to 1200°C. Rev Sci Instrum 64:2340–2345

Bebout GE, Fogel ML, Cartigny P (2013) Nitrogen: highly volatile yet surprisingly compatible. Elements 9:333–338

Bebout GE, Ryan JG, Leeman WP, Bebout AE (1999) Fractionation of trace elements by subduction-zone metamorphism-effect of convergent-margin thermal evolution. Earth Planet Sci Lett 171:63–81

Bergin EA, Blake GA, Ciesla F, Hirschmann MM, Li J (2015) Tracing the ingredients for a habitable earth from interstellar space through planet formation. Proc US Natl Acad Sci 112:8965–8970

Busigny V, Bebout GE (2013) Nitrogen in the silicate earth: speciation and isotopic behavior during mineral-fluid interactions. Elements 9:353–358

Busigny V, Cartigny P, Philippot P (2011) Nitrogen in ophiolitic meta-gabbros: a reevaluation of modern nitrogen fluxes in subduction zones and implication for the early earth atmosphere. Geochim Cosmochim Acta 75:7502–7221

Carmichael ISE, Ghiorso MS (1990) Controls on oxidation-reduction relations in magmas. In: Nicholls J, Russell JK (eds) Modern methods of igneous petrology: understanding magmatic processes. The Mineralogical Society of America, Washington, DC, pp 191–212

Carroll MR, Draper DS (1994) Noble gases as trace elements in magmatic processes. Chem Geol 117:37–56

Cody GD, Alexander CMOD, Yabuta H, Kilcoyne ALD, Araki T, Ade H, Dera P, Fogel ML, Militzer B, Mysen B (2008) Organic thermometry for chondritic parent bodies. Earth Planet Sci Lett 272:446–455

Dalou C, Hirschmann MM, von der Handt A, Mosenfelder J, Armstrong LS (2017) Nitrogen and carbon fractionation during core–mantle differentiation at shallow depth. Eart Planet Sci Lett 458:141–151

De Corte K, Cartigny P, Shatsky VS, Sobolev NV, Javoy M (1998) Evidence of fluid inclusions in metamorphic microdiamonds from the Kokchetav massif, northern Kazakhstan. Geochim Cosmochim Acta 62:3765–3773

Elkins LT, Fischer TP, Hilton DR, Sharp ZD, McNight S, Walker J (2006) Tracing nitrogen in volcanic and geothermal volatiles from the Nicaraguan volcanic front. Geochim Cosmochim Acta 70:5215–5235

Fogel ML, Steele A (2013) Nitrogen in extraterrestrial environments: clues to the possible presence of life. Elements 9:367–372

Gessmann CK, Rubie DC (2000) The origin of the depletions of V, Cr and Mn in the mantles of the earth and moon. Earth Planet Sci Lett 184:95–187

Goldblatt C, Claire M, Lenton T, Matthews A, Watson A, Zahnle K (2009) Nitrogen-enhanced greenhouse warming on early earth. Nat Geosci 2:894–896

Grewal DS, Dasgupta R, Sun C, Tsuno K, Castin C (2019) Delivery of carbon, nitrogen, and sulfur to the silicate earth by giant impact. Sci Adv 23. https://doi.org/10.1126/sciadv.aau3669

Halama R, Bebout GE, John T, Scambelluri M (2014) Nitrogen recycling in subducted mantle rocks and implications for the global nitrogen cycle. Internat. J Earth Sci 103:2081–2099

Hall A (1999) Ammonium in granites and its petrogenetic significance. Earth Sci Rev 45:145–165

Halliday AN (2013) The origins of volatiles in terrestrial planets. Geochim Cosmochim Acta 105:146–171

Honma H, Itihara Y (1981) Distribution of ammonium minerals in metamorphic and granitic rocks. Geochim Cosmohim Acta 45:983–988

Javoy M, Pineau F (1991) The volatiles record of a “popping” rock from the mid-Atlantic ridge at 14 N: chemical and isotopic composition of gas trapped in the vesicles. Earth Planet Sci Lett 107:598–611

Johnson B, Goldblatt C (2015) The nitrogen budget of earth. Earth-Sci Rev 148:150–173

Kadik AA, Koltashev VV, Kryukova EB, Plotnichenko VG, Tsekhonya TI, Kononkova NN (2015) Solubility of nitrogen, carbon, and hydrogen in FeO-Na2O-Al2O3-SiO2 melt and liquid Iron alloy: influence of oxygen fugacity. Geochem Int 53:849–868

Kadik AA, Kurovskaya NA, Ignat'ev YA, Kononkova NN, Koltashev VV, Plotnichenko VG (2011) Influence of oxygen fugacity on the solubility of nitrogen, carbon, and hydrogen in FeO-Na2O-SiO2-Al2O3 melts in equilibrium with metallic iron at 1.5 GPa and 1400°C. Geochem Int 49:429–438

Kadik AA, Litvin YA, Koltashev VV, Kryukova EB, Plotnichenko VG, Tsekhonya TI, Kononkova NN (2013) Solution behavior of reduced N-H-O volatiles in FeO-Na2O-SiO2-Al2O3 melt equilibrated with molten Fe alloy at high pressure and temperature. Phys Earth Planet Inter 214:14–24

Kaminsky FV, Wirth R (2017) Nitrides and carbonitrides from the lowermost mantle and their importance in the search for Earth’s “lost” nitrogen. Am Mineral 102:1667–1676

LeLosq C, Mysen BO, Cody GD (2015) Water and magmas: insights about the water solution mechanisms in alkali silicate melts from infrared, Raman, and 29 Si solid-state NMR spectroscopies. Progr Earth Planet Sci 22:2. https://doi.org/10.1186/s40645-015-0052-7

Li J, Agee CB (1996) Geochemistry of mantle-core differentiation at high pressure. Nature 381:686–689

Li Y, Huang RF, Wiedenbeck M, Keppler H (2015) Nitrogen distribution between aqueous fluids and silicate melts. Earth Planet Sci Lett 411:218–228

Li Y, Keppler H (2014) Nitrogen speciation in mantle and crustal fluids. Geochim Cosmochim Acta 129:13–32

Li Y, Wiedenbeck M, Schcheka S, Keppler H (2013) Nitrogen solubility in upper mantle minerals. Earth Planet Sci Lett 377-378:311–328

Libourel G, Marty B, Humbert F (2003) Nitrogen solubility in basaltic melt. Part I. effect of oxygen fugacity. Geochim Cosmochim Acta 67(21):4123–4136

Litasov KD, Shatskiy A, Ponomarev DS, Gavryushkin PN (2017) Equations of state of iron nitrides ε-Fe3Nx and γ-Fe4Ny to 30 GPa and 1200 K and implication for nitrogen in the Earth’s core. J Geophys Res 122:3574–3584

Mallik A, Li Y, Wiedenbeck M (2018) Nitrogen within the Earth’s atmosphere-mantle system assessed by recycling in subduction zones. Earth Planet Sci Lett 482:556–566

Marty B (2012) The origins and concentrations of water, carbon, nitrogen and noble gases on earth. Earth Planet Sci Lett 313-314:56–86

McCammon C (2005) Mantle oxidation state and oxygen fugacity: constraints on mantle chemistry, structure and dynamics. In: van der Hilst RD, Bass JD, Matas J, Trampert J (eds) Earth’s deep mantle: structure, composition, and evolution. American Geophysical Union, Washington DC, pp 221–242

Mitchell EC, Fischer TP, Hilton DR, Hauri EH, Shaw AM, de Moor JM, Sharp ZD, Kazahaya K (2010) Nitrogen sources and recycling at subduction zones: insights from the Izu-Bonin-Mariana arc. Geochem Geophys Geosyst 11. https://doi.org/10.1029/2009GC002783

Miyazaki A, Hiyagon H, Sugiura N (1995) Solubility of nitrogen and argon in basalt melt under oxidizing conditions. In Farley KA (ed) Volatiles in the Earth and Solar System, pp 276–283. American Inst Physics, Washington DC

Miyazaki A, Hiyagon H, Sugiura N, Hirose K, Takahashi E (2004) Solubilities of nitrogen and noble gases in silicate melts under various oxygen fugacities: implications for the origin and degassing history of nitrogen and noble gases in the earth. Geochim Cosmochim Acta 68(2):387–401

Mysen BO (2018a) Mass transfer in the Earth’s interior: fluid-melt interaction in aluminosilicate-C-O-H-N systems at high pressure and temperature under oxidizing conditions. Proc Earth Planet Sci 5:6. https://doi.org/10.1186/s40645-017-0161-6

Mysen BO (2018b) Solution mechanisms of COHN fluids in melts to upper mantle temperature, pressure, and redox conditions. Am Mineral 103:1780–1788

Mysen BO, Fogel ML (2010) Nitrogen and hydrogen isotope compositions and solubility in silicate melts in equilibrium with reduced (N+H)-bearing fluids at high pressure and temperature: effects of melt structure. Am Mineral 95(7):987–999

Mysen BO, Richet P (2019) Silicate glasses and melts: structure and properties, vol 728, 2nd edn. Elsevier, New York

Mysen BO, Tomita T, Ohtani E, Suzuki A (2014) Speciation of and D/H partitioning between fluids and melts in silicate - D-O-H-C-N systems determined in-situ at upper mantle temperatures, pressures, and redox conditions. Am Mineral 99:578–588

Mysen BO, Yamashita S, Chertkova N (2008) Solubility and solution mechanisms of NOH volatiles in silicate melts at high pressure and temperature—amine groups and hydrogen fugacity. Am Mineral 93:1760–1770

Nieder R, Benbi DK (2008) Carbon and nitrogen in the terrestrial environment. p. 430. Springer, Berlin

O'Neill HSC (1991) The origin of the Moon and the early history of the Earth—a chemical model. Part 2: the Earth. Geochem Cosmochim Acta 55:1159–1172

Palya AP, Buick IS, Bebout GE (2011) Storage and mobility of nitrogen in the continental crust: evidence from partially melted metasedimentary rocks, Mt. Stafford, Australia. Chem Geol 281:211–226

Plessen B, Harlov DE, Henry D, Guidotti CV (2010) Ammonium loss and nitrogen isotopic fractionation in biotite as a function of metamorphic grade in metapelites from western Maine, USA. Geochim Cosmochim Acta 74:4759–4771

Polyanov YN, Bataleva YV, Sokol AG, Borzdov YM, Kupriyanov IN, Reutsky VN, Sobolev NV (2014) Mantle–slab interaction and redox mechanism of diamond formation. Proc Natl Acad Sci U S A 51:20408–20413

Righter K, Drake MJ (1999) Effect of water on metal-silicate partitioning of siderophile elements: a high pressure and temperature terrestrial magma ocean and core formation. Earth Planet Sci Lett 171:383–399

Roskosz M, Bouhifd MA, Jephcoat AP, Marty B, Mysen BO (2013) Nitrogen solubility in molten metal and silicate at high pressure and temperature. Geochim Cosmochim Acta 121:15–28

Roskosz M, Mysen BO, Cody GD (2006) Dual speciation of nitrogen in silicate melts at high pressure and temperature: an experimental study. Geochim Cosmochim Acta 70:2902–2918

Rubie DC, Frost DJ, Mann U, Asahara Y, Tsuno K, Nimmo F, Kegler P, Holzheid A, Palme H (2011) Heteroegeneous accretion, composition and core-mantle differentiation of the Earth. Earth Planet Sci Lett 301:31–42

Sano Y, Takahata N, Nishio Y, Fischer TP, Williams SN (2001) Volcanic flux of nitrogen from the Earth. Chem Geol 171:263–271

Smith EM, Kopylova MG (2014) Implications of metallic iron for diamonds and nitrogen in the sublithospheric mantle. Can J Earth Sci 51:510–516

Wade J, Wood BJ (2005) Core formation and the oxidation state of the Earth. Earth Planet Sci Lett 236:78–95

Watenphul A, Wunder B, Heinrich W (2009) High-pressure ammonium-bearing silicates: implications for nitrogen and hydrogen storage in the Earth’s mantle. Amer Mineral 94:283–292

Watenphul A, Wunder B, Wirth R, Heinrich W (2010) Ammonium-bearing clinopyroxene: a potential nitrogen reservoir in the Earth's mantle. Chem Geol 270:240–248

Yoshioka T, Wiedenbeck M, Shcheka S, Keppler H (2018) Nitrogen solubility in deep mantle and the origin of the Earth’s primordial nitrogen budget. Earth Planet Sci Lett 488:134–143

Zhang C, Duan Z, Li M (2010) Interstitial voids in silica melts and implication for argon solubility under high pressures. Geochim Cosmochim Acta 74:4140–4149

Zhang Y, Yin QZ (2012) Carbon and other light element contents in the Earth’s core based on first-principles molecular dynamics. Proc Natl Acad Sci U S A 109:19579–19583


References

Slush

[1.2] National Geographic Data Center - National Satellite, Data, and Information Service. http://www.ngdc.noaa.gov/mgg/image/2minrelief.html (2006)

[1.3] International Polar Foundation Information Site: http://www.sciencepoles.org/index.php?/home/

[1.4] Zhang D, Yong Y, Xin Y, Hong L, Zhou P, and Zhou Y. Colwellia polaris sp. Nov., a psychrotolerant bacterium isolated from Arctic sea ice. International Journal of Systematic and Evolutionary Microbiology (2008) Vol. 58 p. 1931-1934

[1.5] Raymond J, Fritsen C, Shen K. An Ice-binding Protein from an Antarctic Sea Ice Bacterium. FEMS Microbiology Ecology (2007) Vol. 61(2) p. 214-221

[1.6] Huston A, Haeggstrom J, Feller G. Cold Adaptation of Enzymes: Structural, Kinetic, and Microcalorimetic characterizations of an aminopeptidase from the Arctic psychrophile Colwellia and of Human Leuikotriene A4 Hydrolase. Biochemica et Biophysica Acta (2008) June 13.

[1.7] Huston A, Methe B, Deming J. Purification, Characterization, and Sequencing of an Extracellular Cold-Active Aminopetidase Produced by Marine Psychrophile Colwellia Starin 34H. American Society for Microbiology (2004) Vol. 70(2) p. 3321-3328

[1.8] Gilbert J, Hill P, Dodd C, Laybourn J. Demonstration of Antifreeze Protein Activity in Antarctica Bacteria. Microbiology (2004) Vol. 150 p. 171-180

Lake Vostok

[2.1] Inman M. Microbial ecology. The Dark and Mushy Side of a Frozen Continent. Science2007 Jul 6317(5834):35-6.

[2.2] Studinger M, Bella RE, Karnera GD, Tikkua AA, Holtb JW, Morseb DL, Richterb TG, Kempfb SD, Petersb ME, Blankenshipb DD, Sweeneyc RE, Rystromc VL. Ice cover, landscape setting, and geological framework of Lake Vostok, East Antarctica. Earth and Planetary Science Letters2003205:195-210.

[2.3] Lavire C, Normand P, Alekhina I, Bulat S, Prieur D, Birrien J-L, Fournier P, Hänni C, Petit J-R. Presence of Hydrogenophilus thermoluteolus DNA in accretion ice in the subglacial Lake Vostok, Antarctica, assessed using rrs, cbb and hox. Environmental Microbiology20068(12):2106-14.

[2.4] Siegert MJ, Ellis-Evans JC, Tranter M, Mayer C, Petit JR, Salamatin A, Priscu JC. Physical, chemical and biological processes in Lake Vostok and other Antarctic subglacial lakes. Nature2001 Dec 6414(6864):603-9.

[2.5] Karl DM, Bird DF, Bjorkman K, Houlihan T, Shackelford R, Tupas L. Microorganisms in the accreted ice of Lake Vostok, Antarctica. Science1999 Dec 10286(5447):2144-7.

[2.6] Christner BC, Mosley-Thompson E, Thompson LG, Reeve JN. Isolation of bacteria and 16S rDNAs from Lake Vostok accretion ice. Environ Microbiol2001 Sep3(9):570-7.

[2.7] Raymond JA, Christner BC, Schuster SC. A bacterial ice-binding protein from the Vostok ice core. Extremophiles2008 Jul 12.

[2.8] Hayashi NR, Ishida T, Yokota A, Kodama T, Igarashi Y. Hydrogenophilus thermoluteolus gen. nov., sp. nov., a thermophilic, facultatively chemolithoautotrophic, hydrogen-oxidizing bacterium. International Journal of Systematic Bacteriology199949:783-6.

Ross Dependency

[3.1] Dennett, Mark R., Mathot, Slyvie, Caron, David A., Smith, Walker O. Jr. and Lonsdale, Darcy J. “Abundance and distribution of phototrophic and heterotrophic nano- and microplankton in the southern Ross Sea”. Elsevier Science Ltd. 2001. [1]

[3.2] “Diversity of Ross Sea Fish”. Science Learning Hub. 25 February 2008. [2]

[3.3] Norris, Katina Bucher. “Dimethylsulfide Emission: Climate Control by Marine Algae?”. ProQuest. November 2003. [3]

[3.4] Smith, Walker O Jr, Ainley, David G., and Cattaneo-Vietti, Riccardo. “Trophic interactions within the Ross Sea continental shelf ecosystem”. Philosophical Transactions of the Royal Society B: Biological Sciences. 6 December 2006. p. 95-106. [4]

[3.5] Ward, Paul. “Antarctica Climate Data and Climate Graphs McMurdo, Amundsen-Scott (South Pole) and Vostok Stations”. Cool Antarctica. 17 August 2008. [5]

[3.6] Williams, Nigel. “Chill Wind Over Antarctic Biodiversity”. Current Biology. 9 March 2004. Volume 14, Issue 5. p.R169-R170. [6]

[3.7] Price, P. Buford. “Life in Solid Ice”. Cornell University Library. 2 July 2005. p. 1-11. [7]

[4.1] "Antarctica - The World Factbook". United States Central Intelligence Agency (2007-03-08). Retrieved on 2007-03-14.

[4.2] "Weather in the Antarctic". British Antarctic Survey. Retrieved on 2006-02-09.

[4.3] Lyons, W.B et al. 1998. A Late Holocene desiccation of Lake Hoare and Lake Fryxell, McMurdo Dry Valleys, Antarctica. Antarctic Science. Vol 10 (3): pgs 247-256.

[4.4] Foreman, C., B. Sattler, J. Mikucki, D. Porazinska and J.C. Priscu. 2007. Metabolic Activity and Diversity of Cryoconites in the Taylor Valley, Antarctica. Journal of Geophysical Research - Biogeosciences. Pgs 1-43.

[4.5] Powers, Laura E., Diana W. Freckman, Mengchi Ho and Ross A. Virginia. (1995). McMurdo LTER: Soil properties associated with nematode distribution along an elevational transect in Taylor Valley, Antarctica. Antarctic Journal of the United States. 30 (5): 282-283.

[4.6] Imperio T, Viti C, Marri L. (2008). Alicyclobacillus pohliae sp. nov., a thermophilic, endospore-forming bacterium isolated from geothermal soil of the north-west slope of Mount Melbourne (Antarctica). Int J Syst Evol Microbiol. 58 (Pt 1):221-5.

[4.7] Wood, S. et al. (2008). Sources of edaphic cyanobacterial diversity in the Dry Valleys of Eastern Antarctica. The ISME Journal. Vol 2, pgs 308–320.

[4.8] Ho, Mengchi, Ross A. Virginia, Laura E. Powers, and Diana W. Freckman. (1995). Soil chemistry along a glacial chronosequence on Andrews Ridge, Taylor Valley. Antarctic Journal of the United States. Vol 30 (5): 310-311.

[4.9] Hogg I.D., Craig Cary S., Convey P., Newsham K.K., O'Donnell A.G., Adams B.J., Aislabie J., (. ), Wall D.H. (2006) Biotic interactions in Antarctic terrestrial ecosystems: Are they a factor? Soil Biology and Biochemistry. 38 (10), pp. 3035-3040.

[4.10] de la Torre, J. R., B. M. Goebel, E. I. Friedmann, and N. R. Pace. (2003). Microbial diversity of cryptoendolithic communities from the McMurdo dry valleys, Antarctica. Appl. Environ. Microbiol. 69:3858-3867. [PubMed].

[4.11] Stackebrandt E, Rainey FA, and Ward-Rainey NL. (1997). Proposal for a new hierarchic classification system, Actinobacteria classis nov. Int J Syst Bacteriol. 47:479-491.

[4.12] Mitsui, A. et al. (1986). Strategy by which nitrogen-fixing unicellular cyanobacteria grow photoautotrophically. Nature 323, 720–722.

[4.13] Hogg I.D., Craig Cary S., Convey P., Newsham K.K., O'Donnell A.G., Adams B.J., Aislabie J., (. ), Wall D.H. (2006). Biotic interactions in Antarctic terrestrial ecosystems: Are they a factor? Soil Biology and Biochemistry. 38 (10), pp. 3035-3040. (same as (9))

[4.14] Rosmarie Honegger. (1991). FUNCTIONAL ASPECTS OF THE LICHEN SYMBIOSIS. Annu. Rev. Plant Physiol. Plant Mol. Biol. Vol 42. pgs 553-7.

[4.15] Hunt, H. W. (1987). The detrital food web in shortgrass prairie. Biology and Fertility of Soils. Vol 3, pgs 1-2.

[[4.17] Gerday C., Aittaleb M., Bentahir M., Chessa J.-P., Claverie P., Collins T., D'Amico S., (. ). (2000). Feller G. Cold-adapted enzymes: From fundamentals to biotechnology. Trends in Biotechnology. 18 (3), pp. 103-107.

[4.18] Rivkina, E. I. Friedmann, C. P. McKay, and D. A. Gilichinsky, E. M. (2000). Metabolic Activity of Permafrost Bacteria below the Freezing Point. Appl Environ Microbiol. 66(8): 3230–3233.

[4.19] Wynn-Williams D.D., Edwards H.G.M. (2000). Proximal Analysis of Regolith Habitats and Protective Biomolecules in Situ by Laser Raman Spectroscopy: Overview of Terrestrial Antarctic Habitats and Mars Analogs. Icarus. 144 (2), pp. 486-503.

[4.20] Lancaster, N. (2002). Flux of Eolian Sediment in the McMurdo Dry Valleys, Antarctica: A Preliminary Assessment. Arctic, Antarctic, and Alpine Research. Vol. 34, No. 3 (Aug., 2002), pp. 318-323.

[4.21] Fell J.W., Scorzetti G., Connell L., Craig S. (2006). Biodiversity of micro-eukaryotes in Antarctic Dry Valley soils with <5% soil moisture. Soil Biology and Biochemistry. 38 (10), pp. 3107-3119.

[4.22] Wall D.H., Virginia R.A. (1999) Controls on soil biodiversity: Insights from extreme environments. Applied Soil Ecology, 13 (2), pp. 137-150.

[4.23] Aislabie, J., McLeod, M., Fraser, R. (1998). Potential for biodegradation of hydrocarbons in soil from the Ross Dependency, Antarctica. Applied Microbiology and Biotechnology. Volume 49, Number 2.

[4.24] STEVENS, M.I. and HOGG, I. D.. 2006. Trends in Antarctic Terrestrial and Limnetic Ecosystems: Antarctica as a global indicator. Vol 1. Pgs 1-13..

[4.25] D’Amico, Salvino, et al. (2006). Psychrophilic microorganisms: challenges for life. European Molecular Biology Organization reports. VOL 7 | NO 4, 385–389.

[4.26] David J. Saul a , Jackie M. Aislabie b, *, Caroline E. Brown a , Lisa Harris a , Julia M. Foght c. (2006). Hydrocarbon contamination changes the bacterial diversity of soil from around Scott Base, Antarctica. FEMS Microbiology Ecology. Volume 53, Issue 1, Pages 141-155.

[4.27] Gupta, R. S., Johari, V. (1998). Signature Sequences in Diverse Proteins Provide Evidence of a Close Evolutionary Relationship Between the Deinococcus-Thermus Group and Cyanobacteria. Journal of Molecular Evolution. Volume 46, Number 6.

[4.28] Horneck G. (2000). The microbial world and the case for Mars. Planetary and Space Science. 48 (11), pp. 1053-1063.

[4.29] Stibal, M., Tranter, M., Laboratory investigation of inorganic carbon uptake by cryoconite debris from Werenskioldbreen, Svalbard. Journal of Geophysical Research, Vol. 112, G04S33, Pg 1-9, Copyright 2007, American Geophysical Union.

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.

[5.3] Peck, Lloyd S. “Prospects for surviving climate change in Antarctic aquatic species.” Front Zool. (2005) 2:9.

[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.

[5.5] “Antarctic Weather.” www.antarcticconnection.com

[5.6] Gosink, J. J., Staley J. T. “Biodiversity of gas vacuolate bacteria from Antarctic sea ice and water.” Applied and Environmental Microbiology. 1995 September 61(9): 3486–3489.

[5.7] BOWMAN, JOHN P., et al. “Diversity and Association of Psychrophilic Bacteria in Antarctic Sea Ice.” Applied and Environmental Microbiology. Aug. 1997, p. 3068–3078 Vol. 63, No. 8.

[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.

[5.10] Duck, Hugh, Craig Carlson, “Walker smith Bacterial growth in experimental plankton assemblages and seawater cultures from the Phaeocystis antarctica bloom in the Ross Sea, Antarctica.” MICROBIAL ECOLOGY. (1999) Vol. 19: 215-227.

[5.11] D’Amico, Salvino, et al. “Psychrophilic microorganisms: challenges for life.” European Molecular Biology Organization reports. (2006) VOL 7 | NO 4, 385–389.

[5.12] Price, Buford P., Todd Sowers. “Temperature dependence of metabolic rates for microbial growth, maintenance, and survival.” Proceedings of the National Academy of Sciences. January 22, 20

Freshwater Ice

[6.1]: Carpenter, E. J., S. Lin, and D. G. Capone. 2000. Bacterial activity in South Pole snow. Appl. Environ. Microbiol. 66:4514-4517.

[6.2]: Vincent WF, Quesada A (1994) Ultraviolet radiation effects on cyanobacteria: implications for Antarctic microbial ecosystems. Antarctic Res Ser 62:111–124

[6.3]: Campbell JW, Aarup T (1989) Photosynthetically available radiation at high latitudes. Limnology and Oceanography 34:1490–1499

[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.

[6.5]: Jones, A. E., and J. D. Shanklin. 1995. Continued decline of total ozone over Halley, Antarctica, since 1985. Nature 376:409-411

[6.6]: M. R. James. et al. 1996. Biodiversity in extreme aquatic environments: Lakes, ponds and streams of the Ross Sea sector, Antarctica. Biodiversity Conservation. 5: 145 1-1471.

[6.7]: Vincent WF, Rae R, Laurion I, Howard-Williams C, Priscu JC (1998) Transparency of Antarctic ice-covered lakes to solar UV radiation. Limnology and Oceanography 43:618–624

[6.8]: Howard-Williams, C. (2007). Ecological processes in Antarctic inland waters: interactions between physical processes and the nitrogen cycle. Antarctic science, 19(2), 205-217.

[6.9]: John C. Priscu, Craig F. Wolf, Cristina D. Takacs, Christian H. Fritsen, Johanna Laybourn-Parry, Emily C. Roberts, Birgit Sattler and Berry Lyons. BioScience, Vol. 49, No. 12, McMurdo Dry Valleys (Dec., 1999), pp. 997-1008

[6.11]: Cavicchioli, R. 2006. Cold-adapted Archaea. Nat. Rev. Microbiol. 4:331-343.

[6.12]: Tindall, B. J. 2004. Prokaryotic diversity in the Antarctic: the tip of the iceberg. Microb. Ecol. 47:271-283.

[6.13]: Ellis-Evans, J. (1996). Microbial diversity and function in Antarctic freshwater ecosystems. Biodiversity and conservation, 5(11), 1395-1431.

[6.14]: Stingl, U. (2008). Dilution-to-extinction culturing of psychrotolerant planktonic bacteria from permanently ice-covered lakes in the McMurdo dry valleys, antarctica. Microbial ecology, 55(3), 395-405.

[6.15]: Andrassy, I (2007). Nematodes from saline and freshwater lakes of the Vestfold Hills, East Antarctica, including the description of Hypodontolaimus antarcticus sp n. Polar biology, 30(6), 669-678.

[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.

[6.17] Kepner, R. (2000). UV radiation and potential biological effects beneath the perennial ice cover of an antarctic lake. Hydrobiologia, 427(1-3), 155-165.

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


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