Introduction: Phosphate as an Essential Mineral

“The story of phosphorus is a long, fascinating one. But we are here interested primarily in knowing about its role in agriculture,” wrote Vincent Sauchelli in “Manual on Phosphates in Agriculture” published in 1942.

“Therefore in order to start at the beginning or our story we shall have to go back to the year 1840 – the year when Justus von Liebig, a German scientist, made an historical address before the British Association of Science in which he for the first time gave a clear, intelligent exposition of the role of minerals in plant growth and laid the ground work for modern agricultural science. He was the first to show that insoluble phosphates such as bone could be made to release their phosphorus in a form more quickly accessible to growing plants if they were caused to react with sulfuric acid. That suggestion stimulated John Bennett Lawes, an Englishman, to treat coprolites, a phosphorus bearing ore fairly abundant in Great Britain, with sulfuric acid and to test the resultant phosphate as a plant nutrient. In 1842 Lawes was given a patent on this idea, which permitted him to establish the first ‘superphosphate’ works. From then on is fertilizer history.

Within 20 years after Lawes got his patent the British were producing 150,000 tons a year of superphosphate. Then occurred the discovery of sources of mineral phosphates – rich deposits of rock phosphate in South Carolina in 1867 and in Florida in 1887. These discoveries gave American industry the opportunity to take the lead in the mining of rock phosphates and the production of superphosphate – a lead which has been maintained ever since.”

In a message to the United States Congress in 1938, President Franklin D. Roosevelt underscored the importance of phosphate to agriculture and people.

“The phosphorus content of our land, following generations of cultivation, has greatly diminished,” President Roosevelt said. “It needs replenishing. I cannot over-emphasize the importance of phosphorus not only to agriculture and soil conservation but also the physical health and economic security of the people of the nation. Many of our soil deposits are deficient in phosphorus, thus causing low yield and poor quality of crops and pastures…”

Why?

Phosphorus (P) is required by every living plant and animal cell. Deficiencies in available P in soils are a major cause of limited crop production. Phosphorus deficiency also is probably the most critical mineral deficiency in grazing livestock, according to “The Effect of Soils and Fertilizers on Human and Animal Nutrition,” U. S. Department of Agriculture (USDA) Information Bulletin No. 378, issued in 1975. When P fertilizers are added to soils deficient in the available form of this element, increased crop and pasture yields ordinarily follow.

Phosphorus is one of the primary nutrients essential for plant growth and crop production. It is a non-renewable resource that must be mined from nature. It cannot be artificially produced. We do not, however, mine phosphorus itself. We mine phosphate minerals.

Phosphorus is highly reactive and is not found in its elemental form in nature. It occurs in nature as phosphate, which is a charged group of atoms, or an ion. It is made up of a phosphorus atom and four oxygen atoms (PO₄) and carries three negative charges. The phosphate ion combines with various atoms and molecules within living organisms to form many different compounds essential to life.

Some examples of phosphate’s role in living matter include:

• Giving shape to DNA (deoxyribonucleic acid), which is a blueprint of genetic information contained in every living cell. A sugar-phosphate backbone forms the helical structure of every DNA molecule.
• Playing a vital role in the way living matter provides energy for biochemical reactions in cells. The compound adenosine triphosphate (ATP) stores energy that living matter gets from food (and sunlight in plants) and releases it when it is required for cellular activity. After the energy, in the form of a high-energy phosphate bond, is released the ATP becomes a lower-energy adenosine diphosphate (ADP) or a still lower-energy adenosine monophosphate (AMP) molecule. These will be replenished to the higher-energy ATP (or ADP) state with the addition of phosphate by various mechanisms in living cells.
• The forming and strengthening of bones and teeth.

Humans get phosphate from the foods they eat. These examples show the amount of phosphorus* (mg/100 grams) in various foods.

• Milk 93
• Lean Beef 204
• Potatoes 56
• Broccoli 72
• Wheat Flour 101
Cheddar Cheese 524

* NOTE: Although phosphorus is not found in elemental form in food, by convention the phosphate content of foods is expressed in terms of its phosphorus content.

Plants get phosphate from the soil along with nitrogen, potassium and a number of other nutrients they need to thrive. Fertilizer is added to nutrient-deficient soil to replenish these vital chemicals. Animals get phosphate from their food.

The bulk of the phosphate we mine – about 90% – is used to produce phosphate fertilizers. Another 5% is used to make animal feed supplements. The remaining 5% goes into making a variety of products from soft drinks to toothpaste to metal coatings.

Phosphate is a limited resource that cannot be replaced. As such, an international group of earth science and mineral resource agencies have designated it a strategic mineral resource. This group includes Australia, Canada, the Federal Republic of Germany, the Republic of South Africa and the United States of America.

“The International Strategic Minerals Inventory Summary Report – Phosphate” (USGS Circular 930-C) is a cooperative effort of this international group and was published in 1984 by the U.S. Geological Survey. It describes Phosphorus as “an important component of the cell tissues of plants and animals; it is necessary for the structure, growth, and propagation of living organisms. Phosphorus enters the organic food chain from the soil through the roots of plants. The human body contains about 1 percent by weight phosphorus, most of it in the bones and teeth. The human body requires a daily intake of 0.6-0.7 g of phosphorus.”

 

 

A cell is the unit of life, as in a tiny space separated from external space by a thin closed membrane system, all necessary apparata for life are equipped and a single nucleus spatially and temporally controls all the functions of life (12).   We have believed that the uninuclear organization and principal functions of a cell, such as DNA replication, protein biosynthesis and respiraion, should almost be the same in mammals and tall trees. However, is this always true for all living things? Or, is it only a naiive oversimplification?

We know, on the otherhand, that there is alternative cellular organization, called coenocyte or apocyte in which a cell (to call “cell” may not be appropriate: sometimes, called acellular structure) contains many nuclei. There are various kinds of coenocytes, widely spreading among phylogenetic branches. Are the celular mechanisms of coenocytes also the same as in the ordinary uninuclear cells? Some coenocytes are very large and easy to handle, and hence used for experimental materials for electrophysiological and cell physiological studies.

Coenocytic cells can be classified into several groups in term of theirposition and status in the individuals (Fig. 1).
a: Xanthophycean alga (Chromista, Stramenopiles), Vaucheria and Zygomycetous fungus, Phycomyces, Oomycetes, etc. are coenocytic throughout their cell cycles, except when they are mechanically injured or at the formation of sex organs or zoosporangiophores.

b: Slime molds, such as Physarum polycepharum . are conocytic only in a limited phase of life cycle. Marine green algae Bryopsis, Caulerpa, Acetabularia, etc. are well known large coenocytes. But their diploid (2n) sporophytes, as well as their gametes and zoosopores are nuninuclear cells.

c: Internodal cells of Chara (Charophyte) are giant coenocytes, whereas their node cells are ordinary uninuclear cells. Since the Chara internodal cells reach 0. 5 mm in diameter and 20 cm in length, they have been suitable materials in cell physiology. Differentiation of sex organs and new internodal cells are only from these node cells. The internodal cell contains numerous giant nuclei which are circulated in the large cell by the protoplasmic streaming.Each giant nucleus contains more than1,000 copies of genome. These giant nuclei divide amitotically, and the internodal cell eventually lacks the ability of differentiation.

d: Cladophora spp. (also Aegagropira linnaei = Marimo), Acrosiphonia (green algae) , and Griffithsia(red algae) are consist of sausage-shaped coenocytic tubes (Fig. 1). The common characteristics which lead coenocytic organization is that cytokinesis does not follow mitotic nuclear division.

Following questions are, however, heretofore asked: How is the coenocytic status maintained? Have their Genes for cytokinesis been lost or disfunctioned?

New questions also arise: Is there any causal relationship between being a coenocyte and being a giant cell? Is coenocytic organization ecologically more profitable? How are the internuclear distances regulated? Which nucleus or a group of nuclei does take leadership in nuclear division and cell cycle rotation? How is the signal for nuclear division transmitted to others in the different region? Are individual nuclei taking part different functions, such as CPUs in a parallel-task computer? Does the function of the nuclei (gene expression) depend on the site of residence? And so on.

We don’tstill have any answer to these questions. However, we are finding some interesting features and functions which are specific to coenocytes, through detailed observation of cytoskeletons and nuclear movement.

We recently found an admirable function of Vaucheria in photo-cytomorphogenesis, which is only possible in coenocytes. The body of Vaucheria (V. terrestris sensu G嗾z =V. frigida) consists of a sparsely branched coenocytic tube of 50-70 ｵm in diameter(Fig. 2). Tips of the branches exhibit typical tip growth andpositive- as well as negative phototropism. Vaucheria has thus been very useful material for the photoresponses of tip-growing cells (1-10).

When a narrow region of the cell was irradiated with blue light(400- 500 nm) of moderate intensity, a new growth center was induced at the center of the irraidated region 4 h (at the earliest) after the onset of light. The initiated growth center then bulged out and developed as a new branch (Fig. 3). Only blue light is effective and yellow-red light is completely inert. This is an experimentally induced branching. But, the similar thing must occurs in natural habitats: Vaucheria grows as a mat on wet soil near the stream. In fall, the mat would be shaded by fallen leaves. In the shaded region no photosyntehsis would take place. The ability to form a branch from the opening would have been evolutionary and ecologically profitable trait.Thus, study of the photocytomorpho-genetic response is very important in photobiology and cell physiology.

We found that the accumulation of nuclei was indispensable for this blue light-induced branching. The accumulation of chloroplasts which starts immediately and completed 1 h after the onset of light was by itself not sufficient (Fig. 4) (9-10). The nuclear accumulation started 30- 40 min after the onset of light. Not only nuclei, but also protoplasm, which also included chlorplasts rushed into the lighted region.

Density of nuclei in the irradiated region reached 200% of that in the adjacent dark region by 5 h. The increase in number of nuclei was never due to induction of nuclear division in the lighted region. This was performed by careful observation with many specimen fixed at intervals of 10 min.

Accumulation of protoplasm continued and after 3 h the central vacuole was occasionally teared off. Then, about 4 h after the onset of blue light, cell wall at the center of the irradiated region was softend, and a bulge developed to an actively growing branchlet.

Surprizingly, it was microtubular bundle that translocated the nuclei. During interphase every nucleus had a long microtubule bundle at its head(Fig. 5) (11, 12). Centrosome (including centrioles) serves as an MTOC (12). Additional 2 (or 3?) short microtubule bundles are extending backward from the centrosome region. Observing the movement of nuclei under optical microscope (DIC optics), we confirmed that the frontal microtubule bundles pulled nuclei in longitudinal direction. If the microtubule bundles are destroyed, nuclei are not accumulated, and hence no branch is induced (11). We are now intending to identify the microtubule associated motor protein and to elucidate the motor mechanism.
　Nuclear accumulation is probaly necessary for producing cellulytic enzymes, ion channel proteins and deliver exocytotic vesicles to the presumptive site of branching. It is not known yet that the accumulated nuclei themselves are the site of blue light reception and that only in those nuclei new expression of genes start. By the way, the present results also suggest that the nuclear control for the morphogenesis is rather a short range, as is the case in uninuclear fern protonema cell.
　Now, let’s compare the Vaucheria system with morphogenesis of multicullular plants. In multicellular plants morphogenesis always requires a local cell division(13). If a high nuclear density is principally necessary for creating a new shape, all multicellular plants must inevitably use nuclear- and cell division. In coenocytic cells, however, the high nuclear density can be achieved by gathering nuclei into desired locus without waiting for the next mitosis. Only coenocytic cells can adopt this principle. But we don’t know yet, whether this is also the case in coenocytic algae other than Vaucheria.
　Then, what on earth the roles of individual nuclei in the coenocytic cell? Is the coenocytic organization, monarchy, anarchy, feudalismic, or republic? what is necessary for maintaining coenocytic organization other than deletion of prevention of cytokinesis? Such exciting questions lead us to a new subdiscipline, CELL ECOLOGY. Recently, we have found in Vaucheria that the nuclear division started mainly at the growing apex and the phase of the mitotis propgated from tip to the base as a mitotic wave (14). In a narrow region (about 200 µm in length) the mitosis occur synchrounously. Probably, the mitotic wave is driven via the production of cyclin- like proteins by the dividing nuclei. This may indicate that the nuclein the apical growing region plays a role as the leaders in the coenocytic continuum. ヾimilar　internucleus communication in a coenocytic cell can be observed in the green alga, Bryopsis plumosa (15,16) aritificially fused protoplasts between gametophyte and sporophyte.

　It is not always guaranteed that a phenomenon or a function found in Arabidopsis is also acting in other plants. Also, common senses in multicellular plants are not usually common sense in unicellular or coenocytic organisms. Also, the reason d’etre of coenocyte cannot be solved by the study of unicellular and multicellular organisms. Instead, from the study of coenocytes a quite new, heretofore overlooked important points on the mechanism of cell division could be found. We started to organize “coenocytes Research Group” a few years ago. 

 

 

In 1985 Ian Hall, a mycologist at the Invermay Agricultural Centre near Dunedin began research on the Périgord black truffle (Tuber melanosporum). He believed that it would be possible to establish truffle growing in New Zealand, with the aim of supplying out-of-season Northern Hemisphere markets. By 1987 Invermay had produced its first batch of Périgord black truffle infected plants and in the Spring of that year two truffières (truffle plantations) were established in North Otago. In 1988, truffières were planted in nine other areas of New Zealand, from North Canterbury to Gisborne. In July 1993 a few Périgord black truffles were found in Alan Hall’s Gisborne truffière 5 years after planting – the first in the Southern Hemisphere. Small numbers of black truffles continued to form in the 0.5 hectare truffière during the following few years as well as up to 100kg of an inferior truffle (Tuber maculatum). However, in March and April 1997, 8.5 years after planting, large numbers of immature Périgord black truffles began to form and by May mature truffles were being harvested. By mid June 1997, the 0.5ha truffière had produced 6kg of Périgord black truffles with one tree producing 1.75 kg. Some of the truffles were particularly large and weighed 750g or more. Alan Hall’s truffière has continued to produce commercial quantities of black truffle, and has been joined by truffieres in the Bay Of Plenty, Kapiti, Nelson, North Canterbury and near Taumarunui. There are now over 100 truffieres of all sizes in New Zealand, the largest being near Christchurch and North of Auckland. Annual production remains small but it is expected to grow rapidly over the next five years as plantations mature, and growers refine their management techniques. Ian Hall’s team has significantly expanded their production of Perigord black truffle inoculated trees in recent years, and introduced two new species of truffle – the Burgundy truffle (T. uncinatum) and the bianchetto (T. borchii). Trial plantations of these species began in 2001.

Note: recent genetic evidence indicates that this species should no longer be considered in the genus Coprinus. The new proposed name is Parasola plicatilis (Fries) Redhead.

Photographer: Nathan Wilson

Location: My front lawn is in Los Angeles, California.

Time: Spring 1995

Habitat: Lawns.

Comments: This delicate species makes up for its small size through numbers. For a while, last spring between 10 and 20 caps would appear on my lawn early each morning only to shrivel up and disappear by 10 am. It is now a year later and caps are continuing to show up, though not in as great numbers, probably because I cut back a bit on my watering – or maybe I’m just sleeping in a bit longer.

Processing: The original image was taken on 35mm Fuji Velvia using a Nikon 55mm macro lens. The image was scanned on a Power Mac 8100/100 using a Microtek ScanMaker 35t slide scanner and Adobe Photoshop.

The original photograph was awarded an honorable mention in the limited pictorial category of the 1995 North American Mycological Society photo contest.

When: 1995-07-15

 

 

Merry Christmas from BioStim

If you are looking for a gardening gift for yourself or a green thumb you know try our store at https://biostim.com.au

abstract

Keystones are defined as relatively low biomass species with a structuring role in their food
webs. Thus, identifying keystone species in a given ecosystem may be formulated as: (1) estimating the impact on the different elements of an ecosystem resulting from a small change to the biomass of the species to be evaluated for its ‘keystoneness’; and (2) deciding on the keystoneness of a given species as a function of both the impact estimated in (1) and its own biomass. Experimental quantification of interaction strength necessarily focus on few
species, and require a priori assumptions on the importance of the interactions, which can
bias the identification of keystone species. Moreover, empirical measurements, although
very important, are expensive and time consuming and, owing to the spatio-temporal heterogeneity of habitats, physical conditions, and densities of organisms, published results tend to be case-specific and context-dependent.

Although models can only represent but a caricature of the complexity of the real world,
the modelling approach can be helpful since it allows overcoming some of the difficulties
mentioned. Here we present an approach for estimating the keystoneness of the functional groups (species or group of species) of food web models. Network mixed trophic impact analysis, based on Leontief’s economic input–output analysis, allows to express the relative change of biomasses in the food web that would result from an infinitesimal increase of the biomass of the observed group, thus identifying its total impact.

The analysis of the mixed trophic impacts presented here was applied to a suite of massbalance models, and the results allow us to rank functional groups by their keystoneness. Overall, we concluded that the straightforward methodology proposed here and the broad use of Ecopath with Ecosim (where mixed trophic impact analysis is implemented) together give a solid empirical basis for identification of keystone functional groups.

Introduction

Keystones are defined as species with a structuring role within ecosystems and the food webs that interconnect in spite of a relatively low biomass and hence food intake (Power et al., 1996). It may be noted that the
low biomass requirement eliminates species that structure ecosystems by virtue of their high biomass, such as

Corresponding author at: Dept. Oceanography, Istituto Nazionale di Oceanografia e di Geofisica Sperimentale—OGS, Borgo Grotta Gigante
42/. 34010 Sgonico (TS), Italy. Tel.: +39 040 2140376; fax: +39 040 2140266.
E-mail addresses: [email protected], [email protected] (S. Libralato).0304-3800/$ – see front matter © 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.ecolmodel.2005.11.029

trees in terrestrial forests, or seagrass and kelp in coastal ecosystems.Keystone species affect the communities of which they are part in a manner disproportionate to their abundance (Power et al., 1996). Keystone species strongly influence the abundances of other species and the ecosystem dynamic (Piraino et al., 2002). Therefore, it is important to identify keystone species, notably to maintain ecosystem integrity, and biological diversity in the face of exploitation and other stresses (Naeem and Li, 1997; Tilman, 2000). However, it is expected that all species of a given ecosystem rank in a continuum of levels of ‘keystoneness’, with only some designated to be keystone species.Identifying keystone species in a given ecosystem may be thus formulated as

1. Estimating the interaction strength as the impact on the
different elements of an ecosystem resulting from a small
change to the biomass of the species to be evaluated for its
keystoneness.
2. Deciding on the keystoneness value of a given species as a
function of the impact estimated in (1) and its biomass.

Several studies report on field-based, experimental quantification of interactions strength, as evaluated through the
impacts induced by changes in abundance of one species on the other species in a community (Paine, 1992; Wootton, 1994; Wootton et al., 1996; Berlow, 1999). However, these experiments necessarily focused on few species; thus, they required a priori assumptions on the importance of the interactions, in order to exclude the uninteresting species from the experiment, which can bias the identification of keystone species (Wootton, 1994; Bustamante et al., 1995).

Although the importance of indirect interactions is recognized (Wootton, 1993, 1994; Yodzis, 2001), their explicit consideration within a purely experimental approach is difficult. Most indirect interactions are weak, which seemingly justifies their being neglected. However, indirect interactions can also be magnified by cascading effects (Brett and Goldman, 1996; Pace et al., 1999), and thus need to be taken into account.

Moreover, empirical measurements of interaction strengths are usually limited to easily accessible, sessile
or errant macrobenthic species (Paine, 1992, 2002; Wootton, 1994; Wootton et al., 1996; Berlow, 1999), or microscopic
species (Naeem and Li, 1997), because these are easier to manipulate and control than nektonic species (especially
large fish, or marine mammals), for which only indirect evidence is available (Power et al., 1996).

Finally, empirical measurements, although very important,are expensive and time consuming (Ernest and Brown, 2001).Consequently, and owing to the spatio-temporal heterogeneity of habitats, physical conditions, and densities of organisms(Paine, 1994, 2002; Piraino et al., 2002), published results tend to be case-specific and context-dependent (Power et al., 1996).

The modelling approach allows overcoming some of the difficulties of the experimental quantification of keystoneness. Through a model it is possible to estimate the strength of the interactions between model functional groups (here referred to as ‘species’, although often composed of groups of species with similar sizes and feeding habits). Therefore, the modelling approach provides at least a pre-screening analysis and allows for improved planning of subsequent field experiments.

This suggestion is not new. Previous estimates of keystone species from mathematical models exists, based on successive elimination of functional groups from a trophic web and evaluating impacts on the other functional groups using a graph theoretical method (Jordan et al., 1999; Jordan, 2001; Sole and Montoya, 2001), or evaluating changes in the biomass of ecosystem with dynamic models (Okey et al., 2004a).

However, estimating the trophic impact on the functional groups of an ecosystem resulting from a change (increase) of
the biomass of a single group can be achieved more directly and rigorously through the mixed trophic impact matrix, M, as adopted for food webs by Hannon (1973) from input–output analysis of Leontief (1951). Each element of the matrix represents the relative change of biomass that would result from an infinitesimal increase of the biomass of the functional groups in the rows (Ulanowicz and Puccia, 1990). Thus, M can be used to estimate the total effect of one functional group on all the others in a given model.

The analysis of the mixed trophic impacts presented here was applied to a suite of mass-balance models built with the software package Ecopath with Ecosim (http://www.ecopath.org; Christensen and Walters, 2004), allowing the estimation of keystoneness for the functional groups of models representing marine ecosystems spread over
the world. The results allow us to rank groups by their keystoneness, which then can be compared with ranking from
previous studies, and with the ecological experience resulting from previous experimental research.

Also, the use of models constructed using the same approach, i.e., using Ecopath with Ecosim, allows standardizing the evaluation of species’ roles and the quantification of the interaction strengths in various environments, thus providing at least some of the pre-screening alluded to above, and required for the design of experimental studies.
The present paper aims to answer the following questions:

(1) How can we use the matrix M obtained for mass-balance
models to quantify the total impact of one functional
group on the others in the ecosystem?
(2) Do the resulting estimates obtained in this manner make
ecological sense?
(3) How do we ensure that our measure of keystoneness
correctly balances the strength of interactions measured
through the total effects (by and on species) and the effect
that species exert due to their biomass?
(4) What are, in general, the major features of keystone
species identified in a comparative analysis of models representing different ecosystems?

Methods

Ecopath approach

Ecopath is the core routine of the Ecopath with Ecosim (EwE), a software package based on an approach proposed by Polovina (1984) and subsequently upgraded with a variety of ecologi-cal and theoretical approaches (Christensen and Pauly, 1992; Walters et al., 1997, 2000; Pauly et al., 2000; Christensen and Walters, 2004). Ecopath allows construction of a massbalance model of a given trophic network by representing the ecosystem functional groups as interacting by means of feeding relationships and, when necessary, subjected to fishing (Christensen and Walters, 2004). The balance of mass (energy, or nutrients) for any functional group (i) of the network is obtained by setting its production equal to the sum of the consumption components, expressed as

where production, on the left side of the equation, is expressed as the product between the production–biomass ratio (P/Bi) and the biomass (Bi), and the right-hand side terms are the sum of the predation terms, each expressed as the product of the consumption–biomass ratio (Q/Bj), the biomass of the predators (Bj) and the proportion of the prey i in the diet of the predator j (DCij); the net flow trough the boundaries of the system, i.e., dispersal (Ei); the fishing exploitation, represented through the catches (Yi); the accumulation or depletion of biomass (BAi); and non-predation natural mortality, expressed by means of the ecotrophic efficiency (EEi). The resulting system of equations, when solved, provides a snapshot of the flows within a trophic web (numerous examples are reported in Christensen and Pauly, 1993; see also http://www.ecopath.org).

Mixed trophic impact

Given the mass-balance model of a trophic network, the mixed trophic impact is estimated for each pair of functional groups (i, j, interacting directly or not) of the trophic web, by means of the net impact matrix. According to Ulanowicz and Puccia (1990), the net impact of i on j (qij) is given by the difference between positive effects, quantified by the fraction of the prey I in the diet of the predator j (dji), and negative effects, evaluated through the fraction of total consumption of I used by predator j (fij). Therefore the resulting matrix of the net impacts, Q, has elements:

The mixed trophic impact mij is then estimated by the product of all the net impacts qij for all the possible pathways
in the trophic web that link the functional groups i and j. Ulanowicz and Puccia (1990) demonstrated that the matrix of
the mixed trophic impacts, M, can be obtained by the inverse of the matrix Q, as it is calculated in EwE (Christensen et al.2004). Table 1 shows the elements of such matrix M derived from a food web representing the ecosystem off the coast of Newfoundland (Bundy, 2001).

The elements mij of the matrix M quantify the direct and indirect impacts that each (impacting) group i has on
any (impacted) group j of the food web (Ulanowicz and Puccia, 1990). Positive/negative values of mij indicate the increase/decrease of biomass of the group j due to a slight increase of biomass of the impacting group i. Thus, the
mixed trophic impacts represent the first order partial derivatives, in term of biomass, of the Ecopath master equation
(1). This can be illustrated by Ecosim simulations, as shown below.

Moreover, from Eq. (2) one can tell that negative elements of matrix M indicate a prevailing of negative effects, i.e. the
effects of the predator on the prey; analogously, positive elements of M indicate prevailing effects of the prey on the predator. Therefore, negative elements of M can be associated to prevailing top-down effects and positive ones to bottom-up effects.

Ecosim

The dynamic routine of the EwE package, Ecosim, is based on a set of differential equations, derived from the Ecopath’s master equation (1), allowing dynamic representation of the system variables, i.e. biomasses, predation, andproduction (Christensen and Walters, 2004). In order to describe dynamically the predator–prey interaction, Ecosim uses Lotka–Volterra relationships modified to account for foraging arena theory (Walters et al., 1997, 2000), which allow to avoid the unrealistic Lotka–Volterra assumption of uniform and random distribution of interactions, typically assumed with the mass-action functions (Walters and Martell, 2004). In foraging arena theory, rather, the biomass of the prey available to predators is only a vulnerable fraction of total biomass, with exchanges rates between the vulnerable and the invulnerable states calculated using vulnerability coefficients set by the user (Christensen and Walters, 2004).

Comparison between simulation outputs and matrix M

In order to test if the elements of M represent the relative change of biomass that would result from an infinitesimal
increase of the biomass of the functional groups in the rows, a set of Ecosim simulations (covering a period of 100 years) were done. Since biomass and production are multiplicative terms in EwE formulations, changing proportionally one or the other has identical resulting effects. Therefore, since changes in production are easier to implement in Ecosim than biomass changes, simulations were done perturbing production. In each simulation, the initial (Ecopath) production was kept constant for the first 10 years and then perturbed. From year 10 to year 20 of each simulation, indeed, the initial production of the functional group being observed (corresponding to the
group in the row of the M matrix) was decreased linearly to 90% of its initial value, then maintained constant until the end of the simulation and the resulting ecosystem changes observed. Following Christensen and Walters (2004), a forcing function was used, in Ecosim, to represent this production decrease. The perturbation on the observed group produced dynamic changes on the biomass of other groups in the web. The resulting relative changes in the biomass (excluding detritus and the observed group), were compared with the corresponding elements of the row of the matrix M. Therefore, each mij was

 

Estimating total impact

Given that there is agreement between the mij and Sij (see below), it is appropriate to estimate the total impact of one
functional group on the ecosystem through the mixed trophic impact. Since each impact can be either negative or positive, we define our proposed measure of the overall effect of each group as 

in which the effect of the change in biomass on the group itself (i.e., mii) is not included.The normalized flows in Eq. (2) bound the sum of the elements of the rows and columns of matrix Q between the interval −1 to +1 (Ulanowicz and Puccia, 1990), which guarantees that the overall effect, estimated as the sum of the elements of M as in Eq. (4), does not need to be weighted by the numbers of groups used to describe the trophic network.

Accounting for the positive and negative contributions to the overall effect estimated as in Eq. (4), allows evidencing for a given group, respectively, bottom-up and top-down effects contributing to its overall effect.

Identifying an index of keystoneness

Several alternatives for combining overall effect and biomass in our keystoneness index were explored. The biomass component was adequately represented by the contribution of the functional group to the total biomass of the food web (Power et al., 1996), that is:

was strongly influenced by the biomass proportions, attributing high keystoneness to functional groups with low biomass (as required) and low overall effect (which should not be the case; see Fig. 1A).Therefore, in order to balance the two components (overall effect and biomass), we define our index of keystoneness as follows: 

groups such as macrophytes. Moreover, the negative and positive contributions to the overall effect, as outlined above, allow calculating the bottom-up and top-down effects contributing to the keystoneness index of Eq. (7).

The estimation of the total impacts and of the ‘keystoneness’ proposed here (Eq. (7)) was applied to each living functional group (thus excluding detritus groups) of a suite of 33 Ecopath models considered well described and
detailed, i.e., with a minimum of 24 functional groups used for describing the ecosystem. These models represent the
trophic web of marine ecosystems that differ for location, period and type of habitat represented. The proposed analysis was applied also to 9 models representing different upwelling ecosystems in different periods, for a total of 42 models analysed.

Results

The comparison between the observed changes resulting from the Ecosim simulations, Sij, and the changes predicted by means the mixed trophic impact, mij, are given in Figs. 2–4, respectively, for the Prince William Sound, the Gulf of Thailand and the North Pacific model. The comparisons showed, with few exceptions, a high Spearman’s (rank) correlation (Zar,1999) between changes observed by means of the dynamic simulation (abscissa) and the correspondent row of the matrix M (ordinate of each plot of the figures). The bisecting line is also shown, together with the rank correlation coefficient between observed and predicted values. The Prince William Sound functional groups (Fig. 2), showed low rank correlations only for the herbivorous zooplankton and adult salmon (both with R2 = 0.02), while the analysis on the Gulf of Thailand model (Fig. 3) resulted in low correlations for rays (R2 = 0.03), phytoplankton (R2 = 0.05), ‘trashfish’ (R2 = 0.09), Priacanthus spp. (R2 = 0.12) and juveniles Nemipterus spp. (R2 = 0.13). The lowest rank correlation for the North Pacific model (Fig. 4) was R2 = 0.17 for the large blue shark group. However, most of the functional groups show high agreement between simulated biomass change and mixed trophic impacts. Thus the overall rank correlations were 54.8%, 51.5% and 54.5%, respectively, for the Prince William Sound, the Gulf of Thailand and the North Pacific. Results of non-parametric test of correlation significance (Zar, 1999) evidenced that the above reported overall rank correlations are all significant at p < 0.001.

The high agreement between the mij and Sij, show that it is legitimate to use Eq. (4) to draw inferences on total
impacts from the M matrix, and subsequently applying Eq. (7) to calculate the ‘keystoneness’ of its various functional
groups. Fig. 5 represents the estimated keystoneness index for the functional groups of four selected models, representing the ecosystems of Newfoundland (after Bundy, 2001), Eastern Tropical Pacific (Watters et al., 2003), Chesapeake Bay (Baird and Ulanowicz, 1989) and Bolinao reef (Alin˜ o et al., 1993).

As might be seen, the keystoneness index estimated here show a common pattern in different ecosystems, and allows
ranking the groups of each model by decreasing keystoneness. The keystone functional groups are those that have value of the proposed index close to or greater than zero.

Different groups of marine mammals (cetaceans, harp and hooded seals) show high keystoneness in the Newfoundland ecosystem, where capelin, a forage species, ranks second. Toothed whales rank second in the Eastern Pacific, between large and small sharks, which rank first and third, respectively. For the Chesapeake and Bolinao ecosystems, zooplankton has the highest keystoneness.

Tables 2 and 3 lists, for the 42 Ecopath models analysed, the four functional groups that ranked highest in term of their keystoneness. The top-down effect as percentage contributions to the keystoneness for each species, evaluated through the proportion of the negative values contributing to the sum in Eq.(4), is also reported in Tables 2 and 3.

Table 2 contains the results of the analysis applied to 33 models considered well described and detailed on the basis
of the number of groups used to describe the ecosystems (minimum 24 groups, maximum 59 groups). Marine mammals often have high keystoneness, and rank first (Alaska gyre, Azores, Newfoundland, Norwegian Barents Sea models) or second (Easter tropic Pacific models, Floreana, Georgia Strait, Newfoundland 1985–1987) in many models. Sea birds rank third in Lancaster model and fourth in the Prince William Sound model. Sharks and rays have high keystoneness in many ecosystems, ranking first (Biscaya, Easter tropic Pacific, Floreana, Hong Kong, Morocco models) or second (Gulf of Thailand and Western Gulf of Mexico) in many models. All these

functional groups have effects on the other components of the ecosystem mainly via top-down impacts.A few ecosystems (North Coast Central Java, Prince Williams Sound) show high keystoneness for phytoplankton, while in coastal and semi-enclosed marine environments(Bolinao reef, Chesapeake Bay, Georgia Strait, Gulf of Thailand), the zooplankton group has high keystoneness.

Table 3 presents analogous results for the nine models of upwelling ecosystems. The seabirds are important
in the California (both models) and Peru (1960) upwelling ecosystems, while marine mammals rank second and third
in the California models. Mackerel has high keystoneness in California (77–85), Peru and Northwest Africa. The zooplankton and phytoplankton groups rank for keystoneness among the first four in all the upwelling ecosystems analysed. Moreover, models of the same upwelling system in different periods seem to show an increase of keystoneness of intermediate trophic levels concomitant with a decrease of keytoneness of top predators over time: marine mammals are ranking second in California model for 1965–1972.while they rank third in the model for the later period (1977–1985); in Peru 1960 cormorants are ranking first while later periods show high keystoneness for lower TL functional groups (large scombrids in 1964–1971 and horse mackerel in 1973–1981).

Discussion

The high general agreement between the mixed trophic impacts estimated by the mass-balance routine, Ecopath,
and the observed relative changes in the biomasses obtained with long-term Ecosim simulations, allowed use of the mixed trophic impact matrix M as a straightforward basis to quantify the effect one functional group has on all the other groups in the ecosystem. Thus, the method proposed allows estimating the keystoneness of the species or group of species in a model without having to perform the time-dynamic simulations, as performed, e.g. by Okey et al. (2004a), thus avoiding differences induced by different simulation protocols.

The wide use of EwE and the easy implementation of the method proposed here allow standardizing the procedure to
estimate the keystoneness of functional groups in models of different marine ecosystems and of the same ecosystem at different periods or scales. Although the methodology has the potential for ranking groups across models we examined here rankings of keystoneness within models.

Generally, marine mammals ranked high in most ecosystems, but in some, they had low rank; thus, spotted dolphin
and baleen whales rank 24 and 33 in Eastern Pacific model. Similarly, skates and sharks ranked high in the Newfoundland model, but very low in the Eastern tropical Pacific model (dogfish was only 30th). Seabirds appear to have high keystoneness in shallow and very productive environments (upwelling systems), but low keystoneness in open seas, ranking e.g., last in Newfoundland and the Easter Tropic Pacific. In shallow coastal ecosystems, phyto- and zooplankton can have high keystoneness. Indeed, the lower part of the trophic web appears to be very important in these ecosystems, where other benthic groups also tend to have high keystoneness index.

Thus, we concur with Power et al. (1996) and Piraino et al. (2002), that keystones are not straightforwardly predicable.
Certainly, and perhaps surprisingly, there is no general correlation between trophic level and keystoneness.

The index proposed assign low keystoneness to functional groups with high abundance, whether they have high impacts or not, thus allowing us to distinguish between keystone species, and dominant and structuring species—with which keystones must not be confounded (Power et al., 1996). Macrobenthic producers generally have low keystoneness, e.g., in the Bolinao reef model, where seagrasses and seaweeds have low keystoneness index.

Another important result is that keystone species do not always exert their high impact by means of top-down effects,
a feature initially suggested to be a defining characteristic of keystone species (Paine, 1969), and thus proposed for identifying keystones (Davic, 2003). In fact, although results highlight that keystone functional groups exert their effect via topdown in many ecosystems (e.g., Newfoundland and Eastern tropical Pacific), keystoneness via bottom-up effects appears also very important in others (e.g., North Sea, Prince Williams Sound). These results are not contradicting previous works highlighting the high importance of top-down effects in keystoneness (Paine, 1966; Menge et al., 1994; Estes et al., 1998) and confirm that bottom-up influences can also be important
(Bustamante et al., 1995; Menge, 1995). Moreover, upwelling systems show a prevalence of keystone functional groups with intermediate positions in trophic webs. This indicates that these intermediate functional groups contribute to the mixture of top-down and bottom-up control typical of wasp-waist ecosystems (Cury et al., 2000). Moreover, changes over time of keystone functional groups in upwelling systems seem to evidence the increase of keystoneness of intermediate functional groups and the concomitant decrease of keystoneness of high trophic level groups thus suggesting an increase of wasp-waist control over time.

Conclusion

Since the first definition of keystone species by Paine (1969), their importance for conservation purposes has been widely recognized. However, difficulties in experimental approaches and the different roles assumed by species in time and space (Paine, 1994; Menge et al., 1994; Estes et al., 1998) lead to increasing scepticism about the original definition of the keystone species concept and the flourishing of different definitions (Mills et al., 1993; Bond, 2001; Davic, 2003). Therefore, although the importance of the concept is well recognized, a widely accepted approach for quantifying keystoneness is still lacking (Bond, 2001).

The straightforward methodology proposed here may contribute to filling this gap. The mixed trophic matrix upon
which it relies allows including direct and indirect effects of trophic interactions. Moreover, the broad use of EwE will facilitate application of the methodology to the large number of ecosystems for which models exist, thus providing a broad empirical basis for the new approach. In view of the key role experiments must continue to play (Paine, 1966, 1994; Menge et al., 1994; Power et al., 1996) the methodology proposed can also be used for a priori identification of keystone species, thus focusing empirical studies.

Acknowledgements

Simone Libralato gratefully acknowledges the Istituto Centrale per la Ricerca Applicata al Mare-ICRAM (Chioggia) for supporting his visiting period at Fisheries Centre—UBC, during which this work was deeply improved. S. Libralato also acknowledges Fabio Pranovi (Universita Ca’ Foscari, Venezia) for advice and support. Villy Christensen and Daniel Pauly gratefully acknowledge support from the Sea Around Us Project, initiated and funded by the Pew Charitable Trusts, Philadelphia. D. Pauly also acknowledges support from Canada’s Natural Science and Engineering Research Council.

references

 

Thank you to all those who submitted reviews in 2024. 

Excel randomly picked Harry R as the winner.

Summary

Emitting b radiation with a half-life of 5730 years, Carbon 14 follows the cycle of the stable element C, one of the components of the living materials, in which it is diluted. Carbon-14 is indeed around 10^-12 times less abundant than stable carbon. The main source of exposure is due to naturally occurring ¹⁴C (cosmogenic origin).

About the impact of chronic releases, the consensus is that ¹⁴C behaves in the same manner as the stable ¹²C isotope (representing 99% of carbon). Carbon-14 transfers between two compartments of the environment are generally evaluated based on the assumption that the isotopic ratio between the radioactive carbon and the stable carbon (considered to be ¹²C) is maintained, between the organism and the surrounding environment. This assumes that the transfer of the trace radionuclide ¹⁴C is identical to that of ¹²C and that equilibrium between the two compartments is achieved. Under this assumption, the impact on the environment and populations can only be evaluated for environmental releases and concentrations that are constant over time, generally by using average annual values.

The environmental toxicity of ¹⁴C is only related to radioactive emissions of the pure, low-energy b type. This toxicity is mainly the result of internalisation, essentially by ingestion.

Characteristics

Chemical characteristics

Carbon-14 (¹⁴C) is a radioactive carbon isotope present in infinitesimal quantities in the atmosphere. Carbon-12 and carbon-13 are the stable carbon isotopes and respectively represent 98.9% and 1.1% of the total carbon. Carbon-14 only exists in trace quantities. The chemical forms of ¹⁴C vary according to the method of production. In the environment, ¹⁴C exists in two main forms:

– as ¹⁴CO₂, it acts as stable carbon dioxide, which means it can remain in gas form in the air, becoming bicarbonate and carbonate in water

– during photosynthesis, ¹⁴CO₂ is incorporated into the organic material, forming its carbon skeleton. Equilibrium between the specific activity of atmospheric carbon and that of organic material is then finally reached and maintained by carbon recycling.

Nuclear characteristics

Carbon has 15 isotopes, with masses of 8 to 22. Only isotopes 12 and 13 are stable. The radioactive half-life is higher than a year only for carbon-14, its maximum value for the other isotopes being around 20 minutes.Carbon-14, a beta^– emitter, gives rise to stable ¹⁴N with 100% yield.

Origins

Natural origins

Natural ¹⁴C results from cosmic neutrons acting on nitrogen atoms in the stratosphere and in the upper troposphere (¹⁴N +n →¹⁴C+¹p). The annual production level is around 1.40 x 10¹⁵ Bq and the atmospheric stock of carbon-14 at equilibrium is around 140 x 10¹⁵ Bq (UNSCEAR, 2008). Production fluctuates due to variation in cosmic ray intensity. This fluctuation results from various factors that are not yet well understood, but mainly include the 11-year solar cycle and, on a larger temporal scale, variations in the terrestrial magnetic field that serves as a shield against cosmic rays (Garnier-Laplace et al., 1998).

Artificial origins

During nuclear explosions, the emitted neutrons interact with atmospheric nitrogen, as cosmic neutrons do, to form carbon-14, according to the same reaction as above: ¹⁴N +n →¹⁴C+¹p.

 Nuclear explosions carried out before 1972 released around 3.5 x 10¹⁷ Bq of carbon-14. Later explosions increased this amount by around 1% (UNSCEAR, 2008).

 In nuclear reactors, carbon-14 is produced from reactions in the fuel, the core structural materials and the moderator. The production rate depends on the spectrum and the neutron flux, on cross-sections and on the concentration of the following target elements: uranium, plutonium, nitrogen and oxygen. Water in the primary coolant circuit of pressurised water reactors contains excess hydrogen that combines with oxygen from radiolysis. In this reducing environment, compounds such as methane (CH₄) and ethane (C₂H₆) form. Most of the carbon-14 released in a pressurised water reactor is in the form of alkanes. Various estimations indicate that the annual production rate for a light water reactor (pressurised or boiling water reactor) is between 0.5 and 1.9 x 10¹² Bq/GWe/year, with carbon-14 mainly taking organic forms (CH₄). The rest is released during reprocessing, or remains in the fuel cladding and is later disposed of as solid waste (Garnier-Laplace et al., 1998).

• Releases by irradiated fuel reprocessing plants

Spent nuclear fuel ¹⁴C is released during the dissolution step in reprocessing plants. Depending on the operating mode, these releases are continuous or discontinuous. In reprocessing plants that use the PUREX process (e.g. the AREVA NC La Hague plant), the ¹⁴C is mainly released as CO₂. Commissioning of the UP3 and UP2-800 plants at La Hague resulted in increased annual gaseous ¹⁴C releases starting in the early 1990s. In 2009, the gaseous releases of carbon-14 at the site corresponded to 1.45 x 10¹³ Bq and the liquid releases corresponded to 6.12 x 10¹² Bq. Carbon-14 in fuel cladding is not released during dissolution and remains trapped. It is disposed of later as solid waste.

At the Sellafield plant in the UK in 2009, the ¹⁴C gaseous releases reached 3.8 x 10¹¹ Bq and the liquid releases, 8.2 x 10¹² Bq (Sellafield Ltd, 2009).

• Various sources (medical, industrial, research) 

 In research, carbon-14 is widely used in carbonate form for isotopic labelling of molecules. The activities used are greater than 1 GBq. For example, carbon-14 is used to study metabolic dysfunction related to diabetes and anaemia. It can also be used as a marker to track the metabolism of new pharmaceutical molecules. More generally, carbon-14 can be used to uncover new metabolic pathways, and to identify their normal functioning and any departures from it, e.g. for photosynthesis (Calvin and Benson, 1948) or, more recently, for the methylaspartate cycle in halobacteria (Khomyakova et al., 2011).

It is assumed that all ¹⁴C used for labelling molecules will be released into the atmosphere as CO₂. According to UNSCEAR, the annual production of ¹⁴C is equivalent to 3 x 10¹⁰ Bq per million inhabitants in developed countries and to 5 x 10¹³ Bq worldwide. This estimation is based on the results of a 1978 US study. A 1987 British estimation led to values at least twice as high (UNSCEAR, 1993).

Environmental concentrations

• Carbon-14 background in the environment and changes over the last 60 years

In the terrestrial environment, the consensus (relatively well supported by observations) is that the specific activity, expressed in becquerels of ¹⁴C per kilogram of total carbon, is constant in the environmental components and at equilibrium with the specific activity of atmospheric CO₂ (Roussel-Debet et al., 2006, Roussel-Debet, 2007, 2009). Uninfluenced by nuclear facilities, the ¹⁴C specific activities for the biological compartments of the terrestrial environment reached their maximum values (more than 400 Bq/kg of C) in the mid-1960s, due to fallout from atmospheric nuclear arms testing, then at its height (Figure 1). These activities have slowly decreased since then (by less than 0.5% per year) with the end of testing and the continuous increase in CO₂ from fossil fuels (gasoline, coal, gas). The specific activities of terrestrial biological compartments are currently around 238 Bq ¹⁴C/kg C (2009 measurements), which is very close to 1950 values (226 Bq/kg C), before atmospheric testing.

In aquatic environments, the specific activity of ¹⁴C varies with its dilution in carbon substances, particularly carbonates from old sedimentary rocks lacking carbon-14. Unlike the terrestrial environment, ¹⁴C in freshwater ecosystems is not in equilibrium with atmospheric CO₂: freshwater-specific activity is then lower, around 200 Bq/kg C.

Based on the specific activity and the total proportion of carbon in the various environmental matrices (air, plants, animals, and thus food products), the activity concentration for the ¹⁴C in these matrices can be estimated (Figure 2). The more carbon the product contains (sugars, oils, grains, etc.), the higher the activity.

Depending on the proportion of carbon per wet mass unit of food product, the activity concentration of these products varies between less than 15 (lettuce, mussels) and more than 80 (grains) Bq/kg wet. Atmospheric activities vary from 3 x 10^-2 to 7 x 10^‑2 Bq/m³. Carbon-14 thus has the highest environmental activities amongst the radionuclides released from nuclear facilities.

• Influence of nuclear facilities

 With atmospheric releases of around 2 x 10¹³ Bq/year of ¹⁴C, mainly as CO₂, the AREVA-NC La Hague plant causes an added carbon-14 activity (above the natural background) regularly detectable in the site’s terrestrial environment, leading to specific activities of 500 to 1000 Bq/kg C, and occasionally 2000 Bq/kg C. The corresponding activity concentrations range from 20 to 140 Bq/kg of wet grass or vegetables, compared with a background of around 5 to 20 Bq/kg of wet material in this type of matrix. In milk and meat, this contamination is also significant although much less so, probably due to a feeding component outside the area influenced by the atmospheric releases. Note that the maximum radioactivity in the air at ground level after dispersion, set at 1 Bq/m³ by the French order authorising AREVA-NC La Hague releases, would correspond to specific activity in plants of 5000 Bq/kg C, if attained at all times throughout the year.

The carbon-14 addition around nuclear power plants (atmospheric releases of 0.2 to 1×10¹² Bq/year) is extremely limited: the associated specific activity is around 3 Bq/kg C in addition to the 243 Bq/kg of C representing the average background for 1994-2003 (Roussel-Debet et al., 2006), i.e. an added activity of around 1%. This low level is the result not only of low releases, but also of a clear predominance of releases in the form of methane (CH₄), which plants cannot assimilate.

In rivers, the carbon-14 released by nuclear power plants is diluted in the dissolved stable carbon from carbonates, which are found in sediment. This significantly decreases the specific activity of carbon-14 in physical components. For semi-underwater aquatic plants, dilution also occurs in the atmospheric CO₂ used during photosynthesis; the associated specific activities rarely exceed 400 Bq/kg C. For reasons that remain to be elucidated, fish do not seem to benefit from these dilution phenomena. Their specific activity under the influence of nuclear power plants regularly exceeds 600 Bq/kg C and may reach 1000 Bq/kg C.

Metrology, analytical techniques and detection limits

 Carbon-14 in an environmental sample may be quantified by activity measurement or by atom counting. These two destructive techniques require converting the sample to CO₂ (Maro et al., 2008).

Activity measurement 

The carbon contained in the test portion is transformed to carbon dioxide from which a sample is prepared for measurement by liquid scintillation (AFNOR, 2006).

Two sample preparation methods are mainly used: combustion by oxydiser and benzene synthesis (Fournier et al., 1999).

Preparation of samples by oxydiser

The sample is placed in a cellulose cone, which is inserted in a platinum filament. The entire unit is placed in a combustion chamber. Voltage applied to the ends of the filament in the presence of O₂ causes combustion of the sample. The combustion gases are pushed by nitrogen in a column containing Carbosorb®, which traps CO₂ in the form of carbamate. This mixture is eluted from the column by the scintillation liquid and then collected for measurement.

The oxydiser allows to prepare several samples per day for counting. The test portions are generally less than 0.5 g of the dry sample. They must be rich enough in carbon to undergo a complete oxidation.

Combustion yield must be determined on a reference sample labelled for ¹⁴C. This reference sample must be as close as possible in nature and composition to the samples to be analysed.

The ¹⁴C naturally contained in the combustion cone cellulose contributes to the increase in background and thus in higher measurement uncertainty. Background must thus be determined as precisely as possible.

The expression of the sample’s activity in Bq of ¹⁴C per kg of carbon also requires measuring its elementary carbon content, generally by gas chromatography.

The measurement uncertainty, around 30 to 40% (k=2) for activities of around 260 Bq.kg^-1 of carbon (natural level in the environment), makes it difficult to detect low concentrations with this method. This uncertainty can, however, be reduced by increasing the test portions or by combining the measurements of several test portions from the same sample. 

• ¹⁴C analysis by benzene synthesis

The sample is burned in the presence of under pressure oxygen in a combustion bomb. The CO₂ produced is then reduced by a heated reaction with lithium to obtain lithium carbide (Li₂C₂), the hydrolysis of which produces acetylene (C₂H₂), which is trimerised by catalysis in benzene (C₆H₆).

Atom counting

• Principal

• ¹⁴C measurement by accelerator (AMS)

After decarbonation and combustion of the sample, the CO₂ obtained is reduced by H₂ in the presence of powdered iron. The carbon is deposited on the powdered iron and the mixture is pressed into a target to allow for measurement by mass spectrometry. The sample’s ¹⁴C activity is calculated by comparing ¹⁴C, ¹³C and ¹²C beam intensities, measured sequentially, with the CO₂ reference intensities.

The test portions consist in around 0.10 g of material. Uncertainty at the level of the environmental background corresponds to 2 to 3% (k=2).

Accelerator Mass Spectrometry (AMS) is characterised by high sensitivity, which is obtained by good separation of ¹⁴C from other ions having the same mass (particularly nitrogen). It is favoured for low-quantity samples or those containing low levels of organic materials (soil, sediment, sea water, air samples, etc.).

 The activity concentration results are expressed in Bq/kg of dry material, Bq/kg of wet material or Bq/kg of carbon.

The counting vial is prepared by weighing out synthesised benzene and scintillants. Spectroscopy-quality benzene is added if needed.

The activity of the ¹⁴C present in the vial is then measured using liquid scintillation. The result can be directly converted into Bq/kg of carbon.

The test portions consist of 7 to 10 g of finely ground, dry sample. The chemical processing time for one sample is 3 days, 2 more days being necessary for counting. Uncertainty at the level of the environmental background corresponds to 6 to 7% (k=2).

This method is suitable for solid dry samples containing high carbon and for water matrices in the form of carbonate (e.g. barium carbonate). For water matrices, CO₂ is extracted from the sample by acid attack (e.g. addition of orthophosphoric acid) rather than by combustion bomb. The rest of the protocol does not change.

The analysis methods involving oxydiser or benzene synthesis are not well suited for carbon-poor matrices, such as soil and sediment.  

Mobility and bioavailability in terrestrial environments

Carbon-14 data and the models on the fate of this radionuclide in terrestrial environments (Scott et al., 1991; Sheppard et al., 1994; Garnier-Laplace et al., 1998; Fontugne et al., 2004; Tamponnet, 2005a and b) are based on knowledge of the carbon cycle at equilibrium (Ouyang and Boersma,1992). Carbon-14 is integrated in the carbon cycle, which is very complex due to the presence of inorganic and organic carbon, in solid, liquid or gaseous forms (Figure 3).

Soil

The average quantity of carbon in organic material of cultivated soils is in France around 20 g of carbon per kg of dry soil. The soil solution carbon can be in the form of CO₂, carbonate (CO₃^2-) or bicarbonate (HCO₃^–), depending on the pH and the quantity of calcium ions.

Plants

The average CO₂ quantity of gaseous soil phase varies from 0.5 to 1%. It increases in the presence of plants (due to root respiration, the pH decreases and the dissolved CO₂ increases by around 38% per pH unit).

Root absorption of carbon by plants is negligible. Root incorporation from carbonate ions, poorly understood, appears to represent 5% maximum of the total carbon incorporated in a plant. Most of the carbon is assimilated by leaves as CO₂ during photosynthesis. Isotopic discrimination, which depends on the plant’s photosynthetic cycle, is negligible (¹⁴C / ¹²C ratio less than 5% maximum between the plant and the atmospheric CO₂).

CO₂ emanation from the mineralisation of organic soil residues and root respiration tends to increase the concentration of CO₂ in the air, at the plant cover level. The daily flux of CO₂ released by the soil appears to be 2 to 13 g per m². This flux appears to contribute around 10% to the total carbon assimilated by leaves during photosynthesis (Le Dizès-Maurel et al., 2009).

Animals

More than 99% of the carbon incorporated by livestock comes from their feed. Carbon from inhalation is negligible, as is carbon from ingestion of water or soil.

Mobility and bioavailability in continental aquatic environments

Carbon-14 data and the models on the fate of this radionuclide in continental aquatic environments (Sheppard et al., 1994; Garnier-Laplace et al., 1998) are based on knowledge of the carbon cycle at equilibrium (Stumm and Morgan, 1981; Amoros and Petts, 1993).

The ¹⁴C organic compounds released by nuclear facilities are incorporated into the organic carbon of the hydrosystem that receives them (Figure 4).

The inorganic carbon released by nuclear facilities or present in the hydrosystem takes the form of species in the carbonate system (CO_{2 aqueous}/HCO₃^–/CO₃^2-), which is one of the main chemical systems involved in controlling freshwater pH. In most running waters, pH varies from 6 to 9, with bicarbonate forms dominating. Carbon-14 in liquid effluents, released as carbonates, is incorporated in the inorganic carbon. Isotopic dilution varies according to atmospheric exchanges, run-off contribution and exchanges with hydrogeological systems. In all cases, the specific activity of inorganic ¹⁴C must be considered in terms of the value measured in situ for total CO₂, according to the following equation: [CO2]total = [CO2]aq + [HCO3–] + [CO32-].

Water and sediment

Carbon-14 is integrated in the carbon cycle of continental hydrosystems where the main forms are organic carbon (dissolved organic carbon/DOC, 1 to 3 mg of carbon per litre; and particulate carbon, which is highly variable from one hydrosystem to another) and inorganic carbon (essentially in the form of dissolved bicarbonate, 1 to 120 mg of carbon per litre). Humic and fulvic acids represent from 50 to 75% of the DOC, whilst the colloidal forms represent 20%. The particulate forms are also varied: allogenic detrital forms, living organisms and compounds from their decay.

Plants

Transfers to plants are governed by photosynthesis. Photosynthesis is mainly carried out by higher plants, periphytic and planktonic algae, and cyanobacteria. In schematic terms, it can be considered the dominant biological process that influences the concentration of inorganic carbon in the hydrosystem; respiration and bacterial fermentation can be considered negligible. On average, the concentration of total carbon in freshwater plants is 5 x 10⁴ mg of carbon per kilogram of wet material.

Animals

Transfers to animals are governed by ingestion. For aquatic organisms, the processes of respiration and osmoregulation that use inorganic carbon are similarly negligible in the animal’s carbon balance compared to transfers via food ingestion. Carbon concentration in animals varies from one species to another.

Mobility and bioavailability in marine environments

The mechanisms of ¹⁴C transfer in marine and freshwater environments are identical, and the models are based on the assumption that equilibrium is reached due to environmental carbon recycling. Most of the ¹⁴C released into the sea is in dissolved inorganic form and is incorporated by organic material. Close to release points, when the variations in the quantities released are rapid and large, equilibrium between the specific activities of the organic material and the sea water is not always reached (Fiévet et al., 2006).

Sea water

In the Channel, the research of Douville et al. (2004) indicates that the ¹⁴C in sea water at Cap de La Hague mainly takes the form of dissolved inorganic carbon (dissolved CO₂, HCO^3-, CO₃^2-), which is the predominant form of carbon in sea water, with activities between 300 and 800 Bq.kg^-1 of carbon.

Seeweed

As in the case of freshwater plants, the transfer of ¹⁴C to seaweed occurs by photosynthesis. The total carbon concentration in seaweed is roughly equivalent to the freshwater plant concentration. This concentration was found to be 8 ´ 10⁴ mg of carbon per wet kilogram of the brown seaweed Fucus serratus, an example of the algal flora of north-western European coasts. Used as a model compartment for ¹⁴C exchanges between sea water and a photosynthetic organism, this alga was used to estimate a biological half-life for ¹⁴C of around 5 months. The value of this parameter explains the absence of equilibrium close to the release point (Cap de La Hague), where the variations in seawater ¹⁴C concentration are large and rapid, due to the history of releases by the AREVA NC reprocessing plant (Fiévet et al., 2006).

Animals

As in the case of the terrestrial and freshwater animals, transfers to marine animals are mainly governed by ingestion. Although cell membranes are permeable to bicarbonates dissolved in water, the quantity of absorbed carbon that they represent is low compared to the carbon incorporated in organic material. The carbon concentration by unit of wet weight in marine animals varies a great deal from one organism to another, especially due to the different water contents (e.g. jellyfish, bivalves, gastropods, echinoderms, crustaceans, fish, etc.). The limpet has been used as a model compartment for ¹⁴C exchanges between sea water and a grazing animal, making it possible to estimate a biological half-life for ¹⁴C of around 8 months. This half-life integrates all the transfer pathways between the sea water and the gastropod’s flesh, including ¹⁴C incorporation from the animal’s food source. Biological half-life is estimated to be around 1 month in mussels, which are used as a model of filtering organisms (Fiévet et al., 2006). Although there is great variability in the speed of carbon recycling between sea water and the different biological compartments, these half-life values clearly explain why a state of equilibrium is not reached where the sea water ¹⁴C concentration may vary rapidly, close to release points for example.

Mobility and bioavailability in semi-natural ecosystems

This section is based on the international literature review conducted for the revision of the IAEA handbook on parameter values for predicting radionuclide transfer in terrestrial and temperate continental aquatic environments (IAEA, 2010).

Forests

There is no specific information on the mobility and bioavailability of carbon-14 in forest ecosystems.

Artic ecosystems

There is no specific information on the mobility and bioavailability of carbon-14 in arctic ecosystems.

Alpine ecosystems

There is no specific information on the mobility and bioavailability of carbon-14 in alpine ecosystems.

Environmental dosimetry

The effects of exposure to ionising radiation depend on the quantity of energy absorbed by the target organism, expressed by a dose rate (µGy/h). This dose rate is evaluated by applying dose conversion coefficients (DCCs, µGy/h per Bq/unit of mass or volume) to radionuclide concentrations in exposure environments or in organisms (Bq/unit of mass or volume).

The characteristic ¹⁴C DCCs were determined without considering decay products and without RBE weighting. Version 2.3 of EDEN software (Beaugelin-Seiller et al., 2006) was used, taking into account shape, dimensions and chemical composition of the organisms and of their environments, as well as their geometrical relations. The modelled species were chosen as examples.

Except in the particular case of the fescue (10^-3 µGy/h per Bq/kg wet), internal exposure is generally characterised by DCCs of around 10^-5 µGy/h per Bq/kg wet.

External exposure is characterised by lower DCCs that vary according to the organism, within a range of 10^-10 and 10^-5 µGy/h per Bq/kg.

 For more details on how to calculate DCC, see the Environmental Dosimetry Sheet.

Environmental toxicity

 

 

 

 

 

 

 

 

 

 

History of Taxonomy

Science creates categories and classification systems to make sense of the natural world. In the case of living organisms, which include millions of species that evolved through several billions of years of Earth history, and whose characteristics (especially fossil species) and evolutionary relationships are often imperfectly understood, classification becomes arbitrary. Add to this the fact that specialists working in different fields may have different approaches or preferences, and it is easy to see how the subject can become confusing, and ideas and methodologies have changed radically over time.

Aristotle was the first to give the first detailed classification of living things. His classification of animals was:

Blooded (vertebrates)
Viviparous quadrupeds (land mammals)
Birds
Oviparous quadrupeds (reptiles and amphibians)
Fish
Cetaceans (Aristotle did not realize their mammalian nature)
Bloodless (invertebrates)
Land arthropods (insects, arachnids, myriapods)
Aquatic arthropods (mostly crustaceans)
Shelled animals (shelled mollusks, echinoderms, etc.)
Soft animals (cephalopods, etc.)
Plant animals (cnidarians, etc., which superficially resemble plants)

However, he had made no effort to classify plants or fungi. His ideas were essentially based on the idea of the scala naturae, the “Natural Ladder” according to which the entire natural world could be arranged in a single continuum. During the medieval period, this became incorporated into the idea of the Great Chain of Being.

Classical and medieval thinkers used logical and philosophical categories, but these were based on the most general principles, and while perhaps useful for abstract philosophy, were not much use in understanding the natural world. In the theocratic Middle Ages, this didn’t matter much, but with the progressive advance of knowledge during the Renaissance, the Age of Reason, and the Enlightenment, there developed an interest in the secular world for its own sake. Botanists especially were fascinated by exoteric new plants discovered during the voyages of exploration. It is not coincidental then that the father of modern biological classification was a botanist, Carl Linne, better known by his Latin name Linnaeus. Linnaeus’s simple yet brilliant idea was to distinguish nomenclature – the science of naming – from description. He, therefore, rejected the long-winded descriptive names of plants used by his predecessors and contemporaries and replaced them with a simple two-name system, a generic and a specific (think surname and given name, e.g. Smith, John). These were then grouped in hierarchies such as class, order, and so on. With only slight refinements, the Linnaean system is the scientific, biological classification system still used today.

Scientists and naturalists like Linnaeus in Sweden, and later the anatomist and naturalist Georges Cuvier in France, and Owen in England, and their colleagues and co-workers, established in the 18th and early 19th century the science of what we now know as Taxonomy. Taxonomy is concerned with discovering, identifying, describing, and naming organisms. For this to work it requires institutions to hold collections of these organisms, with relevant data, carefully curated: such institutes include Natural History Museums, Herbaria, and Botanical Gardens. Richard Owen for example established the British Museum of Natural History in London, where his statue still resides.

Linnaeus, like his 18th-century contemporaries, had a static, biblical view of the world. All the species that existed and that he described were the same as those created by God, and every species that ever lived was still alive today. This simple worldview was undermined in the late 18th and early 19th century by the discovery of fossil species different from anything alive. This led to the birth of paleontology, under men like Cuvier and Owen. Cuvier, the father of paleontology, who was the first to name and correctly identify many fossil animals (e.g.: Pterodactylus, Mosasaurus, Didelphys, Palaeotherium) was still a creationist but explained the existence of strange armoured fish, ichthyosaurii, tertiary mammals, mastodons, and the rest in terms of repeated catastrophies, after which God would recreate the world. The biblical flood was considered the most recent of these catastrophes.

Owen, who named the order (now superorder) Dinosauria, instead adopted a Goethean concept of evolving archetypes (but not of physical evolution; Owen was strongly opposed to Darwin’s theory when it came out). By these sorts of mechanisms, Cuvier and Owen could explain the existence of antediluvial (before the flood) monsters. All this changed with Darwin’s discovery of the principle of evolution. Darwin, Huxley, and Haeckel established the evolutionary paradigm, and, like Cuvier and Owen, had no problem identifying prehistoric life with Linnaean categories. What evolution did was to make the Linnaean system more dynamic? Thus, Huxley was able to show that Archaeopteryx, the first bird (Class Aves) was also a transitional form between reptiles (Class Reptilia) and modern birds. This synthesis of Darwinian science and Linnaean taxonomy was further elaborated on in the mid-20th century by vertebrate paleontolgists Romer and Simpson and came to be later known as Evolutionary Systematics

In the 1980s, an alternative to Evolutionary Systematics, called Phylogenetic Systematics, or Cladistics became popular, especially among vertebrate paleontologists. Cladistics is more properly considered under the next Unit, Phylogeny. The central difference between the Linnaean and Cladistic systems is that one is a taxonomic, classification system, the other a means of constructing phylogenetic hypotheses; or in less jargonesque language, deciding which of several possible evolutionary trees is likely to be the more correct one (which doesn’t mean it is the right one, as discoveries can always overturn the current hypotheses) [1]. Over the past few years, an attempt has been made to develop a formal, cladistic system of taxonomy and nomenclature to replace the linnaean system, called the Phylo Code, but this is yet to catch on at a wider level in biology.

One might suppose that classification should reflect phylogeny, and that phylogeny would automatically result in a superior classification system, but this is not necessarily the case. Taxonomies may involve organisms that appear to be closely related but are not, phylogenies can result in unwieldy systems, or phylogenetic definitions can be overturned by discoveries and hypothesis Taxonomies can be overturned as well, but are generally more robust (Benton 2007).. The most reasonable approach therefore is to acknowledge the usefulness of both descriptive classification and phylogenetic hypotheses as two equally partial and complementary means of understanding the natural world. MAK120229

Notes:

[1] Contrary to popular belief, cladistics does not describe the actual evolutionary path of life. That is, it is not concerned with or describes the evolution of later organisms from common ancestors in the way that, say, Darwin or more recently Richard Dawkins do, and what the Evolutionary systematics of Romer and Simpson also describes. It simply provides a way of generating hypotheses regarding the way living organisms are related to each other. Cladograms, in other words, are not evolutionary trees. What cladistics does do is provide a more precise and verifiable method of creating hypotheses regarding the evolutionary relationships of past and current organisms (Phylogeny, a word invented in the late 19th century by Haeke), but used here ina somewhat different context).