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).

 

 

 

I. The history of Biological Control may be divided into 3
periods:

A. The preliminary efforts when living agents were released 
haphazardly with no scientific approach. Little precise information
exists on successes during this time. Roughly 200 A.D. to 1887 A.D.;

B. The intermediate period of more discriminating BC started with
the introduction the Vedalia beetle, Rodolia cardinalis Mulsant, for
control of the cottony cushion scale in 1888. The period extended from 1888
to ca. 1955; and

C. The modern period is characterized by more careful planning and more
precise evaluation of natural enemies. The period from 1956 to the present.

II. Early History: 200 A.D. to 1887 A.D.

A. 200 A.D. to 1200 A.D: BC agents were used in augmentation

1. The Chinese were the first to use natural enemies to control insect pests.
Nests of the ant Oecophylla smaragdina were sold near Canton in the
3rd century for use in the control of citrus insect pests such as
Tesseratoma papillosa (Lepidoptera)

2. Ants were used in 1200 A.D. for control of date palm pests in Yemen
(south of Saudia Arabia). Nests were moved from surrounding hills
and placed in trees

3. Usefulness of ladybird beetles recognized in the control of aphids and scales
in 1200 A.D.

B. 1300 A.D. to 1799 A.D.: BC was just beginning to be recognized.

1. Aldrovandi noted the cocoons of Apanteles glomeratus on a parasitized
Pieris rapae in 1602 A.D., but thought cocoons were insect eggs

2. Vallisnieri interpreted the phenomenon of insect parasitism (parasitoid)
in 1706 A.D. However the honor of being the first to understand insect
parasitism may belong to the microbiologist Van Leeuwenhoek who
illustrated and discussed a parasite of a sawfly that feeds on willow in a
publication in 1701.

3. The first insect pathogen was recognized by de Reaumur in 1726. It
was a Cordyceps fungus on a noctuid

4. In 1734, de Reaumur suggested to collect the eggs of an “aphidivorous
fly” (actually a lacewing) and place them in greenhouses to control
aphids

5. The mynah bird, Acridotheres tristis, was successfully introduced from
India to Mauritius (off the coast of Madagascar) for control of the red
locust, Nomadacris septemfasciata, in 1762

6. In the late 1700s, birds were transported internationally for insect
control

7. Control of the bedbug, Cimex lectularius, was successfully
accomplished by releases of the predatory pentatomid Picromerus
Bidens in 1776 in Europe

C. 1800 A.D. to 1849 A.D. During this period advances were made in
Europe which was both applied and basic

1. In the 1800’s, Darwin discussed “Ichneumonids” as natural control
factors for cabbage caterpillars

2. Malthus (in England) published Essays on the Principles of
Population in 1803

3. Hartig (Germany) suggested the rearing of parasites from parasitized
caterpillars for mass releases in 1827

4. Kollar (Austria) put forth the concept of “natural control” in 1837

5. Verhulst (1838) described the logistic growth equation but the idea
lay dormant until 1920 when rediscovered by Pearl. Expressed
the idea of “environmental resistance”.

6. During the 1840s releases of predators were used for control of the
gypsy moth and garden pests in Italy

D. 1850 to 1887. During this time the focus on BC switched to the United
States.

1. From 1850 to 1870 enormous plantings of many crops were being
grown in the United States (especially California) and were initially
free of pests. Later farmers saw their crops destroyed by hordes of
alien pests

2. Asa Fitch (New York) suggested importing parasites from Europe
to control the wheat midge, Contarinia tritici, in 1856. No action
was taken. In 1860 parasites were requested from Europe, but none
were received

3. During this period, Benjamin Walsh (Illinois) actively worked
for the importation of natural enemies to control the exotic insects
in the United States but was unsuccessful. Fortunately, he
influenced Charles V. Riley greatly who was in Missouri during
Walsh’s campaign

4. The first practical attempt at BC of weeds occurred in 1863 when
segments of the prickly pear cactus, Opuntia vulgaris, infested with
the imported cochineal insect, Dactylopius ceylonicus, were
transported from northern to southern India

5. Riley conducted the 1st successful movement of parasites for
biological control when parasites were moved from Kirkwood,
Missouri, to other parts of the state for control of the weevil
Conotrachelus nenuphar in 1870

6. LeBaron transported apple branches infested with oyster-shell scale
parasitized by Aphytis mytilaspidis from Galena to Geneva,
Illinois in 1871

7. In 1873 Riley sent the predatory mite Tyroglyphus phylloxerae to
France to control the grape phylloxera. The mite was established
but did not exert control as hoped.

8. Trichogramma sp. (egg parasites) were shipped from the U.S. to
Canada for control of lepidopterous pests in 1882

9. In 1883 the USDA imported Apanteles glomeratus from England
for control of P. rapae (the imported cabbageworm). Parasites
were distributed in DC, Iowa, Nebraska, and Missouri. First
intercontinental shipment of parasites.

III. The Intermediate Period: 1888 to 1955

A. 1888 to 1889: The Cottony Cushion Scale Project

1. Cottony cushion scale, Icerya purchasi Maskell, was introduced into
California in ca. 1868 around the Menlo Park (CA) area (near San
Francisco)

2. It spread to southern California and by 1887 was threatening to destroy
the infant citrus industry

3. C. V. Riley (Chief of the Division of Entomology, USDA) employed
Albert Koebele and D. W. Coquillett in research on control of the
cottony cushion scale

4. No method was working in 1887

5. Koebele was sent to Australia in 1888 to collect natural enemies of the
scale

6. He sent ca. 12,000 individuals of Cryptochaetum iceryae and 129
individuals of Rodolia cardinalis (the vedalia beetle)

7. Within the year, the cottony cushion scale ceased to be a substantial
pest

8. The vedalia beetle controls the scale mainly in the inland desert areas
and C. iceryae controls it in the coastal areas of California.

B. 1890 to 1899: Growing pains for BC

1. Following the success in 1889, California put pressure on Riley to
send Koebele back to Australia in search of parasites for other scales
parasites in California

2. Koebele went on foreign exploration, but on his return, he was recalled
from California. Koebele resigned from his position and went to work for
the Republic of Hawaii in 1893. He worked on BC projects in the
interest of Hawaii until 1912 when he retired due to ill health.

3. Due to the success of the Vedalia beetle, great emphasis was placed on
importation of coccinellids for BC initially in California and Hawaii. It
is believed that California was set back many years by promoting
mostly biological control projects and not researching alternative
control methodologies.

4. L. O. Howard replaced C. V. Riley as Chief of the Division of
Entomology, USDA in 1894. Howard was prejudiced against BC due to
the problems he saw in California

5. George Compere began as a foreign explorer for California in 1899

C. 1900 to 1930: New faces and more BC projects

1. The Gypsy Moth Project in New England (1905-1911). W. F. Fiske
was in charge in Massachusetts. Howard conducted foreign exploration
in Europe and arranged for parasites to be imported to the U.S. Many
prominent entomologists employed on the project: Harry Scott Smith,
W. R. Thompson, P. H. Timberlake.

2. The Lantana Weed Project in Hawaii (1902) First published work on
BC of weeds. Koebele went to Mexico and Central America looking
for phytophagus insects which were sent to R. C. L. Perkins in
Hawaii.

3. The Sugar-cane Leafhopper Project in Hawaii (1904-1920). Hawaiian
Sugar Planters Association (HSPA) created a Division of Entomology
in 1904. R. C. L. Perkins was appointed superintendent. The staff consisted of O. H.

Swezey, G. W. Kirkaldy, F. W. Terry, Alexander
Craw, and Albert Koebele. Later Frederick Muir was employed due to
Koebele’s health problems. Muir found the highly effective predator
Tytthus (= Cyrtorhinus) mundulus (Miridae) in Queensland, Australia,
in 1920.

4. Berliner described Bacillus thuringiensis in 1911 as the causative agent of
bacterial disease of the Mediterranean flour moth

5. Prof. H. S. Smith was appointed superintendent of California State
Insectary, Sacramento, CA, in 1913. The facility moved to the University
of California’s Citrus Experiment Station in 1923 (now UC Riverside).
Smith started another facility in Albany, CA, in 1945. Riverside and
Albany (UC Berkeley) is made up Department of Biological Control, UC.

6. The USDA Laboratory for Biological Control was established in France in 1919.

7. The Imperial Bureau of Entomology created the Farnham House
Laboratory for BC work in England in 1927. This was later directed by
W. R. Thompson in 1928.

D. 1930 to 1955: Expansion and decline of BC

1. From 1930 to 1940 there was a peak in BC activity in the world with
57 different natural enemies were established at various places.

2. World War II caused a sharp drop in BC activity.

3. BC did not regain popularity after WW II due to the production of
relatively inexpensive synthetic organic insecticides. Entomological
research switched predominantly to pesticide research.

4. In 1947 the Commonwealth Bureau of Biological Control was
established from the Imperial Parasite Service. In 1951 the name was
changed to the Commonwealth Institute for Biological Control (CIBC).
Headquarters are currently in Trinidad, West Indies.

5. In 1955 the Commission Internationale de Lutte Biologique contre les
Enemis des Cultures(CILB) was established. This is a worldwide
organization with headquarters in Zurich, Switzerland. In 1962 the
CILB changed its name to the Organisation Internationale de Lutte
Biologique contre les Animaux et les Plants Nuisibles.
This organization is also known as the International Organization for
Biological Control (IOBC). Initiated the publication of the journal
“Entomophaga” in 1956, a journal devoted to biological control of
arthropod pests and weed species.

IV. The Modern Period: 1957 to Present.

A. In 1959, Vern Stern et al. (1959) conceived the idea of economic injury
level and economic threshold which would permit growers to make
informed decisions on when they needed to apply control tactices in their
cropping systems and therefore eliminated the need for scheduled pesticide
treatments.

B. Interest developed nationwide in ecology and the environment after 1962
with the publishing of the Rachel Carson’s book “Silent Spring.”

C. “Silent Spring” helped stimulate the implementation of the concept of
Integrated Pest Management (IPM) in the late 1960’s, and biological
control was seen as a core component of IPM by some. More emphasis
was placed on conservation BC than classical BC.

D. In 1964, Paul DeBach and Evert I. Schliner (Division of Biological
Control, University of California, Riverside) publish an edited volume
titled “Biological Control of Insect Pests and Weeds” which becomes a major reference

source for the biological control community. This was a California-based book

with international application.

E. In some areas in the USA (e.g., California, North Carolina, Kansas,
Texas), IPM scouting was commercialized in the 1970’s and natural
enemies were relied upon to suppress pests in crops such as cotton,
alfalfa, citrus, soybeans, and other crops.

F. During the 1970s and 1980’s, Brian Croft and Marjorie Hoy made
impacts by using pesticide-resistant natural enemies in cropping systems.

G. In 1983, Frank Howarth published his landmark paper titled “Biological
Control: Panacea or Pandora’s Box” and significantly impacted classical
BC efforts by concluding that classical BC of arthropods significantly
contributed to extimction of desirable species (e.g., endemic).

1. This eventually forced a rethinking of legislative guidelines as well as
introduction methods which are still being changed today.

2. In Hawaii, BC efforts were diminished significantly and have not risen
to levels before 1985.

3. Research efforts into this area were stimulated with the general results
that many of Howarth’s claims were unjustified, but some impacts
were discovered. No species extinctions have been demonstrated to
have resulted from classical BC efforts to date.

H. In the 1990’s, two additional biological control journals appeared,
“Biological Control – Theory and Application in Pest Management”
(Academic Press) and “Biocontrol Science and Technology” (Carfax
Publishing). Additionally, “Entomophaga” changed its name to
“Biocontrol” in 1997.

 

 

Figs and their pollen carriers, fig-wasps have become very popular at least among biologists. Their relationship, in which we can see another extremely complicated relationship in nature, contains one of the hidden keys needed for solving the mysteries of co-evolution and the ecosystem.

This mutual relationship is believed to have begun about 90 million years ago, and approximately 750 varieties of figs (Ficus) now thrive. Amazingly, as a new kind of fig evolves, a fig-wasp corresponding to that new kind of fig appears. But a complicated co-evolution like this can be explained by Darwin’s theory of evolution, just as other happenings in nature can. His theory suggests that every creature evolves to leave the largest number of descendants in the next generation.

Plants, which have animals carry their pollen, offer the carriers a reward such as honey, or provide a carrier’s larva with a place to grow. Cabbage leaves rich in nutrition didn’t evolve to be a salad, but to be host food for larva of butterflies that carry cabbage pollen. Unfortunately, such larva become a horticulturist’s enemies.

The relationship between fig and fig-wasps in which an insect carries pollen and its larva eats the fruit is also seen in the cycad group and the yucca plant, a plant growing in arid areas in America. In this relationship, a contradictory relationship exists; that is, more pollen carriers are active more eggs are laid, and therefore more seeds are eaten even though the carriers help the plant bear fruit.

Fig flowers, which have the egg of a fig-wasp become a gall (an insect’s knot), food for larva, and fig flowers which don’t have an egg become seeds if they are pollinated. The number of female fig-wasps that develop into adults and fly out of the fig plant carrying pollen represents the number of carried pollen; that is, the adaptation rate of the male fig (the rate used to measure the size of the population of a certain genetic model in the next generation).

The number of seeds indicates the adaptation rate of a female fig. On the other hand, to become a host plant for male fig-wasps is fruitless for figs because male fig-wasps don’t have wings and can’t carry pollen. Therefore, figs don’t want to have too many male fig-wasps. Ideally, figs want to produce about the same number of female fig-wasps and seeds to create a balance between the number of female fig-wasps and fruit for themselves. On the other hand, fig-wasps want to lay as many eggs as possible to obtain the greatest possible benefit. Both the figs and fig-wasps co-exist by maintaining this subtly balanced relationship.

If the figs have a pollen carrier in all their flowers, no seeds will be left for the next generation. On the contrary, if figs reject pollen carriers too often, there won’t be enough pollen carried out for pollination. They both need to maintain this relationship. Some figs are heterosexual so male figs raise fig-wasps to carry pollen and female figs fruit seeds. Fig-wasps can’t mate in the female fig syconium, but the wasps can’t distinguish a male syconium from a female syconium. And the wasps that unfortunately come into a female syconium only help the fig pollinate.

Throughout the world, figs grow most abundantly in the forest of Ranbil on Borneo Island in Malaysia, and about 80 varieties of figs can be observed there. While we were studying there in 1998, there was a drought, and no fig flowers bloomed during that time. On that occasion, we made an interesting observation that fig-wasps came back right away as the flowers of the homosexual figs revived, but the wasps took two to three years to come back to the flowers of the heterosexual figs. Who knows when a change of weather in the tropical rainforest will create a new condition and give us clues to solve this mystery? I can’t take my eyes off the Ranbil forest.