Sources: Cavalier-Smith (1985), Fonseca et al. (2000). The values of ft from Table 5.1 are shown for comparison. a Figures presented by Marques et al. (1997).

Sources: Cavalier-Smith (1985), Fonseca et al. (2000). The values of ft from Table 5.1 are shown for comparison. a Figures presented by Marques et al. (1997).

for others (Algae, Echinodermms, Amphibians, Reptiles, Birds and Mammals) B < BDNA • However, the pDNA-distribution was shifted towards greater values: on average, the DNA-method overestimates the weighting factor. This is easily explained, since the number of non-repetitive genes is, as a rule, less than the total genome size. For instance, the overall C-value for Insect is equal to 0.143 (D. melanogaster), but the non-repetitive DNA is 0.102 (71.4%). This latter value of C was used when the weighting factor was calculated for Table 5.2. As a result, Bi < PY^^ Another example: the overall C-value for Mammals is about 3.37, but the non-repetitive DNA in H. sapiens genome is 2.04 (60.6%). When we use the latter value as the DNA content in Table 5.2, we get very close estimations of weighting factor for Homo sapiens (Bi = 701 and p]yNA = 657). When we do not take into account the reduction of non-nonsense genes then the weighting factors significantly differ from each other (fii = 393 and PDNA = 985 for Mammals).

There is no doubt that the right estimation of P-values should be based on the number of proteins that are controlling the processes in the cells of various organisms. The knowledge of all human genomes is available today, and it has been found that the number of non-nonsense genes is not 250,000 as indicated in Table 5.1 but rather 40,000. On the other hand, it is also clear that the number of amino acids controlled by one gene is more than 700 for H. sapiens. It may, for some genes, be as high as 38,000 (Hastie, 2001). The weighting factor in Table 5.1 may therefore still be approximately correct. The importance of the proteins can be seen from the intensive analytical work on finding the composition of the human proteins, or as they are called now, the genetically determined proteins—proteomes. This word is used to underline that the genetically produced proteins are of a particular importance. The great interest in the proteomes is due to their control of the life processes. Many proteins may become the medicine of the future. The application of enzymes in industrial productions is just in its infancy, as there is an enormous potential to control many more industrial processes by enzymes.

On the one hand the key to finding better ^-values is the proteomes. On the other hand, our knowledge about the proteomes in various organisms is very limited—more limited than for the number of non-nonsense genes. It may be possible, however, to put together our knowledge about non-nonsense genes, the overall DNA content, the limited knowledge about the proteomes and the evolution tree and see some pattern emerging, which could be used to give better but still very approximate ^-values at this stage. For H. sapiens it is presumed that 200,000 proteomes are produced by the cells and that they contain about 15,000 amino acids in average (Nielsen, 2003). In this case the ^-value would be 12,275, or considerably higher than the value in Table 5.1. However, other values in the table might be changed in a similar manner.

5.9. Why living systems have such a high level of exergy?

A frog weighing 20 g at the temperature of environment 25°C will have the exergy content of 20 X 62.3 X 298 X 337 = 0.125 GJ, while a dead frog will have only the exergy content of 371 kJ, although they have the same chemical composition, at least until a few seconds after the frog has died. The difference is rooted in the information, or rather in the difference of the useful information. The dead frog possesses the information for a few seconds after its death (the amino acid composition has not yet been decomposed), but the difference between the live and dead frog is the ability to utilise the enormous information stored in its genes and proteom.

The amount of information stored in a frog is surprisingly high. The number of amino acids placed in the right sequence is 8.4 X 107 and for each of the 8.4 X 107 amino acids the number of possibilities is 20. This amount of information is able to ensure reproduction and is transferred from generation to generation which ensures that the evolution can continue because an already favourable combination of properties is conserved through the genes. Because of the very high number of amino acids, 8.4 X 107, it is not surprising that there will always be a minor difference between frogs in the amino acid sequence. It may be a result of mutations or of a minor mistake in the copying process. This variation is important because it gives possibilities to "test" which amino acid sequence gives the best result with respect to survival and growth. The best, representing the most favourable combination of properties, will offer the highest probability of survival and ensure maximum growth so that corresponding genes will therefore prevail. Survival and growth mean more exergy resulting in a larger distance from thermodynamic equilibrium. Exergy could therefore be considered a thermodynamic function, which could be used to quantify Darwin's theory. It is interesting in this context that exergy also represents the amount of energy needed to tear down the system (Svirezhev, 1998b). It means that the more exergy the system possesses the more difficult it becomes to "kill" the system and therefore the probability of survival is higher.

5.10. Summary of the important ecological issues

Introduction of the concept of exergy facilitates the interpretation of the Second Law of Thermodynamic in an ecological context. All ecological processes are irreversible, and exergy (work capacity) is therefore inevitably lost as heat to the environment and, as there is no temperature gradient to utilise, energy that can do work is converted into energy that cannot do work.

The exergy transfer processes in the biosphere can be considered as a Carnot Cycle, driven by the solar radiation. The exergy efficiency of the Carnot Exergy Cycle is, under the given constraints, about 1.5%, irrespective of whatever we consider—the climate cycle (absorption of solar radiation by the atmosphere), the photosynthesis or the GCC. It explains why the photosynthesis has an efficiency as low as about 1.5%. From an evolutionary point of view it may be possible to explain this low efficiency by the factors determining the selection. As energy has been continuously supplied by the sun during the entire evolution, the main competition has been about the elements that are needed to build up living matter because the elements have always been available in a limited amount. The constraints on the exergy efficiency of the solar radiation determine that the exergy efficiency is 1.5%.

Pollution implies a loss of exergy, for instance dispersion or increased concentration of a toxic substance. When the dispersion is global the exergy loss is very significant.

It can be shown that the exergy of an ecosystem corresponds to the amount of energy that is needed to kill the system. It means that the more exergy a system has the more difficult it will be to kill it. Higher exergy levels therefore mean higher overall buffer capacity, which is equal to the sum of resistances against changes. The exergy is, in other words, a useful variable to apply for assessment of ecosystem health.

It can be shown that exergy has two contributions: one determined by the amount of biomass and one covering the information (in the form of Kullback's measure of information). In other words, exergy expresses both the biomass and the information. Higher organisms carry more information and therefore have more exergy than lower organisms with the same biomass. An approximate calculation of living organisms' exergy is possible by Eq. (7.10). The weighing factor ¡3 expresses the information content. Note that a second after an organism has died the biochemical composition is still the same as for the living organism, but now the information embodied in the biochemical composition including the amino acid sequence of the enzymes for the life processes cannot be utilised and they are therefore worthless—the weighing factor 3 becomes 1.0 in Eq. (7.10).

An ecosystem that will develop towards a higher exergy level can only increase the biomass to a certain point (corresponding to the amount of the most limiting elements in its environment). Therefore, a mature system is characterised by attributes that are based on information (higher biodiversity, more complicated network and so on; see Table 2.5), while a system at the early state of ecosystem development has relatively fast growth of the biomass. The level of information can, in principle, increase infinitely, which can explain the continuous evolution.

The efficiency of the exergy gained from the solar radiation is dependent on Kullback's measure of information, K, for the system and the efficiency with which the ecosystem is able to capture the solar radiation. An ecosystem in an early state (see Table 2.5) will only have a low K-value. It implies, with reference to Fig. 5.4, that the active surface (the ecosystem) works as a classical thermodynamic machine, performing mechanical and chemical work (a build up of biomass). The radiation efficiency cannot exceed about 80% due to physical constraints but, by development of a high K value at the time, when a high radiation efficiency has been obtained (a high concentration of biomass that as a parabola captures as much solar radiation as possible), it is possible for the ecosystem to work as an information machine. The point b (see Fig. 5.4) is, by the development of K, moving toward a and b may correspond for a mature ecosystem to a radiation efficiency of more than 80%, which implies that the ecosystem is working as an information machine (information increases, but not the biomass). This is beneficial for further development of the ecosystem because it is hardly possible to do more chemical work for the ecosystem when the elements needed for the construction of biomass are already used up or are there already in the form of living matter. As seen, the shift of the ecosystem between a classic thermodynamic machine doing chemical work and an information machine takes place at the proper state in the ecosystem development.

This chapter has demonstrated clearly that the application of exergy facilitates the thermodynamic explanation of ecosystem reactions; but how can we calculate exergy for an ecosystem? It is hardly possible due to the enormous complexity of ecosystems. It may, however, be possible and useful to calculate the exergy of an ecosystem model. Assessment of ecosystem health by the use of exergy and development of structurally dynamic models, that can account for adaptation and shifts in species composition, require that we can calculate, at least relatively, the exergy for an ecosystem model. If we can calculate the exergy of the components of the model, including the organisms, we can just sum up the contributions to the exergy of the ecosystem model. In principle, we should also add the exergy of the information contained in the ecological network; it is minor compared with the enormous amount of information embodied in the organisms when we are using (simple) models but it may be significant for real ecosystems. It is not difficult to calculate the exergy of living organisms provided that we know the information content. In spite of big progress in genetics, there is still a long way to go (see the alternative methods in Sections 5.8 and 5.9) before we have all the information needed to make more accurate exergy calculations. It is for instance clear that not only the genes determining the amino acid sequence transfer important information for the life processes, but also there are genes that function as the directory and other genes that are important spare parts, although there are also redundant genes. Fortunately, the application of exergy for ecosystem health assessment and for the development of structural dynamic modelling is rather robust and it seems that relative approximate values of exergy are sufficient. To emphasise, we get, by the calculation presented in Section 5.7, only relatively approximate values and it would be preferable to indicate the exergy result as an exergy index. Moreover, several exergy balance calculations can be performed and lead to interesting results, as shown throughout this volume.

Chapter 6

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