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(continued)

(continued)

Table 6.3 (Continued)

Early organisms Plants Animals ^-values

Mollusca, bivalvia, gastropoda 310

Mosquito 322

Flowering plants 393

Fish 499

Amphibia 688

Reptilia 833

Aves (birds) 980

Mammalia 2127

Monkeys 2138

Anthropoid apes 2145

Homo sapiens 2173

Note: ^-values = exergy content relatively to the exergy of detritus (Jorgensen et al., 2005).

Upper limit determined by limiting element and/or

Upper limit determined by limiting element and/or

Information

Figure 6.1 While further development of physical structure is limited either by a limiting element or by the amount of solar energy captured by the physical structure, the present most concentrated amount of information, the human body, is very far from its limit.

Information

Figure 6.1 While further development of physical structure is limited either by a limiting element or by the amount of solar energy captured by the physical structure, the present most concentrated amount of information, the human body, is very far from its limit.

thereby gives the organisms additional possibilities to survive. The information is by autocatalysis (see Chapter 4) able to provide a pattern of biochemical processes that ensure survival of the organisms under the prevailing conditions determined by the physical-chemical conditions and the other organisms present in the ecosystem. By the growth and reproduction of organisms the information embodied in the genomes is copied. Growth and reproduction require input of food. If we calculate the eco-exergy of the food as just the about mentioned average of 18.7kJ/g, the gain in eco-exergy may be more; but if we include in the energy content of the food the exergy content of the food, when it was a living organism or maybe even what the energy cost of the entire evolution has been, the gain in eco-exergy will be less than the eco-exergy of the food consumed. Another possibility is to apply emergy instead of energy. Emergy is defined later in this chapter (Box 6.2). The emergy of the food would be calculated as the amount of solar energy it takes to provide the food, which would require multiplication by a weighting factor >> 1.

(3) The disappearance and the copying of information, that are characteristic processes for living systems, are irreversible processes. A made copy cannot be taken back and the death is an irreversible process. Although information can be expressed as eco-exergy in energy units it is not possible to recover chemical energy from information on the molecular level as know from the genomes. It would require a Maxwell's Demon that could sort out the molecules and it would, therefore, violate the second law of thermodynamics. There are, however, challenges to the second law (e.g., Capek and Sheehan, 2005) and this process of copying information could be considered one of them. Note that since the big bang enormous amounts of matter have been converted to energy (E = mc2) in a form that makes it impossible directly to convert the energy again to mass. Similarly, the conversion of energy to information that is characteristic for many biological processes cannot be reversed directly in most cases. The transformation matter —■ energy —■ molecular information, which can be copied at low cost is possible on earth, but these transformation processes are irreversible.

(4) Exchange of information is communication and it is this that brings about the self-organization of life. Life is an immense communication process that happens in several hierarchical levels (Box 2.2). Exchange of information is possible with a very tiny consumption of energy, while storage of information requires that the information is linked to material, for instance are the genetic information stored in the genomes and is transferred to the amino-acid sequence.

A major design principle observed in natural systems is the feedback of energy from storages to stimulate the inflow pathways as a reward from receiver storage to the inflow source (H.T. Odum, 1971b). See also the "centripetality" in Chapter 4. By this feature the flow values developed reinforce the processes that are doing useful work. Feedback allows the circuit to learn. A wider use of the self-organization ability of ecosystems in environmental or rather ecological management has been proposed by H.T. Odum (1983, 1988).

E.P. Odum's idea of using attributes to describe the development and the conditions of an ecosystem has been modified and developed further during the past 15 years. Here we assess ecosystem development using ecological orientors to indicate that the development is not necessarily following in all details E.P. Odum's attributes because ecosystems are ontically open (Chapter 3). In addition, it is also rare that we can obtain data to demonstrate the validity of the attributes in complete detail. This recent development is presented in the next section.

The concept of ecological indicators has been introduced —15-20 years ago. These metrics indicate the ecosystem condition or the ecosystem health, and are widely used to understand ecosystem dynamics in an environmental management context. E.P. Odum's attributes could be used as ecological indicators; but also specific indicator species that show with their presence or absence that the ecosystem is either healthy or not, are used. Specific contaminants that indicate a specific disease are used as indicators. Finally, it should be mentioned that indicators such as biodiversity or thermodynamic variables are used to indicate a holistic image of the ecosystems' condition; further details see Chapter 10. The relationship between biodiversity and stability was previously widely discussed (e.g., May, 1973), who showed that there is not a simple relationship between biodiversity and stability of ecosystems. Tilman and his coworkers (Tilman and Downing, 1994) have shown that temperate grassland plots with more species have a greater resistance or buffer capacity to the effect of drought (a smaller change in biomass between a drought year and a normal year). However, there is a limit—each additional plant contributed less (see Figure 6.2). Previously, it has been shown that for models there is a strong correlation between eco-exergy (the definition; see Chapter 2) and the sum of many different buffer capacities. Many experiments (Tilman and Downing, 1994) have also shown that higher biodiversity increases the biomass and therefore the eco-exergy. There is in other words a relationship between biodiversity and eco-exergy and resistance or buffer capacity.

Box 6.1 gives the definitions for ecological orientors and ecological indicators. In ecological modeling, goal functions are used to develop structurally dynamic models. Also the definition of this third concept is included in the box.

Number of species

Figure 6.2 Results of the Tilman and Downing (1994) grassland experiments. The higher the number of species the higher the drought buffer capacity, although the gain per additional plant species decreasing with the number of species.

Number of species

Figure 6.2 Results of the Tilman and Downing (1994) grassland experiments. The higher the number of species the higher the drought buffer capacity, although the gain per additional plant species decreasing with the number of species.

Box 6.1 Definitions of orientors, indicators, and goal functions

Ecological orientors: Ecosystem variables that describe the range of directions in which ecosystems have a propensity to develop. The word orientors is used to indicate that we cannot give complete details about the development, only the direction. Ecological indicators: These indicate the present ecosystem condition or health. Many different indicators have been used such as specific species, specific contaminants, indices giving the composition of groups of organisms (for instance an algae index), E.P. Odum's attributes and holistic indicators included biodiversity and thermodynamic variables such as entropy or exergy.

Ecological goal functions: Ecosystems do not have defined goals, but their propensity to move in a specific direction indicated by ecological orientors, can be described in ecological models by goal functions. Clearly, in a model, the description of the development of the state variables of the model has to be rigorously indicated, which implies that goals are made explicit. The concept should only be used in ecological modeling context.

It has been possible theoretically to divide most of E.P. Odum's attributes into three groups, defining three different growth and development forms for ecosystems (Jorgensen et al., 2000):

I. Biomass growth that is an attribute and also explains why P/B and R/B decreases with the development and the nutrients go from extrabiotic to intrabiotic pools.

II. Network growth that corresponds directly to increased complexity of the ecological network, more complex life and mineral cycles, a slower nutrient exchange rate and a more narrow niche specialization. It also implies a longer retention time in the system for energy and matter.

III. Information growth that explains the higher diversity, larger animals, longer life span, more symbiosis and feed back control and a shift from r-strategists to ^-strategists.

IV In addition, we may of course also have boundary growth—increased input, as we can observe for instance for energy during the spring. It is this initial boundary flow that is a prerequisite for maintaining ecosystems as open far-from-equilibrium systems.

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