openLandscapes - Ecosystem Theories

University of Kiel, Ecology Centre, Msc Environmental Science, a seminar paper
1st version completed 10/30/2009 by Antje Chiu Werner and Claudia Lorena Quan Rodas (schnee_nebel@yahoo.com and alouatta18@gmail.com)
2nd version (adapted, changes) 2010 by Trang Huynh (tranghuynh44@gmail.com)
Status: completed

"A theory is the more impressive the greater the simplicity of its premises, the more different kinds of things it relates, and the more extended its area of applicability. Therefore the deep impression that classical thermodynamics made upon me. It is the only physical theory of universal content which I am convinced will never be overthrown, within the framework of applicability of its basic concepts."

— Albert Einstein, Autobiographical Notes (c. 1940s)

Ecosystem Theories: Thermodynamics

Abstract

Classical thermodynamics might be considered one of the most important theories and yet one of the most widely misunderstood branches of physics. It explains the fluxes and effects of energy in systems and probably its central role in all living processes. However, when we look at it from the physics perspective, we make certain assumptions, which lead to the common thought that thermodynamic laws seem to contradict most if not every single system bearing life. Here we present a short review of the most relevant literature on thermodynamic laws and their application into living systems, starting from the classical approach and moving into the thermodynamic analysis of ecosystems.

Key words: ecosystem theories, thermodynamic laws, exergy, entropy

Content

1. Definitions

Thermodynamics: Study of the energy conversion into work and heat and its relation to macroscopic variables such as temperature and pressure (Perrot 1998).

Exergy: Maximum work possible in a system during a process that brings the system into equilibrium with a heat reservoir (Perrot 1998).

Entropy: Energy broken down in irretrievable heat (De Rosnay 1979) A non-conserved thermodynamic state function, measured in terms of the number of microstates a system can assume, which corresponds to a degradation in usable energy (Scott and Eagleson 2004)

Ecosystem: Community of different species interdependent on each other, together with their non-living environment (Lawrence 2005).

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2. Introduction

When analysing ecosystems from a thermodynamic point of view, the trouble that arises is that classical thermodynamics assume that the analysed system is in thermodynamic equilibrium (Rubí 2008). Being at thermodynamic equilibrium implies that the molecular activity organizes itself so that statistically the system is at rest, meaning that all the compounds of that system have their lowest free energy and can therefore not perform any work. Consequently this also means this system cannot bear life (Jørgensen 1997, Rubí 2008). Ecosystems are considered from a thermodynamic point of view as open (they allow energy and matter flux) and far from equilibrium systems. It was Schrödinger (1944) who led to what is now known as the concept of non-equilibrium thermodynamics when trying to link life with the underlying theorems of thermodynamics (Schneider and Sagan 2005). Furthermore, as this document will try to illustrate, studying living systems from a non-equilibrium perspective showed that biology is not an exception of physics (Prigogine 1986, Schneider and Kay 1994).

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3. The first law of thermodynamics

The first law of thermodynamics deals with the principle of conservation of energy in a system. It states that energy cannot be created nor destroyed, but transformed, and therefore we should not refer to energy consumption or creation (Jørgensen 2000a). It also means that the total energy in the universe is constant. In living systems this law is of fundamental importance because it explains the relationship between any organism and its environment by “examining the exchange of energy in the form of flow of heat and the transport of mass” (Stevenson 1979). It is the basic tool needed to understand how any organism is integrated in its environment, and to describe quantitatively the energy fluxes (balances, inputs, outputs) that underlie the relationships occurring in the system (Stevenson 1979). The example below taken from Petrusewicz (1967) shows the fluxes of energy in an animal population (Figure 1).

Figure 1. Energy flux in an animal population (Petrusewicz 1967).

The first law of thermodynamics may also explain several species adaptations in terms of physiology, distribution and behaviour according to the different temperature gradients they inhabit. It also highlights the importance of microclimate patterns. Some of the examples Stevenson (1979) mentions involve the activity patterns of moose. The activity peaks occur usually far from midday and mainly in shaded places or close to water bodies in order to avoid overheating. It is also suggested that these behavioural patterns may change seasonally. Cacti offer another good example of the development of water storage structures as a response to the dry systems they inhabit in North and South America. These structures however, vary according to the geographical position in which they occur. The reason is that although both inhabit dry systems, in the South America’s system freezing temperatures occur during the nights, which could easily break these structures and cells and consequently kill the plant.

Other examples mentioned by Stevenson (1979) include the interstitial distribution of anemones in direct-sun light protected areas like cracks or crevices in the rocks in order to survive since they are regularly exposed to terrestrial ecosystems. The amount of wax in leafs to avoid desiccation or the distribution of the crystals in the wax covering the cuticle of conifer needles represents another example. The arrangements of these crystals allow a more efficient reflection of short wavelengths (blue) radiations; moreover, the amount of these crystals increases with altitude and higher temperatures where the amount of radiation also increases. In addition, calculations showed that the reflection of UV radiation becomes also more efficient. These adaptations permit conifers to avoid and survive the high amounts of UV radiation present in those habitats.

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4. The second law of thermodynamics

The second Law of Thermodynamics gives us the direction of all the processes and the universe itself. It not only states that a particularly amount of energy diminishes every time it is used suggesting that it is impossible to avoid wasting energy and eventually running down but that every process occurring in the universe increases the disorder in it (Rubí 2008). Moreover it states that the final state of any system is Thermodynamic Equilibrium. In this way, it also introduces two key concepts: Exergy and entropy, which describe how a succession of equilibrium can be irreversible only at a cost in energy from its surroundings (Rubí 2008, Kay and Schneider 1994, Jørgensen 1997, Jørgensen and Marques 2001). In this sense, entropy becomes the way of measuring this irreversibility and it reaches its highest value at thermodynamic equilibrium while exergy becomes the amount of energy produced by a process and can be consumed (Jørgensen 1997, Jørgensen and Marques, 2001) and therefore constitutes a measure of the distance between a given state of the system with respect to what it would be if at thermodynamic equilibrium (Jørgensen and Mejer 1979).

Eco-exergy can be approximately calculated according to S. E. Jørgensen and G. Bendoricchio.

(1)

(2)

Where

Ex is total exergy of community (kJ/unit of volume or area)
Ex_Str is the structural exergy of community,
C_i is biomass concentration of species i,
N is number of species,
β_i is conversion factor expressing the information (informative genes) that the ith species is carrying. Different organism groups have dissimilar β_i values (table 1).

Table 1. Synthesis of Exergy/Biomass Conversion factors for different groups of organisms (Silow & Morky, 2010)

Group	Exergy conversion factor	Group	Exergy conversion factor
Minimal cell	5.8	Brachiopoda	109
Bacteria	8.5–12	Seedless vascular plants	158
Archaea	13.8	Rotifera	163
Yeasts	18	Insecta	167-446
Alga	15-298	Chironomida	300
Cyanobacteria	15	Moss	174
Dynophyta	18	Crustaceans	230-300
Green microalgae	20	Cladocera	232
Diatoms	66	Copepoda	240
Macrophyta (alga)	67-298	Amphipoda	290
Rhodophyta	92	Mollusca	297-450
Protozoa	31-97	Bivalves	297
Amoeba	38	Gastropoda	312-450
Gastrotricha	97	Gymnosperm	314
Fungi	61	Macrophytes (Phanerogam)	356-520
Nemertina	76	Flowering plants	383-543
Worms	91-133	Fish	499-800
Cnidaria	91	Amphibia	688
Plathelminthes	120	Reptilia	833
Oligochaeta	130	Aves	980
Nematoda	133	Mammalia	2127
Sponges	98	Homo sapiens	2173

However, these calculation was based on approximation and assumption, so in different condition these values would be different (Jorgensen, 2000b). Moreover, the accuracy of β values decides reliability of values of exergy (Austoni et al, 2007).

Living systems maintain and create a high state of internal organization (Jørgensen 2000a) and therefore low entropy. A classical example of it is known as the Bénard problem which shows that order can shade into chaos and back to order as a system deviates from equilibrium by an input of energy. In this example a thin layer of fluid is heated from below creating a temperature gradient. At first heat will be conducted from bottom to top, but when the gradient becomes too large and conduction is not efficient anymore and maintaining the dissipation rate becomes too hard, the particles will start to move and a process called convection will occur (Figure 2) (Schneider and Kay 1994).

Figure 2. Bénard cells as self-organized energy dissipative structures. A) Cells diagram in two dimensions (Wikipedia 2009). B) Relation between energy gradient and heat dissipation rate for conduction and conduction + convection processes (Schneider and Kay 1994).

For even larger gradients, this process becomes turbulent. Far from being chaotic, the convection process is orderly and creates hexagonal cells as if the fluid were a crystal (Getlin and Brausch 2003) (Figure 3). A similar process occurs when clouds are formed; however the reason we do not have hexagonal clouds (a phenomena that seldom occurs) is the fact that the convection patterns are highly affected by the irregular surface of the Earth.

Figure 3. Hexagonal shaped Bénard convection cells created with aluminium flakes on a copper plate. Shape anomalies showed on the plate correspond to tiny dent in the bottom surface (Van Dyke 1982).

Systems, in which self-organization can emerge from a spontaneous breaking of spatial and temporal symmetry as a result of an energy exchange with its environment, were called by Prigogine (1977) as Dissipative Systems or Structures. These systems are in consequence thermodynamically non-isolated and open and they operate far from equilibrium (i.e. producing entropy) due to the presence of a continuous source of energy (i.e. Solar radiation). From this starting point, Schneider and Kay (1994) suggested that “ [...] life exists on Earth as another means of dissipating the solar induced gradient and that as such is a manifestation of the restated second law”. This means that if ecosystems are moving towards chaos in order to be more effective in consuming exergy the trend of any living system is towards increasing their complexity.

One study showed that the more complex system is the more self-organization capacity it has. The maize field had less self-organization capacity than its reference ecosystem-beech forest because most of indicators (nitrogen use efficiency, total biomass, number of species, and so on) were higher values toward beech forest, although the maize field can capture more exergy than beech forest. The result was shown in amoeba diagram as below (fig.4).

Figure 4. Capacity for self-organization of maize field and beech forest (Kutsch et al., 2001)

Another important concept related with the second law of thermodynamics is Thermodynamic Information. This is the amount of information required (not necessarily the information we actually possess) to describe a system (Jørgensen 2000a). The number of microstates will define the complexity of the system and therefore the amount of thermodynamic information that is required to describe it. A microstate is defined as one of the huge numbers of different accessible arrangements of the molecules' motional energy for a particular macrostate. On the other hand a macrostate is the thermodynamic state of any system and can be exactly characterized by measuring its physical properties (temperature, volume, pressure, etc.). Thus, statistically a macrostate does not change over time if the properties do not change from which it is possible to say that entropy measures the total number of ways in which a particular macrostate can be constituted microscopically. Thus, the higher the complexity in a system, the further it is from thermodynamic equilibrium and therefore the less entropy it has (Jørgensen 2000). The way in which the second law of Thermodynamics explains how living systems and their theories (natural selection, evolution, among others) develop, leaded to the suggestion of a tentative fourth law of thermodynamics (see below).

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5. The third law of thermodynamics

The third law of Thermodynamics defines the relationship between entropy production and temperature. It states that the entropy of a pure chemical compound and also entropy production by chemical reactions between pure crystalline compounds are equal to zero at a temperature of 0 Kelvin degrees (absolute zero temperature) (Jørgensen 2000a). In other words, as temperature approaches absolute zero, the entropy of a system approaches a constant minimum (Basu 2001). To understand better the third law is necessary to remember that temperature is a measurement of the average kinetic energy of the molecules in an object or system (Basu 2001).

At absolute 0 (zero) temperature, molecules in a system lack of kinetic energy or movement. Under these conditions the order is absolute and no more order can be created. Exergy is equal to 0 (zero) because any useful work can’t be done, also entropy does not exist and cannot be created (Jørgensen 2000a). This is represented in Figure 5 in which the relationship between exergy consumption and entropy production is analysed in relation to the states of absolute order, self-organization and thermodynamic equilibrium (chaos). This graphic makes show how exergy consumption and entropy production are maximized at the organization state.

Figure 5. Comparison of different systems, from very ordered to chaotic, in relation to their exergy consumption and entropy production capacities (Günther 2006).

Knowing the relation between temperature and exergy consumption and entropy production, is easy to imagine that organization can only occur in a specific temperature interval. A temperature greater than 2.726 ± 0.01 K (temperature of deep space) is required before organization can be created (Jørgensen 2000a). With temperatures above this value and a simple energy flow, order creation is inevitable due to the formation of self-organized dissipative structures. However, the temperature interval to allow the formation of living systems is more limited. All biological systems depend on the sun photons capture at molecular level performed by the autotrophs. This process is dependent on the diffusion velocity of the molecules, which is faster in gaseous phases compared with liquids and solids. Nevertheless, many of the important components of carbon-based life on earth do not occur in gaseous phases making the liquid phase of particular importance for the organization of living systems (Jørgensen 2000a). For these reasons temperature has to be enough to allow a sufficient fast creation of order through the diffusion processes.

On the other hand, maximum temperature limits also occur. The macromolecules that conform the most part of the living organisms are subject to denaturalisation; this is especially true for the proteins. Increases in temperature will increase the macromolecule formation but also the reverse process of denaturalisation, this could impede the organization and biomass accumulation in organisms. However, the influence of temperature can be reduced by the use of enzymes, which are molecules (usually proteins) that function as catalysts in virtually all biochemical reactions (Lawrence 2005). An enzyme reduces the activation energy (temperature) required to perform a specific biochemical reaction. In this way enzymes are able to balance the anabolic and catabolic process allowing the biomass accumulation. However, temperature limits to life exist because enzymes are also proteins and they are subject to denaturalisation are higher temperatures (Jørgensen 2000a).

As a result of the minimum temperatures requirements to fast enough diffusion processes and maximum temperatures possible for enzymes functioning, most part of the carbon-based living systems on earth occur at mean temperatures between -13.15 y 66.85^oC (260-340 K). However, life can also exist in some extreme environments due to the presence of special adaptations mainly present in some Archaebacteria (Bergey and Holt 1994) called Thermophiles, which can live even at temperatures above 100^oC.

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6. A tentative fourth law of thermodynamics

Ecosystems are self-organized dissipative structures, which remain far from thermodynamic equilibrium. Because the three classical thermodynamic laws are based on systems in thermodynamic equilibrium a tentative fourth law has been proposed in order to explain better the particular characteristics of these structures and their possible routes of development. Some of the factors considered in the formulation of this law are reviewed below.

Ecosystems attempt to develop toward a higher level of exergy that means increase exergy consumption and storage and also entropy production (Jørgensen 2000b). Nevertheless, to predict the future state of an ecosystem with exactitude is considered almost impossible because of their feedbacks mechanisms, different histories and stochastic events. However, it is possible to analyse their changes toward a higher exergy level in terms of the Neo-Darwinian theory of evolution. This theory establishes that the organisms; species and combination of species with characteristics that give them highest probabilities of survival and growth are the fittest and will be selected. This statement can be described in quantitative terms with the help of thermodynamics saying that the most fitted combination of species is the one that will be able to contribute most to the work content or exergy of the system due to their biomass with embodied information (Jørgensen 2000b). The specific components (e.g. species) and state of the system depend however also of the prevailing conditions in the system. For example, the plants with the crassulacean acid metabolic photosynthetic pathway (CAM) are favoured under harsh and arid environments even though this pathway is less efficient, than the two other possible photosynthetic pathways (C3, C4), in terms of grams of plant biomass formed per unit of energy received (Shugart 1998).

Taking into account the previously explained considerations it has been possible to formulate the tentative forth law of thermodynamics that attempts to explain which organizational structures will be created or which factors establish the direction of development of an ecosystems. This has been expressed as: “If a system receives a through flow of exergy, the system will utilise this exergy to move away from thermodynamic equilibrium. If the system is offered more than one pathway to move away from thermodynamic equilibrium, the one yielding most stored exergy, i.e. with the most ordered structure or the longest distance to thermodynamic equilibrium by the prevailing conditions, will have a propensity to be selected” (Jørgensen 2000b). This tentative law can be proved by inductive methods therefore many modelling cases have been studied and they have all supported this law (Jørgensen 2000b).

The relations between exergy storage and the state of development of an ecosystem can then be described using the Holling’s cycle, (Figure 6). As the system develops from the early stages to a “mature” one, structures become more complicated, exergy storage increases, connectedness between elements increases (specialization), organisms change from R to K strategies and more information is stored per unit of biomass (i.e. genes). One of the limitations for further development in the ecosystems is the energy availability because of physical limits of the sun energy caption. The most efficient ecosystems (i.e. tropical rain forests) are able to capture approximately 70-80% of the total solar energy received by radiation. Another limitation is matter availability, because the ecosystem stops the growth of biomass when the most limiting inorganic components has been fully utilised for biomass construction. However, a system very close to these physical limits can still continue growing through improvements in matter recycling and increases in information, which allow the appearance of more efficient organisms (Jørgensen et al. 2007).

Figure 6. Modified Holling’s cycle. Ecosystem succession as a function of structural and functional items (Jørgensen et al. 2007).

It is important to mention that in mature ecosystems a big paradox exists. These systems are extremely far from thermodynamic equilibrium and the energy necessary to keep them working increases with their complexity. This makes the system very vulnerable to perturbations that can destroy it and begin a phase know as “creative destruction”. This phase is also included in the Holling’s cycle as the one of energy and matter release which allows the system return to the earlier stages of the cycle. Both parts of the cycle, the increase of complexity toward maturity and the creative destruction phase, are considered indispensable in the natural long-term development of ecosystems (Jørgensen et al. 2007).

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7. Applications of thermodynamics to ecosystem assessment

Thermodynamics and its relationship with ecosystems can be seen by many people as a very theoretical topic. However, thermodynamic principles have many important practical applications in ecosystem assessment. For example, different bodies reflect dissimilar amounts of the solar radiation that they received. The ones more efficient in exergy capture and degradation reflect less of the solar energy. This reflected energy could be measured directly using spectrophotometers or indirectly by the temperature of the energy emitted by the system. In this way it is possible for example to compare between different ecosystems using the amount of energy that they reflect as an indicator of their complexity and exergy consumption and storage capacity. This approach has many technical difficulties, as e.g. the standardization of the solar energy irradiance between places, however it has been used in some studies to analyse and compare different ecosystems. For example the global maps of outgoing longwave radiation, show in the tropical zones the correlation between the areas covered by tropical rain forest and the corresponding reduction in the solar energy reflected in these areas (Figure No.7). Applications of these concepts in a local scale can also be found in the publications of Luvall and Holbo (1989) and Luvall et al. (1990).

Figure 7. Global Outgoing Longwave Radiationd (NOAA 2007)

Another important practical application of thermodynamics is the use of measurements of energy transfer as a common currency to unify the data analysis from many areas of ecology and evolutionary biology as suggested by Brown (1995). Compared with other terms in thermodynamic theory, exergy was mostly used in ecosystem assessment. For example, exergy and structural exergy were used as ecological indicators for the development state of the Lake Chaohu ecosystem (Fuliu,1997), and an application to the recovery process of marine benthic communities (Libralato et al. 2006) compared recovery speed of muddy and sandy benthic communities, and many others emperical studies.

Finally, thermodynamic variables also can be used as indicators of sustainability, for example to compare the efficiency of different processes or as useful information when deciding between 2 different development paths, or simply as a good alternative for the use of monetary evaluations. An example of this approach is given by the research of Sewalt et al. (2001), who compare the eco-efficiency, from the thermodynamic point of view, of electricity production in a waste incinerating plant and a natural gas fired power plant, with surprising results.

These last applications however, have been largely criticized when they were first suggested by H. T. Odum because they imply different “qualities” of energy that are embodied (Brown and Ulgiati 2004). This concept suffered several changes while trying to find an equivalent unit to describe the embodied energy, which later developed into the concept of emergy (embodied energy). Brown and Ulgiati (2004) present a historical overview of the concepts and theories that gave origin to emergy.

Odum (1996) defined emergy as "a measure of energy used in the past and thus is different from a measure of energy now. The unit of emergy (past available energy use) is the emjoule to distinguish it from joules used for available energy remaining now" or in other words, the total sum of energy necessary for an entire products’ lifecycle. The concept of virtual water could represent an analogous example to emergy.

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8. Case study

Exergy was used as a Tool for Ecosystem Health Assessment (Silow & Morky, 2010)

The study was taken place in lake Baikal. Exergy was measured on plankton community with the data from 1951-1999, while structural exergy was calculated with three Baikal plankton groups – diatom phytoplankton, cyanobateria and the rest of phytoplankton and zooplankton. The result showed long-term changes of exergy of plankton community (fig 8).

Figure 8. Long-term variability of exergy (g.m-3) of lake Baikal plankton in 1951–1999 in 0–50 m layer (Silow & Morky, 2010)

As can be seen from the graph, eco-exergy fluctuated every year, but generally increased through measurement period. It can be understood by the development of physical-biological structure of ecosystem, the increase in complexity and information of ecosystem because the more complex systems are the more exergy they store (Silow & Mokry, 2010). In addition, the study indicated that exergy increased with the increase of nutrient input and decreased with accumulation of toxic substances. However, with the same disturbance, exergy of under-ice community sinked while exergy of open water community moved up. The authors explained that the reasons were the differences in abiotic environmental conditions, in species composition of phytoplankton, in structural exergy content in planktonic community.

Therefore, exergy can be a good indicator for ecological health of Baikal ecosystems, which was still in good state of development. However, to be more precise, it is necessary to use more indicators for eco-health assessment because there are some drawbacks of exergy calculation such as accurate biological information of assessed ecosystem, other computional or physical factors.

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9. Conclusion

Ecosystems are self-organized systems and energy dissipative structures created by the flow of solar energy. Therefore, they can be analysed and studied using thermodynamic approaches. However, because ecosystems are far from thermodynamic equilibrium some considerations have to be taken at the moment to apply the classical laws of this discipline on them. Also some new tentative thermodynamic laws had been suggested to explain the specific characteristics the ecosystem functions.

Thermodynamics is not only important for the study of ecosystems from the theoretical point of view. It also has important practical applications for ecosystems assessment and evaluation of sustainability on different development pathways.

In larger scale, exergy consumption in biosphere was caused by the exergy uptaken by plant life via photosynthesis, and the exergy of the waste heat emitted to environment (Rosena & Scott, 2003). However, exergy consumption by civilization through devices for the production, conversion, distribution, storage and use of energy is eventually more than biosphere’s consumption. Rosena & Scott also said that entropy production from human would affect biosphere and biosphere would impact human through climate change. Therefore, the solution for reducing global warming is how to balance entropy on earth. It would be increasing the energy use efficiency (exergy/energy input), and wasting as little heat as possible.

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