**3.2.2 Eco-exergy and aquatic ecology**

326 Thermodynamics – Interaction Studies – Solids, Liquids and Gases

Cyanobacteria 15 Moss 174 Dynophyta 18 Crustaceans 230–300

microalgae 20 Cladocera 232 Diatoms 66 Copepoda 240

(alga) 67–298 Amphipoda 290 Rhodophyta 92 Mollusca 297–450

Amoeba 38 Gastropoda 312–450 Gastrotricha 97 Gymnosperm 314

Protozoa 31-97 Bivalves 297

Nemertina 76 Flowering plants 393–543 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

**3.2 Eco-exergy and structural exergy applications in ecology and environmental** 

Table 1. Exergy/Biomass Conversion factors for different groups of organisms, after Silow &

We have seen above exergy approach was demonstrated to be very fruitful during the analysis of the application of thermodynamic principles and laws to the main fundamental concepts of ecology at the end of the XX century. The analysis of three thermodynamic laws expressions in ecological rules together with exergy analysis led to formulation of the 10 Ecological Laws, in particular the Fourth (Ecological) Law of Thermodynamics, EL9 (Patten

Non-equilibrium thermodynamics models based on the concept of exergy provided a common basis for representing many aspects of ecosystem development and response to environmental impacts as a single measure (Pykh et al., 2000). The use of exergy made possible the investigation of the flows of an ecosystem in terms of exergy and to arrange the system as a hierarchically ordered sequence of systems, thermodynamically embedded in each other (Nielsen, 2000). Experiments with mathematical models supported the

Fungi 61 Macrophytes

**3.2.1 Eco-exergy in theoretical ecology and in aquatic ecology** 

et al., 1997; Jørgensen et al., 1999; Straškraba et al., 1999; Jørgensen, 2006b).

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

Group

(Phanerogam) 356–520

Exergy conversion factor, *β*

Exergy conversion factor, *β*

Group

Green

Mokry, 2010

**science** 

Macrophyta

There are very few researches devoted to analysis of plankton communities with the aid of exergy. The implications of body sizes of phytoplankton and zooplankton for total system dynamics by optimizing exergy as a goal function for system performance indicator with mathematical models have been analyzed (Ray et al., 2001). A structurally dynamic model based on phosphorus nutrient limitation has been developed for Lake Mogan located nearby Ankara, Turkey. Exergy was applied as a goal function to consider the dynamic adaptation and the seasonality of plankton species (e.g., size shifts) (Zhang et al, 2003a, b).

The ecosystem of the North Sea integrity was approved to be reflected in exergy capture, storage capacity, cycling, matter losses, and heterogeneity (the diatom/non-diatom ratio of planktonic algae was used) with ecosystem model. Its feasibility was assessed as an ecosystem model of the North Sea, for the Elbe plume, after prior satisfactory calibration. The modeling effort suggested that drastic nutrient load reduction from the Elbe alone would have a limited effect on the larger German Bight: even a 60% reduction scenario would only lead to moderate changes in all five indicators (Windhorst et al., 2005).

More representative and multiple are applications of exergy to benthos communities. Exergy was used in optimization models of phytobenthos (Nielsen, 1997). Exergy concept allowed the finding of the best adapted water plants species in a given environmental condition and to explain in a satisfactory way the observed distributions of them in the Lagoon of Venice, Italy (Coffaro et al., 1997).

Exergy storage was estimated for benthic communities of sandy and muddy bottoms of the North Adriatic Sea subjected to experimental disturbance, induced by means of a controlled trawl fishing haul. The results showed a decrease of local exergy content in the disturbed area, with the minimum, both in sandy and muddy bottom, one month after the experimental disturbance. The exergy of the benthic community increased to the reference level, i.e., the surrounding control area, in accordance with the proposed hypothesis on the dynamics of exergy storage during a systems' development (Libralato et al., 2006).

The changes of exergy and specific exergy were studied with data of benthic macrofauna in the Mondego estuary (Western Portugal). Estimates for the exergy indices provided useful indications for the evaluation of environmental impact due to the eutrophication process (Fonseka et al., 2002).

Export of exergy was estimated for benthic communities on the South-Western Atlantic Coast of France. This export was mainly composed of the migration of grazing fish during the warm season, and of cultivated bivalves during the cold season (Leguerrier et al., 2007).

In the following study a self-organizing map for patterning exergy of benthic macroinvertebrate communities of 650 sampling sites in the Netherlands, including 855 species was implemented. Using these datasets, authors have calculated exergy of five trophic functional groups for each sampling site on the basis of the biomass data. Exergy of

Some Applications of Thermodynamics for Ecological Systems 329

exergy and structural exergy of plankton community response to different chemical stressors analyzed in mesocosms experiments. Results obtained with mesocosms and microcosms demonstrate structural exergy decrease in experiments proportionally to a value of the added toxicant concentration, while other parameters (biomasses of components, total biomass of community, total exergy) fluctuated (Silow & Oh, 2004; Silow. 2006). Here we present the results of exergy calculations for natural plankton community of

Yearly average values of structural exergy during 1951–1999 fluctuated around their longterm average within the limits "long-term average ± mean square deviation" (154,9±26,0) without any trends. More interesting is the picture for total eco-exergy for the same period.

We have also analysed the long-term dynamics of exergetic parameters for four limnological seasons at Baikal: inverted stratification (limnological Winter, under-ice season, February – April), spring overturn (limnological Spring, ice melting, May – June), direct stratification (limnological Summer, July – October), fall overturn (limnological Autumn, November – January)1. Analysis of eco-exergy and structural exergy behaviour during different seasons cleared that the positive trend of eco-exergy is observed during limnological Summer

Dynamics of pelagic plankton biomass in Baikal for 1951-1999 is given in Fig. 2. There is neither expressed directional change of total biomass, nor changes of biomasses of different components (only slight positive trend of zooplankton biomass). Long-term oscillations of individual components are easily observed. Taking into account all discussed above and remembering the relative constancy of the total biomass of pelagic plankton, we can try to explain the tendency of its exergy to increase according to three listed above strategies (EL8 – increase of biomass, increase of network, increase of information). According to the first strategy it is the primary production increase, based on the mass development of small sized alga in summer period. Actually it is observed in the lake (Izmesyeva & Silow, 2010). According to the second strategy it might be some recently observed structural changes in the plankton community (Hampton et al., 2008; Moore et al., 2009; Silow, 2010), and the third startegy is realized through the growth of share of larger zooplankton, like Cladocerans (Pislegina & Silow, 2010). Total biomass of plankton community and its individual components remains constant, while the total exergy of the community tends to increase. This increase can be explained with the principles of S.E. Jørgensen (section 2.1) – the principles of exergy maximization (EL9 and EL10) via the growth of solar exergy consuming capacity, sophistication of ecosystem networking and increase of ecosystem

The calculated values of structural exergy for different seasons in the lake Baikal plankton for the second half of XX century, on the basis of long-term monitoring data, fluctuate within their natural limits (long-term average ± mean square deviation) and do not demonstrate any positive or negative trends (Fig. 2, Fig. 4). It points to the lack of expressed

1 Lake Baikal is dimictic lake, characterized by two periods of stratification – inverted, when upper layer (0-50 m) of water has the temperature 0–1 ºC, layer 50-250 m – 1-4 ºC, direct, when temperature of upper layer decreases from 12 ºC at surface to 5-6 ºC at 50 m, layer 50-250 m – 4-5 ºC, and two overturns with

temperature at 0-250 m is about 4 ºC. Below 250 m temperature is constant about 3,3 ºC.

It demonstrates well expressed linear trend of increase with r2 = 0.31 (Fig. 2).

the lake Baikal.

(Fig. 3, 4).

information storage (EL8).

unfavourable changes in the lake Baikal pelagic.

different trophic groups responded differently to different water types reflecting characteristics of target ecosystems (Park et al., 2006).

Eco-Exergy and Specific Eco-Exergy were used to characterize the state of the community during the recovery process after damage to the benthic communities caused by ecological engineering Yangtze River, China (Zhang et al., 2009). Changes of the macro-benthic community structure (Venice lagoon, Italy) over almost 70 years were pictured, showing a sharp decrease in its diversity and system efficiency, estimated with the use of exergy (Pranovi et al., 2008).
