Exergy vs information in ecological successions: Interpreting community changes by a classical thermodynamic approach |
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| Authors: | A. Ludovisi |
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| Affiliation: | 1. Unité Mixte de Recherche Biologie des ORganismes et Ecosystèmes Aquatiques (BOREA), MNHN, UPMC, UCBN, CNRS-7208, IRD-207, Université de Caen Basse Normandie, Esplanade de la Paix, 14032 Caen, France;2. CNRS, Laboratoire d’Océanologie et de Géosciences, UMR LOG CNRS 8187, Université des Sciences et Technologies, Lille 1–BP 80, 62930 Wimereux, France;3. Centre Scientifique de Monaco, Marine Department, 8 Quai Antoine Ier, MC-98000 Monaco, Principality of Monaco, France;4. CNRS UMS 829, Sorbonne Universités (UPMC University Paris 6), Observatoire Océanologique de Villefranche-sur-Mer, 06230 Villefranche-sur-Mer, France;5. Irstea–Aquatic Ecosystems and Global Changes Unit, 50 avenue de Verdun, 33612 Gazinet Cestas cedex, France;6. Unité Mixte de Recherche LIttoral ENvironnement et Sociétés, Université de la Rochelle, Institut du Littoral et de l’Environnement, 2 rue Olympe de Gouges, 17000 La Rochelle, France;7. Unité Mixte de Recherche Laboratoire des Sciences de l’Environnement Marin (CNRS/UBO/IRD/Ifremer), Institut Universitaire Européen de la Mer, Rue Dumont d’Urville, 29280 Plouzané, France;8. Unité Mixte de Recherche IPSL/LSCE, CNRS-CEA-UVSQ, Bâtiment 712, Orme des Merisiers, 91191 Gif sur Yvette, France;9. AZTI–Tecnalia, Marine Research Division, Herrera Kaia Portualdea z/g, 20110 Pasaia, Basque Country, Spain;1. Copenhagen University, University Park 2, 2100 Copenhagen Ø, Denmark;2. Department of Learning and Philosophy, Aalborg University, A.C. Meyers Vænge 15, 2450 Copenhagen SV, Denmark;1. Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, 28403 NC, USA;2. Ecological Modelling Laboratory, Department of Zoology, Visva-Bharati University, Santiniketan 731 235, India;3. Duke Network Analysis Center, Social Science Research Institute, Duke University, Durham, 27708 NC, USA;1. Systems Ecology & Ecological Modelling Laboratory, Department of Zoology, Visva-Bharati University, Santiniketan 731235, India;2. Department of Environmental Science, Vidyasagar University, West Bengal, India;3. Department of Conservation Biology, Durgapur Govt. College, West Bengal, India;4. Department of Zoology, K.C. College, Hetampur, Birbhum, West Bengal, India;1. Laboratório de Ecologia de Bentos, Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, CP 486, CEP31270-901 Belo Horizonte, MG, Brazil;2. MARE-Marine and Environmental Sciences Centre, Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Portugal;1. Ecological Modelling Laboratory, Department of Zoology, Visva-Bharati University, Santiniketan, India;2. School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa;3. Department of Biological Sciences, Towson University, Towson, MD, USA;4. Advanced Systems Analysis Program, International Institute for Applied Systems Analysis (IIASA), Austria |
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| Abstract: | This work proposes a methodology based on classical thermodynamics, which allows the variation in ecosystem composition to be interpreted within the framework of the exergy concept. The basic equation of exergy [Mejer, H., Jorgensen, S.E., 1979. Exergy and ecological buffer capacity. State-of-the-art in Ecological Modelling 7, 829–846] was decomposed into three terms – size (C), structural information (I) and concentration (X) – and their significance as indicators of ecosystem state was evaluated by simulating different scenarios of development in a simplified freshwater ecosystem. In order to calculate the exergy terms, the most critical issue in using exergy in an ecological context, i.e. the estimate of reference equilibrium values for organic matter and organisms, had to be faced. With this aim, the equations of classical thermodynamics in solution were applied, and “virtual” values of concentration at equilibrium were calculated for a number of organic compounds (VEC) and freshwater organisms (VECE). The results of the simulation showed that, whereas exergy and the exergy terms inherently connected with the a-biotic component varied consistently with the incorporation of biomass into the ecosystem, the structural information of the biotic component followed different, even opposite, pathways of variation, which were dependent only on the change in the size spectrum of the community. Due to the strict dependence of the VECE values on organism size, the increase of structural information with increasing abundance of large and complex species is also consistent with the general pattern of succession delineated by the classical r–K model. Structural information is therefore proposed as an indicator of the development state, as well as an ecological orientor, whose maximisation is expected during ecosystem development. However, since an increase in structural information is not necessarily accompanied by an increase in exergy, a sort of “antagonism” between these two related orientors emerges, whose resolution may contribute to shed light on the fundamental forces which drive ecosystem development. |
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