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The Summary of the Principle of Optimal Diversity of Biosystems.

Authors:
  • Biodiversity Conservation Center

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The paper summarizes the principle of optimum diversity of biosystems, which suggests that biodiversity is a parameter to be optimized. In fact, diversity is considered as a major adaptation of biosystems to environmental conditions. Biosystems with the optimal diversity have maximum efficiency and probability of survival. Paper discusses the diversity of two hierarchical levels - population (phenotypic diversity within a population) and coenotic (number of species in community). It is shown that the optimal values of diversity are determined by the amount of a resource in the environment, the degree of environmental stability and by the evolutionary level of organisms. The adaptation of biosystems to environmental conditions occurs through the optimization of diversity at the population and community levels during their interaction. Optimal values of species diversity increase in more stable and “rich” environments, while optimal values of intrapopulation diversity decrease in more stable environments and is independent of the intensity of resource flow. These opposite reactions allow us to make an assumption of the different roles of intrapopulation diversity and species diversity in a fluctuating environment: intrapopulation diversity is the basis of adaptation to environmental instability, while species diversity enables a community to use resources to the maximum and effectively. The predictions of the principle of optimal diversity does not contradict the basic array of empirical data, and in some cases they are confirmed. This allowes us to accept the principle of optimal diversity as a working hypothesis and put forward on this basis specific hypotheses about mechanisms of diversity optimisation in the ecological, microevolutionary and evolutionary processes. The final section of the paper discusses the findings from the principle of optimal biodiversity for strategies for sustainable environmental management and biodiversity conservation.
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... However, so far they are not used in the field of biodiversity research to their full capacity. The optimal biodiversity principle (Bukvareva, 2014;Bukvareva and Aleshchenko, 2013b) was proposed as the theoretical approach to initiate research in this direction. This principle suggests that inner diversity of a biological system (i.e. ...
... The optimal biodiversity principle was analyzed by the following theoretical mathematical models: the model of phenotypic diversity in a population (Aleshchenko and Bukvareva, 1991); the two-level hierarchical model "population -community" without possibility of divergence of ecological niches (Aleshchenko and Bukvareva, 2010); the two-level hierarchical model "population -community" with the possibility of ecological niches divergence (Bukvareva and Aleshchenko, 2013a). The formal description of all models and short overview of modelling results were presented in the summary of the principle (Bukvareva, 2014). ...
... The above mentioned models (Bukvareva, 2014) showed that the optimal diversity values depend on parameters of the environment and characteristics of species. Theoretical predictions that may be of interest for landscape research relate primarily to the dependence of the optimal diversity values on the degree of environmental stability and the amount of resource available to organisms. ...
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... However, this does not mean that ecosystem assets in these regions are less valuable for biodiversity conservation and provision of ES. Relatively low levels of biodiversity and phytomass in undisturbed ecosystems in these regions are adaptations to climatic and geographical conditions, providing the most effective and sustainable ecosystem functioning under these conditions [42,43]. ...
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Chapter
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Optimization, Niche and Neutral Mechanisms in the Formation of
  • E Bukvareva
  • G Aleshchenko
Bukvareva E., Aleshchenko G. 2013. Optimization, Niche and Neutral Mechanisms in the Formation of Biodiversity // American Journal of Life Sciences V. 1. N.