Abstract

The forest studied is highly heterogeneous. This heterogeneity grow as increase the size of the vegetation cover, because for the level I approach was found average value of 1:14, 1:10, 1:13 and 1:9 for the Jentsch Quocient index, respectively, for the total population, population without liana, population without palm tree and population without liana and palm tree, increasing these values in the level II to 1:3 and level III to I:2. The same value at the quocient index in the level II and III, for the four fraction of the population due the fact in this level the presence of liana and palm tree is insignificant in relation to the total number of individuals. However, in the level I, the removal increases the quociente index and so, the forest heterogeneity of this population. DOI: http://dx.doi.org/10.12957/ric.2014.10505

Highlights

  • The quociente index parameters and degree of homogeneity, though they are not included in calculating the Importance Value Index" (IVI) or Importance Value Index Expanded" (IVIA), because they reflect characteristics of the ecosystem and not the species, it should be evaluated in the structural analysis, as they represent futher information in making decisions the management

  • The method of structural analysis of a forest based on quantitative elements, or analytical method searches ranking of species according to their importance within the forest ecosystem

  • The other parameters are calculated in a relative way, providing as Curtis et al (1951. 1956. 1959), Lamprecht (1964), Veja (1968), Finol (1971), Longhi (1980), e Carvalho (1982), the raking of species within the horizontal structure of the forest through the "Importance Value Index" (IVI), which is the sum of the abundance parameters, dominance and frequency

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Summary

Introduction

The quociente index parameters and degree of homogeneity, though they are not included in calculating the IVI or IVIA, because they reflect characteristics of the ecosystem and not the species, it should be evaluated in the structural analysis, as they represent futher information in making decisions the management.

Results
Conclusion
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