Quantitative Approach of the Ecological Response of Six Species Vascular Plants along an Altitudinal Gradient in Dongling Forest Mountain (Beijing)

Quantitative Approach of the Ecological Response of Six Species Vascular Plants along an Altitudinal Gradient in Dongling Forest Mountain (Beijing)

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Author(s): MABESSIMO Cléoface Landry, Niu Shukui

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DOI: 10.18483/ijSci.663 345 1228 22-27 Volume 4 - Apr 2015


In the current climate change crisis, lack of detailed climate knowledge of the behavior of species and the distribution of their abundance, can be an obstacle on sustainable biodiversity conservation. The goal of our paper is to study the ecological behavior of species along an altitudinal gradient in order to predict their abundance. Six species of vascular plants, sampled in Dongling forest have been the subject of this study. The use of the Gaussian regression allowed calculating three ecological parameters including the optimum, tolerance and maximum, from which six distribution models have been designed; the expected model was the bell curve (Gaussian curve). Among the six species sampled, only Quercus liaotungensis reacted with the expected response to the abundance of its plants. Moreover, the species Acer mono, Betula platyphylla, Juglan mandshurica and Pinus tabuliformis have reacted with answers of parabola-shaped curves; However, Deutzia grandiflora was the only species to react with a totally different response of two other groups of species curve. However, the calculated ecological parameters allowed obtaining an equation for prediction of abundance for each species. Although species response curves to a gradient can take many forms, our results have highlighted that only one species reacts with the expected response. In view of the fragility of China's natural ecosystems, it is urgent that similar studies, on a large scale, can be conducted in order to determine the ecological behavior of a large number of species, to strengthen their capacity for management and conservation.


Vascular Plants, Abundance, Models, Prediction, Dongling


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