Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 452866
Title Bayesian estimation of shrubs diversity in rangelands under two management systems in northern Syria
Author(s) Niane, A.A.; Singh, M.; Struik, P.C.
Source Open journal of Ecology 4 (2014). - ISSN 2162-1985 - p. 163 - 173.
DOI https://doi.org/10.4236/oje.2014.44017
Department(s) Centre for Crop Systems Analysis
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2014
Abstract The diversity of shrubs in rangelands of northern Syria is affected by the grazing management systems restricted by the increase in human and livestock populations. To describe and estimate diversity and compare the rangeland grazing management treatments, two popular indices for diversity, the Shannon index and the Simpson index, were studied for the four combinations of two sites, Hammam and Obeisan, and two grazing methods, Closed and Open, using frequentist and Bayesian approaches. We simulated the a priori and a-posteriori distributions of the Shannon and Simpson diversity indices, where from a range of values for a constant in the a priori distribution the best value normalizing the distribution of the diversity indices was chosen. The Bayesian diversity estimates were higher than their frequentist counterparts and had lower standard errors. The grazing methods at each site and sites under each grazing method delivered significant diversity of shrub species. The Bayesian estimates resulted in lower p-values than the frequentist approach for two cases reflecting in Bayesian method’s higher power. Bayesian approach is recommended as it has a wider framework for inference on diversity studies.
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