Comparative analyses of the scaling diversity index and its applicability

Yue, Tianxiang; C.N., Sachin; Wu, Shan; Zhan, Jing

doi:10.1080/01431160600887714

Cited by 15 publications

(14 citation statements)

References 46 publications

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“…The Shannon-Wiener diversity index performed poorly in retaining information on species richness and species abundance from a given community, making comparisons of species richness and their abundances across highland localities difficult if not impossible; similar results were also found by Yue et al (2007) in other study cases. information on species composition and abundance is critical since knowing the species present in a given locality is necessary to understand causes of species geographical distribution, function of species in a community, and even more critical for conservation of species (Primack 1998).…”

Section: Discussionsupporting

confidence: 65%

Conceptual and statistical problems associated with the use of diversity indices in ecology

Barrantes¹,

Sandoval²

2008

RBT

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Diversity indices, particularly the Shannon-Wiener index, have extensively been used in analyzing patterns of diversity at different geographic and ecological scales. These indices have serious conceptual and statistical problems which make comparisons of species richness or species abundances across communities nearly impossible. There is often no a single statistical method that retains all information needed to answer even a simple question. However, multivariate analyses could be used instead of diversity indices, such as cluster analyses or multiple regressions. More complex multivariate analyses, such as Canonical Correspondence Analysis, provide very valuable information on environmental variables associated to the presence and abundance of the species in a community. in addition, particular hypotheses associated to changes in species richness across localities, or change in abundance of one, or a group of species can be tested using univariate, bivariate, and/or rarefaction statistical tests. The rarefaction method has proved to be robust to standardize all samples to a common size. Even the simplest method as reporting the number of species per taxonomic category possibly provides more information than a diversity index value. Rev. Biol.

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Section: Discussionsupporting

confidence: 65%

Conceptual and statistical problems associated with the use of diversity indices in ecology

Barrantes¹,

Sandoval²

2008

RBT

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“…We are also aware that the Brillouin index is a better choice for estimating heterogeneity in patchy or categorical data (Pielou, 1975), and that the number of patches per class in the area analysed may be insufficient for correct performance (Yue et al, 2007;Steinhardt et al, 1999). However, the Brillouin index is highly dependent on the number of patches, and the scale effects we focused on depend very much on class proportion.…”

Section: Analysis Of Landscape Patternmentioning

confidence: 98%

“…Limitations, such as the capacity to obscure the evenness component of diversity (Odum, 1969;Yue et al, 2007) are assumed taking into account that the aim is to obtain a quantitative expression of the spatial variation in heterogeneity, with no distinction among their components. We are also aware that the Brillouin index is a better choice for estimating heterogeneity in patchy or categorical data (Pielou, 1975), and that the number of patches per class in the area analysed may be insufficient for correct performance (Yue et al, 2007;Steinhardt et al, 1999).…”

Section: Analysis Of Landscape Patternmentioning

confidence: 99%

Use of simulated and real data to identify heterogeneity domains in scale-divergent forest landscapes

Díaz-Varela

Francisco

Álvarez‐Álvarez

2009

Forest Ecology and Management

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“…To distinguish the relative differences between the algorithms relating to terrain form, the terrain roughness of different DEMs can be quantified by implementing a scaling diversity index (SDI). SDI is a statistical index originally designed to estimate richness and evenness of ecological diversity, and it has been widely used in ecosystem studies (Yue et al 2007). In this study it is used to estimate elevation diversity differences between the two terrain types at various resolutions.…”

Section: Comparisons Between Estimated Slope Valuesmentioning

confidence: 99%

“…where p i is the proportion of each elevation value in relation to the whole investigation area; m is the total number of different elevation values; A is the area in hectares, e is the constant value of 2.71828, and r is the DEM resolution (Yue et al 2007).…”

Section: Comparisons Between Estimated Slope Valuesmentioning

confidence: 99%

Estimating Slope From Raster Data – A Test of Eight Algorithms at Different Resolutions in Flat and Steep Terrain

Tang

Pilesjö

Persson

2013

Geodesy and Cartography

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Different slope algorithms can result in totally different estimates. In the worst case, this may lead to inappropriate and useless modelling estimates. A frequent lack of awareness when choosing algorithms justifies a thorough comparison of their characteristics, making it possible for researchers to select an algorithm which is optimal for their purpose. In this study, eight frequently used slope algorithms applied to Digital Elevation Models (DEMs) are compared. The influences of the resolution of the DEM (0.5, 1, 2, and 5 metres), as well as the terrain form (flat and steep terrain), are considered. It should be noted that the focus of the study is not to compare estimates with ‘ground truth’ data, but on the comparisons between the algorithms, and the ways in which they might differ depending on resolution and terrain. Descriptive statistics are calculated in order to characterize the general characteristics of the eight tested algorithms. Eight combinations of DEM resolution and terrain form are analysed. The results show that the Maximum and Simple Difference algorithms always yield higher mean slope values than the other algorithms, while the Constrained Quadratic Surface algorithm produces the lowest values compared to the others. It is concluded that the estimated slope values are heavily dependent on the number of neighbouring cells included in the estimation. An Analysis of Variance (ANOVA) of estimated slope values strongly indicates (at the significance level of 0.01) that the tested algorithms yield statistically different results. The eight algorithms produce different estimates for all tested resolutions and terrain forms but one. The differences are more pronounced in steep terrain and at a higher resolution. More detailed pairwise comparisons between estimated slope values are also carried out. It is concluded that the smoothing effects associated with the Constrained Quadratic Surface algorithm are greater in steeper terrain, showing significantly lower estimates than other algorithms. On the other hand, the Maximum and Simple Difference algorithms show significantly higher estimates in almost all cases, except the combination of steep terrain and low resolution. With an increase in grid cell size, the loss of information contents in DEMs leads to lower estimated slope values as well as smaller relative differences between algorithms. Based on the results of this study it is concluded that the choice of algorithm results in different estimated slope values, and that resolution and terrain influences these differences significantly.

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Comparative analyses of the scaling diversity index and its applicability

Cited by 15 publications

References 46 publications

Conceptual and statistical problems associated with the use of diversity indices in ecology

Conceptual and statistical problems associated with the use of diversity indices in ecology

Use of simulated and real data to identify heterogeneity domains in scale-divergent forest landscapes

Estimating Slope From Raster Data – A Test of Eight Algorithms at Different Resolutions in Flat and Steep Terrain

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