DER: An algorithm for comparing species diversity between assemblages

Guisande, Cástor; Heine, Jürgen; García‐Roselló, Emilio; González‐Dacosta, Jacinto; Vilas, Luis González; Perez-Schofield, J. Baltasar Garcıa

doi:10.1016/j.ecolind.2017.05.049

Cited by 25 publications

(10 citation statements)

References 51 publications

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“…To compare the differences between assemblages in terms of rarity (Leroy’s rarity index [ 54 , 55 ]), heterogeneity of the grassland community (Menhinick index [ 56 ]), evenness (McIntosh index [ 57 ])and taxonomic distinctness [ 58 , 59 ] (Dstar) the four components of diversity were combined into a single measurement using the DER_algorithm in the EcoIndR package in R [ 60 ]. The algorithm calculates the polar coordinates of all samples with all possible combinations for the four indices and then calculates the convex hull and mean Euclidean distances between samples.…”

Section: Methodsmentioning

confidence: 99%

Pastoralism in the highest peaks: Role of the traditional grazing systems in maintaining biodiversity and ecosystem function in the alpine Himalaya

Ingty

2021

PLoS ONE

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Rangelands cover around half of the planet’s land mass and provide vital ecosystem services to over a quarter of humanity. The Himalayan rangelands, part of a global biodiversity hotspot is among the most threatened regions in the world. In rangelands of many developing nations policies banning grazing in protected areas is common practice. In 1998, the Indian state of Sikkim, in the Eastern Himalaya, enacted a grazing ban in response to growing anthropogenic pressure in pastures and forests that was presumably leading to degradation of biodiversity. Studies from the region demonstrate the grazing ban has had some beneficial results in the form of increased carbon stocks and regeneration of some species of conservation value but the ban also resulted in negative outcomes such as reduced household incomes, increase in monocultures in lowlands, decreased manure production in a state that exclusively practices organic farming, spread of gregarious species, and a perceived increase in human wildlife conflict. This paper explores the impact of the traditional pastoral system on high elevation plant species in Lachen valley, one of the few regions of Sikkim where the grazing ban was not implemented. Experimental plots were laid in along an elevation gradient in grazed and ungrazed areas. Ungrazed areas are part of pastures that have been fenced off (preventing grazing) for over a decade and used by the locals for hay formation. I quantified plant species diversity (Species richness, Shannon index, Simpson diversity index, and Pielou evenness index) and ecosystem function (above ground net primary productivity ANPP). The difference method using movable exlosure cages was used in grazing areas to account for plant ANPP eaten and regrowth between grazing periods). The results demonstrate that grazing significantly contributes to greater plant species diversity (Species richness, Shannon index, Simpson diversity index, and Pielou evenness index) and ecosystem function (using above ground net primary productivity as an indicator). The multidimensional scaling and ANOSIM (Analysis of Similarities) pointed to significant differences in plant species assemblages in grazed and ungrazed areas. Further, ecosystem function is controlled by grazing, rainfall and elevation. Thus, the traditional transhumant pastoral system may enhance biodiversity and ecosystem function. I argue that a complete restriction of open grazing meet neither conservation nor socioeconomic goals. Evidence based policies are required to conserve the rich and vulnerable biodiversity of the region.

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Section: Methodsmentioning

confidence: 99%

Pastoralism in the highest peaks: Role of the traditional grazing systems in maintaining biodiversity and ecosystem function in the alpine Himalaya

Ingty

2021

PLoS ONE

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“…Further annotation of phylotypes was performed with the Ribosomal Database Project database using the Seqmatch function to define the discriminatory power of each sequence read; annotation was performed according to criteria published previously. 8 The EcoIndR package 24 (version 1.0, 2017) from R was used for calculating the rarity index, based on the equation of Leroy, 25,26 the Simpson (1l) and Shannon (H 0 ) indices, the taxonomy index, 27 and the richness (S). Statistical analyses were performed with Prism 7 (GraphPad Software, La Jolla, CA).…”

Section: Impactmentioning

confidence: 99%

Analysis of Transcriptionally Active Bacteria Throughout the Gastrointestinal Tract of Healthy Individuals

Vasapolli¹,

et al. 2019

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See Covering the Cover synopsis on page 907; see editorial on page 927. BACKGROUND & AIMS:The microbiome varies along the human gastrointestinal (GI) tract with exposure to luminal and mucosal factors. We analyzed active bacterial communities at 8 locations along the GI tract using high-throughput sequencing techniques. METHODS: We collected saliva, mucosal, and fecal samples from healthy adults (10 men and 11 women; mean age, 59 ± 12.3 years) who underwent upper and lower GI tract endoscopy in Germany from December 2015 through September 2016. Biopsies were taken from stomach, antrum, corpus, duodenum, terminal ileum, ascending colon, and descending colon. RNA was extracted from all samples and reverse transcribed into complementary DNA; V1-V2 regions of 16S ribosomal RNA genes were amplified and sequenced on an Illumina MiSeq platform. Abundances of the taxa in all taxonomic ranks in each sample type were used to construct sample-similarity matrices with the Bray-Curtis algorithm. Significant differences between a priori-defined groups were evaluated using analysis of similarity. RESULTS: After taxonomic annotation, 4045 phylotypes, belonging to 169 genera and 14 different phyla, were identified. Each subject had a different bacterial community. We identified distinct microbial consortia in saliva, upper GI tract, lower GI tract, and fecal samples. The predominant genera in the upper GI tract (Gemella, Veillonella, Neisseria, Fusobacterium, Streptococcus, Prevotella, Pseudomonas, and Actinomyces) were almost absent from the lower GI tract, where the microbial communities mainly comprised Faecalibacterium, Ruminococcus, and Bacteroides. The bacterial communities in the upper GI tract were characterized by greater richness and heterogeneity (measured by the Shannon index) than those in the lower GI tract. We detected Helicobacter pylori in only the upper GI tract. CON-CLUSIONS: In an analysis of saliva, mucosal, and fecal samples from 21 healthy adults, we found each individual, and each GI region, to have a different bacterial community. The fecal microbiome is not representative of the mucosal microbiome. We propose a systematic method to analyze the bacterial communities of the GI tract.

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“…Using the DER function of the EcoIndR package [48] [49] of the R software package [50], five diversity components representing different biodiversity metrics were estimated for each river basin: species richness (SR), geographic rarity (GR), rarity index (LR), taxonomic diversity (TD), and functional richness (FRic). GR reflected the average rarity of all species present in each river basin and was calculated as the inverse of the relative frequency of occupied basins [51].…”

Section: Biodiversity Metrics and Biological Traitsmentioning

confidence: 99%