Ecological Diversity: Measuring the Unmeasurable

This paper deals with the study of vegetation analysis of tree species in Ganesh and Ramnagar community forests (CF) of the Terai region. For the study, altogether 63 concentric sample plots (each plot of 500 m 2 ) were laid using systematic sampling with a sampling intensity of 0.5%. A total of 967 individual trees representing 16 different tree species were identified. Both CF was dominated by monospecies such as Mallotus philippensis (Ganesh CF) and Shorea robusta (Ramnagar CF) based on the Importance Value Index (I.V.I). Likewise, the Shannon-Wiener diversity index was higher in Ganesh CF whereas the dominance index was low. Similarly, the index of evenness and richness was also slightly higher in Ganesh CF than Ramnagar CF. The study provided information about the structure, composition, and dominance of tree species which is essential for conservation and sustainable management of community forests. Therefore, this article serves as an example of establishing community forests so as to initiate the conservation and preservation of local biodiversity.

show abstract

“…Simpson index of dominance is inversely related to diversity; therefore, it is usually expressed as 1-D or 1/D (Daly et al, 2018).…”

Section: Index Of Dominance (C)mentioning

confidence: 99%

Vegetation Analysis in Community Forests of Terai Region, Nepal

2019

View full text Add to dashboard Cite

show abstract

“…There are several goals in forming such a metric, described in the introduction. Luckily the problem has been considered extensively in the context of measuring ecology diversity of a habitat (for a review see [1]), and there are several parallels. In ecology there is a need to understand not just how many members of each species are present in a habitat, but to capture what this means in terms of diversity.…”

Section: Effective Population Size Measurementmentioning

confidence: 99%

“…We argue in this paper that an effective measure of the cast size is useful, and such theoretically appealing metrics can be derived from those used to measure ecological diversity 2 [1]. Such a metric could be useful in • better predictors for movie success;…”

Section: Introductionmentioning

confidence: 99%

How the Avengers assemble: Ecological modelling of effective cast sizes for movies

2020

View full text Add to dashboard Cite

The number of characters in a movie is an interesting feature. However, it is non-trivial to measure directly. Naive metrics such as the number of credited characters vary wildly. Here, we show that a metric based on the notion of "ecological diversity" as expressed through a Shannon-entropy based metric can characterise the number of characters in a movie, and is useful in taxonomic classification. We also show how the metric can be generalised using Jensen-Shannon divergence to provide a measure of the similarity of characters appearing in different movies, for instance of use in recommender systems, e.g., Netflix. We apply our measures to the Marvel Cinematic Universe (MCU), and show what they teach us about this highly successful franchise of movies. In particular, these measures provide a useful predictor of "success" for films in the MCU, as well as a natural means to understand the relationships between the stories in the overall film arc.

show abstract

“…The first quantifies the diversity within a sample, the latter the diversity between samples. As various diversity metrics exist in ecology to calculate α-diversity; we use the Hill numbers to quantify community diversity (Hill, 1973), as proposed by recent reviews of Leinster and Cobbold (2012) and Daly et al (2018). If we let p = p 1 , ..., p S represent the vector of relative abundances, describing the abundance of S bacterial populations, then we can define the richness (D 0 ) and evenness (D 1 , D 2 ) of a microbial community as follows:…”

Section: Cytometric Fingerprinting Using Gaussian Mixture Modelsmentioning

confidence: 99%

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

Rubbens

Props

Kerckhof

et al. 2019

Preprint

View full text Add to dashboard Cite

Motivation:Microbial flow cytometry allows to rapidly characterize microbial community diversity and dynamics. Recent research has demonstrated a strong connection between the cytometric diversity and taxonomic diversity based on 16S rRNA gene amplicon sequencing data. This creates the opportunity to integrate both types of data to study and predict the microbial community diversity in an automated and efficient way. However, microbial flow cytometry data results in a number of unique challenges that need to be addressed. Results: The results of our work are threefold: i) We expand current microbial cytometry fingerprinting approaches by using a model-based fingerprinting approach based upon Gaussian Mixture Models, which we called PhenoGMM. ii) We show that microbial diversity can be rapidly estimated by PhenoGMM. In combination with a supervised machine learning model, diversity estimations based on 16S rRNA gene amplicon sequencing data can be predicted. iii) We evaluate our method extensively by using multiple datasets from different ecosystems and compare its predictive power with a generic binning fingerprinting approach that is commonly used in microbial flow cytometry. These results confirm the strong connection between the genetic make-up of a microbial community and its phenotypic properties as measured by flow cytometry.

show abstract

Ecological Diversity: Measuring the Unmeasurable

Cited by 226 publications

References 95 publications

Vegetation Analysis in Community Forests of Terai Region, Nepal

Vegetation Analysis in Community Forests of Terai Region, Nepal

How the Avengers assemble: Ecological modelling of effective cast sizes for movies

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

Contact Info

Product

Resources

About