Quantifying Biodiversity: Methods and Applications

A special issue of Diversity (ISSN 1424-2818). This special issue belongs to the section "Biodiversity Conservation".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 10335

Special Issue Editor

Department of Applied Mathematics, National Chung Hsing University, Taichung 402, Taiwan
Interests: ecological statistics; biodiversity index estimation; ecological modeling

Special Issue Information

Biodiversity is a comprehensive concept of large amounts of ecological or biological information. In addition, the term biodiversity can be closely related to ecological conservation, monitoring, and management. As such, under different contexts of sampling schemes or model assumptions, a lot of biodiversity indices have been developed for simply and objectively measuring the concept quantitatively.  However, it is not easy to directly assess biodiversity using observations or survey data in field work. Instead, in order to accurately and reliably quantify and infer assemblage biodiversity, proposing well-developed statistical toolkits is always welcome and essential in practical applications and monitoring ecosystems.

Diversity welcomes submissions of reviews as well as practical, modeling or observational studies to this Special Issue on topics including but not limited to: formulating new biodiversity indices, proposing novel statistical methods of well-known typical indices, creating novel sampling methods, and exploring new findings in biodiversity assessment and comparison.

Dr. Tsung-Jen Shen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biodiversity index
  • statistical model
  • biodiversity sampling
  • biodiversity assessment
  • biodiversity conservation

Published Papers (3 papers)

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Research

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16 pages, 1649 KiB  
Article
Comparing Nonparametric Estimators for the Number of Shared Species in Two Populations
Diversity 2022, 14(4), 243; https://doi.org/10.3390/d14040243 - 26 Mar 2022
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Abstract
It is often of interest to biologists to evaluate whether two populations are alike with respect to a similarity index; assessing the numbers of shared species is one way to do this. In this study, we propose two Turing-type estimators for the probability [...] Read more.
It is often of interest to biologists to evaluate whether two populations are alike with respect to a similarity index; assessing the numbers of shared species is one way to do this. In this study, we propose two Turing-type estimators for the probability of discovering new shared species and two jackknife-type estimators for the number of shared species in two populations. We use computer simulation and empirical data analysis to evaluate the proposed approach. The jackknife-type estimators provide stable and reliable estimates, for both the probability of discovering new shared species and the number of shared species. We also compare the jackknife-type estimates with that of using sample coverage to estimate the number of shared species. The estimate of using sample coverage has better performance in the case of even populations, while the jackknife-type estimates have smaller bias in the case of unbalanced populations. When combined with a stopping rule based on the probability of observing new shared species, confidence intervals based on the proposed jackknife-type estimators can provide better coverage probability for the true number of shared species. The jackknife-type estimates can provide coverage probability close to 0.95 in all examples. Full article
(This article belongs to the Special Issue Quantifying Biodiversity: Methods and Applications)
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9 pages, 1147 KiB  
Article
Applicability of Common Algorithms in Species–Area Relationship Model Fitting
Diversity 2022, 14(3), 212; https://doi.org/10.3390/d14030212 - 12 Mar 2022
Viewed by 1886
Abstract
The species–area relationship (SAR) describes a law of species richness changes as the sampling area varies. SAR has been studied for more than 100 years and is of great significance in the fields of biogeography, population ecology, and conservation biology. Accordingly, there are [...] Read more.
The species–area relationship (SAR) describes a law of species richness changes as the sampling area varies. SAR has been studied for more than 100 years and is of great significance in the fields of biogeography, population ecology, and conservation biology. Accordingly, there are many algorithms available for fitting the SARs, but their applicability is not numerically evaluated yet. Here, we have selected three widely used algorithms, and discuss three aspects of their applicability: the number of iterations, the time consumption, and the sensitivity to the initial parameter setting. Our results showed that, the Gauss–Newton method and the Levenberg–Marquardt method require relatively few iterative steps and take less time. In contrast, the Nelder–Mead method requires relatively more iteration steps and consumes the most time. Regarding the sensitivity of the algorithm to the initial parameters, the Gauss–Newton and the Nelder–Mead methods are more sensitive to the choice of initial parameters, while the Levenberg–Marquardt method is relatively insensitive to the choice of initial parameters. Considering that the Gauss–Newton method and the Levenberg–Marquardt method can only be used to fit smooth SAR models, we concluded that the Levenberg–Marquardt model is the best choice to fit the smooth SARs, while the Nelder–Mead method is the best choice to fit the non-smooth SARs. Full article
(This article belongs to the Special Issue Quantifying Biodiversity: Methods and Applications)
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Review

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25 pages, 589 KiB  
Review
An Overview of Modern Applications of Negative Binomial Modelling in Ecology and Biodiversity
Diversity 2022, 14(5), 320; https://doi.org/10.3390/d14050320 - 21 Apr 2022
Cited by 16 | Viewed by 5944
Abstract
Negative binomial modelling is one of the most commonly used statistical tools for analysing count data in ecology and biodiversity research. This is not surprising given the prevalence of overdispersion (i.e., evidence that the variance is greater than the mean) in many biological [...] Read more.
Negative binomial modelling is one of the most commonly used statistical tools for analysing count data in ecology and biodiversity research. This is not surprising given the prevalence of overdispersion (i.e., evidence that the variance is greater than the mean) in many biological and ecological studies. Indeed, overdispersion is often indicative of some form of biological aggregation process (e.g., when species or communities cluster in groups). If overdispersion is ignored, the precision of model parameters can be severely overestimated and can result in misleading statistical inference. In this article, we offer some insight as to why the negative binomial distribution is becoming, and arguably should become, the default starting distribution (as opposed to assuming Poisson counts) for analysing count data in ecology and biodiversity research. We begin with an overview of traditional uses of negative binomial modelling, before examining several modern applications and opportunities in modern ecology/biodiversity where negative binomial modelling is playing a critical role, from generalisations based on exploiting its Poisson-gamma mixture formulation in species distribution models and occurrence data analysis, to estimating animal abundance in negative binomial N-mixture models, and biodiversity measures via rank abundance distributions. Comparisons to other common models for handling overdispersion on real data are provided. We also address the important issue of software, and conclude with a discussion of future directions for analysing ecological and biological data with negative binomial models. In summary, we hope this overview will stimulate the use of negative binomial modelling as a starting point for the analysis of count data in ecology and biodiversity studies. Full article
(This article belongs to the Special Issue Quantifying Biodiversity: Methods and Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Applicability of common algorithms in species-area relationship model fitting

Abstract: The species-area relationship (SAR) describes a law of species richness changes as the sampling area varies, which is of great significance in the fields of biogeography, population ecology and conservation biology, and has been studied for more than 100 years. Accordingly, there are many algorithms available for fitting the SARs, but their applicability is not numerically evaluated yet. Here, we choose three typical and widely used algorithms, and discuss their applicability from three aspects: the number of iterations, the time consumption, and the sensitivity to the initial-parameter setting. Our results showed that, the Gauss-Newton method and the Levenberg-Marquardt method require relatively few iteration steps but consume more time, the Nelder-Mead method requires relatively more iteration steps but consumes the least time. Regarding the sensitivity of the algorithm to the initial parameters, the Gauss-Newton method and the Nelder-Mead method are more sensitive to the choice of initial parameters, while Levenberg-Marquardt method is relatively insensitive to the choice of initial parameters. Considering that the Gauss-Newton method and the Levenberg-Marquardt method can only fit the smooth SAR models, we argued conclusively that the Levenberg-Marquardt is the best choice to fit the smooth SARs, while the Nelder-Mead method is the best choice to fit the non-smooth SARs.

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