Revisiting the dimensionality of biological diversity

Nakamura, Gabriel; Gonçalves, Larissa Oliveira; Duarte, Leandro da Silva

doi:10.1101/508002

Cited by 9 publications

(11 citation statements)

References 15 publications

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“…In this paper, we provided a straightforward solution to: i) account for distances in an Information Theory based metric, and ii) provide a generalized formula in order to avoid point description and account for the continuum of diversity. Diversity can be represented by different dimensions (Nakamura et al, 2020). Considering one single metric to account for the whole continuum of diversity metrics might be a powerful addition to the main framework.…”

Section: Discussionmentioning

confidence: 99%

“…Those areas with a higher diversity dimensionality, namely a higher variability into the diversity spectrum would need a generalized measure to be fully undertaken. On the contrary, ecosystems with a lower dimensionality would have a lower difference among the different diversity measures with a flat curve of the diversity spectrum (Nakamura et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

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From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy

Rocchini

Marcantonio

et al. 2021

Preprint

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AimThe majority of work done to gather information on Earth diversity has been carried out by in-situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannon’s Information Theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, namely they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time.InnovationThe parametric Rao’s quadratic entropy, coded in R, i) allows to represent the whole continuum of potential diversity indices in one formula, and ii) starting from the Rao’s quadratic entropy, allows to explicitly make use of distances among pixel reflectance values, together with relative abundances.Main conclusionsThe proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy

Rocchini

Marcantonio

et al. 2021

Preprint

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“…Obviously, in most of the Information Theory based metrics, only one layer can be used, considering those indices related to relative abundance, apart from the Rao’s Q and the Cumulative Residual Entropy (CRE). In the Rao’s Q index, multidimensional systems can be used to calculate spectral distance (see also Nakamura et al (2020) on the dimensionality of diversity), while in the CRE it is possible to calculate a multidimensional cumulative distribution to be used in the estimates (Drissi et al, 2008). In general, remotely sensed data are actually the approximation of more complex systems, which depends on the original radiometric and spectral resolution.…”

Section: Discussionmentioning

confidence: 99%

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

Thouverai

Marcantonio²,

Bacaro

et al. 2020

Preprint

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The variation of species diversity over space and time has been widely recognised as a key challenge in ecology. However, measuring species diversity over large areas might be difficult for logistic reasons related to both time and cost savings for sampling, as well as accessibility of remote ecosystems. In this paper, we present a new R package - rasterdiv - to calculate diversity indices based on remotely sensed data, by discussing the theory beyond the developed algorithms. Obviously, measures of diversity from space should not be viewed as a replacement of in-situ data on biological diversity, but they are rather complementary to existing data and approaches. In practice, they integrate available information of Earth surface properties, including aspects of functional (structural, biophysical and bio-chemical), taxonomic, phylogenetic and genetic diversity. Making use of the rasterdiv package can result useful in making multiple calculations based on reproducible open source algorithms, robustly rooted in Information Theory.

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“…Data are available from the Github Digital Repository: <https://github.com/GabrielNakamura/Dimensionality> (Nakamura et al ).…”

Section: Discussionmentioning

confidence: 99%

Revisiting the dimensionality of biological diversity

2019

Self Cite

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Biodiversity can be represented by different dimensions. While many diversity metrics try to capture the variation of these dimensions they also lead to a ‘fragmentation’ of the concept of biodiversity itself. Developing a unified measure that integrates all the dimensions of biodiversity is a theoretical solution for this problem, however, it remains operationally impossible. Alternatively, understanding which dimensions better represent the biodiversity of a set of communities can be a reliable way to integrate the different diversity metrics. Therefore, to achieve a holistic understand of biological diversity, we explore the concept of dimensionality. We define dimensionality of diversity as the number of complementary components of biodiversity, represented by diversity metrics, needed to describe biodiversity in an unambiguously and effective way. We provide a solution that joins two components of dimensionality – correlation and the variation – operationalized through two metrics, respectively: evenness of eigenvalues (EE) and importance values (IV). Through simulation we show that considering EE and IV together can provide information that is neglected when only EE is considered. We demonstrate how to apply this framework by investigating the dimensionality of South American small mammal communities. Our example evidenced that, for some representations of biological diversity, more attention is needed in the choice of diversity metrics necessary to effectively characterize biodiversity. We conclude by highlighting that this integrated framework provides a better understanding of dimensionality than considering only the correlation component.

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Revisiting the dimensionality of biological diversity

Abstract: 2

Cited by 9 publications

References 15 publications

From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy

From zero to infinity: minimum to maximum diversity of the planet by spatio-parametric Rao’s quadratic entropy

Measuring diversity from space: a global view of the free and open source rasterdiv R package under a coding perspective

Revisiting the dimensionality of biological diversity

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