Leaving Gondwana: The Changing Position of the Indian Subcontinent in the Global Faunal Network

Halliday, Thomas J. D.; Holroyd, Patricia A.; Gheerbrant, Emmanuel; Prasad, G. V. R.; Scanferla, Agustín; Beck, Robin M. D.; Krause, David W.; Goswami, Anjali

doi:10.1007/978-3-030-49753-8_9

Cited by 5 publications

(3 citation statements)

References 85 publications

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“…2018; Halliday et al . 2020; Neige et al . 2021; Pelletier 2021; Penn‐Clarke & Harper 2021; Gibert et al .…”

Section: Discussionunclassified

“…There are many different and well-established methods, such as ordinary cluster analysis, ordination analysis, or network analysis, that are commonly used to analyse and identify distinct fossil assemblages and to gain insight into the processes and mechanisms driving their spatial and hierarchical organization (e.g. Dunhill et al 2016;Rojas et al 2017;Jattiot et al 2018;Halliday et al 2020;Neige et al 2021;Pelletier 2021;Penn-Clarke & Harper 2021;Gibert et al 2022;Viglietti et al 2022). However, the hespdiv method offers several unique advantages over these methods (summarized in Table 1) in studying the Bretskyan hierarchy.…”

Section: Evaluating Hespdiv's Effectiveness For Bretskyan Hierarchy S...mentioning

confidence: 99%

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hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography

Daumantas,

Spiridonov

2024

Palaeontology

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The objective determination of boundaries of bioregions is a nontrivial problem. Here we present a new method family, HespDiv, and an algorithm for its implementation in a new R package: hespdiv. The hespdiv algorithm performs iterative hierarchical nonlinear spatial subdivisions of taxic data into topologically contiguous geographic regions. Bioregions obtained in this way can be viewed as realistic and causally important entities belonging to eco‐genealogical Bretskyan hierarchy. The possibilities of the hespdiv algorithm are successfully demonstrated on the Miocene mammal data from the contiguous United States, where a hierarchical set of bioregions was determined. The algorithm works in a decision‐tree‐like manner by generating multiple split‐lines, that each subdivide a study area and data into two parts per iteration per region. The performance of each split‐line is measured using a combination of data generalization and comparison functions that can be custom made or selected from pre‐set subdivision methods. In each iteration, the best split‐line is used to produce a subdivision, until no more adequate split‐lines are found. The process results in a hierarchy of subdivisions of distribution of taxonomic occurrences in space. Benefits of hespdiv include the delineation of spatially contiguous clusters, flexibility, and unique representations of spatial hierarchy tree. The package is shown to be effective in determining significant contiguous spatial structures of biota, so called geobiomes, in sufficiently sampled time intervals and regions. The flexibility and ease to use of the approach allows its application to the whole range of palaeo‐variables and environmental proxies.

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“…2018; Halliday et al . 2020; Neige et al . 2021; Pelletier 2021; Penn‐Clarke & Harper 2021; Gibert et al .…”

Section: Discussionunclassified

Section: Evaluating Hespdiv's Effectiveness For Bretskyan Hierarchy S...mentioning

confidence: 99%

hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography

Daumantas,

Spiridonov

2024

Palaeontology

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“…Moreover, the factors and modes of macroevolution apparently vary with time-for example, the Cambrian explosion or Ediacaran-Cambrian radiation and post-Cambrian evolution (Gould 1990;Erwin 2011;Mitchell et al 2019); environment (Jablonski et al 2006;Miller and Foote 2009;Kiessling et al 2010;Boyle et al 2013;Spiridonov et al 2015;Tomašových et al 2015); and timescales (Van Dam et al 2006;Spiridonov et al 2017b;Crampton et al 2018;Beaufort et al 2022). Furthermore, macroevolution is strongly influenced by Earth's systems: geological, climatic, and paleoceanographic factors (Marshall et al 1982;Lieberman and Eldredge 1996;Lieberman 2003;Saupe et al 2019;Carrillo et al 2020;Halliday et al 2020), but also by biotic interactions, which can translate into patterns that are apparent on extremely long timescales of tens to hundreds of millions of years (Vermeij 1977(Vermeij , 2019Jablonski 2008;Erwin 2012). There are also questions on the role of general stochasticity and path dependence/memory in evolutionary dynamics (Schopf 1979;Hoffman 1987;Gould 2001Gould , 2002Cornette and Lieberman 2004;Erwin 2011Erwin , 2016.…”

Section: Introductionmentioning

confidence: 99%

The Fractional MacroEvolution Model: a simple quantitative scaling macroevolution model

Lovejoy,

Spiridonov

2024

Paleobiology

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Scaling fluctuation analyses of marine animal diversity and extinction and origination rates based on the Paleobiology Database occurrence data have opened new perspectives on macroevolution, supporting the hypothesis that the environment (climate proxies) and life (extinction and origination rates) are scaling over the “megaclimate” biogeological regime (from ≈1 Myr to at least 400 Myr). In the emerging picture, biodiversity is a scaling “crossover” phenomenon being dominated by the environment at short timescales and by life at long timescales with a crossover at ≈40 Myr. These findings provide the empirical basis for constructing the Fractional MacroEvolution Model (FMEM), a simple stochastic model combining destabilizing and stabilizing tendencies in macroevolutionary dynamics, driven by two scaling processes: temperature and turnover rates. Macroevolution models are typically deterministic (albeit sometimes perturbed by random noises) and are based on integer-ordered differential equations. In contrast, the FMEM is stochastic and based on fractional-ordered equations. Stochastic models are natural for systems with large numbers of degrees of freedom, and fractional equations naturally give rise to scaling processes. The basic FMEM drivers are fractional Brownian motions (temperature, T) and fractional Gaussian noises (turnover rates, E+) and the responses (solutions), are fractionally integrated fractional relaxation noises (diversity [D], extinction [E], origination [O], and E− = O − E). We discuss the impulse response (itself representing the model response to a bolide impact) and derive the model's full statistical properties. By numerically solving the model, we verified the mathematical analysis and compared both uniformly and irregularly sampled model outputs with paleobiology series.

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An Eocene Greenhouse Forested India: Were Biotic Radiations Triggered by Early Palaeogene Thermal Events?

Sahni

Patnaik

2022

Journal of the Geological Society of India

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During the last two decades, work carried out in the Eocene lignite sequences of Rajasthan and Gujarat has thrown considerable light on the terrestrial biotas during the drift of India and radically changed concepts regarding how the Indian plate was populated, the issues of cosmopolitanism and endemism, and the nature of the mixed sal-dominated forested community that supported a diverse mammalian fauna. While Madagascar and Australia, India’s erstwhile Gondwanan neighbours, supported highly endemic faunas, the biota of India shows strong affinities with Africa, Madagascar, Europe, and North America.

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Leaving Gondwana: The Changing Position of the Indian Subcontinent in the Global Faunal Network

Cited by 5 publications

References 85 publications

hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography

hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography

The Fractional MacroEvolution Model: a simple quantitative scaling macroevolution model

An Eocene Greenhouse Forested India: Were Biotic Radiations Triggered by Early Palaeogene Thermal Events?

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