Applications of species distribution modeling to paleobiology

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Applications of species distribution modeling to paleobiology. / Svenning, Jens-Christian; Fløjgaard, Camilla; Marske, Katharine Ann; Nogues, David Bravo; Normand, Signe.

In: Quaternary Science Reviews, Vol. 30, No. 21-22, 10.2011, p. 2930-2947.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Svenning, J-C, Fløjgaard, C, Marske, KA, Nogues, DB & Normand, S 2011, 'Applications of species distribution modeling to paleobiology', Quaternary Science Reviews, vol. 30, no. 21-22, pp. 2930-2947. https://doi.org/10.1016/j.quascirev.2011.06.012

APA

Svenning, J-C., Fløjgaard, C., Marske, K. A., Nogues, D. B., & Normand, S. (2011). Applications of species distribution modeling to paleobiology. Quaternary Science Reviews, 30(21-22), 2930-2947. https://doi.org/10.1016/j.quascirev.2011.06.012

Vancouver

Svenning J-C, Fløjgaard C, Marske KA, Nogues DB, Normand S. Applications of species distribution modeling to paleobiology. Quaternary Science Reviews. 2011 Oct;30(21-22):2930-2947. https://doi.org/10.1016/j.quascirev.2011.06.012

Author

Svenning, Jens-Christian ; Fløjgaard, Camilla ; Marske, Katharine Ann ; Nogues, David Bravo ; Normand, Signe. / Applications of species distribution modeling to paleobiology. In: Quaternary Science Reviews. 2011 ; Vol. 30, No. 21-22. pp. 2930-2947.

Bibtex

@article{74a3dbc1ac574bdbbe89c6cc00fbfdb2,
title = "Applications of species distribution modeling to paleobiology",
abstract = "Species distribution modeling (SDM: statistical and/or mechanistic approaches to the assessment of range determinants and prediction of species occurrence) offers new possibilities for estimating and studying past organism distributions. SDM complements fossil and genetic evidence by providing (i) quantitative and potentially high-resolution predictions of the past organism distributions, (ii) statistically formulated, testable ecological hypotheses regarding past distributions and communities, and (iii) statistical assessment of range determinants. In this article, we provide an overview of applications of SDM to paleobiology, outlining the methodology, reviewing SDM-based studies to paleobiology or at the interface of paleo- and neobiology, discussing assumptions and uncertainties as well as how to handle them, and providing a synthesis and outlook. Key methodological issues for SDM applications to paleobiology include predictor variables (types and properties; special emphasis is given to paleoclimate), model validation (particularly important given the emphasis on cross-temporal predictions in paleobiological applications), and the integration of SDM and genetics approaches. Over the last few years the number of studies using SDM to address paleobiology-related questions has increased considerably. While some of these studies only use SDM (23%), most combine them with genetically inferred patterns (49%), paleoecological records (22%), or both (6%). A large number of SDM-based studies have addressed the role of Pleistocene glacial refugia in biogeography and evolution, especially in Europe, but also in many other regions. SDM-based approaches are also beginning to contribute to a suite of other research questions, such as historical constraints on current distributions and diversity patterns, the end-Pleistocene megafaunal extinctions, past community assembly, human paleobiogeography, Holocene paleoecology, and even deep-time biogeography (notably, providing insights into biogeographic dynamics >400 million years ago). We discuss important assumptions and uncertainties that affect the SDM approach to paleobiology – the equilibrium postulate, niche stability, changing atmospheric CO2 concentrations – as well as ways to address these (ensemble, functional SDM, and non-SDM ecoinformatics approaches). We conclude that the SDM approach offers important opportunities for advances in paleobiology by providing a quantitative ecological perspective, and hereby also offers the potential for an enhanced contribution of paleobiology to ecology and conservation biology, e.g., for estimating climate change impacts and for informing ecological restoration.",
author = "Jens-Christian Svenning and Camilla Fl{\o}jgaard and Marske, {Katharine Ann} and Nogues, {David Bravo} and Signe Normand",
year = "2011",
month = oct,
doi = "10.1016/j.quascirev.2011.06.012",
language = "English",
volume = "30",
pages = "2930--2947",
journal = "Quaternary Science Reviews",
issn = "0277-3791",
publisher = "Pergamon Press",
number = "21-22",

}

RIS

TY - JOUR

T1 - Applications of species distribution modeling to paleobiology

AU - Svenning, Jens-Christian

AU - Fløjgaard, Camilla

AU - Marske, Katharine Ann

AU - Nogues, David Bravo

AU - Normand, Signe

PY - 2011/10

Y1 - 2011/10

N2 - Species distribution modeling (SDM: statistical and/or mechanistic approaches to the assessment of range determinants and prediction of species occurrence) offers new possibilities for estimating and studying past organism distributions. SDM complements fossil and genetic evidence by providing (i) quantitative and potentially high-resolution predictions of the past organism distributions, (ii) statistically formulated, testable ecological hypotheses regarding past distributions and communities, and (iii) statistical assessment of range determinants. In this article, we provide an overview of applications of SDM to paleobiology, outlining the methodology, reviewing SDM-based studies to paleobiology or at the interface of paleo- and neobiology, discussing assumptions and uncertainties as well as how to handle them, and providing a synthesis and outlook. Key methodological issues for SDM applications to paleobiology include predictor variables (types and properties; special emphasis is given to paleoclimate), model validation (particularly important given the emphasis on cross-temporal predictions in paleobiological applications), and the integration of SDM and genetics approaches. Over the last few years the number of studies using SDM to address paleobiology-related questions has increased considerably. While some of these studies only use SDM (23%), most combine them with genetically inferred patterns (49%), paleoecological records (22%), or both (6%). A large number of SDM-based studies have addressed the role of Pleistocene glacial refugia in biogeography and evolution, especially in Europe, but also in many other regions. SDM-based approaches are also beginning to contribute to a suite of other research questions, such as historical constraints on current distributions and diversity patterns, the end-Pleistocene megafaunal extinctions, past community assembly, human paleobiogeography, Holocene paleoecology, and even deep-time biogeography (notably, providing insights into biogeographic dynamics >400 million years ago). We discuss important assumptions and uncertainties that affect the SDM approach to paleobiology – the equilibrium postulate, niche stability, changing atmospheric CO2 concentrations – as well as ways to address these (ensemble, functional SDM, and non-SDM ecoinformatics approaches). We conclude that the SDM approach offers important opportunities for advances in paleobiology by providing a quantitative ecological perspective, and hereby also offers the potential for an enhanced contribution of paleobiology to ecology and conservation biology, e.g., for estimating climate change impacts and for informing ecological restoration.

AB - Species distribution modeling (SDM: statistical and/or mechanistic approaches to the assessment of range determinants and prediction of species occurrence) offers new possibilities for estimating and studying past organism distributions. SDM complements fossil and genetic evidence by providing (i) quantitative and potentially high-resolution predictions of the past organism distributions, (ii) statistically formulated, testable ecological hypotheses regarding past distributions and communities, and (iii) statistical assessment of range determinants. In this article, we provide an overview of applications of SDM to paleobiology, outlining the methodology, reviewing SDM-based studies to paleobiology or at the interface of paleo- and neobiology, discussing assumptions and uncertainties as well as how to handle them, and providing a synthesis and outlook. Key methodological issues for SDM applications to paleobiology include predictor variables (types and properties; special emphasis is given to paleoclimate), model validation (particularly important given the emphasis on cross-temporal predictions in paleobiological applications), and the integration of SDM and genetics approaches. Over the last few years the number of studies using SDM to address paleobiology-related questions has increased considerably. While some of these studies only use SDM (23%), most combine them with genetically inferred patterns (49%), paleoecological records (22%), or both (6%). A large number of SDM-based studies have addressed the role of Pleistocene glacial refugia in biogeography and evolution, especially in Europe, but also in many other regions. SDM-based approaches are also beginning to contribute to a suite of other research questions, such as historical constraints on current distributions and diversity patterns, the end-Pleistocene megafaunal extinctions, past community assembly, human paleobiogeography, Holocene paleoecology, and even deep-time biogeography (notably, providing insights into biogeographic dynamics >400 million years ago). We discuss important assumptions and uncertainties that affect the SDM approach to paleobiology – the equilibrium postulate, niche stability, changing atmospheric CO2 concentrations – as well as ways to address these (ensemble, functional SDM, and non-SDM ecoinformatics approaches). We conclude that the SDM approach offers important opportunities for advances in paleobiology by providing a quantitative ecological perspective, and hereby also offers the potential for an enhanced contribution of paleobiology to ecology and conservation biology, e.g., for estimating climate change impacts and for informing ecological restoration.

U2 - 10.1016/j.quascirev.2011.06.012

DO - 10.1016/j.quascirev.2011.06.012

M3 - Journal article

VL - 30

SP - 2930

EP - 2947

JO - Quaternary Science Reviews

JF - Quaternary Science Reviews

SN - 0277-3791

IS - 21-22

ER -

ID: 37871363