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Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modelling

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  • Trevor H. Booth

    (CSIRO Land and Water)

Abstract

Almost all climate change studies of plants and animals adopt an ‘equilibrium assumption’ that analyses of natural distributions provide reliable estimates of species climatic requirements. Yet commercial forestry trials around the world have shown that many tree species can grow successfully under climatic conditions somewhat different from those of their natural distributions. Under climate change it is reasonable to assume that a long-lived tree species, already well-established at particular sites, may be able to display some of the climatic adaptability shown in trials outside its natural distribution. The purpose of this paper is to outline how some species distribution modelling (SDM) and ecological niche modelling (ENM) studies have estimated species climatic requirements beyond those shown by conventional analyses of just their natural distributions, and to show how recent developments are facilitating these analyses. Some of the earliest SDM studies of trees demonstrated the desirability of assessing species climatic requirements using data from outside, as well as within, their natural distributions. In recent years, with the advent of large biodiversity databases and some revised SDM analysis methods, there has been a revival of interest in measuring species climatic requirements using data from beyond their realized niches. It is recommended that at least for tree species, natural distribution data, and where possible results from plantings beyond natural distributions, should be analysed in climate change studies. When this is not possible, some alternative methods of estimating species climatic requirements are identified and some of their advantages and disadvantages are considered.

Suggested Citation

  • Trevor H. Booth, 2017. "Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modelling," Climatic Change, Springer, vol. 145(3), pages 259-271, December.
  • Handle: RePEc:spr:climat:v:145:y:2017:i:3:d:10.1007_s10584-017-2107-9
    DOI: 10.1007/s10584-017-2107-9
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    Cited by:

    1. Pecchi, Matteo & Marchi, Maurizio & Burton, Vanessa & Giannetti, Francesca & Moriondo, Marco & Bernetti, Iacopo & Bindi, Marco & Chirici, Gherardo, 2019. "Species distribution modelling to support forest management. A literature review," Ecological Modelling, Elsevier, vol. 411(C).
    2. Jiménez, L. & Soberón, J., 2022. "Estimating the fundamental niche: Accounting for the uneven availability of existing climates in the calibration area," Ecological Modelling, Elsevier, vol. 464(C).
    3. Trevor H. Booth & Paul R. Muir, 2020. "Climate change impacts on Australia's eucalypt and coral species: Comparing and sharing knowledge across disciplines," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.
    4. Diederik Strubbe & Laura Jiménez & A. Márcia Barbosa & Amy J. S. Davis & Luc Lens & Carsten Rahbek, 2023. "Mechanistic models project bird invasions with accuracy," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Loehle, Craig, 2018. "Disequilibrium and relaxation times for species responses to climate change," Ecological Modelling, Elsevier, vol. 384(C), pages 23-29.
    6. Quanzhong Zhang & Haiyan Wei & Zefang Zhao & Jing Liu & Qiao Ran & Junhong Yu & Wei Gu, 2018. "Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data," Sustainability, MDPI, vol. 10(10), pages 1-16, September.
    7. Floris M. Beest & Efrén López-Blanco & Lars H. Hansen & Niels M. Schmidt, 2023. "Extreme shifts in habitat suitability under contemporary climate change for a high-Arctic herbivore," Climatic Change, Springer, vol. 176(4), pages 1-14, April.
    8. Yuncheng Zhao & Mingyue Zhao & Lei Zhang & Chunyi Wang & Yinlong Xu, 2021. "Predicting Possible Distribution of Tea ( Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China," Agriculture, MDPI, vol. 11(11), pages 1-18, November.

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