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Robust modelling of the impacts of climate change on the habitat suitability of forest tree species

Author

Listed:
  • de Rigo, Daniele
  • Caudullo, Giovanni
  • San-Miguel-Ayanz, Jesús
  • Barredo, José I.

Abstract

In Europe, forests play a strategic multifunctional role, serving economic, social and environmental purposes. However, forests are among the most complex systems and their interaction with the ongoing climate change – and the multifaceted chain of potential cascading consequences for European biodiversity, environment, society and economy – is not yet well understood. The JRC PESETA project series proposes a consistent multi-sectoral assessment of the impacts of climate change in Europe. Within the PESETA II project, a robust methodology is introduced for modelling the habitat suitability of forest tree species (2071-2100 time horizon). Abies alba (the silver fir) is selected as a case study: a main European tree species often distributed in bioclimatically complex areas, spanning over various forest types and with multiple populations adapted to different conditions. The modular modelling architecture is based on relative distance similarity (RDS) estimates which link field observations with bioclimatic patterns, projecting their change under climate scenarios into the expected potential change of suitable habitat for tree species. Robust management of uncertainty is also examined. Both technical and interpretation core aspects are presented in an integrated overview. The semantics of the array of quantities under focus and the uneven sources of uncertainty at the continental scale are discussed (following the semantic array programming paradigm), with an effort to offer some minimal guidance on terminology, meaning and methodological limitations not only of the proposed approach, but also of the broad available literature – whose heterogeneity and partial ambiguity might potentially reverberate at the science-policy interface. ► How to cite: ◄ de Rigo, D., Caudullo, G., San-Miguel-Ayanz, J, Barredo, J.I., 2017. Robust modelling of the impacts of climate change on the habitat suitability of forest tree species. Publication Office of the European Union, Luxembourg. 58 pp. ISBN:978-92-79-66704-6 , https://doi.org/10.2760/296501

Suggested Citation

  • de Rigo, Daniele & Caudullo, Giovanni & San-Miguel-Ayanz, Jesús & Barredo, José I., 2017. "Robust modelling of the impacts of climate change on the habitat suitability of forest tree species," MPRA Paper 78623, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:78623
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    File URL: https://mpra.ub.uni-muenchen.de/78623/1/MPRA_paper_78623.pdf
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    References listed on IDEAS

    as
    1. Peter Brang & Marc Hanewinkel & Robert Jandl & Andrej Breznikar & Bernhard Maier, 2013. "Managing Alpine Forests in a Changing Climate," Chapters, in: Gillian Cerbu & Marc Hanewinkel & Giacomo Al. Gerosa & Robert Jandl (ed.), Management Strategies to Adapt Alpine Space Forests to Climate Change Risks, IntechOpen.
    2. Michelle Nijhuis, 2012. "Forest fires: Burn out," Nature, Nature, vol. 489(7416), pages 352-354, September.
    3. Sillero, Neftalí, 2011. "What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods," Ecological Modelling, Elsevier, vol. 222(8), pages 1343-1346.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. J. B. Cuartas & Tim Frazier & Erik Wood, 2021. "The application of cascading consequences for emergency management operations," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 2919-2938, September.

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    More about this item

    Keywords

    Abies alba; array of factors; artificial neural networks; bioclimatic predictors; climate change; climate change impacts and adaptation assessment; change factor; data-transformation modelling; data uncertainty; diversity; environmental modelling; Europe; extrapolation uncertainty; forest resources; free scientific software; free software; fuzzy; GDAL; genetic diversity; geospatial; Geospatial Semantic Array Programming; GNU bash; GNU/Linux; GNU Octave; habitat suitability; integrated modelling; integration techniques; Mastrave modelling library; Maximum Habitat Suitability; modelling uncertainty; multiplicity; PESETA series; Python; regional climate models; Relative Distance Similarity; robust modelling; Semantic Array Programming; semantic constraints; semantics; spatial disaggregation; SRES-A1b; tree species habitat suitability;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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