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Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?

Author

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  • Singer, Alexander
  • Johst, Karin
  • Banitz, Thomas
  • Fowler, Mike S.
  • Groeneveld, Jürgen
  • Gutiérrez, Alvaro G.
  • Hartig, Florian
  • Krug, Rainer M.
  • Liess, Matthias
  • Matlack, Glenn
  • Meyer, Katrin M.
  • Pe’er, Guy
  • Radchuk, Viktoriia
  • Voinopol-Sassu, Ana-Johanna
  • Travis, Justin M.J.

Abstract

Environmental change is expected to shift the geographic range of species and communities. To estimate the consequences of these shifts for the functioning and stability of ecosystems, reliable predictions of alterations in species distributions are needed. Projections with correlative species distribution models, which correlate species’ distributions to the abiotic environment, have become a standard approach. Criticism of this approach centres around the omission of relevant biotic feedbacks and triggered the search for alternatives. A new generation of mechanistic process-based species distribution models aims at implementing formulations of relevant biotic processes to cover species’ life histories, physiology, dispersal abilities, evolution, and both intra- and interspecific interactions. Although this step towards more structural realism is considered important, it remains unclear whether the resulting projections are more reliable. Structural realism has the advantage that geographic range shifting emerges from the interplay of relevant abiotic and biotic processes. Having implemented the relevant response mechanisms, structural realistic models should better tackle the challenge of generating projections of species responses to (non-analogous) environmental change. However, reliable projections of future species ranges demand ecological information that is currently only available for few species. In this opinion paper, we discuss how the discrepancy between demand for structural realism on the one hand and the related knowledge gaps on the other hand affects the reliability of mechanistic species distribution models. We argue that omission of relevant processes potentially impairs projection accuracy (proximity of the mean outcome to the true value), particularly if species range shifts emerge from species and community dynamics. Yet, insufficient knowledge that limits model specification and parameterization, as well as process complexity, increases projection uncertainty (variance in the outcome of simulated model projections). The accuracy–uncertainty-relation reflects current limits to delivering reliable projections of range shifts. We propose a protocol to improve and communicate projection reliability. The protocol combines modelling and empirical research to efficiently fill critical knowledge gaps that currently limit the reliability of species and community projections.

Suggested Citation

  • Singer, Alexander & Johst, Karin & Banitz, Thomas & Fowler, Mike S. & Groeneveld, Jürgen & Gutiérrez, Alvaro G. & Hartig, Florian & Krug, Rainer M. & Liess, Matthias & Matlack, Glenn & Meyer, Katrin M, 2016. "Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?," Ecological Modelling, Elsevier, vol. 326(C), pages 63-74.
  • Handle: RePEc:eee:ecomod:v:326:y:2016:i:c:p:63-74
    DOI: 10.1016/j.ecolmodel.2015.11.007
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    Cited by:

    1. Singer, Alexander & Schweiger, Oliver & Kühn, Ingolf & Johst, Karin, 2018. "Constructing a hybrid species distribution model from standard large-scale distribution data," Ecological Modelling, Elsevier, vol. 373(C), pages 39-52.
    2. Loehle, Craig, 2018. "Disequilibrium and relaxation times for species responses to climate change," Ecological Modelling, Elsevier, vol. 384(C), pages 23-29.
    3. Tiphaine Guillet & Lauriane Mouysset, 2022. "Productive versus environmental objectives of agricultural policies dealing with climate change: a French case study," Post-Print hal-03919917, HAL.
    4. Gerling, Charlotte & Wätzold, Frank, 2019. "Evaluating policy instruments for the conservation of biodiversity in a changing climate," MPRA Paper 95512, University Library of Munich, Germany.
    5. Barber-O'Malley, Betsy & Lassalle, Géraldine & Chust, Guillem & Diaz, Estibaliz & O'Malley, Andrew & Paradinas Blázquez, César & Pórtoles Marquina, Javier & Lambert, Patrick, 2022. "HyDiaD: A hybrid species distribution model combining dispersal, multi-habitat suitability, and population dynamics for diadromous species under climate change scenarios," Ecological Modelling, Elsevier, vol. 470(C).
    6. Wieland, Ralf & Kerkow, Antje & Früh, Linus & Kampen, Helge & Walther, Doreen, 2017. "Automated feature selection for a machine learning approach toward modeling a mosquito distribution," Ecological Modelling, Elsevier, vol. 352(C), pages 108-112.
    7. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    8. Schouten, Rafael & Vesk, Peter A. & Kearney, Michael R., 2020. "Integrating dynamic plant growth models and microclimates for species distribution modelling," Ecological Modelling, Elsevier, vol. 435(C).
    9. Malishev, Matthew & Kramer-Schadt, Stephanie, 2021. "Movement, models, and metabolism: Individual-based energy budget models as next-generation extensions for predicting animal movement outcomes across scales," Ecological Modelling, Elsevier, vol. 441(C).
    10. Mathilde, Godefroid & Tom, Zeimes & Lorenzo, Bramanti & Pascal, Romans & Marzia, Bo & Margherita, Toma & Bruno, Danis & Philippe, Dubois & Charlène, Guillaumot, 2023. "Low vulnerability of the Mediterranean antipatharian Antipathella subpinnata (Ellis & Solander, 1786) to ocean warming," Ecological Modelling, Elsevier, vol. 475(C).

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