Community dynamics under environmental change

How can next generation mechanistic models improve projections of species distributions?

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

*Corresponding author for this work

Research output: Contribution to journalArticle

32 Citations (Scopus)

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. (C) 2015 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)63-74
Number of pages12
JournalEcological Modelling
Volume326
Early online date17 Dec 2015
DOIs
Publication statusPublished - 24 Apr 2016

Keywords

  • Prediction
  • Bias
  • Precision
  • Species interaction
  • SDM
  • Protocol
  • Uncertainty
  • plant-pollinator interactions
  • density-dependent dispersal
  • climate-change impacts
  • population-dynamics
  • range shifts
  • evolutionary processes
  • biotic interactions
  • local adaptation
  • intraspecific competition
  • ecological communities

Cite this

Community dynamics under environmental change : How can next generation mechanistic models improve projections of species distributions? / Singer, Alexander; Johst, Karin; Banitz, Thomas; Fowler, Mike S.; Groeneveld, Juergen; Gutierrez, 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.

In: Ecological Modelling, Vol. 326, 24.04.2016, p. 63-74.

Research output: Contribution to journalArticle

Singer, A, Johst, K, Banitz, T, Fowler, MS, Groeneveld, J, Gutierrez, AG, Hartig, F, Krug, RM, Liess, M, Matlack, G, Meyer, KM, Pe'er, G, Radchuk, V, Voinopol-Sassu, A-J & Travis, JMJ 2016, 'Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?', Ecological Modelling, vol. 326, pp. 63-74. https://doi.org/10.1016/j.ecolmodel.2015.11.007
Singer, Alexander ; Johst, Karin ; Banitz, Thomas ; Fowler, Mike S. ; Groeneveld, Juergen ; Gutierrez, 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. / Community dynamics under environmental change : How can next generation mechanistic models improve projections of species distributions?. In: Ecological Modelling. 2016 ; Vol. 326. pp. 63-74.
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AU - Groeneveld, Juergen

AU - Gutierrez, Alvaro G.

AU - Hartig, Florian

AU - Krug, Rainer M.

AU - Liess, Matthias

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AU - Radchuk, Viktoriia

AU - Voinopol-Sassu, Ana-Johanna

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N1 - Acknowledgements This paper is based on the findings and the follow up discussions of a workshop at the INTECOL 2013 conference in London called ‘Species interactions and survival in dynamic landscapes: How predictable is the future?’ organized by Karin Johst, Alexander Singer and Justin MJ Travis. We thank all participants for the interesting discussions. The work was further facilitated by the working group rangeShifter supported by sDiv, the Synthesis Centre for Biodiversity Sciences within the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118). AGG was funded by a CONICYT-PAI (number 82130046) grant. GP was funded by the FP7 projects SCALES (contract 226852) and EU BON (308454).

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AB - 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. (C) 2015 Elsevier B.V. All rights reserved.

KW - Prediction

KW - Bias

KW - Precision

KW - Species interaction

KW - SDM

KW - Protocol

KW - Uncertainty

KW - plant-pollinator interactions

KW - density-dependent dispersal

KW - climate-change impacts

KW - population-dynamics

KW - range shifts

KW - evolutionary processes

KW - biotic interactions

KW - local adaptation

KW - intraspecific competition

KW - ecological communities

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DO - 10.1016/j.ecolmodel.2015.11.007

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VL - 326

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EP - 74

JO - Ecological Modelling

JF - Ecological Modelling

SN - 0304-3800

ER -