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

Alexander Singer, Karin Johst, Thomas Banitz, Mike Fowler Orcid Logo, Jürgen Groeneveld, Alvaro G. Gutiérrez, 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

Ecological Modelling

Swansea University Author: Mike Fowler Orcid Logo

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DOI (Published version): 10.1016/j.ecolmodel.2015.11.007

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 distr...

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Published in: Ecological Modelling
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa24476
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Projections with correlative species distribution models, which correlate species&#x2019; 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&#x2019; 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&#x2013;uncertainty-relation reflects current limits to delivering reliable projections of range shifts. We propose a protocol to improve and communicate projection reliability. 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spelling 2016-01-13T14:48:51.1880181 v2 24476 2015-11-17 Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions? a3a29027498d4b43a3f082a0a5ba16b4 0000-0003-1544-0407 Mike Fowler Mike Fowler true false 2015-11-17 SBI 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. Journal Article Ecological Modelling 31 12 2015 2015-12-31 10.1016/j.ecolmodel.2015.11.007 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University 2016-01-13T14:48:51.1880181 2015-11-17T23:25:08.7148931 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Alexander Singer 1 Karin Johst 2 Thomas Banitz 3 Mike Fowler 0000-0003-1544-0407 4 Jürgen Groeneveld 5 Alvaro G. Gutiérrez 6 Florian Hartig 7 Rainer M. Krug 8 Matthias Liess 9 Glenn Matlack 10 Katrin M. Meyer 11 Guy Pe’er 12 Viktoriia Radchuk 13 Ana-Johanna Voinopol-Sassu 14 Justin M.J. Travis 15
title Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
spellingShingle Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
Mike Fowler
title_short Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
title_full Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
title_fullStr Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
title_full_unstemmed Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
title_sort Community dynamics under environmental change: How can next generation mechanistic models improve projections of species distributions?
author_id_str_mv a3a29027498d4b43a3f082a0a5ba16b4
author_id_fullname_str_mv a3a29027498d4b43a3f082a0a5ba16b4_***_Mike Fowler
author Mike Fowler
author2 Alexander Singer
Karin Johst
Thomas Banitz
Mike Fowler
Jürgen Groeneveld
Alvaro G. Gutiérrez
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
format Journal article
container_title Ecological Modelling
publishDate 2015
institution Swansea University
doi_str_mv 10.1016/j.ecolmodel.2015.11.007
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 0
active_str 0
description 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.
published_date 2015-12-31T03:29:02Z
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score 11.013194

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