Table S1 - List of studies presenting integrated models that incorporate several processes driving species ranges and community structure. We
differentiated pure theoretical approaches from empirical ones that have been applied to real case studies (column Type). Uni-species means that
the framework models a single species at a time. Each cross indicates the inclusion of a process.
Reference
Description
Type
System
Level
Albert et al. 2008
Spatially explicit
model of range
dynamics with plant
functional types and
ecological succession
SDM coupled with a
metapopulation model
Empirical
Plants
Multispecies
Empirical
Herbivorous
mammals
Boulangeat et al.
2012
SDM with indices for
dispersal limitations
and co-occurrence
Empirical
Brotons et al. 2012
SDM with spatiallyexplicit population
model
Cabral et al. 2012
Cheung et al. 2010
Anderson et al.
2009
Abiotic
constraints
X
Dispersal
Unispecies
X
X
Plants
Multispecies
X
X
Empirical
Birds
Unispecies
X
X
Environmentally
constrained
demography coupled
with spatially explicit
dispersal
Empirical
Plants
Unispecies
X
X
SDM coupled with a
Empirical
Fish
Uni-
X
X
X
Biotic
interactions
X
X
X
Evolution
Benefits of model
integration
Integration of processes
increases the accuracy of
climate change predictions
and facilitates interactions
with land use change
Differential responses of the
metapopulation centroid, at
the leading and trailing
edges are caused by
spatially explicit dispersal
Accounting for dispersal
and biotic interactions
changes the estimated
response to abiotic
constraints
Interactions between
dispersal and habitat
suitability allow modelling
of observed changes in
distributions.
Integration of demographic
constraints increases
extinction risks following
global change due to an
interaction between spatial
distribution, immigration
and local abundance
Integration of a dynamic
spatially-explicit larval
dispersal model
species
bioclimate envelope model
that includes larval
dispersal driven by ocean
current through an
advection–diffusion–
reaction model
Cheung et al. 2012
Ecophysiological
model for individual
fish growth coupled
with a dynamic species
distribution model
Empirical
Fish
Unispecies
X
X
Dullinger et al.
2012
Space and demography
explicit model of range
dynamics
Empirical
Plants
Unispecies
X
X
Engler & Guisan
2009
Species distribution
model coupled with a
spatially explicit
dispersal model
(cellular automaton)
Empirical
Plants
Unispecies
X
X
Kearney et al. 2009
Bio-energetic model of
egg tolerance to
desiccation with
response to selection
SDM coupled with a
stochastic population
model
Empirical
Mosquitoe
larvae
Unispecies
X
X
Empirical
Plants
Unispecies
X
X
Geneticecophysiological
model for individual
Empirical
Trees
Unispecies
X
Keith et al. 2008;
Fordham et al.
2012
Kramer et al. 2008;
Kramer et al. 2010
X
X
Maximum body weight of
fish is expected to decrease
due to the joint effect of a
change in species
distribution and in
physiology, due to warmer
and less oxygenated oceans
The comparison to SDMs
reveals that a majority of
species will not be able to
track suitable habitats in
mountainous areas, creating
an extinction debt
The impact of realistic
dispersal scenarios on range
shifts depends on the
landscape configuration
(e.g. mountains or rivers)
and the pace of climate
change
Identification of key traits
responsible for evolutionary
change under climate
warming
Extinction risk following
global change depends on
the dispersal mode, life
history type, disturbances
and distribution pattern
Potential evolutionary
response to climate change
depends on generation time
trees
Meier et al. 2012
Combination of SDM
with forest gap model
Empirical
Forests
Multispecies
X
X
X
Mokany et al. 2012
Combining correlative
models of alpha and
beta diversities with a
neutral metacommunity model.
SDM coupled with a
metapopulation model
Empirical
Plant
Multispecies
X
X
X
Empirical
Ants
Unispecies
X
X
Smolik et al. 2010
SDM coupled with an
interacting particle
system
Empirical
Plant
Unispecies
X
X
Spooner et al. 2011
SDM model coupled
with host–affiliate
species–discharge
relationships
Empirical
Fish
Multispecies
X
Thomas et al. 2012
Eco-evolutionary
modelling framework
called adaptive
dynamics
Empirical
Phytoplankton
Unispecies
X
Roura-Pascual et
al. 2009
X
X
and the density of mother
trees, and therefore
interactively on land-use
Interspecific competition
slows the response to
climate change, especially
for late successional species
One of the few approaches
integrating metacommunity modelling,
dispersal and abiotic
constraints.
Demonstrate the influence
of historical and
environmental factors in
shaping the distribution of
the Argentine ant
(Linepithema humile) in
Spain
Integrated model better fits
invasion data than isolated
models and provide a less
biased estimation of both
dispersal and habitat
suitability parameters
Demonstrate the potential
of combining
environmental niche models
to host–affiliate
relationships in order to
predict coextirpations from
global change
Using the species' thermal
tolerance curve allows to
calculate species growth
rates under any given
environmental temperature
while accounting for trait
change on ecological time
Williams et al.
2008
Dispersal-constrained
SDM
Empirical
Plants
Unispecies
X
X
X
Wintle et al. 2005
Metapopulation model
coupled with
landscape-level forest
dynamics model
Empirical
Birds
Unispecies
X
X
X
Pagel & Schurr
2012
Environmentally
constrained
demography coupled
with spatially explicit
dispersal
Simulation
Populationlevel
Unispecies
X
X
Duputié et al. 2012
Multiple trait model
and demographic
model over a
continuous space and
diffusive dispersal
Theoretical
Populationlevel
Unispecies
X
X
Gravel et al. 2011
Colonizationextinction dynamics on
islands constrained by
prey availability
Evolutionary model of
predator-prey
dynamics with explicit
gene flow
Theoretical
Food webs
Multispecies
Theoretical
Predator-prey
Multispecies
Moorcroft et al.
2006
Diffusion model for
forest trees interacting
with pathogens
Theoretical
Forestpathogen
Multispecies
Norberg et al. 2012
Metacommunity model
Theoretical
Competition
Multi-
Holt & Barfield
2009; Holt et al.
2011
X
X
X
X
X
X
X
X
X
X
X
X
X
scales
Facilitates early detection of
new populations before
high abundance threatens
native biodiversity
Land-use practices
(forestry) and community
dynamics (succession) at
one level propagates at
higher levels and influences
persistence
An integrated statistical
methodology allows a nonbiased characterization of
the fundamental niche and
more accurate predictions
of range shift
Accurate understanding of
the multivariate selective
pressure is necessary to
determine whether species
track optimum conditions
by dispersal or locally adapt
Regional food web structure
constrains distribution and
the local food web structure
A complex interplay
between predator's
generality, gene flow and
landscape configuration can
either limit or expand prey
range
Enemy-victim interactions
can speed up migration
rates by several orders
through a Janzen-Connell
effect
Complex interactions
with species sorting,
local adaptation and
dispersal
Atkins & Travis
2010; Schiffers et
al. 2013
Spatially explicit
allelic simulation
model with seed and
pollen dispersal
species
Theoretical
Individual
Unispecies
X
X
X
among the four processes.
High genetic variance and
low dispersal minimize
extinction risks under
climate change
Complex interactions
between dispersal, habitat
heterogeneity and local
adaptation limit potential
evolutionary rescue in a
climate change context
1
Albert C.H., Thuiller W., Lavorel S., Davies I.D. & Garbolino E. (2008). Land-use change
and subalpine tree dynamics: colonization of Larix decidua in French subalpine
grasslands. Journal of Applied Ecology, 45, 659-669.
2
Anderson B.J., Akçakaya H.R., Araújo M.B., Fordham D.A., Martinez-Meyer E., Thuiller W.
& Brook B.W. (2009). Dynamics of range margins for metapopulations under
climate change. Proceedings of the Royal Society of London B, Biological Sciences,
276, 1415-1420
3
Atkins K.E. & Travis J.M.J. (2010). Local adaptation and the evolution of species' ranges
under climate change. Journal of theoretical Biology, 266, 449-457.
4
Boulangeat I., Gravel D. & Thuiller W. (2012). Accounting for dispersal and biotic
interactions to disentangle the drivers of species distributions and their
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5
Brotons L., De Caceres M., Fall A. & Fortin M.J. (2012). Modeling bird species distribution
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6
Cabral J., Jeltsch F., Thuiller W., Higgins S., Midgley G.F., Rebelo A., Rouget M. & Schurr F.
(2012). Impacts of past habitat loss and future climate change on the range
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7
Cheung W.W.L., Lam V.W.Y., Sarmiento J.L., Kearney K., Watson R., Zeller D. & Pauly D.
(2010). Large-scale redistribution of maximum fisheries catch potential in the
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8
Cheung W.W.L., Sarmiento J.L., Dunne J., Frolicher T.L., Lam V.W.Y., Deng Palomares M.L.,
Watson R. & Pauly D. (2012). Shrinking of fishes exacerbates impacts of global
ocean changes on marine ecosystems. Nature Clim. Change, advance online
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9
Dullinger S., Gattringer A., Thuiller W., Moser D., Zimmermann N.E., Guisan A., Willner W.,
Plutzar C., Leitner M., Mang T., Caccianiga M., Dirnböck T., Ertl S., Fischer A.,
Lenoir J., Svenning J.-C., Psomas A., Schmatz D.R., Silc U., Vittoz P. & Hülber K.
(2012). Extinction debt of high-mountain plants under twenty-first-century
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10
Duputié A., Massol F., Chuine I., Kirkpatrick M. & Ronce O. (2012). How do genetic
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11
Engler R. & Guisan A. (2009). MIGCLIM: Predicting plant distribution and dispersal in a
changing climate. Diversity and Distributions, 15, 590-601.
12
Fordham D.A., Akcakaya H.R., Araujo M.B., Elith J., Keith D.A., Pearson R., Auld T.D.,
Mellin C., Morgan J.W., Regan T.J., Tozer M., Watts M.J., White M., Wintle B.A.,
Yates C. & Brook B.W. (2012). Plant extinction risk under climate change: are
forecast range shifts alone a good indicator of species vulnerability to global
warming? Glob. Change Biol., 18, 1357-1371.
13
Gravel D., Massol F., Canard E., Mouillot D. & Mouquet N. (2011). Trophic theory of island
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14
Holt R.D. & Barfield M. (2009). Trophic interactions and range limits: the diverse roles of
predation. Proceedings of the Royal Society B-Biological Sciences, 276, 1435-1442.
15
Holt R.D., Barfield M., Filin I. & Forde S. (2011). Predation and the evolutionary dynamics
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16
Kearney M., Porter W.P., Williams C., Ritchie S. & Hoffmann A.A. (2009). Integrating
biophysical models and evolutionary theory to predict climatic impacts on
species' ranges: the dengue mosquito Aedes aegypti in Australia. Functional
Ecology, 23, 528-538.
17
Keith D.A., Akçakaya H.R., Thuiller W., Midgley G.F., Pearson R.G., Phillips S.J., Regan H.M.,
Araújo M.B. & Rebelo T.G. (2008). Predicting extinction risks under climate
change: coupling stochastic population models with dynamic bioclimatic habitat
models. Biol. Lett., 4, 560–563.
18
Kramer K., Buiteveld J., Forstreuter M., Geburek T., Leonardi S., Menozzi P., Povillon F.,
Schelhaas M., du Cros E.T., Vendramin G.G. & van der Werf D.C. (2008). Bridging
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potential of European beech. Ecological Modelling, 216, 333-353.
19
Kramer K., Degen B., Buschbom J., Hickler T., Thuiller W., Sykes M.T. & de Winter W.
(2010). Modelling exploration of the future of European beech (Fagus sylvatica L.)
under climate change - Range, abundance, genetic diversity and adaptive
response. Forest Ecology and Management, 259, 2213–2222.
20
Meier E.S., Lischke H., Schmatz D.R. & Zimmermann N.E. (2012). Climate, competition
and connectivity affect future migration and ranges of European trees. Global
Ecology and Biogeography, 21, 164-178.
21
Mokany K., Harwood T.D., Williams K.J. & Ferrier S. (2012). Dynamic macroecology and
the future for biodiversity. Glob. Change Biol., 18, 3149-3159.
22
Moorcroft P.R., Pacala S.W. & Lewis M.A. (2006). Potential role of natural enemies during
tree range expansions following climate change. Journal of theoretical Biology,
241, 601-616.
23
Norberg J., Urban M.C., Vellend M., Klausmeier C.A. & Loeuille N. (2012). Ecoevolutionary responses of biodiversity to climate change. Nat. Clim. Change,
doi:10.1038/nclimate1588.
24
Pagel J. & Schurr F.M. (2012). Forecasting species ranges by statistical estimation of
ecological niches and spatial population dynamics. Global Ecology and
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25
Roura-Pascual N., Bas J.P., Thuiller W., Hui C., Krug R.M. & Brotons L. (2009). From
introduction to equilibrium: reconstructing the invasive pathways of the
Argentine ant in a Mediterranean region. Glob. Change Biol., 15, 2101-2115.
26
Schiffers K., Bourne E.C., Lavergne S.b., Thuiller W. & Travis J.M.J. (2013). Limited
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27
Smolik M.G., Dullinger S., Essl F., Kleinbauer I., Leitner M., Peterseil J., Stadler L.M. & Vogl
G. (2010). Integrating species distribution models and interacting particle
systems to predict the spread of an invasive alien plant. Journal of Biogeography,
37, 411-422.
28
Spooner D.E., Xenopoulos M.A., Schneider C. & Woolnough D.A. (2011). Coextirpation of
host-affiliate relationships in rivers: the role of climate change, water withdrawal,
and host-specificity. Glob. Change Biol., 17, 1720-1732.
29
Thomas M.K., Kremer C.T., Klausmeier C.A. & Litchman E. (2012). A Global Pattern of
Thermal Adaptation in Marine Phytoplankton. Science, 338, 1085-1088.
30
Williams N.S.G., Hahs A.K. & Morgan J.W. (2008). A dispersal-constrained habitat
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31
Wintle B.A., Bekessy S.A., Venier L.A., Pearce J.L. & Chisholm R.A. (2005). Utility of
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