Lags in the response of mountain plant 
communities to climate change
Authors: Jake M. Alexander, Loic Chalmandrier, Jonathan 
Lenoir, Treena I. Burgess, Franz Essl, Sylvia Haider, 
Christoph Kueffer, Keith McDougall, Ann Milbau, Martin A. 
Nunez, Anibal Pauchard, Wolfgang Rabitsch, Lisa J. Rew, 
Nathan J. Sanders, and Loic Pellissier
This is the peer reviewed version of the following article: see citation below, which has been 
published in Global Change Biology and can be found in final form at https://dx.doi.org/10.1111/
gcb.13976. This article may be used for non-commercial purposes in accordance with Wiley 
Terms and Conditions for Self-Archiving.
Alexander, Jake M. , Loic Chalmandrier, Jonathan Lenoir, Treena I. Burgess, Franz Essl, Sylvia 
Haider, Christoph Kueffer, Keith McDougall, Ann Milbau, Martin A. Nunez, Anibal Pauchard, 
Wolfgang Rabitsch, Lisa J. Rew, Nathan J. Sanders, and Loic Pellissier. "Lags in the response of 
mountain plant communities to climate change." Global Change Biology 24, no. 2 (November 
2017): 563-579. DOI: 10.1111/gcb.13976.
Made available through Montana State University’s ScholarWorks 
scholarworks.montana.edu 
Lags in the response of mountain plant communities to
climate change
Jake M. Alexander1,2 | Lo€ıc Chalmandrier3,4 | Jonathan Lenoir5 | Treena
I. Burgess6 | Franz Essl7 | Sylvia Haider8,9 | Christoph Kueffer2 |
Keith McDougall10 | Ann Milbau11 | Martin A. Nu~nez12 | Anıbal Pauchard13,14 |
Wolfgang Rabitsch15 | Lisa J. Rew16 | Nathan J. Sanders17,18,19 | Lo€ıc Pellissier3,4
1Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
2Institute of Integrative Biology, ETH Zurich, Z€urich, Switzerland
3Landscape Ecology, Institute of Terrestrial Ecosystems, ETH Zurich, Z€urich, Switzerland
4Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
5UR «Ecologie et Dynamique des Systemes Anthropises» (EDYSAN, FRE 3498 CNRS-UPJV), Universite de Picardie Jules Verne, Amiens, France
6Centre for Phytophthora Science and Management, School of Veterinary and Life Sciences, Murdoch University, Perth, Australia
7Division of Conservation, Landscape and Vegetation Ecology, University of Vienna, Vienna, Austria
8Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
9German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
10Department of Ecology, Environment and Evolution, La Trobe University, Wodonga, Victoria, Australia
11Research Institute for Nature and Forest (INBO), Brussels, Belgium
12Grupo de Ecologıa de Invasiones, INIBIOMA, CONICET, Universidad Nacional del Comahue, Bariloche, Argentina
13Laboratorio de Invasiones Biologicas (LIB), Facultad de Ciencias Forestales, Universidad de Concepcion, Concepcion, Chile
14Institute of Ecology and Biodiversity (IEB), Concepcion, Chile
15Department Biodiversity & Nature Conservation, Environment Agency Austria, Vienna, Austria
16Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT, USA
17The Rocky Mountain Biological Laboratory, Crested Butte, CO, USA
18Center for Macroecology, Evolution, and Climate, Natural History Museum of Denmark, Copenhagen, Denmark
19Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA
Abstract
Rapid climatic changes and increasing human influence at high elevations around
the world will have profound impacts on mountain biodiversity. However, forecasts
from statistical models (e.g. species distribution models) rarely consider that plant
community changes could substantially lag behind climatic changes, hindering our
ability to make temporally realistic projections for the coming century. Indeed, the
magnitudes of lags, and the relative importance of the different factors giving rise
to them, remain poorly understood. We review evidence for three types of lag: “dis-
persal lags” affecting plant species’ spread along elevational gradients, “establish-
ment lags” following their arrival in recipient communities, and “extinction lags” of
resident species. Variation in lags is explained by variation among species in physio-
logical and demographic responses, by effects of altered biotic interactions, and by
aspects of the physical environment. Of these, altered biotic interactions could
contribute substantially to establishment and extinction lags, yet impacts of biotic 
interactions on range dynamics are poorly understood. We develop a mechanistic 
community model to illustrate how species turnover in future communities might 
lag behind simple expectations based on species’ range shifts with unlimited disper-
sal. The model shows a combined contribution of altered biotic interactions and dis-
persal lags to plant community turnover along an elevational gradient following 
climate warming. Our review and simulation support the view that accounting for 
disequilibrium range dynamics will be essential for realistic forecasts of patterns of 
biodiversity under climate change, with implications for the conservation of moun-
tain species and the ecosystem functions they provide.
K E YWORD S
alpine ecosystems, biotic interactions, climate change, climatic debt, migration, novel
interactions, range dynamics, range expansion
1 | INTRODUCTION
Mountains are experiencing extensive changes in land-use and cli-
mate and increasing levels of biological invasion, with temperature
increasing faster than global averages at high elevations in many
mountain ranges (Mountain Research Initiative EDW Working Group,
2015). Such rapid changes in temperature are expected to result in
extinctions of cold-adapted plant species in mountains (e.g.
Dirnb€ock, Essl, & Rabitsch, 2011), coupled with a dramatic turnover
in alpine plant communities (Dullinger, Gattringer, et al., 2012;
H€ulber et al., 2016). In some regions, projections using species distri-
bution models (SDMs) predict up to 100% species turnover in alpine
plant communities by 2100 (Engler et al., 2009, 2011). Nonetheless,
time lags in species’ responses to changing climate over the next
50–200 years could be substantial (Bertrand et al., 2011; Corlett &
Westcott, 2013; Svenning & Sandel, 2013). Lags could lead to a dis-
crepancy between realized community changes and expectations
from SDMs that do not account for time lags in biotic responses
(“disequilibrium dynamics”; Svenning & Sandel, 2013). Indeed, while
range expansions to higher elevation have occurred in many regions
(e.g. Lenoir, Gegout, Marquet, de Ruffray, & Brisse, 2008), so far
losses of cold-adapted species appear to have been relatively few, at
least for plants on boreal-temperate mountains of Europe (Kulonen,
2017; Pauli et al., 2012). Therefore, to more accurately forecast the
nature and temporal dynamics of mountain plant community change
over the next century, it will be necessary to understand the pro-
cesses contributing to lags in species’ responses to environmental
change (Bertrand et al., 2016).
Empirical studies reveal considerable variation in species’ range
dynamics along elevational gradients, with asynchrony in the rate, or
even direction, of range expansions and contractions (e.g. Crimmins,
Dobrowski, Greenberg, Abatzoglou, & Mynsberge, 2011; Lenoir
et al., 2008). Partly this variation in response can be explained by
individualistic responses of species to a complex suite of climatic
changes, with alterations to the mean, variation and seasonality of
temperature and precipitation, as well as land-use changes (Crimmins
et al., 2011). But even accounting for this, species vary inherently in
their rates of spread and consequently in their ability to track cli-
mate change (“dispersal lags”; Essl et al., 2015; Svenning & Sandel,
2013), their ability to establish in communities at higher elevation
(“establishment lags”) and in their ability to persist at their trailing
range edge (“extinction lags”; Dullinger, Gattringer, et al., 2012;
Lenoir & Svenning, 2013).
Several processes can influence the magnitude of dispersal,
establishment and extinction lags, and therefore the rate of com-
munity turnover in mountain ecosystems following environmental
change. These include: (1) intrinsic species attributes, such as dis-
persal ability (Engler et al., 2009), physiology and demographic
rates (Kroiss & HilleRisLambers, 2014), (hereafter “physiology and
demography”); (2) biotic interactions (HilleRisLambers, Harsch,
Ettinger, Ford, & Theobald, 2013; Kaarlej€arvi, Eskelinen, & Olofs-
son, 2013); and (3) features of the physical environment (Elsen &
Tingley, 2015; Randin et al., 2009; Scherrer & K€orner, 2011).
Recent theoretical (Urban, Tewksbury, & Sheldon, 2012), empirical
(reviewed by Wisz et al., 2013) and experimental (e.g. Alexander,
Diez, & Levine, 2015) work has emphasized the potentially crucial
role for biotic interactions to influence range dynamics. This is
especially likely in mountains, where steep environmental gradients
give rise to abrupt transitions between bioclimatic zones, most
apparent at the subalpine-alpine ecotone between forest and
alpine vegetation (Descombes, Vittoz, Guisan, & Pellissier, 2017;
K€orner, 2003; Mayor et al., 2017). Species’ range expansions
across this ecotone will therefore precipitate interactions between
alpine taxa and novel competitors (Alexander et al., 2015; le Roux
& McGeoch, 2008) and natural enemies (Rasmann, Pellissier,
Defossez, Jactel, & Kunstler, 2014), which might strongly influence
alpine species’ persistence and ecosystem properties. Nonetheless,
the possible consequences of such altered interactions remain
poorly studied, and difficult to integrate into predictive biodiversity
models.
Our aim here is to outline how different demographic, community
and physical processes might affect the dynamics of plant community
change in mountain—and especially alpine—ecosystems following cli-
mate change. We focus on mountains because of their inherent soci-
etal and conservation value (K€orner, 2004), but also because the
compressed climatic gradients in mountains make them ideal model
systems to study impacts of climate change, and its interaction with
other global change drivers such as land-use changes and non-native
species (Pauchard et al., 2016; Sundqvist, Sanders, & Wardle, 2013).
In particular, we expect community changes to occur more rapidly
across compressed elevational climatic gradients, providing opportuni-
ties to develop and test predictions about key processes that might be
applied to more extensive lowland areas. First, we outline how differ-
ent processes contribute to disequilibrium dynamics in mountain plant
communities. We show that while the importance of dispersal lags is
generally well-appreciated, comparatively much less is known about
how biotic interactions could influence range dynamics. Therefore, we
next develop a process-based and dynamic community model to illus-
trate how dispersal lags and competitive interactions could influence
expectations for range shifts and rates of community turnover along
an elevational gradient under climate change. Finally, we discuss the
possible implications of disequilibrium dynamics for the future of
mountain ecosystems, and highlight areas for further research.
2 | LAGS IN RANGE SHIFTS OF PLANT
SPECIES WITH CLIMATE CHANGE
Species’ range shifts occur as the result of population expansion at
the leading range edge and/or contraction at the trailing range edge
(Lenoir & Svenning, 2013). The pace at which range shifts occur
following climate change will depend on factors influencing: (1) dis-
persal at the leading edge; (2) the likelihood of a species establishing
populations and increasing in abundance (hereafter “establishment”)
beyond their current range edge once propagules arrive; and (3) local
extinction at the trailing edge (Svenning & Sandel, 2013; Urban
et al., 2012; Figure 1). The extent to which species show lags in
these three processes—dispersal, establishment and extinction—de-
termines the rate and synchronicity of species’ range shifts, and ulti-
mately community turnover. Three broad classes of factors increase
and/or decrease the magnitude of these lags and how they vary
across species: (a) intrinsic physiological and demographic responses
of the range-shifting species themselves; (b) interactions with other
species; and (c) characteristics of the physical environment
(Figure 1). In the following sections we consider how these factors
(a, b and c) could influence each type of lag (1, 2 and 3), and review
examples from the recent literature demonstrating these processes
operating in mountains.
2.1 | Dispersal lags
2.1.1 | Physiology and demography
As research into biological invasions has shown (e.g. Pysek et al.,
2009), variation in dispersal lags across plant species will depend to
a large extent on functional traits influencing dispersal and popula-
tion spread (Matteodo, Wipf, Stöckli, Rixen, & Vittoz, 2013), particu-
larly the length of the juvenile period, fecundity and dispersal ability.
On average, an extended time to maturity will increase the likelihood
of a dispersal lag (Kroiss & HilleRisLambers, 2014; Lenoir & Sven-
ning, 2013), because spread rates will be less likely to keep pace
with the rate of climate change. Indeed, herbaceous plants with
Subalpine
plant community
Dispersal 
lag
Alpine
plant community
Establishment 
lag
Extinction 
lag
Short generation time, high 
fecundity, high dispersability 
Animal vectors
Rugged mountain 
topography
Short dispersal 
distances
High propagule input
Biotic resistance of 
resident community
Natural-enemy release
(Human) disturbance
Demographic inertia
(Novel) species interactions
Microrefugia
Missing mutualists
Human transportation
F IGURE 1 The magnitude and pace of
alpine plant community turnover with
climate change will be influenced by rates
of species dispersal from lower elevations
(“dispersal lags”), their rates of
establishment and population growth in
alpine communities (“establishment lags”)
and extinction rates of resident alpine
species (“extinction lags”). We include
factors highlighted by our review that will
increase (pointed arrows) or decrease (flat
arrows) the magnitude of lags (factors
related to focal species physiology and
demography, biotic interactions, and the
physical environment coloured orange,
green and blue, respectively, in the online
version)
shorter life cycles have shifted further towards higher elevation than
have woody plants with longer life cycles under contemporary cli-
mate change (Lenoir et al., 2008). Spread rates also depend on
propagule production by reproductive adults (Molau & Larsson,
2000) and the dispersal ability of those propagules (i.e. the shape of
their dispersal kernel; Muller-Landau, Wright, Calderon, Hubbell, &
Foster, 2002). In general, plants producing many small seeds will
spread faster than those producing few heavy seeds, which could
explain the greater elevation range shifts of small-seeded plants in
the Italian Alps (Parolo & Rossi, 2008). Species more associated with
human-induced dispersal, and particularly lowland non-native spe-
cies, might also have an advantage as mountains become increasingly
connected by roads and other human corridors (e.g. power lines;
Pauchard et al., 2016). Lower elevation native species are also
transported to alpine areas by agricultural or recreational activities
(Pickering, Mount, Wichmann, & Bullock, 2011). Finally, evolution of
traits conferring greater dispersal ability will also tend to reduce dis-
persal lags. While there is some evidence of this occurring in plants
(e.g. Williams, Kendall, & Levine, 2016), we are not aware of any
empirical examples from mountain environments.
2.1.2 | Biotic interactions
Biotic interactions could influence dispersal lags for plants relying on
animal vectors for dispersal (Svenning et al., 2014). The high mobility
of most animals compared to plants (Lenoir & Svenning, 2013)
means that interactions with animal vectors are unlikely to limit rates
of plant dispersal, assuming that the vector species can migrate inde-
pendently of the plant species it transports (i.e. it is not a specialist
on that plant species) (but see Neuschulz, Merges, Bollmann, Gugerli,
& B€ohning-Gaese, 2017). Indeed, animal-dispersed plants are per-
haps better able to track climate change than those relying on wind
or passive dispersal. For example, bears disperse seeds of cherry
trees upwards several hundred metres in elevation as they follow
plant phenology during the summer season (Naoe et al., 2016), and
birds regularly disperse seeds across large distances (Viana, Santa-
marıa, & Figuerola, 2016). Interactions with animal vectors could
therefore accentuate variation among plants in spread rates.
2.1.3 | Physical environment
The magnitude of dispersal lags will further depend on barriers to
dispersal such as landscape configuration and heterogeneity. When
suitable habitat patches are isolated, even species with high dispersal
potential could have a low probability of reaching them. The rugged
topography of mountains, with suitable habitat patches separated by
valleys or regions of different bedrock, can present significant dis-
persal barriers (but see Elsen & Tingley, 2015; Thiel-Egenter et al.,
2011). In the European Alps, nearly half of 183 alpine plants have
failed to fill their potential climatic niche, suggesting substantial post-
glacial recolonization lags, and this is more pronounced for species
for which suitable habitat patches (calcareous substrate) are more
isolated (Dullinger, Willner, et al., 2012).
Other evidence suggests that dispersal lags along elevational gra-
dients could be small due to the compressed climatic gradients in
mountains. Engler et al. (2009) found that dispersal did not strongly
limit the speed of mountain species’ responses to climate change;
simulations of unlimited dispersal provided a similar outcome to pro-
jections constrained by dispersal kernels. Empirical studies have
found smaller lags in range expansion along elevational than latitudi-
nal gradients (Bertrand et al., 2011; Jump, Matyas, & Pe~nuelas,
2009; but see Chen, Hill, Ohlem€uller, Roy, & Thomas, 2011), or even
little lag at all (Beckage et al., 2008). Furthermore, short dispersal
distances mean that many propagules might already be present out-
side of the current range of climatically suitable areas of adult plants.
This is the case, for example, for alpine areas in Sweden, where 11
species were found in the seed bank up to 500 m higher in elevation
than the upper limit of established populations (Molau & Larsson,
2000). These species might experience no dispersal lag once climate
becomes permissive for population establishment, depending on how
long dormant propagules remain viable in the seed bank. Finally, high
rates of dispersal to higher elevations might be expected due to
greater land area at, and so relatively higher propagule pressure
from, lower elevations, although over two-thirds of mountain ranges
do not show monotonic declines in area with elevation (Elsen & Tin-
gley, 2015). Altogether, these lines of evidence suggest that dispersal
lags might only moderately constrain the rate of range expansion,
especially for species with broad habitat requirements.
2.2 | Establishment lags
2.2.1 | Physiology and demography
Having arrived at a new site, the probability of a plant species estab-
lishing will depend on traits such as seed size and germination
requirements, mode of reproduction (selfing vs. outcrossing) and
propagule number. Germination and seedling establishment are
major bottlenecks in arctic and alpine plant life history (Graae et al.,
2011; Shevtsova et al., 2009) and show very low rates under field
conditions, even when propagule inputs are high. For instance, along
an elevational gradient in northern Sweden, average seedling emer-
gence of 17 species was 7.5%, and their subsequent mortality rate
80%, when growing without competition (Milbau, Shevtsova, Osler,
Mooshammer, & Graae, 2013); under high-arctic conditions on Sval-
bard, germination was generally below 5%, compared to c. 80%
under optimal lab conditions for the same seed source (M€uller,
Cooper, & Alsos, 2011). Therefore, propagules must arrive in suffi-
cient numbers to increase the probability of establishment, and over-
come other limitations of small population size such as Allee effects
or a limited capacity to adapt genetically to the new environmental
conditions.
2.2.2 | Biotic interactions
The probability of establishment in alpine communities will depend
on interactions with the resident plant community, natural enemies
and mutualists (HilleRisLambers et al., 2013). Vegetation cover facili-
tates alpine plants by creating soil and microclimatic conditions
favourable for plant growth (K€orner, 2003; Michalet, Sch€ob, Lortie,
Brooker, & Callaway, 2014). In particular, strong facilitative effects
have been demonstrated after experimental neighbour removal (Call-
away et al., 2002; Choler, Michalet, & Callaway, 2001; Olsen,
T€opper, Skarpaas, Vandvik, & Klanderud, 2016), or observed for
plants growing in the presence vs. absence of nurse plants (Cavieres
et al., 2014). Facilitation can also be density dependent, as when
higher canopy cover of adult trees promotes tree seedling recruit-
ment near treeline (Maher & Germino, 2006). Altogether, facilitation
in alpine vegetation could promote the establishment of populations
towards species’ high elevation range limits (Choler et al., 2001; le
Roux, Virtanen, Heikkinen, & Luoto, 2012), something that has also
been observed for some non-native plants (Cavieres, Quiroz, &
Molina-Montenegro, 2008).
In the context of these facilitative effects of vegetation cover,
competition within closed alpine communities can continue to play
an important role even under relatively harsh environmental condi-
tions (Chesson & Huntly, 1997). This appears to be especially the
case at the seedling recruitment stage, which can be greatly reduced
in closed alpine vegetation, presumably because of competition
(Gough, 2006; Graae et al., 2011; Lembrechts et al., 2016; Milbau
et al., 2013; Moen, 1993). For example, Graae et al. (2011) observed
early establishment rates of 0.9% in undisturbed and 11% in dis-
turbed tundra vegetation. The extent to which vegetation cover
facilitates or inhibits recruitment will therefore depend on the bal-
ance between competition and exposure to severe environmental
conditions. As an example of this, a theoretical study suggests that
seedling recruitment could be greater in small rather than large gaps,
because small gaps balance relaxed competition with a favourable
microclimate (Lembrechts, Milbau, & Nijs, 2015). Nonetheless,
recruitment from seed occurs frequently in some alpine areas, such
as in the Australian Alps (Venn & Morgan, 2009), where negative
effects of vegetation on recruitment are also weaker (Venn, Morgan,
& Green, 2009). Interaction strengths are also likely to change with
ontogeny, for example with competitive effects at the seedling stage
weakening (Gough, 2006) or becoming increasingly facilitative (le
Roux, Shaw, & Chown, 2013) as the plant grows.
Given the many different ways in which species can interact with
each other and their environment, generalization is difficult.
Nonetheless, reconciling these empirical and theoretical observa-
tions, we suggest that facilitation will most strongly influence climate
change range dynamics in sparsely vegetated habitats, such as high
alpine screes or in drought-prone mountain regions (Michalet et al.,
2014). Here, establishment lags might be protracted by the coloniza-
tion of pioneer species that can facilitate later-arriving species. By
contrast, in already densely vegetated alpine meadows we expect a
stronger role for competition, with colonization of new species con-
tingent on disturbance creating “safe-sites” for establishment (Vittoz,
Randin, Dutoit, Bonnet, & Hegg, 2009). The strength of competition
from alpine communities could increase with climate warming (e.g.
Alexander et al., 2015; Olsen et al., 2016), potentially reducing
establishment success further (Hampe, 2011). In addition, it will take
time for founding individuals to expand population size towards car-
rying capacity (cf. “abundance lag” in Essl et al., 2015). This process
could be accelerated by traits conferring high competitive ability
and/or high relative growth rate, which might trade-off with traits
conferring successful dispersal or initial establishment (Turnbull,
Rees, & Crawley, 1999). Furthermore, colonizing species from war-
mer areas are likely to possess faster growth rates than alpine spe-
cies under climate change conditions (K€orner, 2003), potentially
conferring a competitive advantage (Alexander et al., 2015) and pro-
moting population expansion.
Natural enemies accompanying plants as they expand their
ranges from lower elevation might also influence establishment lags.
Establishment lags should be reduced for species that experience
“enemy release” (Engelkes et al., 2008) by dispersing faster than the
natural enemies that regulate their population abundance at lower
elevation. For example, release from seed predation might promote
the spread of tropical trees to higher elevations in the Andes (Hillyer
& Silman, 2010). However, natural enemies already present in alpine
ecosystems are likely to increase establishment lags of plants arriving
from lower elevation. Mammalian herbivores can prevent establish-
ment at the upper range margin of some forbs (Kaarlej€arvi et al.,
2013) and trees (Brown & Vellend, 2014), contributing to the forma-
tion of treelines (Speed, Austrheim, Hester, & Mysterud, 2010).
Range expansion might also be constrained by soil pathogens (Brown
& Vellend, 2014). For example, the broad host range dieback patho-
gen Phytophthora cinnamomi, already present in subalpine regions of
Australia, may negatively impact not only existing flora, but also any
range expanding species from lower elevations, including non-native
plants (Burgess et al., 2017).
Finally, lagged range shifts of mutualists such as pollinators or
soil microbiota might increase plant establishment lags, especially
when mutualists have poor dispersal ability and engage in specialized
interactions (Corlett & Westcott, 2013). For example, the availability
of pollinator services might already limit range expansion of some
species at their high elevation range edge (HilleRisLambers et al.,
2013). Similarly, the spread of non-native trees into new environ-
ments has been slowed or halted by a lack of appropriate mycor-
rhizal fungi (Nu~nez, Horton, & Simberloff, 2009).
2.2.3 | Physical environment
As previously noted, low establishment rates in alpine ecosystems
are attributed to limiting abiotic conditions like high irradiance,
drought and extremes of temperature, and the extent to which these
are ameliorated by vegetation cover (Eckstein, Pereira, Milbau, &
Graae, 2011; Graae et al., 2011; Milbau et al., 2013). In areas where
vegetation cover reduces establishment success, successful recruit-
ment might only occur when disturbances create ephemeral safe
sites for establishment (Milbau et al., 2013). Although alpine areas
are naturally disturbed (e.g. by freeze-thaw dynamics, landslides and
animals), certain attributes of human disturbances (e.g. frequency,
intensity, nutrient release) cause them to more strongly promote the
establishment of colonizing (non-native) plant species from lower
elevation (Lembrechts et al., 2016). Therefore, establishment lags
might be strongly influenced by the extent of human disturbances in
alpine areas, easing the barriers for some species.
Soil development itself will limit the establishment of propagules
across the alpine elevational gradient, especially for species that
require deep organic soils. In the Swiss Alps, alpine species that are
able to colonize scree habitats have increased in frequency markedly
over the last century, in contrast to alpine species with a preference
for organic soil (Kulonen, 2017). Poorly-developed alpine soils with
low water-retaining capacity can limit seedling growth and survival,
which is expected to constrain the range expansion of tree and
grassland species beyond the current limits of alpine meadows (Ford,
2014; HilleRisLambers et al., 2013). But soil development could also
constrain establishment at lower elevations as well. For example, low
water-holding capacity in thin alpine soils could explain colonization
lags by spruce (Picea abies) in the Swiss Alps during the Holocene,
and with future climate change (Henne, Elkin, Reineking, Bugmann,
& Tinner, 2011). Alpine soil development can take hundreds to thou-
sands of years, and be further slowed on steep slopes prone to soil
erosion (Theurillat et al., 1998). Therefore in general, soil develop-
ment is likely to contribute substantially to establishment lags, even
given increased rates of soil formation due to warmer climate (Sven-
ning & Sandel, 2013).
2.3 | Extinction lags
2.3.1 | Physiology and demography
Populations of alpine species that are physiologically unable to toler-
ate altered climatic conditions due to climate change, especially
those at the low-elevation distribution limit, will rapidly become
locally extinct. For example, Ranunculus glacialis does not tolerate
warmer conditions when transplanted to lower elevations (Prock &
K€orner, 1996). Population dieback is occurring in some areas, caused
primarily by interactions between extreme climatic conditions (e.g.
summer drought or severe frost) and plant pathogens. Examples
include the shrub Nematolepis ovatifolia in alpine areas of Australia
(Green, 2016), and the cushion plant Azorella macquariensis on the
sub-Antarctic Macquarie Island (Bergstrom et al., 2015).
Extinction lags could, however, be substantial for many other
alpine species, even if climate change has a negative effect on popu-
lation dynamics (Dullinger, Gattringer, et al., 2012; Essl et al., 2015;
Svenning & Sandel, 2013). Some herbaceous alpine plant species,
including those dominating alpine grasslands such as Carex curvula in
the European Alps, have persisted many centuries through extensive
fluctuations in climate (De Witte, Armbruster, Gielly, Taberlet, &
St€ocklin, 2012). Long-lived species with extensive resources stored
in roots, rhizomes and stems can persist for decades or even cen-
turies after cessation of reproduction, as is the case for some trees
(Hampe & Jump, 2011). In addition, recruitment might continue from
long-lived soil seed banks, or after living individuals have disap-
peared altogether from the community (Ouburg & Eriksson, 2004).
Finally, many alpine species are widely distributed and possess an
intrinsic ability to tolerate temperature and water stresses (K€orner,
2003), equipping them to tolerate climate changes. Widely dis-
tributed species tend to have broad ecological tolerances, making
them less vulnerable to extinction following climate change (Slatyer,
Hirst, & Sexton, 2013). Such species can also host higher levels of
genetic variability in ecologically relevant traits (Sheth & Angert,
2014), potentially fostering adaptation to environmental change and
so delaying extinction lags. Together, therefore, there could be con-
siderable inertia in alpine plant populations, delaying local extinction
due to unfavourable climate.
2.3.2 | Biotic interactions
Changing species interactions can play a more decisive role in popu-
lation declines and local extinction following climate change than
direct climatic effects (Cahill et al., 2013). Altered biotic interactions
could therefore accelerate local extinction, firstly, due to changes in
interactions among species already co-occurring in alpine communi-
ties today. For instance, some alpine plants might be competitively
suppressed under climatic warming by neighbours that respond more
favourably to altered climate (Niu & Wan, 2008), such as those
already found at lower elevation. Biotic interactions among alpine
species can shift from facilitative to competitive as climatic condi-
tions change (Olsen et al., 2016). Climate change could also alter the
trophic interactions regulating population size, such as with pollina-
tors, herbivores and pathogens. For example, some species of Phy-
tophthora occur with susceptible hosts in alpine areas of the
Australian Alps, but disease outbreak is only expected to occur once
climate warms sufficiently to become conducive to disease expres-
sion (Burgess et al., 2017), with potentially large impacts on host
plant populations (Cahill, Rookes, Wilson, Gibson, & McDougall,
2008). Pathogens have also been implicated in population collapses
in an alpine ecosystem on the sub-Antarctic Macquarie Island (Berg-
strom et al., 2015). In contrast, mutualistic interactions can enhance
plant species’ climatic tolerance (Afkhami, McIntyre, & Strauss,
2014), potentially delaying or avoiding local extinction, as suggested
for effects of ectomycorrhizal fungi on range contraction rates of
North American trees (Lankau, Zhu, & Ordonez, 2015).
Competitors and natural enemies can restrict a species’ distribu-
tion to a subset of the environmental conditions it would physiologi-
cally be able to tolerate (i.e. set the limits to its realized climate
niche; Figure 2). Therefore, the local persistence of some alpine
plants near their lower range margin might hinge on the outcome of
their interactions with new competitors or natural enemies that
migrate-in from lower elevations. For example, a plant community
from low elevation suppressed the survival and growth of three focal
alpine plants much more strongly than an alpine plant community
when growing under a warmer low elevation climate (Alexander
et al., 2015). Partly this might be driven by functional trait differ-
ences between subalpine and alpine species, with traits such as taller
stature and faster growth conferring a competitive advantage to
lower elevation species once climate at high elevations becomes
permissive. Although upwards range shifts of lower elevation taxa
have been well-documented, direct evidence that competitive
replacement is already occurring is scant. However, in mountains of
China (Zong, Xu, Dege, Wu, & He, 2016) and Japan (Kudo, Amagai,
Hoshino, & Kaneko, 2011), the climate-related range expansion of
two grass species into alpine areas has led to local reductions in spe-
cies richness.
Interactions with new natural enemies could also accelerate the
local extinction of alpine plants. Alpine communities can contain a
low abundance of certain taxa found more commonly at lower eleva-
tions, such as herbivorous insects (Pellissier et al., 2012). As a result,
alpine plants tend to be less defended against herbivores than low-
land species (Bruelheide & Scheidel, 1999; Pellissier et al., 2012).
More generally, the arrival of functionally new herbivores from lower
elevations with climate warming might increase the competitive
advantage of better-defended, co-evolved lowland plants (Rasmann
et al., 2014). This arrival might accelerate the extinction of local
alpine plant species, thus favouring the establishment of other spe-
cies from lower elevation and in the end precipitate rapid community
change. Overall, the effect of low elevation species on extinction
lags of alpine plants will depend on their rates of dispersal and
establishment, as well as their direct (e.g. through competition, her-
bivory) and indirect (e.g. by facilitating the arrival of other lowland
species) effects on alpine plant population dynamics.
2.3.3 | Physical environment
The complex topography and spatial heterogeneity of mountain envi-
ronments contribute to the occurrence of microsites within close
proximity that differ strongly in climatic characteristics (Randin et al.,
2009; Scherrer & K€orner, 2011). This microsite variation might also
help increase extinction lags at the landscape scale by providing
microrefugia for populations to persist locally, as climatic relics,
under deteriorating regional climate (Hampe & Jump, 2011). How-
ever, the effectiveness of such microhabitats for long-term species
survival relies on long-term microclimate stability (Lenoir, Hattab, &
Pierre, 2017). Other features of the physical environment that
increase dispersal and establishment lags, such as topographic
D
ec
re
as
in
g 
te
m
pe
ra
tu
re
Population growth
Range expansion 
at leading edge
Local extinction at 
trailing edge
Species A with no 
dispersal or 
establishment lag
Species B with 
dispersal lag
Range expansion 
at leading edge
Local persistence at 
trailing edge
Species B realized 
and fundamental 
niche
Species A 
realized niche
Species A 
fundamental 
niche
SDM prediction for Species A 
following climate warming
Process-based prediction 
following climate warming
(a)
(c)(b)
Species’ realized niches and elevational distribution under current climate
Realized niche 
conservatism
Realized niche 
expansion
Species A
Species B 
X
F IGURE 2 Range predictions for a focal alpine species (species A) following climate change. Species A shows a trade-off between
population growth rate and tolerance of low temperature, such that it can tolerate a greater range of temperature than species B, but is
outcompeted by species B under warmer climatic conditions (i.e. it has a broader fundamental niche than species B, but a realized niche
restricted to cooler temperatures) (a). Following climate warming, a simple species distribution modelling (SDM) approach for species A, that
assumes realized niche conservatism, would predict high elevation range expansion and low elevation range contraction (b). However, if
species B is constrained by a dispersal lag, as shown, then the prediction of local extinction at the lower elevation range margin for species A
would be incorrect. Instead species A would persist at its current lower elevation range margin, expanding its realized niche, due to the
absence of competition from species B (c). This outcome would be predicted by process-based models that account independently for effects
of climate and competition on population growth rates
isolation, will also tend to increase extinction lags by sheltering
alpine plants from interactions with lowland taxa. For example, rocky
habitats like scree that are difficult for lowland competitors to colo-
nize, and slow soil development more generally, could provide
microrefugia for some alpine plants under a warmer climate (Kulo-
nen, 2017). Indeed, populations of some alpine plants can persist at
low elevations in habitats, such as rocky slopes, where they are shel-
tered from competition with lowland species (Spillmann & Holdereg-
ger, 2008).
3 | PREVALENCE AND PREDICTION OF
LAGGED RANGE DYNAMICS IN ALPINE
COMMUNITIES
Based on our review, dispersal lags could be substantial for some
species spreading to higher elevations, but on the whole the
available evidence suggests that dispersal lags will contribute less to
disequilibrium range dynamics than establishment and extinction
lags. Existing vegetation could facilitate range expansion into spar-
sely vegetated alpine habitats (Cavieres et al., 2008), but biotic pres-
sures (e.g. herbivory, competition) in temperate/arctic alpine
ecosystems with closed vegetation can impose strong barriers to
establishment (e.g. Kaarlej€arvi et al., 2013; Milbau et al., 2013).
Therefore, even when low elevation species arrive in a climatically
suitable area, we could expect a considerable lag before they are
able to establish and increase population size. Coupled with this,
several lines of evidence suggest that extinction lags in alpine spe-
cies could also be substantial; some species have persisted in situ
through large climatic fluctuations (De Witte et al., 2012), and there
appears to have been little local extinction at trailing edges so far
(Pauli et al., 2012), in contrast with the many examples of range
expansions (but see Cannone, Sgorbati, & Guglielmin, 2007). These
observations are consistent with the view that species’ low elevation
range edges tend not to be set by direct climatic effects, which
might be favourable for plant growth following climate change, but
rather by negative biotic interactions (Gaston, 2003; Hargreaves,
Samis, & Eckert, 2014; MacArthur, 1972).
The accumulation of species’ dispersal, establishment and
extinction lags will affect the magnitude of community turnover
(i.e. changes in species composition and abundance) with climate
change (Bertrand et al., 2011). Evidence of alpine community turn-
over during the last decades of climate change exist (Gottfried
et al., 2012; Wipf, St€ockli, Herz, & Rixen, 2013), but so far the
documented change in alpine communities are the result of colo-
nization of species from lower elevation (i.e. a thermophilization of
the flora) without clear negative effects on resident species.
Regions such as the Swiss Alps have already experienced more
than 1°C of warming in the 20th century, yet its effect on the
vegetation is sometimes barely perceptible (Vittoz et al., 2009).
Taken together then, lags in the dispersal, establishment and local
extinction of individual species will collectively determine the rate
of community turnover.
The need to forecast the impact of global changes on species
assemblages has fuelled the development of a variety of predictive
models (e.g. Thuiller, Lavorel, Araujo, Sykes, & Prentice, 2005). Due
to their simplicity of application, statistical models such as species
distribution models (SDMs) have been a particularly popular
approach to forecast the consequence of global changes on species
assemblages (e.g. Descombes, Pradervand, Golay, Guisan, & Pellissier,
2016; Engler et al., 2011; Thuiller et al., 2005). SDMs fitting relation-
ship between species’ occurrence (or abundance) and climate allow
species assemblages to be predicted in space and time (e.g. Guisan
& Rahbek, 2011). Though they can integrate dispersal (Engler et al.,
2009), SDMs do not usually account explicitly for biotic interactions
(Wisz et al., 2013), so they are unable to consider processes such as
establishment or extinction lags. Rather, mechanistic approaches that
account independently for effects of climate and biotic interactions
on population dynamics would be needed to model such disequilib-
rium situations (Schurr et al., 2012; Zurell et al., 2016). Meta-com-
munity models could prove particularly useful in identifying the
range of circumstances under which strong lags should arise (Jackson
& Sax, 2010). In the next section, we develop a process-based meta-
community model that directly manipulates dispersal processes while
indirectly manipulating establishment and extinction processes
through competition. The model illustrates the effects of lags in dis-
persal, establishment and extinction on community turnover com-
pared to expectations of species-specific range shifts with unlimited
dispersal (e.g. SDMs). Our objective is to illustrate the sort of mod-
elling approach that might be used to predict disequilibrium range
dynamics under climate change.
4 | LAGGED RANGE DYNAMICS AND
COMMUNITY TURNOVER ALONG AN
ELEVATIONAL GRADIENT: A META-
COMMUNITY MODEL
Meta-community models could help provide forecasts of disequilib-
rium range dynamics (Jackson & Sax, 2010), including dispersal,
establishment and extinction lags. Some previous attempts have
been made to model mechanisms of plant community responses to
climate changes, for example including dispersal kernels (Engler et al.,
2009) or demographic effects (Cotto et al., 2017; Dullinger, Gattrin-
ger, et al., 2012) within SDMs. Combining SDMs with demographic
parameters, Cotto et al. (2017) showed that perennial species can
persist in unsuitable habitats longer than predicted by their climatic
tolerance, causing delayed range losses. Nevertheless, to integrate
the effect of biotic interactions on the turnover of entire communi-
ties, a mechanistic model is needed that not only allows dispersal
limitation and demographic inertia, but also periods of transient co-
occurrence in communities caused by lags in the establishment of
new species and their replacement of weaker competitors.
We formulate a model to describe the assembly of a meta-com-
munity along a temperature gradient, and the response of the sys-
tem to climate warming, inspired by a Lotka-Volterra model of
interspecific competition. The model does not attempt to consider
all processes highlighted by our review; it includes variation among
species in demographic rates (dispersal, growth) and impacts of one
type of biotic interaction (competition), but for simplicity omits non-
climatic impacts of the physical environment on lags.
We developed a 1-dimensional stepping stone model represent-
ing a meta-community of 500 communities along a linear tempera-
ture (elevation) gradient varying between 0 and 20°C. A plant
species’ distribution along the gradient depends on dispersal, its
growth response to temperature, and effects of competitors (see
Appendix S1). The model simulates the dispersal and growth dynam-
ics of a set of plant species within a meta-community. The popula-
tion size of species i in cell j at time t + 1, Pi,j,t+1, can be calculated
from the population of species i in cell j at time t, Pi,j,t, in two succes-
sive operations. In each community, all species export a fraction (d)
of their local population to the two adjacent communities in the 1-
dimensional landscape:
P0i;j;t ¼ ð1 dÞ  Pi;j;t þ
d
2
 ðPi;jþ1;t þ Pi;j1;tÞ (1)
Weak dispersal to neighbouring cells will cause dispersal lags
under scenarios of warming. Next, we derive the species’ population
sizes after taking into account population growth and competitive
interactions:
Pi;j;tþ1 ¼ P0i;j;t þ Dt P0i;j;t½giðTj  TminiÞ  ciP0i;j;t  li
X
k
P0k;j;t (2)
Competition enters the model in three ways. First, fast-growing
species have a competitive advantage over slow-growing species
because they more rapidly attain higher abundance within a commu-
nity. Therefore, growth rate (gi) captures variation among species in
their innate competitive ability. A large difference in gi between an
invader species and those in the established communities will allow
the invader to quickly expand its population with a small establish-
ment lag. Second, species vary in their sensitivity to competition (li),
i.e. the extent to which their population size is directly reduced by
the abundance of both hetero- and conspecific neighbours. When
the invader is a greatly superior competitor (i.e. li invader  li estab-
lished species), the population size of the established community will
offer weak resistance to the invader’s growth, resulting in a small
establishment lag for the invader. Combined with a higher growth of
the invader, this implies short extinction lags for the established spe-
cies. Third, population size shows additional sensitivity to the abun-
dance of conspecific neighbours, given by a coefficient ci that is set
to be constant across all species and is needed to stabilize the coex-
istence of multiple species (Chesson, 2000). We assume a fundamen-
tal trade-off between competitive ability and tolerance of low
temperatures (Jones & Gilbert, 2016; Loehle, 1998; le Roux et al.,
2013), so that fast-growing species (high gi) are less sensitive to
competition (low li) but restricted to growing under warmer tempera-
tures (high Tmini), while slow-growing species (low gi) suffer greater
competitive suppression (high li) but can tolerate lower temperatures
for growth (low Tmini) (Grime, 1977). This widely held assumption in
fact remains largely untested (Jones & Gilbert, 2016), but is consis-
tent with observations that species are principally limited by abiotic
factors at their colder range edge (Pellissier et al., 2013) but biotic
interactions at the warmer edge (Alexander et al., 2015), and is fur-
ther supported by the successful growth of many alpine plants in
lowland botanical gardens (Vetaas, 2002).
We investigate the rate of temporal community turnover, quanti-
fied as b-diversity between the communities present at a given point
along the gradient before and after a 3°C increase in temperature,
using the b-diversity of Jost (2007) and Tuomisto (2010) (see
Appendix S1). We compared the meta-community model with the
expectation of community turnover when all species shift their range
independently and without dispersal limitation (i.e. by simply stacking
SDM projections). In this scenario, communities are simply trans-
posed to higher elevations under the +3°C climate warming scenario
without any dispersal lag or altered competitive interactions. Smaller
values for temporal b-diversity in the process-based simulation rela-
tive to the SDM-stacking indicate greater time lags in community
turnover.
To test the influence of competition and dispersal on the
response of the meta-community to climate warming, we simulated
a set of 500 scenarios in which we randomly sampled the values of
the following three parameters (Appendix S1): dispersal rate d; mean
and coefficient of variation in gi in the species pool; and mean and
coefficient of variation in li in the species pool (see Table S1 for
parameter distributions). We first explored scenarios varying the
average values of these parameters across the entire gradient, to
quantify how the strength of those processes influence community
turnover. Second, we explored scenarios differing in the coefficient
of variation (CV) of gi or li along the gradient, hypothesizing that a
greater CV (i.e. greater asymmetry in growth or sensitivity to compe-
tition between low- and high-elevation species) should promote fas-
ter turnover under temperature change. Each meta-community was
subjected to an initial burn-in period to reach equilibrium (see
Appendix S1), and was then exposed to a gradual climate warming
of 3°C and allowed to reassemble along the elevational gradient. We
computed temporal community turnover and fitted a linear mixed
effects model to quantify the effects on community turnover of dis-
persal rate, mean and CV of growth rate and sensitivity to competi-
tion, with species pool as a random effect. We also controlled for
initial meta-community structure (after the initial burn-in period) by
including mean a- and spatial b-diversity across the gradient as fixed
effects. To exclude edge effects on our estimates of temporal turn-
over, we removed low and high elevation communities from the lin-
ear model estimation (see Appendix S1). Our baseline for comparing
the results of the meta-community model and the SDM-stacking
approach were the meta-communities after the initial burn-in period,
which differed between each set of parameters (Figures 3 and 4).
We documented lags in the dynamics of species dispersal and
replacement across the gradient, with an average 10% lower tempo-
ral community turnover (b-diversity) compared to the expectations
obtained by simply stacking SDMs (Figures 3, 4 and S1c). Both out-
comes from our process-based model and expectations obtained by
stacking SDM projections followed the same pattern along the tem-
perature gradient: a low turnover at high elevation and a high turn-
over at low elevations. This reflects the structure of the meta-
community in our model; the gradient in species diversity is not lin-
ear, since species coexistence results from an interactive effect of
temperature-related growth on population sizes acting together with
the species’ intrinsic abilities to tolerate competition (Figures S1d, S2
and S3).
Both dispersal rate and competition significantly affected the
extent of community turnover following climate warming. Temporal
turnover increased with higher dispersal rates (Table 1), bringing it
closer to the expectations obtained using SDM-stacking (Figures 3
and 4), as expected from previous work (Engler et al., 2009). Under
high dispersal meta-communities had a temporal turnover close to
the one predicted by SDM stacking regardless of other model
parameters (Figures 3a-d and 4a–d), and also an accelerated colo-
nization of previously empty communities at the mountain summits
(Figures 3 and 4). However, the effects of competitive ability cap-
tured by the mean and coefficient of variation among species in
growth rate (gi) were nearly three to five times greater than the
effect size of the log-transformed dispersal rate on community turn-
over over time (Table 1). When growth rates were high across the
whole gradient, competitive replacement and hence community turn-
over occurred more rapidly (Figure 3c,d,g,h). A greater variation in
growth rate between warm- and cool-adapted species also
accelerated the rate of temporal turnover in community composition
(Figure 3a,b,e,f). The effects on community turnover over time of
species’ sensitivity to competition were qualitatively similar to
growth rate but smaller in magnitude (Table 1; Figure 4). Because in
our simulations climate change occurred gradually over time, these
results are likely to be conservative with respect to the relative
importance of competition and dispersal on temporal community
turnover. Nevertheless, the model outputs are constrained by the
architecture of the models and the number of time steps considered
within the 3°C change and confrontations with empirical data are
needed to provide realistic expectations.
Our simulation exercise with a meta-community model indicates
how the demographic parameters gi, li and d influence lags in com-
munity turnover under climate change and how forecasts might dif-
fer from single species models based on their realized niche and
with unlimited dispersal. Beyond the use of SDMs in community
forecasts under climate change, our results advocate the develop-
ment and calibrations of process-based models to reach more accu-
rate temporal estimates of community changes. This agenda will
require methodological developments (e.g. Harsch et al., 2017;
Urban et al., 2016) and the collection of appropriate demographic
data, e.g. using experiments (Alexander, Diez, Hart, & Levine, 2016).
Despite the complexity of mechanisms described in the review sec-
tion, our model focuses on competition because of its central impor-
tance for regulating plant population dynamics, even under relatively
SDMSimul. SDMSimul.
SDMSimul. SDMSimul.
SDMSimul.
0
1
β
SDMSimul.
SDMSimul. SDMSimul.
H
ig
h 
di
sp
er
sa
l
Low CV High CV
Lo
w
 d
is
pe
rs
al
Low mean High mean
(a) (b)
Simulated and expected community turnover due to temperature change
(e) (f)
(c) (d)
(g) (h)
Growth rate
F IGURE 3 Temporal community turnover along an elevational temperature gradient (b-diversity, represented by the gradient from white to
red, see legend in (d)) following climate warming. The left section of the mountains represents the results of simulations (“Simul.”), while the
right section represents the expected turnover obtained by simply stacking species distribution model projections (“SDM”). Shown are eight
scenarios that differ depending on dispersal ability (rows) and growth rate within the species pool (a, e: coefficient of variation [CV] = 0.1; b, f:
CV = 1/√3; c, g: mean = 0.2; d, h: mean = 0.5; a–d: d = 101; e–h: d = 104). In each panel, all other parameters except the ones specified in
the header of their line and column were set to the average value of their respective distribution (see Table S1). The lowest elevation
communities are not displayed (see Appendix S1). Communities at the highest elevations resulting from colonization of previously unoccupied
habitat are coloured in blue, while communities that remain empty despite warming are in light grey. Note that outcomes of temporal
community turnover obtained from stacking SDM projections can also differ slightly among panels due to different initial conditions (i.e. initial
burn-in period that allowed the meta-communities to equilibrate)
harsh environmental conditions (Chesson & Huntly, 1997). Nonethe-
less, the model structure might be extended to accommodate a more
complex suite of positive and negative interactions (Brooker, Travis,
Clark, & Dytham, 2007; Svenning et al., 2014), including interactions
with higher trophic levels. In addition, the model as written corre-
sponds to environmental conditions typical for temperate mountains
like the European Alps, where water is generally not limiting along
the gradient (with the exception of some internal valleys). Further
modifications would be required to accommodate other environmen-
tal contexts, such as mountains where vegetation is shaped by
drought at lower elevations or where climate change increases,
rather than decreases, climatic stress at high elevation. Finally, while
we illustrate the potential of dispersal, establishment and extinction
lags in shaping community turnover over time, we are still far from
being able to calibrate such a model with empirical data.
5 | DISEQUILIBRIUM DYNAMICS IN
ALPINE PLANT COMMUNITIES:
IMPLICATIONS AND RESEARCH NEEDS
Two main findings emerge from our review and simulation of dise-
quilibrium dynamics in alpine plant communities. Firstly, they confirm
that lags in species’ range responses to recent climate change along
elevation gradients occur, and that the conditions promoting lags are
common and likely to influence rates of community turnover in the
future. This has implications for the future structure and functioning
of alpine communities, which are discussed below. Secondly, biotic
interactions play a crucial role in mediating disequilibrium dynamics,
SDMSimul. SDMSimul.
SDMSimul. SDMSimul.
SDMSimul.
0
1
β
SDMSimul.
SDMSimul. SDMSimul.
H
ig
h 
di
sp
er
sa
l
Low CV High CV
Lo
w
 d
is
pe
rs
al
Low mean High mean
(a) (b)
Simulated and expected community turnover due to temperature change
(e) (f)
(c) (d)
(g) (h)
Sensitivity to competition
F IGURE 4 Temporal community turnover along an elevational temperature gradient (b-diversity, represented by the gradient from white to
red, see legend in (d)) following climate warming. The left section of the mountains represents the results of simulations (“Simul.”), while the
right section represents the expected turnover obtained by stacking species distribution models projection (“SDM”). Shown are eight scenarios
that differ depending on dispersal ability (rows) and sensitivity to competition within the species pool (a, e: coefficient of variation [CV] = 0.1;
b, f: CV = 1/√3; c, g: mean = 0.7; d, h: mean = 1.5; a–d: d = 101; e–h: d = 104). In each panel, all other parameters except the ones specified
in the header of their line and column were set to the average value of their respective distribution (see Table S1). The lowest elevation
communities are not displayed (see Appendix S1). Communities at the highest elevations resulting from colonization of previously unoccupied
habitat are coloured in blue, while communities that remain empty despite warming are in light grey. Note that outcomes of temporal
community turnover obtained from stacking SDM projections can also differ slightly among panels due to different initial conditions (i.e. initial
burn-in period that allowed the meta-communities to equilibrate)
TABLE 1 Parameter estimates from the linear mixed effects
model linking temporal community turnover (b-diversity) to dispersal
rate, mean growth rate (gi), the coefficient of variation in growth
rate, mean sensitivity to competition (li), the coefficient of variation
in sensitivity to competition, mean a-diversity across the gradient
and initial spatial b-diversity across the gradient. Because the
covariates and response variable were standardized, the estimates
are also effect sizes (*p < .05; ***p < .001). The marginal R2 = 0.360
and conditional R2 = 0.411 indicate the amount of variance
explained by the fixed effects, and by the combination of fixed and
random effects, respectively. For all fixed effects, df = 492
Parameter Estimate ( SD)
Intercept 0.000 (0.013)ns
Mean of gi 0.356 (0.018)***
Coefficient of variation in gi 0.591 (0.031)***
Mean of li 0.117 (0.013)***
Coefficient of variation in li 0.216 (0.013)***
Dispersal (log transformed) 0.124 (0.012)***
Mean a-diversity across the gradient 0.006 (0.012)ns
Initial b-diversity across the gradient 0.059 (0.027)*
and likely have a particularly strong influence on establishment and
extinction lags. Together, these findings imply that modelling of dise-
quilibrium dynamics in a way that accounts for changing biotic inter-
actions will be necessary to forecast the response of alpine
communities to climate change. Our simulation model offers an
example of one way forward, and other tools are becoming available
(Evans, Merow, Record, McMahon, & Enquist, 2016; Pagel & Schurr,
2012; Zurell et al., 2016), rooted in the common demographic basis
of both range dynamics and impacts of biotic interactions on popula-
tion dynamics (Alexander et al., 2016).
Whilst our simulation model is informative, we can as yet say lit-
tle about the relative contribution of different lags to disequilibrium
dynamics without data on demographic rates and biotic interactions
for a large number of species. Parameterizing such demographic
models will be a huge challenge, since potentially all species can
respond differently to changing environmental conditions. One
approach might be to measure demographic rates for a subset of
species under a range of controlled environmental conditions, either
in the field or phytotron, and correlate these with functional trait
proxies that are easier to measure for a large number of species
(Alexander et al., 2016). For example, such trait-based models have
been developed to predict dispersal potential (Tackenberg, 2003),
and therefore might also be applied to predict demographic
responses to abiotic gradients (i.e. to estimate something approach-
ing a species’ fundamental niche). Traits can also be used as proxies
for the outcome of biotic interactions, especially for trophic interac-
tions (Morales-Castilla, Matias, Gravel, & Araujo, 2015) and to some
extent for competition among plants (e.g. Kraft, Godoy, & Levine,
2015).
There are a number of further implications of disequilibrium
community dynamics not addressed here, which constitute open
questions for future research. Firstly, how long will novel communi-
ties persist following climate change? Novel communities arising
from asynchronous migration might be transient, especially when
community turnover is rapid, and converge towards the composition
of communities typical of lower elevations today (Cannone et al.,
2007). However, they might be more persistent when environmental
conditions themselves are novel (Williams & Jackson, 2007), or if pri-
ority effects or rapid evolution stabilize a novel community structure.
Secondly, what are the consequences of disequilibrium dynamics for
alpine ecosystem structure and function? For instance, considerable
time will pass before trees invading alpine habitats will deliver the
full quantity and quality of forest ecosystem services (e.g. timber
production, protection against avalanches; Essl et al., 2015; Svenning
& Sandel, 2013). Similarly, alpine meadows composed of fewer, func-
tionally different species might not deliver ecosystem services in the
same way as current alpine meadow communities (K€orner, 2004).
Thirdly, what are the implications of disequilibrium dynamics for the
conservation and management of biodiversity in mountains? The
prevalence of lags underscores the need for networks of protection
promoting (or sometimes reducing) connectivity, that do more than
just protect the highest elevation ecosystems (Plassmann, Kohler,
Badura, & Walzer, 2016). Understanding the nature and causes of
lags at leading and trailing range edges might inform active manage-
ment strategies for particular species or habitat types of conserva-
tion concern. Finally, the examples we have reviewed illustrate the
usefulness of elevational gradients, and alpine ecosystems in particu-
lar, as model systems to observe disequilibrium dynamics and also to
conduct experiments at the scale of species’ ranges. Disequilibrium
dynamics are likely to be magnified in lowland areas by the greater
spatial scales involved, so studies from mountains should provide
lower limits for the magnitude of lags that we should expect
following climate change. But one should extrapolate biological
responses from mountainous to lowland regions with caution, since
climatic changes with latitude covary with numerous factors, such as
photoperiod, geological history and regional species pools, that tend
to be relatively constant across elevational gradients. Nonetheless,
we hope that further insights and methodological approaches devel-
oped in mountains will eventually be scaled-up to help predict dise-
quilibrium dynamics across broader latitudinal and longitudinal
gradients.
ACKNOWLEDGMENTS
JMA, TIB, SH, JL, NJS and LP initiated discussions on the paper; LC
and LP developed the simulation model; JMA led the writing; all
authors contributed to discussions and revising drafts of the paper.
This paper arose from a workshop “Biosecurity in Mountains and
Northern Ecosystems: Current Status and Future Challenges” from 3
to 5 June 2015, organized by the Mountain Invasion Research Net-
work (MIREN) and supported through funding by the Mountain
Research Initiative (MRI) of the University of Bern (Switzerland), the
Marcus Wallenberg Foundation for International Scientific Collabora-
tion, the Oscar and Lili Lamms Remembrance Foundation, the Arctic
Research Centre at Umea University (ARCUM), and the Climate
Impacts Research Centre (CIRC). JMA received funding from the
European Union’s Horizon 2020 research and innovation programme
under grant agreement No. 678841. FE was supported by the Aus-
trian Science Foundation (FWF, grant I2096-B16). LJR was sup-
ported by the National Institute of Food and Agriculture, US
Department of Agriculture Hatch: MONB00363. NJS was supported
by a National Science Foundation Dimensions of Biodiversity grant
(NSF-1136703) and the Carlsberg Foundation through the Carlsberg
Foundation’s Semper Ardens program. AP was supported by ICM
P05-002 and CONICYT PFB-23. LP was supported by the NSF grant
Nr. 31003A 162604 (Life3web project).
ORCID
Jake M. Alexander http://orcid.org/0000-0003-2226-7913
Jonathan Lenoir http://orcid.org/0000-0003-0638-9582
Treena I. Burgess http://orcid.org/0000-0002-7962-219X
Franz Essl http://orcid.org/0000-0001-8253-2112
Sylvia Haider http://orcid.org/0000-0002-2966-0534
Christoph Kueffer http://orcid.org/0000-0001-6701-0703
Ann Milbau http://orcid.org/0000-0003-3555-8883
Martin A. Nu~nez http://orcid.org/0000-0003-0324-5479
Anıbal Pauchard http://orcid.org/0000-0003-1284-3163
Lisa J. Rew http://orcid.org/0000-0002-2818-3991
Nathan J. Sanders http://orcid.org/0000-0001-6220-6731
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