REV I EWS AND
SYNTHESES Predicting species distribution: offering more than
simple habitat models
Antoine Guisan1* and Wilfried
Thuiller2,3
1Laboratoire de Biologie de la
Conservation (LBC),
De´partement d’Ecologie et
d’Evolution (DEE), Universite´ de
Lausanne, Baˆtiment de Biologie,
CH-1015 Lausanne, Switzerland
2Climate Change Research
Group, Kirstenbosh Research
Center, South African National
Biodiversity Institute, Post Bag
x7, Claremont 7735, Cape Town,
South Africa
3Macroecology and
Conservation Unit, University
of E´vora, Estrada dos Leo˜es,
7000-730 E´vora, Portugal
*Correspondence: E-mail:
antoine.guisan@unil.ch
Abstract
In the last two decades, interest in species distribution models (SDMs) of plants and
animals has grown dramatically. Recent advances in SDMs allow us to potentially
forecast anthropogenic effects on patterns of biodiversity at different spatial scales.
However, some limitations still preclude the use of SDMs in many theoretical and
practical applications. Here, we provide an overview of recent advances in this field,
discuss the ecological principles and assumptions underpinning SDMs, and highlight
critical limitations and decisions inherent in the construction and evaluation of SDMs.
Particular emphasis is given to the use of SDMs for the assessment of climate change
impacts and conservation management issues. We suggest new avenues for incorporating
species migration, population dynamics, biotic interactions and community ecology into
SDMs at multiple spatial scales. Addressing all these issues requires a better integration
of SDMs with ecological theory.
Keywords
Dispersal, ecological niche theory, future projections, habitat suitability maps, population
dynamics, prediction errors, predictive biogeography, spatial scales, species distribution
models.
Ecology Letters (2005) 8: 993–1009
I N TRODUCT ION
The fascinating question of how plants and animals are
distributed on Earth in space and time has a long history
which has inspired many biogeographers and ecologists to
seek explanations. Most modelling approaches developed
for predicting plant or animal species distributions have
their roots in quantifying species–environment relation-
ships. Three phases seem to have marked the history of
species distribution models (SDMs) (S. Ferrier, personal
communication): (i) non-spatial statistical quantification of
species–environment relationship based on empirical data,
(ii) expert-based (non-statistical, non-empirical) spatial
modelling of species distribution, and (iii) spatially
explicit statistical and empirical modelling of species
distribution.
Earliest found examples of modelling strategies using
correlations between distributions of species and climate
seems to be those of Johnston (1924), predicting the
invasive spread of a cactus species in Australia, and Hittinka
(1963) assessing the climatic determinants of the distribution
of several European species (quoted in Pearson & Dawson
2003). Earliest developments in computer-based predictive
modelling of species distribution seem to originate in the
mid-1970s, stimulated by the numerous quantification of
species–environment available at that time (Austin 1971).
The earliest species distribution modelling attempt found so
far in the literature seems to be the niche-based spatial
predictions of crop species by Henry Nix and collaborators
in Australia (Nix et al. 1977).
These were succeeded, in the early 1980s, by the
pioneering simulations of species distribution by Ferrier
(1984). At about the same time, the publication of two
seminal books (Verner et al. 1986; Margules & Austin 1991,
resulting from a workshop in 1988) also contributed largely
to promote this new approach, resulting in a growing
number of species distributions models proposed in the
literature. These advances were largely supported by the
Ecology Letters, (2005) 8: 993–1009 doi: 10.1111/j.1461-0248.2005.00792.x

2005 Blackwell Publishing Ltd/CNRS

parallel developments in computer and statistical sciences,
and by strong theoretical support to predictive ecology as

more rigorously scientific, more informative and more
useful ecology (Peters 1991).
As a result, the number of related publications increased
very significantly since the early 1990s, and the first partial
reviews, such as those published by Franklin (1995) and
Austin (1998), appeared shortly before the turn of the
century. A large symposium on modelling species occur-
rence, organized in Snowbird, Utah, in September 1999,
additionally provided a large review of the twentieth century
state-of-the-art in this field (Scott et al. 2002). A synthesis
review of this pre-2000 period can be found in Guisan &
Zimmermann 2000).
In recent years, predictive modelling of species distribu-
tion has become an increasingly important tool to address
various issues in ecology, biogeography, evolution and,
more recently, in conservation biology and climate change
research (see Table 1).
In this paper, we review the recent achievements in
developing species distribution models (SDMs) and address
some of their limitations. We devote particular attention to
the challenge of projecting the impacts of climate change on
the distribution of biodiversity, which currently yields some
of the most spectacular progress in SDM research. To set
the scene, we first define SDMs and provide an overview of
basic ecological theory and working assumptions underpin-
ning them. We then discuss some methodological issues,
decisions to be made during the process of model building
and evaluation, and the implications for conservation and
management. We then summarize important challenges that
must be addressed to overcome the limitations of SDMs.
WHAT ARE SDMS AND HOW DO THEY WORK?
Species distribution models are empirical models relating
field observations to environmental predictor variables,
based on statistically or theoretically derived response
surfaces (Guisan & Zimmermann 2000). Species data can
be simple presence, presence–absence or abundance obser-
vations based on random or stratified field sampling, or
observations obtained opportunistically, such as those in
natural history collections (Graham et al. 2004a). Environ-
mental predictors can exert direct or indirect effects on
species, arranged along a gradient from proximal to distal
predictors (Austin 2002), and are optimally chosen to reflect
the three main types of influences on the species (modified
from Guisan & Zimmermann 2000; Huston 2002; Fig. 1):
(i) limiting factors (or regulators), defined as factors controlling
species eco-physiology (e.g. temperature, water, soil com-
position); (ii) disturbances, defined as all types of perturbations
affecting environmental systems (natural or human-induced)
and (iii) resources, defined as all compounds that can be
assimilated by organisms (e.g. energy and water). These
relationships between species and their overall environment
can cause different spatial patterns to be observed at
different scales (Fig. 1), often in a hierarchical manner
(Pearson et al. 2004). For instance, a gradual distribution
observed over a large extent and at coarse resolution is likely
to be controlled by climatic regulators, whereas patchy
distribution observed over a smaller area and at fine
resolution is more likely to result from a patchy distribution
of resources, driven by micro-topographic variation or
habitat fragmentation (Fig. 1; see examples in Scott et al.
2002). The environmental data related to these three main
Table 1 Some possible uses of SDMs in ecology and conservation biology
Type of use References
Quantifying the environmental niche of species Austin et al. (1990), Vetaas (2002)
Testing biogeographical, ecological and evolutionary hypotheses Leathwick (1998), Anderson et al. (2002),
Graham et al. (2004b)
Assessing species invasion and proliferation Beerling et al. (1995), Peterson (2003)
Assessing the impact of climate, land use and other
environmental changes on species distributions
Thomas et al. (2004), Thuiller (2004)
Suggesting unsurveyed sites of high potential of occurrence for
rare species
Elith & Burgman (2002), Raxworthy et al. (2003),
Engler et al. (2004)
Supporting appropriate management plans for species recovery
and mapping suitable sites for species reintroduction
Pearce & Lindenmayer (1998)
Supporting conservation planning and reserve selection Ferrier (2002), Arau´jo et al. (2004)
Modelling species assemblages (biodiversity, composition) from
individual species predictions
Leathwick et al. (1996), Guisan & Theurillat (2000),
Ferrier et al. (2002)
Building bio- or ecogeographic regions No published example found
Improving the calculation of ecological distance between patches
in landscape meta-population dynamic
and gene flow models
No published example found
994 A. Guisan and W. Thuiller

2005 Blackwell Publishing Ltd/CNRS