ARTICLE IN PRESS
Perspectives
in Plant Ecology,
Evolution and
Systematics
Perspectives in Plant Ecology, Evolution and Systematics 9 (2008) 171–189
www.elsevier.de/ppees
The state of plant population modelling in light of environmental change
Florian Jeltscha,, Kirk A. Moloneyb, Frank M. Schurra,
Martin Köchya, Monika Schwagerc
a
Plant Ecology and Conservation Biology, University of Potsdam, Am Neuen Palais 10, D-14469 Potsdam, Germany
Department of Ecology, Evolution and Organismal Biology, 253 Bessey Hall, Iowa State University, Ames, IA 50011-1020, USA
c
Department of Mathematics and Statistics, P.O. Box 68, University of Helsinki, FIN-00014 Helsinki, Finland
b
Received 6 July 2007; accepted 4 November 2007
Abstract
Plant population modelling has been around since the 1970s, providing a valuable approach to understanding plant
ecology from a mechanistic standpoint. It is surprising then that this area of research has not grown in prominence
with respect to other approaches employed in modelling plant systems. In this review, we provide an analysis of the
development and role of modelling in the field of plant population biology through an exploration of where it has been,
where it is now and, in our opinion, where it should be headed. We focus, in particular, on the role plant population
modelling could play in ecological forecasting, an urgent need given current rates of regional and global environmental
change. We suggest that a critical element limiting the current application of plant population modelling in
environmental research is the trade-off between the necessary resolution and detail required to accurately characterize
ecological dynamics pitted against the goal of generality, particularly at broad spatial scales. In addition to suggestions
how to overcome the current shortcoming of data on the process-level we discuss two emerging strategies that may
offer a way to overcome the described limitation: (1) application of a modern approach to spatial scaling from local
processes to broader levels of interaction and (2) plant functional-type modelling. Finally we outline what we believe to
be needed in developing these approaches towards a ‘science of forecasting’.
r 2007 Rübel Foundation, ETH Zürich. Published by Elsevier GmbH. All rights reserved.
Keywords: Mechanistic; Process-based modelling; Forecasting; Scaling up; Plant functional types
Introduction
In 1798, Malthus published his seminal work An
Essay on the Principle of Population leading to a long
standing interest in the dynamics of populations among
zoologists. In stark contrast to this, population phenomena in the plant sciences were largely ignored even
Corresponding author. Tel.: +49 331 977 1954;
fax: +49 331 977 1948.
E-mail address: jeltsch@uni-potsdam.de (F. Jeltsch).
through much of the last century (Harper and White,
1974). Indeed, as recently as 1974 Harper and White
(1974) lamented that ‘‘the reluctance of botanists to
concern themselves with numbers is the more strange
because there are fewer of the problems of search, capture,
and estimation that bedevil demographic research with
animals’’. By the mid-1970s, however, the lack of a
population perspective among botanists began to
change slowly. An early indication of this can be seen
in a quote from Solbrig (1976) who stated that ‘‘(plant)
population biology is a synthetic discipline with the aim of
1433-8319/$ - see front matter r 2007 Rübel Foundation, ETH Zürich. Published by Elsevier GmbH. All rights reserved.
doi:10.1016/j.ppees.2007.11.004
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understanding the mechanisms that govern the growth and
reproduction of individuals and populations in order to be
able to make predictions regarding future states under
normal and abnormal environmental conditions’’. Following quickly on the heels of this statement was Harper’s
(1977) classic text The Population Biology of Plants,
which subsequently fuelled an explosion in the growth
of the field of plant population biology. An integral
component of this growth was the development of
various modelling techniques for studying and characterizing population dynamics. These range from the,
by now, classic demographic models derived from
animal ecology (Harper and White, 1974) to more
recent, spatially explicit computer simulation models
(Jeltsch and Moloney, 2002).
The early development of plant population modelling
was largely influenced by theories and approaches
already well established in animal ecology. Since then
there has been a cross-fertilization of modelling techniques between these disciplines, such that more recent
developments largely reflect the progress of the discipline of ecological modelling in general. Some of the
more recent developments have included an increasing
scepticism regarding equilibrium approaches, starting in
the 1970s and 1980s (Reddingius, 1971; May, 1976), the
implicit and more recent explicit inclusion of spatial
components in demographic modelling (e.g. Levins,
1970; see also Jeltsch and Moloney, 2002) and an
increasing focus on more specific questions, rather than
a search for general theories (e.g. Grant and Price,
1981). From the methodological point of view, model
development started by incorporating predominantly
analytical, mathematical approaches (mainly differential
and difference equations). This however has been
increasingly supplemented or supplanted by simulation
approaches, e.g. cellular automata (Czaran and Bartha,
1992) and individual-based models (IBMs) (Grimm,
1999). The shift away from pure analytical modelling
has run in parallel with the development of computer
technology and has been important in allowing the
consideration of complex ecological relationships that
cannot be solved easily using analytical techniques.
What all of these approaches have in common is that
they are process-based and bottom-up; i.e. they use
processes at a lower hierarchical level (typically individuals) to determine the dynamics at a higher level
(typically the population or community).
In the current situation of rapid regional and global
environmental change, there is a strong need for
ecological forecasts. According to Clark et al. (2001),
‘‘ecological forecasting is definedyas the process of
predicting the state of ecosystems, ecosystem services,
and natural capital, with fully specified uncertainties,
and is contingent on explicit scenarios for climate, land
use, human population, technologies, and economic
activity.’’ The above-mentioned aim of plant population
modelling to understand mechanisms in order to make
predictions would lead us to expect that population
modelling is a main contributor to ecological forecasting
under environmental change. But is this really the case?
The impression one gets from the current literature is
that plant population modelling has established a
reasonable niche, but only plays a minor role in
comparison to experimental and other modelling
approaches that are commonly employed in vegetation
science. For evidence of this claim, a comparison of the
number of publications in the ISI Web of Sciences
database using the search phrase [‘‘plant population’’
AND (‘‘model*’’ OR ‘‘simulat*’’)] shows a moderate
increase in publication rate from the 1980s to the
present, whereas during the same time period the
phrase [‘‘vegetation’’ AND (‘‘model*’’ OR ‘‘simulat*’’)]
shows an increase from 12 publications in the first
6 years of the 1970s to 8682 publications from 2000 to
the present (Fig. 1). Clearly, the difference in the rate of
increase and the magnitude of the publication numbers
indicates that plant population modelling has not yet
become a key approach in the plant sciences. But what is
the main difference between plant population and
vegetation modelling? While plant population modelling
is necessarily a dynamic, process-based, bottom-up
approach that considers key demographic processes,
vegetation modelling does include static and purely
correlative approaches, as well as purely physiological
models.
Many vegetation models that do not explicitly include
demographic processes focus on broad spatial scales,
typically adopting a top-down rather than a bottom-up
approach (e.g. Bondeau et al., 2007; Dormann, 2007;
Jakob et al., 2007). An important subset of vegetation
Fig. 1. Rate of publication of papers of ‘‘plant population’’
versus ‘‘vegetation’’ modeling during successive 7-year periods
presented on a log scale. (See text for more details on the
search criteria used in generating these data.)
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models that directly address issues of environmental
change are coupled climate–vegetation models (e.g.
Kutzbach et al., 1996; Claussen, 1994; Claussen et al.,
1998). These have developed rapidly since the 1990s, a
prominent example of which is provided by dynamic
global vegetation models (DGVMs). DGVMs simulate
transient global vegetation dynamics coupled to climate
models and are generally constructed from a biogeochemical point of view (Foley et al., 1998). The flow of
matter and energy between plants and the environment
is modelled by linking together physiological and
physical processes. Important questions tackled with
DGVMs include the effects of increased levels of
atmospheric CO2 on climate, incorporating feedback
mechanisms between climate and vegetation dynamics
(Levis et al., 2000; Cramer et al., 2001) and a
consideration of how change in vegetation will affect
ecosystem functioning (Thuiller et al., 2006). Although
some DGVMs have recently begun to include coarse
representations of population dynamics in their structure, they mostly characterize plant growth on a
physiological basis (Sitch et al., 2003; Morales et al.,
2005). As such, we do not include DGVMs in our
consideration of plant population models.
Another important development in the field of
vegetation modelling is the climate envelope modelling
approach. This is also not yet based on a population
approach, but focuses on the effects of climate change
on (plant) species distributions (see Thuiller et al.,
2008). For a complete review on this type of climate
change modelling see Guisan and Thuiller (2005) and
Heikkinen et al. (2006). The climate envelope and the
DGVM modelling approaches are both closely related
to the study of the impact of environmental change
on vegetation, which suggests that the increasing rate
of publications incorporating vegetation modelling is
probably due in part to an increasing concern about
the negative consequences of environmental change
(see also Botkin et al. (2007) for a recent evaluation of
different modelling approaches related to climate
change effects).
Interestingly, the role of mechanistic plant population
modelling with regard to environmental change is, to
date, largely restricted to problems of local population
extinction, often in response to habitat loss and
fragmentation (e.g. population viability analyses –
PVA, Menges, 2000). In contrast, investigations into
global change impacts on populations are still dominated primarily by statistical, correlational approaches
or dynamic models that focus on physiological responses, although recent attempts have been made to
include population dynamics (see Thuiller et al., 2008;
Sato et al., 2007). In fact, current projections of
species responses to changing environment and land
use mostly rely on phenomenological models of species
distributions (such as habitat models or climate envel-
173
ope models) (Thomas et al., 2004; Thuiller, 2004; Botkin
et al., 2007). Although widely applied, these approaches
are increasingly questioned since they ignore several
mechanisms and processes that are likely to modify the
response to a shift in environmental conditions (see
Thuiller et al., 2008). Many of these mechanisms operate
at the level of individuals and populations, including the
effects of genetic variation and evolutionary processes
(Travis and Dytham, 2002; Botkin et al., 2007);
phenotypic or behavioural plasticity (Chun et al.,
2007), competition and other biotic interactions, buffering effects produced by source–sink interactions or
metapopulation dynamics, and migration or dispersal
limitations in response to land management and landscape changes (Menges, 2000; Higgins et al., 2003;
Leibold et al., 2004).
In principle, well designed (plant) population models
can be used to provide an improved understanding of
complex factors and processes influencing population
and community change (e.g. Jeltsch et al., 1996, 1998,
1999; Wiegand et al., 2000; Colasanti et al., 2001; Bauer
et al., 2002; Jeltsch and Moloney, 2002; Cousins et al.,
2003; Berger et al., 2004; Wiegand et al., 2004a, b;
DeAngelis and Mooij, 2005; Grimm et al., 2005a, b;
Wiegand et al., 2006; Tews et al., 2006). Furthermore,
dynamic modelling of processes at the level of individuals and populations allows the direct analysis of
transient dynamics resulting from environmental
change, thereby avoiding ad hoc equilibrium assumptions (Botkin et al., 2007). However, given this potential,
the question remains, Why does plant population
modelling still play a minor role in vegetation science?
In this review, our goal is to analyse the development
and role of modelling in the field of plant population
biology through an exploration of where it has been,
where it is now and, in our opinion, where it should be
headed. In particular, we will focus on the role of
modelling in environmental change research. Based on a
comparative analysis of the historical roots and the
current situation, we speculate on the future of plant
population modelling and the necessary steps that must
be taken to realize the full potential of this approach.
A critical element limiting the current application of
population modelling in environmental research is
the trade-off between the necessary resolution and
detail required to accurately characterize ecological
dynamics pitted against the goal of generality. However,
in addition to an intensified emphasis on novel
approaches to model validation and data acquisition
we see two emerging strategies as a way to overcome
these limitations: (1) application of a modern approach
to spatial scaling from local processes to broader
levels of interaction and (2) plant functional-type
(PFT) modelling. Finally, we discuss what is needed
to develop these approaches towards a ‘science of
forecasting’.
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A brief overview of the history of population
modelling
The 1920s of the last century marked the beginning of
theoretical, mathematical population ecology – a period
later dubbed the ‘golden age of theoretical ecology’
(Scudo and Ziegler, 1978). Though there had been
earlier mathematical descriptions of population growth,
e.g. geometric growth recognized by Thomas Malthus at
the end of the 18th century and logistic growth
formulated by Verhulst in 1838, these went largely
unnoticed (McIntosh, 1985). Only through the eventual
work of Lotka (1925), Volterra (1926) and Gause (1934)
did the focus of ecology shift from a static community
approach to theoretical and experimental population
ecology. According to Hutchinson and Deevey (1949):
‘‘Perhaps the most important theoretical development in
general ecology has been the application of the logistic
by Volterra, Gause and Lotka to 2 species cases’’.
Though these early population models originating from
theory in physical sciences were very general they were
clearly developed for and were initially oriented towards
animal populations. The key research question of this
period concerned the quest to discover the internal
factors that drive (interacting) populations. The final
hope was to develop ecology, which was ‘chaotic and
non-systematic’ (Clements, 1905) at the beginning of the
20th century, towards an exact natural science, such as
physics (McIntosh, 1985). Unfortunately this hope was
not supported by subsequent experiments and empirical
tests of hypotheses generated by the simple models.
Instead, the frequent failure of these tests led to some
disappointment and increasing criticism in the scientific
community. Criticisms were commonly based on the
observation that organisms do not behave like identical
elements such as gas molecules (e.g. Smith, 1952). Only
much later, with the rise of individual-based population
models, could this unrealistic simplifying assumption be
overcome (see below). Despite the limited acceptance in
the broader ecological community, the general approach
based on the modification and expansion of the logistic
growth or the Lotka-Volterra equations has since been
widely applied in population modelling (e.g. Christiansen and Fenchel, 1977; Neuhauser and Pacala, 1999;
Turchin, 2003).
From 1940 to 1960, two new major topics were
introduced into population modelling. First, there was
an increasing interest in quantifying the number and
abundance of species in the field (e.g. Fisher et al., 1943;
Preston, 1948). Second, initial attempts were made to
describe the spatial spread of populations (e.g. Skellam,
1951). The latter approach started in a straightforward
way by applying simple diffusion equations, known
from chemistry and physics, to population modelling
(Okubo, 1980). This introduced spatial aspects into
plant population modelling, which had previously been
for the most part ignored. However, this development
still lacked a general consideration of the effects of
heterogeneity in the environment, a key topic in the
1960s and 1970s of the last century.
Gadgil (1971), one of the pioneers of spatial population ecology, brought a consideration of environmental
heterogeneity into the realm of population dynamics. He
envisaged the density of a population as a function of
the spatial and temporal composition of the environment and brought much clarity into thinking about the
impact of immigration and emigration on population
dynamics. He did this through the development of
theoretical models that were built on earlier considerations of Cohen (1967) and Levins (1968), explicitly
including the number and area of suitable sites for
colonization, the spatial distribution and the carrying
capacity of these sites, the time for which the sites
remain habitable, and the dispersal characteristics of the
species in the structure of the model.
Gadgil’s work followed two of the most influential
ideas in ecology, which were developed a few years
before: MacArthur and Wilson’s (1967) theory of
island-biogeography and the metapopulation concept
of Levins (1969, 1970). Both theories were based on the
role of habitat patchiness and spatial isolation in
determining species or population distributions. In
addition to the novel inclusion of spatial heterogeneity
both theories postulated the revolutionary idea that
ecological systems were characterized by a dynamic
equilibrium. This concept contrasted with the paradigm
of a static, stable equilibrium, which was the classic
belief at that time (and, in some cases, still is today).
The acceptance of the idea that ecological systems are
not stable, in a static sense, was a major step forward in
theoretical ecology, emancipating it from its roots in
theoretical physics and chemistry. The increasing criticism of equilibrium concepts during that time period
(e.g. Reddingius, 1971) also inspired a systematic
analysis of the stability of populations, including the
seminal work of Robert May on the relationship
between complexity and stability in natural communities
(e.g. May, 1973, 1974, 1976). This led directly to the first
theoretical investigations of the role of stochasticity and
discontinuity in ecological systems (e.g. Cohen, 1966;
Goodall, 1967; Noy-Meir, 1973). The role of environmental stochasticity has since become a highly relevant
topic, particularly with respect to population extinction
risks (e.g. Menges, 1990; Mace and Lande, 1991; Boyce,
1992) and the theory of PVA (Menges, 2000; Beissinger
and McCullough, 2002).
The inclusion of both spatial heterogeneity and
temporal stochasticity were the two main steps necessary
to link theoretical ecology to real ecological systems.
However, this came at the cost of introducing greater
complexity. Further developments were therefore possible only with the increase in computational power that
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came during the 1980s and 1990s. For the first time,
computer simulations allowed the examination of
complex, stochastic, model systems typically beyond
the scope of analytical mathematical models. It was also
possible to give up the simplifying assumption that all
individuals were identical entities, giving rise to IBMs,
an alternative approach to tackling population level
questions (e.g. Shugart and West 1980; Huston et al.,
1988; Grimm, 1999).
The first IBMs were forest gap models, which started
appearing in the 1970s (e.g. Botkin et al., 1972). These
models were designed to forecast the growth and
mortality of individual trees and the regeneration of
species in small forested areas (see Botkin, 1993). Gap
models have since been widely applied in forest
modelling (e.g. Shugart and West, 1980; Shugart, 1984;
Davis and Botkin, 1985) addressing a broad range of
ecological questions (e.g. forecasting possible effects of
global warming on forests, Botkin, 1993; Shugart and
Smith, 1996). While the first IBMs were still only
spatially implicit (Botkin et al., 1972; Shugart, 1984;
Botkin, 1993), increasing computational power soon
allowed the explicit inclusion of spatial aspects of
landscape heterogeneity or ecological processes into
the approach (e.g. Pacala et al., 1993; Pacala and
Deutschman, 1995; Moloney and Levin, 1996; Bugmann, 2001). IBMs soon became an important tool for
simulating vegetation dynamics in a more realistic way
than was possible using the previously employed
analytical models. Even so, the merits of the individual-based approach to understanding a variety of
ecological systems is still under heated debate (Grimm
et al., 2005a).
Though individual-based approaches brought a new,
bottom-up way to look at the organization of ecological
systems (Grimm, 1999; Grimm et al., 2005a) they were
less suitable to tackle very specific spatial questions, e.g.
dealing with pattern formation in ecological systems at
larger spatial scales or covering whole communities.
Here, parallel spatially explicit simulation approaches
were developed (e.g. reviewed in Czaran and Bartha,
1992; Jeltsch and Moloney, 2002) with one of the most
prominent examples being so-called ‘cellular-automata
models’ (e.g. Hogeweg, 1988; Jeltsch and Wissel, 1994).
These models describe the dynamics of ‘landscapes’ (at
different spatial scales) by assigning discrete ecological
states (e.g. states of a succession, numbers of individuals, etc.) to local ‘cells’ (e.g. patch). The dynamics of
these states is typically influenced by interactions with
direct neighbouring cells, simulating small-scale spatial
processes such as local dispersal or competition. The
initially simple and deterministic ‘automata’ approach
was however soon adjusted to more realistic ecological
systems that include stochasticity as well as different
ranges of neighbourhood interactions. These more
general grid-based models became, in particular, useful
175
for exploring complex pattern formation and the effects
of landscape heterogeneity and fragmentation for
(plant) population dynamics (e.g. Czaran and Bartha,
1992; Jeltsch and Wissel, 1994; Jeltsch et al., 1996, 1997;
Jeltsch and Moloney, 2002).
The new simulation-based approaches, such as IBMs
and cellular-automata models, have allowed the exploration of more complex ecological questions that
could not be tackled before. Interestingly the corresponding increase in methodological sophistication has
been accompanied by a trend towards more specific
ecological questions. While the early population modelling approaches were oriented towards general ecological theory these later studies were oriented towards a
‘‘specific theory with a link to reality and data instead of
a general theory as an end in itself’’ (Grant and Price,
1981).
The current situation in plant population
modelling
Comparing the overarching themes in (plant) population modelling of the last 80 years with the research
areas prevalent today, we identify continuous interest in
four topics: (i) demography and population dynamics of
single species, (ii) mechanisms of species interactions,
(iii) number and relative abundance of species in a
community context, and (iv) spatial population structure and dynamics. While these major research areas
have persisted, the specific foci and questions within
them have changed over the decades with the introduction of increasing methodological sophistication and
expanding knowledge. With regard to (i), the demography and population dynamics of single species, current
focal research questions include the role of individual
variability (e.g. Grimm et al., 1999; Weiner et al., 2001;
Stoll et al., 2002; Xiao et al., 2006) and genetic variation
(e.g. Porcher and Lande, 2005), the causes of complex
dynamics, such as cycles and chaos, in plant populations
(e.g. Bauer et al., 2002; Freckleton and Watkinson,
2002; Gonzalez-Andujar et al., 2006; Logofet et al.,
2006; Pastor and Durkee Walker, 2006), and, as a
primarily applied topic, species persistence under
environmental change (e.g. Jeltsch et al., 2000; Menges,
2000; Lamont et al., 2001; Beissinger and McCullough,
2002; Wiegand et al., 2004a, b; Grimm et al., 2005b).
Current key questions dealing with (ii), mechanisms of
species interactions, encompass effects of competition
and facilitation at the level of the individual plant (e.g.
Damgaard et al., 2002; Berger et al., 2002, 2004; Wang
et al., 2004; Schneider et al., 2006; see also Berger et al.,
2008) and more general mechanisms of coexistence
of plant species (e.g. Mertens et al., 2002; Matsinos
and Troumbis, 2002; Amarasekare, 2003; Amarasekare
et al., 2004, Gardner and Engelhardt, 2008).
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Clearly, the question of species coexistence links to
species diversity or topic (iii), the number and relative
abundance of species in a community. Here, numerous
modelling studies deal with the drivers and consequences of species diversity (e.g. Hubbell 2001, 2005; He
and Legendre, 2002; Chave et al., 2002; Chave, 2004;
Mouquet et al., 2006), although not all of these models
explicitly incorporate population dynamics in their
design.
While several of the examples mentioned above
include spatial aspects to a certain degree, a growing
number of studies acknowledge explicitly the need to
understand (iv) spatial processes and population structure
in plant ecology. Key questions currently explore the
mechanisms of seed dispersal (Higgins et al., 2003;
Levine and Murrell, 2003; Davies et al., 2004; Tews
et al., 2004; Mistro et al., 2005; Katul et al., 2005; Schurr
et al., 2007; Jongejans et al., 2008), and its role for
population dynamics. This issue is in particular important for understanding and managing the spread of
invasive species (e.g. Higgins et al., 2001; Wang et al.,
2003; Clark et al., 2003; Volin et al., 2004; Rinella and
Sheley, 2005) or the spread of genes of genetically
modified organisms (e.g. Thompson et al., 2003;
Kuparinen and Schurr, 2007). The interactions of
environmental heterogeneity and spatial population
processes often lead to pattern formation in ecological
systems. Vegetation patterns can also influence population processes leading to complex feedback mechanisms.
So it is not surprising that pattern formation and its
consequences is still an important topic in spatial plant
population modelling (e.g. Jeltsch and Moloney, 2002;
Rademacher et al., 2004; van de Koppel and Rietkerk,
2004; Wiegand et al., 2004a, b; Aitkenhead et al., 2004;
Sherratt, 2005). In particular, spatial vegetation pattern
can be of crucial importance both for theoretical or
applied questions related to the dynamics and survival
of species in fragmented landscapes (e.g. Geertsema
et al., 2002; Jacquemyn et al., 2003; Kondoh, 2003;
Hanski and Gaggiotti, 2004; Snäll et al., 2005; Tews
et al., 2006; Körner and Jeltsch, 2008).
The current situation in plant population modelling
indicates that a broad range of methods are available for
exploring the impact of environmental change on
ecological systems. Specifically individual-based approaches allow a consideration of, e.g. the response to
a changing environment through phenotypic plasticity
and evolutionary change (e.g. Botkin et al., 2007).
Equally important are spatially explicit approaches that
allow the modelling of species migration, range shifts,
invasions, or landscape changes in response to environmental change. As shown above, some of the key topics
regarding climate change, habitat loss/fragmentation
and alien plant invasion are already addressed in
bottom-up, mechanistic population models. However,
the number of these studies is still small in comparison
with top-down vegetation modelling approaches exploring the impacts of environmental change. Thus the
question still remains why plant population modelling
has gained much less momentum in the last decades
than vegetation modelling in general; or, in other words,
why are demographic processes driving plant population
dynamics largely ignored in many current fields of
vegetation modelling?
Problems and limitations of plant population
modelling
Process-based understanding is a prerequisite for
forecasting under non-equilibrium conditions. The
strength of plant population modelling in providing
this type of understanding is typically focused at the
level of interacting (either identical or distinguishable)
individuals. Clearly, this strength comes at a cost: the
amount of information to be processed and the
complexity generated are relatively high. Furthermore,
mechanistic population models require data on demographic or physiological processes rather than static
data on occurrence or abundance. This often limits the
spatial scale tackled, the number of modelled species, or
the generality of results. Guisan and Thuiller (2005)
conclude in a recent review on species distribution
models that ‘‘mechanistic models, while very appealing
at the species level, are often too data-hungry to be of
general use in nature management and biodiversity
assessment’’.
Obviously, a general dilemma in (plant) population
modelling pits the necessary resolution and detail of
modelled processes against generality. Given the overarching environmental problems caused by global
change these limitations also restrict the relevance of
plant population modelling. The great advantage of topdown and coarse-grained approaches in vegetation
modelling, and probably also the main reason why
these approaches are applied more often, is that they
focus on broader scales and restrict themselves to a
limited range of characteristics, such as species number,
biomass per unit area (e.g. Woodward et al., 1995;
Ruimy et al., 1996), leaf area indices (LAI) (Chase et al.,
1996; Kucharik et al., 2000) or broad-scale shifts in the
distribution of biomes (Doherty et al., 2000).Clearly,
these types of output are of greater relevance with
regard to forecasting impacts on global ecosystem
services than are more specific, localized data derived
from process-based, bottom-up models. Furthermore,
integrative vegetation measures such as biomass production, LAIs, or biome boundaries are much easier to
measure (e.g. in combination with remote sensing
techniques) than more detailed spatio-temporal population or community changes. Consequently, top-down
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and coarse-grained vegetation models are often easier to
test and validate than mechanistic population models
(e.g. Wiegand et al., 2003, 2004b). The latter aspect
contributes to a certain amount of scepticism directed at
spatial simulation approaches in population ecology
(e.g. Ruckelshaus et al., 1997; but see Mooij and
DeAngelis, 2003).
However, the advantages of top-down, coarse-grained
versus bottom-up, fine-grained approaches in vegetation
modelling also come at a cost: without a mechanistic
basis of forecasting, such as that provided by including
population processes, it is difficult to extrapolate easily
to new conditions or produce realistic projections
contrasting different scenarios of environmental change
(Sitch et al., 2003; Pearson and Dawson, 2003; Hampe,
2004; Thuiller, 2004; Araújo and Pearson, 2005; Botkin
et al., 2007; Albert et al., 2007; Thuiller et al., 2008).
For example, without understanding the mechanisms of
long distance seed dispersal, it is difficult to reliably
forecast the dynamics of range shifts (Schurr et al.,
2007). Or, forecasts of biome shifts will remain
preliminary, unless an understanding of the spatial
population dynamics of competing dominant life forms
under variable environmental conditions is incorporated
(e.g. Jeltsch et al., 1999).
Given the limitations of both (mechanistic) bottomup and (integrative) top-down approaches, it is obvious
that the challenges of environmental change require
approaches that include process-based, mechanistic
methods, but at the same time lead to integrative results
that are relevant with regard to ecosystem services at
broad spatial scales.
Plant population modelling: quo vadis?
While there is obviously a trend towards including
some mechanistic population level aspects into current
top-down or plant physiology-based approaches in
vegetation modelling (e.g. Albert et al., 2007), the
question arises as to what is required for the complementary approach, i.e. to expand and thus increase the
relevance of the bottom-up approach of plant population modelling. The key problems we have to face here
are scale, the availability of suitable data, and generalization.
Population models across scales: the process of
up-scaling
To become more relevant with regard to the
challenges that are posed by environmental change,
plant population modelling has to be successful in
scaling up its valuable mechanistic characterization of
177
local dynamics to larger spatial scales. A good example
for this is the scaling-up process from the physiology of
single trees to landscapes in forest gap models (e.g.
Reynolds et al., 2001; Galitskii, 2003; Mladenoff, 2004).
A successful scaling up from localized population
processes to the landscape or beyond is a necessary
prerequisite for tackling current problems such as
regional species shifts, plant species migrations or
changes in large-scale ecosystem services provided by
vegetation (e.g. Schneider, 2001).
If the environment was homogeneous and interactions
linear and non-spatial, scaling of population properties
and dynamics would be a simple matter of multiplication (compare Wiens, 1989). However, in scalingup, researchers often must consider the patchiness of the
environment, spatial interactions, non-linearities in the
interaction, and their variation with space, time, and
population density as well as scale-crossing processes
(e.g. Fuhlendorf and Smeins, 1996; McGill and Collins,
2003). In addition, processes that can be considered
invariant and easy to characterize at one scale may
become variable and important to characterize at
another (Peters, 1986; Peters and Herrick 2004; Urban,
2005; Wu et al., 2006).
One promising way to scale up a plant population
model is to make a model of the scaling process. This
may be called hierarchical modelling, nested modelling,
or meta-scaling but implies that results of simulations
with validated fine-grained models are condensed by
applying formal statistical analyses to quantitative or
qualitative relationships with high explanatory power
and therefore, reliability (compare Urban, 2005). These
relationships are then used as instructions in coarsegrained models. The fine-grained simulations must cover
the range of conditions expected in the coarse-grain
model, either as an independent variable or by using
realistic scenarios. The condensation may be in time,
space, or other units, including individuals or taxa. For
example, Köchy (2006) (see Fig. 2) simulated the
performance of individual annual plants in semi-arid
climates to study the effect of changes in rainfall
variability. The density of the seed bank and annual
mean water availability were the most important
predictors of biomass. In order to simulate the dynamics
of annual plants at the landscape scale with a grain of
25 m2, he carried out simulations for a range of classes
of observed seed bank densities in factorial combination
with classes of mean annual precipitation (representing
climate), soil types and plant species. The simulation
results were represented as non-linear regressions of
biomass on annual precipitation for each class of seed
bank density. In addition, the variation of the simulation results was represented by calculating the regressions for five quantiles of biomass. Thus, the dynamics
of annual plants in the landscape model (Köchy et al.,
unpublished manuscript) were modelled by selecting the
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Fig. 2. Example for hierarchical modelling as a scaling approach: For the simulation of climate change effects, in particular
precipitation, on the production of vegetation at the landscape scale in the Middle East, Köchy et al. (unpubl.) constructed a
spatially explicit model with 1.5 km 1.5 km extent and 5 m 5 m grain. Regional projections for three climate change scenarios
were available with a grain of 0.51 0.51 (ca. 50 km 50 km) for single 30-year time slices. For the landscape model the projections
had to be downscaled to obtain time series for specific points on a climatic aridity gradient. In addition, stochastic time series were
required to assess the variability of the landscape simulations due to climate. To this end a gamma distribution was fitted to the
yearly data in each cell; the distribution parameters were regressed on mean annual precipitation to interpolate the values for specific
climates. Vegetation in the target region is dominated by annual plants and shrubs. The response of these vegetation types to
variation in precipitation has been described by validated, process-based, fine-grained models for annuals (Köchy, 2006; Köchy
et al., unpublished manuscript) and shrubs (Malkinson and Jeltsch, 2007). The fine-grained models were used to simulate the
vegetation-type responses to the most important predictors of biomass of annuals (annual precipitation, seed bank density, soil
structure, plant ecotype) and cover of shrubs (annual precipitation) on smaller scales. The simulation output was then aggregated by
regressions and transition probabilities for categories of mean annual precipitation and seed bank density. Regressions and
transition probabilities were used to define the vegetation response in individual cells of the landscape model. This aggregated
mechanistic description was simple and fast enough to use at the larger scale. Net facilitative and competitive effects of shrubs on
annuals based on field experiments are added in the landscape model at the grain level.
appropriate seed bank and climate combination, drawing a quantile at random, and then using the corresponding non-linear regression to calculate biomass
based on the annual rain volume. This integrated the
effects of germinability, density-dependent competition,
and daily rain variability of the fine-grained model into
the coarse-grain model. This approach has been
described more formally by Berk and De Leeuw (2006)
in Wu et al. (2006). The latter reference gives an
excellent overview of scaling problems and analyses in
ecology, including error propagation. Recent reviews
and strategic papers on scaling up are also given by
Rastetter et al. (2003), Miller et al. (2004), Peters and
Herrick (2004), and Urban (2005).
Data requirements
The lack of suitable data can hamper the development
and assessment of plant population models. Consequently, building shared data sets available to the
ecological community would provide an invaluable
asset. Mechanistic models require detailed data at the
process-level for model parameterization that are often
difficult and time consuming to obtain, at least if more
than a few species are involved (Ruckelshaus et al.,
1997; Guisan and Thuiller, 2005; Botkin et al., 2007). In
forestry, such data have been accumulated through a
long history of management. For herbs, in contrast,
several initiatives to compile comparable data sets have
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only started to be assembled recently (e.g. Knevel et al.,
2003; Poschlod et al., 2003; Kühn et al., 2004). Most of
these data banks include relevant plant species traits
(e.g. traits related to seed dispersal) covering some basic,
yet not all, necessary information on mechanisms
driving population level processes. Here, clearly, greater
emphasis should be put on quantitative data that can be
used to parameterize key population processes, such as
germination, establishment, and mortality. Also a sound
understanding of (i) trade-off structures between traits
and of (ii) correlations between important factors that
are difficult to measure (so-called ‘‘hard traits’’ – e.g.
dispersal distances, relative growth rates, seed bank
parameters, competitive ability, etc.) and their more
easily measurable counterparts (so-called ‘‘soft traits’’ –
e.g. seed weight as a surrogate for dispersal distance)
would be highly valuable (e.g. Weiher et al. 1999; Diaz
et al., 2004). A valuable understanding of these relationships would encompass their quantitative expression,
ranges of validity, and external factors influencing these
interactions.
The same desire for the development of a stronger
focus in assembling process-level data holds for current
initiatives to assemble large data sets from natural
history collections, such as the Global Biodiversity
Information Facility (2007), or to assemble knowledge
about ecosystems and biodiversity, such as the Millennium Ecosystem Assessment (2005). Though the data
compiled in these initiatives include some information
relevant for mechanistic modelling, they focus more on
broader scales and pattern than on processes. Adding an
additional focus on underlying mechanisms and processes would clearly improve our basis to model,
understand, and predict the dynamics of a broader
range of species and communities in a bottom-up
approach. In particular, a close interaction between
model data requirements and large data collection
campaigns could be fruitful (see below, e.g. Storkey,
2006).
In principle, long-term data sets of vegetation systems
also provide a suitable basis to derive population level
processes. However, such data sets are relatively rare
and often difficult to decipher with regard to underlying
mechanisms (e.g. Kahmen and Poschlod, 2004; Silvertown et al., 2006). An interesting and recent alternative
to detect long-term population processes and provide
missing population demography data of herbaceous
species is the novel field of herb chronology, i.e. the
analysis of annual growth rings in the secondary root
xylem of perennial forbs (Dietz, 2002). Similar to the
better known tree rings, discernible annual rings in the
secondary root xylem have proven to be a common
phenomenon in many perennial forb species with
persistent main roots (Dietz, 2002; von Arx and Dietz,
2006). This method has been successfully applied to
determine age structures, to conduct demographic
179
analyses, and investigations into population development, or life history responses to different growth
conditions such as climatic or other environmental
variations (e.g. Dietz and Ullmann, 1998; Rixen et al.,
2004; Dietz and von Arx, 2005; von Arx et al., 2006).
The potential strength of linking this novel approach of
data collection with plant population modelling has yet
not been fully explored.
Generalization across species–plant functionaltype population models
A further prerequisite for population models to
become more relevant with regard to environmental
change is to develop modelling strategies that go beyond
the dynamics of single populations, species or communities. The question thus is, whether it is possible to
combine the advantages of a mechanistic bottom-up
approach, which simulates population processes, with a
more general approach that goes beyond the species
concept. One recent strategy that holds promise is to
combine population modelling with the PFT approach.
We consider models that are based on PFTs as
plant population models if they describe populations
and their dynamics in a process-based bottom-up
approach as defined above. Since we are not aware of
any existing review of this combined approach we will
summarize the current state of PFT modelling in a little
more detail.
Functional types are initially defined as ‘‘a nonphylogenetic classification leading to a grouping of
organisms that respond in a similar way to a syndrome
of environmental factors’’ (Gitay and Noble, 1997; but
see Lavorel and Garnier (2002) for a more recent
definition of response and effect types). The classification
is based on a user-defined set of functional traits that are
considered important for a species’ response to the
environment. Although not necessarily phylogenetic,
this also commonly includes taxonomic relationships,
because relevant traits are often heritable and functionally similar within a taxon.
This concept has found wide application in plant
ecology, both in empirical and modelling studies.
However, we think that for modelling studies in
particular the potential of the approach has not yet
been fully exploited. Reviewing the literature, we see
three different strategies as to how the PFT concept has
been employed, and where it may have a strong
potential for future development. We refer to these as
‘‘functional group’’, ‘‘functional trait’’ and ‘‘functional
species’’ strategies. The strategies provide different
perspectives but are not mutually exclusive, and overlap
between and within studies and models.
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Functional group strategy
Functional trait strategy
The functional group strategy follows the initial idea
of pooling species into PFTs. Models of this type
simulate community dynamics with a limited number of
PFTs instead of real species, assuming that the dynamics
of PFTs encapsulate the most important features of the
species within a particular group. Although not necessarily called a functional-type model, this strategy is in
fact adopted by most mechanistic plant population
models that consider communities as a whole instead of
focussing on one or few species of interest (e.g. Wiegand
et al., 1995; Jeltsch et al., 1996, 1997, 1998; Colasanti
et al., 2001; Groeneveld et al., 2002; Cousins et al., 2003;
Reineking et al., 2006). Typically plant modelling
studies (both population models and other vegetation
models) applying the functional group strategy address
questions regarding the composition and persistence of
PFTs in a community under the impact of environmental change (e.g. Epstein et al., 2000; Mouillot et al.,
2002; Köhler and Huth, 2004), fire (e.g. Pausas, 1999;
Franklin et al., 2005), other types of disturbance (e.g.
grazing: Cousins et al., 2003; land use change: Albert
et al, 2007; logging: Huth and Ditzer, 2001), or habitat
loss and landscape structure (e.g. Cousins et al., 2003;
Pausas, 2003). The definition of PFTs in these models
depends mostly on the specific purpose of the model,
and includes fire response types, life history strategies,
life forms, physiological types and many more. For
example, DGVMs, as well as other vegetation models
that are not based on a population approach, apply a
functional group strategy. However, the resolution of
PFTs in DGVMs is necessarily coarse, as it is essential
to match the scale of the overall model (Cramer, 1997).
In fact, the resolution is too coarse to be practical on
smaller scales, making it difficult to address questions of
conservation and land management (Campbell et al.,
1999), or to forecast details of future community
composition.
Despite the loss of information produced by grouping
species into functional groups, a careful choice of PFTs
can provide an understanding that is particularly useful
in practice (e.g. Campbell et al., 1999). Cousins et al.
(2003) state in a study on species-rich grasslands in a
fragmented landscape that ‘‘a handful of caricatural
model plants may answer more questions on grassland
management and conservation y than individual
species models’’. This pragmatic view is related to the
idea that ‘‘landscapes cannot be designed for single
species’’ (Cousins et al., 2003; Opdam et al., 2002).
However, even as the PFT approach provides understanding and forecasts at the level of the community, it
finds its limitation when it comes to understanding
species composition within PFTs, species diversity, and
may be in particular when considering rare species that
are not well characterized by ‘typical’ PFTs.
A second strategy incorporating the notion of
functional groups focuses on traits of species. The
primary questions are: Which functional traits make
species sensitive to specific environmental forces, or
which traits cause a specific behaviour in plants? These
questions are prominent in empirical studies on the
effect of grazing and disturbance (e.g. McIntyre et al.,
1995; Diaz et al., 2001), and on the process of invasion
by alien plant species (e.g. review of Richardson and
Pysek, 2006). The value of the approach may lie
primarily in its ability to forecast which plants will
show a specific behaviour and in the development of an
understanding as to what causes this behaviour. In
population modelling studies, the focus on functional
traits is surprisingly rare, and often confined to
theoretical studies (e.g. Schippers et al., 2001). An
excellent exception, however, is provided by the study of
Higgins and Richardson (1998). They used a spatially
explicit population model to determine what plant traits
were critical for the successful invasion of pine trees into
the southern hemisphere, focussing on the interactions
among habitat type, disturbance and plant traits. They
subsequently derived rules regarding the combination of
habitat and traits of invasive species that would produce
the greatest susceptibility to invasion. The study
effectively demonstrated a potential strength of mechanistic population models in providing a predictive
understanding of the interactions between plant traits
and the environment.
Functional species strategy
Some modelling approaches keep individual species in
the model, but generalize their descriptions to a defined
set of functional traits. We refer to this approach as the
‘‘functional species’’ strategy. If the set of traits is simple
enough and consists of easily measurable characteristics,
models of this type can in principle be parameterized for
a large number of species. Studies that parameterize a
mechanistic population model for all species of interest
are, however, rare, with the primary examples being
composed of 10 or fewer woody species (e.g. Pacala
et al., 1996; de Groot et al., 2003). An exception is the
study of Bugmann and Solomon (1995) (see also
Bugmann, 1996) who parameterized a forest model
along a climatic gradient for 72 species in North
America and 20 species in Europe. Alternatively,
‘‘functional species’’ models can be parameterized using
hypothetical species that cover the whole trait space
systematically. Models of this type cannot make
predictions on specific species, but contribute to
explaining and forecasting species diversity in relation
to the environment. This strategy has been adopted in
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various kinds of models (e.g. growth and persistence of
single species – Kleidon and Mooney, 2000; competition
and population dynamics in a community – Pachepsky
et al., 2001). For example, Reineking et al. (2006) could
show the effect of spatio-temporal variation in resource
supply on plant diversity in a species-rich succulent
community in arid Southern Africa. In their mechanistic
population model, species were defined by a set of four
simple traits related to resource allocation and plant size
that provided the necessary basis for niche differentiation. Despite the difficulties of parameterizing models of
this type for real species, some recent developments are
promising: the increasingly standardized measurements
of functional traits and their availability in databases
(see above; e.g. Knevel et al., 2003), the increasing
understanding of trade-off structures between plant
traits (e.g. Diaz et al., 2004), and the interaction between
modelling studies and field studies for screening large
numbers of species for specific traits (e.g. Storkey, 2006).
In summary, irrespective of the particular strategy,
the PFT approach is a successful method of generalizing
plant population model processes and results over a
large number of species. However, in all three
approaches to functional trait modelling the challenge
lies in finding an optimal choice of traits and processes
that is precise enough to be useful, general enough to
avoid the necessity that every system has to be treated by
a separate model, and pragmatic enough that traits can
be easily measured for all PFTs or even species of
interest.
Towards a science of forecasting
The future role of the bottom-up approach of plant
population modelling will not only depend on the
approach taken (e.g. up-scaling or PFT modelling), the
spatial scales considered or the generality of results but,
in addition to offering its inherent strength of providing
an improved understanding of mechanisms and principles, will also have to prove that it can significantly
contribute to forecasting. To make quantitative forecasts, ecological models will have to be linked with
empirical information (Clark et al., 2001). Such information can enter the forecasting process in two ways:
as prior information on model parameters, or as
independent data (‘‘patterns’’) to which model predictions are compared. The interaction between models and
empirical information then defines key aspects of
forecasting such as model selection, model parameterization, and the quantification of forecast uncertainty.
The realization that a linkage between process-based
models and empirical information is essential for
forecasting has triggered the development of a wide
range of tools and concepts in population modelling. In
181
particular, the concept of ‘pattern-oriented modelling’
has been developed as a strategy for making use of
multiple sources of information to infer parameter
values and to choose between alternative models
(Wiegand et al., 2003; Grimm et al., 2005a). Patternoriented modelling can also incorporate a ‘virtual
ecologist’, a virtual observer that ‘samples’ a processbased ecological model and ‘records’ data which can
then be compared to empirical observations (Grimm
et al., 1999; Wiegand et al., 2003). An unsolved problem
is that pattern-oriented modelling involves a number of
ad hoc decisions such as which observed ‘‘patterns’’ are
contrasted against model predictions, how to measure
the fit of model predictions to observations, how to
select between alternative models, and how to quantify
forecast uncertainty. All these decisions will influence
the choice of models and parameter values and hence
the mean and variability of forecasts.
In our opinion, the pattern-oriented approach to
(plant) population modelling can be strengthened by
embedding it in a more rigorous statistical framework.
The statistical linkage between empirical information
and complex ecological models is covered in a number
of reviews (e.g. Hilborn and Mangel, 1997; Burnham
and Anderson, 1998; Clark, 2005; Clark and Gelfand,
2006). Central to this linkage is the concept of likelihood: likelihood is the probability of obtaining a set of
observed data under a model with a given set of
parameters. In a classical (frequentist) framework, the
best set of parameters is the one that maximizes the
likelihood of the observations given the model. Bayesian
statistics goes one step further by combining the
likelihood with prior information on the distribution
of parameters to derive the posterior distribution of
parameter values. Bayesian approaches are thus related
to ideas of pattern-oriented modelling in population
ecology where prior information defines the range over
which parameters are varied and independent data are
used to narrow down this range to a subset of parameter
values for which the model provides a good fit (Wiegand
et al., 2003). The parallels to pattern-oriented modelling
are particularly strong in hierarchical Bayesian statistics
which can assimilate multiple data sets (multiple
‘‘patterns’’) collected at different scales and hierarchical
levels (Clark, 2005). For each data set, Hierarchical
Bayes allows one to formulate an observation model
(Clark, 2005), the statistical equivalent of a ‘virtual
ecologist’. Hierarchical Bayesian statistics thus enable
population modellers to do pattern-oriented modelling
in a mathematically rigorous way. A considerable
number of studies have used Hierarchical Bayes to
statistically combine population models and empirical
information (see Ellison, 2004). Some of these studies
cover topics relevant to plant population modelling such
as phytoplankton dynamics (Cottingham and Schindler,
2000), the spread of plant populations (Clark et al.,
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2003), temporal and inter-individual variation in tree
recruitment (Clark et al., 2004), competition between
plant genotypes (Damgaard, 2004) or the ecological and
evolutionary dynamics of plant–pathogen interactions
(Ovaskainen and Laine, 2006).
Currently, the application of Bayesian approaches
tends to be limited to relatively simple population
models. This can be explained by the fact that complex
process-based population models require computer
intensive simulations, and the likelihood of these models
is hard or even impossible to compute (e.g. Marjoram
et al., 2003). However, the number of simulations can be
reduced by using methods such as Markov Chain Monte
Carlo analysis which provide computationally efficient
means of estimating the posterior distribution of model
parameters (Clark, 2005). Moreover, approximate
Bayesian computation (Marjoram et al., 2003) helps in
situations where the true likelihood cannot be evaluated.
As computer power increases and development of
methods proceeds, more and more complex population
models will thus be fitted to data by means of statistical
analysis. This holds substantial promise, because statistical approaches offer solutions to the problems of
model selection and uncertainty quantification that
population modelling has long struggled with.
Model selection is necessary because one and the same
ecological phenomenon can be described with a variety
of different models. Complex models represent much
ecological detail but are data hungry and case specific.
Simple models, on the other hand, may ignore
important processes but are easier to parameterize and
more generally applicable. From a statistical perspective, complex models tend to have high variance in
parameter estimation and forecasts whereas simple
models tend to generate biased predictions (Burnham
and Anderson, 1998). This bias-variance trade-off is
inevitable and the aim of statistical model selection is to
find an optimal model that combines low bias with low
variance in parameters and forecasts. In a frequentist
framework, information-theoretical measures such as
Akaike’s Information Criterion (AIC) can be used to
select the optimal model (Burnham and Anderson,
1998). These measures combine model fit (measured by
the likelihood) and model complexity (measured as the
effective number of parameters). They can furthermore
be used to average forecasts of multiple models based
on the degree to which these models are empirically
supported (Burnham and Anderson, 1998). Similar
methods are available in a Bayesian framework
although there is less agreement as to the proper
approach to model selection (Carlin et al., 2006).
A comparison of Bayesian and frequentist criteria for
model selection has been performed by Link and Barker
(2006).
Uncertainty analysis allows the quantification of how
forecasts are affected by different sources of uncertainty.
According to Higgins et al. (2003), three types of
uncertainty are particularly relevant for plant population modelling: model uncertainty is uncertainty in the
representation of ecological processes, parameter uncertainty is uncertainty in parameter estimates, and
inherent uncertainty arises from stochasticity in the
modelled processes. Uncertainty analysis thus goes
beyond simple error propagation by quantifying the
overall forecast uncertainty that arises from the interactions among model, parameter and inherent uncertainty. Moreover, uncertainty analysis can also be used
to quantify the relative importance of these different
sources of uncertainty (e.g. Clark et al., 2003; Higgins
et al., 2003). This can be highly relevant for guiding
future empirical research: if model and parameter
uncertainty dominate, forecasts can be improved
through the collection of more empirical information.
On the other hand, if inherent uncertainty dominates,
forecasts will remain inherently stochastic, even though
empirical information allows perfect determination of
the ‘true’ model structure and its ‘true’ parameter values
(Clark et al., 2003; Higgins et al., 2003). Uncertainty
analysis can thus help to direct empirical population
ecology in a situation where environmental change poses
urgent questions and resources for data collection are
limited. Finally, as discussed in the concluding section,
we believe that population modelling itself will strongly
benefit from the statistical coupling of population
models and empirical information.
Conclusion
Several decades of plant population modelling have
led to a variety of approaches and specific research
topics. All these approaches have in common that they
are process-oriented and, in this sense, mechanistic,
bottom-up and dynamic. Although, these basic features
would in principle make demographic models perfect
candidates for investigating and forecasting the effects
of environmental change, their application in this field is
still limited.
We see four different key steps that need to be taken
to significantly increase the relevance and contribution
of plant population modelling to environmental change
research:
1. New strategies for model scaling up should be applied
to mechanistic small-scale population models and the
validity of the strategies should be tested against the
initial models and empirical data. Only successful
examples of this approach will prove that plant
population modelling has the potential to also tackle
large-scale environmental problems.
2. Initiatives on assembling and collecting large data
sets should put greater emphasis on the inclusion of
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process-level data. Only a broader base of quality
data suitable for parameterization of population
models will allow the systematic analysis and
comparison of different systems. Also, these data
will be required to systematically explore a set of
species that is large enough to derive a general
understanding of the response to environmental
change.
3. The growing interest in empirical research on PFTs
and the increasing availability of information on
plant functional traits should feed into process-based
modelling approaches. A linkage between the classically static/correlational functional-type approach
and dynamic modelling techniques could provide an
understanding that overcomes the inherent restrictions of both approaches. From the perspective of
empirical research and static models this could
provide knowledge of the mechanisms that cause
observed correlations and indicate limitations where
the assumption of a static response of species to the
environment fails. From the perspective of population modelling it provides a necessary strategy for
developing models beyond single case studies and
allows the development of generalized answers of
how species respond to environmental change.
4. We need a better statistical linkage of models and
empirical information: statistical approaches will help
find the best model for up-scaling and the level of PFT
aggregation that is optimal for forecasting. Tighter
linkage of population models and empirical information will also strengthen the position of plant population modelling itself: in comparison to DGVM and
bioclimatic modelling, plant population modelling
shows a bewildering diversity of models – putting these
models in a rigorous statistical framework will facilitate
model comparison/selection and lead to a unification of
modelling approaches where this is sensible.
While we feel that steps 1–4 are necessary prerequisites to ensure that population modelling gets the
attention it deserves in forecasting the effects of
environmental change, we should not forget that the
understanding of underlying principles and mechanisms
is the basis of any natural science and also of any
scenario development. Thus, in addition to the new
developments in plant population modelling outlined
above, we should also continue to develop the ‘old’
strength of population modelling that goes back to the
earliest models of the last century, i.e. the development
of a conceptual understanding of complex ecological
systems using simplified yet elegant models. Only the
combination of such conceptual and more realistic
approaches will, in the long-term, allow us to successfully face the current threats of global and regional
environmental changes.
183
Acknowledgements
We gratefully acknowledge the financial support of
the Deutsche Forschungsgemeinschaft DFG (BU 1386
to F. Jeltsch), the German Ministry of Science and
Education (BMBF) in the framework of the GLOWAJordan River Project (to F. Jeltsch and M. Köchy) and
the Velux Foundation (to K. Moloney). F. Schurr and
F. Jeltsch acknowledge support from the European
Union through Marie Curie Transfer of Knowledge
Project FEMMES (MTKD-CT-2006-042261). We are
grateful to A. Guisan, W. Thuiller and an anonymous
reviewer for helpful comments on an earlier version of
this manuscript.
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