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1. Summary
Ecologists seek to understand the factors that lead some exotic plant species to become ecologically
dominant and widespread. This attention is justified because these invasive species can alter
community composition, impact ecosystem function, affect species’ evolutionary trajectories, and
lead to species extinctions. Despite these impacts, finding characteristics that are consistently
shared by invasive species is difficult. The climatic niche of plants (the climatic conditions under
which population growth is positive) potentially facilitates plant invasions. Some groups of
invasive plants have native ranges that span large latitudinal gradients. Other invasive species have
experienced shifts of climatic niche during the invasion process. Niche shifts, both recent and deep
in species evolutionary history, likely influence whether immigrant species become established,
naturalized and, subsequently, invasive. The evolutionary dynamics of the climatic niche, per se,
have seen very little work and there is substantial disagreement over the occurrence, magnitude and
rate of climatic niche shifts. These evolutionary niche dynamics are the focus of this proposal.
The main goal of this project is to understand the influence of evolutionary history, especially the
history of shifts of the climatic niche, on the invasiveness of exotic species. Studying the
evolutionary history of niche dynamics is not specific to invasive species. Invasive species are a
convenient set of model systems (i.e. genera) in which the literature suggests that it is likely that
evolution of the climatic niche influences the presence of a detectable ecological quality:
invasiveness. Without the criterion of presence of invasives, we could have selected genera at
random or used other criteria that would not likely lead the research to have similarly broad
interest. In this research we: (a) use a bioinformatics approach to obtain existing data on species
distribution and molecular variation within genera that contain invasive species; (b) modify and use
existing software to test the degree to which alternative evolutionary models suffice in describing
phylogenetic patterns of niche evolution within these genera; (c) test for historical correlates of
niche shift and invasiveness by drawing on information from phylogenetic reconstructions, plant
functional traits, and climatic niche characteristics of species.
The first part of the work focuses on collection of (a) ecological, occurrence, and climate data that
are used to describe the distribution-climate relationships of species, (b) data on species functional
traits, and (c) existing sequence data for phylogeny reconstructions. We extract species distribution
data from online databases and a wide array of other data sources, including the primary literature.
We obtain molecular data directly from GenBank. Plant tissue for additional sequencing will be
collected at national herbaria and botanical gardens. We model and quantify niche optima and
limits using species distribution models and we construct phylogenetic trees of species. We model
the evolution of the climatic niche using existing open-source software, examining potential effects
of both selection and random evolution on niche variability. We produce a supertree of the genera
and, using both niche and functional trait data, develop general linear models to examine the
relationship of these variables to establishment and invasion by exotic species. Finally, we expand
existing software to examine how heterogeneous selection among subclades affects the likelihood
of niche shifts and species invasiveness.
Beyond the importance of understanding the factors that contribute to species invasiveness, this
research will contribute to understanding how niche shifts contribute to evolution within genera and
affect species geographical distributions. By using genera that include invasive species in
Switzerland and elsewhere, the project will contribute to efforts to identify potentially invasive
species before they become introduced. We will gain better understanding of the role of selection
and random evolution in niche shifts of invasive species, closely related species, and their
ancestors. By helping us to understand the evolutionary history of niche shifts, this research will
improve confidence in the use of species distribution models to predict the potential distributions of
invasive species. Finally, understanding evolutionary processes that enable species to expand into
new environments will help to predict the impacts of climate change on plant biodiversity.
2.1 State of Research
2.1.1 Niche dynamics and species invasions
Ecologists seek to understand the factors that lead some exotic
species to become ecologically dominant and widespread
(Callaway & Maron 2006). This attention is justified because
these invasive species can impact ecosystem function, affect
species’ evolutionary trajectories, alter community
composition, and lead to species extinctions (Clavero &
Garcia-Berthou 2005; Strayer et al. 2006; Vellend et al. 2007).
General trends in factors that are associated with invasiveness
have been difficult to identify (Levine et al. 2003); each taxon
has different correlates of invasiveness. For example, large
seed size, presence of fleshy fruits, and opportunity for
vertebrate dispersal are related to invasiveness in pines
(Rejmánek & Richardson 1996). In a Mediterranean climate in
California, invasive species have faster seedling growth, higher
specific leaf areas, and significantly more root biomass than do
less invasive species (Grotkopp & Rejmanek 2007). Species
with a history of being invasive elsewhere or having vegetative
reproduction are more likely to become invasive than species
lacking these characteristics (Kolar & Lodge 2001). Further,
introduced species that are less-closely related to species in
receiving communities can encounter ecologically dissimilar
species, find free resources, gain a competitive advantage or
escape from natural enemies, and thus become invasive (Fargione
et al. 2003; Mack 2003; Cavender-Bares et al. 2004; Strauss et
al. 2006). These observations suggest that there are multiple
factors that affect invasiveness and that they vary in importance
among taxa.
Figure 1. The relationship between fundamental
and realized niche during a founder event and
subsequent niche expansion. There are four
snapshots in time. The rate of increase of
populations (r, on the y-axis) is shown as a
function of location along a moisture gradient. (a)
The realized niche (dark green) of the species in
comparison with its fundamental niche (light
green). The dashed green line indicates the
moisture optimum. Individuals from one end of the
moisture gradient, together defining a realized
niche (red area) and a fundamental niche (dotted
red line), disperse. (b) The population establishes
within its fundamental niche (light read area),
which is small in comparison to that of the species
in its original range (black arrows indicate
difference). (c) The population expands (black
arrows) in its use of habitat (i.e. the realized niche)
into portions of the fundamental niche that were
not occupied in the original range. (d) Directional
selection favors toleration of drier conditions than
those tolerated by the first colonists. Thin black
arrows indicate the evolution of the fundamental
niche and expansion of the realized.
One potential difficulty in identifying general patterns in the
causes of invasiveness is that each invasive species has a unique
evolutionary history. This history might contribute substantially
to determining which species become invasive. For example, the
factors associated with invasiveness in plants likely depend on
plant growth form and other characteristics of species in ways
that vary idiosyncratically within clades (Herron et al. 2007).
Evidence suggests that herbaceous invasive species, when
compared to non-invasive species, have relatively large native
ranges, with large latitudinal breadth. Thus, the species
experience variation in climate in their native range. These
species may spread geographically in a new region because of their broad tolerance to climatic
conditions, making the species preadapted to a wide range of climates (Rejmánek 1995, 1996;
Rejmánek & Richardson 1996; Herron et al. 2007). In this sense, the same traits that allow species
to spread across their native continents could be advantageous for establishment and range
expansion under similar climates (Thuiller et al. 2005b). These results suggest a direct link
between a species’ invasiveness and the evolutionary trajectory of its climatic niche.
In addition to the influence on invasiveness that is associated with adaptation to a range of climates,
invasive plants can exhibit altered distribution-climate relationships between the native and invaded
ranges (Broennimann et al. 2007) and differ from closely related species. These changes can
evolve over a wide range of time scales (Pearman et al. in press-a). The ecological and
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evolutionary processes involved (Sexton et al. 2002; Maron et al. 2004; Muller-Scharer et al. 2004)
can alter species’ environmental requirements, i.e. the species environmental niche (Hutchinson
1957). What is missing is research to identify contributions of the recent and ancient evolutionary
history of the climatic niche to invasiveness, by comparing characteristics of species.
Two aspects of the niche concept are essential when considering or analyzing distributions of
invasive species. The fundamental environmental niche is genetically and physiologically
determined, while the realized environmental niche includes, additionally, constraints arising from
interspecific competition (Hutchinson 1957). It is the realized niche that can be estimated using
field observations. The distinction between realized and fundamental niche is important to
describing niche changes (‘dynamics’, Fig. 1) because a putative niche change, a shift for example,
might result from a change in the realized niche only, for instance owing to release from natural
enemies in a new environment, while the fundamental niche remains unchanged, or to a change of
both the realized and fundamental niches (Pearman et al. in press-a). Ideally, one would model the
fundamental niche and then constrain it by the effects of competition. Unfortunately, this requires
an expensive, experimental approach that has been possible for only a few species. For these
reasons we restrict our work to the realized niche. Recent findings suggest that shifts of the realized
niche, expansions and other dynamics occur in previously unrecognized situations (Ackerly 2003;
Davis et al. 2005; Dietz & Edwards 2006).
There is a substantial literature surrounding niche dynamics. One of the most important and
potentially wide-reaching issues in this literature is ‘niche conservatism’, which speculates that the
climatic aspects of the realized environmental niche remain unchanged or change only slowly or
infrequently over hundreds to millions of years (Peterson et al. 1999; Wen 1999; Qian & Ricklefs
2004). The idea of niche conservatism currently influences the study of species distributions, and
has generated considerable debate (Wiens & Graham 2005). This is because the idea that most
environmental niches change little is used to justify applying niche-based species distribution
models (SDMs) to predict species distributions in space and across time (Guisan & Zimmermann
2000; Guisan & Thuiller 2005). In this way, the invoking of niche conservatism has been used by
authors to study and predict the establishment and spread of invasive species (Peterson & Vieglais
2001; Dirnbock et al. 2003; Thuiller et al. 2005b) and other phenomena related to global change.
In reality, there has
been little basis for
evaluating whether
the assumption of
niche conservatism
holds when
predicting changes in
species distributions.
Reciprocal prediction
using SDM’s of
Figure 2. Niche shift in an invasive plant, spotted knapweed (Centaurea maculosa). (a) The modeled
distribution of spotted knapweed in Europe. The gray area indicates predicted presence when the models
species distributions
have been calibrated data from the European range. When this ‘European model’ is projected to the
between native and
western United States (b), the predicted range of the species is shown again in gray. However, when the
models are calibrated with occurrence data from the USA, the species is predicted to occur in the black
invaded portions of the
area. Projection of the ‘United States’ model to Europe (a) leads to predicted presence, shown also in
range has
black. The gray and black hatched areas in each panel show that there is little area that both models
predict as occupied. This provides a spatially explicit representation of a shift in the species realized
demonstrated rapid
climatic niche (Broennimann et al. 2007).
niche changes during
invasions by exotic species, for example fire ants (Solenopsis invicta) (Fitzpatrick et al. 2007) and
spotted knapweed (Centaurea maculosa, Fig. 2) (Broennimann et al. 2007). These empirical results
suggest that the uncritical assumption of any particular kind of niche dynamic, as done in
estimating risk of establishment or future distribution of invasive species, appears to be
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questionable at best. If niches of species are not static, then they can expand, contract, or shift.
These observations suggest that it is critical to understand when particular niche dynamics may
occur, in order to help predict which species become invasive and what their eventual distributions
might be.
2.1.2 Niche dynamics and a role for phylogenetics
Niche conservatism has been used to signify that niches appear to be statistically correlated over
time (Martinez-Meyer et al. 2004; Knouft et al. 2006; Martinez-Meyer & Peterson 2006), and that
the fundamental niche has a tendency to resist evolution (Levin 2005). Further, niche conservatism
might be assigned to various patterns resulting from niche dynamics. For example, stasis of the
fundamental and realized niches (e.g. through stabilizing selection and competitive dominance,
respectively), phylogenetic signal (e.g. owing to finite rates of random divergence of the
fundamental or realized niche of species over time) and evolutionary constraints (Blomberg &
Garland 2002) could all be considered niche conservatism. Also, given enough time, niche stasis is
not the null expectation for evolution of the fundamental niche (Blomberg & Garland 2002).
Phylogenetic analysis of niche characteristics has provided
evidence of both recent and ancient niche dynamics. Niche
shifts (Cavender-Bares et al. 2006; Lovette & Hochachka
2006) can occur in as few as one hundred years, or deep in a
species’ evolutionary pasts (Cavender-Bares et al. 2006;
Broennimann et al. 2007). Efforts to clarify how invasiveness
is associated with characteristics of the environmental niche,
how these associations change over time, and how the
distribution of these changes varies geographically and among
related lineages will help to elucidate the influence of recent
and ancient niche dynamics on the invasiveness of exotic
species. Furthermore, we still lack sufficient information to
form an expectation as to in which species, under which
ecological conditions, in which clades, and over what time
periods niche changes will be observed. No study has yet to
analyze the combined effects of characteristics of the climatic
niche and plant functional traits on invasiveness in a way that
controls for the evolutionary relatedness of the species being
compared.
Figure 3. Detecting a phylogenetic signal in the
environmental niches of oaks (Quercus) depends on
Comparisons based on combining phylogenetic methods with
environmental variation and phylogenetic tree size. (a) Niche
empirical data on climatic niche characteristics can contribute
shifts: over-dispersed pattern of the occurrence of related
species in relation to the moisture differences among types of
to a better understanding of niche dynamics by providing a
forest (xeric, mesic and hydric); no phylogenetic signal. (b)
framework in which different patterns of distribution of niche
With larger environmental and taxonomic scope, phylogenetic
signal in habitat preference is observed. Redrawn with
characteristics on tree nodes are expected (Losos et al. 2003;
permission from (Cavender-Bares et al. 2006).
Cavender-Bares et al. 2004). For example, a matrix of the
distances of niche characteristics among species within a clade
can be compared to a matrix of phylogenetic distances using a Mantel test or similar randomization,
and the presence or absence of significant correlation is interpreted as the presence or absence of
phylogenetic signal in the trait (Losos et al. 2003; Knouft et al. 2006; Lovette & Hochachka 2006).
Nonetheless, this approach reduces a phylogeny to a distance matrix, which, while providing a
measure of the concordance between ecological and phylogenetic distances, omits information on
the timing and distribution of niche changes on the tree. One study has shown that taxa vary in
where niche shifts occur in a phylogeny (Cavender-Bares et al. 2006). Some taxa demonstrate
shifts with recent speciation events; others show shifts deep in the past (Figure 3a,b). In the
presence of significant correlation between phylogenetic distance and distance among
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environmental niche values of species, the question becomes whether there is more or less niche
similarity than expected, given the amount of genetic change that has occurred. Phylogenetic
analysis, using information contained in tree topology, can be applied to characteristics of the
realized niche in a similar way to how it is applied to analyze the evolution of morphological
characters (Silvertown et al. 2001; Graham et al. 2004). These studies indicate that application of
advanced techniques to model evolutionary processes are needed to better understand the
evolutionary dynamics of the climatic niche and how these dynamics may be related to the invasion
process.
The application of evolutionary models of character change supports understanding of niche
dynamics by helping to choose among particular evolutionary processes that might have influenced
patterns of niche change. For example, random evolution of a character (e.g. an aspect of the
realized niche of a species) can be modeled with the Brownian motion model of character change.
The resulting among-species distribution of the magnitude and direction of character change is
Gaussian, with a single expectation of no change and a finite standard deviation (Felsenstein 1988).
This process results in among-species variance in character values (e.g. niche metrics) that
increases with the square root of elapsed time. The Ornstein-Uhlenbeck (OU) family of models, of
which Brownian motion character change is a special case, incorporates the opportunity for
selection to act, in addition to random effects, as a source of character differences among species
(Hansen 1997). Selection in these models can include both directional and stabilizing selection via
a hierarchical model structure that includes one or more terms for ecological optima for extant
species, in addition to a term for Brownian motion (Butler & King 2004). Parameter estimation is
possible through maximum likelihood, whereas a model comparison approach can determine which
model of character change is most supported by the data (Butler & King 2004). In an example,
Losos et al. (Losos et al. 2003) failed to reject a Brownian motion model in favor of constrained
evolution affecting the realized niche in Cuban Anolis lizards. Other methods to detect nonBrownian evolution (e.g. via detecting that potential environmental niches are filled non-randomly
in association with speciation events) are also possible by correlating phylogenetic contrasts with
the height of the nodes at which the contrasts are assembled. A significant correlation leads to
rejection of the Brownian motion model because trait evolution occurs systematically throughout
the tree (Freckleton & Harvey 2006).
The combining of niche modelling and phylogenetic methods has demonstrated shifts of realized
climatic niche that are associated with speciation and ecological diversification. For example,
Knouft et al. (Knouft et al. 2006) examined the degree to which phylogenetic similarity paralleled
similarity of realized climatic niches of Cuban Anolis lizards. The authors recognized that a
negative correlation between evolutionary distance and niche similarity would suggest that niches
tend to show phylogenetic signal, perhaps similar to Brownian motion evolution. They used a
phylogenetic tree of 11 Cuban Anolis species to calculate the sum of the branch lengths between
pairs of individuals, each representing a distinct species, and determined the niche similarity of the
species in terms of climatic niche dimensions. The authors found no significant correlation
between niche similarity and phylogenetic distance, indicating no detectable phylogenetic signal in
the characteristics of the climatic niche, i.e. radical niche changes had occurred. In a similar
example, Graham et al. used climatic variables, species occurrence data, SDMs and a phylogeny to
describe the evolution of species climatic niches in three clades of dendrobatid frogs in Ecuador
(Graham et al. 2004). The authors tested for directionality in the evolution of pairs of sister species
by comparing the phylogeny of the frogs (a maximum likelihood tree) to what might be produced if
trait evolution were to follow a Brownian motion model. They failed to reject the adequacy of a
Brownian-like evolutionary process. The door is now open for us to investigate within a
phylogenetic context the importance of niche characteristics and change to species invasions.
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2.2 Current Research
2.2.1 PETER B. PEARMAN—SPECIES DISRIBUTIONS, NICHE MODELING
I am currently a Research Scientist at the Federal Research Institute WSL. My interests lie in
understanding factors that are associated with variation in the spatial distribution of species and
biological diversity. I have been working in this area since the beginning of my graduate studies,
through two postdoctoral positions and on into my career as a senior scientist. I have addressed this
issue using experimental (Pearman 1993, 1995b; Pearman et al. 2004; Pearman & Garner 2005),
modeling (Pearman & Wilbur 1990; Pearman et al. in press-b), and population genetic approaches
(Garner et al. 2003; Garner et al. 2004), with both terrestrial (Marsh & Pearman 1997; Pearman
1997, 2002; Pearman & Weber 2007) and aquatic systems, in the field and in the lab, with plants
and with animals. I have consistently placed my work in leading ecological journals.
My initial work on species distributions addressed effects of fragmentation and subdivision of
habitat. Using analytical, statistical and numerical tools I determined that intermediate degrees of
subdivision confer stabile dynamics on populations that would otherwise exhibit chaotic behavior
(Pearman & Wilbur 1990). I then tested the results and assumptions of my model by emulating the
process of the distribution of reproductive effort, using crossed-factorial experiments in a system of
aquatic mesocosms. The experiments showed that the form of competition in habitat patches
changed with variation in the size of the habitats, revealing additional complexity that was not
captured in the original model (Pearman 1993). I added an additional layer of ecological
complexity to my experimental system by including competing species and predators to address
how trophic interactions within a community could influence the effects I had found. These
experiments demonstrated that manifestation of effects of competition and predation on prey
populations was mediated by both direct and indirect effects of size variation in habitat (Pearman
1995b). Together, these studies illuminated the interaction between habitat subdivision and
multiple ecological processes on spatial variation in population dynamics.
One of the principal questions at that time I finished my Ph.D., considering the growing recognition
of the impacts of deforestation on tropical systems, was how does the distribution of species
respond to forest disturbance and fragmentation. It was known that species diversity decreased
with massive deforestation, but the scale of disturbance to which communities responded was little
understood. I addressed this issue in a post-doctoral research program I established in the upper
Amazon Basin. Published work from this program demonstrated that with increasing forest
disturbance, the number of bird and amphibian species increased in some ‘winning’ guilds while
other ‘losing’ guilds lost species. Further, the scale of environmental disturbance with which these
changes were related varied greatly among guilds. In some guilds, loss of member species was
associated with disturbance one to two kilometers from a sampling site, while changes in other
guilds were only related to nearby forest disturbance (Pearman 1995a, 1997, 2002). A
contemporaneous study showed that the species that persisted in isolated forest fragments was
related to species mobility and the isolation of forest remnants (Marsh & Pearman 1997).
Perhaps the most influential idea in studying species distributions is that of the ecological niche of
species (Hutchinson 1957). Niche-based species distribution models (Guisan & Zimmermann
2000) have been employed to predict the response of species to environmental change (Thuiller et
al. 2005a). However, this approach assumes niche conservatism over time. I evaluated the
evidence for niche shifts and found great variation in niche dynamics among systems. I then
proposed a research program to investigate the phylogenetic characteristics that are associated with
niche shifts (Pearman et al. in press-a), based on recent work that shows patterns of niche shifts can
vary among subclades and that both time and phylogenetic tree size are influential in shift detection
(Cavender-Bares et al. 2006). Empirical study of altered climate-distribution relationships of plants
has indicated that certain ecological characteristics, such as competitive ability, might be associated
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with the tendency of the climate-distribution relationships of species to remain stable over long
periods (thousands of years), suggesting great niche stability (Pearman et al. in press-b). Other
species appear to have experienced radical niche shift during the last 6000 years. The current
proposal builds on this work to investigate phylogenetic trends in dynamics of the climatic niche.
2.2.2 Nicolas Salamin—Phylogenetics
I am currently a group leader in the Department of Ecology and Evolution at the University of
Lausanne. My research areas have been centred ever since my M.Sc. on theoretical aspects of
phylogenetic reconstructions and can be grouped in three different subjects: (1) the development
and assessment of methods of phylogenetic reconstructions (Salamin et al., 2002, 2003, 2005;
Savolainen et al., 2002), (2) the analysis of speciation processes using phylogenetic trees (Salamin
and Davies, 2004; Strauss et al., 2006; Savolainen et al., 2006), and (3) the study of the
evolutionary history of the grasses (Christin et al., 2007, 2008; Hodkinson et al., 2007). I have also
been working in the area of bioinformatics with a particular focus on phylo-informatics. I have
written a series of software programs to implement supertree reconstruction methods (Salamin et
al., 2002), Maximum likelihood estimation (Christin et al., 2008), Markov chain Monte Carlo
algorithm (Salamin and Felsenstein, in prep.) and other more-specific tasks in several different
programming languages (see <www.unil.ch/phylo/software.html>).
The theoretical aspects of building large trees formed a major component of my PhD and my more
recent postdoctoral position. I have thus been looking at different ways of creating comprehensive
and large phylogenetic trees. An interesting approach uses a type of meta- analysis to build a tree,
called supertree, from a set of source trees containing subsets of the total number of overall
terminal taxa (Salamin et al., 2002). One important property of supertrees is that the terminal taxa
of the source trees do not need to be identical; sufficient overlap is enough. This permits combining
several smaller source trees to form a more comprehensive supertree. Beside the empirical testing
of diverse supertree methods, the largest and most comprehensive estimation of grass evolutionary
history was obtained (Salamin et al., 2002), and used to investigate macro-evolutionary (Salamin
and Davies, 2004; Hodkinson et al., 2007) and ecological questions (Strauss et al., 2006) related to
the grass family. I have also used extensively computer simulations to assess the impact of low
taxon sampling on resolving grass evolutionary history (Salamin, 2002). One of my main
contributions to this area of research was a large simulation study that looked at the affect of
building phylogenetic trees for very large number of taxa (up to 13,000 were used; Salamin et al.,
2005). This work received the Ernst Mayr Award by the Society of Systematic Biologists. My
research group is now continuing research on large multi-gene tree reconstruction following a
successful SNF grant that started last October (grant no 3100AO-116412).
My research has recently been focused on developing methods that use trees to test hypotheses
about the evolutionary history of organisms. My group has recently published two papers on the
evolution of C4 photosynthesis in the grass family (Christin et al., 2007, 2008). In these papers, we
integrated phylogenetic trees with mathematical models to investigate a key step in the evolution of
the C4 photosynthetic pathway in this important plant family. Furthermore, a series of
collaborations resulted in papers published in high impact factor journals. For instance, the
occurrence of sympatric speciation on an oceanic island was demonstrated by a combination of
population genetics and phylogenetics techniques on a group of palm trees (Savolainen et al.,
2006). I have also been collaborating on studies of co-speciation between fig wasps and figs
(Ronsted et al., 2005), where we showed that speciation in the figs and their pollinators happened at
the same time. Phylogenetic trees can as well be used to study ecological communities. One of my
collaborations has been looking at invasive species (Strauss et al., 2006) and has shown, using a
supertree of the grass family, that exotic species less related to native species are more invasive.
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Finally, following my last post-doctoral position, I have been developing methods to estimate the
rate of speciation and extinction within a group of organisms. These methods are based on
stochastic processes, such as the birth and death process, and implement an importance sampling
approach using Markov chain Monte Carlo to integrate over the uncertainty of the phylogeny while
estimating the rate of speciation (Salamin and Felsenstein, in prep.). I have therefore gained
experience in Maximum Likelihood estimation on phylogenies and numerical integration with one
of the most well-known figures in my field.
2.2.3 Collaborators—external resources In addition to the two principal investigators, three
collaborators increase the depth of experience in the two types of modeling that are associated with
the project (Table 1). We selected collaborators to compliment and extend the depth of experience
of each of the co-PIs. Antoine Guisan is Associate Professor at the University of Lausanne. Along
with Niklaus Zimmermann of the WSL, these two collaborators provide us with a wealth of
experience in plant ecology and species distribution modeling. Marguerite Butler of the University
of Hawaii is a molecular evolutionist and author of software to model the OU process. Additional
collaborators include three leading scientists active in research on invasive species (Table 1).
Table 1. Expertise of each participant in the project
Applicant
Name and Institute
Expertise
Main
applicant
Peter Pearman
Species distribution models; analysis of species distributions,
Federal Research Institute ‘WSL’ generalized linear models; geographic information systems
Co-applicant Nicolas Salamin
Department of Ecology and
Evolution, Univ. Lausanne
Bioinformatics, phylogenetic reconstructions, molecular evolution
Resource
persons
GIS; SDMs; plant ecology
SDMs, plant ecology, active in NCCR
Molecular evolution
Plant ecology, active in NCCR
Plant population genetics, active in NCCR
Invasive species, active in NCCR
Nick Zimmermann (WSL)
Antoine Guisan (DEE)
Marguarite Butler (U. Hawaii)
Heinz Mueller (U. Freiburg)
Markus Fischer (U. Bern)
Urs Schaffner (CABI)
2.3 Detailed Research Plan
2.3.1 Goal, focus and objectives
The main goal of this project is to understand the influence of evolutionary history, especially the
history of shifts of the environmental niche, on the invasiveness of exotic plants. We do this with
regard to the evolution of characters of the climatic niche, species functional traits, and gene
phylogenies. Our approach is unique in that we will test specific evolutionary hypotheses about
niche dynamics using models of evolution and comparative analyses. As such, our research
compliments other research on endangered species, in Switzerland and elsewhere, by providing
analyses above the level of species. However, studying the evolutionary history of niche dynamics
is not specific to invasive species. Invasive species indicate a convenient set of model systems (i.e.
genera) in which the literature suggests that it is likely that evolution of the characteristics of the
climatic niche could influence a detectable ecological quality of some species: invasiveness.
Without this criterion, we would essentially have to select genera at random for study of niche
dynamics. Furthermore, the use of other criteria for the choice of genera would not lead the
research to have similar scientific impact. By using genera that include invasive species in
Switzerland and elsewhere, the project will contribute to efforts to identify potentially invasive
species before they become introduced. To realize this goal, we establish the following objectives:
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(a) use a bioinformatics approach to obtain existing data on species distribution and molecular
variation within genera that contain invasive species in Switzerland. These data come mainly from
eight distinct types of sources;
(b) use and modify existing software to test the degree to which alternative evolutionary models
suffice in describing phylogenetic patterns of niche evolution within these genera;
(c) test for historical and ecological correlates of niche shift and invasiveness by drawing on
information from phylogenetic reconstructions, functional traits, and climatic niche characteristics
of species, using generalized linear models of the occurrence of invasive and exotic species within
genera.
These objectives are addressed in order in the following three work packages.
Work Package 1: Data collecting and supplementation
Data adequacy:
a) Niche modelling data. Niche modelling requires sufficient occurrence data for calibrating
SDMs. Ideally, we would obtain the locations of 50 occurrences of each study species although
half that many would be sufficient in cases where data are limited. We note that although a random
sample of locations would be ideal, this is essentially never possible and for this reason we take a
bioinformatics approach. Presence within 50x50km squares (‘atlas’ data) will be sufficient for
modelling medium to broadly distributed species (>60,000 km2). For species with smaller
distributions, occurrence data with a resolution of 100-500 m will be sufficient because the climate
data we will use (below) has 1km resolution. We will include data on position uncertainty of each
observation as a basis for quality control and filtering of observations that lack sufficiently precise
spatial resolution. Occurrence data provides information on where a species is known to occur.
Data on species absence is much less available. We will use ‘pseudo-absence’ data, consisting of
random points chosen from around and within each species range. The use of pseudo-absences
along with modeling algorithms that require absence data produces better results that do techniques
designed for use with presence-only data (Zaniewski et al. 2002).
b) Phylogenetic trees. There are 53 plant species indicated in the Swiss Red List of plants as actual
or likely invasive pests (Moser et al. 2002). Sequence data is available for most, but not all, of the
genera represented by these invasive species. The phylogenetic information available in sequence
databases for the species with sufficient sequence data (invasive species and additional congenerics
sequenced) is presented in Table 2. In this project, we aim to expand the phylogenetic information
of five to six of the genera listed in Table 2 in order to get phylogenetic trees with at least 50%
coverage in terms of number of species. Our strategy is to collect species from herbaria and
botanical gardens and perform DNA extraction and sequencing on these samples. In order to do
that, we have on-going collaborations with the botanical gardens of Geneva, Zurich, Trinity
College Dublin and Kew London. Further sampling at other botanical gardens or herbaria (e.g.
Madrid, St. Louis, Paris, Bejing, Quito) will be likely useful and can be done concurrently with
collection of distribution data. We also aim to sequence at least two DNA regions for each genus
selected. According to the current available data (Table 2), the ITS region has been the most widely
used marker. We are aware of the potential difficulties in sequencing this nuclear region for certain
plant species, in particular the occurrence of divergent copies of the same gene not homogenized by
concerted evolution (Feliner & Rosselló 2007). However, it would be ideal to have one sequence
in common for all genera investigated, and ITS is the best candidate gene for this task. The second
DNA region used will depend greatly on the genera selected, but the plastid region trnL-F is a good
candidate as it is commonly sequenced in plants and is available for several genera with invasive
species (Table 2). Furthermore, we have already experience with this marker in grasses (Hodkinson
et al. 2002). For the two markers selected, the extraction, PCR and sequencing protocols will be
based on the procedures described in the papers associated with DNA markers found in Genbank
8
(Table 2). All the laboratory work will be done in the DEE where we have all the facilities required
to do these analyses and the experience to help the PhD student in this task.
Once the molecular data are available, phylogenetic trees will be reconstructed using typical
phylogenetic reconstruction methods such as Maximum Likelihood and Bayesian inference.
Calibrated trees will be obtained by Bayesian optimisation of the evolutionary rates within each
lineage. Fossil information is not easily available for most of the genera listed in Table 2, and
relative divergence date will be used when necessary. The access to the vital-it cluster from the
Swiss Institute of Bioinformatics will be relevant during this step of the analyses.
Table 2. Number of species and the corresponding DNA regions available in genbank (as of release
159, Oct 2006) for the relevant genera.
Genus
Acer
Amorpha
Phylo.
psbA- trnD- psbMtrnL- tRNAmycatpBinfo.
ITS
rbcL rpl16
matk
26S ndhF ETS trnK 18S
rps16
trnH trnT trnD
trnF Leu
like
rbcL
clusters
12
107 66
52
52
53 64 32
96
7
1
5
Artemisia
3
88
Bidens
Buddleja
3
2
36
4
6
Cornus
Erigeron
6
2
59
90
Helianthus
10
56
Impatiens
Lonicera
12
3
124
6
28
Lupinus
Oenothera
13
8
110
53
25
Parthenocissus
2
Prunus
22
82
Quercus
Rhus
21
5
78
31
Robinia
Rosa
1
10
4
51
72
Rudbeckia
Senecio
2
10
21
109
8
Solidago
3
11
4
Vaccinium
Veronica
6
4
58
122
4
6
25
24
54
21
24
4
11
4
8
8
56
85
4
16
30
4
32
29
6
42
39
62
26
4
38
37
22
29
5
60
71
52
31
22
9
5
102
45
39
40
11
21
4
6
5
16
Species distribution, niche and traits:
WP 1a—Data mining
Data on plant species distributions and climate are available in several forms and from a
variety of sources.
a. Publicly-available species atlases are a source of distribution data at a large (regional to
global) scale. Data from atlases of species distribution will be sufficient for distribution modeling
of species with large ranges. For example, the Atlas Florae Europeae (Jalas & Suominen 19721999), a digitized version of distribution maps for over 4000 European species (Hultén & Fries
1986) and the United States Climate-Vegetation Atlas <http://pubs.usgs.gov/pp/p1650-a> all
provide usable data.
9
b. Publicly available ad hoc collections and museum records can also inform us about the
distribution of species. We will obtain records of species presence by searching the databases of
the Global Biodiversity Information Facility (GBIF, http://www.gbif.org), and others. This
institution provides direct links and downloads from online databases at 235 museums and herbaria.
c. Designed sampling programs are probably the most reliable and precise type of data for
creating SDMs. Data directly from programs, normally with a standardized sampling design, and
potentially with valid randomization, will provide additional occurrence data for select species with
restricted distributions. These sources include data from programs in Switzerland (e.g.,
MODIPLANT at University of Lausanne), S. Africa, and at the European level (EU Forest
inventory data), etc.
d. Some data on species occurrence is available from databases holding species
composition data from vegetation sample plots, for example the SALVIAS database
<http://www.salvias.net>, VEGBANK <http://vegbank.org/vegbank/index.jsp>, the US Long Term
Ecological Research Network <http://www.lternet.edu/>, and others.
e. Data are also available from collections in national herbaria. Although older herbarium
records are not often geographically referenced, national herbaria may provide some occurrence
data, as well as maps from national publications and guides.
f. Additional information on exotic, naturalized, and invasive species will be obtained
extensive web databases such as DAISIE <http://www.daisie.se> ,
<http://invasivespeciesinfo.gov>, various other databases found at
<http://www.invasivespeciesinfo.gov/resources/databases>, <http://www.invasive.org>, and
<http://plants.usda.gov>, in addition to sources from the primary literature.
g. Data on current climate are needed in order to fit and calibrate SDMs. The most
efficient source of climate data for creating niche-based SDMs of species distributed at the global
or continental scale is the WorldClim climate dataset <http://www.worldclim.org> (Hijmans et al.
2005). This set of 1km-resolution maps, each of global extent, was constructed by interpolating
data from thousands of weather stations. In addition to the primary variables (e.g., monthly
precipitation, mean annual temperature, etc.), 19 additional variables that are particularly relevant
to species distributions are available for download. All these data are currently stored on WSL
computers and have been used successfully in modeling species distributions at the continental
scale (Pearman et al. in press-b).
h. We need data on plant functional traits for developing generalized linear models of
invasiveness. We recognize that some functional traits may be associated with species
invasiveness. Our intention is not to study functional traits, per se, but instead to include functional
covariates in each of the models, for each tree, we analyze. We will compile a list of plant
functional traits of the species that are modeled. We will collect for each species complete data on
canopy height, leaf distribution, leaf dry matter, leaf mass, leaf size, specific leaf area index, seed
mass, seed dispersal mechanism, shoot growth form, re-growth possibilities, whether the plant is
herbaceous or woody, and others. This list will be distilled from internet databases of plant
functional traits such as the LEDA ‘Traitbase’, <http://www.leda-traitbase.org>, BiolFlor
http://www.ufz.de/biolflor/index.jsp), the Mariwenn database of plant functional traits
<http://ecofog.cirad.fr/Mariwenn/>, other online and restricted databases, and directly from the
primary literature. Additional data will come from databases held in the DEE-UNIL and at the
WSL (by Pascal Vittoz and Niklaus Zimmermann).
WP 1b—Species distribution models
a. Niche modeling. Species distribution models are widely used to study the characteristics
of the environmental niche of species (Guisan & Zimmermann 2000). Algorithms for developing
SDMs can vary in their ability to fit species distribution data. We will evaluate the potential
importance of this variation to estimating evolutionary history of the environmental niche. This is
facilitated by use of the modeling techniques that are available in the R package BioMod (Thuiller
2003) and in other R packages. We will include familiar algorithms (generalized linear models,
10
generalized additive models) and newer algorithms that have been show to exhibit higher
performance, such as generalized boosted regression trees and the maximum entropy algorithm
(Elith et al. 2006; Elith et al. in-press).
Modeling generally proceeds by first assembling the climate and species distribution data in
geographic information system (GIS) layers. If no species absence data are available, random
pseudo-absence locations are generated. Climate data are extracted from the GIS layers at each
species presence point and at pseudo-absence points. A probabilistic occurrence model is fit using
one or more modeling algorithms. Then the model is re-projected over the climate (and other) data
from the area of interest to determine for each map pixel the probability of species occurrence.
These probability surfaces are often used, along with a criterion for a ‘prediction threshold’ to
predict an area in which the species may occur. We will skip this and determine directly from the
probability surfaces both the species environmental optima and the observed environmental
conditions that are associated with niche margins.
b. Niche parameters. Variation among species in climatic niche characteristics can be
related to changes in optimum values in a particular parameter (a quantitative difference), or be
related to changes in which climate parameters most influence species distribution (a qualitative
difference). Both of these aspects could contribute to niche change. We will identify highly
influential parameters and quantify the parameter values that are optimal for the species, as
modeled under each of the algorithms. In evaluating occurrence based on SDM results, optimal
parameter values are defined by us as the mean values from all area of the predicted distribution
with the top five-percent of the probability of species occurrence. In addition, the factors that limit
species distributions are likely most influential at the edge of species ranges. To determine
boundary conditions at niche margins, we will extract the yearly coldest and warmest monthly
temperatures and driest monthly moisture values (least precipitation, minimum potential
evapotranspiration) that correspond to the lowest 5% quantile +/- 1% of the probabilities of species
presence. Species-specific traits that represent conditions at or near range limits are likely targets
for selection, adaptation, and evolution (Holt 2003; Davis et al. 2005; Hampe & Petit 2005; Bridle
& Vines 2007) and may evolve differently than species climate optima. These values of optimal
and marginal environments will be used in further analyses.
WP 1—Expected results: Given the large number of data sources at our disposal, in addition to the
scientific literature, we fully expect to obtain sufficient distribution data for the majority of species
within thoughtfully chosen genera. The ability to gather distribution data in national-language
publications and herbaria will help in completing data for some genera. Addressing niche
differences at a global scale will elucidate substantial among-species variation in niche parameters.
Model building of species distributions will provide models with good to very-good fit, as has been
found in other studies using atlas-type data (Thuiller et al. 2005a; Thuiller et al. 2005b; Pearman et
al. in press-b).
Work Package 2: Evidence of niche shifts in invasive species
WP 2a—Tests of evolutionary models
This work package section addresses questions regarding the evolution of niche traits in clades that
include invasive species:
Q1. Is there evidence for non-Brownian niche evolution in clades that contain invasive species?
Q2. If more than one optimum is identified, then:
Q2.1 Is the shift happening only in the invasive species, closely related species, or larger
aggregations of clades?
Q2.2 Is the shift unique within the genus or are their similar shifts evident in the history of the
genus?
Q2.3 Is the shift gradual during the evolution of the invasive species and/or other species, or has it
been evolved through large and rapid changes?
11
Methods:
We will use the phylogenetic trees for each genus to reconstruct the evolutionary history of the
niche characteristics as estimated with SDMs. We will use a recently developed method that
applies OU stochastic process to model how niche changes through time along the branches of a
phylogenetic tree (Butler and King, 2004; 2006). The method is implemented in the ‘OUCH’
package that is available as part of the R software project, <http://www.r-project.org>.
To test Q1 and Q2.1 and Q2.2, we will apply likelihood ratio tests between the null model of
Brownian evolution and an alternative model allowing a different optimum for the niche of
invasive species. The OU process can also be made more complex by allowing several optimum
evolutionary regimes for different lineages in a phylogenetic tree (Fig. 4). It is then possible to test
restricted models against more general ones using likelihood ratio tests. These tests will be
performed on each genus sampled.
To test Q2.3, we will follow the approach advocated by Pagel (1997). To test gradual vs. saltational
change of a character through time, it is possible to investigate if the probability of character
change is linked to branch lengths of a phylogenetic tree. The approach is to incorporate a
parameter in the model to reduce the effect of the branch lengths in the likelihood calculation. It is
then possible to test by likelihood ratios if this model is different from a model in which branch
length does not change during the likelihood calculation. This step will be implemented in the
MLtree software (Christin et al. 2008) written by NS <http://www.unil.ch/phylo/software>.
WP 2a—Expected results: We anticipate that certain species, for which ancient niche shifts allowed
occupation of a broad range of climates, have had a greater tendency to become exotics than
species in lineages that did not undergo niche expansion. We expect to find that species from
subclades with high rates of niche shifts will tend to become exotic or invasive exotic species.
Species from clades with high levels of random evolution, or in which selection has erased
phylogenetic signal, are likely to become exotics and, potentially, invasive. We would be surprised
if evolution within plant genera were generally similar to BM evolution because other forms of
evolution have been reported (Cavender-Bares et al. 2006). It is possible that we would not reject
the adequacy of the BM model when applied to a specific genus, but still conclude that niche
similarity among some species is a result of homoplasy. If BM is not rejected but niche shifts are
evident, then niche shifts do not involve multiple optima, but evolutionary process (selection or
neutral effects) rapidly alters the position of a species’ niche in relation to the other species in the
clade. This would suggest a scale effect on the detection of non-random evolution and further
suggest that evolutionary models should be fit at a sub-clade and generic level, as we plan to do.
WP 2b—Generalized linear models
This work package section develops linear models in order to test multiple influences on species
establishment and invasiveness. There are a number of hypotheses that can be extracted from the
literature to help explain the invasiveness of some exotic species.
H1: Invasive species tend to have broad tolerance to climatic variation, i.e. they have a relatively
large climatic niche, perhaps having a range covering large latitudinal variation (Roy 1990;
Rejmánek 1996; Rejmánek & Richardson 1996; Prinzing et al. 2002; Herron et al. 2007).
H2: Invasive species tend to present evidence of a recent shift, shown by significant difference
between niche position in the native and invasive range (Broennimann et al. 2007).
H3: Invasiveness can be related to ploidy level (Broennimann et al. 2007) and specific plant
functional traits.
In a phylogenetic context, these hypotheses can be examined by comparing sub-clades that have
invasive species with sub-clades that lack invasive species. By including additional plant
12
characteristics (functional traits) in our analysis of climatic niche characteristics, we will be able to
identify and control for effects of each trait that may be correlated with invasiveness arising from
climatic niche and its evolutionary history. We will test for correlated effects of plant functional
traits, current characteristics of the climatic niche, and the evolutionary history of niche dynamics
of species by applying a rigorous statistical modeling approach, based on generalized linear
models, combined with the multidisciplinary, bioinformatics approach already described above.
Approach
1. We will use the species distribution models, plant functional trait data, and phylogenetic trees
from WPs 1 and 2a to provide data on each plant species in the study genera. Each species will
be an observation and the relationship between invasiveness (high, low, zero), traits, climatic
niche characteristics and relatedness will be developed as follows:
2. In order to combine all the species analyzed independently in WP2 into a single tree, we will
combine all the genus-level trees with the well-corroborated Angiosperm Phylogeny Group tree
using supertree techniques (APGII; (The Angiosperm Phylogeny Group 2003)). The APG tree is
necessary as our independent trees do not have overlapping taxa, and thus a backbone tree of the
relationships between genera is necessary.
3. In order to determine the effect of shared evolutionary history on the model developed in point 1,
we will use a GEE approach (Paradis and Claude, 2002) to explain the relationships between
invasiveness and the included factors. The ‘ape’ package of the R software will be used to run this
analysis. We will test the significance of the different factors using a stepwise approach based on
the Akaike information criterion (AIC).
WP 2b—Expected results:
The results presented in the papers listed under H1 and in the introduction (above) suggest that
some plant functional traits will be associated with invasiveness. The identity of those traits will
likely differ among clades within the supertree, if only because there are no functional traits that are
highly and consistently associated with species invasiveness across taxa. Range size may be
related to invasiveness, but it may also be related to the establishment per se of exotics, and provide
no additional information about reasons for species invasiveness. If invasive and exotic species
tend to be closely related, phylogenetic relationships should be significantly related to both
invasiveness and non-invasive ‘exoticness’.
Work Package 3: Understanding invasiveness in a phylogenetic context
WP 3—Theoretical development and application
The nature of what makes a lineage invasive is still fairly unknown. In particular, it is still unclear
what type of selection might be acting on the development of invasiveness. The use of OU process
can highlight whether directional selection might be involved, but if the OU model is rejected,
other selection pressure could be invoked. For example, invasiveness could be due to a relaxed
stabilizing selection. It is thus essential to have models that can test the strength of stabilizing
selection on niche evolution.
Due to its reliance on neutral evolution, the Brownian motion (BM) model does not adequately
describe many patterns of change in adaptive characters. Modifications of BM in comparative
methods have been proposed and centered mainly on either weakening the strength of the BM to
reach non-phylogenetic evolution (e.g. Mooers et al. 1999; Freckleton et al. 2002) or transforming
the phylogenetic tree in order to improve the fit of the BM model (Grafen 1989; Gittleman and Kot
1990; Garland et al. 1992; Pagel 1997). The first approach is flawed in that BM is a pure drift
process and it is not possible to obtain a selection model from it by simply weakening its strength.
The second approach results in statistically valid tests, but distortion of the phylogenetic tree
renders the interpretation of the model difficult and inference about the implied evolutionary
process is blurred. The fundamental limitation of these BM-based methods is that they take no
13
account of selection. However, tools able to model selection directly have been proposed.
Following the suggestion of Felsenstein (1988), Hansen (1997) proposed to model character
evolution by means of the Ornstein-Uhlenbeck (OU) process with multiple evolutionary optima.
This model has received little attention until recently, when Butler and King (2004) proposed a
maximum likelihood based method to estimate evolutionary optima in different lineages (Fig. 4).
Consider the evolution of a quantitative character X along one branch of a phylogenetic tree. The
change in X over time can be decomposed into deterministic and stochastic parts. The former may
be interpreted as the force of selection acting on the character, the latter as the effect of random
drift and other, unmodeled, forces. In mathematical terms, the OU process can be expressed as
dX(t) = α [θ − Χ(t)]dt + σdB(t) ,
(1)
where dX(t) is the infinitesimal change in the character X over the time interval t , dB(t) is random
noise, α measures the strength of selection, θ gives the optimum trait value, and σ measures the
intensity of the random fluctuations in the evolutionary process. The OU model can be viewed as a
generalization as it simplifies to BM when α = 0 .
Figure 4. Simulated evolution under stabilizing and directional selection in replicate lineages that follow Brownian
motion (BM) or OU models of evolution. In A, the left-most frame shows BM evolution of lineages around a single
optimum. Other frames in A show lineages that begin at an arbitrary niche character value and evolve under
directional selection toward an optimum value. Stabilizing selection is the same in all four frames. In B, same as in A
except that there is half as much stabilizing selection around the optimum.
Using niche models, one can blend in phylogenetic information by assuming that each lineage in
the tree evolves according to its own OU process, that is, that there is one climatic optimum (or
other niche metric) per branch of the phylogeny. Complex evolutionary scenarios can be modeled
by allowing different branches of the phylogeny to have different optima. When analyzing whether
the climatic niche is changing in invasive lineages, defining different niche optima is important, but
it is also essential to characterize the evolutionary path followed by the different lineages to reach
this optimum. One simple way to do that would be to model the intensity of the random
fluctuations σ and test whether it differs between specific invasive lineages. Given the equation in
(1), it is possible to model the Brownian motion term of OU and the optimum term as multivariate
normal (Butler & King 2004). Maximum likelihood estimation of parameters is then possible using
nonlinear optimization techniques. The ‘OUCH’ package is written for R software to estimate the
14
optimality parameter, θ . In this part of the project, we want to focus on the σ parameter and design
a method that will be able to test if it differs between lineages. We will take advantage of the
existing OUCH package (Butler & King 2005), which is open-source, to modify it in order to
develop a method that can detect differences in σ in different lineages. We want to test if two
subclades within a tree have a similar mean niche optimum, but a different variance parameter.
This will allow us to test if these lineages differ in terms of evolutionary constraints (sometimes
‘stabilizing selection’) on their distributions.
WP 3—Expected results:
Some indications of niche shifts in invasive plants (Broennimann et al. 2007) suggest that we will
find that more than one optimum is tracked by congeneric species. Within clades, we expect that
subclades will show variation in parameters described in the OU process. This is because some
subclades may be distributed among areas of continents that differ in climate (e.g. temperate areas),
while other subclades span relatively little climatic variability (Hoffmann 2005), may have
diverged ecologically at different points in their evolutionary history (Cavender-Bares et al. 2006;
Lovette & Hochachka 2006), or have different rates of evolution. We expect invasive species to
come from clades that have experienced low levels of stabilizing selection.
2.4 Project Execution
2.4.1. Timetable
We outline the foreseen development of the project in Table 3.
Table 3 ENNIS project timeline
WP Tasks
1a
1b
2a
2b
3
Collect Species trait data
Species distribution data
Climate data
(done)
Existing sequence data
Collect additional tissue from herbaria
Conduct Species Distribution Modeling
Construct species x niche-and-trait matrix
Extraction and sequencing
Phylogenetic tree construction
Multi-model analysis with original OUCH package
Supertree construction
General linear model analysis of invasiveness
Expansion and optimization of OUCH code
Analysis of heterogeneous selection in clades
Literature review
International conferences
Paper submission
Writing of Ph.D. dissertation
Participant
PhD PBP NS
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Year 1
Year 2
Year 3
S1
S1
S1
S2
S2
S2
*
*
*
*
*
*
*
*
*
*
2.4.2 Milestones and control
We have developed a plan for receiving feedback and criticism during the project. We do this so
that we can understand the degree to which we are achieving our goal and meeting our objectives.
We include the use of independent sources of feedback and control so to increase the rigor of our
planned evaluations (Table 4, next page).
2.4.3. Research risks, limitations and responses
Substantial delays, especially at the beginning of the project are unlikely because we already have
much useful data in our possession and many sources for more. It might happen that we fail to find
variation in the evolution of niche dynamics within clades or observable phylogenetic signal. This
15
is, however, unlikely because the phylogenetic trees with we will be working are large (10s to 100s
of species).
Table 4. Logical framework of benchmarks
Goal: Understand Evolutionary history of invasive species
Purposes
Objectives
a Data collection and
niche modeling
Indicator of success
Sources of verification
-Sequence, trait and
distribution data for at least
50 % of species in genus
World Species Lists- the complete list of lists
http://species.enviroweb.org/, also
http://www.ncbi.nlm.nih.gov/sites/entrez?db=tax
onomy
b Test evolutionary
-Comprehensive modeling of
hypotheses
clades and subclades
c Modeling invasiveness -Final GEE models of traits
and niche on super tree
:
Comparison with existing trees, support from
likelihood ration tests and AIC in model
comparisons
Activities
Focused on completion Prepared talks, posters,
of objectives
research plans reviewed
during labgroup meetings
Presentation of work at departmental seminars,
congresses and symposia, at least 2 presentations
per objective, direct feedback
Products
Directed toward
communication of
results
Peer review of submitted manuscripts,
publication
At least one paper for each
objective and one review
paper for a general interest
journal
The power to detect phylogenetic signal increases with tree size (Cavender-Bares et al. 2006). It
may also be difficult to collect species occurrence data, as well as sufficient data on the ecological
and life history characteristics of the species. We think that this is unlikely for several reasons.
The first is that we will be addressing only described species, which means that specimens are
available in (at least) a small number of herbaria. Secondly, much information on species range
and functional characteristics can be found in on-line databases of museum collections through
GBIF. Third, published treatments of regional flora, field guides, and species atlases provide
substantial coverage of much of the northern hemisphere. At the outset we cannot specify the
functional traits upon which we will focus. They will, in any case, be ones that are available for
many species, be found in databases and the literature. Field or experimental collection of
expensive functional trait data is beyond the scope of the project. This is not crucial to the study’s
success because in combination, the methods we apply will demonstrate the feasibility of the
approach we develop here, use available methods and provide a basis for the application of
additional approaches in the future.
2.4.5 Future directions
Future research of PBP will focus on niche dynamics of plants and animals, and will continue to
have an evolutionary perspective that is developed through collaboration. Demonstration of a role
for evolutionary history in determining invasiveness and/or current niche dynamics will lead to
opportunities to apply the methods used here to additional genera of interest. Clades that
demonstrate substantial niche stability (stasis) should be good candidates for improved predictions
of climate change effects on plants. Understanding of evolution of the realized climatic niche will
lead to questions and research regarding evolution of the fundamental niche. Careful choice of
genera and species could allow joint testing of hypotheses regarding the parallel evolution of the
fundamental and realized niches.
2.5 Significance of the Project
Neither niche stasis nor rapid niche shift prevail in existing studies. Better understanding of the
evolutionary history of climatic niche shifts of invasive species and their relatives should improve
our ability to predict which clades will likely produce invasive species in the future. The project
will elucidate the role of evolutionary history of species’ climatic requirements in determining
which species become invasive and which become exotic do not spread become dominant
16
community members. It would be helpful to know whether niche shifts are likely to occur in the
case of biological invasions. This research will put niche shifts fully into the context of
evolutionary relationships. It would also be useful to identify clades or parts of clades that show
long-term niche stasis. This is because one of the most important actions that can be taken to boost
confidence in the results of predictive models of species distributions is to identify biological
characteristics, both current and historical, that are associated with species having relatively stable
niche dynamics. These patterns will point to the species for which predictions based on species
distribution models will be especially trustworthy.
The project will be uniquely significant in training a student in areas of ecology and evolution that
have historically seen very little contact. The project will train the student with deep knowledge of
phylogenetic methods. These methods are increasingly used in ecology and provide an important
tool for separating ecological and historical factors in their influence on ecological patterns of
abundance, distribution, and community composition. At the same time, the student will acquire
broadly applicable GIS and statistical modeling tools that will enhance his/her ability to put
evolutionary patterns into a geographical context. Understanding the factors that influence species’
geographical distributions will allow the student to develop comparative analysis of ecological
characters that have not often been examined from a systematic point of view. By focusing on a
systematic approach to the study of species distributions and invasive species in Switzerland and
elsewhere, the student’s work will complement other current research on invasive species, in
Switzerland and abroad (e.g. the NCCR program). The student will benefit by sitting at the WSL
during the first three semesters and then at the UNIL during the second three semesters. This will
give the student opportunities that arise from extensive interactions with specialist groups at both
the WSL and UNIL.
The project will be broadly significant to the community of integrative biologists in that we will
provide a comparative analysis of species climatic niche in the context of alternative evolutionary
mechanisms that can affect niche dynamics. In doing this, the project will bring together two
historically important yet separate conceptual areas, community ecology and molecular
phylogenetics. To disseminate the results of these studies, the work will be published in leading
journals in ecology and evolution (Ecology Letters, Evolution, Ecology). In addition, we will
publish a more general-interest paper on climatic niche evolution and invasive species in a general
interest journal (e.g. a short review in TREE, BioScience, Nature, or similar).
References
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