Download How functional is functional? Ecological groupings in terrestrial

Biodivers Conserv (2011) 20:2333–2345
DOI 10.1007/s10531-011-9995-1
REVIEW PAPER
How functional is functional? Ecological groupings
in terrestrial animal ecology: towards an animal
functional type approach
Niels Blaum • Eva Mosner • Monika Schwager
Florian Jeltsch
•
Received: 13 August 2010 / Accepted: 18 January 2011 / Published online: 30 January 2011
Springer Science+Business Media B.V. 2011
Abstract Understanding mechanisms to predict changes in plant and animal communities is a key challenge in ecology. The need to transfer knowledge gained from single
species to a more generalized approach has led to the development of categorization
systems where species’ similarities in life strategies and traits are classified into ecological
groups (EGs) like functional groups/types or guilds. While approaches in plant ecology
undergo a steady improvement and refinement of methodologies, progression in animal
ecology is lagging behind. With this review, we aim to initiate a further development of
functional classification systems in animal ecology, comparable to recent developments in
plant ecology. We here (i) give an overview of terms and definitions of EGs in animal
ecology, (ii) discuss existing classification systems, methods and application areas of EGs
(focusing on terrestrial vertebrates), and (iii) provide a ‘‘roadmap towards an animal
functional type approach’’ for improving the application of EGs and classifications in
animal ecology. We found that an animal functional type approach requires: (i) the
identification of core traits describing species’ dependency on their habitat and life history
traits, (ii) an optimization of trait selection by clustering traits into hierarchies, (iii) the
assessment of ‘‘soft traits’’ as substitute for hardly measurable traits, e.g. body size for
dispersal ability, and (iv) testing of delineated groups for validation including experiments.
N. Blaum (&) F. Jeltsch
Plant Ecology and Conservation Biology, University of Potsdam, Maulbeerallee 2,
14469 Potsdam, Germany
e-mail: [email protected]
E. Mosner
Conservation Biology, University of Marburg, Karl-von-Frisch-Straße 8, 35032 Marburg, Germany
Present Address:
E. Mosner
Ecological Interactions, Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany
M. Schwager
Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25,
60325 Frankfurt (Main), Germany
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Keywords Ecological classification Functional type Guild Functional trait Trait selection Effect group Response group Environmental relationships
Introduction
Understanding causes and mechanisms of changes in community structure of plants and
animals is a key challenge of ecology for predicting future patterns of occurrence, abundance and diversity under global change. The need to transfer knowledge gained from
single species to a more generalized approach has led to the development of categorization
systems where species’ similarities in life strategies and traits are classified into ecological
groups (EGs) like functional groups/types or guilds. The main advantage of applying EGs
over single species approaches in understanding biodiversity changes to environmental
alterations is the generalization of results. For example, developing management strategies
for biodiversity conservation requires a process based understanding of multiple species,
which is practically impossible to implement in a reasonable time and cost frame using
single species approaches. Instead, using an EG approach, where species are described by
across species traits will include the sensitivities of multiple species into a management
decision. Also the assessment of functional group richness instead of simply measuring
species richness is favourable since functional groups are directly related to traits rather
than to an evolutionary based taxonomic classification, which may not relate to the
capacity of focal organisms to provide a specific ecosystem service (Tilman et al. 1997).
EGs have been applied in several fields of ecology, e.g. to clarify general relationships
between EGs and ecosystem functioning (e.g. Du Toit and Cumming 1999; Diaz and
Cabido 2001; Brodie et al. 2009), to classify habitats (e.g. Degraaf and Chadwick 1984;
Kurosawa 2009), to predict changes due to environmental alterations (e.g. Croonquist and
Brooks 1991; Wiegand et al. 1997; Kissling et al. 2008), and in landscape management and
nature conservation (e.g. Verner 1984; Cousins and Lindborg 2004; Todd and Andrews
2008; Barbaro and van Halder 2009).
While functional classification systems have a long tradition in plant ecology their
application in animal ecology remains challenging. The first case of a functional classification system can be dated back to 300 BC when Theophrastus classified plants into trees,
shrubs and herbs. Well established schemes include structural classifications (Raunkiaer
1934), r-/K-strategies (MacArthur and Wilson 1967; Pianka 1970), C–S–R strategy types
(Grime 1979), plant functional types (PFT) (Montalvo et al. 1991; Lavorel et al. 1997;
Weiher et al. 1999; Lavorel and Garnier 2002; Diaz et al. 2004), and response (community
level) and effect (ecosystem level) groups (Hooper et al. 2002; Violle et al. 2007; Lavorel
et al. 2007).
Nevertheless, the selection criteria for setting up EGs are manifold and some studies
demonstrate that their application can also be misleading (Jaksic et al. 1996). In particular,
the integration of results from different studies on similar topics/ecosystems requires a
standardized methodology for EGs to allow for comparisons and meta-analyses. In plant
ecology, this refinement of methods to set up EGs has been the focus of recent developments (Pillar 1999; Cornelissen et al. 2003; Poschlod et al. 2003). In contrast, similar
approaches in animal ecology are scarce, and although EGs are applied continuously,
general problems in assignment and applicability have not been solved yet.
In this review, we aim to initiate a discussion on a further development of functional
classification systems in animal ecology focusing on vertebrates. We first clarify the usage
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of terms for different groupings. Second, we summarize the methods of species assignment
into EGs and provide application areas for these EGs (e.g. in nature conservation and
landscape management). Finally, we propose prospects for further refinement of approaches in animal ecology.
Literature survey
We searched the ISI Web of Science for publications between 1960 and 2010 focusing on
ecological categorization using the terms: functional group, functional trait, functional
type, functional classification, functional response type, adaptive syndrome, and guild.
Overall, 1,487 papers matched with one of the above mentioned terms and are applied
in plant and animal ecology. EGs in animal ecology across all taxonomic groups are mostly
described by the term guild (69%, Fig. 1). While functional type is dominant in plant
ecology (38%), this term is used in only 7% of the animal ecological studies. Functional
groups, used in 19% of the animal ecological studies (31% in plant ecology) and are mostly
restricted to aquatic systems (Johnson et al. 2003; Bremner et al. 2003; Dumay et al. 2004).
The term functional trait in animal ecology was used in less than 5% (17% in plant
ecology). The terms functional classification, functional response type and adaptive syndrome play a minor role in both, animal and plant ecology (\5%).
Terms and definitions of EGs
Root (1967) introduced EGs to animal ecology and defined the term ‘‘guild’’ as ‘‘[…] a
group of species that exploit the same class of environmental resources in a similar way’’
and ‘‘[…] that overlap significantly in their niche requirements’’. A few years later, the
proportion of applied terms
within plant/animal ecology [%]
80
70
plant ecology (N=844)
60
animal ecology (N=826)
50
40
30
20
10
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Sy
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Fu
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la
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Fig. 1 Relative amount of concepts used for ecological groupings in animal and plant ecology publications
(ISI Web of Science Literature Survey, 1960–2010)
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term ‘‘functional type’’ was devised by Cummins (1974) to describe groups of marine
invertebrates depleting the same class of food resources. Gitay and Noble (1997) suggested
using the term ‘‘functional group’’ for a ‘‘[…] non-phylogenetic classification leading to a
grouping of organisms that respond in a similar way to a syndrome of environmental
factors’’. Other authors have applied the term to describe species that do not respond in a
similar way to ecosystem functions but perform the same ecosystem service e.g. by
influencing nutrient cycling, productivity, water uptake, or trophic levels (Box 1996; Diaz
and Cabido 1997; Blondel 2003; Diaz et al. 2004; Dumay et al. 2004).
Within these definitions, mainly two approaches constitute the basis for delineating
EGs: (i) the resource centred (Root 1967) and (ii) the functional approach (Cummins
1974). The resource centred approach is mostly associated with the term ‘‘guild’’ and
focuses on the common sharing of resources within a group of species. The functional
approach can be divided into two opposing views of species-environment interactions to
categorize groups. First, using attributes of species’ responses to environmental conditions
(functional response group/type) and second, applying attributes of species’ effects on their
environment (functional effect group/type) (Diaz and Cabido 2001).
The resource-centred approach
EGs in animal ecology are mostly based on the resource-centred guild approach (Root
1967) where resource use is either related to food or to suitable reproduction sites. This
approach assumes that guilds comprise species ‘‘that overlap significantly in their niche
requirements’’. Pianka (1980) extended this concept characterizing guilds as ‘‘arenas of
intense interspecific competition, with strong interactions within guilds but weak interactions between members of different guilds’’.
These ideas have set the starting point for a number of studies investigating the intensity
of competition between and within guilds. However, ecological surveys on this topic led to
ambiguous results with all kinds of competitive relationships within (e.g. Brown et al.
1979; Adams 2007) and between guilds (e.g. Fritz et al. 2002), and even missing competition within proclaimed guilds (e.g. Steffan-Dewenter and Tscharntke 2000; Westphal
et al. 2006). When resource use as the selected parameter characterizes the EGs (guilds),
competition between group members is likely (Degraaf and Chadwick 1984). On the
contrary, competition between different groups or species of different groups might occur
when species are unspecific in resource use i.e. generalists, and are thus assigned to only
one group although they should also rank among others (Hawkins and MacMahon 1989;
Elliott et al. 2007). Finally, the use of similar resources might not lead to competition, if
other factors are more influential for community structure (e.g. presence of predators,
superabundant resources). Although the consideration of interactions between species of
the same group and between groups is important, environmental changes are likely to
affect interactions and relationships between the members of an EG (Voigt et al. 2003;
Resetarits and Chalcraft 2007).
The functional type/group approach
While guilds describe the similarity in resource sharing and competition without consideration of processes and functions, functional groups in animal ecology refer so far to
species that perform a similar ecosystem function/service without any relation of competition (Blondel 2003). In this refined definition, functional groups are seen as effect
groups and are complemented by guilds comparable to functional response groups in plant
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ecology, where plants are grouped in relation to their functional responses to disturbances
(e.g. fire and resprouting ability) (e.g. Lavorel and Garnier 2002). However, the similarity
between guilds and functional response groups is restricted to cases where resource use
determines the response to the environment (Blondel 2003). In cases without a focus on
resource exploitation, e.g. regarding the response to environmental perturbations, functional response groups might need to be assigned differently. For example, if perturbation
leads to an increase in predator abundance, functional response groups should reflect the
ability of species to struggle with new conditions e.g. predator avoidance behaviour or a
high population growth rate.
Therefore, we suggest (i) to expand the functional group/type approach by including
both, response and effect groups, and (ii) to use the term functional group when relationships with the environment are observed, while guild is applicable when species and
their performance are studied (Lavorel and Garnier 2002; Blondel 2003).
Assignment of species to EGs
Principally, two different approaches are applied for the assignment of species to EGs
(Woodward and Cramer 1996). First, expert knowledge on species parameters of the focal
community is used for the definition of the grouping categories. However, this a priori
approach can lead to artificial assemblages of syntopic species, which are often taxonomically related, whereas the type of relation remains unclear (Jaksic 1981; Terborgh and
Robinson 1986; Blondel 2003).
Second, EGs are identified a posteriori by using quantitative statistical methods, e.g.
nearest neighbour statistics, cluster analysis, principal components analysis, canonical
correlation, and Monte Carlo techniques (Hawkins and MacMahon 1989; Simberloff and
Dayan 1991; Pillar 1999). Here, a variety of species’ parameters is sampled and analyzed
to identify patterns of species aggregation, which are subsequently used to designate EGs.
Despite the ‘‘objectivity’’ of a posteriori approaches, the statistical analysis can also lead to
artificial results (Hawkins and MacMahon 1989; Simberloff and Dayan 1991), and the
decision on which parameters are measured and included into the analysis remains
ambiguous (Terborgh and Robinson 1986; Hawkins and MacMahon 1989).
Although, both approaches are commonly used in animal ecology, no clear trend for
favouring one over another can be identified. In any case, classification of species into EGs
needs rigorous testing to ensure validity of assumptions and results (Barnett et al. 2007).
Applications of EGs in animal ecology
Community comparisons
The comparison of EGs between animal communities of similar environmental conditions
derives from the idea that guilds are basic building blocks within communities (Hawkins
and MacMahon 1989). Since general ecological mechanisms are assumed to act similarly
in different regions with comparable environmental conditions, EGs are thought to recur
over these regions due to similar ecological and evolutionary pressures. Furthermore,
evolutionary trade-offs between traits should lead to similar correlations between traits
within EGs (Terborgh and Robinson 1986; Simberloff and Dayan 1991; Blondel 2003). For
example, in a study on North American desert mammal communities (MacMahon 1976)
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functional similarity was examined using foraging habits for classification. The comparison of different deserts revealed fundamental differences between hot and cold deserts, but
a high functional similarity among all hot deserts. This occurred despite differences among
hot deserts in terms of abiotic and biotic features (e.g. amount of annual precipitation).
Although functional diversity of feeding guilds did not reflect all habitat characteristics in
detail, however, on a broader scale, the potential value of EG-approaches for predicting
community organization was emphasized.
The functional similarity between different areas may also depend on the spatial scale of
comparisons. Parker et al. (2001) described vertebrate communities (amphibian, reptile,
bird and mammal species) in a taxonomic and in a feeding guild categorization, both, on a
regional and subcontinental scale. On the regional scale, all measures (guild structure,
guild richness and species richness) reflected environmental conditions (vegetation structure and elevation) well. However, in a comparison between subcontinents, guild structure
and species richness showed a less clear trend where guild structure was not merely
explainable by vegetation structure, and species richness was partially unrelated to vegetation complexity. This lack of consistency on the subcontinental scale possibly emerged
from processes like speciation, extinction and dispersal (Parker et al. 2001).
Community comparisons are also applied to analyze habitat variables that shape
communities (e.g. Mac Nally 1994; Adamik et al. 2003; Blaum et al. 2007). For example,
species richness and composition of bird guilds in forests change substantially in relation to
horizontal and vertical structural diversity of the studied forest patch. Richness within
guilds was higher in structurally rich forests compared to monocultures (Adamik et al.
2003). Similarly, composition and species richness of carnivore guilds in Kalahari
rangelands strongly depended on the structural diversity of the vegetation (Blaum et al.
2007).
Across taxa comparisons
In plant ecology, the functional classification of EGs sensu Gitay and Noble (1997)
explicitly emphasizes ‘‘non-phylogenetic’’ groupings as a basic criterion. In contrast, in
animal ecology only few studies investigated multi- and across taxa EGs including
mammals, birds, reptiles and arthropods (MacMahon 1976; Brown et al. 1979; Jaksic
1981; Jaksic et al. 1993; Parker et al. 2001) because such studies are extremely time and
cost intensive, require data collection and analysis at different spatial scales, and are
coupled with methodological difficulties (Blondel 2003). One example where functional
effect groups across taxonomic levels were investigated was a study on fish and salamander
predators and their effects on their prey communities (Chalcraft and Resetarits 2003). In
this study, the degree of functional similarity between species that share similar traits but
originate from different taxa was assessed by classifying predator species according to
taxon, gap size or microhabitat use. Results show that a similarity in traits across taxa can
result in a similar effect on prey communities but only for specific response variables. For
example, the trait ‘‘habitat use’’ had a significant effect on prey species richness but not on
prey biomass.
Despite these limitations the classification of EGs across-taxa are crucial for detecting
climate- or land use-induced changes of ecosystems. For example, mammal and bird guilds
were used to indicate anthropogenic-induced changes in wetland areas (Croonquist and
Brooks 1991). While mammal guilds were insensitive in their response to habitat change,
habitat specificity- and seasonality-guilds of birds indicated habitat disturbances. The
contrast between the sensitivity of the different taxonomic guilds was explained by the
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relatively higher sedentariness of mammals as compared to birds, which can readily disperse into suitable habitats.
Application of EGs in nature conservation and landscape management
The application of EGs is particularly relevant in nature conservation and landscape
management where EGs are used to analyze and indicate the effects of environmental
changes on community structure as a result of anthropogenic (e.g. land use, climate) and
natural drivers (e.g. fire, windbreak) (Verner 1984; Szaro 1986; Du Toit and Cumming
1999; Thiollay 1999; Caro and O’Doherty 1999; O’Connell et al. 2000; Miller and Cale
2000; Rodewald and Yahner 2001; Schulze et al. 2004; Barlow and Peres 2004; Blaum
et al. 2007, 2009; Todd and Andrews 2008; Klingbeil and Willig 2009; Barbaro and van
Halder 2009). The advantage of using EGs in nature conservation is the reduction of
working expenditures to investigate multiple species/communities. In this context, classification criteria are mostly chosen to be simple and based on e.g. food resources and
habitat features are applied. However, too much simplification might be counterproductive
where more than simply resource use could fundamentally structure an animal’s life like
for example a particular landscape/habitat context (Ewers and Didham 2006). An approach
where resource use was included in ‘‘higher resolution’’ was the study of Verner (1984)
aiming to apply the guild concept in management of bird populations. Classifying species
by their foraging and nesting site requirements, he defined guilds distinguished between
different ‘‘zones of importance’’. For example, guilds of ‘‘primary feeding zones’’ comprised species using this particular area primarily for the respective function (more than
50% of their time expenditure in terms of foraging activities). As Verner himself already
realized this assignment might become difficult when species use different habitats or
structures in nearly equal amounts like generalists often do (Roberts 1987). Nevertheless, it
is important to consider not only an association between species and their environment
itself but also the strength of correlation of this association. O’Connell et al. (2000) applied
such a ‘‘detailed’’ classification system for a so called Bird Community Index (BCI) as an
indicator system for landscape-scale environmental stressors. Species were assigned to 16
behavioural and physiological guilds, which represented functional, compositional and
structural elements. Guilds were categorized as ‘‘specialist’’ or ‘‘generalist’’ depending on
their relationship to the corresponding elements. Each species was assigned to more than
one guild and thus species could be generalists for one trait while being specialist for
another. This approach appeared to be representative for landscape gradients as an independent verification revealed.
The selection of a suitable ecological group to indicate environmental changes requires
the explicit inclusion of environment sensitive traits of species, and species with weak
abilities for adaptation (Croonquist and Brooks 1991; O’Connell et al. 2000; Reynaud and
Thioulouse 2000). Caro and O’Doherty (1999) stated that such species should have small
body sizes since small animal species often show a stronger sensitivity to environmental
changes due to shorter generation periods, smaller home ranges and lower mobility (Du
Toit and Cumming 1999). Moreover, high metabolic rates of small sized species lead to
more rapid incorporation of environmental pollutants, and shorter generation times that
allow earlier observation of changes in population dynamics. In addition to small body
size, particular taxonomic groups might appear more useful as indicators than others.
However, it seems important to know how species of all taxonomic orders respond to
environmental changes, especially since differing responses are to be expected among
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different taxonomic groups (Voigt et al. 2003). Therefore, considering more than one
indicator group and a classification of indicators based on multiple traits is reasonable as
well as a selection of indicative species based on their ‘‘vulnerability’’ (Ewers and Didham
2006). However, when the considered species’ traits sum up to a high number of possible
trait combinations, using indicator groups might not lead to a simplification and nothing is
won (Blackburn et al. 2005). In any case, a comprehensive knowledge of species’ traits and
behaviour is necessary for a proper classification and prediction of species responses
(Elliott et al. 2007). For example, Szaro (1986) tested several ways of classifying bird
species into functional guilds by behavioural attributes and into structural guilds by habitat
use. However, the species within particular guilds might responded individually different
to environmental changes and the presence or absence of a specific guild may therefore not
always indicate a change in the environment (Szaro 1986; Miller and Cale 2000; Mac
Nally et al. 2008).
We therefore suggest to apply classification systems which (i) account for more than
just resource use where necessary and consider different species’ traits, (ii) consider a
particular resolution of species habits like differential usage of habitat, and (iii) consider
species flexibility to respond to their environment, i.e. generalists versus specialists, sensitive versus insensitive species, and others.
Conclusion—towards an animal functional type approach
During the last few years EGs have made a big step in plant ecology. A clear emphasis has
been set on identifying the most important plant traits for population and community
responses to environmental changes and for the relationship between species traits and
ecosystem functioning. For that reason, much effort has been invested into analyzing
(i) how plant traits correlate to their environment and (ii) how functional trait lists and
classification systems can be improved grasping the most determining processes in plant’s
life.
The development of comparable classification systems in animal ecology beyond the
resource-centered guild approach remains a challenge.
Nevertheless, we think that further development of EG-approaches in animal ecology
can lead to fundamental insights in different ecological fields, including applied conservation. Indeed, the need for multiscale and multitaxa approaches in animal ecology considering life-history traits has been highlighted recently (Summerville et al. 2006;
Cushman et al. 2008; Barbaro and van Halder 2009), also in light of the strong potential
link between functional classification, trait complementarity between species, and the
likely role of animal communities in providing ecosystem services (Philpott et al. 2009). A
first step to improve methodologies of EG-studies is to develop appropriate criteria for
their successful application in nature conservation and landscape management. In this
respect, we consider the following points important.
Application of a functional approach
In animal ecology, EGs are so far mostly defined as guilds, and the classification is based
on the usage of food resources and specific habitat structures. Although resources are
mostly important in defining the suitability of a habitat, a species’ response to environmental changes may be determined by a variety of other traits, e.g. dispersal ability,
demographic rates, competitive abilities, predator avoidance etc. Assessing the response of
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communities to environmental changes requires therefore a functional classification system
that goes beyond the resource centred guild approach (Barnett et al. 2007) using existing
functional relationships between habitats and the requirements of species with particular
life histories (McGill et al. 2006). This approach allows for the explicit consideration of
life history traits, which provides a process based understanding linking species to their
habitat.
Identification of ‘‘core traits’’
Clearly, the definition of core traits for EGs is difficult to make due to the high variability
in behaviour, foraging strategies, and morphology of animals. However, developing a core
list of traits for animal functional types is essential for generalizations and may greatly
improve the application of any kind of classification system in animal ecology. Similar to
approaches in plant ecology, a classification system must focus on the central life traits of
species. For animals, these are both, (i) traits describing species’ dependency on their
habitat as well as (ii) life history traits that are related to the processes of birth, survival and
movement. A good starting point for such a classification system may be systems comparable to the r-, K-strategies, the C–S–R system in plant ecology, trophic levels etc.,
which allow accounting for species’ ability to adapt to changes. However, as has been
demonstrated, so far applied approaches exposed limitations and therefore new classification schemes should try to overcome revealed problems.
Optimization of trait selection
To keep the system as simple as possible, the most important traits should be identified and
if necessary clustered into hierarchies according to the suggestions of Lavorel et al. (1997)
for plant species groups.
Assessment of ‘‘soft traits’’
As substitute for hardly measurable traits, soft traits should be identified to allow for
feasibility of investigation. For example, body size within a taxon could be used as a
substitution for home range size (Haskell et al. 2002) and dispersal ability (Sutherland et al.
2000). For example, in mammal species of different feeding types the dispersal distance
increases with body size and allometric scaling equations can be used as a suitable soft trait
(Sutherland et al. 2000). Also other body related soft traits such as wing length and wing
shape in birds were identified as suitable substitutes for the dispersal ability of birds
(Böhning-Gaese et al. 2006; Dawideit et al. 2009).
Rigorous, objective testing of delineated groups
This is needed to approve the validity of the groups for the respective study system. This
could lead to an aggregation of the so far often exclusively applied approaches of a priori
and a posteriori methodologies in delineating groups. Moreover, experimental testing of
groupings should be carried out where possible.
Although the identification of functional groups in animal ecology is unquestionable
more complex than in plant ecology, we are convinced that improved classification systems
will provide a crucial step in understanding and predicting community changes under the
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current threats of global changes. Given the current rates of worldwide species losses we
can no longer afford to focus on single species only. Instead, we urgently need a more
generalizing approach that enables us to link easy measurable traits of larger sets of species
with likely community responses to expected environmental changes. Only on this basis
we will be able to develop long-term strategies to successfully manage and conserve
biodiversity.
Acknowledgments We are grateful for comments on an earlier version of the manuscript by Eva Rossmanith, Stuart Pimm and an anonymous reviewer. The work was funded by the German Ministry of
Education and Research in the framework of BIOTA Southern Africa (01LC0624I).
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