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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 123 2334 Biodivers Conserv (2011) 20:2333–2345 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 123 Biodivers Conserv (2011) 20:2333–2345 2335 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 na tio m nd Sy ive Fu nc Ad ap t lR es lC na io ct Fu n ro Ty e ns po ss la tio nc Fu e pe n ifi na ca lT ra i tio t e lT yp tio nc Fu Fu n ct io na na lG G ro ui ld up 0 Fig. 1 Relative amount of concepts used for ecological groupings in animal and plant ecology publications (ISI Web of Science Literature Survey, 1960–2010) 123 2336 Biodivers Conserv (2011) 20:2333–2345 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 123 Biodivers Conserv (2011) 20:2333–2345 2337 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) 123 2338 Biodivers Conserv (2011) 20:2333–2345 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 123 Biodivers Conserv (2011) 20:2333–2345 2339 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 123 2340 Biodivers Conserv (2011) 20:2333–2345 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 123 Biodivers Conserv (2011) 20:2333–2345 2341 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 123 2342 Biodivers Conserv (2011) 20:2333–2345 current threats of global changes. Given the current rates of worldwide species losses we can no longer afford to focus on single species only. 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