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ERSPECTIVES

urrent use of and future needs for soil invertebrateunctional traits in community ecology

enjamin Peya,b, Johanne Nahmanic, Apolline Auclercd, Yvan Capowieze,aniel Cluzeauf, Jérôme Cortetg, Thibaud Decaënsh, Louis Deharvengi,lorence Dubsj, Sophie Joimelk, Charlène Briardf, Fabien Grumiauxl,arie-Angélique Laportem, Alain Pasquetn, Céline Pelosia, Céline Perninl,

ean-Francois Pongeo, Sandrine Salmono, Lucia Santorufok,p, Mickaël Heddea,∗

INRA, UR251 PESSAC, RD 10, 78026 Versailles Cedex, FranceCESAB/FRB, Domaine du Petit Arbois, Avenue Louis Philibert, 13545 Aix-en-Provence, FranceCentre d’Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, Université de Montpellier II, Montpellier, FranceUniversity of Michigan, Department of Ecology and Evolutionary Biology, Ann Arbor, MI, USAINRA, UR1115 “Plantes et Systèmes Horticoles”, Domaine Saint-Paul, 84914 Avignon Cedex 09, FranceUniversité de Rennes 1, UMR CNRS 6553 “EcoBio”, Station Biologique, 35380 Paimpont, FranceUniversité Paul Valéry Montpellier III, Centre d’Ecologie Fonctionnelle et évolutive, Laboratoire deoogéographie, UMR 5175 CEFE, route de Mende, 34199 Montpellier Cedex 5, FranceUFR Sciences et Techniques, EA 1293 “ECODIV”, Université de Rouen, 76821 Mont Saint Aignan Cedex, FranceCNRS, UMR 7205, Muséum National d’Histoire Naturelle, CP50, 45 rue Buffon, 75005 Paris, FranceIRD, UMR BIOEMCO, Centre France Nord, 93143 Bondy Cedex, FranceINRA/INPL, UMR 1120 “Laboratoire Sols et Environnement”, Nancy-Université, 2 avenue de la Forêt de Haye,P 172, 54505 Vandœuvre-lès-Nancy Cedex, France

Université de Lille 1, EA 4515 “Laboratoire Génie Civil & géo Environnement”, Lille Nord de France,cologie Numérique et Ecotoxicologie - Bat SN3, 59655 Villeneuve d’Ascq Cedex, FranceIRD, UMR 228 ESPACE-DEV, 500 rue Jean-Francois Breton, 34093 Montpellier Cedex, FranceUR AFPA, Faculté des Sciences et Technologies, Université de Lorraine, Boulevard des Aiguillettes, BP 239,4506 Vandœuvre-lès-Nancy Cedex, FranceCNRS, UMR 7179, Muséum National d’Histoire Naturelle, 4 Avenue du Petit-Château, 91800 Brunoy, FranceDepartment of Structural and Functional Biology, University of Naples Federico II, Complesso Universitario dionte Sant’Angelo, Via Cinthia, 80126 Naples, Italy

eceived 3 July 2013; accepted 27 March 2014

bstract

Please cite this article in press as: Pey, B., et al. Current use of and future needs for soil invertebrate functional traits in community ecology.Basic and Applied Ecology (2014), http://dx.doi.org/10.1016/j.baae.2014.03.007

Soil invertebrates are assumed to play a major role in ecosystem dynamics, since they are involved in soil functioning.unctional traits represent one of the main opportunities to bring new insights into the understanding of soil invertebrateesponses to environmental changes. They are properties of individuals which govern their responses to their environment. As

∗Corresponding author. Tel.: +33(0)130833270; fax: +33 01 30 83 32 70.E-mail address: [email protected] (M. Hedde).

ttp://dx.doi.org/10.1016/j.baae.2014.03.007439-1791/© 2014 Gesellschaft für Ökologie. Published by Elsevier GmbH. All rights reserved.

dx.doi.org/10.1016/j.baae.2014.03.007

dx.doi.org/10.1016/j.baae.2014.03.007

mailto:[email protected]

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o clear conceptual overview of soil invertebrate trait definitions is available, we first stress that previously-described conceptsf trait are applicable to soil invertebrate ecology after minor modification, as for instance the inclusion of behavioural traits.

decade of literature on the use of traits for assessing the effects of the environment on soil invertebrates is then reviewed.rait-based approaches may improve the understanding of soil invertebrate responses to environmental changes as they help tostablish relationships between environmental changes and soil invertebrates. Very many of the articles are dedicated to the effectf one kind of stress at limited spatial scales. Underlying mechanisms of assembly rules were sometimes assessed. The patternsescribed seemed to be similar to those described for other research fields (e.g. plants). The literature suggests that trait-basedpproaches have not been reliable over eco-regions. Nevertheless, current work gives some insights into which traits might beore useful than others to respond to a particular kind of environmental change. This paper also highlights methodological

dvantages and drawbacks. First, trait-based approaches provide complementary information to taxonomic ones. However theiterature does not allow us to differentiate between trait-based approaches and the use of a priori functional groups. It alsoeveals methodological shortcomings. For instance, the ambiguity of the trait names can impede data gathering, or the use ofraits at a species level, which can hinder scientific interpretation as intra-specific variability is not taken into account and mayead to some biases. To overcome these shortcomings, the last part aims at proposing some solutions and prospects. It concernsotably the development of a trait database and a thesaurus to improve data management.

usammenfassung

Man nimmt an, dass wirbellose Bodentiere eine wichtige Rolle bei der Ökosystemdynamik spielen, da sie am Funktionierener Böden beteiligt sind. Funktionelle Merkmale bilden eine der wichtigsten Möglichkeiten für ein neues Verständnis der Reak-ion von Bodenwirbellosen auf Umweltänderungen. Es handelt sich um Eigenschaften von Individuen, die deren Reaktion aufie Umwelt bestimmen. Da es keinen klaren konzeptionellen Überblick über die Merkmalsdefinitionen für Bodenwirbelloseibt, betonen wir zunächst, dass existierende Konzepte nach geringen Modifikationen auf die Ökologie von Bodenwirbellosennwendbar sind, wie z.B. das Einbeziehen von Verhaltensmerkmalen. Anschließend betrachten wir ein Jahrzehnt der Literaturum Gebrauch von Merkmalen bei der Abschätzung der Effekte der Umwelt auf Bodenwirbellose. Merkmalsbasierte Ansätzeönnen unser Verständnis der Reaktionen von Bodenwirbellosen auf Umweltänderungen verbessern, da sie helfen, Beziehun-en zwischen Umweltänderungen und Bodenwirbellosen zu etablieren. Sehr viele der Artikel widmen sich dem Effekt einestressfaktors auf begrenzten räumlichen Skalen. Die zugrundeliegenden Mechanismen von Vergemeinschaftungsregeln wurdenanchmal bestimmt. Die beschriebenen Muster scheinen denen von anderen Forschungsgebieten (z.B. Pflanzen) ähnlich zu

ein. Die Literatur legt nahe, dass merkmalsbasierte Ansätze über Ökoregionen hinweg nicht zuverlässig sind. Nichtsdestotrotzassen aktuelle Arbeiten erkennen, welche Merkmale nützlicher als andere sein könnten, um auf spezielle Umweltveränderungenu reagieren. Diese Arbeit stellt auch methodische Vor- und Nachteile heraus. Zuerst liefern merkmalsbasierte Ansätze Infor-ationen, die taxonomische ergänzen. Indessen erlaubt uns die Literatur nicht, zwischen merkmalsbasierten Ansätzen und demebrauch von a-priori definierten funktionellen Gruppen zu unterscheiden. Sie zeigt auch methodische Unzulänglichkeiten. Soann z.B. die Mehrdeutigkeit von Merkmalsbezeichungen das Sammeln von Daten behindern, oder der Gebrauch von Merk-alen auf der Artebene, der die wissenschaftliche Interpretation erschweren kann, da die intraspezifische Variabilität nicht

erücksichtigt wird und zu gewissen Verzerrungen führen kann. Um diese Unzulänglichkeiten zu überwinden, hat der letzte Teilum Ziel, einige Lösungen und Ausblicke vorzuschlagen. Dies betrifft namentlich die Entwicklung einer Merkmalsdatenbanknd eines Thesaurus’ um die Datenverwaltung zu verbessern.

2014 Gesellschaft für Ökologie. Published by Elsevier GmbH. All rights reserved.

eywords: Behaviour; Community ecology; Constraint; Database management system; Disturbance; Ecological preference; Life-history

sTmnd&

rait; Soil fauna; Thesaurus

ntroduction

The current biodiversity estimation of soil fauna assumeshat soil is the third biotic frontier after tropical forestanopies and ocean abysses (André, Noti, & Lebrun 1994;iller 1996; Swift, Heal, & Anderson 1979; Wolters 2001).

Please cite this article in press as: Pey, B., et al. Current use of and future

Basic and Applied Ecology (2014), http://dx.doi.org/10.1016/j.baae.201

he soil fauna encompasses both the obligate and facul-ative inhabitants of soil and soil annexes (Wolters 2001).oil annexes are simple structures which diversify the soil

mit

urface (e.g. tree stumps) (Gobat, Aragno, & Matthey 1998).he soil includes a variety of animals from almost allajor taxa that compose the terrestrial animal commu-

ities and may represent as one quarter of all currentlyescribed biodiversity (Decaëns, Jimenez, Gioia, Measey,

Lavelle 2006). Soil invertebrates are assumed to play a

needs for soil invertebrate functional traits in community ecology.4.03.007

ajor role in ecosystem dynamics, since they are involvedn soil functioning (e.g. carbon transformation and seques-ration, regulation of microbial activity or community

dx.doi.org/10.1016/j.baae.2014.03.007

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B. Pey et al. / Basic and App

tructure, nutrient turnover, aggregation). Consequently, soilnvertebrates contribute to the provision of many ecosystemervices such as nutrient cycling or soil structure maintenanceBarrios 2007; Kibblewhite, Ritz, & Swift 2008; Lavelle et al.006).Studying soil invertebrate responses to environmental

hanges is of great interest. In various research fields (e.g.lant ecology), functional components of communities haveevealed valuable insights into the understanding of orga-isms’ responses to the environment (Garnier & Navas 2012;cGill, Enquist, Weiher, & Westoby 2006). Originally, taxaere grouped into a priori functional groups based on certain

characteristics” which they shared. The classification intouch functional groups is based on subjective expert judge-ent. For instance, several plant functional types existed,

ased on their life form or growth form (Lavorel, McIntyre,andsberg, & Forbes 1997). Conclusions were drawn from

hese a priori functional groups’ richness (Villéger, Mason, &ouillot 2008). However these approaches led to several lim-

tations (Villéger et al. 2008) such as (i) a loss of informationy imposing a discrete structure on functional differencesetween taxa, which are usually continuous (Fonseca &anade 2001; Gitay & Noble 1997), (ii) a non-robust wayf obtaining results depending on the choice of the func-ional group types in the analysis (Wright et al. 2006) andometimes (iii) a failure to take account of abundance (Díaz

Cabido 2001). As an alternative to the taxonomic and ariori functional group approaches, trait-based approachesave been developed (Lavorel & Garnier 2002; McGill et al.006). Traits can be divided into response and effect traits. Anffect trait is an individual property which affects an upperevel of organization (e.g. ecosystem processes). Responseraits, also called functional traits, are properties of indi-iduals which govern their responses to their environmentStatzner, Hildrew, & Resh 2001; Violle et al. 2007). In theollowing, traits will mean response traits. Unlike a prioriunctional groups, trait-based approaches are based on objec-ive relations between individual properties (= traits) andhe environment. In other research fields, notably for plants,rait-based approaches have brought several new insightso the understanding of organisms’ responses to environ-

ental changes, by improving predictability and reducingontext dependence (Garnier & Navas 2012; Webb, Hoeting,mes, Pyne, & LeRoy Poff 2010). Prediction involves that

relationship must be found between soil invertebratesnd environmental changes through their traits. It has beenemonstrated that community assembly mechanisms are gov-rned by rules. The literature tends to support the existencef environmental filters which filter a sub-set of individualsf the regional pool to form local communities (Keddy 1992;cGill et al. 2006). Furthermore, environmental filters can

e categorized according to the scale on which they work.



rom larger scales to smaller ones, filters are (i) dispersallters which select individuals according to their dispersalapacity, (ii) abiotic filters which select individuals accord-ng to their capacity to live under certain abiotic conditions

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ology xxx (2014) xxx–xxx 3

nd (iii) biotic filters which represent the selection result-ng from the interactions between individuals (Belyea &ancaster 1999; Garnier & Navas 2012). Reducing con-

ext dependency implies that trait-based approaches haveo be: (i) generic over eco-regions and (ii) reliable what-ver kind of environmental change is considered. Enoughrait-based approach studies have been made on plants tossociate one or more traits with one or more environmen-al changes in any eco-region (Garnier & Navas 2012). Fornstance, “leaf area” responds gradually to complex envi-onmental change such as climate change over eco-regionsMoles et al. 2009; Thuiller, Lavorel, Midgley, Lavergne, &ebelo 2004).To our knowledge, attempts to relate terrestrial invertebrate

esponses in terms of their “characteristics” to environmen-al stress began at the end of the ninetieth century (Statznert al. 2001). In 1880, Semper (in Statzner et al. 2001)ssessed the temperature-induced switch from partheno-enetic to sexual reproduction in aphids. During the followingears, authors were convinced that environmental stress andcharacteristics” of terrestrial insects were linked (Buxton923; Hesse 1924; Pearse 1926; Shelford 1913 – all intatzner et al. 2001). For instance, Buxton (1923 – intatzner et al. 2001) related “characteristics” of terrestrial

nsects such as the presence of wings or the tolerance ofarvae to a lack of food and water to harsh environmentalonditions of deserts (e.g. drought, torrential rain, whirl-inds).Despite this early interest, no clear conceptual and method-

logical overview has been made for such “characteristics” ofoil invertebrates, which are now called traits. Originally, asor plants, most previous studies assessed soil invertebrateesponses to their environment using taxonomic structurend/or composition of communities. As soil invertebrateaxonomic diversity is huge, authors tried to simplify ity grouping together individuals by shared properties. Therouping also dealt with the lack of knowledge of taxonomy.or instance, eco-morphological groups, such as epigeic,necic and endogeic groups of earthworms (Bouché 1972),piedaphic, hemiedaphic and euedaphic groups of springtailsGisin 1943) or terrestrial isopods (Schmallfuss 1984) andunctional guilds such as the distinction between ecosystemngineers, litter transformers and micropredators (Lavelle

Spain 2001) were used. For instance, eco-morphologicalroups bring together individuals based on subjective expertudgments of some of the ecological or biological “charac-eristics” they share. For instance, epigeic earthworms areigmented and live near the soil surface, whereas endogeicarthworms are unpigmented and live deep in the soil. As forlants, all of these groupings have been used as a priori func-ional groups and should present the same disadvantages (seebove). Experience in other research fields led us to think that


sing functional trait-based approaches for soil invertebratesepresents one of the main opportunities to bring new insightsnto the understanding of soil invertebrate responses to thenvironment.

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Table 1. Definitions of trait concepts for soil invertebrates.

Concept Definitions

MPPB trait Any morphological, physiological,phenological or behavioural(MPPB) feature measurable at theindividual level, from the cell to thewhole-organism level, withoutreference to any other level oforganization.

Performance trait Performance traits describe growth,reproduction and survival,considered as being the threecomponents of fitness (Violle et al.2007). For soil invertebrates thereare for instance: biomass, offspringoutput and survival.

Ecological preference The optimum and/or the breadth ofdistribution of a trait on anenvironmental gradient.

Population parametersderived from traits

The median, mean and/or breadth ofdistribution of a trait (aggregatedvalues of a MPPB or a performance

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To our knowledge, no attempt has been made to clearlyefine functional trait concepts for soil invertebrates. Theoncept already existed but was used in other research fields.s a consequence, we first determine whether the actual def-

nitions around the notion of traits are applicable to soilnvertebrates. Second, to summarize the current advancesn the understanding of soil invertebrate responses to thenvironment through their traits, a one-decade literatureeview was made. It also aimed to focus on current method-logical advantages and drawbacks of soil invertebraterait-based approaches. The last part envisages solutions androspects for overcoming current conceptual and method-logical drawbacks. It notably deals with the development ofco-informatics tools.

re existing trait definitions applicable tooil invertebrates?

From work on terrestrial plants (Lavorel et al. 2007)r aquatic invertebrates (Bonada, Prat, Resh, & Statzner006), traits are being defined as properties of organismseasured at the individual level (Violle et al. 2007). Further-ore, a trait is qualified as “functional” when it influences

he organism’s performance and consequently its fitnessBlanck, Tedesco, & Lamouroux 2007; Nylin & Gotthard998; Southwood 1977; Violle et al. 2007; Webb et al.010). Some authors distinguish the performance traits fromorphological, phenological and physiological traits (“M-P-” traits). Performance traits describe growth, reproductionnd survival, considered as being the three componentsf fitness (Arnold 1983; McGill et al. 2006; Violle et al.007). Three main performance traits are recognized inlant ecology: vegetative biomass, reproductive output andeasured plant survival (Violle et al. 2007). Conversely,

M-P-P” traits are supposed to influence fitness indirectlyy influencing performance traits. In addition, plant abi-tic preferences are denominated “Ellenberg’s numbers” andeflect optima/ranges in environmental gradients (Ellenberg988). In aquatic invertebrate ecology, traits are usually splitnto biological and ecological traits (Dolédec, Statzner, &ournard 1999). Biological traits include M-P-P and life-istory traits, while ecological traits reflect behaviour andcological optima/ranges in environmental gradients.

Regarding soil fauna, many functional traits consideredn the literature are related to morphology, physiology orhenology (Barbaro & van Halder 2009; Pérès et al. 2011;ibera, Doledec, Downie, & Foster 2001; Vandewalle et al.010) matching the definition proposed by Violle et al.2007). The literature used, for instance, carabid beetle eyeiameter or wing form for morphology, carabid beetle breed-ng season for phenology (Ribera et al. 2001; Vandewalle



t al. 2010) or springtail reproductive mode for physiologyMalmstrom 2012). However, behaviour, such as “huntingtrategy” (Langlands, Brennan, Framenau, & Main 2011), is

crucial component in animal fitness that was not taken into

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trait).

ccount in Violle’s definition as the definition was stated forlants. For animals other than soil invertebrates, behaviouras semantically included (i) in a “biological traits” group,

ii) in an “ecological traits” group or (iii) in a semanticallyedicated “behavioural traits” group (Bonada, Dolédec, &tatzner 2007; Frimpong & Angermeier 2010; Relya 2001).ehaviour can be defined as an organized and directed bio-

ogical response to variations in the environment to suit thendividual’s requirements (adapted from Barnard 2004). Thenvironment refers both to the biocenosis and the biotope.e propose to extend Violle et al.’s (2007) definition of a

unctional trait for soil invertebrates as follows: “any morpho-ogical, physiological, phenological or behavioural (MPPB)eature measurable at the individual level, from the cell to thehole-organism level, without reference to any other level ofrganization” (Table 1). Furthermore, as for plants, we canistinguish MPPB traits from performance traits. The per-ormance traits for soil invertebrates could be for instance:iomass, offspring output and measured survival. Populationarameters can be derived from the median, mean and/orreadth of distribution of a trait (aggregated values of a MPPBr a performance trait, Table 1).In addition, some of the functional traits used in the

iterature refer to properties of the environment in which indi-iduals of a given species live. For instance, authors used theerm “soil moisture preferences” (Makkonen et al. 2011) toxpress the breadth of the occurrence distribution of individ-als of a species along a soil moisture gradient. We propose


o call “ecological preference” any value which results fromhe optimum and/or the breadth of distribution of a trait alongn environmental gradient (Table 1).

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Finally, authors called “life-history traits” (Stearns 1992)r “life-cycle traits” a wide range of data such as mois-ure preference (Bokhorst et al. 2012), adult daily activityBarbaro & van Halder 2009) or body size estimated for

species (Malmstrom 2012). Life-history traits need toe renamed, depending on their nature. In our examples,oisture preference will be classified as an “ecological pref-

rence”, while adult daily activity and body size estimated for species are “population parameters derived from a trait”.

rait-based approaches for soil invertebrateommunity ecology

ethods for literature review

A literature review was made from the ISI Web of Knowl-dge research platform using the search terms “trait” andsoil” or “ground” with each vernacular or taxonomic namef four groups: earthworms, ground beetles, spiders andpringtails. The taxonomic groups were chosen because theyepresent a wide range of biological strategies and wereften used as bio-indicators. Papers were selected accord-ng to several criteria described below. The term “trait”

ust have directly concerned soil invertebrates. To keephe scope of our study as restricted as possible, we onlyelected studies dealing with the effects of environmentalhanges on soil invertebrates. We did not include approachesxclusively dealing with other ecological questions or dedi-ated to evolutionary questions (e.g. adaptation, speciation).owever, we are aware that ecological and evolutionary ques-

ions can overlap, notably when considering links betweenhylogeny and trait conservation (Cavender-Bares, Kozak,ine, & Kembel 2009). Reviews (with no original data)nd methodological papers were excluded. Searches wereimited to papers published since 2000 as the use of theerm “trait” in soil invertebrate ecological studies is quiteecent. We may have failed to find some papers as the wordtrait” was not used in some papers even though a trait-ased approach was used. This highlights the fact that therait concept suffers from semantic inconsistency for soilnvertebrates as stated in the previous section. However, wehose to look for literature on measurable criteria (as men-ioned above), especially by using the search word “trait”,ather than on studies based on expert knowledge, evenhough this meant excluding a considerable number of papers.or instance, some studies using a trait-based approachave not been collected, e.g. for carabid beetles (Vanbergent al. 2010), springtails (Ponge, Dubs, Gillet, Sousa, &avelle 2006), earthworms (Jimenez, Decaëns, & Rossi012), spiders (Cristofoli, Mahy, Kekenbosch, & Lambeets



010; Lambeets, Vandegehuchte, Maelfait, & Bonte 2008;ambeets, Vandegehuchte, Maelfait, & Bonte 2009; Le Violt al. 2008) and for multiple groups (Bell et al. 2008; Deange, Lahr, Van der Pol, & Faber 2010; Hedde, van Oort,

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Lamy 2012; Jennings & Pocock 2009; Moretti & Legg009). However, we are confident in the representativenessf the literature review, which found 4, 17, 4 and 6 papers forarthworms, ground beetles, spiders and springtails respec-ively (Table 2 ).

cientific advances and drawbacks

All the literature showed, as for other research fields, thatome environmental filters filter a sub-set of individuals from

regional pool to form local communities according to somef their traits. Most of the studies were dedicated to assessoil invertebrate response to some kind of stress (Table 2).or instance, Barbaro and van Halder (2009), Driscoll andeir (2005) and Ribera et al. (2001) assessed mechanisms

f carabid beetle responses to habitat types according to theirraits (e.g. body size, wing development, Table 2). Underly-ng mechanisms of assembly rules were sometimes assessed.or instance, Decaëns, Margerie, Aubert, Hedde, and Bureau2008) demonstrated that some abiotic environmental filtersed to a trait convergence for earthworms. Decaëns et al.2011) revealed that the variability of morphological earth-orm traits was lower in the regional species pool and higher

n the local species pool compared to what would have beenxpected by chance. As very few examples were given, suchatterns cannot be used as general patterns for soil inverte-rate assembly rules. However, the patterns described seemedo be similar to those described in the introduction for otheresearch fields. These results claimed that soil invertebraterait-based approaches help to improve predictability of com-

unity assembly in relation to environmental changes as theyaterialize relationships between traits and environmental

hanges.Almost all of the studies assessed the responses of soil

nvertebrates in relation to only one kind of environmentalhange. Some exceptions were found. For instance, Gobbit al. (2010) aimed to assess both the abiotic effect ofeglaciation and the biotic effect of plant communities onarabid beetle communities. While individual studies usuallyealt with a single change, environmental changes studiedere diverse among studies. They included “natural” changes

uch as habitat type, fire, flooding or climatic events and alsoanthropic” changes such as invasive tree species or humanractices on cultivated fields or forests (Table 2). In addi-ion, studies were geographically limited to the regional scalesensu Belyea & Lancaster 1999). Some exceptions occurred,.g. Vandewalle et al. (2010) who sampled carabid beetles ineveral European countries. They assumed that the responsesf functional diversity indices calculated from traits (e.g.ao index of diversity, Botta-Dukat 2005) to habitat com-osition and landscape heterogeneity were consistent across


eographical regions.To conclude, we cannot be confident in trait genericity

ver eco-regions, as this was rarely studied (Vandewalle et al.010). Despite these shortcomings in reducing the context

dx.doi.org/10.1016/j.baae.2014.03.007

Please cite this article in press as: Pey, B., et al. Current use of and future needs for soil invertebrate functional traits in community ecology.Basic and Applied Ecology (2014), http://dx.doi.org/10.1016/j.baae.2014.03.007


6 B. Pey et al. / Basic and Applied Ecology xxx (2014) xxx–xxx

Table 2. Results of the literature review and some of the properties of the selected articles. LIT: trait data from the literature, OMS: originalmeasurements of traits. Without any specific information, we assumed that trait data had been derived from the literature.

Reference Soilinvertebrategroup

Environmental change LIT or OMS Traits

Decaëns et al. (2011) Earthworms Habitat (different agedpastures)

LIT (Ecological category), body length,diameter, weight, epithelium type,pigmentation

Decaëns et al. (2008) Earthworms Habitat LIT Size, weight, pigmentation, (ecologicalcategories, ecological features)

Fournier et al. (2012) Earthworms Flooding LIT + OMS Length, width, weight, segment number,pH optimum, pH range, prostomium type,(ecological type), C/N (soil) preference

Pérès et al. (2011) Earthworms Contamination andland use

LIT Body pigmentation, body wall thickness,food, reproductive strategy, rarity

Bonte, Lens, andMaelfait (2006)

Spiders Sand dynamics LIT + OMS Mean size, local activity-density, nichebreath, ballooning, seasonal activity,generation time, diurnal activity

Buchholz (2010) a Spiders Climate (drought) – –Langlands et al. (2011) Spiders Fire LIT + OMS Burrowing, body size (length),

cephalothorax heavy sclerotization,abdominal scutes, ballooning, time tomaturity, phenology, hunting strategy, dietspecialization (ants), flattened body

Tropek, Spitzer, andKonvicka (2008) a

Spiders Stone quarry – –

Bokhorst et al. (2012) Springtails Climate (winterwarming)

LIT + OMS (Life form), biomass, body length,moisture preference, vertical stratification

Huebner, Lindo, andLechowicz (2012)

Springtails Fire LIT Dente shape, eye number, total bodylength, furcula, pigmentation, body scales,PAO, antennae length, antennal organ,sexual dimorphism

Lindberg & Bengtsson(2005)

Springtails Climate (drought) LIT + OMS Depth distribution, reproductive mode,habitat specialization, (ecological category)

Makkonen et al. (2011) Springtails Climate LIT Ocelli number, body size, bodypigmentation level, body pigmentationpattern, modified hairs or scales, furcadevelopment, antenna/body, moisturepreference, habitat width

Malmstrom (2012) Springtails Fire LIT + OMS Habitat (vertical stratification), body size,reproductive mode, dispersal traits

Vandewalle et al.(2010)

Springtails Invasive tree species LIT Ocelli, antenna length, furca, hairs/scales,pigmentation

Barbaro and vanHalder (2009)

Ground beetles Habitat(fragmentation)

LIT European trend, European rarity, regionalrarity, biogeographic position, dailyactivity, diet, overwintering, body colour,breeding season, body size (mm), wingdevelopment, adult activity period

Cole et al. (2002) Ground beetles Habitat (agriculturalmanagement)

LIT Size (length), overwintering, life cycleduration, adult food, daily activity,breeding season, emergence, main activity,wing morphology, locomotion

Driscoll and Weir(2005)

Ground beetles Habitat(fragmentation)

LIT Flight, trophic group, adult primaryposition, size

Gerisch et al. (2012) Ground beetles Flooding LIT Wing morphology, overwintering strategy(reproduction season), body size

Gerisch (2011) Ground beetles Flooding LIT Body size, wing morphology, reproductionperiod, overwintering stage, daily activity,colour elytra, body pubescence, food type

dx.doi.org/10.1016/j.baae.2014.03.007


B. Pey et al. / Basic and Applied Ecology xxx (2014) xxx–xxx 7

Table 2 (Continued)

Reference Soilinvertebrategroup

Environmental change LIT or OMS Traits

Gobbi et al. (2010) Ground beetles Deglaciated terrain andplants

– Brachypterous, autumn-breeding,predators, average body length

Gobbi and Fontaneto(2008)

Ground beetles Habitat LIT Wing morphology, body length, diet

Grimbacher & Stork(2009)

Ground beetles Climate (seasonality) LIT + OMS Feeding ecology, body size, habitat strata,mean period of activity

Jelaska et al. (2010) Ground beetles Habitat (naturaltemperate forests)

LIT + OMS Body size

Karen et al. (2008) Ground beetles Habitat (forest cycleplantation)

LIT Broad habitat associations, body size,wing-type, microhabitat associations

Liu, Axmacher, Wang,Li, and Yu (2012)

Ground beetles Habitat (humanpractices onsemi-natural habitatsand cultivated fields)

LIT & OMS Trophic status, body size

Ribera et al. (2001) Ground beetles Habitat (landdisturbance)

LIT + OMS Eye diameter, antenna length, pronotummaximum width, pronotum maximumdepth, elytra maximum width, metafemurlength, metatrochanter length, metatarsilength, metafemur maximum width, totallength, leg colour, body colour, wingdevelopment, pronotum shape,overwintering, adult food, daily activity,breeding season, main period of adultemergence, main period of adult activity

Silva, Aguiar, Faria eSilva, and Serrano(2011)

Ground beetles Habitats (orchard andriparian)

LIT Moisture preferences

Tropek et al. (2008) a Ground beetles Stone quarry – –Vandewalle et al.

(2010)Ground beetles Habitat (composition

and landscapeheterogeneity)

LIT Wing form, body pubescence, body length,elytra width, elytra length, femora length,femora width, tibae length, metatarsuslength, pronotum height, pronotum length,eye diameter, antennae length, body black,body pale, legs black, legs pale, anthropic

Verhagen, Diggelen,and Vermeulen(2008)

Ground beetles Habitat (removal oftopsoil on formeragricultural fields)

LIT Habitat preference (characterization andamplitude), dispersal capacity (flying),occurrence, size

Warnaffe and Dufrene(2004)

Ground beetles Habitat (forestmanagement)

LIT Mean size, wing development

e abstran

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aOnly abstracts could be obtained of these papers, not full articles. Whilames can be given here.

ependence, the literature currently gives us some insights aso which traits might be more useful than others to respondo a particular kind of environmental change. For instance, itas been shown that ground beetle wing development variesith habitat type in different contexts (Barbaro & van Halder009; Driscoll & Weir 2005; Gobbi & Fontaneto 2008; Gobbit al. 2010; Ribera et al. 2001; Vandewalle et al. 2010). Toake the trait-based approaches reliable whatever the kind



f environmental changes, we have to establish relationshipsetween each kind of environmental change with one or sev-ral traits.

rwD

cts contained the search word “trait”, no information on LIT, OMS or trait

ethodological advantages and drawbacks

omplementarity with other approachesFrom a methodological point of view, trait-based

pproaches bring new insights into the understanding ofoil invertebrate responses to stress, compared to taxo-omic approaches (Cole et al. 2002; Gobbi & Fontaneto008; Langlands et al. 2011). First, inverse trends between


esults obtained by trait-based and taxonomic approachesere reported. For example, Gerisch, Agostinelli, Henle, &ziock (2012) showed that the species diversity of ground

dx.doi.org/10.1016/j.baae.2014.03.007


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eetle communities increased whereas functional diversityfunctional evenness and divergence) decreased with increas-ng flooding disturbances. This combined approach led theuthors to conclude that flooding disturbance increased theumber of species but that species were functionally redun-ant. Otherwise, Gobbi and Fontaneto (2008) showed thatround beetle traits such as wing morphology, diet and bodyize responded to habitat diversity, while species richnessnd a taxonomic diversity index based on phylogeny did not.he authors therefore claimed that trait-based approacheshould be favoured for assessing mechanisms of carabid bee-le responses to habitat disturbance rather than taxonomicpproaches. In other cases, trait-based approaches comple-ented the conclusions based on taxonomic approaches.or instance, in a study by Fournier, Samaritani, Shrestha,itchell, and Le-Bayon (2012), community-weighted means

f earthworm traits (e.g. body length and width, pH optimumnd range) were more strongly correlated with environmen-al variables (e.g. total carbon, gravel sizes, type of cover,uch as mosses, woody debris) than species compositionnd taxonomic diversity. However, no study aimed at com-aring approaches based on a priori functional groups (e.g.co-morphological groups) with trait-based approaches.

eficiencies in trait definitions, data treatment andathering structureThe literature review revealed semantic inconsistencies for

rait names. For instance, the type of materials eaten by soilnvertebrates (e.g. carnivorous) and the way they feed onhem (e.g. as predators, i.e. by killing their preys) are twooncepts. However, the literature revealed several categori-al traits whose attributes could describe several of the aboveoncepts simultaneously. For instance, “food of the adult”Cole et al. 2002; Ribera et al. 2001) referred both to the typef food eaten (e.g. plant, springtails) but also to the way itas eaten (e.g. generalist predators) whereas “diet” (Barbaro

van Halder 2009) refers only to the first one. Such draw-acks occurred within a taxon but also among taxa. They caninder data gathering in so far as they can cast doubt on arait’s scientific meaning.

At the moment, soil invertebrate trait-based approachessed traits at the species level. Such a process can lead towo main biases. A first bias occurs when the trend of theelationship between the mean trait of N species and annvironmental gradient is in the opposite direction to the rela-ionships between this environmental gradient and individualrait values. The second bias is that using traits at the speciesevel hides individual heterogeneity.

Traits can be described in two formats, numerical data (e.g.ye diameter, Ribera et al. 2001) or by text (e.g. pigmentation,ing form, Vandewalle et al. 2010). Format heterogeneity

nd the missing data impeded the use of traits. It has been



uggested that traits should be encoded into a limited numberf subsets (Chevenet, Dolédec, & Chessel 1994; Hedde et al.012). For all of these reasons, some authors discretized datanto attributes, e.g. by fuzzy coding procedures (e.g. body size

dw(m

ology xxx (2014) xxx–xxx

lasses, Jelaska et al. (2010) or diet, Pérès et al. 2011). Whenorking on one or several taxonomic groups, it was crucial toe able to deal with different data formats. However when thisas done, the way data were transformed by fuzzy coding wasot clearly explained. This impedes the comparison betweentudies using a trait shared by one or several groups but notecessarily using the same coding procedure (e.g. differentategories for the diet) (Barbaro & van Halder 2009; Gerisch011). It also limits the reuse of an encoded trait from theiterature as readers do not know exactly how the trait wasncoded.

Exploiting existing literature was preferred to time-onsuming trait measurements on sampled specimens.

hatever the methodology, the review of literature under-ined the lack of a data-compilation structure for soilnvertebrate traits. Depending on the author, a trait could beescribed from different literature sources. Cole et al. (2002)nd Karen et al. (2008) described body size trait values forebria brevicollis (Fabricius) from two different literature

ources. As a consequence, works do not benefit each other aso data-compilation allows authors to have access on existingrait data.

A general shortcoming which is not often considered in theurrent literature is the fact that traits used in a study can benter-correlated (“trait syndromes”) (Poff et al. 2006). Inter-orrelation can therefore cause that traits appear decoupledrom environmental changes (Poff et al. 2006; Statzner,olédec, & Hugueny 2004). Generally, trait selection for

nalyses was a priori justified on the basis of the biologicalunction they are supposed to be linked with. For instance,anglands et al. (2011) selected the body shape of spiders,s spiders with flattened bodies are supposed to shelter betterrom fire. Apart from this view, no analysis has been describedo identify “trait syndromes” before performing linking traitso environmental variables. Exception was made for certaintudies (Gobbi & Fontaneto 2008).

uture needs: eco-informatics at a crossroad

The following prospects are not limited to the four taxased in the literature search. They are suitable for all the soilnvertebrate taxa. Large amounts of data from multiple dataources need to be characterized and integrated into a uni-ed corpus in order to improve soil invertebrate trait-basedpproaches. Current eco-informatics literature provides aasis for a global scheme to structure ecological data (Garnier

Navas 2012; Madin et al. 2007). Between non-robust datatorage by scientists (e.g. spreadsheets, relational databaseystems) (Jones, Schildhauer, Reichman, & Bowers 2006)nd their exploitation by software tools (e.g. “R Statisticalackage”) (R Development Core Team 2010), an interme-


iate level is needed. It requires linking data with metadata,hich are information used to document and interpret data

Jones et al. 2006). Such a level would greatly enhance dataanagement (storage, integrating, querying, and analysing)

dx.doi.org/10.1016/j.baae.2014.03.007


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y producing robust traceability. One way is to construct aatabase management system (DBMS) for soil invertebrateraits which could associate metadata with data. First arescientific” metadata describing scientific data (e.g. informa-ion usually provided in the Materials and methods section).cientific metadata provide all the necessary informationor acquiring, interpreting and using scientific data. Sec-nd are “computer” metadata required for computerisatione.g. metadata required for the database structure, semanticetadata). They principally allow acquisition and automated

nput, analysis and processing of scientific data by the com-uter (Michener 1997, 2006). Associating data to metadatan a DBMS provides several advantages. Data longevitydata history) and quality (control of the nature of data)re increased. Data could be easily reused and integrated.inally data sharing is facilitated (Jones et al. 2006; Michener006). DBMS per se possesses sorting, indexing and query-ng functions which increase data interpretation and usePorter 1998). A few databases for soil invertebrates alreadyxist: for instance, Edaphobase (Russell et al. 2012), ColtraitSalmon & Ponge 2012), the Dutch soil invertebrate traitatabase (from M.P. Berg) (Makkonen et al. 2011), Macro-auna (Lapied, personal communication), and Ant ProfilerBertelsmeier, Luque, Confais, & Courchamp 2012). Never-heless, they do not always contain trait data or are not alwaysn a format which allows collaborative data sharing. Evenf they fulfil such criteria, they tend to be concerned with amall part of the whole diversity of soil invertebrates (usually

single group is concerned). Computer science solutions cur-ently exist to gather data from different sources (Jones et al.006; Michener 2006), so previous soil invertebrate databaseshould not be seen as isolated islands (Jones et al. 2006) buts complementary bricks which can be combined to createew soil invertebrate trait databases. However, combiningata from different formats, especially from spreadsheets, isot easy (Jones et al. 2006).

Among the existing solutions, semantic data integrations a promising way which preserves the scientific mean-ng of data. Semantic approaches deal with the differencesn the terms used (terminology) and the scientific conceptsormulated by soil invertebrate experts over time (Laporte,

ougenot, & Garnier 2012; Madin et al. 2007). To achievehis, the soil invertebrate scientific community is requiredo standardize meaningful and precise terms that cover theiromain of interest. Trait names are especially concerned, tak-ng a central position in trait-based approaches in the contextf the responses of soil invertebrates to their environment.

thesaurus of a particular domain reflects a communitygreement on a set of terms established in a given area andts organization through a well-designed structure. Further-

ore, a thesaurus is recognized as a knowledge organizationystem and bypasses ambiguity issues in natural language,



ontrolling and clarifying the access and exchange of infor-ation and facilitating communication. The main concern

ocuses on access, sharing and dissemination of informationithin the soil invertebrate scientific community. First, a soil

lab


nvertebrate trait thesaurus can serve as a stable referenceesource, specifically when published in RDF (Resourceescription Framework) language (Manola & Miller 2004)

nd available as linked data on the web. A second prospect iso include such a thesaurus in soil invertebrate trait databaseso facilitate data management. A third, more long-termrospect, involves the use of the thesaurus as a prerequisiteor the construction of a soil invertebrate trait ontology. Toonclude, it would be of major assistance for the soil inverte-rate scientist community to have access to knowledge-basedodels enabling the efficient answering of questions, which,

or example, may require the data aggregation of differentraits from several taxa.

Effort on data management using eco-informatics toolsill fill some gaps revealed by the literature review. First, itill strengthen current scientific advances. By increasing the

ollection of trait data and associated environmental param-ters, it will offer the possibility of considering the actions ofeveral environmental filters on different spatial and temporalcales (see section “Scientific advances and drawbacks”). Itill also aim to establish consistent “population parameterserived from traits” and “ecological preferences” (Table 1)y increasing the number of literature sources informing traitalues used to calculate them. All of this will contribute to aetter general understanding of soil invertebrate responses tohe environment from local to biogeographical scales, whichas not always possible from independent single studies.he data gathering structure should also improve knowledgef soil invertebrate group interactions, since it will becomeossible to work on several groups and taxa with severalomparable traits.

Second, it will help with some methodological short-omings. It will improve the possibility of dealing with (i)nter-correlation of traits and (ii) bias when using traits onhe species level (see section “Deficiencies in trait defini-ions, data treatment and gathering structure”). On the oneand (i), “trait syndromes” could be more easily revealedecause the data gathering structure should provide a largeody of available documented traits. We recommend test-ng for inter-correlation of traits before drawing conclusionse.g. fuzzy correspondence analysis, “ade4” R package,hessel, Dufour, & Thioulouse 2004). One other solutionhich has not been tested for soil invertebrates since not

nough trait data have yet been gathered, is the screeningethod (Bernhardt-Römermann et al. 2008). This allows the

est combination of traits to be found for an environmentalhange. On the other hand (ii), with the increasing number ofrait values measured on individuals rather than compiled atpecies or higher taxonomic level, it will provide the oppor-unity to put much more intraspecific variability into thessessment of functional diversity. It is a way to overruleias when using traits at a species level.


Although the data gathering structure will enable the col-ection of data documenting traits from all sources (e.g.rticles, books) and from all formats, i.e. numerical data (e.g.ody size distribution) and literal data (e.g. text descriptions

dx.doi.org/10.1016/j.baae.2014.03.007


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f diets), it will not deal with the definition of similar fuzzyoding protocols (see section “Deficiencies in trait defini-ions, data treatment and gathering structure”). For instance,e propose two main protocols: one for traits described byumerical values and another for traits described by textualata (see Appendix A).

cknowledgements

The authors wish to thank the CESAB (Centre de Synthèset d’Analyse sur la Biodiversité) and the FRB (Fondation poura Recherche sur la Biodiversité) (FRB “BETSI” 11000502)or their financial support. We also thank Baptiste LaporteFRB/CESAB) for his advice on computer science. Finally,e kindly thank M. Berg and three anonymous reviewers

or greatly improving the scientific content of the manuscripthrough their comments.

ppendix A. Supplementary data

Supplementary data associated with this article cane found, in the online version, at http://dx.doi.org/0.1016/j.baae.2014.03.007.

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