The soil fauna consists of a variety of animals which may represent as much as a quarter of all currently described biodiversity [1]. Of this, soil invertebrates are known to be highly involved in soil behaviour (e.g. carbon transformation and sequestration, soil aggregation) and therefore in the provision of ecosystem services [2]–[4]. As a consequence, soil ecologists aim to understand the interactions between soil invertebrates and their environment. Functional trait-based approaches are methodologies which can help us to understand soil invertebrates’ response to their environment through their traits. In this paper, we consider functional traits as being characteristics of individuals that affect their fitness and govern their responses to their surrounding environment [5]–[8]. The main advantages of trait-based approaches are that they (i) improve the prediction of the relationship between soil invertebrates and environmental changes and (ii) reduce the dependence of such predictions on time and space [8]. Trait-based approaches have confirmed the existence of environmental filters which filter a sub-set of individuals from the regional pool to form local communities [9]. Furthermore, trait-based approaches have been shown to be reliable over eco-regions and for whatever kind of environmental change is considered [10].

The current use of traits for soil invertebrates resulted from isolated initiatives which produced large amounts of unconnected heterogeneous data [8]. Without efforts to integrate such data, the emergence of new knowledge from combining, reusing or sharing it will remain scarce and time-consuming. Our main aim is to provide soil invertebrate scientists with tools which allow data identification, availability and interoperability [11]. The semantic web offers such kinds of tools by being based on the key principles of metadata, controlled vocabularies and ontologies [11].

The integration of the trait data on soil invertebrates is a key issue which can be resolved through semantic data integration. It deals with the variation of the terms employed (terminology) over time. It preserves their meanings (scientific concepts) and also captures their interrelationships [12], [13]. In the following, trait scientific concepts will be called “concepts” and trait terms which pertain to trait scientific concepts will be called “terms”. As has been stressed by some authors, semantic inconsistencies can not only impede data integration [14] but could also lead to ambiguous scientific data interpretation [13]. For instance, concerning the problem of data integration, some authors employed either the term “body length” [15] or “body size” [16] to describe the same concept, i.e. the length of the body, for two soil invertebrate taxonomic groups respectively. Without a semantic link between these two terms, data integration is impossible as data were described by two different terms. Is the concept associated with these two terms the same? Only a semantic structure would remove any doubt and identify these two terms as synonyms. Such semantic inconsistencies also exist within a given soil invertebrate taxonomic group. For instance, the development of ground beetle wings has been called “wing morphology” [17], [18], “wing form” [10] or “wing type” [19]. Otherwise, concerning examples of misunderstanding scientific data interpretation, the type of food eaten by soil invertebrates (e.g. carnivorous which means that they eat animals, usually alive), the way they feed on them (e.g. predators, which means that they feed by killing their live prey) or finally their trophic position in the food chain (e.g. tertiary consumers which eat animals feeding on herbivores) refer to different concepts. Nevertheless, the literature contained several categorical traits whose attributes described several of the above concepts simultaneously. For instance, the term “food of the adult” [20], [21] referred both to the type of materials ingested (e.g. plants, springtails) but also to the way the materials were eaten (e.g. generalist predators). The terms “feeding guilds” [22] or “food strategy” [23] are other terms used. Another example is that, to refer to the body colour, some authors employed the term “body colour” for carabid beetles [21], [24] while others used “body pigmentation” for earthworms [15], [25]. However, the concept of “coloration” is different from the concept of “pigmentation” since pigmentation does not necessarily imply the presence of colour. As soon as the traits are not clearly defined, confusion will emerge from comparisons between trait data from multiple literature sources.

As far as we know there has been no attempt to deal with these shortcomings for soil invertebrates. One solution is to build a thesaurus, which is a list of terms used in a particular topic, with some of their properties, organized into a hierarchy according to their meanings, i.e. their concepts. The aim of this paper is to present a first thesaurus for soil invertebrate trait-based approaches, called T-SITA.

The thesaurus for soil invertebrate trait-based approaches (T-SITA) was constructed through a web-based tool, designed for the collaborative construction of thesauri in ecology: the Thesauform [13].

T-SITA is freely available at the following URL address: http://t-sita.cesab.org/Thesauform/BETSI_vizIndex.jsp. It contains 71 traits and 24 ecological preferences.

Two types of semantic search engines are offered to access the T-SITA content. The first one is a classic semantic search engine which allows thesaurus terms to be found through an auto-completed search field and/or through a navigation tree (Fig. 1). It reflects how the experts of soil invertebrate ecology first organized the terms into a conceptual hierarchical tree and then amended it during the editing phase. To have access to information on a given term, it is necessary to click on it in the tree. Then a new web page appears with the properties of the term (Fig. 2).

The second search engine available is a faceted search engine (Fig. 3). It is defined as being “a technique for accessing a collection of information allowing users to explore by filtering available information. It allows the assignment of multiple classifications to an object” [27]. It enables users to filter thesaurus terms on characteristics they share that are called ‘facets’. At the moment in T-SITA, terms can be filtered according to several facets which gather terms either by their expression basis, the organ concerned, the main biological function concerned, their nature by distinguishing the traits from ecological preferences [8] and finally by the environmental property concerned. Each facet is divided into several categories that the user can select (when selected, the categories are coloured green). For instance, the “expression basis” was divided into four categories (area, length, mass, time) so that the user can select one/several of them. A dynamically updated list of terms appears then in the result part according to the selected category(ies) (Fig. 3). Simultaneously multiple selection of facet categories is possible. For instance, the user can select simultaneously the category “growth and development” from the “biological function” facet and the category “by mass” from the “expression basis” facet. He will find three terms: the “body weight”, the “fresh body weight” and the “dry body weight” in the result part since they correspond to both filters (Fig. 3). To access the information on a given term, the user has to click on it in the result part. Then a new web page appears displaying the complete information on the term.

The web of data focuses on the sharing of data on the web. SKOS (Simple Knowledge Organization System) (Isaac 2008) is the W3C standard dedicated to thesaurus representation, based on RDF triples [28]. SKOS was designed to provide an existing thesaurus standard (ANSI 2005) by providing a common format to adequately manage not only thesauri, but also any knowledge organizational system. T-SITA, built using Thesauform [13], is defined through the SKOS format and makes full use of it. As a consequence, T-SITA serves as a stable reference resource, specifically when available as linked data on the web. Additionally, T-SITA has been coupled with a soil invertebrate trait database to enhance data integration and data scientific integrity. The BETSI database will be soon in production. It is a relational database defined under the database management system PostgreSQL (http://www.postgresql.org/docs/9.1/static/reference.html), which contains soil invertebrate data on traits and ecological preferences. The linkage of the thesaurus and the database presents huge advantages.

First, each insertion of trait data in the BETSI database is under the control of T-SITA, which improves data quality in the database. Indeed, a trait term must be present in T-SITA in order to insert related data in the BETSI database, so data contributors to the BETSI database must consult T-SITA before inputting data. It guides the data integration and limits scientific misunderstanding. Concerning the data integration, it allows integrating trait data from two trait terms that represent the same concept (see section 1). For instance, sclerotization of the body is called “integument sclerotization” or “cuticule sclerotization” in the literature. As both terms are defined as synonyms in T-SITA, data inserted from these two terms will be identified in the database as belonging to the same trait concept. As a consequence, a data query concerning the sclerotization of the body will return data from both terms. Concerning the data interpretation, contributors to the database have to associate their data with a term coming from a limited set of trait terms. Each of them represents a trait concept which does not overlap with other trait concepts in the thesaurus. Two trait terms representing the same concept are synonyms. This can resolve the problems of scientific misunderstanding mentioned in section 1. Data contributors who want to insert raw data describing the “body colour” trait for carabid beetles and the “body pigmentation” trait for earthworms will realize that both data apply to the same concept. As a consequence, they will insert the data under the same trait term “body colour” in T-SITA and not under the trait term “body pigmentation” which refers to a different concept.

Second, in the web page describing the trait/ecological preference information in T-SITA, the dynamic link with the database enriches information about a given trait/ecological preference by providing statistics coming from the BETSI database (Fig. 2). It displays for each trait or ecological preference (as soon as a unit has been allocated to them, see section 2), the number of raw data observations input into the database and how many species they concern (Fig. 2). In addition, the statistics are aggregated according to the level of the tree hierarchy. When clicking on an above term (no unit, see Section 2), the number of raw data observations input into the database and how many species they concern are aggregated from its trait/ecological preference daughter terms. For instance, the “body dimension” term displays the aggregated statistics coming from its daughter terms, i.e. the “body length” and “body width” trait terms.

Interoperability between T-SITA and the database is dynamic. Therefore, each time the content is modified either via the database or the Thesauform, the modification is instantaneously updated in the other tool without any human intervention.

Harmonization of trait data for soil invertebrates requires a handbook to answer questions such as: what is really to be understood by this trait term, how can I measure it? T-SITA forms the first step in this huge task by being, to our knowledge, the first initiative to deal with the semantics of traits and ecological preferences for soil invertebrates. It reflects the agreement of a scientific expert community to fix the semantic properties (e.g. definition) of approximately 100 traits and ecological preferences. In addition, T-SITA has been successfully linked with a fully operational database on soil invertebrate traits. Such a link enhances data integration and improves data scientific integrity.

The future of T-SITA depends on the tool used to build it (Thesauform), which allows improvements in the current content by (i) performing other complete procedures (edit, validate, supervise), (ii) including other scientific experts and (iii) including new trait/ecological preference properties such as methods of measurement. Finally, a more long-term prospect for T-SITA could be its use for the construction of a soil invertebrate trait ontology.