Landscape-scale distribution patterns of earthworms inferred from soil DNA
Introduction
Earthworm communities play an important role in terrestrial ecosystems as ecosystem engineers (Lavelle et al., 1997): they regulate aeration, water infiltration, and nutrient cycling in soils. Their distribution is mainly influenced by soil properties and vegetation type (Curry, 2004, Salomé et al., 2011). At the landscape-scale, human land-use induces a strong spatial heterogeneity of earthworm communities by altering their biological, physical and chemical habitat and food supply (Grossi et al., 1995, Grossi and Brun, 1997, Curry, 2004, Stauffer et al., 2014). As the heterogeneity of earthworm communities translates to some extent into the spatial patterns of ecosystem functions (Ettema and Wardle, 2002, Blouin et al., 2013, Hedde et al., 2013), earthworms are increasingly used as bioindicators of soil quality (Römbke et al., 2005, Pérès et al., 2011).
Assessment of land-use effect on the diversity of soil biota is difficult to implement over large areas due to technical constraints, bias linked with current methods of extraction, and the general lack of taxonomic skills. Current International Standard for earthworm sampling is based on handsorting and/or chemical expellant (NF EN ISO 23611-1, 2011). This method is time consuming (Bartlett et al., 2006) and its efficiency depends on soil parameters, season, species characteristics and life stages (Lawrence and Bowers, 2002, Coja et al., 2008). Moreover, taxonomic assignment is often difficult, especially for juveniles, and does not account for cryptic species. During the past decade, DNA barcoding was successfully used for earthworm identification (Rougerie et al., 2009, James et al., 2010, Decaëns et al., 2013). This approach provides a more accurate estimation of taxonomic richness by accounting for both juveniles and cryptic diversity (King et al., 2008, Richard et al., 2010, Klarica et al., 2012). Despite these progresses in the taxonomic identification of specimens, problems inherent to earthworm sampling methodologies, particularly their variable efficiency and difficulties to implement them over large areas, limit our understanding of earthworm diversity.
Recently, Bienert et al. (2012) demonstrated the potentiality of a more integrative metabarcoding approach based on soil extracellular DNA and next-generation sequencing to identify earthworm species (Lumbricidae). Persistence of DNA in soils allows overcoming constraints linked to earthworm sampling methodologies, while benefiting from the taxonomic precision of DNA barcoding. The pioneer study of Bienert and colleagues constituted a proof of concept but also pointed out several directions to improve earthworm detection. Among them, authors identified soil sampling, more particularly the coverage of the studied area and the depth of soil cores, as the critical step towards an exhaustive inventory of earthworm diversity using environmental DNA metabarcoding (Bienert et al., 2012).
Here, we optimized the sampling step of this non-invasive soil DNA approach for assessing its potential to characterize earthworm communities at the landscape-scale, in order to propose a robust methodology complementary to classical earthworm surveys. For this purpose, (i) we compared the results obtained by a classical earthworm survey across a mountain range to those deriving from DNA extracted from a mixture of soil plus litter, collected following the same spatial sampling scheme. Then, (ii) we assessed an alternative spatial soil sampling procedure for soil DNA covering the entire surface of the studied plots. Finally, (iii) we compared factors influencing the observed distribution patterns, in relation with land-use temporal trajectories.
Section snippets
Study area
The study was conducted in “Quatre Montagnes” area, a 255 km2 landscape in the Vercors mountain range (Northern French Alps, elevations range between 1200 and 1600m a.s.l.). Following previous works (FORGECO project, https://forgeco.cemagref.fr/), we selected 18 homogeneous 1 ha plots, corresponding to the three dominant land covers of the studied area (n = 6): old beech coppice, young spruce plantation and pasture (Table 1 and Supplementary Table S1). Land-use trajectories of these three types
Results
A total of 13 species was visually identified by the classical earthworm survey. Despite the conservative data filtering procedure, a total of 16 earthworm molecular operational taxonomic units (MOTUs) was detected across the 18 plots with the soil DNA sampling scheme performed on subplots (Table 2). Taxonomic assignment of MOTUs to a reference DNA database with > 95% identity allowed discriminating seven different species. Eight MOTUs could not be identified to the species level due to
Discussion
The soil DNA approach allowed detecting an overlooked earthworm diversity irrespective of soil sampling strategies. As suggested by Bienert et al. (2012), extracting DNA from the O and A horizons simultaneously allowed the detection of epigeic, endogeic and anecic species. However, differences in earthworm diversity were found between the classical survey and the soil DNA-based survey performed on subplots. This could be explained by different sampling time and/or by the fact that the three
Conclusion
This study highlighted the potential of environmental DNA metabarcoding to investigate distribution patterns of earthworm communities and their drivers at the landscape-scale. Soil DNA can provide consistent fingerprints of earthworm communities, particularly relevant in the context of land-use change studies, as well as for landscape management applications. The field protocol is relatively easy to implement and adapt. It allows an entire plot surface coverage with less intensive sampling
Acknowledgments
The comments of three anonymous reviewers greatly improved an early version of the manuscript. This research was conducted on the long term research site “Zone Atelier Alpes”, a member of the ILTER-Europe network. It was jointly funded by “Zone Atelier Alpes” through metaLOVER project and by the French National Research Agency through MetaBar (ANR-11-BSV7-020-01) and FORGECO (ANR-09-STRA-02-01) research programs. Authors specifically thank E. Mermin, P. Tardif, N. Daumergue and T. Goïtré
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