QBS-ar and QBS-ar_BF index toolbox for biodiversity assessment of microarthropods community in soil
Creators: name: D'Avino, Lorenzo
name: Gambelli, Miguel Angel
name: Bigiotti, Gaia
name: Vitali, Francesco
name: Tondini, Elena
name: L'Abate, Giovanni
name: Jacomini, Carlo
name: Cassi, Fabrizio
name: Menta, Cristina
name: QBS-ar SISS working group
Publisher: name: Zenodo
Year: 2024
Subjects: Soil biodiversity
Mesofauna
Soil quality
QBS data template
Data manipulation

description: This toolbox contains a proposal and detailed instructions for the standardization of the QBS-ar index procedure. The QBS-ar index (Parisi, 2001; Parisi et al., 2005) is an index aimed at assessing soil-dwelling microarthropod communities in relation to their soil adaptation level. The core of QBS-ar index principle is: the higher is soil quality, the higher will be the number of microarthropod groups well adapted to soil habitats living there. Soil quality here stands for good stability, high organic matter content, and good biodiversity level. Worth of notice, QBS-ar is not comprehensive of the whole soil biodiversity or whole soil quality. 

For each sample to be assessed with the QBS-ar method, three different samplings are performed 5-10 meters apart (Menta et al., 2018), obtaining three subsamples (replicates) that are considered representative of an area homogeneous for slope and vegetation. 1-page guidelines for sampling and extraction were detailed in file ‘1.QBS-ar sampling and extraction guidelines.pdf’. 

The functional characteristics for soil adaptation of microarthropods are the reduction of visual structures as microphthalmia or even anophthalmia, the reduction or loss of pigmentation and dehydration adaptations (thinner cuticle, shorter setas or chaeta loss), appendage reduction (as shorter and/or smaller antennae, legs, furca), miniaturization, streamlined body form. Starting from these principles, soil organisms could be divided into a discrete set of Biological Forms (BFs, eco-morpho-types) according to their morphological adaptation to soil (Menta et al., 2018); in the EXCALIBUR and EJP-Minotaur projects, CREA Research Centre for Agriculture and Environment, in collaboration with the National QBS-ar working group (D’Avino et al., 2021), recognized no less than 54 different BFs, 16 of which were classified as less frequent. Each BF is shortly described and  associated with a score, the so-called Eco-Morphological Index (EMI), which ranges from 1 to 20 in proportion to the increasing degree of the soil adaptation of microarthropods. As reported by Parisi et al. (2005), some taxa show only one single EMI value because all species belonging to these taxa report the same adaptation level to the soil. Other groups show a range of EMIs in relation to the different adaptation levels of species to the soil. In general, eu-edaphic forms get EMI = 20, hemi-edaphic get an EMI rating proportionate to their degree of soil adaptation, while epedaphic (epigean) forms get EMI score = 1 (Menta et al., 2018). In QBS-ar whenever two eco-morphological forms are present in the same group, the final score is determined by the higher EMI. In other words, the most highly adapted microarthropods belonging to a group determine the overall EMI score for that group (Parisi et al 2005). This statement is not valid for the QBS-ar_BF proposed here, for which every biological form concurs to the calculation of QBS-ar_BF index, regardless of whether or not it belongs to the same group (i.e. class or order). Moreover, to assess variability between subsamples (replicates) an index based on spectral analysis (D’Avino, 2019) is proposed. The file ‘2.Sheet for QBS-ar record and calculation_v2.xlsx’ provides the selection of 54 most common BFs in European soils and should be used as a template for data registration during the microarthropods identification and count at the stereomicroscope. As outlined in the “README” sheet of the excel file, the user should register the abundance of each BF and other sampling-related information, and the template will automatically perform a series of calculations and will format several output sheets. In particular it calculates: mean abundance of microarthropods and relative class of abundance, QBS-ar, QBS-ar_BF, variability index community based on spectral analysis. 

One example of the compilated sheet is reported as file ‘2a.Filled_template_example_v2.pdf’. The sampling was carried out during MINOTAUR project, the example was implemented starting from file ‘2. Sheet for QBS-ar record and calculation.xlsx’. 

 The file "2.1.Sheet for QBS-ar record and calculations_v2.1.xlsx" contains updates to the previous version:





In the ‘QBS-ar sheet’ the formulas for calculating Density n.m-2, Class n.m-2, Density n.kg-1 and S1 in Pauropoda_20 have been corrected




In the ‘”Export_Subsamples” sheet, the formula of Pauropoda_20 has been corrected




In the ‘Export_MINOTAUR’ sheet the formulas for Hymenoptera_AB, Hymenoptera_Number_taxa, Diplopoda_AB and Diplopoda_Number_taxa have been corrected




In the ‘Codelist’ sheet, Pest_control_method_Insecticide was added between Main_Pest_control_methods



The file "2.Sheet for QBS-ar record and calculation. vers_3.xlsx" is the latest version of the sheet. It contains several updates regarding the metadata that can be entered, updated descriptions of the biological forms and their common name. The order of the biological forms has been changed from the previous version: Hymenoptera-L_10 has been moved between the rare forms and Symphyla_20 has been moved between Chilopoda_20 and Pauropoda_20. One example of the compilated sheet is reported as file ‘2a.Filled_template_example_v3.pdf’.

This file also contains a new printable sheet called "Data_report" that automatically summarizes the values of the QBS indices, diversity indices (Shannon, Evenness, Simpson and Richness) and abundances per square meter of the different biological forms along with the graphs.

The collection, merging and organization of data from the precompiled sheet could be a cumbersome task, especially for surveys with many different samples. The user would need to copy-paste each row in the “Export_Sample” or “Export_Subsample” sheets (from file 2.) to a new file, to perform overall analysis; an error-prone operation. To ease this process, we have compiled a short R script that can be found in this submission as the third file ‘3.QBS-ar data merging_v2.R’. By using the script, the user would automatically obtain three files (csv and/or xlsx, that will be stored in a “Results” folder) resulting from the merging of any number of QBS precompiled templates collected in a folder:  

1. The result of the merge of “Export_Sample” sheets 

2. The result of the merge of “Export_Subsample” sheets  

3. The result of the merge of  "Export_MINOTAUR" sheets 

The latest version of the script is “3.QBS-ar data merging_v3.R”

It is recommended to use this version for the file “2.Sheet for QBS-ar record and calculation. vers_3.xlsx”

 

Cited reference 

D’Avino, L., Menta, C., Jacomini, C., Cassi, F., L’Abate, G., La Terza, A., Staffilani, F., Pocaterra, F., Piazzi, M., Parisi, V. (2021). The Italian skill network of Soil Biological Quality assessed by microarthropods’ community, in: FAO. 2021. Keep soil alive, protect soil biodiversity –Global Symposium on Soil Biodiversity 19–22 April 2021. Proceedings. Rome. ISBN [978-92-5-135218-2] pages 182-188. Available at https://doi.org/10.4060/cb7374en (accessed 11/11/2024) 

D’Avino 2019. Soil mesofauna QBS-ar index in: Malusa et al. Common guidelines for analytical methods.  https://cordis.europa.eu/project/id/817946/results/it (Accessed 11/11/2024) 

Menta, C., Conti, F. D., Pinto, S., Bodini, A. (2018). Soil Biological Quality index (QBS-ar): 15 years of application at global scale. Ecological Indicators, 85, 773-780. 

Parisi V. (2001). La qualità biologica del suolo. Un metodo basato sui microartropodi [In Italian] Acta Naturalia de L’Ateneo Parmense 37, 97-106. 

Parisi, V., Menta, C., Gardi, C., Jacomini, C., Mozzanica, E. (2005). Microarthropod communities as a tool to assess soil quality and biodiversity:  a new approach in Italy. Agriculture, Ecosystems & Environment, 105 (12), 323-333.