Mapping Soil Biodiversity in Europe and the Netherlands

Abstract

Soil is fundamental for the functioning of terrestrial ecosystems, but our knowledge about soil organisms and the habitat they provide (shortly: Soil biodiversity) is poorly developed. For instance, the European Atlas of Soil Biodiversity and the Global Soil Biodiversity Atlas contain maps with rather coarse information on soil biodiversity. This paper presents a methodology to map soil biodiversity with limited data and models. Two issues were addressed. First, the lack of consensus to quantify the soil biodiversity function and second, the limited data to represent large areas. For the later issue, we applied a digital soil mapping (DSM) approach at the scale of the Netherlands and Europe. Data of five groups of soil organisms (earthworms, enchytraeids, micro-arthropods, nematodes, and micro-organisms) in the Netherlands were linked to soil habitat predictors (chemical soil attributes) in a regression analysis. High-resolution maps with soil characteristics were then used together with a model for the soil biodiversity function with equal weights for each group of organisms. To predict soil biodiversity at the scale of Europe, data for soil biological (earthworms and bacteria) and chemical (pH, soil organic matter, and nutrient content) attributes were used in a soil biodiversity model. Differential weights were assigned to the soil attributes after consulting a group of scientists. The issue of reducing uncertainty in soil biodiversity modelling and mapping by the use of data from biological soil attributes is discussed. Considering the importance of soil biodiversity to support the delivery of ecosystem services, the ability to create maps illustrating an aggregate measure of soil biodiversity is a key to future environmental policymaking, optimizing land use, and land management decision support taking into account the loss and gains on soil biodiversity.

Methods and approaches to advance soil macroecology

“…Assembling data from multiple sources is complicated due to variability in taxonomic resolutions, unresolved taxonomies (Cameron, Decaëns, Lapied, Porco, & Eisenhauer, 2016) and lack of standardization of sampling techniques that cause technical factors (e.g., sampling protocol, primer and sequencing platform) to be an important source of inter-study variability (Ramirez et al, 2018). Some studies choose to only include data collected using specific methodologies to reduce inter-study variability (e.g., European earthworm diversity maps, Rutgers et al, 2016Rutgers et al, , 2019), yet, non-standardized data sets can still provide important global insights into the ecological preferences and geographical ranges of species (Ramirez et al, 2015). These data will complement standardized sampling protocols when analysed appropriately, for example using meta-analytical or machine learning approaches (Hendershot, Read, Henning, Sanders, & Classen, 2017;Ramirez et al, 2018).…”

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

“…DSM is an effective implement to organize, harmonize, and visualize the detailed soil information, and defines soil differences over an area of interest based on a set of soil-environmental relationships. DSM has been applied to diminish the uncertainty of soil biodiversity and quantify the relationships between soil properties and ecosystems. , One of the most well-documented frameworks for DSM is the scorpan-SSPFe framework that is established based on a spatial soil prediction function employing “scorpan” factors (stands for soil, climate, organisms, relief, parent material, age and n for space) with autocorrelated error (SSPFe) . However, with sparse and interrupted data sets and uncertain accuracy of legacy data, , the major knowledge gap of DSM comes from the uncertainty of the covariates’ evaluation and the sparsity of data sets .…”

Quantitative Evaluation of Soil Functions: Potential and State

“…The prediction and mapping of soil biodiversity based on inherent and manageable soil and site attributes is considered as currently not feasible by the LANDMARK project due to the lack of indicators and specific reference values with respect to soil types, climate and land use, as well as models (Staes et al, 2018). However, there are some recent approaches to assess the actual state of the habitat for biological activity based on, e.g., geographic location, soil pH, soil organic matter content, texture, land use and climate (Aksoy et al, 2017;Rutgers et al, 2016Rutgers et al, , 2019 or by using the QBS index (Qualità Biologica del Suolo), which assumes that the habitat function of soils is reflected by a higher number of microarthropods well adapted to soil habitats (Parisi et al, 2005), in combination with SOC content and bulk density (Calzolari et al, 2016).…”