Proximal Soil Sensing – A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?

Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Publisher Copyright: © 2021, The Author(s).Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.Peer reviewe

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change

Earthworm population dynamics as a consequence of long-term and recently imposed tillage in a clay loam soil

Earthworm abundances were tracked from 1997-2012 in established tillages (since 1983) and recently imposed tillages (since 1997) for a Brookston clay loam soil (Orthic Humic Gleysol) at Woodslee, Ontario. Tillages included: long-term fall moldboard plowing (CT83) and its 1997 conversion to no tillage (NT97-CT83); long-term no tillage (NT83) and its conversion to moldboard plowing (CT97-NT83); long-term ridge tillage (RT83) and its conversion to moldboard plowing (CT97-RT83); and long-term bluegrass sod (BG83) and its conversion to moldboard plowing (CT97-BG83). Lumbricus terrestris and Aporrectodea turgida were the most abundant of six species identified. NT83 had the greatest earthworm numbers except for 2012 when RT83 had equal abundance because of increased Ap. turgida juveniles. Populations in NT97-CT83 increased significantly from 1997-2012 as a result of reduced mechanical disturbance and greater surface residues. During 1997, 1999 and 2003, mean abundance in CT97-BG83 was not different from BG83, which likely occurred because buried sod continued to provide ample food. CT97-RT83 showed a decline in earthworm populations relative to RT83. The CT97-NT83 treatment had the most significant earthworm decline reflecting substantial increase in soil disturbance. Characterizing tillage system effects on earthworm dynamics (e.g., diversity, occurrence, adult and juvenile abundance) will provide essential input for landscape models.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Use of deep learning for structural analysis of computer tomography images of soil samples

Soil samples from several European countries were scanned using medical computer tomography (CT) device and are now available as CT images. The analysis of these samples was carried out using deep learning methods. For this purpose, a VGG16 network was trained with the CT images (X). For the annotation (y) a new method for automated annotation, ‘surrogate’ learning, was introduced. The generated neural networks (NNs) were subjected to a detailed analysis. Among other things, transfer learning was used to check whether the NN can also be trained to other y-values. Visually, the NN was verified using a gradient-based class activation mapping (grad-CAM) algorithm. These analyses showed that the NN was able to generalize, i.e. to capture the spatial structure of the soil sample. Possible applications of the models are discussed

Univariate statistics of average individual counts of earthworm species observed at all measurement plots during the years 1997 till 2007.

<p>Univariate statistics of average individual counts of earthworm species observed at all measurement plots during the years 1997 till 2007.</p