Abstract
BackgroundEarthworms 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.Methodology/Principal FindingsProximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species.Conclusions/SignificanceOur findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.
Highlights
Understanding the spatial variation of soil biota and its abiotic and biotic drivers is a keystone for recognizing the soil ecosystem functioning and its influences on ecosystem services the soil environment provides [1,2]
Spatial patterning of soil biota ranges from the micro scale to the regional scale and strongly depends on the hierarchical nested soil variation occurring in space [1,3,4]
According to the theory of geographical dimension, which is rooted in landscape ecology [6], the scale of spatial studies is linked to the methods and models used for describing the spatial processes [7]
Summary
Understanding the spatial variation of soil biota and its abiotic and biotic drivers is a keystone for recognizing the soil ecosystem functioning and its influences on ecosystem services the soil environment provides [1,2]. Species distribution models (SDM) provide valuable insights into the drivers of these spatial patterns over a wide range of scales [3]. For collecting soil data more efficiently at the field scale, soil sensors and mobile sensor platforms have been recently developed. They allow for spatially dense mapping of physico-chemical soil properties on cultivated land and may help to improve SDM and their output. 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. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils
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