Schirrmann Michael, Joschko Monika, Gebbers Robin, Kramer Eckart, Zörner Mirjam, Barkusky Dietmar, Timmer Jens
Leibniz Institute for Agricultural Engineering Potsdam-Bornim, Max-Eyth-Allee 100, 14469, Potsdam, Germany.
Leibniz Centre for Agricultural Landscape Research (ZALF), Institute for Landscape Biogeochemistry, Eberswalder Str. 84, 15374, Muencheberg, Germany.
PLoS One. 2016 Jun 29;11(6):e0158271. doi: 10.1371/journal.pone.0158271. eCollection 2016.
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.
METHODOLOGY/PRINCIPAL FINDINGS: Proximal 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/SIGNIFICANCE: Our 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.
蚯蚓对于维持土壤生态系统功能至关重要，并且可作为土壤肥力的指标。然而，蚯蚓的检测耗时费力，这阻碍了在整块田地以高采样密度评估蚯蚓数量。用于近地土壤传感（PSS）的移动陆地传感器平台的最新进展提供了新工具，可利用各种传感原理收集土壤的密集空间信息。然而，PSS在评估蚯蚓栖息地方面的潜力在很大程度上尚未得到探索。本研究调查了PSS数据是否有助于在农业土壤物种分布模型中对蚯蚓数量进行空间预测。
方法/主要发现：在一块采用两种管理策略（少耕/传统耕作）且土壤质地从砂土到壤土的田地里，实时收集了近地土壤传感数据，例如土壤电导率（EC）、pH值和近红外吸光度（NIR）。PSS与在42个样地进行的一项为期11年的蚯蚓观测研究的观测结果相关。从0.5×0.5×0.2立方米的土壤块中采集蚯蚓样本并鉴定到物种水平。传感器数据与少耕条件下观测到的蚯蚓数量高度相关，但与传统耕作条件下观测到的蚯蚓数量相关性较低。这可能表明管理方式会影响传感器与蚯蚓之间的关系。广义相加模型和状态空间模型表明，基于EC、pH值和NIR传感器的数据融合进行建模，比不使用传感器数据或仅使用单个传感器数据进行建模产生的结果更好。对于单个蚯蚓物种，由于蚯蚓的栖息地需求不同，特定的传感器组合比其他组合更合适。与更常见的物种相比，PSS对具有特定土壤栖息地偏好的蚯蚓物种进行空间预测的准确性更高。
结论/意义：我们的研究结果表明，PSS有助于在田间尺度上对蚯蚓数量进行空间建模，并且将支持物种分布建模，以试图理解农业生态系统中土壤与蚯蚓的关系。
