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- ItemGlobal data on earthworm abundance, biomass, diversity and corresponding environmental properties(London : Nature Publ. Group, 2021) Phillips, Helen R. P.; Bach, Elizabeth M.; Bartz, Marie L. C.; Bennett, Joanne M.; Beugnon, Rémy; Briones, Maria J. I.; Brown, George G.; Ferlian, Olga; Gongalsky, Konstantin B.; Guerra, Carlos A.; König-Ries, Birgitta; López-Hernández, Danilo; Loss, Scott R.; Marichal, Raphael; Matula, Radim; Minamiya, Yukio; Moos, Jan Hendrik; Moreno, Gerardo; Morón-Ríos, Alejandro; Motohiro, Hasegawa; Muys, Bart; Krebs, Julia J.; Neirynck, Johan; Norgrove, Lindsey; Novo, Marta; Nuutinen, Visa; Nuzzo, Victoria; Mujeeb Rahman, P.; Pansu, Johan; Paudel, Shishir; Pérès, Guénola; Pérez-Camacho, Lorenzo; Orgiazzi, Alberto; Ponge, Jean-François; Prietzel, Jörg; Rapoport, Irina B.; Rashid, Muhammad Imtiaz; Rebollo, Salvador; Rodríguez, Miguel Á.; Roth, Alexander M.; Rousseau, Guillaume X.; Rozen, Anna; Sayad, Ehsan; Ramirez, Kelly S.; van Schaik, Loes; Scharenbroch, Bryant; Schirrmann, Michael; Schmidt, Olaf; Schröder, Boris; Seeber, Julia; Shashkov, Maxim P.; Singh, Jaswinder; Smith, Sandy M.; Steinwandter, Michael; Russell, David J.; Szlavecz, Katalin; Talavera, José Antonio; Trigo, Dolores; Tsukamoto, Jiro; Uribe-López, Sheila; de Valença, Anne W.; Virto, Iñigo; Wackett, Adrian A.; Warren, Matthew W.; Webster, Emily R.; Schwarz, Benjamin; Wehr, Nathaniel H.; Whalen, Joann K.; Wironen, Michael B.; Wolters, Volkmar; Wu, Pengfei; Zenkova, Irina V.; Zhang, Weixin; Cameron, Erin K.; Eisenhauer, Nico; Wall, Diana H.; Brose, Ulrich; Decaëns, Thibaud; Lavelle, Patrick; Loreau, Michel; Mathieu, Jérôme; Mulder, Christian; van der Putten, Wim H.; Rillig, Matthias C.; Thakur, Madhav P.; de Vries, Franciska T.; Wardle, David A.; Ammer, Christian; Ammer, Sabine; Arai, Miwa; Ayuke, Fredrick O.; Baker, Geoff H.; Baretta, Dilmar; Barkusky, Dietmar; Beauséjour, Robin; Bedano, Jose C.; Birkhofer, Klaus; Blanchart, Eric; Blossey, Bernd; Bolger, Thomas; Bradley, Robert L.; Brossard, Michel; Burtis, James C.; Capowiez, Yvan; Cavagnaro, Timothy R.; Choi, Amy; Clause, Julia; Cluzeau, Daniel; Coors, Anja; Crotty, Felicity V.; Crumsey, Jasmine M.; Dávalos, Andrea; Cosín, Darío J. Díaz; Dobson, Annise M.; Domínguez, Anahí; Duhour, Andrés Esteban; van Eekeren, Nick; Emmerling, Christoph; Falco, Liliana B.; Fernández, Rosa; Fonte, Steven J.; Fragoso, Carlos; Franco, André L. C.; Fusilero, Abegail; Geraskina, Anna P.; Gholami, Shaieste; González, Grizelle; Gundale, Michael J.; López, Mónica Gutiérrez; Hackenberger, Branimir K.; Hackenberger, Davorka K.; Hernández, Luis M.; Hirth, Jeff R.; Hishi, Takuo; Holdsworth, Andrew R.; Holmstrup, Martin; Hopfensperger, Kristine N.; Lwanga, Esperanza Huerta; Huhta, Veikko; Hurisso, Tunsisa T.; Iannone, Basil V.; Iordache, Madalina; Irmler, Ulrich; Ivask, Mari; Jesús, Juan B.; Johnson-Maynard, Jodi L.; Joschko, Monika; Kaneko, Nobuhiro; Kanianska, Radoslava; Keith, Aidan M.; Kernecker, Maria L.; Koné, Armand W.; Kooch, Yahya; Kukkonen, Sanna T.; Lalthanzara, H.; Lammel, Daniel R.; Lebedev, Iurii M.; Le Cadre, Edith; Lincoln, Noa K.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.
- ItemProximal Soil Sensing - A Contribution for Species Habitat Distribution Modelling of Earthworms in Agricultural Soils?(San Francisco, California, US : PLOS, 2016) Schirrmann, Michael; Joschko, Monika; Gebbers, Robin; Kramer, Eckart; Zörner, Mirjam; Barkusky, Dietmar; Timmer, JensBackground: 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.
- ItemRapid and low-cost insect detection for analysing species trapped on yellow sticky traps(London : Nature Publishing Group, 2021) Böckmann, Elias; Pfaff, Alexander; Schirrmann, Michael; Pflanz, MichaelWhile insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role in increased acceptance and application of IPM in practice. In this study, we tested the differentiation of two whitefly species and their natural enemies trapped on yellow sticky traps (YSTs) via image processing approaches under practical conditions. Using the bag of visual words (BoVW) algorithm, accurate differentiation between both natural enemies and the Trialeurodes vaporariorum and Bemisia tabaci species was possible, whereas the procedure for B. tabaci could not be used to differentiate this species from T. vaporariorum. The decay of species was considered using fresh and aged catches of all the species on the YSTs, and different pooling scenarios were applied to enhance model performance. The best performance was reached when fresh and aged individuals were used together and the whitefly species were pooled into one category for model training. With an independent dataset consisting of photos from the YSTs that were placed in greenhouses and consequently with a naturally occurring species mixture as the background, a differentiation rate of more than 85% was reached for natural enemies and whiteflies.