Earthworm distribution and abundance predicted by a process-based model |
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| Institution: | 1. School of Biological Sciences, University of Reading, UK;2. Department of Bioscience, Aarhus University, Denmark;3. Environment Department, University of York, UK;4. Environmental Safety, Syngenta Ltd., Bracknell, UK;5. EcoRisk Solutions Ltd., Norwich, UK;1. Universidade Positivo, Curitiba, Brazil;2. EmbrapaFlorestas, Estrada da Ribeira Km. 111, C.P. 319, Colombo 83411-000, Brazil;3. Universidade do Estado de Santa Catarina – Centro de Ciências Agroveterinárias, Lages, Brazil;4. Univeristy of Iowa, Iowa City, United States;5. ECODIV Laboratory, EA 1293, SFR SCALE, Université de Rouen, 76821 Mont Saint Aignan cedex, France;6. Universidade do Estado de Santa Catarina – Centro de Educação Superior do Oeste, Chapecó, Brazil;1. INRA, AGROCAMPUS OUEST, UMR 1069 SAS, F-35000 Rennes, France;2. CNRS, Université Rennes 1, UMR 6553 ECOBIO, F-35000 Rennes, France;1. Sorbonne Universités, UPMC Univ Paris 06, UPEC, Paris 7, CNRS, INRA, IRD, Institut d’Ecologie et des Sciences de l’Environnement de Paris, 75005, Paris, France;2. UMR 1121, Université de Lorraine – INRA, Laboratoire Agronomie et Environnement, 2 Avenue Forêt de la Haye, 54518 Vandoeuvre, France;3. Université Paris Est Créteil (UPEC), UPMC Univ Paris 06, Paris 7, CNRS, INRA, IRD, Institut d’écologie et des sciences de l’environnement de Paris, 94010 Créteil Cedex, France;1. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany;2. Institute of Biology, Leipzig University, Johannisallee 21, 04103 Leipzig, Germany;3. J.F. Blumenbach Institute of Zoology and Anthropology, Georg August University Göttingen, Berliner Straße 28, 37073 Göttingen, Germany;1. Department of Earth and Environmental Sciences, KU Leuven, University of Leuven, Celestijnenlaan 200E Box 2411, BE-3001 Leuven, Belgium;2. Forest & Nature Lab, Department Forest and Water Management, Ghent University, Geraardsbergsesteenweg 267, B-9090 Gontrode, Belgium;3. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, D-04103 Leipzig, Germany;4. Martin Luther University Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, D-06108 Halle (Saale), Germany;5. Laboratory of Plant and Microbial Ecology, University of Liège, Botany Bât. 22, Quartier Vallée 1, Chemin de la Vallée 4, 4000 Liège, Belgium;6. Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark;7. University Stefan cel Mare of Suceava, Forestry Faculty, 13 Universitatii Street, 720229 Suceava, Romania;8. Natural Resources Institute Finland, Box 68, FI-80101 Joensuu, Finland;9. Centre of Evolutionary and Functional Ecology (CEFE UMR 5175, CNRS – University of Montpellier – University Paul-Valéry Montpellier – EPHE), 1919 route de Mende, 34293 Montpellier Cedex 5, France;10. Białowieża Geobotanical Station, Faculty of Biology, University of Warsaw, Sportowa 19, 17-230 Białowieża, Poland;11. Department of Geobotany, Faculty of Biology, University of Freiburg, Schänzlestraße 1, 79104 Freiburg, Germany;12. Department of Agri-Food and Environmental Sciences, University of Firenze, Piazzale delle Cascine 28, 50144, Firenze, Italy;13. Department of Special Botany and Functional Biodiversity, University of Leipzig, 04103 Leipzig, Germany;14. Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, E-28006 Madrid, Spain |
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| Abstract: | Earthworms are significant ecosystem engineers and are an important component of the diet of many vertebrates and invertebrates, so the ability to predict their distribution and abundance would have wide application in ecology, conservation and land management. Earthworm viability is known to be affected by the availability and quality of food resources, soil water conditions and temperature, but has not yet been modelled mechanistically to link effects on individuals to field population responses. Here we present a novel model capable of predicting the effects of land management and environmental conditions on the distribution and abundance of Aporrectodea caliginosa, the dominant earthworm species in agroecosystems. Our process-based approach uses individual based modelling (IBM), in which each individual has its own energy budget. Individual earthworm energy budgets follow established principles of physiological ecology and are parameterised for A. caliginosa from experimental measurements under optimal conditions. Under suboptimal conditions (e.g. food limitation, low soil temperatures and water contents) reproduction is prioritised over growth. Good model agreement to independent laboratory data on individual cocoon production and growth of body mass, under variable feeding and temperature conditions support our representation of A. caliginosa physiology through energy budgets. Our mechanistic model is able to accurately predict A. caliginosa distribution and abundance in spatially heterogeneous soil profiles representative of field study conditions. Essential here is the explicit modelling of earthworm behaviour in the soil profile. Local earthworm movement responds to a trade-off between food availability and soil water conditions, and this determines the spatiotemporal distribution of the population in the soil profile. Importantly, multiple environmental variables can be manipulated simultaneously in the model to explore earthworm population exposure and effects to combinations of stressors. Potential applications include prediction of the population-level effects of pesticides and changes in soil management e.g. conservation tillage and climate change. |
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| Keywords: | Individual based model Earthworm Energy budget Food availability Soil water potential Local movement |
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