More than 75 percent decline over 27 years in total flying insect biomass in protected areas.
Caspar A. Hallmann,Martin Sorg,Eelke Jongejans,Henk Siepel,Nick Hofland,Heinz Schwan,Werner Stenmans,Andreas Müller,Hubert Sumser,Thomas Hörren,Dave Goulson,Hans de Kroon +11 more
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This analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying insect biomass over the 27 years of study, and shows that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline.Abstract:
Global declines in insects have sparked wide interest among scientists, politicians, and the general public. Loss of insect diversity and abundance is expected to provoke cascading effects on food webs and to jeopardize ecosystem services. Our understanding of the extent and underlying causes of this decline is based on the abundance of single species or taxonomic groups only, rather than changes in insect biomass which is more relevant for ecological functioning. Here, we used a standardized protocol to measure total insect biomass using Malaise traps, deployed over 27 years in 63 nature protection areas in Germany (96 unique location-year combinations) to infer on the status and trend of local entomofauna. Our analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying insect biomass over the 27 years of study. We show that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline. This yet unrecognized loss of insect biomass must be taken into account in evaluating declines in abundance of species depending on insects as a food source, and ecosystem functioning in the European landscape.read more
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Moth biomass increases and decreases over 50 years in Britain.
TL;DR: Analysing data from the world’s longest-running insect population database, the authors find that recent declines in UK moth biomass were preceded by a larger increase, highlighting the need for long-term data to detect trends and identify their causes robustly.
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Complex long-term biodiversity change among invertebrates, bryophytes and lichens.
Charlotte L. Outhwaite,Charlotte L. Outhwaite,Richard D. Gregory,Richard D. Gregory,Richard E. Chandler,Ben Collen,Nick J. B. Isaac +6 more
TL;DR: By analysing changes in occupancy among >5,000 species of invertebrate, bryophytes and lichens in the United Kingdom over the past 45 years, the authors find substantial turnover in community composition among all groups, although average declines are evident only among terrestrial non-insect invertebrates.
Journal ArticleDOI
Integrated pest management: good intentions, hard realities. A review
Jean-Philippe Deguine,Jean-Noël Aubertot,Rica Joy Flor,Françoise Lescourret,Kris A.G. Wyckhuys,Alain Ratnadass +5 more
TL;DR: Agroecological Crop Protection is proposed as a concept that captures how agroecology can be optimally put to the service of crop protection, an interdisciplinary scientific field that comprises an orderly strategy at the field, farm, and agricultural landscape level and a dimension of social and organizational ecology.
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Global Patterns and Drivers of Bee Distribution.
Michael C. Orr,Alice C. Hughes,Alice C. Hughes,Douglas Chesters,John Pickering,Chao-Dong Zhu,John S. Ascher +6 more
TL;DR: A uniquely comprehensive checklist of bee species distributions and >5,800,000 public bee occurrence records are combined to describe global patterns of bee biodiversity, providing a new baseline and best practices for studies on bees and other understudied invertebrates.
Posted ContentDOI
Deep learning and computer vision will transform entomology
Toke T. Høye,Johanna Ärje,Kim Bjerge,Oskar Liset Pryds Hansen,Alexandros Iosifidis,Florian Leese,Hjalte M. R. Mann,Kristian Meissner,Claus Melvad,Jenni Raitoharju +9 more
TL;DR: This work connects recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates and shows how deep learning tools can convert the big data streams into ecological information.
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