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Weather explains the decline and rise of insect biomass over 34 years

Nature volume 628, pages 349–354 (2024)Cite this article

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Abstract

Insects have a pivotal role in ecosystem function, thus the decline of more than 75% in insect biomass in protected areas over recent decades in Central Europe1 and elsewhere2,3 has alarmed the public, pushed decision-makers4 and stimulated research on insect population trends. However, the drivers of this decline are still not well understood. Here, we reanalysed 27 years of insect biomass data from Hallmann et al.1, using sample-specific information on weather conditions during sampling and weather anomalies during the insect life cycle. This model explained variation in temporal decline in insect biomass, including an observed increase in biomass in recent years, solely on the basis of these weather variables. Our finding that terrestrial insect biomass is largely driven by complex weather conditions challenges previous assumptions that climate change is more critical in the tropics5,6 or that negative consequences in the temperate zone might only occur in the future7. Despite the recent observed increase in biomass, new combinations of unfavourable multi-annual weather conditions might be expected to further threaten insect populations under continuing climate change. Our findings also highlight the need for more climate change research on physiological mechanisms affected by annual weather conditions and anomalies.

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Fig. 1: Insect biomass sampled by Malaise traps in Germany from 1989 to 2022.
Fig. 2: Observed biomass per day versus a linear combination of the weather components based on the training data.

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Data availability

Raw data for all analyses are publicly available from figshare (https://doi.org/10.6084/m9.figshare.23536551).

Code availability

Annotated R code, including the data needed to reproduce the statistical analyses and figures, is publicly available from figshare (https://doi.org/10.6084/m9.figshare.23536551).

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Acknowledgements

J.M., A.M. and Y.Y. received funding for this study from the Bavarian Ministry of Science and the Arts through the Bavarian Climate Research Network (bayklif). The collection of validation data was supported by the DFG research unit BETA-FOR supported by the German Research Foundation grant no. 459717468, the research project ‘The impact of tree mortality in Bavaria 2018–2019 on forest resilience and biodiversity’ financed by the Bavarian Ministry of the Environment and Consumer Protection, and by the project ‘Effects of silvicultural interventions on biodiversity—New methods allow new insights (L062)’ financed by the Bavarian State Ministry for Food, Agriculture and Forestry. We further acknowledge the E-OBS dataset from the EU-FP6 project Uncertainties in Ensembles of Regional Re-Analysis (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the European Climate Assessment & Dataset project (https://www.ecad.eu). H. Bussler, S. Schmidt and J. Bittermann provided important suggestions during the selection of temporal windows potentially critical for insects in Malaise traps. We thank R. C. Burner and K. Vierling for linguistic correction of the manuscript.

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Authors and Affiliations

  1. Field Station Fabrikschleichach, Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Rauhenebrach, Germany

    Jörg Müller, Oliver Mitesser, Julia Rothacher, Julia Freund, Clara Wild & Marina Wolz

  2. Bavarian Forest National Park, Grafenau, Germany

    Jörg Müller

  3. Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland

    Torsten Hothorn

  4. Ecoclimatology, School of Life Sciences, Technical University of Munich, Freising, Germany

    Ye Yuan & Annette Menzel

  5. Ecosystem Dynamics and Forest Management Research Group, School of Life Sciences, Technical University of Munich, Freising, Germany

    Sebastian Seibold

  6. Berchtesgaden National Park, Berchtesgaden, Germany

    Sebastian Seibold

  7. Forest Zoology, TUD Dresden University of Technology, Tharandt, Germany

    Sebastian Seibold

  8. Institute for Advanced Study, Technical University of Munich, Garching, Germany

    Annette Menzel

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Contributions

J.M. conceived the idea of this manuscript. J.M., A.M. and T.H. designed the concept of the study. Y.Y. analysed the meteorological data, J.M., T.H. and O.M. ran the final analyses. J.R., J.F., C.W. and M.W. collected validation data. J.M., A.M., S.S. and T.H. wrote the first manuscript draft and finalized the manuscript. All authors commented on the manuscript.

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Correspondence to Jörg Müller.

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Nature thanks Ed Harris, Oliver Schweiger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Map of training and validation data.

Each dot represents the location of a Malaise trap. Grey dots show location of the training data from 1989 to 2016, coloured dots show the location of the traps in the different years of the validation data. The map was created in ArcGIS Pro using the EU country layer: https://uneplivemapservices.unep.org/arcgis/rest/services/EU_Countries/MapServer (June 22, 2023).

Extended Data Fig. 2 Temporal patterns of weather anomalies during the period of 1989 to 2016.

For biomass samples from Hallmann et al.1; Spearman’s correlation coefficient for monotone associations with year is reported. Grey line shows lowess smoother and black line linear function.

Extended Data Fig. 3 Monthly climate changes in Germany for the period 1961–2020.

Climate data (temperature, precipitation, sunshine duration) are shown for the last four 30-year reference periods (1961–1990 to 1991–2020), whereby conditions in the first period (1961–1990) are taken as baseline and deviations are given either in °C (temperature) or as % (precipitation, sunshine) for the subsequent three reference periods. Data from the German Meteorological Service (https://www.dwd.de/EN/ourservices/zeitreihen/zeitreihen.html). These data show rising temperatures over the longer time period, particularly in April and during the winter.

Extended Data Fig. 4 Multiplicative change of the effects of year.

As smoothed factor in our model 5. The confidence band of the effect includes the 1. This indicates that there is no more relevant effect of year (p = 0.12; see annotated R code), once the prediction from model 5 is included as an offset.

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Müller, J., Hothorn, T., Yuan, Y. et al. Weather explains the decline and rise of insect biomass over 34 years. Nature 628, 349–354 (2024). https://doi.org/10.1038/s41586-023-06402-z

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