Annual occupancy estimates for butterflies, grasshoppers and dragonflies in Bavaria (Germany), 1980-2019
Cite this dataset
Engelhardt, Eva Katharina et al. (2022). Annual occupancy estimates for butterflies, grasshoppers and dragonflies in Bavaria (Germany), 1980-2019 [Dataset]. Dryad. https://doi.org/10.5061/dryad.4f4qrfjf5
Recent climate and land-use changes are having substantial impacts on biodiversity, including population declines, range shifts, and changes in community composition. However, few studies have compared these impacts among multiple taxa, particularly because of a lack of standardized time series data over long periods. Existing datasets are typically of low resolution or poor coverage, both spatially and temporally, thereby limiting the inferences that can be drawn from such studies. Here, we compare climate and land-use driven occupancy changes in butterflies, grasshoppers, and dragonflies using an extensive dataset of highly heterogeneous observation data collected in the central European region of Bavaria (Germany) over a 40-year period. Using occupancy models, we find occupancies (the proportion of sites occupied by a species in each year) of 37% of species have decreased, 30% have increased and 33% showed no significant trend. Butterflies and grasshoppers show strongest declines with 41% of species each. By contrast, 52% of dragonfly species increased. Temperature preference and habitat specificity appear as significant drivers of species trends. We show that cold-adapted species across all taxa have declined, while warm-adapted species have increased. In butterflies, habitat specialists have decreased, while generalists increased or remained stable. The trends of habitat generalists and specialists both in grasshoppers and semi-aquatic dragonflies however did not differ. Our findings indicate strong and consistent effects of climate warming across insect taxa. The decrease of butterfly specialists could hint towards a threat from land-use change, as especially butterfly specialists’ occurrence depends mostly on habitat quality and area. Our study not only illustrates how these taxa showed differing trends in the past, but also provides hints on how we might mitigate the detrimental effects of human development on their diversity in the future.
The main dataset (Engelhardt_etal_2022_OccupancyEstimates.csv) provides annual estimates of species occupancy for 300 insect species in the German federal state of Bavaria from 1980 to 2019. Species include 163 butterfly (Lepidoptera, Rhopalocera), 66 grasshopper (Orthoptera), and 71 dragonfly and damselfly (Odonata) species. Occupancy estimates were generated using Bayesian occupancy models, using observations of species collected by the Bavarian Environment Agency (Bayerisches Landesamt für Umwelt) for the database of species in Bavaria ("Bayerische Artenschutzkartierung (ASK)", www.lfu.bayern.de/natur/artenschutzkartierung) on a grid of 5x5km squares.
Our occupancy models are based on Outhwaite et al. (2018, 2019) and Bowler et al. (2021), the code is given in the file Engelhardt_etal_2022_OccupancyModels.R which uses BUGS-files (Engelhardt_etal_2022_BUGScode_moreGenerations.txt, Engelhardt_etal_2022_BUGScode_oneGeneration.txt) as well as information on the number of generations per year for butterflies (Engelhardt_etal_2022_Lepidoptera_NumGenerations.csv). Occupancy models are hierarchical models consisting of a state submodel describing the best estimate of occupancy, and an observation submodel describing the detection probability of a species. Each species' occupancy was estimated as the mean proportion of sites occupied each year, including uncertainty estimates and convergence statistics.
We provide an overview of the occupancy model results in the file Engelhardt_etal_2022_DataOverview.R, explored species' trends in the file Engelhardt_etal_2022_InsectTrends.R, and analyzed possible drivers of these trends in the file Engelhardt_etal_2022_InsectAttributes.R. Data on species' traits was compiled from various public sources linked in the publication.
The annual occupancy estimates published here are the first large-scale, long-term trend estimates for this set of species in a European model region, broadening the knowledge of central European biodiversity change.
Format of the data:
Engelhardt_etal_2022_OccupancyEstimates.csv - Comma-separated values (CSV)
Engelhardt_etal_2022_Lepidoptera_NumGenerations.csv - Tab-delimited values
Bavarian Climate Research Network bayklif (project ‘mintbio’)
Deutsche Forschungsgemeinschaft, Award: DFG FZT 118, 202548816