Identifying typical patterns of land-use and landscape structure in citizen science butterfly monitoring

• Cluster analysis reveals links between land use, landscape structure, and butterflies. • Butterfly diversity is highest in grassland- and forest-dominated landscapes. • Low-intensity grassland management seems most important in grassland-dominated landscapes. • Heterogenous landscape structure seems most important in crop-dominated landscapes. • Expanding German butterfly monitoring transects may enhance biodiversity assessments. Agricultural intensification in grasslands, characterized by increased livestock density, frequent mowing, and landscape homogenization, has driven significant declines in insect populations. Butterflies, key indicators of habitat quality, are sensitive to intensification and often used for monitoring. As they are influenced by the landscape context, understanding the landscapes surrounding monitoring transects is crucial for the evaluation of the status of butterflies and the effectiveness of conservation strategies. Using data from the German Butterfly Monitoring Scheme (DEBMS), we applied a nested clustering approach to group landscapes surrounding survey transects by dominant land cover, land-use intensity, and landscape structure. We then related these landscape contexts to butterfly diversity and community composition. Grassland- and forest-dominated clusters had highest species richness, abundance, number of specialists, and endangered species, while urban clusters had the lowest. Most species occurred independently of dominant land-cover types, highlighting the importance of even small proportions of habitats to support populations. We found no statistically significant differences in butterfly communities within crop- and grassland-dominated clusters when comparing different land-use intensity and landscape structure characteristics. However, data indicated higher richness, abundance, and more endangered species on less intensively managed grasslands. Our findings underscore the value of citizen science monitoring schemes in capturing broad-scale patterns in land use and landscape structure. Although clusters did not strongly differentiate distinct butterfly communities, they suggest that maintaining less intensively managed grasslands and expanding semi-natural habitats in agricultural landscapes can enhance butterfly diversity. We highlight the importance of accounting for landscape contexts when interpreting biodiversity patterns and informing conservation strategies.

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Publication Details

Journal
Ecological Indicators
Published
2025-10-22
DOI
https://doi.org/10.1016/j.ecolind.2025.114317
Citations
1
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
1.84
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article

Identifying typical patterns of land-use and landscape structure in citizen science butterfly monitoring

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1 citations
Ecological Indicators
Species Distribution and Climate Change
1.84
article

Identifying typical patterns of land-use and landscape structure in citizen science butterfly monitoring

Elisabeth Kühn, Diana Sietz, Gideon Okpoti Tetteh, Sebastian Klimek, Jens Dauber, Martin Musche, Christian Levers, Toni Kasiske, Marcel Schwieder, Josef Settele, Alexander Harpke
article en
1 citations

Abstract

• Cluster analysis reveals links between land use, landscape structure, and butterflies. • Butterfly diversity is highest in grassland- and forest-dominated landscapes. • Low-intensity grassland management seems most important in grassland-dominated landscapes. • Heterogenous landscape structure seems most important in crop-dominated landscapes. • Expanding German butterfly monitoring transects may enhance biodiversity assessments. Agricultural intensification in grasslands, characterized by increased livestock density, frequent mowing, and landscape homogenization, has driven significant declines in insect populations. Butterflies, key indicators of habitat quality, are sensitive to intensification and often used for monitoring. As they are influenced by the landscape context, understanding the landscapes surrounding monitoring transects is crucial for the evaluation of the status of butterflies and the effectiveness of conservation strategies. Using data from the German Butterfly Monitoring Scheme (DEBMS), we applied a nested clustering approach to group landscapes surrounding survey transects by dominant land cover, land-use intensity, and landscape structure. We then related these landscape contexts to butterfly diversity and community composition. Grassland- and forest-dominated clusters had highest species richness, abundance, number of specialists, and endangered species, while urban clusters had the lowest. Most species occurred independently of dominant land-cover types, highlighting the importance of even small proportions of habitats to support populations. We found no statistically significant differences in butterfly communities within crop- and grassland-dominated clusters when comparing different land-use intensity and landscape structure characteristics. However, data indicated higher richness, abundance, and more endangered species on less intensively managed grasslands. Our findings underscore the value of citizen science monitoring schemes in capturing broad-scale patterns in land use and landscape structure. Although clusters did not strongly differentiate distinct butterfly communities, they suggest that maintaining less intensively managed grasslands and expanding semi-natural habitats in agricultural landscapes can enhance butterfly diversity. We highlight the importance of accounting for landscape contexts when interpreting biodiversity patterns and informing conservation strategies.

Ecological IndicatorsVol. 180
University of the Philippines Los Baños (PH), Helmholtz Centre for Environmental Research (DE), German Centre for Integrative Biodiversity Research (DE), Humboldt-Universität zu Berlin (DE), Johann Heinrich von Thünen-Institut (DE), Technische Universität Braunschweig (DE)
Openalex Percentile: Top 12%
Species Distribution and Climate Change
1.84
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