Mapping butterfly species richness and abundance in mountain grasslands – spatial application of a biodiversity indicator

Assigned Session: FS 3.149: Mountain regions as key biodiversity observatories – challenges and solutions in times of global changes

Abstract ID: 3.7985 | Pending | Talk/Oral | TBA | TBA

Friederike Barkmann (0)
Rüdisser, Johannes (1)
Friederike Barkmann ((0) Universität Innsbruck, Sternwartestraße 15, 6020, Innsbruck, Tyrol, AT)
Rüdisser, Johannes (1)

(0) Universität Innsbruck, Sternwartestraße 15, 6020, Innsbruck, Tyrol, AT
(1) Universität Innsbruck, Sternwartestraße, 6020, Innsbruck, AT

(1) Universität Innsbruck, Sternwartestraße, 6020, Innsbruck, AT

Categories: Biodiversity, Conservation, Ecosystems, Monitoring
Keywords: Butterflies, Species Richness, Grassland, Remote Sensing, Modelling

Categories: Biodiversity, Conservation, Ecosystems, Monitoring
Keywords: Butterflies, Species Richness, Grassland, Remote Sensing, Modelling

Assessing biodiversity in mountainous regions is challenging due to steep environmental gradients, complex small-scale landscape structures, and the resulting high habitat diversity. Even with carefully selected field survey locations, achieving a comprehensive, area-wide biodiversity characterization remains unattainable. Integrating field surveys with remote sensing data through spatial modelling approaches offers a promising solution to address this challenge. We developed such an approach to assess butterfly species richness and abundance in mountain grasslands in Western Austria – a region located in the Eastern Alps. Butterflies proved to be good biodiversity indicators, that can be surveyed comparably easily, inhabit a wide range of terrestrial habitats, react sensitively to changes in environmental conditions, and are representative for other groups of terrestrial insects. The models are based on butterfly monitoring data from 175 systematically selected survey sites of the Viel-Falter butterfly monitoring scheme and high-resolution remote sensing data including Sentinel-2 data. The environmental variables that were derived describe topography, grassland characteristics and the landscape composition and configuration around a site. The models were used to make predictions for butterfly species richness and abundance in the grassland areas of the study region and to analyse their drivers. Especially for species richness the models for predictions were promising with a residual mean squared error of less than 5 species on test data, while the models for abundance were less accurate. The analysis of drivers highlights the positive influence of moderate grassland productivity and forest ecotone structures and the importance of mountain topography. Our study contributes to a better understanding of the distribution and drivers of butterfly diversity and provides valuable information for policy makers and stakeholders at different spatial scales. The presented approach can aid the development of national nature conservation strategies that are especially relevant regarding EU Nature Restoration Law (NRL) and the role of grassland butterflies as designated indicators to monitor its progress. Ongoing technological developments have the potential to further improve the usability of remote sensing data for biodiversity assessments at large spatial extents.


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