Refine
Has Fulltext
- no (3)
Document Type
- Article (3)
Language
- English (3) (remove)
Is part of the Bibliography
- yes (3)
Keywords
- time series (3) (remove)
Institute
- Institut für Biochemie und Biologie (3) (remove)
Recent studies indicated severe decline of insect diversity and abundance across major parts of Central Europe.
Theoretical studies showed that the drivers behind biodiversity loss vary considerably over time. However, these scenarios so far have been insufficiently approved by long-term and large-scale data.
In this study we analysed the temporal trends of butterflies and Zygaenid moths across the federal state of Salzburg, northern Austria, from 1920 to 2019. Our study area covers a large variety of habitats and altitudes.
Various changes of land use and intensification occurred during and shortly before our studied period, with a first wave of habitat destruction starting in the late 19th century, followed by the deterioration of habitat quality since the mid-20th century. We used 59,870 presence-only data of 168 butterfly and burnet moth species.
Each of these species was classified according to ecological characteristics. Break point analyses for non-linear temporal trends in the community composition returned two major time windows.
These time windows coincide with periods characterized by severe habitat destruction and the deterioration of habitat quality due to agricultural intensification. We found significant reductions of the proportion of species requiring specific habitats since 1920 and until today.
We identified additional break points for species requiring high habitat qualities, endangered butterfly species, and sedentary species, particularly after a main break point in the 1960s.
Our findings underline that, apart from habitat destruction, the deterioration of habitat quality is a main driver of biodiversity loss in general.
Therefore, nature conservation should focus on maintaining the highest possible habitat quality.
Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture.
However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land.
We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed.
We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data.
Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and nondrought years. The maps show high spatial consistency and good delineation of field parcels.
Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level.
Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals.
Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions.
Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.
In most biodiversity studies, taxonomic diversity is the measure for the multiplicity of species and is often considered to represent functional diversity. However, trends in taxonomic diversity and functional diversity may differ, for example, when many functionally similar but taxonomically different species co-occur in a community. The differences between these diversity measures are of particular interest in diversity research for understanding diversity patterns and their underlying mechanisms. We analysed a temporally highly resolved 20-year time series of lake phytoplankton to determine whether taxonomic diversity and functional diversity exhibit similar or contrasting seasonal patterns. We also calculated the functional mean of the community in n-dimensional trait space for each sampling day to gain further insights into the seasonal dynamics of the functional properties of the community. We found an overall weak positive relationship between taxonomic diversity and functional diversity with a distinct seasonal pattern. The two diversity measures showed synchronous behaviour from early spring to mid-summer and a more complex and diverging relationship from autumn to late winter. The functional mean of the community exhibited a recurrent annual pattern with the most prominent changes before and after the clear-water phase. From late autumn to winter, the functional mean of the community and functional diversity were relatively constant while taxonomic diversity declined, suggesting competitive exclusion during this period. A further decline in taxonomic diversity concomitant with increasing functional diversity in late winter to early spring is seen as a result of niche diversification together with competitive exclusion. Under these conditions, several different sets of traits are suitable to thrive, but within one set of functional traits only one, or very few, morphotypes can persist. Taxonomic diversity alone is a weak descriptor of trait diversity in phytoplankton. However, the combined analysis of taxonomic diversity and functional diversity, along with the functional mean of the community, allows for deeper insights into temporal patterns of community assembly and niche diversification.