Accounting for geographical variation in species–area relationships improves the prediction of plant species richness at the global scale.
Gerstner, K., Dormann, C.F., Václavík, T., Kreft, H., Seppelt, R., (2013): Journal of Biogeography
The species–area relationship (SAR) is a prominent concept for predicting species richness and biodiversity loss. A key step in defining SARs is to accurately estimate the slope of the relationship, but researchers typically apply only one global (canonical) slope which is overly simplistic. We show that predictions of global species richness patterns can be considerably improved by accounting for variation due to biomes.
Mapping global land system archetypes.
provides a new representation of global land systems based on more than 30 high-resolution datasets on land-use intensity, environmental conditions and socioeconomic indicators. This approach advances our under-standing of the global patterns of human-environment interactions and of the environmental and social conditions associated with different types of land use.
Václavík, T., Lautenbach, S., Kuemmerle, T., Seppelt, R. (2013): Global Environmental Change.
Identifying trade-offs between ecosystem services, land use, and biodiversity: a plea for combining scenario analysis and optimization on different spatial scales:
provides perspectives on the application of exploratory modelling, esp. optimization for a quantitative analysis of trade-off of different types of land use in multifunctional landscape. We also discuss the integration of these as well as scenario analysis for solving regional as well as global aspect of land use conflicts: Seppelt, R., Lautenbach, S., Volk, M., (2013): Current Opinion in Environmental Sustainability.
The impact of Best Management Practices on simulated streamflow and sediment load in a Central Brazilian catchment:
In several Brazilian river basins Best Management Practices, such as terraces or sediment retention basins, are supported by 'Payments for Environmental Services'. By means of process-based scenario simulations, this study quantified the cost-effectiveness of such measures regarding sediment retention and water yield: Strauch, M., Lima, J.E.F.W. , Volk, M., Lorz, C., Makeschin, F., (2013): J. Environ. Manage.
A new multi-scale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field and landscape:
Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales: Lausch, A. et al. (2012) Environmental Monitoring and Assessment. doi:10.1007//s10661-012-2627-8
Spatial and temporal trends of global pollination benefit:
Based on global data on land use and time series for production quantities and production prices of pollination dependent crops, an global increase in pollination benefits was shown and regional hotspots of pollination benefits were identified: Lautenbach, S. et al. (2012) PlosONE e35954. doi:10.1371/journal.pone.0035954
Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures:
This European wide analysis of urban heat island on the base of remote sensing data based on 263 cities revealed the variation of classical urban heat island indicators and identified the need to comparatively quantify several indicators of urban heat islands in parallel to foster comparability: Schwarz, N. et al. (2011) Remote Sensing of Environment, 115, 3175-3186.
Using precipitation data ensemble for uncertainty analysis in SWAT streamflow simulation:
The study shows that ensemble modeling with multiple precipitation inputs can considerably increase the level of confidence in hydrological simulation results, particularly in data-poor regions: Strauch, M., Bernhofer, C., Koide, S., Volk, M., Lorz, C., Makeschin, F. (2012) Journal of Hydrology 414-415, 413-424. doi:10.1016/j.jhydrol.2011.11.014