Suchergebnisse

Die Welt hat nicht nur ein Corona-Problem. Die Gefahren durch Artensterben und Klimawandel sind nach wie vor mindestens ebenso gross - und haben die gleichen Ursachen. Der renommierte Umweltforscher und Agrarökologe Josef Settele analysiert die Gründe und Folgen dieser dreifachen Krise. Er erläutert sie vor allem anhand der Insekten, deren Gefährdung beispielhaft für die der gesamten Artenvielfalt steht. Die Auslöser - eine unkontrollierte Ausbeutung der Natur, immer intensivere Landnutzung und wachsende Verstädterung, sowie ungebremste Abholzungen IBM sind zugleich wesentliche Ursachen für den Ausbruch von Pandemien. Der Klimawandel verstärkt diese Entwicklung und seine Auswirkungen nochmal dramatisch, die drei Komponenten der Triple-Krise befeuern sich gegenseitig. Setteles Ansatz für Wege aus der Krise ist ein umfassender, von der lokalen bis zur globalen Ebene. Ein Weiter-wie-bisher ist für ihn keine Option, sein dringender Aufruf: Nur wenn wir die Natur gemeinsam schützen, schützen wir uns auch selbst! (Verlagstext)

Designing agri-environmental schemes targeted at conservation poses the key question of how many financial resources should be allocated to address a particular aim such as the conservation of an endangered species. Economists can contribute to an answer by estimating the 'optimal level of species conservation'. This requires an assessment of the supply and the demand curve for conservation and a comparison of the two curves to identify the optimal conservation level. In a case study we estimate the optimal conservation level of Large Blue butterflies (protected by the EU Habitats Directive) in the region of Landau, Germany. The difference to other studies estimating optimal conservation is that a problem is addressed where costs and benefits of conservation measures are heterogeneous in space and over time. In our case study we find a corner solution where the highest proposed level of butterfly conservation is optimal. Although our results are specific to the area and species studied, the methodology is generally applicable to estimate how many financial resources should be allocated to conserve an endangered species in the context of agri-environmental schemes.

International audience There is mounting evidence of pollinator decline all over the world and consequences in many agricultural areas could be significant. We assessed these consequences by measuring 1) the contribution of insect pollination to the world agricultural output economic value, and 2) the vulnerability of world agriculture in the face of pollinator decline. We used a bioeconomic approach, which integrated the production dependence ratio on pollinators, for the 100 crops used directly for human food worldwide as listed by FAO. The total economic value of pollination worldwide amounted to €153 billion, which represented 9.5% of the value of the world agricultural production used for human food in 2005. In terms of welfare, the consumer surplus loss was estimated between €190 and €310 billion based upon average price elasticities of –1.5 to –0.8, respectively. Vegetables and fruits were the leading crop categories in value of insect pollination with about €50 billion each, followed by edible oil crops, stimulants, nuts and spices. The production value of a ton of the crop categories that do not depend on insect pollination averaged €151 while that of those that are pollinator-dependent averaged €761. The vulnerability ratio was calculated for each crop category at the regional and world scales as the ratio between the economic value of pollination and the current total crop value. This ratio varied considerably among crop categories and there was a positive correlation between the rate of vulnerability to pollinators decline of a crop category and its value per production unit. Looking at the capacity to nourish the world population after pollinator loss, the production of 3 crop categories – namely fruits, vegetables, and stimulants– will clearly be below the current consumption level at the world scale and even more so for certain regions like Europe. Yet, although our valuation clearly demonstrates the economic importance of insect pollinators, it cannot be considered as a scenario since it does not ...

Ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our ecosystems as well as their ecosystem functions. The relationships between drivers, stress and ecosystem functions in ecosystems are complex, multi- faceted and often non-linear and yet environmental managers, decision makers and politicians need to be able to make rapid decisions that are data-driven and based on short- and long-term monitoring information, complex modeling and analysis approaches. A huge number of long-standing and standardized ecosystem health approaches like the essential variables already exist and are increasingly integrating remote-sensing based monitoring approaches [1-2]. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. This presentation therefore discusses the requirements for using Data Science as a bridge between complex and multidimensional Big Data for environmental health. It became apparent that no existing monitoring approach, technique, model or platform is sufficient on its own to monitor, model, forecast or assess vegetation health and its resilience. In order to advance the development of a multi-source ecosystem health monitoring network, we argue that in order to gain a better understanding of ecosystem health in our complex world it would be conducive to implement the concepts of Data Science with the components: (i) digitalization, (ii) standardization with metadata management adhering to the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, (iii) Semantic Web, (iv) proof, trust and uncertainties, (v) complex tools for Data Science analysis and (vi) easy tools for scientists, data managers and stakeholders for decision-making support [3-4]. REFERENCES: 1.Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J.M., Heurich, M., Jung, A., Klenke, R., Neumann, C., Pause, M., Rocchini, D., Schaepman, M.E.; Schmidtlein, S., Schulz, K., Selsam, P., Settele, J., Skidmore, A.K., Cord, A.F., 2016. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecol. Indic. 70, 317–339. doi:10.1016/j.ecolind.2016.06.022. 2.Lausch, A., Erasmi, S., Douglas, J., King, Magdon, P., Heurich, M., 2016. Understanding forest health with remote sensing - Part I - A review of spectral traits, processes and remote sensing characteristics. Remote Sens. 8, 1029; doi:10.3390/rs8121029. 3.Lausch, A.; Bastian O.; Klotz, S.; Leitão, P. J.; Jung, A.; Rocchini, D.; Schaepman, M.E.; Skidmore, A.K.; Tischendorf, L.; Knapp, S. 2018. Understanding and assessing vegetation health by in-situ species and remote sensing approaches. Methods Ecol. Evol. 00, 1–11. doi:10.1111/2041-210X.13025. 4.Lausch, A., Borg, E., Bumberger, J., Dietrich, P., Heurich, M., Huth, A., Jung, A., Klenke, R., Knapp, S., Mollenhauer, H., Paasche, H., Paulheim, H., Pause, P., Schweitzer, C., Schmulius, C., Settele, J., Skidmore, A.K.,, Wegmann, M., Zacharias, S., Kirsten, T.; Schaepman, M.E., 2018. Understanding forest health with remote sensing -Part III - Requirements for a scalable multi-source forest health monitoring network based on Data Science approaches. (Remote Sens., in review).