Suchergebnisse

Die Saiga-Antilope (Saiga tatarica) bevölkerte einst in riesigen Herden den gesamten eurasischen Steppengürtel von den Karpaten bis in die Mongolei. Heute liegt ihr Hauptverbreitungsgebiet in Kasachstan. Die Art ist ausgezeichnet angepasst an die Herausforderungen dieses Ökosystems und zugleich auch wichtig für seine Erhaltung. Die Saiga-Bestände waren in den letzten zwanzig Jahren seit Ende der Sowjetunion jedoch drastisch eingebrochen. Wilderei vor allem für das Horn der männlichen Tiere, das in der chinesischen Medizin geschätzt wird, aber auch für Fleisch, hatte zu einem starken Rückgang der Bestandszahlen geführt. Intensive Naturschutzmaßnahmen durch internationale und lokale Naturschutzorganisationen und die kasachstanische Regierung lieferten zwar in jüngster Vergangenheit wieder Grund zu Optimismus, was den Erhalt dieser für die Steppengebiete so wichtigen Art angeht. In den vergangenen Jahren aber stellen rätselhafte Massensterben Naturschützer und Forscher vor neue Herausforderungen: Allein im Frühjahr 2015 starben innerhalb weniger Tage nach vorläufigen Angaben 150.000 Tiere.

Shared use of rangelands by livestock and wildlife can lead to disease transmission. To align agricultural livelihoods with wildlife conservation, a multipronged and interdisciplinary approach for disease management is needed, particularly in data-limited situations with migratory hosts. Migratory wildlife and livestock can range over vast areas, and opportunities for disease control interventions are limited. Predictive frameworks are needed which can allow for identification of potential sites and timings of interventions. We developed an iterative three-step framework to assess cross-species disease transmission risk between migrating wildlife and livestock in data-limited circumstances and across social-ecological scales. The framework first assesses risk of transmission for potentially important diseases for hosts in a multi-use landscape. Following this, it uses an epidemiological risk function to represent transmission-relevant contact patterns, using density and distribution of the host to map locations and periods of disease risk. Finally, it takes fine-scale data on livestock management and observed wildlife-livestock interactions to provide locally relevant insights on disease risk. We applied the framework to characterize disease transmission between livestock and saiga antelopes Saiga tatarica in Central Kazakhstan. At step 1, we identified peste-des-petits-ruminants as posing a high risk of transmission from livestock to saigas, foot-and-mouth disease as low risk, lumpy skin disease as unknown and pasteurellosis as uncertain risk. At step 2, we identified regions of high disease transmission risk at different times of year, indicating where disease management should be focussed. At step 3, we synthesized field surveys, government data and literature review to assess the role of livestock in the 2015 saiga mass mortality event from pasteurellosis, concluding that it was minimal. Synthesis and applications. Our iterative framework has wide applicability in assessing and predicting disease spill-over at management-relevant temporal and spatial scales in areas where livestock share space with migratory species. Our case study demonstrated the value of combining ecological and social information to inform management of targeted interventions to reduce disease risk, which can be used to plan disease surveillance and vaccination programmes.

Shared use of rangelands by livestock and wildlife can lead to disease transmission. To align agricultural livelihoods with wildlife conservation, a multipronged and interdisciplinary approach for disease management is needed, particularly in data‐limited situations with migratory hosts. Migratory wildlife and livestock can range over vast areas, and opportunities for disease control interventions are limited. Predictive frameworks are needed which can allow for identification of potential sites and timings of interventions. We developed an iterative three‐step framework to assess cross‐species disease transmission risk between migrating wildlife and livestock in data‐limited circumstances and across social‐ecological scales. The framework first assesses risk of transmission for potentially important diseases for hosts in a multi‐use landscape. Following this, it uses an epidemiological risk function to represent transmission‐relevant contact patterns, using density and distribution of the host to map locations and periods of disease risk. Finally, it takes fine‐scale data on livestock management and observed wildlife–livestock interactions to provide locally relevant insights on disease risk. We applied the framework to characterize disease transmission between livestock and saiga antelopes Saiga tatarica in Central Kazakhstan. At step 1, we identified peste‐des‐petits‐ruminants as posing a high risk of transmission from livestock to saigas, foot‐and‐mouth disease as low risk, lumpy skin disease as unknown and pasteurellosis as uncertain risk. At step 2, we identified regions of high disease transmission risk at different times of year, indicating where disease management should be focussed. At step 3, we synthesized field surveys, government data and literature review to assess the role of livestock in the 2015 saiga mass mortality event from pasteurellosis, concluding that it was minimal. Synthesis and applications. Our iterative framework has wide applicability in assessing and predicting disease spill‐over at management‐relevant temporal and spatial scales in areas where livestock share space with migratory species. Our case study demonstrated the value of combining ecological and social information to inform management of targeted interventions to reduce disease risk, which can be used to plan disease surveillance and vaccination programmes.