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UID:DSC-16599
DTSTART;TZID=Europe/Berlin:20191022T100000
SEQUENCE:1575364564
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20191022T110000
URL:https://www.dresden-science-calendar.de/calendar/de/detail/16599
LOCATION:Andere\,   
SUMMARY:Seidl: Gastvortrag Prof. Dr. Rupert Seidl (BOKU Wien)
CLASS:PUBLIC
DESCRIPTION:Speaker: Prof. Dr. Rupert Seidl\nInstitute of Speaker: Universi
 ty of Natural Resources and Life Sciences (BOKU) Vienna\, Austria\nTopics:
 \nUmwelt\n Location:\n  Name: Andere (Hörssal C1)\n  Street:  \n  City:  
 \n  Phone: \n  Fax: \nDescription: <p><strong>Gastvortrag von<br />  Prof.
  Dr. Rupert Seidl</strong><br />  (https://boku.ac.at/wabo/waldbau/persone
 n/seidl)<br />  <strong>zum Thema:</strong></p>    <p>Disturbances in a ch
 anging world:<br />  Measurement\, Modeling\, Impacts</p>    <p><strong>Ab
 stract:</strong> Disturbances are discrete events in time and space that r
 esult in the loss of live biomass\, disrupting the structure and compositi
 on of ecosystems and altering environmental conditions and resource availa
 bility. Disturbances are ubiquitous in ecosystems around the world\, and a
 gents such as wildfires\, storms\, and insect outbreaks are key drivers of
  natural ecosystem dynamics. Disturbances contribute to the renewal of nat
 ural ecosystems and are an important process in their adaptation to changi
 ng environmental conditions. At the same time\, disturbances are highly cl
 imate sensitive\, and future disturbance regimes are projected to intensif
 y in many regions of the globe. This poses considerable challenges for eco
 system management\, as disturbances can have detrimental impacts on the pr
 ovisioning of ecosystem services to society. Here I will provide a short s
 ynopsis on current developments in measuring and modeling natural disturba
 nces in order to assess their impacts on forest ecosystems. Remote sensing
  has recently improved the availability of consistent disturbance data\, e
 nabling the application of advanced computational approaches from network 
 theory and machine learning. In  addition\, a new generation of process-b
 ased disturbance models is emerging\, driven by novel insights from large-
 scale experiments and empirical studies. Such approaches are increasingly 
 incorporated into dynamic vegetation models\, which serve as powerful plat
 forms for an integrated modeling of disturbance regimes. An improved capac
 ity to project future disturbance regimes is an important element in incre
 asing the robustness of simulations informing future ecosystem management 
 and climate policy.</p>
DTSTAMP:20261103T143048Z
CREATED:20191017T220945Z
LAST-MODIFIED:20191203T091604Z
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