BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//www.bayceer.uni-bayreuth.de//NONSGML kigkonsult.se iCalcreator 2.41.92//
CALSCALE:GREGORIAN
METHOD:PUBLISH
UID:38316631-3430-4638-b933-393634616235
X-WR-CALNAME:BayCMS-Export
X-WR-CALDESC:This is a calendar exported from BayCMS
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
TZUNTIL:20100328T010000Z
BEGIN:STANDARD
TZNAME:CET
DTSTART:20071028T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
RDATE:20081026T030000
RDATE:20091025T030000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:CEST
DTSTART:20080330T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RDATE:20090329T020000
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:www.bayceer.uni-bayreuth.de-bayceer-t60369id
DTSTAMP:20260907T053337Z
DESCRIPTION:Statistical species distribution models are a useful tool to de
 termine the habitat factors that govern the spatial and temporal distribut
 ion of species and to assess the effect of changing environmental conditio
 ns on species distributions. This talk presents species distribution model
 s for rock ptarmigan\, an alpine bird species. A multi-scale approach cons
 idering territory scale and larger scales with 1 km2 as well as 100 km2 gr
 ain size shows the scale dependency of environmental predictors. To analys
 e the effect of climate change on the spatial distribution of this species
 \, scenarios are used to predict the future habitat in 2030\, 2050 and 207
 0. Predictions are calculated with several methods (i.e. standard methods 
 such as GLM\, GAM\, CART as well as sophisticated machine learning and ens
 emble forecasting methods such as Boosted Regression Trees and Random Fore
 st) to analyse the high amount of model uncertainty related to the statist
 ical approach. All models are checked for residual spatial autocorrelation
  and internally validated by bootstrapping to receive unbiased estimates o
 f model performance. The second part of the talk will focus on the applica
 bility of the methods applied in this study in related disciplines such as
  soil landscape modelling and predictive eco-geomorphological modelling.
DTSTART;TZID=Europe/Berlin:20080529T161500
DTEND;TZID=Europe/Berlin:20080529T235959
LOCATION:H 6
SUMMARY:Dr. Boris Schröder\, Institute of Geoecology\, University of Potsda
 m: Model uncertainty in statistical modelling and regionalisation - assess
 ing the effect of climate change on species distribution
TRANSP:TRANSPARENT
END:VEVENT
END:VCALENDAR