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BEGIN:VEVENT

CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=18832e99-2a84ac8f-012a-85e0def0-00000038:Rauch\, Lo
 ri
CREATED:20250428T171215Z
DESCRIPTION:Traditional expert-based biodiversity surveys are increasingly
  replaced by semi-automated sensors that produce completely new types and
  volumes of data. As one example\, our ongoing ERC-synergy project LIFEPL
 AN (PIs: Otso Ovaskainen\, David Dunson\, Tomas Roslin & Brian Fisher) ma
 ps global biodiversity with DNA-\, image-\, and audio-based sampling tech
 nologies. The project has generated >100 years of audio\, >10M camera-tra
 p images\, and >10 billion DNA metabarcoding sequences. These new types o
 f data provide completely new challenges for data processing and interpre
 tation\, the solving of which has motivated the close collaboration betwe
 en statisticians and ecologists in LIFEPLAN. To convert the information i
 n raw DNA-\, audio- and image samples to data on species-level occurrence
 s and/or abundances\, we have improved automated methods for probabilisti
 c species classification. One particular challenge concerning the species
  rich groups of fungi and arthropods is that most species are still unkno
 wn to science and thus lacking from any reference databases. To address t
 his challenge\, we have developed classifiers that robustly assign DNA se
 quencies to earlier known species\, to novel species\, or highlight them 
 as uncertain cases that may represent either earlier known or novel speci
 es. To generate ecological insights and to make predictions on how specie
 s communities respond to the ongoing global change\, we have developed jo
 int species distribution models that integrate the classified biodiversit
 y data with spatiotemporal predictors. One particular challenge in this l
 ine of research is the high dimensionality of the multivariate response (
 the LIFEPLAN data contains detection of ca. 1 million species)\, combined
  with the extreme sparseness of the data (most species have been observed
  only in one sample). To address this challenge\, we have solved earlier 
 computational bottlenecks of by combining high-performance computing with
  common to rare species transfer learning\, enabling models to be fitted 
 to full biodiversity data. The methodological advances generated by the L
 IFEPLAN project have already contributed to more broadly to the society\,
  including e.g. a new smartphone-based approach to citizen science.
DURATION:PT2H
DTSTAMP:20250428T180706Z
DTSTART;TZID=America/New_York:20250429T100000
LAST-MODIFIED:20250428T180706Z
LOCATION;X-BEDEWORK-UID=8a0870ef-4fb9fa3c-014f-ccd34f6a-000042d6:Rubenstei
 n Library Holsti-Anderson Family Assembly Room 153
STATUS:CONFIRMED
SUMMARY:Surveying the World's Biodiversity with DNA\, Audio\, Image\, Mach
 ine Learning and Statistics
UID:CAL-8a000483-92c3adf6-0196-7d62992a-00000ab1demobedework@mysite.edu
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
 ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Lecture_Talk:/user/public-us
 er/Lectures_Conferences/Lecture_Talk
X-BEDEWORK-SPEAKER:Otso Ovaskainen\, Professor of Mathematical and Statist
 ical Ecology\, University of Jyvaskyla
X-BEDEWORK-SUBMITTEDBY:lr3 for Statistical Science (agrp_StatisticalScienc
 e)
X-BEDEWORK-IMAGE-X1:0
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X-BEDEWORK-IMAGE-ALT-TEXT:Otso Ovaskainen
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END:VEVENT
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