Lecture 1 Indrė Žliobaitė

Transcription

1 Machine learning methods for analysis of the global fossil record Lecture 1 Indrė Žliobaitė Indre.zliobaite@helsinki.fi Photo credit: Kayle Reed

2 Biospheric data Ecological and conservation data Fossil databases Weather stations Environment observation sensors Satellite imaging data

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4 What can be answered using machine learning Reconstructing how life was in the past and how life works in general Analyzing relations between organisms and environments, understanding biospheric change processes Analyzing why and how species evolve Understanding circumstances of early human evolution Occurring animals: Species A Species B Species C... Environment: Temperature Precipitation Productivity Seasonality Vegetation type Woody cover... Features of animals: Body mass Slim legs? Sharp teeth?...

5 Why understanding the past?

6 Climate change Source:

7 Extreme events Source:

8 Reconstructing and analyzing climate over time Fortelius et al 2002

9 Circumstances of early human environments Source:

10 Circumstances of early human environments Image credits: Mauricio Anton Source:

11 Slide credit: Mikael Fortelius

12 Estimates for the past, the Turkana basin Ecometric modeling makes quantitative analysis possible, complements qualitative insights with quantitative reasoning Fortelius et al 2016

13 Source: Barnosky et al 2017 Paleobiology and conservation: putting dead to work The geologic record as a natural ecological and evolutionary laboratory Dietl and Flessa 2011 Source: Turkana Basin Institute

14 Seminar agenda and topics

15 To pass the seminar Short presentation of a research article Data analysis project + presentation Implement one method from the article and one modification Attendance Exam?

16 Organization Mondays: lectures or presentations Tuesdays: project consultations come to discuss your projects, for help in interpreting articles, accessing data, implementation and analysis

17 Master projects are welcome (contact me) + A fully funded PhD position is available at the University of Helsinki on computational methods for analysis of the global fossil record, ecological and climate data to better understand history of life, evolutionary processes and environmental change. Apply via UH by October 31,

18 Topics for machine learning 1) Chronofaunas 2) Ecometrics 3) Macroevolution 4) Phylogenetic regression 5) Phylogenetic trees

19 Chronofaunas: (mostly unsupervised learning) tracking faunal communities over time Eronen et al 2009

20 Chronofaunas: (mostly unsupervised learning) identifying chronofaunas Bingahm and Mannila 2014

21 Chronofaunas: (mostly unsupervised learning) seriation - ranking of localities in time Fortelius et al 2006 Puolamaki et al 2006

22 one observation/ learning instance World today Occurring animals: Species A Species B Species C... Features of animals: Body mass Slim legs? Sharp teeth?... Fossil sites 1-5 million years old Occurring animals: Species X Species Y Species Z... Features of animals: Body mass Slim legs? Sharp teeth?... Fossil sites million years old... Environment: Temperature Precipitation Productivity Seasonality Vegetation type Woody cover Ecometrics analyzing ecosystems of the present and the past

23 Estimates for the past, the Turkana basin Age in MA Fortelius et al 2016 Age in MA

24 Why animal traits predict climate? Grazing in open environments requires much more more tooth than browsing in a closed forest foresta/pages/moose_jpg.htm /2-huge-genuine-moose-teeth-molars-alces pathphys/digestion/pregastric/horsepage.html

25 Features of animal teeth Žliobaitė et al 2016 Hypsodonty (HYP) Horizodonty (HOD) Presence of acute lophs (AL) Presence of obtuse lophs (OL) Structural fortification of cusps (SF) Occlusal topography (OT) Coronal cementum (CM)

26 Predicting climate from animal communities (concept drift, transfer learning) Satellite observation data Model fit

27 Ecometrics: (mostly predictive modeling, transfer learning) predicting productivity of environment from animal teeth Global regression, main traits Liu et al 2012 Advanced traits, focus on Africa Fortelius et al 2016

28 Ecometrics: (mostly predictive modeling, transfer learning) Redescription minning local relations between climate and dental traits Galbrun et al in review

29 Galbrun et al in review

30 Macroevolution: (probabilistic modeling, maximum likelihood) Analyzing tempo and mode of evolution Voje 2016

31 Macroevolution: (probabilistic modeling, maximum likelihood) Analyzing tempo and mode of evolution Raia et al 2016

32 Macroevolution: (probabilistic modeling, maximum likelihood) Estimating probabilities of occupancy Foote 2016

33 Phylogenetic regression Freckleton et al 2002 Felsenstein 1985

34 2017 Phylogeny reconstructing the tree of life

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37 Fitting phylogenetic trees Baron et al 2017

38 Fitting phylogenetic trees Baron et al 2017

39 Fossils and fossil databases

40 Richard Owen and MOA leg fossil, 1846 Wikimedia commons

41 Museums catalogues books fossil databases Recorded in global fossil databases, Including: taxonomic identification, fossil age, location, other characteristics Imgage: From Heck's Iconographic Encyclopedia (1851),

42 Sepkoski 1981

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44 Fossils

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50 Fossils are collected all over the world - depositional environment needs to be special for fossil preservation - dating / stratigraphy

51 Photo credit: Mikael Fortelius

52 Photo credit: Mikael Fortelius

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56 Fossil data Described and catalogued - identification of species - measured traits - environmental context - 3D scans Image:

57 NOW database (Helsinki)

58 PDBD Paleobiological database

59 Slide credit: Laura Säilä

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61 Slide credit: Laura Säilä

62 Fossil teeth

63 Slide credit: Laura Säilä

64 NOW database (Helsinki)