Global Modeling of Biodiversity and Climate Change


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Global Modeling of Biodiversity and Climate Change

  1. 1. Global Modeling of Biodiversity and Climate Change Falk Huettmann et al. -EWHALE lab- Biology and Wildlife Department Institute of Arctic Biology University of Alaska-Faibanks Fairbanks Alaska
  2. 2. EWHALE lab
  3. 3. Re. Scientific Thinking and Thought Karl Popper Leo BreimanFelix Shtilmark Herman Daly Dave Carlson
  4. 4. Scientific Landmines ?! Spatial/Geographic Information Systems (GIS) and… Data Sharing (online) Machine LearningPredictions Data Mining Diseases Metadata (Influenza) Sustainability Economic Growth problem Management
  5. 5. Central to our work:Predictions in Space and Time,e.g. done best with Machine Learning -quantitative -spatial -statistical interactions included -one formula -one algorithm -repeatable -testable -transparent -open access
  6. 6. How GIS and machine learning connect… A Work FlowArcGIS 10.2SalfordRGMEPython etc
  7. 7. Tree/CART - Family Binary recursive partitioning Temp>15 Precip <100 Temp<5 YES NOLeo Breiman 1984, and others PURITY METRIC OF NODES
  8. 8. TreeNet The more nodes (~A sequence of CARTs) …the more detail …the slower ‘boosting’ + + + + each explains the remaining variance til the end… ROC ROC curves for accuracy tests Importance ValueVariableLDUSE 100.00 Score |||||||||||||||||||||||||||||||||||||||||| e.g. correctly predicted absence app. 97%TAIR_AUG 97.62 |||||||||||||||||||||||||||||||||||||||||HYDRO94.35 ||||||||||||||||||||||||||||||||||||||||DEM94.01 ||||||||||||||||||||||||||||||||||||||| e.g. correctly predicted presence app. 92%PREC_AUG 90.17 ||||||||||||||||||||||||||||||||||||||POP 82.54 Difficult to interpret ||||||||||||||||||||||||||||||||||HMFPT81.46 |||||||||||||||||||||||||||||||||| =>Apply to a dataset for predictions but good graphs
  9. 9. RandomForest (Prasad et al. 2006,Boosting & Bagging algorithms Furlanello et al. 2003Handles ‘noise’, interactions Breiman 2001)and categorical data fine! Random set 1 Random set of Columns (Predictors) DEM Slope Aspect Climate Land- coverRandom set of Rows 1 Random set 2 (Cases) 2 3 4 5 Average Final Tree from e.g.>2000 trees done by VOTING Bagging: Optimization based on In-Bag, Out-of Bag samples In RF no pruning => Difficult to overfit Difficult to interpret (robust) but good graphs
  10. 10. Machine Learning example with GIS: Spoon-billed Sandpiper and Predictions (where are the wintering grounds of ca. 1000 highly endangered birds…?) (breeding, Kamchatka) (winter)Engler et al.(in prep)
  11. 11. Data means Metadata and Data Management (specifically for GIS, for science projects, machine learning and for graduate students) ___________Field Season 1_________ ___________Field Season 2 & 3_________ Raw Dataset 1 Metadata Raw Dataset 2 Metadata Raw Dataset 3 Metadata etc. Raw Dataset 4 MetadataA. Baltensperger Raw Dataset 5 Metadata => Digital Publications
  12. 12. Two books by the EWHALE lab re. Predictions and related Philosophies as presented here
  13. 13. Students & Projects of the EWHALE lab Andy Baltensperger Katherine Miller Shana Losbaugh Sue Hazlett Tim MulletKeiko Akasofu Herrick
  14. 14. Students & Projects of the EWHALE lab Ben Best Imme Rutzen Betsy Young Brian Young Michal LindgrenZach Meyers
  15. 15. Students & Projects of the EWHALE lab: Visitors Moritz Schmid Laszlo Koever (Uni Goettingen, (Uni Debrecen, Germany) Hungary) David Lieske Dmitry Korobitsyn (Mount Allison, (Uni Archangelsk, Canada) Russia)Cynthia Resendiz(Mexico)
  17. 17. Some Examples of what the EWHALE lab does, internationally (~how Falk spent his sabbatical and time)
  18. 18. Bioice/Iceland: A research cruise “in” a predictive model… ‘RV Meteor’ (Germany)
  19. 19. Ocean View I: A Global Benthos Model…(RandomForest Predictions) Wei et al. (2011). Global Patterns and Predictions of Seafloor Biomass using Random Forests. PLOS 5(12): e15323.
  20. 20. Ocean View II: Dimethylsulfid (DMS), globally per month Humphries et al. (in review)
  21. 21. Spatial Predictions of Arctic (Pelagic) Seabirds What Data are used: Pelagic Seabird Data ?!Public data+ High Quality Relevance of Arctic Content Specimen Collections vs.+ Metadata ?! (Polarstern)
  22. 22. Spatial Predictions of Arctic (Pelagic) Seabirds What Environmental Data were Used (Listed in no order) 1. Distance to ice edge 2. Sea temperature at 10m depth 3. Sea temperature at 0m depth 4. Phosphate concentration at 10m depth 5. Silicate concentration at surfacePublic Sources & 6. Phosphate concentration at surfaceAvailability 7. Salinity at 20m depth 8. Distance to Settlements (!) 9.Salinity at surfaceHuettmann & 10.Silicate10m depthHazlett (2009) 11. Discharge from riversfor 50 layers 12. Distance to shelf edge 13. Seaice thickness 14. Nitrate concentration at surface 15. DMS (Di-Methyl Sufide) at surface (G. Humphries in prep.) 16. Nitrate concentration at 10m depth 17. Bathymetric slope
  23. 23. Spatial Predictions of Arctic (Pelagic) Seabirds How it looks like: Training and Assessment Data Env. DataPresence (blue)vs.Random (red)(Pseudo- + absence) … Algorithm =>Predictions
  24. 24. Spatial Predictions of Arctic (Pelagic) Seabirds How it looks like: Training and Assessment Data Env. DataPresence (blue)vs.Random (red)(Pseudo- + absence)Assessment(green; telemetry … O. Gilg) Algorithm =>Predictions
  25. 25. Spatial Predictions of Arctic (Pelagic) Seabirds How it looks like: Predictions Prediction Surface Legend Red/Yellow=Presence t 1 af Light blue: WeakDr Presence Dark blue: Pseudo- absence
  26. 26. Spatial Predictions of Arctic (Pelagic) Seabirds How it looks like: Predictions and its data Prediction Surface Legend Red/Yellow=Presence t 1 af Light Blue: WeakDr Presence Dark Blue: Pseudo- absence Green: Assessment Data (O.Gilg)
  27. 27. Circumpolar Arctic: 27 Seabird Open Access Predictions Tufted Puffin Horned Puffin Northern Fulmar …add up all Ivory Gull Ross’s Gull Black-legged Kittiwake predictions…Huettmann et al. (2011)
  28. 28. Circumpolar Arctic: Putting Models to Use Seabird vs.=>We are running out of space and time in the Arctic (and anywhere else)
  29. 29. Circumpolar Arctic: Alaskan Crab Ensemble Model => Open Access (Raw Data + Model) in a highly commercial setting!Compiled Raw Crab Data Predicted Crab Pres/Abs (and Abundance) Snow Crab off Alaska (Hardy et al. 2011)
  30. 30. Circumpolar Arctic: Marine Protected Areas (MPA) and Biodiversity MARXAN optimization based on over 60 GIS layers =>Over 20 GIS data layers for each Pole (Arctic and Antarctic)Huettmann and Hazlett (2010)
  31. 31. Antarctica: MPA by WWF-Australiafor the Scientific Committee on Antarctic Research (SCAR) WWF-Australia, SCAR 2012
  32. 32. Antarctica: Isopode Data, Penguin Data etc Kaiser et al., French Antarctic Service Data
  33. 33. What is a Soundscape?• Biological Sounds – Biophony• Geophysical Sounds – Geophony• Anthropogenic Sounds – Anthrophony Mullet et al. (in prep)
  34. 34. Model-Predicting Sound (‘Soundscapes’)Models based on: - 7 permanent sound stations - Stratified according to expected sounds - Rotate 6 sound stations – Input GPS coordinates and related sound data into TreeNet modeling software – Include environmental and human-related covariates (e.g., vegetation, distance to roads, aspect) – Extrapolate sound levels and sound source data to rest of Refuge Mullet et al. (in prep)
  35. 35. Spatial Predictions of Forest Cover in Alaska Young et al. (in prep)
  36. 36. Spatial Predictions of Forest Cover in Alaska Young et al. (in prep)
  37. 37. Spatial Predictions of Forest Cover in Alaska 2010 2050 Young et al. (in prep)
  38. 38. Regionalized IPCC models, e.g. AlaskaTemperature (August and January) (SNAP UAF data) 2099 2008 Murohy et al. (2010)
  39. 39. Alaskan Caribou: Summer & winter ranges 2008 & 2099 2008 2008 2099SummerRange Model in RF with IPCC Murphy et al. (2010)WinterRange
  40. 40. RandomForest: Supervised and Unsupervised ClassificationSupervised Classification: -Multiple Regression (classification or continuous) -Multiple Response e.g. YAIMPUTE RandomForestUnsupervised Classification: 1. Proximity Matrix via Bagging/Voting (RF) 2. Similarity Matrix 3. e.g. Regular Clustering (mclust, PAM) 3. Visualize Result
  41. 41. 11 Cliome Clusters (RF)Climate Cluster Data, Canada & Alaska Credit: M. Lindgren et al.
  42. 42. Now, a topical shift to CircumpolarArctic and Zooplankton Forecastingtil 2100Metridia longa showed the highestincrease in the copepodite life stage from2010 to 2100. Credit: M. Schmid et al.
  43. 43. Calanus hyperboreus showed the highestchange in the predicted relative index ofdepth from 2010 to 2100. Credit: M. Schmid et al.
  44. 44. GMBA Case Study: Himalaya Uplands Plant Database Bernhard Dickoré et al. (red: sampling points) + FGDC NBII/ISO Metadata
  45. 45. A High Priority Ethnomedicinal Plant in Nepal Dactylorhiza hatagirea (Marsh Orchids) 81 “points”Ethnobotanical Use: Tubers are used as nervine tonic and aphrodisiac. It is also used to treat cuts, wounds, cough and anemia.
  46. 46. Prediction of a a High Priority Ethnomedicinal Plant in Nepal Dactylorhiza hatagirea (Marsh Orchids)
  47. 47. MARXAN Solution for the Three Poles: 50% Protection Scenario (birds, glaciers/ice and freezing temperatures)x Legend: Selection Frequency! (the darker the more frequently selected) Note: Terrestrial areas of Arctic & Antarctic are not included, yet
  48. 48. Another book by the EWHALE lab
  49. 49. Avian Influenza (AI) Prediction globally… (all based on Machine Learning!)Global AI model (Ecological Niche) based on K.Herrick-Akasofu,F. Huettmann,J. Runstadler et al.(unpublished; forthcoming thesis chapter)
  50. 50. AcknowledgementsL. Strecker, all co-authors, all EWHALE lab students, NCEAS, Universityof Alaska-Fairbanks, D. Steinberg (Salford Systems Ltd), COML, CAML,ArcOD, GMBA, IPY, A.W. Diamond, and many colleagues worldwide (a 20 years summary...) AND HUGE THANKS TO SALFORD SYSTEMS &Dan Steinberg’s team