Global Modeling of Biodiversity and Climate Change

Falk Huettmann et al.-EWHALE lab-Biology and Wildlife DepartmentInstitute of Arctic BiologyUniversity of Alaska-FaibanksFairbanks Alaska

EWHALE lab

Re. Scientific Thinking and Thought

Karl Popper Leo Breiman

Felix ShtilmarkHerman Daly

Dave Carlson

Scientific Landmines ?!

Data Mining

Machine Learning

Predictions

Metadata

Data Sharing (online)

Economic Growth problem

Diseases(Influenza)

Spatial/Geographic Information Systems (GIS) and…

SustainabilityManagement

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

How GIS and machine learning connect… A Work Flow

ArcGIS 10.2

SalfordRGMEPython etc

Tree/CART - Family

Binary recursive partitioning

Leo Breiman 1984, and others

YES NO

Temp>15

Precip <100

Temp<5

PURITY METRIC OF NODES

TreeNet(~A sequence of CARTs) ‘boosting’

Variable Score LDUSE 100.00 ||||||||||||||||||||||||||||||||||||||||||TAIR_AUG 97.62 |||||||||||||||||||||||||||||||||||||||||HYDRO94.35 ||||||||||||||||||||||||||||||||||||||||DEM94.01 |||||||||||||||||||||||||||||||||||||||PREC_AUG 90.17 ||||||||||||||||||||||||||||||||||||||POP 82.54 ||||||||||||||||||||||||||||||||||HMFPT81.46 ||||||||||||||||||||||||||||||||||

+ + + +

Importance Value ROC curves for accuracy tests

e.g. correctly predicted absence app. 97%

e.g. correctly predicted presence app. 92%

=>Apply to a dataset for predictions

The more nodes…the more detail…the slower

ROCeach explains the remaining variance til the end…

Difficult to interpretbut good graphs

DEM Slope Aspect Climate Land-cover

1

2

3

4

5

Ran

dom

set

of

Row

s(C

ases

)

Random set of Columns(Predictors)

Random set 1

Random set 2

RandomForest (Prasad et al. 2006, Furlanello et al. 2003 Breiman 2001)

Bagging: Optimization based on In-Bag, Out-of Bag samples

In RF no pruning => Difficult to overfit (robust)

Boosting & Bagging algorithms

Difficult to interpretbut good graphs

Handles ‘noise’, interactionsand categorical data fine!

Average Final Treefrom e.g.>2000 treesdone by VOTING

Machine Learning example with GIS:Spoon-billed Sandpiper and Predictions (where are the wintering grounds of ca. 1000 highly endangered birds…?)

Engler et al.(in prep)

(winter)

(breeding,Kamchatka)

Data means Metadata and Data Management (specifically for GIS, for science projects, machine learning and for graduate students)

___________Field Season 1_________

Raw Dataset 1 Metadata

Raw Dataset 2 Metadata

Raw Dataset 3 Metadata etc.

Raw Dataset 4 Metadata

Raw Dataset 5 Metadata

http://mercury.ornl.gov/clearinghouse/

=> Digital Publications

___________Field Season 2 & 3_________

A. Baltensperger

Two books by the EWHALE lab re. Predictions and related Philosophiesas presented here

Students & Projects of the EWHALE lab

Andy Baltensperger

Shana Losbaugh

Keiko Akasofu Herrick

Katherine Miller

Sue Hazlett

Tim Mullet

Betsy YoungBrian Young

Zach Meyers

Ben BestImme Rutzen

Michal Lindgren

Students & Projects of the EWHALE lab

Laszlo Koever(Uni Debrecen, Hungary)

Dmitry Korobitsyn(Uni Archangelsk, Russia)

Cynthia Resendiz(Mexico)

Moritz Schmid(Uni Goettingen, Germany)

Students & Projects of the EWHALE lab: Visitors

David Lieske(Mount Allison, Canada)

Our Business Model

NOT A WETLAB

NOT FOR RE-CHARGE

CONSTANT, STEADY SMALL FLOW

Some Examples of what the EWHALE lab does, internationally

(~how Falk spent his sabbatical and time)

Bioice/Iceland: A research cruise “in” a predictive model… ‘RV Meteor’ (Germany)

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.

Ocean View II: Dimethylsulfid (DMS), globally per month

Humphries et al. (in review)

Spatial Predictions of Arctic (Pelagic) Seabirds

What Data are used: Pelagic Seabird Data ?!

Relevance of ArcticSpecimen Collections vs.

(Polarstern)

Public data

+ High Quality Content

+ Metadata ?!

What Environmental Data were Used (Listed in no order)

Spatial Predictions of Arctic (Pelagic) Seabirds

Public Sources &Availability

Huettmann &Hazlett (2009)for 50 layers

1. Distance to ice edge2. Sea temperature at 10m depth3. Sea temperature at 0m depth4. Phosphate concentration at 10m depth5. Silicate concentration at surface6. Phosphate concentration at surface7. Salinity at 20m depth8. Distance to Settlements (!)9.Salinity at surface 10.Silicate10m depth11. Discharge from rivers12. Distance to shelf edge13. Seaice thickness14. Nitrate concentration at surface 15. DMS (Di-Methyl Sufide) at surface (G. Humphries in prep.)16. Nitrate concentration at 10m depth17. Bathymetric slope

Spatial Predictions of Arctic (Pelagic) Seabirds

How it looks like: Training and Assessment Data

Presence (blue)vs.Random (red)(Pseudo- absence)

+

…Algorithm

Env. Data

=>Predictions

Spatial Predictions of Arctic (Pelagic) Seabirds

How it looks like: Training and Assessment Data

Presence (blue)vs.Random (red)(Pseudo- absence)

Assessment(green; telemetry O. Gilg)

+

…

Env. Data

=>Predictions

…Algorithm

Spatial Predictions of Arctic (Pelagic) Seabirds

How it looks like: Predictions

Prediction SurfaceLegend Red/Yellow=Presence

Light blue: Weak Presence

Dark blue: Pseudo- absence

Draft

1

Spatial Predictions of Arctic (Pelagic) Seabirds

How it looks like: Predictions and its data

Prediction SurfaceLegend Red/Yellow=Presence

Light Blue: Weak Presence

Dark Blue: Pseudo- absence

Green: Assessment Data (O.Gilg)

Draft

1

Circumpolar Arctic: 27 Seabird Open Access Predictions

Ivory Gull Ross’s Gull Black-legged Kittiwake

Tufted Puffin Horned Puffin Northern Fulmar

…add up all predictions…

Huettmann et al. (2011)

Circumpolar Arctic: Putting Models to Use

vs.

=>We are running out of space and time in the Arctic (and anywhere else)

Seabird

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)

Circumpolar Arctic: Marine Protected Areas (MPA) and Biodiversity

Huettmann and Hazlett (2010)

MARXAN optimizationbased on over 60 GIS layers

=>Over 20 GIS data layers for eachPole (Arctic and Antarctic)

Antarctica: MPA by WWF-Australia for the Scientific Committee on Antarctic Research (SCAR)

WWF-Australia,SCAR 2012

Antarctica: Isopode Data, Penguin Data etc

Kaiser et al.,

FrenchAntarctic Service Data

What is a Soundscape?

• Biological Sounds– Biophony

• Geophysical Sounds– Geophony

• Anthropogenic Sounds– Anthrophony

Mullet et al. (in prep)

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)

Spatial Predictions of Forest Cover in Alaska

Young et al. (in prep)

Spatial Predictions of Forest Cover in Alaska

Young et al. (in prep)

Spatial Predictions of Forest Cover in Alaska 2010 2050

Young et al. (in prep)

2008

2099

Temperature (August and January) (SNAP UAF data)

Regionalized IPCC models,e.g. Alaska

Murohy et al. (2010)

Alaskan Caribou:Summer & winter ranges 2008 & 2099

20082008 2099

SummerRange

WinterRange

Murphy et al. (2010)Model in RFwith IPCC

RandomForest: Supervised and Unsupervised Classification

Supervised Classification: -Multiple Regression (classification or continuous)

-Multiple Response e.g. YAIMPUTE

Unsupervised Classification: 1. Proximity Matrix via Bagging/Voting (RF)

2. Similarity Matrix

3. e.g. Regular Clustering (mclust, PAM)

3. Visualize Result

RandomForest

11 Cliome Clusters (RF)Climate Cluster Data, Canada &

Alaska

Credit: M. Lindgren et al.

Now, a topical shift to Circumpolar Arctic and Zooplankton Forecasting til 2100

Metridia longa showed the highest increase in the copepodite life stage from 2010 to 2100.

Credit: M. Schmid et al.

Calanus hyperboreus showed the highest change in the predicted relative index of depth from 2010 to 2100.

Credit: M. Schmid et al.

GMBA Case Study: Himalaya Uplands Plant Database

Bernhard Dickoré et al.

(red: sampling points)

+ FGDC NBII/ISO Metadata

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.

Prediction of a a High Priority Ethnomedicinal Plant in Nepal

Dactylorhiza hatagirea (Marsh Orchids)

x

Note: Terrestrial areas of Arctic & Antarctic are not included, yet

MARXAN Solution for the Three Poles: 50% Protection Scenario(birds, glaciers/ice and freezing temperatures)

Legend:SelectionFrequency!(the darker the more frequently selected)

Another book by the EWHALE lab

Global AI model (Ecological Niche) based on K.Herrick-Akasofu,F. Huettmann,J. Runstadler et al.(unpublished; forthcoming thesis chapter)

Avian Influenza (AI) Prediction globally… (all based on Machine Learning!)

Acknowledgements

L. 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