Integrating Ocean Observing Data to Enhance Protected Species Spatial Decision Support Systems

Integrating Ocean Observing Data to Enhance Protected Species

Spatial Decision Support Systems

Biodiversity and Ecological Forecasting Team Meeting

May 17-19, 2010

Dr. Pat HalpinDr. Karin Forney

Presented by:

Ben Best (Duke)

Elizabeth Becker (NOAA)

Project Team MembersBen Best, Ei Fujioka, Pat Halpin, and Jason Roberts

Marine Geospatial Ecology LabNicholas School of the Environment, Duke University

Lisa Ballance, Jay Barlow, Elizabeth Becker, Steven Bograd, Karin Forney, and Jessica RedfernSouthwest Fisheries Science Center

NOAA - National Marine Fisheries Service

Dave Foley and Daniel PalaciosJoint Institute for Marine and Atmospheric Research,

University of Hawai`i at Manoa

Grant/Cooperative Agreement Number: NNX08AK73G

Cetaceans (whales, dolphins and porpoises)and anthropogenic threats

Threats include

Ship strikes

Fishery bycatch

Naval activities

Anthropogenic sound

Cetaceans protected by US laws

MMPA

ESA

Cetacean distributions are dynamic

Balaenoptera musculus

Blue whale

DataData

DecisionDecision

Marine Habitat Modeling Process

ModelingSDSS

SSTCHLDepthShelfFronts

Sample fin whale densitiesKey predictor variables

Depth, Slope, SSTBal.phy

R S M odel

R efit to 1991-2005

C reated: 08/28/08 15:52:29

30o

35o

40o

45o

1991 1993 1996

W 130o

W 125o

W 120o

30o

35o

40o

45o

2001

W 130o

W 125o

W 120o

2005

W 130o

W 125o

W 120o

0.00010

0.00150

0.00250

0.00350

0.00450

0.00550

0.00650

0.00750

D ensity (Ani/km 2)

Avg91-05

O bs. Seg. D ensity

< 1 < 5 < 13

1991 1993 1996

2001 2005 Ave.

Expansion and Enhancement of the SDSS

Challenge: Marine mammals are highly mobile; distributions change on seasonal, interannual and decadal time scales

La NiñaEl Niño

Expansion and Enhancement of the SDSS

Incorporate additional covariates derived from remotely-sensed data (varies by region)

• Explore the implementation of Nowcast and Forecast capabilities

• Update SDSS and release a package of open-source Desktop GIS tools for end-users

Whale, dolphin and porpoise species:

Pacific: 21 species and 1 guild of beaked whales

Atlantic: 12 species and five species guilds

Nowcast and Forecast Capability development

GHRSST (RSS Inc.): Blended SST Developed by Remote Sensing Systems, Santa Rosa, CA High-resolution (9 km) infrared data Microwave (data for cloudy areas) Optimal interpolation Pixel-by-pixel error characterization

ROMS = Regional Ocean Modeling SystemDeveloped for the NASA-funded FAST Project (Chavez,

Chai, Chao, Barber and Foley) Run by Yi Chao's group at JPL Uses forecast surface fluxes (NCEP) Monthly mean products with 1-9 month lead time

NOWCASTS

(tactical)

FORECASTS

(strategic)

NOWCAST – Dall’s porpoise densityfor novel 2008 survey (July-Nov)

“Daily forecast”“1991-2005 Climatology”

NOWCASTS as short-term forecasts

(weeks)

FORECAST – Striped dolphin densityROMS: Oct/Nov 2008 (as forecast in July)

Expansion and Enhancement of the SDSS

Incorporate additional covariates derived from remotely-sensed data (varies by region)

• Explore the implementation of Nowcast and Forecast capabilities

• Update SDSS and release a package of open-source Desktop GIS tools for end-users

Whale, dolphin and porpoise species:

Pacific: 21 species and 1 guild of beaked whales

Atlantic: 12 species and five species guilds

SDSS: Summarize by Region

Select Region by1. Drop-down

list (OPAREAs, MPAs, EEZ)

2. Enter polygon coordinates

3. Draw on mapReturn:

– Effort, Obs– Min, Max,

Mean– Histogram– Coordinates

ROC Curve to Binary Habitat in SDSS

Example: baleen guild (fin, blue, sei, Bryde’s) in summer

Example: Species distribution modeling with MGET(Marine Geospatial Ecology Tools)

Sample time-series imagery Invoke R from ArcGIS to create plots, etc.

Fit models with R, evaluate using ROC analysis, predict maps from satellite images

False positive rate

Tru

e po

sitiv

e ra

te

Cutoff = 0.020

Binary classification (range map)

Predicted probability of presence

Red: Anticyclonic Blue: Cyclonic

Eddies from AVISO

Additional Derived Covariates

28.0 °C

25.8 °C

Front

Fre

quen

cy

Temperature

Optimal break 27.0 °C

Fronts from Pathfinder / GHRSSTCayula, J-F and P Cornillon (1992)Isern-Fontanet et al (2006)

Hybrid Coordinate Ocean Model (HYCOM)

pros: 1/12 °, cloud-free, 3D, forecastcons: modeled, since 2003, physical only

Hybrid Coordinate Ocean Model (HYCOM)

cons: complicated projection

Minimal Loss Decision Mapping

Loss Decision

Integrated Prob(Encounter)

0-.5 .5 to 1

DecisionGo 0 3

No-go

1 0

Conclusions and Next Steps

• Nowcast and forecast capabilities exceeded expectations

• Incorporate additional ecological relevant covariates (e.g. ROMS-CoSiNE)

• Foundation for climate-response modeling

• Improved SDSS for end-user needs

• Reproducibility with desktop GIS

Thank You!

Funding

• NASA

• SERDP

• NOAA

Project Support

• Marine mammal observers, oceanographers, chief scientists, cruise leaders, officers and crew of surveys

• Yi Chao (JPL)

• Fei Chai (University of Maine)

• NOAA Northeast and Southeast Fisheries Science Centers

Websites

• SDSS – http://seamap.env.duke.edu

• MGET – http://code.env.duke.edu/projects/mget