Ecological Vulnerability Modeling - Asia-Pacific Data-Research...
Transcript of Ecological Vulnerability Modeling - Asia-Pacific Data-Research...
Ecological Vulnerability ModelingDelineating Areas of Vulnerability to Algal Invasion
in Hawaii using a Niche Model
Investigators:
John J. Kineman (Senior Research Scientist, University of Colorado and NOAA)Rozita Abdul-Williams (Graduate Student, Michigan State University and University of
Colorado)
Mark Fornwall (Director, NBII Pacific Basin Information Node and USGS)Derek Masaki (Research Scientist, Pacific Basin Information Node and UH)
Sponsorship ($50,000):
PRIDE (National Climatic Data Center, NOAA)
Purpose of the Project
• Develop and demonstrate a general modeling approach to identifying areas of vulnerability to marine algal invasions (Hawaii pilot).
• Improve collaboration, sharing, and integrated use of physical and biological data to answer spatial ecological questions.
PredictiveMaps
Iteration Revision
EcologicalDistributionModeler
24 April 2002Ecosystem Informatics at NGDC
Define Controlling Variables
Temperature Photosynthetic Radiation
Precipitation Soil Water Holding Capacity
Multi-variateStratification
Selec
tda
ta la
yers
√ temp√ precip√ swhc√ PAR
LUI[N]elevVrelPETLAIPdens
Add data layer / time period
Remove data layer
100100507550504576864525
Weig
htfa
ctor
Rang
e
10<23300<700==<10====<2050<
Resp
onse
fu
nctio
n
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
Define ClassesDefine trainingDefine thresholds
√ System defined
User Definition Delineation Method√Cluster analysis
MaximumliklihoodBaysianprobabilitiesgradient analysis
OutputInput
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
eSc
ale
start timeend timetime step(run avg.)
Spat
ial
Scale
of u
nit
Coarse------
--Fine
Line
frac
tal
dim
ensio
n (FD
)
1.2D---------1.7D
link FD withspatial scale
Preview
Save Model and ExecuteSet preview background image
Previous Next
Reset defaults
FD = 1.3420
Multi-variateStratification
Selec
tda
ta la
yers
√ temp√ precip√ swhc√ PAR
LUI[N]elevVrelPETLAIPdens
Add data layer / time period
Remove data layer
Selec
tda
ta la
yers
√ temp√ precip√ swhc√ PAR
LUI[N]elevVrelPETLAIPdens
Selec
tda
ta la
yers
√ temp√ precip√ swhc√ PAR
LUI[N]elevVrelPETLAIPdens
Add data layer / time period
Remove data layer
100100507550504576864525
Weig
htfa
ctor
100100507550504576864525
Weig
htfa
ctor
Rang
e
10<23300<700==<10====<2050<
Rang
e
10<23300<700==<10====<2050<
Resp
onse
fu
nctio
n
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
Resp
onse
fu
nctio
n
linearlinearlinearlinearlogsqrtlinearsqre(3x-7)1/xlinear
Define ClassesDefine trainingDefine thresholds
√ System defined
User Definition Delineation Method√Cluster analysis
MaximumliklihoodBaysianprobabilitiesgradient analysis
Define ClassesDefine trainingDefine thresholds
√ System defined
User DefinitionDefine ClassesDefine trainingDefine thresholds
√ System defined
User Definition Delineation Method√Cluster analysis
MaximumliklihoodBaysianprobabilitiesgradient analysis
Delineation Method√Cluster analysis
MaximumliklihoodBaysianprobabilitiesgradient analysis
OutputInput
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
eSc
ale
start timeend timetime step(run avg.)
Decadal-Annual-Seasonal--Monthly--Daily-Hourly
Tim
eSc
ale
start timeend timetime step(run avg.)
Spat
ial
Scale
of u
nit
Coarse------
--Fine
Spat
ial
Scale
of u
nit
Coarse------
--Fine
Line
frac
tal
dim
ensio
n (FD
)
1.2D---------1.7D
link FD withspatial scale
Line
frac
tal
dim
ensio
n (FD
)
1.2D---------1.7D
link FD withspatial scale
Preview
Save Model and ExecuteSet preview background image
Previous Next
Reset defaults
FD = 1.3420Preview
Save Model and ExecuteSet preview background image
Previous Next
Reset defaults
FD = 1.3420
test data
�improved data,time series
�new measures�higher resolution�error correction
�validation
�Species Analyst�Biomapper�HSM methods�SWARM
FunctionalFunctionalDefinitionsDefinitions
PhysicalPhysicalPatternsPatterns
• Improve current methods and general informatics for ecosystem management, including monitoring, assessment, and mitigation.
The Problem
• Exotic marine algae are invading island coastal ecosystems, driving out natives and reducing natural ecosystem goods and services.
• Present mitigation plans do not include the ability to estimate potential vulnerability.
• Better predictive capabilities are needed, that are simple and can be added to existing information systems.
Invasive Algae SpeciesHypnea musciformis (L)Acanthophora spicifera (R)
Invasive Algae LocationsIsland of Maui
Relevance to Pacific Island communities, governments, and natural resources
• Impacts of Macroalgae Invasion:– Loss of productivity– Loss of biodiversity– Decrease in the intrinsic value of the reef (aesthetic and tourism impacts)– Changes in community structure of the reef– Erosion of the physical structure of the reef
Healthy Hawaiian Reef Invaded Reef (Cladophora)
Benefits to the Region
• Currently, mechanical removal is the primary method of control
• Response to invasion often comes too late, resulting in severe impact to near shore ecosystems
• Rapid assessment tools would expedite response to potential threats
• Rapid assessment could identify invasive response with respect to changing environmental parameters.
Gracilaria infestation, Waikiki Gracilaria removal, Waikiki
• NOAA and USGS Strategic Plans– “Climate Risk in the Pacific”– Invasive species management, habitat characterization, coastal management, and
ecosystem management (plants and animals)– Biological stewardship, coastal/coastline processes, resource management– Ecology and the environment (ecological processes, biogeography, habitats)– Biological inventories and assessments via the National Biological Information Infrastructure
– Pacific Basin Information Node (PBIN) / Pacific Basin Information Forum (PBIF)– Methods and technology (mathematical modeling, real time monitoring and reporting)– Collaborate with key regional partners (State, Federal and educational organizations)– Disseminate information for decision-making, science, education, public.
• PRIDE Initiative (Report to Congress)– Addresses all three themes (climate and coastal communities, Marine and coastal
ecosystems, and risk management)– Develop critical information resources for the Pacific Region– Coordinate key regional partners in a broad-based multi-disciplinary data enterprise.– Link data resources to address ecosystem management– Develop new tools to assess the ecological consequences of climate variability and change – PRIDE 2004 Call for Proposals
• Pilot projects in data integration across disciplines and institutions• Linking data and models for application to problem-driven science.• “Data Center of the Future” concepts (Ecosystem Informatics)
Relevance to Agency Missions and PRIDE
Benefits to PRIDE
• Standardize modeling of potential ecological distributions.
• Improve collaboration between PRIDE facilities
• Link physical and ecological data and facilities.
• Allow integration with existing PRIDE information systems.
• Provide broad application and flexibility for many kinds of ecological problems.
• Link Pride to other informatic initiatives in the state and the Pacific such as the Pacific Basin Information Node
Project Milestones
• Produce and test a general niche model technique. √
• Define future requirements forintegrating the model into PBIN and other PRIDE information systems.
• Apply in Hawaii to map marine algae invasions using available data. (Aug. 11-19 collaboration at PBIN)
Question: Potential distribution of X = {xi} on A={an}
ŅNicheÓ Modelof Xon A
PotentialDistribution
Units (maps) of X
Validation(Test Maps)
NicheVisualization
and InteractiveEditing
Native Distribution of X
Model Criteria (Characters, constraints,
and scale)Environmental
Database
Delta: Accession and Character Data {xi({ai})}Sampling/Assessment Scheme for xi on {a1, a2, ...an}i
StatisticalDistribution Model, _(x)
SpatialData
Processing
[metadata][archive]
[etc.]
Controlling variables
SpatialStratification
Theory
Env. DataSources
Taxonomic ResourceDatabases
Combined probability
M
Combined probability
M
Combined probability
M
data Model result
The Modeling Approach(hypothetical distribution around Maui using relief data and shoreline
locations as the model constraints)
Key Current and Future Partners
University of Colorado Cooperative Institute for Research in theEnvironmental Sciences (CIRES)
John Kineman, Rozita Abdul-Williams
National Biological Information Infrastructure, Pacific Basin Information Node (PBIN)
Mark Fornwall, Derek Masaki
USGS Water Resources Division Gordon Tribble
University of Hawaii, Manoa Botany Dept Isabella Abbott
NOAA National Ocean Service, Pacific Services Center (PSC)
NOAA National Marine Fisheries Service
University of Hawaii, Manoa Botany Dept. Kim Peyton
Future Collaboration Possibilities
NOAA Coastal Services Center
USDA Natural Resources Conservation Service
University of Hawaii, Manoa; and Waikiki Aquarium Cindy Hunter
University of Hawaii Sea Grant Program Megan Toaspern
University of Hawaii (K-12 education) Alyson Napua Barrows
Maui Community College Cathy Davenport
Organization. University of Hawaii, Marine Gap Analysis Program Noelani Puniwai
Hawaii Natural Heritage Program and Center for Conservation Research and Training, University of Hawaii at Manoa
Barbara (Annie) Gibson
Bishop Museum Richard Pyle
Department of Land and Natural Resources, Division of Aquatic Resources
Skippy Hau
Anticipated Products
• Pilot demonstration of the model– Data layers (environmental and biological)– Model criteria– Data transfer– Off-the-shelf software and linkage
• Preliminary map of invasive potential– Validation
• Pilot study of systems integration issues and potential– Collaboration
Project Status
• Project management established. √– Personnel funded at University of
Colorado and University of Hawaii– Equipment and software acquired √
• General model developed (Univ. of Colorado) √– Tested on physical distribution
(delineating deltas) √– Tested on biotic distribution
(phytoplankton) √
• Data requirements and sources – Being identified for Hawaii demonstration
Combined probability
M
Bathymetry
Phytoplankton
Delta delineation test
Niche model
Benthic habitat
Programmatic Challenges and Opportunities
• Problems– Some funding delays for University partners (resolved).– Data acquisition mostly on schedule, some delays, people very busy.– Need to resolve software bugs and linkage between systems.– Need to publish core methods.
• Future opportunities that have emerged:– Many applications in predictive mapping are emerging.– Strong interest within the Coastal Global Terrestrial Observing System (GTOS).
• Opportunities for follow-on work in Year 2 and beyond:– Apply to a broader range of similar problems, testing and refining the model.– Programming of proven components of the model for greater speed and
efficiency.– Incorporate into or with existing GIS capabilities in PRIDE systems.– Improve distributed linkages with other PRIDE systems.– Expand to other Pacific Islands where appropriate (e.g., through the Pacific
Island RISA and/or PBIF).
Combined probability
M
0 10 20 30 40 500
0.2
0.41.0
• The model runs in off-the-shelf software: Interoperability and speed can be improved by programming it separately, as components are finalized.
• Integration with GIS and other systems can be difficult: The model can be standardized and implemented in or with a GIS environment by creating a model-attribute architecture.
• Method relies on functional response to limiting variables for defining the niche.: This may require new data observations, priorities, and development, including lab studies.
• The model is fundamentally static, representing a theoretical way to build up to complex and/or dynamic behavior: It is thus extensible to dynamic simulations, complex problems, and use of expert knowledge.
Model-Attribute Table
Technical Challenges & Opportunities