Ecological Vulnerability Modeling Delineating Areas of Vulnerability to Algal Invasion in Hawaii using a Niche Model

Transcription

1 Ecological Vulnerability Modeling Delineating 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 AbdulWilliams (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)

2 Purpose of the Project Define Classes Cluster analysis Define training Maximumliklihood Define thresholds Baysianprobabilities System defined gradient analysis temp <23 linear Decadal Coarse 1.2D precip <700 linear swhc 50 = linear FD = Previous Next Annual PAR 75 = linear LUI 50 <10 log Seasonal [N] 50 = sqrt elev 45 = linear Vrel 76 = sqr Monthly PET 86 = e(3x7) LAI 45 <20 1/x Daily Pdens 25 50< linear Fine 1.7D Hourly start time link FD with Set preview background image end time spatial scale Add data layer / time period time step (run avg.) Remove data layer Reset defaults 24 April 2002 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. Improve current methods and general informatics for ecosystem management, including monitoring, assessment, and mitigation. Functional Definitions Iteration Input Select data layers Weight factor Range Response function MultivariateStratification User Definition Delineation Method Preview Save Model and Execute Output Time Scale Spatial Scale of unit Line fractal dimension (FD) Ecological Distribution Modeler Revision Predictive Maps Species Analyst Biomapper HSM methods SWARM Physical Patterns Define Controlling Variables Temperature Photosynthetic Radiation Precipitation Soil Water Holding Capacity Ecosystem Informatics at NGDC test data improved data, time series new measures higher resolution error correction validation

3 The Problem Exotic marine algae are invading island coastal ecosystems, driving out natives and reducing natural ecosystem goods and services. Invasive Algae Species Hypnea musciformis (L) Acanthophora spicifera (R) 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 Locations Island of Maui

4 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)

5 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

6 Relevance to Agency Missions and PRIDE 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 decisionmaking, 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 broadbased multidisciplinary 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 problemdriven science. Data Center of the Future concepts (Ecosystem Informatics)

7 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

8 Project Milestones Produce and test a general niche model technique. Combined probability Combined probability Combined probability M M M Apply in Hawaii to map marine algae invasions using available data. (Aug collaboration at PBIN) data Model result Spatial Data Processing [metadata] [archive] [etc.] Native Distribution of X Controlling variables Question: Potential distribution of X = {x i } on A={a n } Model Criteria (Characters, constraints, and scale) Environmental Database Env. Data Sources Taxonomic Resource Databases Spatial Stratification Theory ŅNicheÓModel of X on A Statistical Distribution Model, _(x) Validation (Test Maps) Niche Visualization and Interactive Editing Potential Distribution Units (maps) of X Define future requirements for integrating the model into PBIN and other PRIDE information systems. Delta: Accession and Character Data {x i ({a i })} Sampling/Assessment Scheme for x i on {a 1, a 2,...a n } i

9 The Modeling Approach (hypothetical distribution around Maui using relief data and shoreline locations as the model constraints)

10 Key Current and Future Partners University of Colorado Cooperative Institute for Research in the Environmental Sciences (CIRES) National Biological Information Infrastructure, Pacific Basin Information Node (PBIN) USGS Water Resources Division University of Hawaii, Manoa; and Waikiki Aquarium University of Hawaii Sea Grant Program University of Hawaii (K12 education) University of Hawaii, Manoa Botany Dept University of Hawaii, Manoa Botany Dept. Maui Community College Organization. University of Hawaii, Marine Gap Analysis Program Hawaii Natural Heritage Program and Center for Conservation Research and Training, University of Hawaii at Manoa Bishop Museum Department of Land and Natural Resources, Division of Aquatic Resources John Kineman, Rozita AbdulWilliams Mark Fornwall, Derek Masaki Gordon Tribble Cindy Hunter Megan Toaspern Alyson Napua Barrows Isabella Abbott Kim Peyton Cathy Davenport Noelani Puniwai Barbara (Annie) Gibson Richard Pyle Skippy Hau Future Collaboration Possibilities NOAA Coastal Services Center USDA Natural Resources Conservation Service NOAA National Ocean Service, Pacific Services Center (PSC) NOAA National Marine Fisheries Service

11 Anticipated Products Pilot demonstration of the model Data layers (environmental and biological) Model criteria Data transfer Offtheshelf software and linkage Preliminary map of invasive potential Validation Pilot study of systems integration issues and potential Collaboration

12 Project Status Project management established. Personnel funded at University of Colorado and University of Hawaii Equipment and software acquired M Combined probability Niche model General model developed (Univ. of Colorado) Tested on physical distribution (delineating deltas) Tested on biotic distribution (phytoplankton) Delta delineation test Data requirements and sources Being identified for Hawaii demonstration Phytoplankton Bathymetry Benthic habitat

13 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 followon 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).

14 M Technical Challenges & Opportunities The model runs in offtheshelf 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 modelattribute architecture. ModelAttribute Table 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 Combined probability