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Boundary delineation ppt - Carrie Kappel
EBM Boundary Delineation
Mike Fogarty, Carrie Kappel, Rebecca Martone
Approach
Ecosystem-based management is fundamentally place-based management.
A first step is boundary definition.
We plan to use nearshore coastal central California as a case study and apply a boundary delineation approach based on existing biophysical data.
Geographical boundaries of ecosystems depend on the scale considered and may vary through time.
Ecosystems are nested within larger systems and open to external processes. For some applications, we will have to account for these “open� boundaries.
Questions
How should boundaries be defined? What constitute coherent, natural units for management within Central California?
What are the best techniques for integrating diverse datasets with varying spatiotemporal resolution?
How can we account for the joint uncertainties associated with integrated data layers?
How can we incorporate temporal variability and how does this variability affect the definition of boundaries at different scales?
How does data availability affect boundary definition? Can we use this technique to prioritize data collection?
Steps
Compile physical and biological data layers in ArcGIS.
Decide on common scale of resolution (e.g. ~1km grid)
Interpolate point data (using kriging or other geospatial technique) to create continuous data layers.
Perform PCA to reduce number of variables.
Perform cluster analysis on eigenvectors from PCA.
Data layers
Bathymetry
Substrate type (hard vs. soft, rugosity)
Habitat type (kelp, seagrass, and others from MLPA)
Kelp coverage and areas of persistent kelp
Fish diversity and abundance (from trawl surveys, PISCO & CRANE surveys)
Invertebrate and algal diversity and abundance (PISCO & CRANE surveys)
Seabird diversity, abundance, colonies
Marine mammal haulouts, rookeries, abundance
Data layers cont.
Sea otter counts, linear densities
Zooplankton biomass (CalCOFI)
Temperature (CalCOFI, AVHRR satellite data)
Salinity (CalCOFI)
Water chemistry (phosphate, oxygen, etc., CalCOFI)
Primary productivity (chla, CalCOFI, SeaWIFS, MODIS)
Upwelling index (PFEL)
Wave exposure (CDIP, Bill O’Reilly, Berkeley/Scripps)
Questions for group
What spatial resolution would be most appropriate?
Are there other similar analyses that already exist that we should know about (e.g. Central California biogeographic analysis, MLPA synthesis, etc.)
Biogeochemical provinces of the ocean based on hydrographic, chl, shape of seasonal cycle, etc. (Longhurst regions)
Marine ecosystems of the world (TNC/WWF)
Are there other data layers that should be included?
Talk to Dave about Navy cetacean datasets
Different components of data, e.g. mean wave field vs. max
Are there other questions we could answer using this approach that we have not considered yet?
Which data layers are the relevant ones given the structural models we develop to track particular ecosystem services
Notes
Narrow down to particular ecosystem (e.g. kelp forest, rocky shore) and look for regions of biophysical similarity within those ecosystems
Incorporate the human activities as well as and look at areas of similar human activity (data layer from human impacts analysis)
Mismatch of scales in mgt might be a way to frame the concept behind analysis
EBM Boundary Delineation
Mike Fogarty, Carrie Kappel, Rebecca Martone
Approach
Ecosystem-based management is fundamentally place-based management.
A first step is boundary definition.
We plan to use nearshore coastal central California as a case study and apply a boundary delineation approach based on existing biophysical data.
Geographical boundaries of ecosystems depend on the scale considered and may vary through time.
Ecosystems are nested within larger systems and open to external processes. For some applications, we will have to account for these “open� boundaries.
Questions
How should boundaries be defined? What constitute coherent, natural units for management within Central California?
What are the best techniques for integrating diverse datasets with varying spatiotemporal resolution?
How can we account for the joint uncertainties associated with integrated data layers?
How can we incorporate temporal variability and how does this variability affect the definition of boundaries at different scales?
How does data availability affect boundary definition? Can we use this technique to prioritize data collection?
Steps
Compile physical and biological data layers in ArcGIS.
Decide on common scale of resolution (e.g. ~1km grid)
Interpolate point data (using kriging or other geospatial technique) to create continuous data layers.
Perform PCA to reduce number of variables.
Perform cluster analysis on eigenvectors from PCA.
Data layers
Bathymetry
Substrate type (hard vs. soft, rugosity)
Habitat type (kelp, seagrass, and others from MLPA)
Kelp coverage and areas of persistent kelp
Fish diversity and abundance (from trawl surveys, PISCO & CRANE surveys)
Invertebrate and algal diversity and abundance (PISCO & CRANE surveys)
Seabird diversity, abundance, colonies
Marine mammal haulouts, rookeries, abundance
Data layers cont.
Sea otter counts, linear densities
Zooplankton biomass (CalCOFI)
Temperature (CalCOFI, AVHRR satellite data)
Salinity (CalCOFI)
Water chemistry (phosphate, oxygen, etc., CalCOFI)
Primary productivity (chla, CalCOFI, SeaWIFS, MODIS)
Upwelling index (PFEL)
Wave exposure (CDIP, Bill O’Reilly, Berkeley/Scripps)
Questions for group
What spatial resolution would be most appropriate?
Are there other similar analyses that already exist that we should know about (e.g. Central California biogeographic analysis, MLPA synthesis, etc.)
Biogeochemical provinces of the ocean based on hydrographic, chl, shape of seasonal cycle, etc. (Longhurst regions)
Marine ecosystems of the world (TNC/WWF)
Are there other data layers that should be included?
Talk to Dave about Navy cetacean datasets
Different components of data, e.g. mean wave field vs. max
Are there other questions we could answer using this approach that we have not considered yet?
Which data layers are the relevant ones given the structural models we develop to track particular ecosystem services
Notes
Narrow down to particular ecosystem (e.g. kelp forest, rocky shore) and look for regions of biophysical similarity within those ecosystems
Incorporate the human activities as well as and look at areas of similar human activity (data layer from human impacts analysis)
Mismatch of scales in mgt might be a way to frame the concept behind analysis
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Mike Fogarty, Carrie Kappel, Rebecca Martone
Approach
Ecosystem-based management is fundamentally place-based management.
A first step is boundary definition.
We plan to use nearshore coastal central California as a case study and apply a boundary delineation approach based on existing biophysical data.
Geographical boundaries of ecosystems depend on the scale considered and may vary through time.
Ecosystems are nested within larger systems and open to external processes. For some applications, we will have to account for these “open� boundaries.
Questions
How should boundaries be defined? What constitute coherent, natural units for management within Central California?
What are the best techniques for integrating diverse datasets with varying spatiotemporal resolution?
How can we account for the joint uncertainties associated with integrated data layers?
How can we incorporate temporal variability and how does this variability affect the definition of boundaries at different scales?
How does data availability affect boundary definition? Can we use this technique to prioritize data collection?
Steps
Compile physical and biological data layers in ArcGIS.
Decide on common scale of resolution (e.g. ~1km grid)
Interpolate point data (using kriging or other geospatial technique) to create continuous data layers.
Perform PCA to reduce number of variables.
Perform cluster analysis on eigenvectors from PCA.
Data layers
Bathymetry
Substrate type (hard vs. soft, rugosity)
Habitat type (kelp, seagrass, and others from MLPA)
Kelp coverage and areas of persistent kelp
Fish diversity and abundance (from trawl surveys, PISCO & CRANE surveys)
Invertebrate and algal diversity and abundance (PISCO & CRANE surveys)
Seabird diversity, abundance, colonies
Marine mammal haulouts, rookeries, abundance
Data layers cont.
Sea otter counts, linear densities
Zooplankton biomass (CalCOFI)
Temperature (CalCOFI, AVHRR satellite data)
Salinity (CalCOFI)
Water chemistry (phosphate, oxygen, etc., CalCOFI)
Primary productivity (chla, CalCOFI, SeaWIFS, MODIS)
Upwelling index (PFEL)
Wave exposure (CDIP, Bill O’Reilly, Berkeley/Scripps)
Questions for group
What spatial resolution would be most appropriate?
Are there other similar analyses that already exist that we should know about (e.g. Central California biogeographic analysis, MLPA synthesis, etc.)
Biogeochemical provinces of the ocean based on hydrographic, chl, shape of seasonal cycle, etc. (Longhurst regions)
Marine ecosystems of the world (TNC/WWF)
Are there other data layers that should be included?
Talk to Dave about Navy cetacean datasets
Different components of data, e.g. mean wave field vs. max
Are there other questions we could answer using this approach that we have not considered yet?
Which data layers are the relevant ones given the structural models we develop to track particular ecosystem services
Notes
Narrow down to particular ecosystem (e.g. kelp forest, rocky shore) and look for regions of biophysical similarity within those ecosystems
Incorporate the human activities as well as and look at areas of similar human activity (data layer from human impacts analysis)
Mismatch of scales in mgt might be a way to frame the concept behind analysis
EBM Boundary Delineation
Mike Fogarty, Carrie Kappel, Rebecca Martone
Approach
Ecosystem-based management is fundamentally place-based management.
A first step is boundary definition.
We plan to use nearshore coastal central California as a case study and apply a boundary delineation approach based on existing biophysical data.
Geographical boundaries of ecosystems depend on the scale considered and may vary through time.
Ecosystems are nested within larger systems and open to external processes. For some applications, we will have to account for these “open� boundaries.
Questions
How should boundaries be defined? What constitute coherent, natural units for management within Central California?
What are the best techniques for integrating diverse datasets with varying spatiotemporal resolution?
How can we account for the joint uncertainties associated with integrated data layers?
How can we incorporate temporal variability and how does this variability affect the definition of boundaries at different scales?
How does data availability affect boundary definition? Can we use this technique to prioritize data collection?
Steps
Compile physical and biological data layers in ArcGIS.
Decide on common scale of resolution (e.g. ~1km grid)
Interpolate point data (using kriging or other geospatial technique) to create continuous data layers.
Perform PCA to reduce number of variables.
Perform cluster analysis on eigenvectors from PCA.
Data layers
Bathymetry
Substrate type (hard vs. soft, rugosity)
Habitat type (kelp, seagrass, and others from MLPA)
Kelp coverage and areas of persistent kelp
Fish diversity and abundance (from trawl surveys, PISCO & CRANE surveys)
Invertebrate and algal diversity and abundance (PISCO & CRANE surveys)
Seabird diversity, abundance, colonies
Marine mammal haulouts, rookeries, abundance
Data layers cont.
Sea otter counts, linear densities
Zooplankton biomass (CalCOFI)
Temperature (CalCOFI, AVHRR satellite data)
Salinity (CalCOFI)
Water chemistry (phosphate, oxygen, etc., CalCOFI)
Primary productivity (chla, CalCOFI, SeaWIFS, MODIS)
Upwelling index (PFEL)
Wave exposure (CDIP, Bill O’Reilly, Berkeley/Scripps)
Questions for group
What spatial resolution would be most appropriate?
Are there other similar analyses that already exist that we should know about (e.g. Central California biogeographic analysis, MLPA synthesis, etc.)
Biogeochemical provinces of the ocean based on hydrographic, chl, shape of seasonal cycle, etc. (Longhurst regions)
Marine ecosystems of the world (TNC/WWF)
Are there other data layers that should be included?
Talk to Dave about Navy cetacean datasets
Different components of data, e.g. mean wave field vs. max
Are there other questions we could answer using this approach that we have not considered yet?
Which data layers are the relevant ones given the structural models we develop to track particular ecosystem services
Notes
Narrow down to particular ecosystem (e.g. kelp forest, rocky shore) and look for regions of biophysical similarity within those ecosystems
Incorporate the human activities as well as and look at areas of similar human activity (data layer from human impacts analysis)
Mismatch of scales in mgt might be a way to frame the concept behind analysis
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