Dawn Phillips' Intro to WILDSPACE 1996

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WILDSPACE TM
Integration and Management of Wildlife Data and
Information ... in Ontario and Beyond
NAESI workshop November 9-10, 2005 Merrickville,
Ontario
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The WILDSPACETM Team
Don McNicol, Don Fillman, Rich Russell Canadian
Wildlife Service Ontario Region Robin Bloom
Canadian Wildlife Service Prairie Northern
Region Isaac Wong, Phil Fong Environment Canada
National Water Research Institute
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The WILDSPACETM Mandate

• For over 30 years, CWS has studied wildlife in
  Ontario and beyond, some work spanning decades
  and covering large areas.

• To maintain the integrity of this information and
  to facilitate its use, Project WILDSPACETM was
  initiated in 1996.

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WILDSPACETM Contributions to NAESI

• A bunch of relevant data
• A platform for data integration
• Lessons learned re multi-source data integration
• Some cool tools to explore biodiversity data
• A nifty habitat modelling routine ( plans for
  more)
• Potential for collaborative development? ?
  Synergy
• Human Resources species/habitat biologists,
  modelling practitioners

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Presentation Objectives

• Project WILDSPACETM
• who why
• contributions to NAESI
• approach components
• WILDSPACETM Decision Support System
• overview of software architecture
• examples of relevant data tools (classic
  biodiversity metrics)
• platform within which to integrate additional
  data
• spatially explicit modelling routine (predictive
  habitat occupancy)
• WILDSPACETM development
• ES-guidance on multi-modelling approach

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Project WILDSPACETM Components
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Project WILDSPACETM Components
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Project WILDSPACETM Components
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Project WILDSPACETM Components
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Project WILDSPACETM Components
the WILDSPACE approach
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Lessons Learned via data integration

• capture data at its most atomic level always
  more efficient to roll-up, but rarely reliable
  (and usually frustrating) to sub-divide later
• build shared or modular query designs to
  aggregate data, to common levels where
  possible/or biologically/ecologically
  meaningful/appropriate
• adopt a core commonality in db structure space,
  time, taxonomy
• facilitate modelling exercises using multi-source
  data, at common scales (spatial, temporal,
  taxonomic)

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WILDSPACETM DSS Architecture highly-integrated
modules
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WILDSPACETM DSS Architecture SPECIES module

• taxonomic queries
• species life history
• Avian Life History
• metadata queries

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WILDSPACETM DSS Architecture SPECIES module
taxonomy

• Taxonomic Schemas currently at 7600 taxa
  deployed approaching 13,000 in development
• hooks to ITIS, AOU, Synthesis of North American
  Flora (Kartesz)

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WILDSPACETM DSS Architecture SPECIES module
taxonomy, images
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WILDSPACETM DSS Architecture SPECIES module
hemispheric range map
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WILDSPACETM DSS Architecture SPECIES module
Avian Life History
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WILDSPACETM DSS Architecture SPECIES module
taxonomic query
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WILDSPACETM DSS Architecture SPECIES module
relevant CWS projects
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WILDSPACETM DSS ArchitectureSPACES module

• GIS 2400 map layers
• hemispheric to local
• spatial queries
• custom site selection
• thematic mapping
• interpolation/contouring

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WILDSPACETM DSS ArchitectureSPACES module
spatial query
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WILDSPACETM DSS ArchitectureData Analysis
Visualization module

• data filtering/joining
• data summary/analysis
• link data to mapping
• time series exploration
• trend analysis
• data export

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Module Data Analysis Visualizationdata
filtering query design, relational tables
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Module Data Analysis Visualizationthematic
mapping max productivity (COGO)
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Module Data Analysis VisualizationCOGO
productivity (max) interpolated surface
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Module Data Analysis Visualizationtrend
analysis estimating equations
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WILDSPACETM DSS ArchitectureKnowledge
Templates module (KT)

• custom routines
• CWS program-specific
• custom data filtering
• dataset joining
• custom data analysis
• habitat selection models
• gaming scenarios

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WILDSPACETM DSS Architectureexploring habitat
composition
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WILDSPACETM DSS Architectureexploring community
composition change
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WILDSPACETM DSS Architectureexploring spatial
similarity in spp. composition
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WILDSPACETM DSS ArchitectureGetting Data IN

• database import (.mdb)
• time series definition
• basemap association
• map layer import (.shp)

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WILDSPACETM DSS ArchitectureGetting Data OUT
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Critical Habitat Protection under the Species at
Risk Act Eastern Loggerhead Shrike (Lanius
ludovicianus migrans)
LIVE DEMO!
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DSS Data Inventory in EOMF
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Presently under development ...
Scenario Gaming System, with Expert System
guidance appropriate/potential model
choice(s) appropriate/potential data
choice(s) linkage to db structure for chosen
model(ling suite) platform to facilitate data
transfer among models scenario builder to plan
parameterizations platform to compile compare
output where, appropriate ecological
situation scale
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Scenario Gaming System
model choice facilitated by Expert System
scenario design (scenario builder)
model design 1
model design 3
model design 2
hooks to inputs, through ES guidance (e.g.,
parameter value adjustment, and/or data filtering
or spatially-mediated process to alter
observations used)
(model processing)
model output 1
model output 2
model output 3
comparison of models by type, by inputs, by
variability
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ES-guidance toward appropriate Model Type