Use Case 4 - Citizen Science - Biodiversity

• Goal
• Enable researcher to discover, access, and integrate a number of potentially useful covariate data to assist with occurrence modeling effort.
  

   

• Still need to elaborate upon useful linkages with genomics and other ecological/behavioral/trait observational data (nesting location and timing, dietary characteristics), via ontologies.

• Summary

• Use Case 1: Predicting species occurrence based on point data (e.g., bird counts) and other biotic & abiotic data. Want to estimate population densities (presence/absence; abundance) of birds (just as an example; others include specimen location information, road-kill sitings, etc.) based on growing database of organismal occurrence data (species/location/date). 
  

  Assist researchers in locating and assembling for further analyses, data sets of covariates (predictor variables) to inform models of occurrence and abundance. Scope expansion-- if one has certain species, what behavioral/life history characteristics are available for these?

• Queries
• 1) what variables are available as covariates for my occurrence data-- is there information about mean daily temperature at lat/lon over xx time span?
  
•      thematic search: weather/climate; soil, land-use, human demographics, hydrology, habitat type
  
•     return number of data sets, can be searched by facets:  sector, realm, institution, author, grain/resolution (in space-time) --> semantics of these can be elaborated and referenced from ontology 
  
•     how determine the catalog of measurements that are available, and the provenance of their values-- modeled surface, nearest gage reading, etc.  E.g. there are many ways to estimate a temperature at some given lat/lon.  Lots of datasets doing it for some given lat/lon.  Which should analyst use?  How would semantics guide in resolving these?
  
• what is the nearest freshwater source (stream/pond/lake) to lat/lon?  what is growth rate of human popu over last XX years at lat/lon?  what is land-use pattern at lat/lon?  what are areas having XX diversity of birds at YY time?  what studies about [nesting, feeding] in bird sp. X have been done in region Y?
• Operations/Tasks
• must have an easy way of discovering and acquiring  useful abiotic and biotic data "coverages" to associate with point data; express model outputs as some standard set of coverages to inform other analyses
• enable flexible querying of a variety of geospatial coverage data relative (expressed in common projection with access to " catalog" of well-defined attributes, which reveal capacity to drill-down or roll-up those measurements, and include details as to their provenance-- owner, methodology for collection, etc.) to those point data in order to better understand underlying ecological drivers for those densities; enable extraction of coverage data as single-value supplements to table (e.g. landuse=agric; drill-down to soybean farm)
• must have flexible ways to define 'co-location'-- interest might be in radius of importance of features for breeding birds-- proximity to food, water, shelter.
•  requires extracting coverage data values of a variety of abiotic and biotic data (land use, human census info, meteorological data, with flexible radii of relevance.  E.g. proximity to freshwater-- 1km away from 3 major bodies of water or 100m away from small stream. Temporal aspects of importance as well.
• Need to incorporate specific data sets and associated metadata: NLCD; MODIS, "individual researcher" observational data sets and intepretation of attributes from these

• CONCRETE: integrate bird point data with MODIS data with meteorological data.  Determine programmatic access to these sources.  (Maybe take lat/lon and semantic map that to Obs model to unify lat/lon anywhere appears, e.g. met data and bird point data).

  eBird mapped to ObsOnt.  Weather underground or IRI Data lib mapped to ObsOnt-- keying on lat/lon in both.

  phylogenetic commu analysis, trait-commu analysis use cases to be developed?

• Data sets and associated metadata
• TBD
  
• Metrics of completion/success
•     We refine some of above points, and predetermine subset of results.  Challenge expected to automate more comprehensive results (larger number of coverages discovered, selected, rescaled, integrated
• Ontologies
TBD

Participants

• Mark*, Shawn, Steve, Damian, Benno, Flip

• General Integration Challenges--
  
•     resolve scale issues: everything has to be at same resolution--system must be able to aggregate so that can union commensurate measures of data.
•     measurement scale alignment-- simple example fahrenheit/celsius
•     rebin/rescale grids to integrate on common scale-- semantic specification of operation across data sets?
  
•     Ecological genetics-- need to engage some practitioners  to help flesh this out. 
  
•     Trait-based community analysis-- need practitioners to help define
•     thematic alignment-- attributes referred to using synonyms, similar-to, coarser-- resolve with ontologies (need specific example)
  
•     Tagging and retrieving data based on epiphenomena-- e.g. northern migrational front/pattern;  involves tracking delta/changes-- when are birds in Mississippi River Delta; when are birds reaching Great Lakes; generally indicating trends--** feedback from analysis/models into metadata/data.  Like in genomics commu-- include both highly structured tagging and idiosyncratic taging.