1. OBIS and species distributions Tony Rees discussion presentation, March 2003 Some fundamental intentions for OBIS... –Choose any species and discover its distribution –Click on a body of water and discover what lives there –Then... Examine/discover correlations with environmental variables, etc. Currently: –OBIS connects to sources of point data, retrieves relevant points, displays on map and/or passes to modelling packages (also displays some relevant ancillary information and provides link to more if available)

2. Point vs. polygon data Point data... –Limited by: restricted distribution of sampling activity (vs world ocean area) only a subset of possible specimens retained (or catches recorded) only a further subset of these acessible to OBIS –“Real” distribution will always require interpolation/ extrapolation (=> ranges, expressible in polygons) –Only then can do meaningful queries as per initial slide Polygon data... –Could need mechanisms for OBIS to: import store query display export

3. Types of polygons May cover more than simple binary (presence/absence) states, e.g... –“Normal” vs. extralimital (occasional/vagrant) distribution –Breeding vs. non-breeding ranges –Seasonal distributions (winter/summer/migration paths etc.) –Juveniles vs. adults –infraspecific variation (subspecies, varieties...) –confidence limits (known vs. doubtful) –historic vs. current (decadal variability) –etc. May also vary by source/ method of construction: –Comprehensive sampling (present vs. definitely absent) –Indicative sampling (with inferred presence/absence where not sampled) –Modelled distribution based on proxies (environmental, other species)

4. Examples of polygons (butterfly data) - actually a gridded dataset with both presence+absence data

5. Examples of polygons - cont’d (bird species)

6. Examples of polygons - cont’d (whale species)

7. Examples of polygons - cont’d (modelled fish distribution)

8. Expression of polygons Polygons expressed in various forms.. –DIGITAL - vector string of points connected by simple lines as above, but with exclusion areas *** need GIS back end for spatial queries and display –DIGITAL- non-vector gridded (tiled) representation nominated regions/zones *** potentially simpler to query and display, but may be less exact –NON-DIGITAL paper / diagrammatic representation ms textual descriptions *** need digitising before accessible to online querying

9. An approach using gridded (tiled) data Express any species distribution as list of tiles (grid squares) in which that species occurs Tile size would be matched to scale of query system would be designed to support, e.g. 1 x 1 deg [100 x 100 km approx.], 0.5 x 0.5 deg. Could then query any tile and rapidly extract a list of what species occur there - especially if secondary (“inverted”) index built based on tile IDs Could accept species distributions as gridded data directly, or as vector polygons (then use polygon converter tool on the data)

10. “C-squares” system does exactly this Provides a unique, easily organised and searchable nomenclature for individual tiles (gridsquares) Prototype “polygon-to-c-square” converter available to look at / try out Distributions expressed in c-squares are straightforward to map using existing c-squares mapper (or OBIS could construct its own) Could accept species distributions as gridded data directly, or as vector polygons (then use polygon converter tool on the data) - True GIS system might be better (ultimately more accurate, smoother diagonal lines/curves, more flexible overlays etc.) but may be slower for searching ?? (considering numbers of species potentially involved)

11. How might this be implemented? ? OBIS master distributions table: Species 1... square A... source x Species 1... square B... source x Species 2... square B... source y etc.... can then easily extract: –all squares occupied by Species 1 –all species in square A –source of any record (also needed for internal tracking) would potentially need to qualify “source” information as to type of record (as previously discussed) could also need to qualify by depth, season, decade (etc.) in “sources” list (table)

12. Getting these data into OBIS... ? A persistent index, regularly refreshed/updated by querying distributed data sources –OBIS could handle the polygon => gridsquare conversion as a background task ? Live broadcast query, polygon/raster data interrogated on-the-fly –would be quickest if polygon => gridsquare conversion had already been undertaken at the partner end... data could potentially be transferred as vector data (polygon boundaries), or gridded data (either standardised, or non-standardised). Need to consider optimum method(s) for transfer, with implications as to where required conversions would be done.

13. “Click on a square” search interface (from current “MarLIN” system)

14. Could generate (for example)... species are registered in OBIS