WP3 contributions to WP1/online content

1. WP3 contributions to WP1/online content Los Baños, 16 May 2006 Sven O Kullander

2. Deliverables 1-4 • Mo. 13 Online: standardized electronic maps with predicted distribution = AQUAMAPS done • Mo. 19 Before-after maps with predicted distribution for at least 10 species • Mo. 26 Maps with predicted seasonal distribution • Mo. 31 Online: Dynamic maps of predicted distribution based on physical models

3. Map components • Species occurrence data (FishBase, GBIF, other sources) • Environmental layers/datasets • Algorithm • Base maps • Projection model (=scientific hypothesis) • Software running 3 on 1 and 2, according to 5 • Visualisation tool (e.g., web interface) displaying results of 6 on 4

4. Biodiversity Informatics Basics • Primary diversity data = Observation data on specimens including scientific name (of taxon), place and time of occurrence (spatiotemporal parameters of scalable objects) • Any additional information about the taxon, the time or the place • A biological hypothesis linking 1 and 2 • An analytical tool testing 3 using at least 1 and 2 • Or go straight from 1 to pattern analysis

5. Biodiversity data LCD • Darwin Core concepts (xml schema for biodiversity information exchange, subset of Dublin Core) • Developed for, but not restricted to DiGIR (Distributed Generic Information Retrieval) • Mandatory fields providing unique identifier for record and scientific name • Descriptive fields for spatial and temporal allocation and level of confidence in scientific name • Used by GBIF, OBIS (w/ extensions), and others, contained in ABCD and covered in TAPIR

6. Biodiversity data components • What – scientific name of a species • Where – Latitude/longitude • Latitude/longitude in decimal degrees (12.3456), using the WGS84 datum (earth model) • When – Date • Day of month, Month of year, Year

7. Environmental data components • What – a measurement (w/ associated metadata) • Where – Latitude/longitude • Point, grid, or polygon • When – Date • Precise time or span of time

8. Science components • How? • Why?

9. Species occurrence is point, environment is area • Relation of occurrence records is to an environmental polygon. Overcome by fitting occurrence points to polygon extent (e.g., c-squares), i.e., modelling species distribution • Resolution is limited by resolution of environmental data set (e.g., 0.5 degree) • Does not matter much because species distribution (in contrast to species occurrence) is also a polygon, and environmental data based on point records • However, ad hoc species and environment polygons cannot be directly related in a database

10. Maps and dynamic maps • c-squares AquaMaps produces semi-dynamic maps. Excellent for any kind of modelling, expanded projection options underway. Works from FB-Kiel, installed in FB-Stockholm. c-squares is the work of Tony Rees, CSIRO, Australia. c-squares ”Server” is open for map plotting from anywhere and anyone. • UMN Mapserver application mapcria is a map application server that can use vector maps, serving db data on a scalable map. Models can be output on the fly. NRM has UMN mapserver running. Installation is not straightforward. Excellent for dynamic mapping.

11. Maps and dynamic maps

12. Models and Modelling Guisan & Thuiller, 2005

13. Models and Modelling • INCOFISH so far based on environmental envelopes. Requires calculating species specific preference table, and probability table for all squares of occurrence. • Other environmental/climatic envelope models: BIOCLIM, DOMAIN • Multivariate models: ENFA • Artificial neural network: ANN • General Linear Modelling: GLM • Maximum Entropy • Except for species distribution data, modelling uses the same environmental data sets, and generally the level of detail of analysis is decided by the set with lowest resolution. • Applied models are normally predictive of present occurrence (spatial prediction or ecological niche prediction). Result can be tested empirically. • Predicting past or future distribution requires data sets modified according to some theory/model about future climate and land extension (future), or historical information (past distribution). Testing model result of spatiotemporal prediction is probably possible, assessing the reliability of the combined occurrence data used + model for climate change over time + environmental data set used + prediction from specific algorithm, is an issue.

14. Models and Modelling (2) • Modelling software mediates occurrence data, environmental data sets, algorithm, and usually map output • Popular now: GARP, DIVA-GIS, Maxent, WhyWhere • OpenModeller is a software that can handle several different algorithms within the same application (Input occurrence data, select environmental data sets, select and run algorithm, obtain map and statistics). Currently uses QuantumGIS for map output.

15. Models, modelling and the client • To provide consistent information to a general public, decision makers, and non-modellers, conforming to trust, communication, and relevance, AquaMaps (controlled occurrence data + c-squares + environmental envelope) probably serves the purpose best. The Aquamap model is being tested/validated by WP3 • A web service implementation of OpenModeller (with AquaMaps model or other) may be most efficient to serve specific predictions with user-selected environmental parameters and taxa. • Or go for a combination? • A decision has to be made regarding use of UMN Mapserver or c-squares mapper or balanced use similar to 1,2 above

16. Data interoperability • Darwin Core is an available, growing standard for Internet based exchange of biological data using xml/http. Minimum requirements and preferably also additional concepts in Darwin Core should be implemented in ALL biological databases • Relations between Darwin Core and environmental/ecological data sets can be standardised. At present they need to be managed programmatically. WP1,3,4,5 should look into this. c-squares is a scalable option implemented in AquaMaps

17. Deliverables 1-4 • Mo. 13 Online: standardized electronic maps with predicted distribution = AQUAMAPS (done) • Mo. 19 Before-after maps with predicted distribution for at least 10 species. Underway with Aquamaps • Mo. 26 Maps with predicted seasonal distribution Underway with AquaMaps • Mo. 31 Online: Dynamic maps of predicted distribution based on physical models. Needs a decision about mapping tool.