Difference between revisions of "Geospatial cluster"

Revision as of 18:18, 31 January 2013

The iMarine Geospatial Cluster activities revolve around the following principle: Enrichment of Species Occurrences data with profiles of environmental parameters

Executive Summary

The iMarine Geospatial Cluster is aimed at organizing collections of requirements gathered from the iMarine Business cases (EU Common Fishery Policy, FAO deep seas fisheries programme, and the UN EAF Ecosystem Approach to fisheries) and at providing infrastructure implementation recommendations. These recommendations are primarily intended for the iMarine project partners and the Communities of Practice (CoPCommunity of Practice.) identified within the Ecosystem Approach.

Introduction and Background (The Problems)

“For many biological observations, we have no data on the prevailing environmental conditions; either this information was never recorded (as is the case for many museum specimens, especially older ones), or the data was collected, but by others than the biologists, and different data streams were never re-united. Digging though archives of sampling campaigns of many years ago is tedious, if not impossible, by loss of essential information on the sampling event”.

Edward Vanden Berghe, Executive Director, Ocean Biogeographic Information System, April 2012

Environmental conditions such as salinity, temperature, or acidity, … are essential for conducting studies and developing applications related to Marine Species Distributions. Nevertheless, it is still a tedious task to collect and present coherently such parameters to the scientists. Through the activities of its Geospatial Cluster, the iMarine project is developing an approach and infrastructure tools in order to tackle this issue. Such activities aim at leveraging the spatial and temporal dimensions of environmental measurements, both being the bridges that can join environmental variables with the current assessments on species occurrences.

Way forward

Oceanographers have created large archives of data, the majority of them publicly accessible. Some examples discussed or assessed within the iMarine project:

• The OBIS Ocean Biogeographic Information System: http://www.iobis.org/
• The Fishbase global information system: http://www.fishbase.org
• The SeaLifebase biological information for biodiversity and ecosystem studies http://www.sealifebase.org
• The WorldFish center ReefBase: http://www.reefbase.org
• The World Register of Marine Species (WoRMS): http://www.marinespecies.org
• The Ecoscope Knowledge Base About Marine Ecosystems: http://www.ecoscopebc.ird.fr
• The Catalog of Life indexing the world’s known species: http://www.catalogueoflife.org
• …

Also, there are vast libraries of remotely-sensed data, some of them being in the public domain. Mining these data sources enables to reconstruct the environmental conditions in the neighbourhood and at the time of the biological observations.

• The NOAA World Ocean Atlas, e.g. 2009: http://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html
• The pan-European infrastructure for ocean and marine data management: http://seadatanet.maris2.nl
• The GMES myOcean Monitoring and Forecasting system for marine applications: http://www.myocean.eu.org
• …

It is unlikely that there will be a complete collection of environmental data for each and every point of interest corresponding to biological observations (e.g. latitude, longitude, depth and time for the OBIS observations). So we will need a step of interpolation between the existing environmental data. This interpolation can be either a statistical interpolation, or based on a model of the variable of interest.

Some further thoughts

A statistical interpolation will most probably be based on a weighted average of measurements of the parameter under consideration in the neighborhood of our 4D point of interest. The problem is that the weighting has to be done over dimensions that do not all behave the same – most obvious is the difference between spatial dimensions and time. I am not sure which models exist for the parameters we’re interested in, and how easily they would be available for our work. There is a difference between remote sensing data and in-situ data – remote sensing data is, first of all, spatially only 2D, which dramatically reduces complexity; and their geographic scope is usually very large, which means that we probably have measurements close to our points of interest. Another type of 2D data is bathymetry; here, the data do not change much in time (at least not on time scales we’re interested in), so we have to deal with only a single ‘layer’. The main source of in-situ data I am aware of is the World Ocean Database, and the Word Ocean Atlas which is derived from the WOD. Both are maintained by the World Ocean Data Center in Silver Spring, near Washington DC; the WDC is operated by the National Oceanographic Data Center of the USA, which is part of NOAA. Obviously, the WDC people know how to create the WOA based on the raw data from the WOD; we might want to look for their collaboration (I have some good contacts there).

What are the environmental variables of interest?

Bathymetry – is easily available, and in a resolution that is sufficient for our purposes; several sources: ETOPO, GEBCO. We could derive some extra parameters from bathymetry, like distance from continent, rugosity or aspect, but these are lower priority. Salinity and temperature – the classic in-situ data. There’s been a lot collected, mainly because these two parameters are influencing the speed of sound in water, so are needed for interpretation of sonar signals – in other words, they have military implications. For this reason, some countries refuse to make the data in their coastal waters public; but there is still *a lot* of data around. And for biodiversity and environmental envelope modelling, they are a priority. pH – very important if we want to be able to deal with global change, including ocean acidification. It’s an in-situ measurement, and not all that much is available. Ocean colour, productivity – the first is used as a proxy for the second; ocean colour reflects chlorophyll. Remote sensing data, so should be relatively easy to deal with? Nutrients – in situ, not as well measured as salinity and temperature, or even pH; lower priority? But is available, just as temperature, salinity and pH is WOA and WOD, so might be low hanging fruits.

Goals and Objectives (The Outputs)

Resources and Constraints (The Inputs)

Strategy and Actions (from Inputs to Outputs)

Appendix A - Resources

Appendix B - Budget

Appendix C - Schedule

Appendix D - Documents

Appendix E - Other