User scenario on Marine Biodiversity AquaMaps Pasquale Pagano National Research Council (CNR) – ISTI Italy

Marine Biodiversity AquaMaps AquaMaps VRE is a virtual environment providing set of services for the generation, standardized dissemination, and mapped visualization of model-based, large-scale predictions of currently known occurrence of marine species

Standardized range maps of marine species Ecological Niche Modelling – extrapolation of known species occurrences data to determine environmental envelopes (species tolerances) – predict future distributions by matching species tolerances against local environmental conditions (e.g. climate change and sea pollution) AquaMaps* – Maps large-scale species distributions based on existing but fragmented and potentially non-representative occurrence data – Uses knowledge of the geographic extents of commercial species available from FAO – Uses information about habitat usage available from online species databases * Initially defined to predict global distributions of marine mammals (by Kashner et al ) and then generalised to marine species. AquaMaps is the only species distribution modelling approach that combines numerical algorithms with expert knowledge

HSPENHSPEC HCAF Species Range Maps Production Workflow Color-coded species range map, using a half- degree latitude and longitude dimensions Defining Environmental Envelopes Generating Species Occurrences Probability Plotting Range Maps HSPEN HCAF HSPEC HSPEN Good Cells Biological Species

Species Range Maps Complexity 0.5 latitude and longitude cells (  35 km 2 equator), > 170k marine cells Large volume of input and output data Less than 7,000 species: – native range = 56,468,301 – suitable range = 114,989,360 Estimation for 50,000 species: – native range = 350,000,000 – suitable range = 715,000,000 [Eli E. Agbayani, FishBase Project/INCOFISH] Very large number of computation One Multispecies map computed on 3.5 % of the marine areas and 25% species requires 125 millions computations One global map (extended to all species and marine cells around the world) requires about 400 billions computations [N. Bailly, WorldFish Center]

AquaMaps is a production environment Produce range maps, multispecies maps plus a variety of thematic maps: taxonomic, climatologic, invasiveness, … Allows researchers to evaluate the impact of climate changes (e.g. 2050) Distribute maps via different services – FishBase, the most widely used biological information system with over one million visitors per month; – Species information systems such as SeaLifeBase and GBIF

Current Limitations To evaluate the marine biodiversity changes in presence of human disasters To provide useful information to mitigate the impact of natural disasters on marine biodiversity 0.5 latitude and longitude cells Increase the Precision

Increasing the precision means … Less than 13,000 species Increase resolution 0.5 deg -> 0.1 deg – Environment DBs => 4,5 million rows – Iterations => 55 billion iterations – Species Prediction DBs => 2.5 billion rows Scientists may tweak parameters – Species Prediction up to 55 billion rows (870 Gb) Roadmap to increase the number of species 13,000 -> 50,000 – 4-times more rows  Change the current technologies – Relational databases – Execution models 8

Expected benefits of VENUS-C Execution models  Sequential  Multithreaded  Batch + MapReduce the overall problem is split into smaller chunks which can be processed in parallel. All partial solutions are then combined / reduced into the overall result + COMPSs COMPSs runtime is responsible for processing a single application as remote tasks, i.e. checking their data dependencies and scheduling their concurrent execution on distributed parallel resources + Generic Worker Distribute the load over multiple machines IO  Relational + Hadoop VFS (local fs, http, ftp, s3, kfs, hdfs) + Cassandra + CDMI + Elasticity Management

Conclusions Venus-C allows to – Perform analysis that otherwise could not be undertaken – Increase the efficiency of research – Sharing results in a community in real time – Maximize the data production – Accelerate the research