Processing Biological Data in i-marine

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1 Processing Biological Data in i-marine Gianpaolo Coro ISTI-CNR i-marine Workshop 14 & 15 May 2013, Bruxelles, Belgium

2 Facilities and Experience - Summary We will show results about: Ecological Niche Modeling Environmental Features Analysis Time Series Analysis Climate changes and impact on species Vessels Monitoring Systems Occurrence Points Reconciliation Taxa Reconciliation

3 Context

4 gcube Data staging The Context of Data Processing in imarine We will show experiments that have been performed by means of the collaborative imarine Data Processing Facilities Data Discovery Data Access OGC - WCS OGC - WFS OGC CSW OpenDAP Postgres Geonetwork GBIF Obis Distributed Storage System Data Processing D4S Statistical Manager D4Science Cluster OGC WPS WPS 52N P1 P2 P.. Windows Azure WPS Hadoop Hadoop Cluster Data Visualization OGC WMS, WFS GeoServer External Geospatial Repositories MyOcean World Ocean Atlas

5 Ecological Niche Modeling

6 Latimeria chalumnae (Smith, 1939) Aquamaps Native Distribution Presence Points (FishBase) We used Presence information from FishBase Absence information simulated through Aquamaps Absence Points Artificial Neural Network Environmental information from Aquamaps Depth; Bottom and Mean Annual Salinity; Bottom and Surface Temperature; Mean Annual Primary Production; Distance from Land; Sea Ice Concentration. To train an Artificial Neural Network and project a native and suitable environment for the Coelacanth Projection Artificial Neural Network Passed a first review for the Ecological Modelling Journal, Elsevier

7 Environmental Features Analysis

8 Habitat Representativeness Score 1. How representative is an environmental feature set with respect to the projection area? 2. Are the features independent of each other? 3. Do the features share hidden common characteristics? We produce niche models but also assess the quality of the models and the features HRS: measures the representativeness of a set of features with respect to a certain area A HRS which is too high means the automatic maps could mean that the automatic maps are unreliable HRS = HRS = 10.61

9 Features Clustering Presence Points (FishBase + Obis) Density Based Clustering DBSCAN (with outliers) Other methods are also available K-Means X-Means

10 Climate changes and Impact on species

11 Species Occupancy in Time Goldback Anthias Impact of climate change over 20 years on species. Analysis on the Aquamaps Suitable Distribution The occupancy decreases in Area 71 and increases in Area 77

12 Vessels Monitoring Systems

13 Vessels Transmitted Information Data Enrichment by means of the e-infrastructure Vessels information processing workflow to calculate Fishing Monthly Effort Alternative ways for vessels activity classification

14 Time Series Analysis

15 Environmental Signal Analysis 17.59; We took data from the MyOcean reporitory (NetCDF Format) Fequencies in 10-8 Hz We traced the Spectrogram We automatically detected a periodicity in the trend Periodicity of 12 months

16 Occurrence Points Reconciliation

17 Probabilistic Algebraic Operations on Occurrence Points Occurrence Data from GBIF Occurrence Data from WoRMs Intersection Occurrence Data from OBIS ᴜ Union - Difference A x,y Event Date Modif Date Author Species Scientific Name d(x,y) < Distance Thr LexicalDistance(A.Author,B.Author) LexicalDistance (A.SciName,B.SciName) > Lexical Thr Evaluate <Take the most recent> B x,y Event Date Modif Date Author Species Scientific Name

18 Taxa Reconciliation

19 Steps: Nomalization Stemming Phonetic Transformation Lexical Distance Integration with the Infra : 1h Interface Generation Time: 0s FIN Taxa Match Performs near matching of a species scientific name with respect to the FishBase repository

20 Semantic Analysis on Species Descriptions The muzzle is short and moderately pointed. The nose does not extend much past the mouth, is not bulbous, and the nostrils point ahead. [..] New Zealand fur seals have rather generic southern fur seal features. The muzzle is moderately long, flat, and pointed, with a fleshy, somewhat bulbous nose [..] Descriptions and Habitat 52,6% Match Killer Whale Semantic Distances 14% 14% 52,6% Antarctic Fur Seal 37% 17% New Zealand Fur Seal 31% Southern Elephant Seal

21 Future Work

22 Future Work Native Today Native 2050 Numerical comparison between remote distribution maps Time Series Forecast and Anomalies Detection Functions Simulation

23 Conclusions The experiments show some of the methods the i-marine Community can use. We stress on: Collaboration: the results can be shared by one user to other users in the same VRE Reproducibility: all the experiments can be easily reproduced by another user Data Accessibility: all the data hosted\accessed by the e-infrastructure are automatically available to be processed Data Import: it is easy to make user s own data available for processing Transparent Computational Effort: the processing effort and the cloud computations are autonomously managed and are transparent to the user Features Accessibility: the processing facilities are accessible from outside by means of standard protocols

24 References 1. G. Coro, A. Gioia, P. Pagano, L. Candela. A Service for Statistical Analysis of Marine Data in a Distributed e-infrastructure. (Sub. to) International Conference on Marine Data and Information Systems (IMDIS 2013). 2. D. Castelli, P. Pagano, L. Candela, G. Coro. The imarine Data Bonanza: Improving Data Discovery and Management through an Hybrid Data Infrastructure. (Sub. to) International Conference on Marine Data and Information Systems (IMDIS 2013). 3. G. Coro, P. Pagano, A. Ellenbroek. Automatic Procedures to Assist in Manual Review of Marine Species Distribution Maps. M. Tomassini et al. (Eds.): International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 13), Springer, Heidelberg (2013). 4. G. Coro, P. Pagano, A. Ellenbroek. Combining Simulated Expert Knowledge with Neural Networks to Produce Niche Models for Latimeria Chalumnae. (accepted with rev.) Ecological Modeling Journal, Ed. Elsevier. 5. G. Coro, L. Fortunati, P. Pagano. Deriving Fishing Monthly Effort and Caught Species from Vessel Trajectories. To be published in Oceans 2013, Proceedings of MTS/IEEE. 6. L. Candela, D. Castelli, G. Coro,P. Pagano, F. Sinibaldi. Species Distribution Modeling in the Cloud. Concurrency and Computation: Practice and Experience, Ed. Wiley. 7. P. Pagano, G. Coro, D. Castelli, L. Candela, F. Sinibaldi, A. Manzi. Cloud Computing for Ecological Modeling in the D4Science Infrastructure. In Proceedings of EGI Community Forum L. Candela, G. Coro, P. Pagano. Supporting Tabular Data Characterization in a Large Scale Data Infrastructure by Lexical Matching Techniques. In M. Agosti et al. (Eds.): IRCDL 2012, CCIS 354, pp Springer, Heidelberg (2012). 9. Castelli, P. Pagano, G. Coro. Variazioni Climatiche ed Effetto sulle Specie Marine (Climate Changes and Effect on Marine Species). In the book: Le Tecnologie del CNR per il Mare (CNR Technologies for the Sea) p. 139, Ed. CNR 2013 (Roma). 10. D. Castelli, P. Pagano, G. Coro, F. Sinibaldi. Modellazione della Nicchia Ecologica di Specie Marine (Marine Species Ecological Niche Modelling). In the book: Le Tecnologie del CNR per il Mare (CNR Technologies for the Sea) p. 140, Ed. CNR 2013 (Roma). 11. D. Castelli, P. Pagano, G. Coro. Elaborazione di Dati Trasmessi da Pescherecci (Processing of Vessel Transmitted Information). In the book: Le Tecnologie del CNR per il Mare (CNR Technologies for the Sea) p. 133, Ed. CNR 2013 (Roma). 12. C. MacLeod. Habitat representativeness score (hrs): a novel concept for objectively assessing the suitability of survey coverage for modelling the distribution of marine species. Journal of the Marine Biological Association of the United Kingdom 90 (07) (2010)

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