Processing Biological Data in i-marine

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Processing Biological Data in i-Marine

Gianpaolo Coro

ISTI-CNR

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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

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The Context of Data Processing in iMarine

External

Geospatial Repositories

MyOcean

Data Discovery

Data Access

_{Data Processing}

OGC

CSW

Geonetwork

OGC - WCS

_OGC

WPS

WPS 52N

P1

P2

P..

WPS Hadoop Hadoop Cluster

World Ocean

Atlas

Distributed Storage System

Data Visualization

OGC WMS, WFS GeoServer

gCu

be

Da

ta

st

ag

ing

D4S Statistical Manager D4Science Cluster Windows Azure

We will show experiments that have been performed by means of the

collaborative

iMarine Data Processing Facilities

OGC - WFS

OpenDAP

GBIF

Obis

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Latimeria chalumnae (Smith, 1939)

Presence Points (FishBase) Aquamaps Native Distribution Absence Points Ar tifici al N eura l N et wo rk Artificial Neural Network

We used…

• Presence information

from FishBase

• Absence information

simulated through

Aquamaps

• 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

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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?

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 = 10.58

HRS = 10.61

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Features Clustering

Presence Points (FishBase + Obis)

Density Based Clustering

DBSCAN

(with outliers)

Other methods are also

available …

K-Means

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Climate changes

and

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Species Occupancy in Time

Impact of climate change over 20 years on

11549 species.

Goldback Anthias

The occupancy decreases

in Area 71 and increases in

Area 77

Analysis on the Aquamaps

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Data Enrichment

by means of the

e-Infrastructure

Vessels information

processing workflow

to calculate Fishing

Monthly Effort

Alternative ways for vessels activity

classification

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Environmental Signal Analysis

17.59; 41.37

We traced the Spectrogram

We automatically detected a

periodicity in the trend

Fequencies in 10-8 _Hz

Periodicity of 12 months

We took data from the

MyOcean reporitory (NetCDF

Format)

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Occurrence Data from GBIF

Occurrence Data from OBIS

Occurrence Data from WoRMs

∩

Intersection

-Difference

ᴜ

Union

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

<Take the most recent>

Evaluate

B

x,y

Event Date

Modif Date

Author

Species Scientific

Name

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FIN Taxa Match

Steps:

• Nomalization

• Stemming

• Phonetic Transformation

• Lexical Distance

Integration with the Infra : 1h

Interface Generation Time: 0s

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52,6% Match

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 [..]

52,6%

17% 14%

37%

31%

Antarctic Fur Seal

New Zealand Fur Seal

Killer Whale

Southern Elephant Seal 14%

Descriptions and Habitat

Semantic Distances

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Future Work

Native Today

Native 2050

Numerical comparison

between remote

distribution maps

Time Series Forecast

and

Anomalies Detection

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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

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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 2013.

8. 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. 21--32. 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) 1269-1277.

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