Invasive species and climate change

BlueBRIDGE receives funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu
Invasive species and
climate change
Gianpaolo Coro
ISTI-CNR
gianpaolo.coro@isti.cnr.it Supporting Blue Growth with
innovative applications based on
EU e-infrastructures
14-15 February 2018, Brussels

"Supporting Blue Growth with innovative applications based
on EU e-infrastructures”, 14-15 February 2018, Brussels
15/02/2018 2
Infra and
VRE
services
Data Production
and Representation
Discovery of patterns
in the data
Ecological Modelling
Discovery of patterns in Eco Models
Assess effects on economy and society
Reuse of data Reuse of processes
From Virtual Research Environments to Open Science

15/02/2018 3
Infra and
VRE
services

• Experiments on Big Data
• Sharing inputs and results
• Save the provenance of experiments
• Supports R-R-R of experiments
• Input/Out
• Parameters
• Provenance
Cloud Computing
Platform
WPS
REST
NEW
Workspace

Geospatial Interpolation
From a set of punctual observations
of a parameter in the sea
To a uniform distribution of the parameter

Standard representation of
geospatial data
From a geospatial dataset
To a standard representation

Presence/absence data
Probab
ility
(1/ 0)
1. Depth mean
2. Depth Standard Deviation
3. Depth Maximum
4. Depth Minimum
5. Distance from Land
6. Ocean Area
7. Ice Concentration Annual
Mean
8. PrimaryProduction Annual
Mean
9. Sea SurfaceTemperature
Standard Deviation
Maximum
Minimum
Range
Annual Mean
14. Sea Bottom Temperature
Annual Mean
15. Salinity Minimum
16. Salinity Maximum
17. Salinity mean
18. Salinity bottom mean
Ready-to-use
Data Mining Algorithms
• Weka
• Rapid Miner
• Knime
• ISTI-CNR gCube
Ecological Engine

WPS
REST
Geospatial data infra.
Work-
-space
WMS
WCS
GeoTiff
NetCDF
OPeNDAP
VRE
• Data preparation
• Ocean Currents
• Parameters
• Signal to noise
estimation
• Correlation
analysis
NetCDF file
Provenance Metadata
(Prov-O)
Out. file
Sharing
Input
User
Other user
OGC StandardsVisualisation
Publication
VRE
Combining Multiple-Infrastructures

15/02/2018 9
Data Production
and Representation
Infra and
VRE
services

10
Time span: 1950, 2050, and 2100 at .5° degrees resolution.
Parameters: Sea surface temperature, primary production, temperature at sea bottom level,
sea surface salinity, salinity at sea bottom level, and ice concentration.
Sources: IPSL weather forecast model adjusted using real observation data from The World
Ocean Atlas, NOAA NCEP Climatology, SeaAroundUs, and the US National Snow and Ice Data
Centres.
Projection scenario: IPCC SRES A2 scenario - a future with independently operating, self-reliant
nations, continuously increasing population and regionally oriented economic development.
Availability: distributed in unstructured textual format via the Web site. Metadata described
in a separate online-excel sheet.
Global Forecast Data from
1950 to 2100
The AquaMaps Consortium
NASA Earth Exchange platform NEX-GDDP
Time span: daily forecasts from 1950 to 2100 at .25° resolution
Parameters: minimum, maximum surface air temperature, precipitations.
Sources: 21 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5).
Projection scenarios: medium mitigation (RCP 4.5) and high concentration (RCP 8.5) of
greenhouse gasses.
Availability: a Cloud service using the NetCDF standard that also embeds metadata.

AquaMaps data (600 MB):
1. Cleaned up and put in NetCDF format;
2. Distributions for 1950, 2050, and 2100 were extracted from the table;
3. A 2016 scenario was produced: real observations were adjusted with respect to
the other scenarios (values were point-by-point automatically checked to be inside the
boundaries of 1950 and 2050, not to go outside the expected maximum and minimum values etc.);
4. A 1999 scenario was produced: point-by-point backwards linear interpolation
from 2016 (slopes in the interpolation were calculated based on the differences between 2050
and 1999 IPSL simulations).
NASA data (200 GB):
1. The same five years of AquaMaps were selected;
2. The 21 models were averaged together and yearly;
3. Average surface air temperature was calculated as the average of min and max
temperatures;
4. Resolution was downscaled to 0.5°;
5. Time series were produced for the RCP 4.5 and RCP 8.5 scenarios separately.
Big Data Preparation
11
 We used the DataMiner* Cloud computing system of the D4Science e-Infrastructure to process the data.
This allows making all the experiments reproducible-repeatable-reusable, in compliance with Open
Science directives..
*Coro, G., Panichi, G., Scarponi, P., & Pagano, P. (2017). Cloud computing in a distributed e‐infrastructure using the web processing
service standard. Concurrency and Computation: Practice and Experience, 29(18).

Surface Air Temperature RCP 4.5
12
Sea Surface Temperature
Surface Air Temperature RCP 8.5Sea Bottom Temperature
Data Visualisation

04-06 Dec.
2017
13
Sea Surface Salinity Salinity at Sea Bottom
Primary ProductionIce Concentration
Precipitations RCP 4.5 Precipitations RCP 8.5

Temperatures Differences
Difference was calculated
between the
Surface Air Temperatures
(SAT) in RCP 4.5 and RCP 8.5
and the
Sea Surface Temperature
(SST)
 Highest discrepancy in
2100;
 Covariate of sea level
change: a small
temperature difference
likely indicates that
there is no land or ice to
isolate air from the sea.
04-06 Dec.
2017
14
SAT-SST RCP 4.5
SAT-SST RCP 8.5

Timeline and Linked Open Data
04-06 Dec.
2017
15
https://dlnarratives.eu/timeline/climate.html

16
Data Production
and Representation
in the data
Infra and
VRE
services

17
Climate Change
Patterns
17
Surface Air – Sea Surface Temperature RCP 4.5
Global Average Poles Equator Tropics
North
Atlantic Oc.
South
Atlantic Oc.
Indian Oc. Pacific Oc.
Oceania
and
Indonesia
Mediterranean
Sea
Time series analysis per area:
• [SAT-SST] in RCP 4.5 recovers in many areas,
• [SAT-SST] in RCP 8.5 always decreases,
• Overall sea level increase and ice melting.
• Sea Surface Salinity increases globally but
decreases locally;
• No temperature recovery at the Poles even
in low emission scenarios
• Etc.
Global Average Poles Equator Tropics North Atlantic Ocean South Atlantic Ocean Indian Ocean Pacific Ocean Oceania and Indonesia Mediterranean Sea
Global Average 1.00
Poles 0.98 1.00
Equator 0.76 0.71 1.00
Tropics 0.98 0.93 0.77 1.00
North Atlantic Ocean 0.72 0.73 0.47 0.69 1.00
South Atlantic Ocean 0.80 0.76 0.76 0.80 0.49 1.00
Indian Ocean 0.95 0.90 0.82 0.98 0.63 0.80 1.00
Pacific Ocean 0.96 0.91 0.82 0.99 0.64 0.80 0.99 1.00
Oceania and Indonesia 0.85 0.80 0.84 0.88 0.56 0.70 0.91 0.91 1.00
Mediterranean Sea 0.41 0.40 0.34 0.43 0.72 0.24 0.42 0.42 0.39 1.00
Time series cross-correlation analysis:
• The Poles reflect the global trend
• The Mediterranean Sea is poorly correlated with other areas
• Temperature is inverse correlated with Primary Production
• Etc.

20
Data Production
and Representation
in the data
Infra and
VRE
services

Modelling the
pufferfish invasion
04-06 Dec.
2017
21
1. Environmental
Features are used to
train different Species
Distribution Models
2. The Models are
merged together to
estimate one habitat
suitability model
3. Real observation are
used to estimate
potential geographical
reachability
4. Forecasted environmental vars.
are used to understand how the
geographical distrib. changes in time
and if it is a stable scenario

The puffer fish Lagocephalus sceleratus
(Gmelin, 1789)
is an invasive species for the Eastern Mediterranean
Sea:
• Entered through the Suez Canal;
• Rapidly invaded the Eastern Mediterranean Sea
reaching the western part of the basin;
• A highly-toxic species that has an opportunistic
behaviour: attacks fishes captured in the nets and lines,
seriously damages fishing gears and catch, fast expanding,
represents 4% of the weight of the total artisanal catches, very
dangerous to humans.
 Future expansion was calculated with respect to
2050;
 Dynamic expansion and convergence was
simulated;
 Impact per area was calculated as density of high
probability cells in the subdivisions;
 The quality of the model was evaluated with
respect to real observation and to published and
grey literature*;
Geographic
reachability today
Best convergence
simulation: in 26
years *G. Coro, L. G. Vilas, C. Magliozzi, A. Ellenbroek, P. Scarponi, P. Pagano. 2017. Forecasting the ongoing invasion of Lagocephalus
sceleratus in the Mediterranean Sea, Ecological Modelling (Elsevier)

23
Data Production
and Representation
in the data
Infra and
VRE
services

Quantity Description
Mean Mean discrepancy between the probabilities
Variance Variance of the discrepancies
Number of errors Number of discrepant locations
Number of
comparisons
Number of compared locations
Accuracy Number of agreed locations divided by the number of
compared locations
Maximum error Maximum recorded discrepancy between two locations
Relative error Average error divided by the maximum error
Maximum error
points
List of the geographic areas of maximum discrepancy
Cohens Kappa A value representing the agreement between the two
maps with respect to agreement by chance
Trend Expansion, contraction, or stationary label indicating if the
second map in the comparison is more or less extended
than the first
Clusters Interpretation
Cluster1
From small- to large-scale conservative
distributions with localized moderate
changes (40%) in one FAO Area
Cluster2
From small- to medium- scale distributions
with high (>=40%) habitat change in all the
FAO Areas
Cluster3
Conservative distributions with small
localized change (from 10% to 20%) in one
or two FAO Areas
Cluster4
From small- and medium- scale
distributions with moderate
change (from 20% to 40%) in all the FAO
Areas
Cluster5
From small- and medium- scale
distributions with low change (from 10% to
20%) in all the FAO Areas
Cluster6
Medium-scale conservative distributions
without sensible changes
Cluster7
Small-scale conservative distributions,
without sensible changes
Global Patterns of Climate Change
Effects on Species Distributions
Discovered classes of change
90.13% agreement with expert assessment
** Coro, G., Magliozzi, C., Ellenbroek, A., Kaschner, K., & Pagano, P. (2015).
Automatic classification of climate change effects on marine species
distributions in 2050 using the AquaMaps model. Environmental and
Ecological Statistics, 1-26.
Cluster Analysis
on the
discrepancy
Statistics**
Calculated for 406 ASFIS-FAO species (100GB data)
Heterodontus zebraMaps comparison
statistics*
* Coro, G., Pagano, P., & Ellenbroek, A. (2014). Comparing heterogeneous distribution maps for marine species. GIScience & remote
sensing, 51(5), 593-611.

25
Data Production
and Representation
in the data
Infra and
VRE
services

Fisheries dependency on
habitat and climate change
26
Alopias vulpinus
Today
2050
• In a yield-biomass equilibrium scenario,
abundancy shift may depend on the effects
of Climate Change on habitat loss;
• Catch-per-unit of effort is moderately
proportional to habitat suitability for
sustainable fisheries (low suitability->no
CPUE);
• Impact, calculated as density of habitat
suitability in time, can be used in stock
assessment predictions;
• Clustering species’ Climate Change responses
can help finding classes of impact on
fisheries;
• If an expert’s fisheries impact assessment
were available for a certain area (e.g. FAO or
EEZ), assessing Climate Change response
similarities between the areas can help
extending the expert’s assessment to other
areas.

27
Data Production
and Representation
in the data
Reuse of data
Infra and
VRE
services

28
Reuse of Data
Environmental Data Forecasts:
http://thredds.d4science.org/thredds/catalog/public/netcdf/ClimateChange/catalog.html
https://goo.gl/cVKuCb
Species Distributions Data (AquaMaps Native Model):
http://thredds.d4science.org/thredds/catalog/public/netcdf/AquamapsNative/catalog.html
Species Distributions in 2050 Data (AquaMaps Native 2050 Model):
http://thredds.d4science.org/thredds/catalog/public/netcdf/AquamapsNative2050/catalog.html
Interactive Timeline – Linked Open Data:
https://dlnarratives.eu/timeline/climate.html
Pufferfish Invasion Data:
https://goo.gl/vvleUG
D4Science e-Infrastructure Catalogue:
https://bluebridge.d4science.org/group/imarine-gateway/data-catalogue
Cloud Computing Platform:
https://wiki.gcube-system.org/gcube/Data_Mining_Facilities

29
Data Production
and Representation
in the data
Reuse of data Reuse of processes
Infra and
VRE
services

1 - Download the AquaMaps model from the e-Infrastructure:
https://bluebridge.d4science.org/group/imarine-gateway/data-catalogue
2 - Produce presence and absence records in native env.:
https://bluebridge.d4science.org/group/biodiversitylab/data-
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.PRESENCE_CELLS_GENERATION
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.ABSENCE_GENERATION_FROM_OBIS
3 - Collect environmental information used by AquaMaps:
https://bluebridge.d4science.org/group/biodiversitylab/geo-visualisation
Or directly: http://data.d4science.org/ctlg/BiodiversityLab/half-degree_cells_authority_file_-_hcaf
4 - Produce Neural Networks and MaxEnt models using the information above:
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.generators.AQUAMAPS_SUITABLE_NEURALNETWORK
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.MAX_ENT_NICHE_MODELLING
5 - Use SVMs as further model
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.SUPPORT_VECTOR_MACHINES_PROJECTOR
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.SUPPORT_VECTOR_MACHINES_MODELLING
6 - Merge and inverse weight – offline post-processing (integrated with the services)
7 - Display as a map using the e-Infrastructure:
miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.SPECIES_MAP_FROM_POINTS
8 – Get reference points from GBIF and OBIS:
https://bluebridge.d4science.org/group/biodiversitylab/species-discovery
An Open Science workflow

Thank you
31

Invasive species and climate change

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