Assessing, Monitoring and Forecasting Biodiversity for Conservation

The case of ,

a Digital Observatory for Protected Areas

http://dopa.jrc.ec.europa.eu

Contact: gregoire.dubois@jrc.ec.europa.eu

221 May 2014

Baseline biodiversity conservation data for decision-making ?

© Timo Elliot

321 May 2014

Large scale assessment of protected areas (PAs) require objective continent-wide data sets and methodologies as opposed to case studies

on individual parks

The donors dilemma for protected areas: how to define funding priorities considering

the huge number of protected areas?

A LEGO Toolbox for Biodiversity Conservation!

DOPA is based on small independentinteroperable web-based componentsdeveloped by experts at different institutions

Components are reusable for other applications

Data & models sometimes exist, but these are often not available and/or reusable

Decision-makers

Delivering the right informationwith the right tools for the right people

Park Managers Public

Researchers

Web Services for Biodiversity Conservation

Encouraging multi-scale, cross-disciplinary approaches combiningin situ data with remote sensing observations

is based on Critical Biodiversity Infrastructures

organized around a set of distributed web servicesto assess, monitor, and forecast biodiversity & ecosystems

Red List of Threatened Species

World Database of Protected Areas

Ecological monitoring and modeling

Species Occurrences

Important Bird Areas & Bird distributions

Comparing protected areas

Protected areas can be compared using 2 indices (i) Biodiversity Value and (ii) Anthropogenic Pressure.

Hartley, A., A. Nelson, P. Mayaux, and JM. Grégoire (2007). The Assessment of African Protected Areas. EUR 22780 EN, Luxembourg, Office for Official Publications of the European Communities. 77 pp.

7 components for the trilogy

1. Assessing protected areas

DOPA Explorer: Beta version released Nov. 2013 describing all PAs ≥ 150 km2 (9 000 PAs). Top-down approach

2. Monitoring and validating assessments

DOPA Validator (2014): bottom-up approach

3. Predicting & forecasting changes in and outside protected areas

DOPA Analyst (2015)

eStation

Example 1: eStation

Remote sensing for Terrestrial monitoring

Fire ecology

Ecological anomalies

Real time monitoring of NDVI, rainfall, … in protected areas

Clerici, M. B. Combal, J.F. Pekel, G. Dubois, J. van't Klooster, J.O. Skøien, E. Bartholomé (2013). The eStation, an Earth Observation processing service in support to ecological monitoring. Ecological Informatics, (18):162-170.

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Historical and near-real time information about fire activity derived from the MODIS burned area and active fire products.

The fire tool: a web base tool developed with and for managers of protected areas

The MODIS products are distributed by NASA FIRMS and the University of Maryland

eMarine

Example 2: eMarine

Remote sensing for Marine monitoring

Physical variablesSea surface temperatureBathymetry (GEBCO)

Bio-optical variablesAbsorption coefficientParticulate backscatter CoefficientDiffuse Attenuation CoefficientChlorophyll ConcentrationSurface productive layer

Biological variablesPrimary production

MODIS-AQUA Chlorophyll-a

Input maps:

• % tree cover

• % herbaceous cover

• % barren cover

• Elevation

• NDVI

• Aridity index

• % water bodies

• Slope

• OTHERS...eHabitat

Example 3: eHabitat

Agriculture

Climate Change

Biofuels

Ecological forecasting for impact assessment of different environmental scenarios

Dubois, G., M. Schulz, J. Skøien, L. Bastin, S. Peedell (2013). eHabitat, a multi-purpose Web Processing Service for ecological modeling. Environmental Modelling & Software, 41:123-133.

Example 3: eHabitat

Habitat irreplaceability & connectivity

Dubois, G., M. Schulz, J. Skøien, L. Bastin, S. Peedell (2013). eHabitat, a multi-purpose Web Processing Service for ecological modeling. Environmental Modelling & Software, 41:123-133.

Mavinga (Angola)

Mupa (Angola)

Portland Bight (Jamaica)

Skøien, J., M. Schulz, G. Dubois, I. Fisher, M. Balman, I. May, É. Ó Tuama (2013). Climate change in biomes of Important Bird Areas – results from a WPS application. Ecological Informatics, 14:38-43

Birdlife Int.Bird habitats

(Cambridge, UK)

GBIFBird occurrences (Copenhagen, DK)

UNEP-WCMCPark –Boundaries(Cambridge, UK)

EC-JRCModeling &

Remote sensing(Ispra, IT)

Example 3: eHabitat

Ecological forecasting of bird distributions according to different climate change scenarios

IUCNRed List status(Nyon, CH)

EO are essential for modeling & large scale assessment

EO alone is not sufficient

EO are very much IT dependent: derived products need to be accessible

EU encourages open data policy. COPERNICUS will lead to Terabytes of free daily imagery

Propagation of errors and uncertainties in data and models

Making information available is not always sharing

Summary & Challenges

Thank You!

The institutional funding activities of the Land Resources Management Unit,

Institute for Environment Sustainability, Joint Research Centre of the European

Commission (2009- ….)

BIOPAMA, (http://www.biopama.org/) funded by a European Development Fund

(DG DEVCO) (2012-2015)

EuroGEOSS (http://www.eurogeoss.eu/) funded by the DG RTD of the European

Commission (2009-2012)

UncertWEB (http://www.uncertweb.org/) funded by the DG CONNECT of the

European Commission (2010-2013)

PacsBIO, funded by an EC Budget support programme from DG DEVCO to

Biodiversity and Protected Areas in Bolivia (2013-2016) PacsBIO

has been supported by the following projects from the European Commission

http://dopa.jrc.ec.europa.eu