biodiversity data for decisi - university of auckland · inauguration, election mc & wg chairs...

Biodiversity data for decision making Jörn P. W. Scharlemann

UNEP-WCMC is UNEP’s Biodiversity Assessment

Centre

Founded in 1981

Mission To provide authoritative

information about biodiversity and ecosystem services in a manner that is useful to decision-makers who are driving change in

environment and development policy

3 Databases for biodiversity assessment

Global Ecoregion Protection. Published in Protected Areas Annual Report: UNEP-WCMC 2008

Global analysis of the protection status of the world’s forests. Biological Conservation 2009

Toward representative protection of the world’s coasts and oceans—progress, gaps, and opportunities. Conservation Letters 2008

Aichi Target 11 By 2020, at least 17 per cent of terrestrial and inland water areas, and 10 per cent of coastal and marine areas, especially areas of particular importance for biodiversity and ecosystem services, are conserved through effectively and equitably managed, ecologically representative and well connected systems of protected areas and other effective area-based conservation measures, and integrated into the wider landscapes and seascapes.

Improvements in WDPA 2006-2011

41% 59%


“New wiki-style, web-interface for learning about and contributing to WDPA”

Interact with Data • Download / Upload Data • Reconcile Data • Edit data on-line • Cut out the middle man • View users (stakeholder) opinions

Global Biodiversity Information Facility (GBIF) Species & Protected Areas Widget

…..more like this to come...

Existing global data for mangroves

Two recent global studies:

World Atlas of Mangroves 2010, published by UNEP, ISME, ITTO, FAO, UNU, UNESCO & TNC

Giri et al, 2010: Global Ecology and Biogeography 20(1), 154-159 UNEP, USGS, NASA

Global Spatial similarity: 0.4

Fitzgerald, C, Giri, C., Kainuma, M., Latham, J., Wilkie, M., Singh, A., Spalding, M., Wood, L. 2011.

Some National Examples Kenya

Australia Indonesia

Work is ongoing to do more in-depth comparison of all countries to try to estimate uncertainties more comprehensively – this is indicative only.

HarmBio Harmonizing

Global Biodiversity Modelling

Jörn P. W. Scharlemann [email protected]

HarmBio COST Action Harmonizing Global Biodiversity Modelling

Aim The harmonization of current models and datasets of terrestrial, freshwater and marine biodiversity to improve the reliability of future projections of biodiversity change under various policy options enabling better environmental decision making.

Modelling climate change

+2 °C EU target

Biodiversity data

GEO-BON

Biodiversity metrics

Biodiversity scenarios

Pereira et al. 2010 Science 330:1496-1501

Modelling

Models are simplified mathematical representations of complex systems

Modelling

Models are simplified mathematical representations of complex systems.

Epidemiological models inform responses to disease pandemics Atmospheric models project future climate Economic models guide financial decisions

Modelling biodiversity change

Statistical niche models

Integrated assessment models

Policy Metrics Models

Scenarios

Metrics Data

Overview

HarmBio


Aim The harmonization of current models and datasets of terrestrial, freshwater and marine biodiversity to improve the reliability of future projections of biodiversity change under various policy options enabling better environmental decision making.


Better models • transparent • scientifically robust • intercomparable • common data and scenarios

Better decisions • based on state-of-the-art • approved projections • feed into assessments, eg. IPBES, post 2010

EU Biodiversity Strategy


Biodiversity and Climate Research Centre, Senckenberg

University of Copenhagen National Museum of Natural Sciences, Madrid Universite Paris-Sud Sapienzia Universita di Roma Alterra Wageningen PBL – NL Environmental Assessment Agency UNEP GRID Arendal Universidade de Lisboa Imperial College London University of East Anglia University of York UNEP-WCMC

University of British Columbia, Canada Yale University, USA

22 participants from 15 COST countries, 1 COST reciprocal country & 2 other countries

CSIRO, Australia


Year 1 Year 2 Year 3 Year 4 WG1: Consensus on metrics WG2: Harmonized datasets WG3: Agree standards for models WG4: Intercomparison of models

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WG1: Consensus on biodiversity metrics

currently every model produces different output metric (species richness, extinction risk, abundance, habitats, ...) multitude of national, regional & global indicators consensus required for comparison, integration into Earth system models and to guide policy ⇒ agree on scientifically robust and policy relevant metrics of biodiversity & processes


WG2: Harmonized datasets

currently every model uses different input data and different data on environmental conditions and human activities consensus required for model comparison long-term access to data and metadata ⇒ agree and compile datasets incl. full description & long-term storage of key datasets; collaborate with GEO-BON, GBIF, etc.


WG3: Standards for biodiversity models

currently every model addresses different components of biodiversity and different processes documentation variable explicit about components and feedbacks consensus required for model comparison and link to global climate models

⇒ build consensus on processes and components essential for model development; standards for documentation and transparency


WG4: Intercomparison of biodiversity models

currently individual models cannot be compared comparison needed to assess model capability to hind-cast historic/observed changes in biodiversity future projections based on standardised scenarios ⇒ facilitate systematic intercomparison and benchmarking of biodiversity models


Year 1 Year 2 Year 3 Year 4 WG1: Consensus on metrics WG2: Harmonized datasets WG3: Agree standards for models WG4: Intercomparison of models

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www.unep-wcmc.org

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...from a technical perspective

.fgdb

Managed Off-line Managed On-line

Global Data Partnership

Strengthen the business case for better data Transform the volume & rate of improvement of coastal ecosystem data Beneficiaries clearer:

• Governments • Citizens • Private Sector

Outcomes Transformed methodologies for data collection

• Citizen Science communities of contributors • Core standards and protocols • Freely available, reliable data • Transparency of contributions • Consistency of monitoring in local initiatives

Enable better decision-making across sectors

• Blue C & Ecosystem Service assessments • Robust basis of market-based instruments • Strengthened local capacity to integrate coastal

issues in national management strategies, EIA, etc

Outputs

Consolidated Partnership networks Consistent global baselines Validated and enriched datasets Improved transparency, accessibility, visualisation of data

biodiversity data for decisi - university of auckland · inauguration, election mc & wg chairs...

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