Application of GLOBIO3 Biodiversity Modelling to KENYA 2 ND JANUARY 2007 MOSES MALOBA

GLOBIO 3- Developed by Netherlands environmental assessment agency (MNP), UNEP WCMC & UNEP GRID ARENDAL Globio3 –Describes biodiversity by calculating remaining mean species abundance of original species relative to their abundance in primary vegetation (pristine condition) Model considers various pressure factors (driving forces) that are either direct or indirect

MODEL DESIGN The core of the model is the description of the major relationships between the pressures/ drivers and their impacts on biodiversity Biodiversity of an ecosystem is considered as a stock entity i.e. the complete set of characteristic species & their abundance. Drivers are divided into two Dependent Independent

GLOBIO3 Design

MODEL INPUTS Land use (agriculture, forestry, settlement) Climate change Infrastructure Fragmentation Nitrogen Deposition

Design of model framework for GLOBIO 3 GLC 2000 IMAGEGLOBIO 2 Land use Nitrogen Climate Infrastructure Land-use effect Nitrogen effect Climate effect Patch size effect Infrastructure effect MSA GLOBIO3

Results from individual pressures are then combined and overall change in biodiversity calculated as Mean species abundance (MSA) Globio3 model depend on other models for some of the input data-IMAGE &Globio2

THE PROCESS OF BIODIVERSITY LOSS Biodiversity decrease 100% 50% Map color 0%

GLOBIO3 OUTPUT Maps figures tables

PRELIMINARY RESULTS NATIONAL MSA MAP OF KENYA MSA GRAPH FOR 8 PROVINCES

PRESSURE FRACTION CONTRIBUTION TO MSA

Total contribution for each pressure

The Wildlife Conservation Problem The Wildlife Conservation Problem Decline in Wildlife population Habitat loss Human -wildlife conflict Drought and diseases High population growth Poaching Increased Poverty Cultivation in Wildlife areas Loss of genetic biodiversity PAC

Key policy questions relevant to KWS What are impacts of pressures on species, ecosystems & ecosystem goods and services? Where are the changes occurring?

Notable land use changes at: 1 –Transmara; 2-Narok-Nakuru; 3-Laikipia-Samburu; 4-Chyulu-Ngai Ndeithya; 5-Taita; 6- Coastal strip; 7-Tana PNR Expansion of Agriculture :

Which are the environmental hotspots?

What is the state of biodiversity in the protected areas?

What are the key pressure factors contributing to biodiversity loss?

NATIONAL BIODIVERSITY MODELLING SUPPORT TO POLICY MAKERS African group Robby, Carla and Moses Enschede, ITC June 29, 2007

Scenario 1: OECD baseline (IMAGE results) Lu demands Increase of agricultural area demands (39%) Reduction of forest and woodlands (10%) Reduction of shrublands (39%) Reduction of Grasslands (27%) Policy option for conservation Complete Protection of all the reserves CLUE (Conversion of Land Use and its Effects

0 – permanent crops 1 – intensive agriculture 2 – extensive agriculture 3 – forest 4 – woodland 5 – scrubland 6 – grassland 7 – others

0 – permanent crops 1 – intensive agriculture 2 – extensive agriculture 3 – forest 4 – woodland 5 – scrubland 6 – grassland 7 – others

TRENDS FOR KENYA 2000 – 2030 SCENARIO 2 Increasing in agriculture by 30% (extensive and intensive combined). Keeping proportion constant between extensive and intensive from the beginning Increasing in perennials by 10% Increasing in built up areas by 15% Decreasing in savannas and natural areas Conversions into agriculture and built up areas are not permitted inside protected areas POLICY: Increase intensive agriculture by 5% & reduction in extensive agric by same.

RESULTS FOR SCENARIO 2

Results communication_ Policy makers Target Organisations : Environment Government Departments – e.g. and Natural Resources Management, etc. Policy Mandate Environmental conservation – Biodiversity (Fauna and Flora), water.

Geo-Information and biodiversity Modeling can benefit this Policy Spatial and temporal visualization of biodiversity status Data Integration from different sources (socio-economic, biophysical, administrative, etc) Results are aggregated and presented in a series of clear, communicative and policy relevant indices and indicators. Use of scenarios to project future trends Test different policy option outcomes Supports decision making at both national and local levels Scenarios for the future are relevant for policy formulation over a range of spatial scales from local to National and global. Policy Target - Environmental Conservation Policy

Biodiversity conservation strategy PROBLEM RECOGNITION POLICY FORMULATION IMPLEMENT CONTROL EVALUATE

Which areas are most vulnerable to Biodiversity loss (hot spots)? What is the relative importance of the different pressures (and interactions)? What trends in land use patterns can be expected (under various scenarios)? What are the likely effects of various response options, i.e. policies and strategies What is the rate of biodiversity loss (in terms of targets) in the future? Key questions – Addressed by Modelling

Per region Per pressure factor Current biodiversity status Information that can be provided

Future Biodiversity Trend

Land use contribution

Poverty map overlay

Combining all Layers: Poverty and Competing Demands for Ecosystem Services in the Upper Tana River Basin Sources: Kenya Central Bureau of Statistics, International Water Management Institute, Africover – Food and Agriculture Organization of the United Nations, Kenya National Environment Management Authority, and World Conservation Monitoring Centre. Mt. Kenya Meru National Park Aberdare Range Tana R.

Role of our organizations and supporting Partners Provision of information to support policy Create awareness on importance of biodiversity conservation Conduct research and communicate the results Ensure sustainability of the GI and Biodiversity Modelling