Application of GLOBIO3

Biodiversity Modelling to KENYA

2ND 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 DESIGNThe 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 2000IMAGE GLOBIO 2

Land use

Nitrogen ClimateInfrastructure

Land-useeffect

Nitrogeneffect

Climateeffect

Patch sizeeffect

Infrastructureeffect

MSAGLOBIO3

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

Biodiversitydecrease

100%

50%Map color

0%

GLOBIO3 OUTPUT

Maps figures tables

PRELIMINARY RESULTS

NATIONAL MSA MAP OF KENYA

MEAN SPECIES ABANDANCE

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8

Total

MSA GRAPH FOR 8 PROVINCES

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8

corrected reduction by agriculture corrected reduction by grazing

corrected reduction by forestry corrected reduction by built up

corrected reduction by nitrogen corrected reduction by climate

corrected reduction by infra corrected reduction by fragmenation

corrected remaining biodiversity

PRESSURE FRACTION CONTRIBUTION TO MSA

Total contribution for each pressure

5.67%

1.34%

6.15%6.17%

0.01%

0.17%

0.09%

0.43%

79.97%

corrected reduction by agriculture corrected reduction by grazingcorrected reduction by forestry corrected reduction by built upcorrected reduction by nitrogen corrected reduction by climatecorrected reduction by infra corrected reduction by fragmenationcorrected remaining biodiversity

The Wildlife Conservation ProblemThe Wildlife Conservation Problem

Decline in Wildlife population

Habitat lossHuman -wildlife

conflict

Drought and diseases

High populationgrowth

PoachingIncreased 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

1 2

3 3

1 2

44

5 5 66

77

Expansion of Agriculture : 1981-2000

Which are the environmental hotspots?

What is the state of biodiversity in the protected areas?

5.67%

1.34%

6.15%6.17%

0.01%

0.17%

0.09%

0.43%

79.97%

corrected reduction by agriculture corrected reduction by grazingcorrected reduction by forestry corrected reduction by built upcorrected reduction by nitrogen corrected reduction by climatecorrected reduction by infra corrected reduction by fragmenationcorrected remaining biodiversity

What are the key pressure factors contributing to biodiversity loss?

NATIONAL BIODIVERSITY MODELLING

SUPPORT TO POLICY MAKERS

African group

Robby, Carla and Moses Enschede, ITCJune 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 crops1 – intensive agriculture2 – extensive agriculture3 – forest4 – woodland5 – scrubland6 – grassland7 – others

2000 2030

0 – permanent crops1 – intensive agriculture2 – extensive agriculture3 – forest4 – woodland5 – scrubland6 – grassland7 – others

2000

2030

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.

2000 2015 2030

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

IMPLEMENT

POLICYFORMULATION

EVALUATE

CONTROL

PROBLEMRECOGNITION

• 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

2000

60%

31%

0%2%7% 0%

MSA remaining

Land_use

Ndep

Climate

infrastructure

fragmentation

Per regionPer pressure factor

Current biodiversity status

Information that can be provided

2000

2030

Future Biodiversity Trend

MSA 2000-2030

0%10%20%30%40%50%60%70%80%90%

100%

2000 2030

fragmentation

infrastructure

Climate

Ndep

Land_use

MSA remainingLegend

MSA

Value

High : 0.9807

Low : 0

Land use contribution

Land_use contribution

0

0.10.2

0.30.4

0.5

0.60.7

0.8

2000 2030

Year

MS

A

Others

Grasslands

Shrublands

Woodlands

Forest

Extensive

Crop area

Land_use

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 NationalPark

Aberdare R

ange

Tana R.

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