Transcript Application of GLOBIO3 Biodiversity Modelling to KENYA

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 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
IMAGE
GLC 2000
Land
use
Nitrogen
Land-use Nitrogen
effect
effect
GLOBIO3
GLOBIO 2
Climate
Infrastructure
Climate Patch size Infrastructure
effect
effect
effect
MSA
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
100%
Biodiversity
decrease
Map color
50%
0%
GLOBIO3 OUTPUT
Maps
 figures
 tables

PRELIMINARY
RESULTS
NATIONAL MSA MAP OF
KENYA
MSA GRAPH FOR 8
PROVINCES
MEAN SPECIES ABANDANCE
12
10
8
6
4
2
0
1
2
3
4
5
Total
6
7
8
PRESSURE FRACTION CONTRIBUTION TO MSA
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
5
6
7
8
corrected reduction by agriculture
corrected reduction by forestry
corrected reduction by grazing
corrected reduction by built up
corrected reduction by nitrogen
corrected reduction by infra
corrected remaining biodiversity
corrected reduction by climate
corrected reduction by fragmenation
Total contribution for each pressure
79.97%
5.67%
1.34%
0.43%
6.17%
6.15%
0.09%
0.17%
corrected reduction by agriculture
0.01%
corrected reduction by forestry
corrected reduction by nitrogen
corrected reduction by infra
corrected remaining biodiversity
corrected reduction by grazing
corrected reduction by built up
corrected reduction by climate
corrected reduction by fragmenation
The Wildlife Conservation Problem
Loss of genetic
biodiversity
Decline in Wildlife
population
PAC
Habitat loss
Cultivation in
Wildlife areas
Drought and
diseases
Poaching
Increased
Poverty
Human -wildlife
conflict
High population
growth
Key policy questions relevant to
KWS
What are impacts of pressures on species,
ecosystems & ecosystem goods and
services?
 Where are the changes occurring?

Expansion of Agriculture : 1981-2000
3
1
3
2
1
2
7
7
4
4
5 6
5
6
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
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?
79.97%
5.67%
1.34%
0.43%
6.17%
6.15%
0.09%
0.17%
corrected reduction by agriculture
0.01%
corrected reduction by forestry
corrected reduction by nitrogen
corrected reduction by infra
corrected remaining biodiversity
corrected reduction by grazing
corrected reduction by built up
corrected reduction by climate
corrected reduction by fragmenation
NATIONAL BIODIVERSITY MODELLING
SUPPORT TO POLICY MAKERS
African group
Robby, Carla and Moses
Enschede, ITC
June 29, 2007
Scenario 1: OECD baseline (IMAGE results)
CLUE (Conversion of Land Use and its Effects
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
2000
2030
0 – permanent crops
1 – intensive agriculture
2 – extensive agriculture
3 – forest
4 – woodland
5 – scrubland
6 – grassland
7 – others
2000
2030
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
POLICY: Increase intensive agriculture by 5% & reduction in
extensive agric by same.
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
RESULTS FOR SCENARIO 2
2000
2015
2030
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.
Policy Target - Environmental Conservation
Policy
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.
Biodiversity conservation strategy
PROBLEM
RECOGNITION
EVALUATE
POLICY
FORMULATION
CONTROL
IMPLEMENT
Key questions – Addressed by Modelling
• 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?
Information that can be provided
Current biodiversity status
2000
MSA per Province 2000
Southern
10%
Northern
11%
2%
0%
7% 0%
Luapula
11%
Western
13%
MSA remaining
Land_use
Eastern
10%
Copperbelt
15%
Ndep
31%
Climate
60%
infrastructure
fragmentation
Central
12%
Nwestern
18%
Per region
Per pressure factor
Future Biodiversity Trend
MSA 2000-2030
2030
2000
Legend
MSA
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
fragmentation
infrastructure
Climate
Ndep
Land_use
MSA remaining
2000
Value
High : 0.9807
Low : 0
2030
Land use contribution
MSA
Land_use contribution
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Others
Grasslands
Shrublands
Woodlands
Forest
Extensive
Crop area
2000
2030
Year
Land_use
Poverty map overlay
Combining all Layers: Poverty and Competing Demands
for Ecosystem Services in the Upper Tana River Basin
Meru National
Park
Mt. Kenya
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.
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