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A Living from LivestockPro-Poor Livestock Policy Initiative
Mapping African buffalo distributions, in relation to livestock disease risk
Buffalo Mapping Meeting
7-8 June, Rome FAO, Canada Room
Tim Robinson and Jennifer Siembieda
A Living from LivestockPro-Poor Livestock Policy Initiative
• Modelling densities of the
African buffalo
• Adjustment for
anthropogenic influence
• The buffalo-cattle
interface
• Conclusions and next
steps
Overview
A Living from LivestockPro-Poor Livestock Policy Initiative
• Based on an approach developed for livestock mapping• Gridded Livestock of the World (GLW)
• Wint and Robinson (2007); Prosser et al. (2011); van Boeckel et al. (2011)
• Assumptions• that buffalo populations in protected areas occur at densities reflecting the
suitability of the habitat to support buffalo
• that available statistics reflect the numbers reasonably closely
• that these habitat characteristics can be relatively well described by multi-
temporal, Fourier processed, remotely-sensed environmental variables
(vegetation indices, temperature variables, etc.),
Buffalo distribution modelling
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo distribution modelling
Buffalo data preparation • Collect information on buffalo numbers in protected areas
Suitability masking• Mask unsuitable areas
• Calculate adjusted observed densities
Sampling and stratification
• Define stratification methods for regressions• Stratified random sampling of predictor variables for 25 bootstraps
AIC stepwise regression analysis
• Log transform dependent variable• Add quadratic terms for independent variables
• Stepwise regression, AIC variable selection
Buffalo density predictions
• Apply regression coefficients to predictor variables• Select best predictions (based on RMSE) from different strata• Average the log buffalo density predictions for 25 bootstraps
Model comparison and validation
• Compare to other buffalo maps• Compute Standard Deviations over 25 bootstraps
• Compare predicted versus observed densities
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo data
Figure 1. Distribution of protected areas (IUCN levels I-V) in Africa superimposed on the distributional extents of the four subspecies of buffalo (Syncerus caffer) on the continent
(Source: IUCN 2010).
Data collection
• FAO reps in 38 countries
• AU-IBAR wildlife focal points (n=35)
• South African National Parks (SanParks)
•Tanzania Wildlife Research Institute
•Rod East: African Antelope Database 1998. IUCN/SSC Antelope Specialist Group
Data statistics
•n=121 vs. n=241
•Range of park areas: 72 to 64,257 km2
•Range of buffalo densities: 0.003 to 20.7 per km2
•Range of buffalo counts: 5 to 138,100
•Most populous: Selous National Park, Tanzania
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo dataAngola 1
Botswana 2
Burkina Faso 1
Burundi 1
Cameroon 1
Central African Republic 9
Chad 2
Cote d'Ivoire 2
Congo 2
Ethiopia 3
Ghana 2
Guinea 1
Kenya 14
Malawi 6
Mali 1
Mozambique 5
Namibia 5
Senegal 1
South Africa 9
Sudan 1
Uganda 9
Tanzania 16
Zambia 10
Zimbabwe 17
A Living from LivestockPro-Poor Livestock Policy Initiative
Predictor variables
• Locational – longitude and latitude
• Anthropogenic
• Distance to roads
• Distance to city lights
• Demographic - human population
• Topographic – slope and elevation
• Vegetation – NDVI, EVI
• Temperature
• Land surface temperature
• Air temperature
• Water and moisture
• Vapour pressure deficit
• Distance to rivers
• Evapotranspiration
• General climatic - LGP
A Living from LivestockPro-Poor Livestock Policy Initiative
Predictor variables
• MODIS satellite data, 2001-6
• Fourier-processed imagery
False colour composite
A Living from LivestockPro-Poor Livestock Policy Initiative
Unsuitability masking and sampling
A Living from LivestockPro-Poor Livestock Policy Initiative
Best result so far ….
• Version 9b
• Reduced set of training data (n-121)
• 200 points per 10,000 square kilometres
• Two stratification schemes
• Subspecies distribution
• Livestock production systems
• Unsuitable areas with mean annual NDVI < 0.2
• Data points in unsuitable areas ignored (not set to 0 density)
Predicted buffalo density
A Living from LivestockPro-Poor Livestock Policy Initiative
Best result so far ….
Predicted buffalo density
A Living from LivestockPro-Poor Livestock Policy Initiative
Best result so far ….
Predicted buffalo density Standard Deviation of mean (n=25)
A Living from LivestockPro-Poor Livestock Policy Initiative
Best result so far ….
• Probabilistic model
Predicted buffalo density Probabilistic Continuous Model (AMD)
A Living from LivestockPro-Poor Livestock Policy Initiative
Adjusting for anthropogenic influence
• Based on the Human Footprint and the Last of the Wild (Sanderson et al. 2002)
• Assumptions:
• Buffalo occur outside
protected areas where
human influence is minimal
• Direct, linear relationship
between human footprint and
reduced habitat suitability
A Living from LivestockPro-Poor Livestock Policy Initiative
Adjusting for anthropogenic influence
• Based on wilderness mapping
• Geographic proxies for Human
Influence
• Summed to give a quantitative
evaluation of HI on the land’s
surface
• Four types of data (9 datasets) as
proxies for HI
• Each dataset was standardized from
0 (low HI) to 10 (high HI) to reflect
their estimated contribution to HI.
A Living from LivestockPro-Poor Livestock Policy Initiative
Adjusting for anthropogenic influence
1. Population density higher human density leads to higher levels of influence on nature
2. Land transformation
3. Accessibility of roads, major rivers and coastlines leads to extraction of resources, pollution and disruption of resources areas
4. Electrical power infrastructure
A Living from LivestockPro-Poor Livestock Policy Initiative
Human Footprint in protected areas
A Living from LivestockPro-Poor Livestock Policy Initiative
Adjusting for anthropogenic influence
0
1
0 10 20 30 40 50 60 70 80 90 100
Ha
bita
t su
ita
bility
Human Footprint
0 = not suitable for buffalo
1 = suitable for buffalo
A Living from LivestockPro-Poor Livestock Policy Initiative
Adjusting for anthropogenic influence
Predicted buffalo density Predicted density x HF
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface
• Where do cattle and buffalo potentially interact?
• Cattle distribution maps
• Links to specific production systems?
• Ruminant production systems
• Links to cattle movements
• Transhumance
• Trade-related movements
• Disease ecology and disease risk
• Combining risk factors
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface: Cattle densities
Modelledcattle density
• Original maps produced for PAAT Information
System
• Gridded Livestock of the World (2007)
• Recent developments
• Improvements to models and data (1 km)
• Monogastrics in Asia
• Ruminants in Africa
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface: Ruminant systems
Land cover (GLC 2000)Ruminant Production
Systems
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface: Ruminant systems
Land cover (GLC 2000)Legend
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface: Ruminant systems
Length of growing period Ruminant production systems
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface: Cattle movements
• Supply and use accounts
• Beef demand mapped against human distribution (GRUMP)
• Beef production mapped against cattle distribution (GLW)
• Difference = production surplus
Beef production surplus (kg per Km2)
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface
Predicted cattle density Predicted buffalo density x HF
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface
Cattle-buffalo interface Ruminant production systems
A Living from LivestockPro-Poor Livestock Policy Initiative
Buffalo-cattle interface
A Living from LivestockPro-Poor Livestock Policy Initiative
Conclusions and next steps
• Buffalo data
• Park boundaries
• More detailed estimates of numbers
• Appropriate suitability masking
• Modelling approach
• Revisit the assumptions made
• Evaluate other statistical approaches
• Appropriate model stratification
• Distributional limits for sub-species
• Anthropogenic effects
• Improve on HF?
• Interaction with ag. landscape
• Incorporate pathogen information
• Disease ecology / nature of interaction