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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


• Modelling densities of the

African buffalo

• Adjustment for

anthropogenic influence

• The buffalo-cattle

interface

• Conclusions and next

steps

Overview


• 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


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


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


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


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


Predictor variables

• MODIS satellite data, 2001-6

• Fourier-processed imagery

False colour composite


Unsuitability masking and sampling


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



Predicted buffalo density



Predicted buffalo density Standard Deviation of mean (n=25)



• Probabilistic model

Predicted buffalo density Probabilistic Continuous Model (AMD)


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



• 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.



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


Human Footprint in protected areas



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



Predicted buffalo density Predicted density x HF


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


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


Buffalo-cattle interface: Ruminant systems

Land cover (GLC 2000)Ruminant Production

Systems



Land cover (GLC 2000)Legend



Length of growing period Ruminant production systems


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)



Predicted cattle density Predicted buffalo density x HF



Cattle-buffalo interface Ruminant production systems


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

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