Mapping African buffalo distributions, in relation to livestock disease risk

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1 Mapping African buffalo distributions, in relation to livestock disease risk Tim Robinson and Jennifer Siembieda Buffalo Mapping Meeting 7-8 June, Rome FAO, Canada Room

2 Overview Modelling densities of the African buffalo Adjustment for anthropogenic influence The buffalo-cattle interface Conclusions and next steps

3 Buffalo distribution modelling 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 multitemporal, Fourier processed, remotely-sensed environmental variables (vegetation indices, temperature variables, etc.),

4 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

5 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 IUCN/SSC Antelope Specialist Group Data statistics n=121 vs. n=241 Range of park areas: 72 to 64,257 km 2 Range of buffalo densities: to 20.7 per km 2 Range of buffalo counts: 5 to 138,100 Most populous: Selous National Park, Tanzania

6 Buffalo data Angola 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

7 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

8 Predictor variables False colour composite MODIS satellite data, Fourier-processed imagery

9 Unsuitability masking and sampling

10 Best result so far. Predicted buffalo density 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)

11 Best result so far. Predicted buffalo density

12 Best result so far. Predicted buffalo density Standard Deviation of mean (n=25)

13 Best result so far. Predicted buffalo density Probabilistic Continuous Model (AMD) Probabilistic model

14 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

15 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.

16 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

17 Human Footprint in protected areas

18 Adjusting for anthropogenic influence 1 Habitat suitability Human Footprint 0 = not suitable for buffalo 1 = suitable for buffalo

19 Adjusting for anthropogenic influence Predicted buffalo density Predicted density x HF

20 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

21 Buffalo-cattle interface: Cattle densities Modelled cattle 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

22 Buffalo-cattle interface: Ruminant systems Ruminant Production Systems Land cover (GLC 2000)

23 Buffalo-cattle interface: Ruminant systems Land cover (GLC 2000) Legend

24 Buffalo-cattle interface: Ruminant systems Length of growing period Ruminant production systems

25 Buffalo-cattle interface: Cattle movements Beef production surplus (kg per Km 2 ) Supply and use accounts Beef demand mapped against human distribution (GRUMP) Beef production mapped against cattle distribution (GLW) Difference = production surplus

26 Buffalo-cattle interface Predicted cattle density Predicted buffalo density x HF

27 Buffalo-cattle interface Cattle-buffalo interface Ruminant production systems

28 Buffalo-cattle interface

29 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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