1. Armed conflicts in drylands:
a statistical investigation of natural and socio-economic causes
Armed conflict distribution in global drylands through the lens of a typology of socio-ecological vulnerability
Till Sterzel*, Matthias Lüdeke*, Marcel Kok+, Carsten Walther*, Diana Sietz**, Indra de Soysa++, Paul Lucas+, Peter Janssen+
+ Netherlands Environmental Assessment Agency (PBL), 3720 AH Bilthoven, The Netherlands
++Norwegian University of Science and Technology, Dept. of Sociology and Political Science, N-7491 Trondheim, Norway
**Wageningen University, Rural Development Sociology Group, Hollandseweg 1, 6706 KN Wageningen, The Netherlands
Reg Environ Change (2014) 14:1419–1435
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

2. Empirical, large N investigation contributing to the discourses:
(1) “neo-Malthusian“ vs. “neo-Durkheimian“ explanation of conflict
(2) linear vs. non-linear models of conflict explanation
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

3. UCDP/PRIO ACD, version 4-2006 at least 25 battle-related deaths’
Armed conflict data:
UCDP/PRIO ACD, version
Uppsala Conflict Data Program and International Peace Research Institute, Oslo
at least 25 battle-related deaths’
geo-referenced
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

4. Colored & dark grey areas: global drylands (CBD, 2007):
- 41% of global land surface % of global population
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

5. Socio-economic & environmental data:
For the choice of core areas see Reynolds et al (2007) and Safriel and Adeel (2008)
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

6. multivariate logistic regression
The linear approch:
multivariate logistic regression
AIC for different logit – models:
(measure for goodness of fit, corrected by number of free parameters, the smaller the better)
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

7. The non-linear approch:
clusters in the 7-dimensional indicator space are related to conflict incidence
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

8. The non-linear approach:
the cluster profiles (=cluster centers which represent the resp. point clouds)
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

9. The non-linear approach:
the cluster profiles and related conflict numbers
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

10. The non-linear approach :
the cluster profiles, conflict numbers and resulting rules
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

11. Quantitative comparison:
explanatory power of linear vs. nonlinear model
AIC for different logit – models:
ROC for the different models:
ROC (receiver operating characteristic)
Shifting threshold of independent variable,
resulting false/true positive/negative
predictions. AUC = 1: exact
AUC = 0.5: no relation
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

12. Empirical, large N investigation contribution to the discourse:
“neo-Malthusian“ vs. “neo-Durkheimian“ explanation of conflict:
The dependence of conflict incidence on natural and social variables
seems to be non-linear.
Depending on the resource endowment different socio/economic variables
determine the probability of conflict occurence.
In this sense both, natural and social varibles play their role
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,

13. Matthias K. B. Lüdeke & Till Sterzel
Cluster (color)
Group characteristics
Main characteristics
Examples of locations
Developed, less marginal (purple)
Very high average income, very low infant mortality rate
Very high agropotential, moderate road density, very high soil erosion mainly caused by cropland irrigation
Arid areas of the HICs– mainly in the USA, Spain, Italy and Australia
Developed, marginal (light blue)
Low agropotential, very low population density and road density, low water availability, low soil erosion through livestock grazing
Resource poor, moderate poverty (yellow)
Most resource-constrained and isolated areas of the world, very low renewable water resources and agro-potential, low soil degradation; very low water availability, very low population density and road density
Low average income, moderate IMR, moderate HWB
Transition zone between pastoral and sporadic, sparse forms of land-use in the desert fringes in America, Africa and Asia, driest deserts in the world (Atacama, Sahara, central Arabian Peninsula)
Resource poor, severe poverty (red)
Lowest HWB - very low average income, very high IMR, pastoral land use
Arid regions of Sub-Saharan Africa and Asia that are dominated by pastoral land-use
Moderate resources,
more populated (dark blue)
Low water availability, medium to very high agropotential, low average income, high IMR, low population density and road density
Very high agropotential, moderate soil erosion
Parallel bands in steppes and savannahs and neighboring desert areas, with the pink cluster more commonly adjacent to the desert. This typically coincides with a land-use gradient from pastoral to agro-pastoral uses
Moderate resources, less populated (pink)
Moderate agropotential, high soil erosion
Resource rich, overuse (green)
High natural resource endowment, high water availability
Very high overuse - very high soil erosion, high agropotential, low HWB; very high population density and road density
Indus River, Tigris-Euphrates river system, Volga River, other irrigated areas like the Aral Sea area, regions adjacent to the eastern Andes
Resource rich, rivers (black)
Moderate HWB (highest in developing country clusters), high disparities, moderate agro-constraints, very high water availability
Matthias K. B. Lüdeke & Till Sterzel
Climate Impacts and Vulnerabilities
Lübeck,