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International Institute for Geo-Information Science and Earth Observation (ITC) 2015

Modelling and mapping the probabilities of lion - livestock conflict areas : a case study of Masai Mara, Kenya.

Angima, V.K. (Vella Kwamboka)

Titre : Modelling and mapping the probabilities of lion - livestock conflict areas : a case study of Masai Mara, Kenya.

Auteur : Angima, V.K. (Vella Kwamboka)

Etablissement de soutenance : University of Twente International Institute for Geo-Information Science and Earth Observation (ITC)

Grade : Master of Science in Geo-Information Science and Earth Observation 2015

Résumé
Lions and livestock play an integral role culturally, socially and economically. They aid in ecosystem structuring and functioning. However, increased human settlements and livestock grazing in conservation areas has contributed to overlap between lion habitats, livestock grazing areas and human settlements . This overlap has augmented interactions between lions and livestock, fuelling conflicts which mostly result in loss of livestock, which is the key component for sustenance of pastoral livelihoods. The main aim of this study therefore was to model and map the probabilities of livestock-lion conflicts at bomas and at livestock grazing areas, for the purposes of assessing the spatial likelihood of lion-livestock conflicts occurring within the study area. To map vegetation cover types available for livestock grazing, hyper-temporal MODIS imagery were used , in combination with vegetation field survey data. Settlements were digitized from Google Earth imagery. This research made use of lion presence data and a set of six environmental predictors to predict lion presence probabilities using Maximum Entropy (MaxEnt) model. Model performance and accuracy was assessed using ROC Curve, Kappa and TSS statistics. Livestock kill count for one conservancy was used to validate the boma lion-livestock conflict probability map. Two formal hypothesis were formulated. Null hypothesis 1 stated that the MaxEnt model performance will be equal to 0.5. Null hypothesis 2 stated that the boma lion-livestock conflict probability map is not valid, based on the 2013/2014 livestock kill count for one of the conservancies (Mara North). The vegetation cover mapping exercise revealed that there were five grassland cover types in the study area, two of which could be easily differentiated from the rest. MaxEnt model for lions performed better than random. Analysis of model accuracy yielded TSS value of 0.497 and a Kappa statistic value of 0.733, indicating a fair model. Jackknife test results showed that wildlife density was the most important predictor of lion presence, followed by distance to bomas. Distance to roads, distance to rivers and vegetation cover types were on the other hand the least important predictors. Two limitations were encountered when conducting this study. Actual lion-livestock conflict data for the entire study area was not available. Hence, validation of the lion-livestock conflict probability maps for the entire study area was not possible. Secondly, actual livestock grazing areas within the study area was lacking. Hence, mapping of lion-livestock conflict probabilities considered all grasslands, representing all potential livestock grazing areas. In conclusion, the 71 classes, median, SD, and trend was not sufficient to differentiate all vegetation cover types. Grassland types situated in more conserved areas had more % grass cover and were more homogenous. Those situated near and in areas with anthropogenic activities were more heterogeneous. Wildlife and livestock densities are the best predictors of lion presence probabilities. Bomas located in areas with high lion presence probability have a very high likelihood of experiencing conflicts.

Mots clés : African Lions, Maasai Mara, Habitat, NDVI, livestock, conflicts, Boma, Species Distribution Models, MODIS.

Version intégrale (ITC)

Page publiée le 26 janvier 2018