Browsing by Person "Ogutu, Joseph O."

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    Increasing anthropogenic disturbance restricts wildebeest movement across east African grazing systems
    (2022) Stabach, Jared A.; Hughey, Lacey F.; Crego, Ramiro D.; Fleming, Christen H.; Hopcraft, J. Grant C.; Leimgruber, Peter; Morrison, Thomas A.; Ogutu, Joseph O.; Reid, Robin S.; Worden, Jeffrey S.; Boone, Randall B.
    The ability to move is essential for animals to find mates, escape predation, and meet energy and water demands. This is especially important across grazing systems where vegetation productivity can vary drastically between seasons or years. With grasslands undergoing significant changes due to climate change and anthropogenic development, there is an urgent need to determine the relative impacts of these pressures on the movement capacity of native herbivores. To measure these impacts, we fitted 36 white-bearded wildebeest (Connochaetes taurinus) with GPS collars across three study areas in southern Kenya (Amboseli Basin, Athi-Kaputiei Plains, and Mara) to test the relationship between movement (e.g., directional persistence, speed, home range crossing time) and gradients of vegetation productivity (i.e., NDVI) and anthropogenic disturbance. As expected, wildebeest moved the most (21.0 km day–1; CI: 18.7–23.3) across areas where movement was facilitated by low human footprint and necessitated by low vegetation productivity (Amboseli Basin). However, in areas with moderate vegetation productivity (Athi-Kaputiei Plains), wildebeest moved the least (13.3 km day–1; CI: 11.0–15.5). This deviation from expectations was largely explained by impediments to movement associated with a large human footprint. Notably, the movements of wildebeest in this area were also less directed than the other study populations, suggesting that anthropogenic disturbance (i.e., roads, fences, and the expansion of settlements) impacts the ability of wildebeest to move and access available resources. In areas with high vegetation productivity and moderate human footprint (Mara), we observed intermediate levels of daily movement (14.2 km day–1; CI: 12.3–16.1). Wildebeest across each of the study systems used grassland habitats outside of protected areas extensively, highlighting the importance of unprotected landscapes for conserving mobile species. These results provide unique insights into the interactive effects of climate and anthropogenic development on the movements of a dominant herbivore in East Africa and present a cautionary tale for the development of grazing ecosystems elsewhere.
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    An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems
    (2024) Ojwang, Gordon O.; Ogutu, Joseph O.; Said, Mohammed Y.; Ojwala, Merceline A.; Kifugo, Shem C.; Verones, Francesca; Graae, Bente J.; Buitenwerf, Robert; Olff, Han
    Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.
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    Small-mammal abundance and species diversity: Land use and seasonal influences in the Serengeti Ecosystem, Tanzania
    (2023) Shilereyo, Monica T.; Magige, Flora J.; Ogutu, Joseph O.; Røskaft, Eivin
    Land use, habitat suitability, and seasonality can fundamentally shape small-mammal abundance, species richness, diversity, evenness, and composition. However, how these characteristics of small mammals are determined by land use, habitat type, and rainfall seasonality is still poorly understood for most ecosystems. We analyze how land use (protection in a national park, pastoralism, and crop agriculture), habitat type, and rainfall seasonality influence small-mammal relative abundance, species richness, and diversity in the Tanzania Serengeti Ecosystem. We used 141 live traps to capture 612 small mammals in the wet and dry seasons of 2017 and 2018. Relative abundance was higher in the pastoral land than in the park or agricultural land and in the dry season in all the three land use types. Species richness and diversity were highest in the park, middling in the agricultural land, and lowest in the pastoral land. The high relative abundance in the pastoral land was primarily due to the numerical dominance of two generalist species in the shrubland (grass rat Arvicanthis niloticus) and cropland (multimammate rat Mastomys natalensis), resulting in low species richness and diversity. High species richness and diversity in the park indicate high habitat heterogeneity, whereas high species diversity in the agricultural land during the dry season reflects high food availability during and soon after harvests. Thus, human activities apparently exert deleterious effects on some specialist small mammals as a result of reduced habitat heterogeneity while promoting the abundance of some generalist species in African savanna ecosystems. However, increased abundance of generalist species reduces small mammal species diversity while increasing the risk of human–small mammal conflicts. We offer several testable hypotheses motivated by our results.