Due to global land surface warming, severe temperature events are expected to occur more frequently and more extremely causing changes in biodiversity and altering movement and survival of large herbivores. There are increasing observations of escalating wildlife range losses worldwide. In this study, we investigated 15 large wild herbivores (4 migratory, 1 dispersing and 10 residents) and their potential range changes in relation to projected temperatures changes based on three Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. Previous studies of Kenyan savannah have shown that increases in temperature can reduce the densities of wildlife significantly and after certain thresholds the species can be lost in those landscapes. The range maps of the 15 species were developed from aerial censuses that have been conducted in the arid and semi-arid lands of Kenya. We analysed temperature changes for the three RCPs for the periods 2030s, 2050s and 2070s. And based on the temperature threshold for each of the 15 species we analysed which wildlife range areas will be lost. Our results project that for the RCP 2.6, 3 out of the 15 species are projected to lose more than 50% of their range by the year 2030s, and 5 out 15 by 2050s and 4 of 15 by 2070s. The second climate scenario of RCP 4.5 projects that by 2030s, 3 species will lose more than 50% of their range, and in 2050s and 2070s 5 species. The RCP 8.5 which is the extreme scenario of temperature changes projects 5 species to lose their range by 50% in 2030s, 7 species by 2050s and 10 species by 2070s. The extent of range loss was different among species but was severe for buffalo, Thomson's ga- zelle, waterbuck, and wildebeest which are also water dependent species. However, the elephant, gerenuk, hartebeest, lesser kudu, and oryx are expected to retain most of their range in all the RCPs scenarios. These range contractions raise serious concerns about the future of wildlife in Kenyan savannah based on projected climate changes. And therefore, it is imperative the wildlife sector develops climate policies and plans that take into account the projected climate scenarios.

This study investigated spatial and temporal trends of rainfall and temperature in the Amboseli ecosystem of Kenya. The analysis were based on historical Climate Hazards group InfraRed Precipitation with Station (CHIRPs) and Climate Hazards group InfraRed Temperature with Station (CHIRTs) data for the period 1960-2014 and the period 2006-2100 for the projections. This data was used due to limitations in the observed station data. Projections of rainfall and temperature were based on Regional Climate Models (RCM) from Coordinated Regional Downscaling Experiment (CORDEX) over the Amboseli ecosystem. The long-term annual and seasonal trends of rainfall and temperature were analyzed via Mann-Kendall's statistical test and linear trend analysis. The annual and seasonal rainfall declined slightly between 1960 and 2014 though not significant. However the temperatures increased more in the annual minimum (1.23 °C) compared to the annual maximum (0.79 °C). The maximum temperatures for the October-November-December (OND) season had highest increases of 0.88 °C while the March-April-May (MAM) season showed an increase of 0.69 °C. The highest increase in minimum temperatures of 1.35 °C was recorded for the June-July-August-September season (JJAS), while the least increase was in MAM (1.04°C). Projected rainfall based on Representative Concentration Pathways (RCPs) for the periods 2006-2100 varied with RCP 2.6 showing a decline for the four seasons. RCP 4.5 and 8.5 project marginal increase in annual and OND with declines in the MAM and JJAS. Projected maximum and minimum temperature for RCP 2.6 indicate increments of less than 1 °C while for RCP 4.5 the maximum range is between 0.57 °C and 1.85 °Cand minimum is between 0.51 °C to 1.98 °C. RCP 8.5 projected maximum increase are the highest between 1.11°C and 4.34 °C and minimum is between 1.34 °C and 5.26 °C based on period-2030, 2050 and 2070. The increase of temperatures and changes in rainfall can have large impacts on the resources in the savanna dry lands of East Africa especially on its livestock, agriculture, wildlife and pastoral and agro-pastoral communities.

Within savanna environments, movements of elephant are influenced by changes in climate especially seasonal rainfall. In this study, we investigated the possible changes in elephant population based on projected rainfall changes using regional climate models (RCM) and Representative Concentration Pathways (RCPs). The relationship between elephant and rainfall was modelled against annual, wet season, dry season rainfall based on various time lags. Future relation between elephant and rainfall was projected based on three RCPs; 2.6, 4.5 and 8.5. There was a strong linear relationship between elephant and October-November-December (OND) rains with time lag of 13 years (Y = −4016.43 + 19.11x, r 2 = 0.459, P = 0.006). The rainfall trends for RCP 2.6 and 4.5 showed a slight increase in annual rainfall for the period 2006-2100 but driven by OND increases. Rainfall increase for RCP 8.5 was significant and was driven by increase in both March-April-May (MAM) and OND. These rainfall dynamics had influence on the projected elephant population in the Amboseli ecosystem. For RCP 2.6 and 4.5 the elephant population increase was 2455 and 2814 respectively. RCP 8.5 elephant population doubled to an average of 3348 elephants. In all the RCPs there are seasonal and yearly variations and absolute number varies from the average. The range of variation is small in RCPs 2.6 and 4.5 compared to RCP 8.5. Evidently, elephant population will increase based on projected rainfall projections surpassing park capacity. It therefore, requires that the Park authority put in place measures that could contain these numbers including opening of blocked wildlife corridors, maintain the cross border movement of Amboseli elephant with Tanzania in that case ensure there is no poaching. Lastly, work with local communities so that they can benefit from tourism through setting How to cite this paper: Aduma, M.M.,