Context Poaching of the African elephant for ivory had been on the increase since 1997 when the Convention on International Trade in Endangered Species (CITES) allowed a one-off legal sale of ivory by several southern Africa countries. In Kenya, reports indicate continuous year-to-year increase in elephant poaching since 2003. Aims The goals of the study were to describe the temporal and spatial patterns of elephant poaching in south-eastern Kenya between 1990 and 2009, and examine relationships between observed patterns of poaching, and human and biophysical variables. The study aimed to answer the following questions: (1) how has elephant poaching varied seasonally and annually; (2) what are the spatial patterns of elephant poaching in the Tsavo Conservation Area (TCA); and (3) what are the relationships between observed patterns of poaching and human and biophysical variables? Methods The study used elephant-poaching data and various GIS-data layers representing human and environmental variables to describe the spatial and temporal patterns of elephant poaching. The observed patterns were then related to environmental and anthropogenic variables using correlation and regression analyses. Key results Elephant poaching was clustered, with a majority of the poaching occurring in the dry season. Hotspots of poaching were identified in areas with higher densities of roads, waterholes, rivers and streams. The Tsavo East National Park and the Tsavo National Park accounted for 53.7% and 44.8% of all poached elephants, respectively. The best predictors for elephant poaching were density of elephants, condition of vegetation, proximity to ranger bases and outposts, and densities of roads and rivers. Conclusions Predictor variables used in the study explained 61.5–78% of the total variability observed in elephant poaching. The location of the hotspots suggests that human–wildlife conflicts in the area may be contributing to poaching and that factors that quantify community attitudes towards elephant conservation may provide additional explanation for observed poaching patterns. Implications The poaching hotpots identified can be a used as starting point by the Kenya Wildlife Service (KWS) to begin implementing measures that ensure local-community support for conservation, whereas on other hotspots, it will be necessary to beef-up anti-poaching activities. There is a need for Kenya to legislate new anti-poaching laws that are a much more effective deterrence to poaching than currently exist.

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Human-wildlife conflict (HWC) is a widespread and persistent challenge to conservation. However, relatively few studies have thus far examined long-term monitoring data to quantify how the type, and severity of HWC varies across species, seasons, years and ecosystems. Here, we examine human-wildlife conflicts in Tsavo and Maasai Mara, two premier wildlife conservation areas in Kenya. Using Kenya Wildlife Service (KWS) data (2001-2016), we show that both the type and severity of conflicts vary among species such that the African elephant (Loxodonta africana), is the leading conflict species in both the Tsavo (64.3%, n= 30664) and Mara (47.0%, n=12487) ecosystems. The next four most notorious conflict animals, in decreasing order, are nonhuman primates (Tsavo 11.4%, n=3502; Mara 11.8%, n=1473), African buffalo (Syncerus caffer, Tsavo 5.5%, n=1676; Mara 11.3%, n=1410), lion (Panthera leo,Tsavo 3.6%, n=1107; Mara 3.3%, n=416) and spotted hyena (Crocuta crocuta, Tsavo 2.4%, n=744; Mara 5.8%, n=729). We group the observed conflict incidences (n= 43,151) into four major conflict types, including crop raiding, the most common conflict type, followed by human and livestock attacks and property damage. The severity of conflicts also varies markedly seasonally and inter-annually. Crop raiding peaks in May-July, during and at the end of the wet season when crops are maturing but is lowest in November during the late dry season and beginning of the early rains. Attacks on humans and livestock increased more than other conflict types in both Tsavo (from 2001) and Mara (from 2013). Relatively fewer people in Mara (7.2%, n=901) than in Tsavo (38.2%, n = 11714) felt threatened by wildlife, suggesting that the Maasai people are more tolerant of wildlife. Minimizing HWC is tightly linked to successfully resolving the broader conservation challenges, including enhancing ecosystem connectivity, community engagement and conservation benefits to communities.

The African elephant (Loxodonta africana) require vast areas to meet their survival needs such as food, mates, water, resting sites, and look up positions; the area referred to as home range. We collared 9 bull and 3 female elephants using satellite-linked Geographic Positioning System (GPS) collars in February 2013. Their movements were monitored up to April 2016 in the wider Amboseli landscape. We estimated their home ranges using 100% minimum convex polygon (MCP) and 95% Fixed Kernel Density Estimator (KDE) methods. A total of 48,852 GPS points were used representing 77% of the expected GPS points. This study revealed that bulls had a larger total home range size (MCP = 32,110 km²; KDE = 3,170 km² compared to females (MCP = 10,515 km²; KDE = 3,070 km²). The 95% confidence interval of the monthly range (95% KDE) for all elephants was 6,130 to 7,025 km² with the minimum and maximum range being 5,200 and 7,790 km² respectively. Females had smaller home ranges during the dry and wet season (MCP: dry = 2,974 km²; wet = 1,828 km²; KDE: dry = 2,810 km²; wet = 3,070 km²) than bulls (MCP: dry = 3,312 km²; wet = 13,288 km²; KDE: dry = 2,960 km²; wet = 3,720 km²). The variations of the elephant home range could have been influenced by an interaction of factors including rainfall, human disturbances and land use (e.g., farms, settlements, road network, and fences), water availability, bush cover, food availability, and tracking period. The most important areas that had key habitats for elephants were scattered throughout the Kenya/Tanzania borderland. The Amboseli-TsavoMagadi-Natron-West Kilimanjaro elephant population roams within specific areas of the landscape. Trans-boundary efforts should be enhanced to ensure sound management of the elephant-habitatpeople interface for continued well-being of the elephant population.