Assessment of human–elephant conflicts in multifunctional landscapes of Taita Taveta County, Kenya

People and wildlife have co-occurred, sharing resources for thousands of years, however, over the last four decades records of human–wildlife conflict have increasingly emerged. Human–elephant conflict is a form of such conflict, resulting from negative interactions between people and elephants. Human–elephant conflict affects local community livelihood and the success of elephant conservation. Tsavo East and Tsavo West National Parks, which cover about 60% of the Taita Taveta County land area, host the single largest elephant population in Kenya. We analysed human–elephant conflict incident data over 15 years (2004–2018) in Taita Taveta County, which forms part of the Tsavo ecosystem in south-eastern Kenya. We identified eight forms of human–elephant conflict comprising elephant threat, crop raiding, property damage, injury to people, human death, elephant death, elephant injury, and livestock death. Three forms of conflict accounted for 97% of the reported incidents, namely elephant threat to humans, constituting the highest number of incidents (62.46%), followed by crop raiding (32.46%) and property damage (2.33%). Conflicts occurred throughout the year, with June to July having the highest number of incidents. Rainfall, distance from the Tsavo national parks, and human population density were used as covariates to explain HEC patterns. This study seeks to provide a detailed evaluation of the spatial–temporal patterns of human–elephant conflict in Taita Taveta County and to yield information useful for human–elephant conflict mitigation and elephant conservation.Peer reviewe

Primates on the farm - spatial patterns of human-wildlife conflict in forest-agricultural landscape mosaic in Taita Hills, Kenya

Human-wildlife conflict (HWC) is a growing concern for local communities living in the vicinity of protected areas. These conflicts commonly take place as attack by wild animals and crop-raiding events, among other forms. We studied crop-raiding patterns by non-human primates in forest-agricultural landscape mosaic in the Taita Hills, southeast Kenya. The study applies both qualitative and quantitative methods. Semi-structured questionnaire was used in the primary data collection from the households, and statistical tests were performed. We used applied geospatial methods to reveal spatial patterns of crop-raiding by primates and preventive actions by farmers. The results indicate most of the farms experienced crop-raiding on a weekly basis. Blue monkey (Cercopithecus mitis) was the worst crop-raiding species and could be found in habitats covered by different land use/land cover types. Vervet monkey (Chlorocebus pygerythrus) and galagos crop-raided farms in areas with abundant tree canopy cover. Only few baboons (Papio cynocephalus) were reported to raid crops in the area. Results also show that the closer a farm is to the forest boundary and the less neighbouring farms there are between the farm and the forest, the more vulnerable it is for crop-raiding by blue monkeys, but not by any other studied primate species. The study could not show that a specific type of food crop in a farm or type of land use/land cover inside the wildlife corridor between the farmland and the forest boundary explain households' vulnerability to crop-raiding by primates. Preventive actions against crop-raiding by farmers where taken all around the studied area in various forms. Most of the studied households rely on subsistence farming as their main livelihood and therefore crop-raiding by primates is a serious threat to their food security in the area.Peer reviewe

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers

Aboveground Biomass Distribution in a Multi-Use Savannah Landscape in Southeastern Kenya: Impact of Land Use and Fences

Savannahs provide valuable ecosystem services and contribute to continental and global carbon budgets. In addition, savannahs exhibit multiple land uses, e.g., wildlife conservation, pastoralism, and crop farming. Despite their importance, the effect of land use on woody aboveground biomass (AGB) in savannahs is understudied. Furthermore, fences used to reduce human–wildlife conflicts may affect AGB patterns. We assessed AGB densities and patterns, and the effect of land use and fences on AGB in a multi-use savannah landscape in southeastern Kenya. AGB was assessed with field survey and airborne laser scanning (ALS) data, and a land cover map was developed using Sentinel-2 satellite images in Google Earth Engine. The highest woody AGB was found in riverine forest in a conservation area and in bushland outside the conservation area. The highest mean AGB density occurred in the non-conservation area with mixed bushland and cropland (8.9 Mg·ha−1), while the lowest AGB density (2.6 Mg·ha−1) occurred in overgrazed grassland in the conservation area. The largest differences in AGB distributions were observed in the fenced boundaries between the conservation and other land-use types. Our results provide evidence that conservation and fences can create sharp AGB transitions and lead to reduced AGB stocks, which is a vital role of savannahs as part of carbon sequestration

East African megafauna influence on vegetation structure permeates from landscape to tree level scales

African savanna elephants (Loxodonta africana) can substantially modify their habitat through their interactions with woody vegetation. Nonetheless, the scale, intensity and characteristics of these relations are not yet fully understood. Consequently, it is unclear how vegetation-megafauna interactions can be disrupted by external factors, such as land management. This study attempted to quantify and characterize structural changes in vegetation caused by elephants, from landscape to tree level scales. We applied multi-scale geospatial tools, including airborne (ALS) and terrestrial laser scanning (TLS), to address the following questions: (1) How do elephants shape landscape level vegetation structure in conservation areas? (2) Are the impacts of elephants evident on individual tree architecture? Our study area was located at the Taita Hills Wildlife Sanctuary in South-eastern Kenya. The occurrence of elephants was estimated using elephant observation records and proximity to elephant tracks. Landscape level structure was assessed using tree density maps calculated based on individually detected treetops from ALS data. Next, TLS measurements of 72 trees were processed using quantitative structural modelling to characterize their architecture. Our results demonstrate a widespread influence of elephants on both landscape and tree level structural characteristics. This influence was strongly mediated by management, as we observed differences in vegetation structure inside and outside conservation areas. Tree density was up to 42% lower (5.84 trees/ha) in conservation areas than in non-conservation areas (10.17 trees/ha). Trees were relatively larger with closer proximity to elephant tracks, while smaller trees were more often observed in areas further away from elephants. At an architectural level, trees closer to elephant tracks had lower ratio between the crown length and the tree height, demonstrating a substantial influence of elephants on the morphological characteristics of trees. Our results highlight the importance of accounting for vegetation fauna interactions when planning conservation areas in African savannahs.Peer reviewe