Understanding the effect of landscape fragmentation and vegetation productivity on elephant habitat utilization in Amboseli ecosystem, Kenya

Gara, Tawanda W.; Wang, Zhihui; Zengeya, Fadzai M.; Ngene, Shadrack; Murwira, Amon; Ndaimani, Henry

doi:10.1111/aje.12346

Cited by 15 publications

(20 citation statements)

References 51 publications

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“…The area falls in the agro-ecological zones V and VI, and is hence classified as arid to semi-arid savanna (Gara, 2014). It is more suitable for pastoralism rather than crop farming and has a high potential for conservation of wildlife and tourism based enterprises.…”

Section: Study Areamentioning

confidence: 99%

“…The rainfall shows spatiotemporal variations during the year. Annual rainfall ranges from 500 to 600 mm in the north to 250 to 300 mm in Amboseli National Park (Gara, 2014). The rain falls in two seasons with short rains being experienced from November to January and long rains from March to April.…”

Section: Study Areamentioning

confidence: 99%

“…Regardless of the methodology used to estimate home range, changes of land use and land tenure systems affect utilization of range by elephants and constrict their home ranges (Doughlas-Hamilton et al, 2005;Gara et al, 2016a, b). The increase in human population and changes in lifestyles have resulted to changes in land use emanating from mushrooming of human infrastructure (e.g., roads, human settlements, fences and crop fields) within elephant ranges (Gara, 2014;Gara et al, 2016a, b). These changes impede wildlife movement and fragment prime elephants habitats (Burn Silver et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…In Kenya, most protected area (parks, reserves and sanctuaries) aimed at in-situ conservation of different types of wildlife are bordered by human settlements (Graham et al, 2009). The Amboseli National Park is surrounded by six community ranches that are used by the Maasai agro-pastoralists for livestock grazing and subsistence and commercial crop farming (Hobbs et al, 2008;Gara, 2014). These ranches act as dispersal areas and migratory corridors for elephants as they endeavor to connect cut off habitats (Burn Silver et al, 2008;Gara, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The Amboseli National Park is surrounded by six community ranches that are used by the Maasai agro-pastoralists for livestock grazing and subsistence and commercial crop farming (Hobbs et al, 2008;Gara, 2014). These ranches act as dispersal areas and migratory corridors for elephants as they endeavor to connect cut off habitats (Burn Silver et al, 2008;Gara, 2014). However, these community ranches have experienced increased landscape fragmentation as a result of sedentarization of the Maasai, intensification of different landuse types and changing land tenure system (Ogutu et al, 2009;Gara et al, 2016a, b).…”

Section: Introductionmentioning

confidence: 99%

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Home range sizes and space use of African elephants (Loxodonta africana) in the Southern Kenya and Northern Tanzania borderland landscape

Ngene¹,

Okello²,

Mukeka³

et al. 2017

Int. J. Biodivers. Conserv.

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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.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Home range sizes and space use of African elephants (Loxodonta africana) in the Southern Kenya and Northern Tanzania borderland landscape

Ngene¹,

Okello²,

Mukeka³

et al. 2017

Int. J. Biodivers. Conserv.

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Suitable habitat of wild Asian elephant in Western Terai of Nepal

Sharma¹,

Panthi²,

Yadav³

et al. 2020

Ecology and Evolution

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BackgroundThere is currently very little available research on the habitat suitability, the influence of infrastructure on distribution, and the extent and connectivity of habitat available to the wild Asian elephant (Elephas maximus). Information related to the habitat is crucial for conservation of this species.MethodsIn this study, we identified suitable habitat for wild Asian elephants in the Western Terai region of Nepal using Maximum Entropy (MaxEnt) software.ResultsOf 9,207 km2, we identified 3194.82 km2 as suitable habitat for wild Asian elephants in the study area. Approximately 40% of identified habitat occurs in existing protected areas. Most of these habitat patches are smaller than previous estimations of the species home range, and this may reduce the probability of the species continued survival in the study area. Proximity to roads was identified as the most important factor defining habitat suitability, with elephants preferring habitats far from roads.ConclusionsWe conclude that further habitat fragmentation in the study area can be reduced by avoiding the construction of new roads and connectivity between areas of existing suitable habitat can be increased through the identification and management of wildlife corridors between habitat patches.

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Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Duporge

Isupova

Reece

et al. 2020

Remote Sens Ecol Conserv

Self Cite

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Satellites allow large‐scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very‐high‐resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very‐high‐resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView‐3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye‐1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state‐of‐the‐art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique.

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Understanding the effect of landscape fragmentation and vegetation productivity on elephant habitat utilization in Amboseli ecosystem, Kenya

Cited by 15 publications

References 51 publications

Home range sizes and space use of African elephants (Loxodonta africana) in the Southern Kenya and Northern Tanzania borderland landscape

Home range sizes and space use of African elephants (Loxodonta africana) in the Southern Kenya and Northern Tanzania borderland landscape

Suitable habitat of wild Asian elephant in Western Terai of Nepal

Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

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