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relationships between landscape fragmentation level, forage abundance and speed of movement. Speed increase from dark green (low speed) to dark red (high speed). (a) Wet season, (b) transition season (c) dry season. note: White dots represent points used to compute the model.
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Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study,...
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... the transition season, the weights for effective mesh size and DMP are all less than 0.5 indicating little support to explain elephant speed of movement as solitary variables. It is worthwhile to note that the interaction effect of landscape fragmentation and DMP explained speed of elephant movement with the lowest AICc, more weights (Table 2) and high R 2 (Figure 4) across all the three seasons. Elephants move faster in landscapes that are highly fragmented and contain very low forage across the three seasons in Amboseli (Figure 3). ...
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... The existence of small elephant metapopulations depends on the intactness of these corridors to access the restricted supplies (Gara et al. 2021). By facilitating movement between core habitats, migration corridors enhance habitat connectivity and support the distribution of elephants across their landscapes (Gara et al. 2017a(Gara et al. , 2017b. Migration via the usage of corridors is proven to ease ecological pressure on ecosystems and allow habitat restoration (Gara et al. 2017a(Gara et al. , 2017b. ...
... By facilitating movement between core habitats, migration corridors enhance habitat connectivity and support the distribution of elephants across their landscapes (Gara et al. 2017a(Gara et al. , 2017b. Migration via the usage of corridors is proven to ease ecological pressure on ecosystems and allow habitat restoration (Gara et al. 2017a(Gara et al. , 2017b. However, human land uses, including urban development and agriculture, have imposed great pressure on corridors, resulting in the narrowing of corridors and hence loss of genetic flow among elephant populations (Chaiyarat et al. 2022). ...
Landscape connectivity is a critical factor influencing the survival and ecological roles of large terrestrial herbivores within dynamic ecosystems. Yet, the increasing fragmentation of habitats due to human activities, such as agricultural expansion and infrastructure development, disrupts natural movement patterns and limits access to essential resources. This is particularly concerning in mesic protected areas, where moderate rainfall supports diverse vegetation but is often bordered by human-dominated landscapes. To address this challenge, the use of Spatial Absorbing Markov Chain (SAMC) provides a robust framework to simulate the African savannah elephant (Loxodonta africana) dispersal and identify critical connectivity nodes within fragmented landscapes. Additionally, assessing and understanding the regenerative potential of these landscapes is vital for evaluating their capacity to sustain wildlife populations and maintain ecological balance. The objectives of this study were to (i) model the ecological connectivity of Mana Pools National Park (MPNP) by assessing spatial and functional linkages among African savannah elephant herds and (ii) predict the regenerative potential of the park's range. We used multi-temporal satellite data (2003, 2013, and 2023), GPS collar data, road transects, and plot-based surveys. The study employed a cellular automata artificial neural network (CA-ANN) to forecast the regenerative potential of the range. Connectivity maps illuminated vital pathways that sustain the elephants' migratory and foraging behaviours, underscoring the holistic interplay of land cover, slope, and terrain in shaping movement patterns. The study identified core micro-corridors and broader sub-landscape linkages essential for maintaining the park's ecological vitality. This interconnectedness serves as a testament to the resilience and regenerative power of the semi-arid savannah. CA-ANN projections predicted a high landscape regenerative capacity by the year 2083. Highlighting diverse geographical priorities for connectivity conservation, the research advocates for integrated, multi-scale actions to preserve these vital linkages. Such insights are pivotal in nurturing the relational integrity of MPNP, ensuring its long-term viability as a sanctuary for elephants and other coexisting life forms. By integrating connectivity modelling and habitat regeneration predictions, this study advances conservation strategies. It highlights the importance of maintaining functional landscapes to preserve ecosystem resilience, enhance biodiversity, and mitigate human-wildlife conflicts in increasingly fragmented ecosystems.
... As elephants expand their ranges in search of resources, they may come into conflict with human activities (Mukomberanwa et al., 2024a). It is crucial to implement strategies that minimize these conflicts, such as establishing buffer zones and community engagement programs that promote coexistence (Gara et al., 2017). The observed seasonal patterns may be influenced by climate change, affecting resource availability (Nampindo et al., 2024). ...
The African savannah elephant (Loxodonta africana) migrate in landscapes with patchily distributed food resources in semi-arid environments. GPS collar data in combination with the Minimum Convex Polygon approach (100% MCP) can be utilised to investigate elephant home ranges and spatial ecology. Mapping of suitable habitats in landscapes with isolated and patchy resources housing threatened and endangered species like the African savannah elephant is critical for conservation of their natural habitat. This study aimed to: (i) investigate the seasonal ranging patterns of the African savannah elephants and (ii) model the preferred habitat of the African savannah elephants in Mana Pools National Park (MNP) in Zimbabwe. Minimum Convex Polygon method was employed to delineate elephant home ranges and the MaxEnt algorithm was used to model their habitat preferences. There were significant differences (p < 0.05) in the size of the home ranges across all the three demarcated seasons (wet, transitional and dry). Elephant habitat preference is mainly driven by the presence, quantity and quality of palatable vegetation close to the Zambezi River in the Mana Pools National Park. GPS telemetry provides smart data for understanding elephant behaviour and movement patterns in semi-arid environments across seasons.
... The differences among seasons illustrate the elephants' resilience to variation in resource availability. The considerable increase in HR during the transitional season shows that elephants are responding to environmental pressures, such as water scarcity or food limitations, by exploring new regions (Gara et al., 2017). Each herd demonstrates varied patterns of HR use, determined by ecological factors and potentially social dynamics. ...
Knowledge of home ranges (HRs) helps conservationists understand movement patterns and can aid management including avoidance of human‐wildlife conflicts. This study examined the African savannah elephant seasonal HRs and space use using telemetry data in Mana Pools National Park, Zimbabwe. The objectives were to (i) compare the HR sizes and (ii) construct utilization distribution of African savannah elephants using the minimum convex polygon (MCP) method and the time‐local convex hull (T‐LoCoH). The results revealed that the dry, transitional, and wet season HR sizes estimated by the MCP method were significantly larger than those of the T‐LoCoH method. Significant differences were observed between core T‐LoCoH home‐range distributions for the wet, transition, and dry seasons. T‐LoCoH more accurately represented the HR size and nuances of repeated movements and internal spaces than the MCP method. The findings show larger‐scale movements in the transition season, which would enhance the potential for human–elephant conflicts.
... Thus crop-raiding is also due to foraging reasons (Kyokuhaire et al. 2023). Moreover, it should also be acknowledged that elephant movements have been studied in the past (Gara et al. 2017;Hema et al. 2017;Sievert et al. 2022;Thompson 2022;Carvalho and Campbell 2023;Kyokuhaire et al. 2023). In tandem with tracking migration patterns of African savannah elephants, monitoring the spatiotemporal availability of palatable vegetation is key for development of park management plans (Thompson 2022). ...
... By comparing indices, experts can enhance their modelling tools to get the most accurate forecasts of animal habitat utilisation (Harmse, Gerber, and van Niekerk 2022). This may entail modifying thresholds or merging various indices into composite variables that better capture environmental appropriateness (Gara et al. 2017). Ultimately, evaluating numerous vegetation indices enriches scientific understanding by exposing complicated correlations between vegetation properties and animal behaviour (Pirotti et al. 2014). ...
... The constancy in total land area masks major shifts within individual habitat categories crucial for elephant survival. Monitoring and adaptive management measures are necessary to preserve habitat resilience amid continuing environmental changes(Gara et al. 2017). ...
African savannah elephants ( Loxodonta africana ) are key ecosystem engineers that migrate over large spatiotemporal scales foraging as they require copious amounts of food and water across habitable landscapes. Therefore a need to understand movement patterns arises in relation to vegetation type and landscape variability, moreso in forage depauparate arid areas such as Gonarezhou National Park (GNP) in Zimbabwe. The objectives of this study were to: (i) assess the performance of vegetation indices in modelling the distribution of African savannah elephants, and (ii) model future landscape variability in Gonarezhou National Park (GNP) in Zimbabwe. Maximum entropy (MaxEnt) algorithm was used to explore the relationship between vegetation indices and distribution of African savannah elephants in the GNP. The Soil Adjusted Vegetation Index (SAVI) performs better relative to other indices in modelling the distribution of African savannah elephants across all habitat types in the GNP. Cellular automata‐Artificial Neural Network (CA‐ANN) showed a significant future decrease (Kruskal Anova; p < 0.05) in landscape suitable to sustain large populations of African savannah elephants in the GNP by the year 2083. Future remote sensing reveals directional insights into the future consequences of current landscape management for African savannah elephant conservation which is a crucial in the sustainability of climate threatened arid protected areas such as the GNP.
... Human-wildlife conflict (HWC) has become a serious concern in a number of nations as a result of the loss of natural vegetative cover ( Gara et al., 2017 ;Haddad et al., 2015 ;Liu et al., 2017 ;Padalia et al., 2019 ;Sharma et al., 2017 ;Sharma et al., 2019 ). Conversion, modification, and fragmentation of the earth's natural areas (or Land Use and Land Cover Change (LULCC)) due to exponential human population growth and widespread demand for land and other natural resources have substantially altered wildlife habitat shape ( Gara et al., 2017 ;Köpke et al., 2021 ;Padalia et al., 2019 ;Santini et al., 2016 ;Sharma et al., 2020 ), resulting in wildlife being increasingly confined to small and sparse habitat fragments. ...
... Human-wildlife conflict (HWC) has become a serious concern in a number of nations as a result of the loss of natural vegetative cover ( Gara et al., 2017 ;Haddad et al., 2015 ;Liu et al., 2017 ;Padalia et al., 2019 ;Sharma et al., 2017 ;Sharma et al., 2019 ). Conversion, modification, and fragmentation of the earth's natural areas (or Land Use and Land Cover Change (LULCC)) due to exponential human population growth and widespread demand for land and other natural resources have substantially altered wildlife habitat shape ( Gara et al., 2017 ;Köpke et al., 2021 ;Padalia et al., 2019 ;Santini et al., 2016 ;Sharma et al., 2020 ), resulting in wildlife being increasingly confined to small and sparse habitat fragments. Choudhury et al., 2008 ;Riddle et al., 2010a ;Sukumar, 2006 ) due to high population pressure and heterogonous landscapes. ...
Protected areas play a crucial role in the conservation and management of wildlife, but land use and land cover change (LULCC) threatens the status of protected areas. Sri Lanka has a history of severe human–elephant conflict (HEC). In the last 15 years, Sri Lanka has recorded the highest mortality of elephants and the second-highest human casualties among countries where the Asian elephant is native. In this study, we conducted a whole of country analysis of the effect of LULCC on protected areas using a land cover change map (1993–2018) recently developed by the authors using Landsat satellite data. Protected area performances were measured using five criteria including LULCC, the protected areas, and categorised into three performance levels. The protected area performances were then compared with number of HEC incidents. We found that 12% of Sri Lanka's protected area was affected by LULCC events, and every individual protected area experienced LULCC. We also found that 86% of elephant death incidents occurred within a 5 km radius of protected areas, with a strong negative correlation with distance from protected areas (r = –0.94, p < 0.05). Some 43% of HEC incidents and 23% of elephant deaths occurred inside protected areas, while 40% of elephant deaths in the last two years occurred inside protected areas. These areas were also found to fragment over time and elephant deaths increased, and showed a strong positive correlation, with fragmentation (r = 0.88, p < 0.05). Wildlife regions that experienced higher LULCC also experienced a greater number of elephant deaths, with a moderately positive correlation (r = 0.54, p < 0.05). Irrespective of the level of performance, all protected areas reported elephant deaths as well as HEC incidents, indicating that protected areas are failing to protect the endangered Elephas maximus population in Sri Lanka. These country-wide insights into protected areas can be used to re-evaluate the function and effectiveness of protected areas in managing and mitigating HEC while providing protection to elephants in Sri Lanka.
... Elephants are known for traversing a mosaic of heterogenous landscapes (defined as the vegetation patches that vary in their composition and spatial arrangements (Beer & van Aarde, 2008)) in search of resources, that is, food and water (Gara et al., 2017). Consequently, elephants cover thousands of kilometers to maximize their quality forage and water intake. ...
... In 2017, 8 elephants from 4 groups (2 elephants from each group) in Sioma Ngwezi landscape were fitted with AWT ultrahighfrequency global position system (GPS) radio collars acquired from Africa Wildlife Tracking (Gara et al., 2017). These collars were fitted on 5 matriarchs and 3 bulls by experts from elephant connections organization. ...
... To know how permeable the landscape was in the wet sea- with weight greater than 45 kg. Given the understanding that elephants have a wider home range between 10 km 2 and 21,000 km 2 than most other animals (Gara et al., 2017), the focal species linkage mapping approach developed in this project would be a conduit through which other species will be guaranteed protection as well. This concept was reiterated by Roever et al. (2013) corridors in the landscape is not an end in itself but may require both political will and knowledge-based management to produce the desired outcome of their functionality. ...
• The influence of environmental factors on the distribution and persistence of African elephants (Loxodonta africana) is pertinent to policy makers and managers to formulate balanced plans for different land-use types.
• The study focuses on movement of elephants and how they utilize foraging areas in Sioma Ngwezi landscape in Zambia by answering the following questions: (1) Which environmental variables and land-cover class predict the movement of elephants during the wet season in Sioma Ngwezi landscape? (2) What is the wet season suitable habitat for elephants in Sioma Ngwezi landscape? (3) What are the major wet season movement corridors for elephants in Sioma Ngwezi landscape?
• We used GPS telemetry data from the collared elephants to assess habitat connectivity. Maximum entropy (MaxEnt) and linkage mapper were the tools used to predict habitat suitability, movement corridors, and barriers in the landscape during the wet season.
• The study identified elevation, land cover, and NDVI as the most important environmental predictors that modify the dispersal of elephants in the landscape during the wet season. Additionally, a total of 36 potential wet season corridors were identified connecting 15 core areas mainly used for foraging and protection from poachers in the landscape. Of these, 24 corridors were highly utilized and are suggested as priority corridors for elephant movement in the landscape.
• The identified wet season habitats and functional corridors may help to combat elephant poaching by patrolling areas with high relative probability of elephant presence. The findings may also help abate human–elephant conflict such as crop-raiding by managing identified corridors that run into agriculture zones in the game management area.
... They also move with lower tortuosity in core areas with high poaching levels than in those with lower poaching levels (Ihwagi et al., 2019), and similarly with lower tortuosity and faster speed when moving through corridors than in their core range when stressed (Jachowski, Slotow, & Millspaugh, 2013). In comparison, while foraging elephants will increase their tortuosity in favourable areas (Duffy, Dai, Shannon, Slotow, & Page, 2011;Vanak, Thaker, & Slotow, 2010), they increase their speed in areas of low forage resources (Gara et al., 2017). ...
Animals living in heterogeneous landscapes are often faced with making a trade-off between maximizing foraging success and avoiding risk. Using high-resolution GPS-tracking data, this study explored the fine-scale movement patterns and risk sensitivity of crop-raiding African elephants, Loxodonta africana, in the anthropogenic landscape of Tsavo, Kenya. We analysed patterns in the speed and tortuosity of elephant movements over the 24 h surrounding crop-raiding events and compared them with those of nonraiding elephants during corresponding periods. Crop-raiding elephants moved faster and straighter (less tortuously) with closer temporal proximity to farmland, which we argue reflects their increased intensity of risk avoidance behaviours in response to approaching humans. Once inside farmland, elephants appeared to reduce movements associated with risk avoidance to forage intensively on crops, decreasing their speed and reducing the likelihood of moving in straight lines while crop raiding. These results highlight trade-offs in the fine-scale movement patterns of elephants living in anthropogenic landscapes with differing levels of habitat quality and exposure to humans, providing new insight into how they perceive the risks associated with crop raiding.
... The reasons why elephants move can also influence the speed at which they walk. When faced with a more fragmented and less productive landscape, elephants tend to increase their speed of passage [73], since less amount of forage does not justify their establishment. Although there are great reasons that may justify migrations of elephant populations, they are considered facultative partially migrants: not all individuals in a population migrate and those who do may not even do it every year, but only when it is more opportune [74]. ...
... This is because habitat size is strongly linked to population size (Bohrer et al., 2014). These small patches are rarely utilised by elephants as noted in Amboseli ecosystems by Gara et al., (2017). Elephants are keystone species and have remained endangered for decades. ...
Understanding the key drivers that influence the potential distribution of herbivore species in changing landscapes has been at the centre of enquiry in wildlife science for many decades. This knowledge is particularly important for keystone species like the African elephant ( Loxodonta africana ) whose population is declining even in conservation areas. The Sebungwe Region is part of the Kavango‐Zambezi Transfrontier Conservation Area and supports ~4,000 elephants. The Sebungwe Region has lost an estimated 76% of its elephant population over the years. This study aimed to determine how the elephant distribution in the Sebungwe Region was affected by human settlement and whether the patches for elephant distribution were large enough for elephant habitation. The prediction of the potential distribution of the elephant was based on presence‐only data modelled through an ensemble algorithm that combined several candidate models to enhance predictive ability. We observed that human settlement drives the potential distribution of elephants in the Sebungwe Region (test AUC = 0.95), and patches from the model were on average <1.5 km ² . Our results provide initial insights into the key habitat factors that drive distribution of elephants in the Sebungwe landscape. Future conservation of the elephant could benefit from our study through systematic planning of settlements, which might help minimise human interaction with wildlife.
... Human population density 0.01 can easily monitor their livestock from a distance. Additionally, these areas are also preferential because they do not restrict livestock movement during grazing, unlike high biomass areas that are highly nutritious but not manoeuvrable ( Gara et al., 2017). ...
Overlapping interests between humankind and nature have resulted in humans and wildlife interacting. These interactions usually emanate from the absence of fences or hard boundaries to restrict animal movement and when this happens, it results in Human Carnivore Conflict (HCC). People residing in areas adjacent to protected areas are the ones mostly at risk of HCC. In this study, we used ensemble modelling to explain predation risk and indicate the key drivers of HCC in Matetsi Communal Area, Zimbabwe. Ensemble modelling involves building a single consensus model from several candidate models. We used seven environmental variables for the modelling process, and these were distance from the park boundary, distance from rivers, Normalised Difference Vegetation Index (NDVI), human population density and livestock density for cattle, goats and sheep. Livestock kill sites were used as the presence data. Our results illustrate that ensemble modelling explains predation risk with a true skill statistic (TSS) of 0.9 for Matetsi Communal Area. This study provides the potential application of ensemble modelling in HCC management through identifying predation risk areas. In identifying predation risk areas, proactive and cost-efficient management strategies for dealing with HCC in specific high-risk areas are plausible.
