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Environmental factors influencing spotted hyena and lion population biomass across Africa.

ABSTRACT: The spotted hyena (Crocuta crocuta Erxleben) and the lion (Panthera leo Linnaeus) are two of the most abundant and charismatic large mammalian carnivores in Africa and yet both are experiencing declining populations and significant pressures from environmental change. However, with few exceptions, most studies have focused on influences upon spotted hyena and lion populations within individual sites, rather than synthesizing data from multiple locations. This has impeded the identification of over-arching trends behind the changing biomass of these large predators. Using partial least squares regression models, influences upon population biomass were therefore investigated, focusing upon prey biomass, temperature, precipitation, and vegetation cover. Additionally, as both species are in competition with one other for food, the influence of competition and evidence of environmental partitioning were assessed. Our results indicate that spotted hyena biomass is more strongly influenced by environmental conditions than lion, with larger hyena populations in areas with warmer winters, cooler summers, less drought, and more semi-open vegetation cover. Competition was found to have a negligible influence upon spotted hyena and lion populations, and environmental partitioning is suggested, with spotted hyena population biomass greater in areas with more semi-open vegetation cover. Moreover, spotted hyena is most heavily influenced by the availability of medium-sized prey biomass, whereas lion is influenced more by large size prey biomass. Given the influences identified upon spotted hyena populations in particular, the results of this study could be used to highlight populations potentially at greatest risk of decline, such as in areas with warming summers and increasingly arid conditions.

SUBMITTER: Jones AK

PROVIDER: S-EPMC8668751 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Humans have shaped carnivore behavior since at least the Middle Paleolithic period, about 42,000 years ago. In more recent times, spotted hyenas (Crocuta crocuta) in Ethiopia have adapted to living in urban areas, while humans have adapted to living with hyenas. Yet, relationships between coexisting humans and carnivores are rarely addressed beyond mitigating conflicts. We provided a case study for how to broadly think about coexistence and how to study it when measuring if humans and carnivores affect one another. We collected data in four Ethiopian cities: Mekelle, Harar, Addis Ababa, and Arba Minch. We held focus groups and key informant interviews that incorporated feedback from 163 people, representing a wide array of religious, economic, and educational backgrounds. We also determined how many hyenas resided in these cities, hyena behavioral responses to humans using a flight initiation test, and problem-solving abilities via puzzle box trials. We found that in three of the cities, hyenas and humans coexist at high densities and frequently encounter each other. While all participants recognized the importance of hyenas as scavengers to maintain a clean environment, there was pronounced variation in cultural perspectives across cities. For example, while the people of Harar revere hyenas in spiritual terms, in Arba Minch hyenas were regarded as nuisance animals. Hyenas were universally respected as a formidable predator across cities but reports of attacks on livestock and humans were few. Flight initiation tests revealed hyenas fled at significantly closer distances in Harar and Addis Ababa than in Mekelle. Hyenas succeeded at solving a puzzle box in Harar but not in Mekelle. These variable behavior in hyenas correlated to different human perceptions. Our case study results suggest that the hyena-human dynamic is highly variable across these locations. We conclude by exploring the implications of these findings for how humans and hyenas can shape one another's behavior. Developing studies to link human perceptions and animal behavior could advance wildlife conservation, especially in urban areas.

Project description:Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO's exploration phase is enhanced, and the SHO's hunting mechanisms are modified with PSO's velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.

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Environmental factors influencing spotted hyena and lion population biomass across Africa.

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