The ecomorphology and musculoskeletal biomechanics of wild western lowland gorillas (Gorilla gorilla gorilla)

King, Charlotte Angela (2024). The ecomorphology and musculoskeletal biomechanics of wild western lowland gorillas (Gorilla gorilla gorilla). University of Birmingham. Ph.D.

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Abstract

Western lowland gorillas are the largest and most sexually dimorphic ape. Their heavily robust musculature and adaptations towards terrestrial quadrupedal locomotion makes them suitable models for understanding the relationship between morphology, ecology and behaviour in arboreal environments. The arboreal environment is characterised by supports that vary in availability, size and compliance, but it was not previously understood how western lowland gorillas find solutions to the challenges of feeding and travelling in the trees. This thesis addresses this gap in our knowledge by presenting new comprehensive data on the arboreal locomotor repertoire of wild western lowland gorillas. A group of fully habituated wild western lowland gorillas were followed for twelve months in the Loango National Park, Gabon. Walking and vertical climbing dominated the gorillas’ arboreal locomotor repertoire, but gap-crossing behaviours made up a considerably larger proportion of their locomotor repertoire than expected. Overall, the gorillas minimised the risk of falling in the supports and hand postures they used when addressing the ecological challenges of accessing different heights, moving in the canopy and dealing with discontinuity.

Despite being phylogenetically close, great apes vary in postcranial morphology and the ecological niches that they inhabit. In order to identify what separates, and what links the locomotor ecology of great apes, focus was then directed towards comparing the arboreal locomotor repertoire of the gorillas with wild chimpanzees and orangutans. The orangutans engaged mostly in suspension, whereas the chimpanzees mostly used vertical climbing, and the gorillas engaged mostly in walking. Interspecific comparisons of support use for gorillas and orangutans revealed that each species differed in the size, type and number of supports that they used when addressing different ecological challenges. Body size also influenced the arboreal locomotor behaviours of the great apes, but in patterns that did not align with classic predictions. Overall, the results suggested that evolutionary selective pressures have shaped the locomotor behaviours used in complex arboreal environments to reduce energy expenditure. However, the variation in support use reflected many aspects, including their morphology, ecological niche, degree of arboreality and strategies to mitigate the risk of falling.

The next step in developing an understanding of how great apes interact with their environment is to study their locomotor biomechanics in natural habitats. Kinematic analysis of great ape locomotion is used to understand the mechanisms of movement. Recent technological advances in deep learning software to recognise and track objects is revolutionising the way in which we can study animal locomotion. Pose estimation uses this framework to detect body landmarks but very few studies have applied this framework in wild settings. Using video footage of wild gorillas, two open-source markerless pose estimation frameworks were compared to manual identification of body landmarks to assess the accuracy of machine learning software packages, DeepLabCut and Open Ape Pose. Overall, it was determined that they are not yet ready to be applied to wild footage because of the complexities of natural habitats and focus should now be directed towards exposing these software packages to more wild footage so that this can be possible in the future.

These studies highlight the importance of studying primates using the ecomorphology framework. Furthermore, by integrating data on the arboreal locomotor behaviour of apes with locomotor kinematics, this makes them suitable referential models for understanding the evolution of hominoid locomotion.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):