2022
DOI: 10.1002/2688-8319.12159
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Non‐local effects of human activity on the spatial distribution of migratory wildlife in Serengeti National Park, Tanzania

Abstract: 1. Human activities are transforming landscapes and altering the structure and functioning of ecosystems worldwide and often result in sharp contrasts between human-dominated landscapes and adjacent natural habitats that lead to the creation of hard edges and artificial boundaries. The configuration of these boundaries could influence local biotic interactions and animal behaviours.2. Here, we investigate whether boundaries of different degrees of 'hardness' affect space utilization by migratory species in Ser… Show more

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Cited by 6 publications
(4 citation statements)
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“…We thus encourage studies to test the general validity of the outlined effects of multiple stressors on MSGs. Such analyses could also employ more nuanced proxies for human land use intensity 31 .…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…We thus encourage studies to test the general validity of the outlined effects of multiple stressors on MSGs. Such analyses could also employ more nuanced proxies for human land use intensity 31 .…”
Section: Discussionmentioning
confidence: 99%
“…illegal hunting) or indirect (e.g. changes in habitat structure and competition with livestock) pathways 30 , 31 . According to the SGH, we thus expected that herbivores were more likely to form MSGs in areas with greater human presence.…”
Section: Introductionmentioning
confidence: 99%
“…Since our method learns spatial patterns of movement directly from data and by‐passes the need for hypothesised relationships to environmental covariates, it creates opportunities in several ecological application areas. Notably, our approach enables the investigation of human‐mediated environmental disturbance on animal movement, even when disturbance effects are non‐local (Kavwele et al, 2022 ) or the level of human activity is difficult to measure. By detecting regions of markedly different movement behaviours, our analysis is able to act as a precursor to environmental data collection rather than being dependent on the prior identification and investigation of spatial covariates.…”
Section: Discussionmentioning
confidence: 99%
“…To locate animals within video frames, we used established computer vision techniques, specifically employing the you only look once (YOLO) (Redmon et al, 2016) single‐shot object detector. YOLO object detectors are a family of deep convolutional neural networks that predict bounding boxes and class probabilities in a single pass through the network and have been applied in several ecological computer vision tasks (Kavwele et al, 2022; Roy et al, 2023; Torney et al, 2019).…”
Section: Case Studymentioning
confidence: 99%