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 Serengeti National Park, Tanzania. We deployed camera traps along transects perpendicular to the national park boundary at three different locales. The transects were located in areas that consisted of two types of human-wildlife interface: a sudden transition from the national park into agro-pastoral land use (termed a 'hard' boundary) and a more gradual transition mediated by a shared usage area (termed a 'soft' boundary).3. Camera traps were placed at 2 km intervals along each 10 km transect from the edge towards the core of the park and were programmed to collect images hourly between dawn and dusk between June 2016 and March 2019. We used a deep neural network to detect the presence of wildlife within images and then used a Bayesian model with diffuse priors to estimate parameters of a generalized linear model with a Bernoulli likelihood. We explored the binomial probability of either wildebeest or zebra presence as a function of distance to the boundary, the rate of grass greening or drying (dNDVI) and the concentration of grass protein.4. There was a strong negative effect of distance to boundary on the probability of detecting wildebeest or zebra; however, this was only observed where the transition from human-dominated landscape to protected areas was sudden. Conversely, soft boundaries had little to no effect on the probability of detecting wildebeest or zebra. The results suggest that boundary type affects migratory species occurrence.

Refining the stress gradient hypothesis for mixed species groups of African mammals

“…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 .…”

“…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.…”

Inferring spatially varying animal movement characteristics using a hierarchical continuous‐time velocity model

“…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.…”

Automated and repeated aerial observations of GPS‐collared animals using UAVs and open‐source electronics

“…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).…”