Inferring an animal’s environment through biologging: quantifying the environmental influence on animal movement

Abstract

Background Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. Methods We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms. Results We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement. Conclusions Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review

“…Generally, grazing herbivores chose plants and plant parts to optimise nutrient ingestion, as well as minimising energy cost and intake of detrimental chemical components [ 55 ]. It has been reported that a relevant proportion of cow movements (37%) is explained by the variation in resource availability [ 56 ]. Worldwide, the most important biotic factors which drive herbivore selectivity are linked to short plants at a wide range of scales and hierarchical levels [ 34 , 57 ], and the magnitude of other factors which effect the activity patterns depends on the specific context of the area [ 58 ].…”

Validating ATLAS: A regional‐scale high‐throughput tracking system

“…These solutions form a diverse toolset for biologging, ranging from autonomous underwater videography (Hawkes et al, 2020), using biomarkers from tail hair to detect movement through landscapes (Kabalika et al, 2020) and inferring movement paths through dead‐reckoning (Bidder et al, 2015). However, few technologies can attain the high‐throughput movement data that are necessary for identifying decision points of individuals (Collet et al, 2017) or groups (Strandburg‐Peshkin et al, 2015), or correlating movements with precise environmental covariates (Eikelboom et al, 2020). One potential alternative for regional‐scale studies is ATLAS (Advanced Tracking and Localisation of Animals in real‐life Systems; Toledo et al, 2020), a high‐throughput system that uses an array of receivers to detect and localise low‐cost and lightweight radio‐transmitters to track animals within a specific study area.…”

Unveiling the intertwined roles of the spatial-temporal environment and behavioural modes in animal movement

“…Internal motives, temporal rhythms and spatial environmental heterogeneity are thus three intricately linked components of the animal movement process. However, animal movement research has often been hampered by the focus on univariate analyses (Eikelboom et al, 2020), while ideally the relationships between animal movement and each of these three components should be modelled and interpreted within each other’s context (Nathan et al, 2008). Only when interpreting animal movement relative to these three components acting in concert can we advance our understanding of the processes shaping animal movement.…”