Is habitat selection in the wild shaped by individual‐level cognitive biases in orientation strategy?

Abstract

Cognitive biases for encoding spatial information (orientation strategies) in relation to self (egocentric) or landmarks (allocentric) differ between species or populations according to the habitats they occupy. Whether biases in orientation strategy determine early habitat selection or if individuals adapt their biases following experience is unknown. We determined orientation strategies of pheasants, Phasianus colchicus, using a dual-strategy maze with an allocentric probe trial, before releasing them (n = 20) into a novel landscape, where we monitored their movement and habitat selection. In general, pheasants selected for woodland over non-woodland habitat, but allocentricbiased individuals exhibited weaker avoidance of non-woodland habitat, where we expected allocentric navigation to be more effective. Sex did not influence selection but was associated with speed and directional persistence in non-woodland habitat. Our results suggest that an individual's habitat selection is associated with inherent cognitive bias in early life, but it is not yet clear what advantages this may offer.

A Guide to Pre-Processing High-Throughput Animal Tracking Data

“…Recent technical advances in wildlife tracking have already yielded exciting new insights from massive high-resolution movement datasets (Aspillaga et al, 2021a,b; Baktoft et al, 2019, 2017; Beardsworth et al, 2021b,c; Corl et al, 2020; Harel et al, 2016; Harel and Nathan, 2018; Oudman et al, 2018; Papageorgiou et al, 2019; Strandburg-Peshkin et al, 2015; Toledo et al, 2020; Tsoar et al, 2011; Vilk et al, 2021), and high-throughput animal tracking is expected to become increasingly more common in the near future. Tackling the very large datasets that high-throughput tracking generates requires a different approach from that used for traditionally smaller volumes of data.…”

“…The package is based on , a fast implementation of data frames; thus it is compatible with a number of data structures from popular packages including , , and objects, which can be converted to data frames (Boone et al, 2020; Calenge et al, 2009; Kranstauber et al, 2011). These pre-processing techniques and package were designed with ATLAS systems in mind, motivated to meet the rapid growth of studies using this high-throughput system worldwide: in Israel (Corl et al, 2020; Toledo et al, 2014, 2016, 2020; Vilk et al, 2021), the UK (Beardsworth et al, 2021b,c), and the Netherlands (Beardsworth et al, 2021a). However, the principles and functions presented here are ready for use with other massive high-resolution data collected by GPS, reverse-GPS or any other high-throughput tracking system.…”

WATLAS: high throughput and real-time tracking of many small birds in the Dutch Wadden Sea

“…Movement is a fundamental aspect of life and tracking wild animals under natural conditions has become central to animal behaviour, ecology, and conservation science (Nathan et al 2008;Kays et al 2015;Allen & Singh 2016;Tucker et al 2018;Hays et al 2019). Animal tracking has revealed extreme and large-scale migratory journeys (Gill et al 2009;Lindström et al 2021) and detailed patterns of habitat use (Dickie et al 2020;Beardsworth et al 2021b), as well as elucidated mechanisms of navigation (Guilford & Biro 2014;Harten et al 2020;Toledo et al 2020), predator-prey dynamics (Fortin et al 2005), and social interactions (Strandburg-Peshkin et al 2015). Insights from animal tracking studies are regularly incorporated in policy and conservation management (Choi et al 2019;Hays et al 2019).…”

Spatial cognitive ability is associated with transitory movement speed but not straightness during the early stages of exploration

“…There are marked individual differences in patterns of movement behaviour [3], which can be highly repeatable [4,5]. An individual's spatial cognitive ability, namely, the ability to collect, process, store and use spatial information has been suggested to influence movement decisions [6][7][8] and improve movement efficiency [2]. This ability can be assayed in both laboratory [9][10][11] and wild populations [12,13] and performances on tasks assaying spatial abilities have been correlated with proxies of fitness including better survival [14,15], increased sexual success [16,17] or reproductive investment [18,19].…”

“…As individuals become familiar with a landscape, their trajectories between places of interest are expected to increase both in straightness and speed [21,22]. However, we may expect differences in movement traits between individuals of differing spatial cognitive abilities, due to a different strategy [8] or capacity for collecting spatial information. First, if individuals initially explore the environment in similar ways, we may expect birds that perform well in the spatial task to take less time to learn about their environment, resulting in quicker improvements in the speed and straightness of transitory paths.…”