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

Abstract

Memories about the spatial environment, such as the locations of foraging patches, are expected to affect how individuals move around the landscape. However, individuals differ in the ability to remember spatial locations (spatial cognitive ability) and evidence is growing that these inter-individual differences influence a range of fitness proxies. Yet empirical evaluations directly linking inter-individual variation in spatial cognitive ability and the development and structure of movement paths are lacking. We assessed the performance of young pheasants ( Phasianus colchicus ) on a spatial cognition task before releasing them into a novel, rural landscape and tracking their movements. We quantified changes in the straightness and speed of their transitory paths over one month. Birds with better performances on the task initially made slower transitory paths than poor performers but by the end of the month, there was no difference in speed. In general, birds increased the straightness of their path over time, indicating improved efficiency independent of speed, but this was not related to performance on the cognitive task. We suggest that initial slow movements may facilitate more detailed information gathering by better performers and indicates a potential link between an individual's spatial cognitive ability and their movement behaviour.

The effects of released pheasants on invertebrate populations in and around woodland release sites

“…The absolute declines, regardless of transect location, could indicate that the release of pheasants has immediate, widespread effects on invertebrate populations in woods surrounding the pens where pheasants are released. However, pheasants typically do not leave their release pen during these first few weeks (Beardsworth et al, 2021;, so we doubt whether these absolute declines are due to direct predation by the pheasants. Instead, declines outside the pen may occur because predation on invertebrates within the pen produces vacant niches that draw invertebrates in from outside and the pen may act as a population sink, so reducing their abundance outside.…”

“…At this time, released pheasants were absent from both transects and we expected there to be no differences in invertebrate populations between transects due to the direct effects of released pheasants (although persistent changes to soils or vegetation from previous releases may drive chronic differences). Survey 2 was conducted 4 weeks after pheasants had been released into the pens between 12 August and 30 September 2016 and 26 July and 30 September 2017, and during which time the pheasants typically remained entirely within the pen (Beardsworth et al., 2021 ). This allowed us to explore the relationship between interior and exterior invertebrate populations compared to the baseline established in Survey 1.…”

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.…”

A guide to pre‐processing high‐throughput animal tracking data

“…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, Whiteside, Capstick, et al., 2021 ; Beardsworth, Whiteside, Laker, et al., 2021 ) and the Netherlands (Beardsworth, Gobbens, et al., 2021 ; Bijleveld et al, 2021 ). However, the principles and functions presented here are ready for use with other massive high‐resolution data collected by GPS (e.g.…”