Cognitive map–based navigation in wild bats revealed by a new high-throughput tracking system

Toledo, Sivan; Shohami, David; Schiffner, Ingo; Lourie, E. M.; Orchan, Yotam; Bartan, Yoav; Nathan, Ran

doi:10.1126/science.aax6904

Cited by 124 publications

(166 citation statements)

References 36 publications

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“…Environmental affordances are, thus, often horizontally stratified either due to limitations on movement or resources and thus the anisotropic perception and coding of space observed in rats (Hayman et al 2011;Grieves et al 2020) may extend to many other animals even if they live in volumetric substrates. Interestingly, research on Egyptian fruit bats (Rousettus aegyptiacus) suggests that their representation of space is isotropic (equally accurate in all 3 dimensions; Yartsev and Ulanovsky 2013) although this was observed in relatively small rooms compared to the extensive home range of wild fruit bats (Tsoar et al 2011;Harten et al 2020;Toledo et al 2020). It remains to be seen if bats represent the vast areas over which they forage (up to 90 km 2 but with a mean flight altitude of just 30 m; Harten et al (2020) supplementary data) isotropically as well.…”

Section: Horizontal Movement Bias In Both Lattice Mazesmentioning

confidence: 99%

Volumetric spatial behaviour in rats reveals the anisotropic organisation of navigation

et al. 2020

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We investigated how access to the vertical dimension influences the natural exploratory and foraging behaviour of rats. Using high-accuracy three-dimensional tracking of position in two- and three-dimensional environments, we sought to determine (i) how rats navigated through the environments with respect to gravity, (ii) where rats chose to form their home bases in volumetric space, and (iii) how they navigated to and from these home bases. To evaluate how horizontal biases may affect these behaviours, we compared a 3D maze where animals preferred to move horizontally to a different 3D configuration where all axes were equally energetically costly to traverse. Additionally, we compared home base formation in two-dimensional arenas with and without walls to the three-dimensional climbing mazes. We report that many behaviours exhibited by rats in horizontal spaces naturally extend to fully volumetric ones, such as home base formation and foraging excursions. We also provide further evidence for the strong differentiation of the horizontal and vertical axes: rats showed a horizontal movement bias, they formed home bases mainly in the bottom layers of both mazes and they generally solved the vertical component of return trajectories before and faster than the horizontal component. We explain the bias towards horizontal movements in terms of energy conservation, while the locations of home bases are explained from an information gathering view as a method for correcting self-localisation.

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Section: Horizontal Movement Bias In Both Lattice Mazesmentioning

confidence: 99%

Volumetric spatial behaviour in rats reveals the anisotropic organisation of navigation

et al. 2020

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“…The idea of scales of space is actively considered in human spatial cognition (e.g., Wolbers & Wiener, 2014;Meilinger, 2008;Montello, 1993) and in ecological studies of animals such as rats (e.g., rats live in large, underground tunnels: Calhoun, 1963; rats move an average of over 675 m per night when searching for a new home: Russell, McMorland, & MacKay, 2010), bats (e.g., up to 100 km: Harten, Katz, Goldshtein, Handel, & Yovel, 2020;Toledo et al, 2020;Tsoar et al, 2011), ants (e.g., Wehner, 2020;Wittlinger et al, 2006), andhoneybees (e.g., von Frisch, 1954). For example, one important consideration about the scales of space is whether we and other animals form globally coherent spatial representations of large-scale spaces (e.g., Wolbers & Wiener, 2014;Meilinger, 2008;Hirtle & Jonides, 1985).…”

Section: The Importance Of Scales Of Spacementioning

confidence: 99%

An Important Step toward Understanding the Role of Body-based Cues on Human Spatial Memory for Large-Scale Environments

Huffman

Ekstrom

2021

Journal of Cognitive Neuroscience

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Moving our body through space is fundamental to human navigation; however, technical and physical limitations have hindered our ability to study the role of these body-based cues experimentally. We recently designed an experiment using novel immersive virtual-reality technology, which allowed us to tightly control the availability of body-based cues to determine how these cues influence human spatial memory [Huffman, D. J., & Ekstrom, A. D. A modality-independent network underlies the retrieval of large-scale spatial environments in the human brain. Neuron, 104, 611–622, 2019]. Our analysis of behavior and fMRI data revealed a similar pattern of results across a range of body-based cues conditions, thus suggesting that participants likely relied primarily on vision to form and retrieve abstract, holistic representations of the large-scale environments in our experiment. We ended our article by discussing a number of caveats and future directions for research on the role of body-based cues in human spatial memory. Here, we reiterate and expand on this discussion, and we use a commentary in this issue by A. Steel, C. E. Robertson, and J. S. Taube (Current promises and limitations of combined virtual reality and functional magnetic resonance imaging research in humans: A commentary on Huffman and Ekstrom (2019). Journal of Cognitive Neuroscience, 2020) as a helpful discussion point regarding some of the questions that we think will be the most interesting in the coming years. We highlight the exciting possibility of taking a more naturalistic approach to study the behavior, cognition, and neuroscience of navigation. Moreover, we share the hope that researchers who study navigation in humans and nonhuman animals will synergize to provide more rapid advancements in our understanding of cognition and the brain.

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“…We present a fully worked-out example of our pre-processing pipeline and residence patch method using movement data from three Egyptian fruit bats tracked using the ATLAS system (Rousettus aegyptiacus; Toledo et al (2020) = 2,173; range = 11,195 -15,542; interval = 8 seconds) per individual. Plotting the tracks showed severe distortions (see Supplementary Material Fig.…”

Section: Worked-out Example On Animal Tracking Datamentioning

confidence: 99%

“…Investigating fine-scale environmental and social drivers and the consequences of movement requires position data from many individuals at high temporal and spatial resolution. Such high-throughput tracking is possible using GPS tags (see recent examples in Harel et al, 2016;Papageorgiou et al, 2019;Strandburg-Peshkin et al, 2015), yet 'reverse-GPS' systems developed to track animals over land (MacCurdy et al, 2009(MacCurdy et al, , 2019Toledo et al, 2014Toledo et al, , 2016Toledo et al, , 2020Weiser et al, 2016) and in aquatic systems (Baktoft et al, 2019(Baktoft et al, , 2017Hussey et al, 2015;Jung et al, 2015) routinely produce high-throughput tracking data at much lower costs. Although high-resolution tracking inherently provides more detailed information about the true path of the tracked animal, high-throughput data presents a two-fold challenge to ecologists.…”

Section: Introductionmentioning

confidence: 99%

“…Investigating fine-scale environmental and social drivers and the consequences of movement requires position data from many individuals at high temporal and spatial resolution relative to the phenomenon of interest (high-throughput tracking). High-throughput tracking is possible using GPS tags (see recent examples in Harel et al, 2016; Papageorgiou et al, 2019; Strandburg-Peshkin et al, 2015); additionally, ‘reverse-GPS’ systems such as ATLAS (Advanced Tracking and Localization of Animals in real-life Systems) developed to track animals over land (Toledo et al, 2014, 2016, 2020; Weiser et al, 2016, see also MacCurdy et al 2009, 2019), and in aquatic systems (Aspillaga et al, 2021; Baktoft et al, 2019, 2017; Hussey et al, 2015; Jung et al, 2015) routinely produce high-throughput location data at much lower costs (Wikelski et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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

Gupte

Beardsworth

Spiegel

et al. 2020

Preprint

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Modern, high-throughput animal tracking studies collect increasingly large volumes of data at very fine temporal scales. At these scales, location error can exceed the animal step size, confounding inferences from tracking data. Cleaning the data to exclude positions with large location errors prior to analyses is one of the main ways movement ecologists deal with location errors. Cleaning data to reduce location error before making biological inferences is widely recommended, and ecologists routinely consider cleaned data to be the ground-truth. Nonetheless, uniform guidance on this crucial step is scarce. Cleaning high-throughput data must strike a balance between rejecting location errors without discarding valid animal movements. Additionally, users of high-throughput systems face challenges resulting from the high volume of data itself, since processing large data volumes is computationally intensive and difficult without a common set of efficient tools. Furthermore, many methods that cluster movement tracks for ecological inference are based on statistical phenomena, and may not be intuitive to understand in terms of the tracked animal biology. In this article we introduce a pipeline to pre-process high-throughput animal tracking data in order to prepare it for subsequent analysis. We demonstrate this pipeline on simulated movement data to which we have randomly added location errors. We further suggest how large volumes of cleaned data may be synthesized into biologically meaningful residence patches. We then use calibration data to show how the pipeline improves its quality, and to verify that the residence patch synthesis accurately captures animal space-use. Finally, turning to real tracking data from Egyptian fruit bats (Rousettus aegyptiacus), we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. To help with fast implementations of our pipeline, and to help standardise methods, we developed the R package atlastools, which we introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data volume combined with knowledge of the tracked individuals biology can be used to reduce location errors. The use of common pre-processing steps that are simple yet robust promotes standardised methods in the field of movement ecology and better inferences from data.

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Cognitive map–based navigation in wild bats revealed by a new high-throughput tracking system

Cited by 124 publications

References 36 publications

Volumetric spatial behaviour in rats reveals the anisotropic organisation of navigation

Volumetric spatial behaviour in rats reveals the anisotropic organisation of navigation

An Important Step toward Understanding the Role of Body-based Cues on Human Spatial Memory for Large-Scale Environments

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

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