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1. Internet on animals: Wi‐Fi‐enabled devices provide a solution for big data transmission in biologging

Author

Timm A. Wild, Martin Wikelski, Stephen Tyndel, Gustavo Alarcón‐Nieto, Barbara C. Klump, Lucy M. Aplin, Mirko Meboldt, and Hannah J. Williams

Subjects
animal movement, embedded systems, Internet of Things, open source, sensing, telemetry, Ecology, QH540-549.5, Evolution, QH359-425
Abstract

Abstract Biologging devices are deployed on animals to collect ultra‐fine‐scale movement data that reveal subsecond patterns in locomotion or long‐term patterns in motion and space use. Often these two data types, although complementary, are rarely collected within the same study, given the limiting factors of memory space, power requirements and the need to retrieve stored data from animals. Biologging requires a revolutionary advancement in data networking to overcome these restrictions that constrain big data collection; for the continuous recording and remote download of fine‐scale movement and environmental data, from long‐term deployments and multiple individuals. Here, we adopt a strategy from the Internet of Things and develop the use of Wi‐Fi as a solution for big data biologging. Our ‘WildFi’ tag uses pre‐existing, or easy‐to‐set‐up, infrastructure in smartphones and Wi‐Fi gateways. We demonstrate the power of memory management and an embedded modular software architecture for functionality, including collective data retrieval at multiple gateways. We find that Wi‐Fi, together with smart embedded software, increases the retrieval efficiency of biologging data by orders of magnitude compared to other available systems: with a transmission speed of 230 kByte/s and range of ≤200 m that is 11 times faster than Bluetooth low energy and >3000 times faster than LoRaWAN. Case studies on a domestic dog (Canis lupus familiaris), aviary‐housed cockatiels (Nymphicus hollandicus) and free‐roaming pangolins (Smutsia temminckii) demonstrate the functionality of the WildFi tag for remote and robust autonomous Wi‐Fi data transmission under a range of conditions. Modularity in software and hardware allows for project‐specific tailoring beyond reconfiguring sampling parameters of a biologger, which we encourage with open‐source sharing of our architecture design. Enhanced communication between animal‐attached devices, Wi‐Fi infrastructure and smartphones, alongside smart and collaborative data retrieval, eases restrictions for big data collection in animal ecology.

Published
2023
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2. The behaviour and activity budgets of two sympatric sloths; Bradypus variegatus and Choloepus hoffmanni

Author

Rebecca N. Cliffe, Ryan J. Haupt, Sarah Kennedy, Cerys Felton, Hannah J. Williams, Judy Avey-Arroyo, and Rory Wilson

Subjects
Bradypus, Choloepus, Sloth, Activity budgets, Circadian rhythm, Biologging, Medicine, Biology (General), QH301-705.5
Abstract

It is usually beneficial for species to restrict activity to a particular phase of the 24-hour cycle as this enables the development of morphological and behavioural adaptations to enhance survival under specific biotic and abiotic conditions. Sloth activity patterns are thought to be strongly related to the environmental conditions due to the metabolic consequences of having a low and highly variable core body temperature. Understanding the drivers of sloth activity and their ability to withstand environmental fluctuations is of growing importance for the development of effective conservation measures, particularly when we consider the vulnerability of tropical ecosystems to climate change and the escalating impacts of anthropogenic activities in South and Central America. Unfortunately, the cryptic nature of sloths makes long term observational research difficult and so there is very little existing literature examining the behavioural ecology of wild sloths. Here, we used micro data loggers to continuously record, for the first time, the behaviour of both Bradypus and Choloepus sloths over periods of days to weeks. We investigate how fluctuations in the environmental conditions affect the activity of sloths inhabiting a lowland rainforest on the Caribbean coast of Costa Rica and examined how this might relate to their low power lifestyle. Both Bradypus and Choloepus sloths were found to be cathemeral in their activity, with high levels of between-individual and within-individual variation in the amounts of time spent active, and in the temporal distribution of activity over the 24-hour cycle. Daily temperature did not affect activity, although Bradypus sloths were found to show increased nocturnal activity on colder nights, and on nights following colder days. Our results demonstrate a distinct lack of synchronicity within the same population, and we suggest that this pattern provides sloths with the flexibility to exploit favourable environmental conditions whilst reducing the threat of predation.

Published
2023
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3. How often should dead-reckoned animal movement paths be corrected for drift?

Author

Richard M. Gunner, Mark D. Holton, David M. Scantlebury, Phil Hopkins, Emily L. C. Shepard, Adam J. Fell, Baptiste Garde, Flavio Quintana, Agustina Gómez-Laich, Ken Yoda, Takashi Yamamoto, Holly English, Sam Ferreira, Danny Govender, Pauli Viljoen, Angela Bruns, O. Louis van Schalkwyk, Nik C. Cole, Vikash Tatayah, Luca Börger, James Redcliffe, Stephen H. Bell, Nikki J. Marks, Nigel C. Bennett, Mariano H. Tonini, Hannah J. Williams, Carlos M. Duarte, Martin C. van Rooyen, Mads F. Bertelsen, Craig J. Tambling, and Rory P. Wilson

Subjects
Biologging, Dead-reckoning, Drift, Global Positioning System (GPS), Animal movement, Animal tracking, Ecology, QH540-549.5, Animal biochemistry, QP501-801
Abstract

Abstract Background Understanding what animals do in time and space is important for a range of ecological questions, however accurate estimates of how animals use space is challenging. Within the use of animal-attached tags, radio telemetry (including the Global Positioning System, ‘GPS’) is typically used to verify an animal’s location periodically. Straight lines are typically drawn between these ‘Verified Positions’ (‘VPs’) so the interpolation of space-use is limited by the temporal and spatial resolution of the system’s measurement. As such, parameters such as route-taken and distance travelled can be poorly represented when using VP systems alone. Dead-reckoning has been suggested as a technique to improve the accuracy and resolution of reconstructed movement paths, whilst maximising battery life of VP systems. This typically involves deriving travel vectors from motion sensor systems and periodically correcting path dimensions for drift with simultaneously deployed VP systems. How often paths should be corrected for drift, however, has remained unclear. Methods and results Here, we review the utility of dead-reckoning across four contrasting model species using different forms of locomotion (the African lion Panthera leo, the red-tailed tropicbird Phaethon rubricauda, the Magellanic penguin Spheniscus magellanicus, and the imperial cormorant Leucocarbo atriceps). Simulations were performed to examine the extent of dead-reckoning error, relative to VPs, as a function of Verified Position correction (VP correction) rate and the effect of this on estimates of distance moved. Dead-reckoning error was greatest for animals travelling within air and water. We demonstrate how sources of measurement error can arise within VP-corrected dead-reckoned tracks and propose advancements to this procedure to maximise dead-reckoning accuracy. Conclusions We review the utility of VP-corrected dead-reckoning according to movement type and consider a range of ecological questions that would benefit from dead-reckoning, primarily concerning animal–barrier interactions and foraging strategies.

Published
2021
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4. Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks

Author

Richard M. Gunner, Mark D. Holton, Mike D. Scantlebury, O. Louis van Schalkwyk, Holly M. English, Hannah J. Williams, Phil Hopkins, Flavio Quintana, Agustina Gómez-Laich, Luca Börger, James Redcliffe, Ken Yoda, Takashi Yamamoto, Sam Ferreira, Danny Govender, Pauli Viljoen, Angela Bruns, Stephen H. Bell, Nikki J. Marks, Nigel C. Bennett, Mariano H. Tonini, Carlos M. Duarte, Martin C. van Rooyen, Mads F. Bertelsen, Craig J. Tambling, and Rory P. Wilson

Subjects
Animal behaviour, Animal movement, Global Positioning System, R (programming language), Track integration, Tri-axial accelerometers, Ecology, QH540-549.5, Animal biochemistry, QP501-801
Abstract

Abstract Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the Additional file 4 as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.

Published
2021
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5. An 'orientation sphere' visualization for examining animal head movements

Author

Rory P. Wilson, Hannah J. Williams, Mark D. Holton, Agustina diVirgilio, Luca Börger, Jonathan R. Potts, Richard Gunner, Alex Arkwright, Andreas Fahlman, Nigel C. Bennett, Abdulaziz Alagaili, Nik C. Cole, Carlos M. Duarte, and David M. Scantlebury

Subjects
animal behaviour, environment framing, head movement, head pitch, head yaw, orientation sphere, Ecology, QH540-549.5
Abstract

Abstract Animal behavior is elicited, in part, in response to external conditions, but understanding how animals perceive the environment and make the decisions that bring about these behavioral responses is challenging. Animal heads often move during specific behaviors and, additionally, typically have sensory systems (notably vision, smell, and hearing) sampling in defined arcs (normally to the front of their heads). As such, head‐mounted electronic sensors consisting of accelerometers and magnetometers, which can be used to determine the movement and directionality of animal heads (where head “movement” is defined here as changes in heading [azimuth] and/or pitch [elevation angle]), can potentially provide information both on behaviors in general and also clarify which parts of the environment the animals might be prioritizing (“environmental framing”). We propose a new approach to visualize the data of such head‐mounted tags that combines the instantaneous outputs of head heading and pitch in a single intuitive spherical plot. This sphere has magnetic heading denoted by “longitude” position and head pitch by “latitude” on this “orientation sphere” (O‐sphere). We construct the O‐sphere for the head rotations of a number of vertebrates with contrasting body shape and ecology (oryx, sheep, tortoises, and turtles), illustrating various behaviors, including foraging, walking, and environmental scanning. We also propose correcting head orientations for body orientations to highlight specific heading‐independent head rotation, and propose the derivation of O‐sphere‐metrics, such as angular speed across the sphere. This should help identify the functions of various head behaviors. Visualizations of the O‐sphere provide an intuitive representation of animal behavior manifest via head orientation and rotation. This has ramifications for quantifying and understanding behaviors ranging from navigation through vigilance to feeding and, when used in tandem with body movement, should provide an important link between perception of the environment and response to it in free‐ranging animals.

Published
2020
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6. Identification of animal movement patterns using tri-axial magnetometry

Author

Hannah J. Williams, Mark D. Holton, Emily L. C. Shepard, Nicola Largey, Brad Norman, Peter G. Ryan, Olivier Duriez, Michael Scantlebury, Flavio Quintana, Elizabeth A. Magowan, Nikki J. Marks, Abdulaziz N. Alagaili, Nigel C. Bennett, and Rory P. Wilson

Subjects
Magnetometer, Magnetic field, Bio-logging, Accelerometer, Animal behaviour, Behavioural consistency, Biology (General), QH301-705.5
Abstract

Abstract Background Accelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers. Methods We calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour. Results Tri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading. Conclusion Magnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry.

Published
2017
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7. Individual Spatial Consistency and Dietary Flexibility in the Migratory Behavior of Northern Gannets Wintering in the Northeast Atlantic

Author

W. James Grecian, Hannah J. Williams, Stephen C. Votier, Stuart Bearhop, Ian R. Cleasby, David Grémillet, Keith C. Hamer, Mélanie Le Nuz, Amélie Lescroël, Jason Newton, Samantha C. Patrick, Richard A. Phillips, Ewan D. Wakefield, and Thomas W. Bodey

Subjects
individual variation, carry-over effects, Geolocator (GLS), stable isotope analysis (SIA), animal migration, Evolution, QH359-425, Ecology, QH540-549.5
Abstract

Migration is a fundamental behavioral process prevalent among a wide variety of animal taxa. As individuals are increasingly shown to present consistent responses to environmental cues for breeding or foraging, it may be expected that approaches to migration would present similar among-individual consistencies. Seabirds frequently show consistent individual differences in a range of traits related to foraging and space-use during both the breeding and non-breeding seasons, but the causes and consequences of this consistency are poorly understood. In this study, we combined analysis of geolocation and stable isotope data across multiple years to investigate individual variation in the non-breeding movements and diets of northern gannets Morus bassanus, and the consequences for changes in body condition. We found that individuals were highly repeatable in their non-breeding destination over consecutive years even though the population-level non-breeding distribution spanned >35° of latitude. Isotopic signatures were also strongly repeatable, with individuals assigned to one of two dietary clusters defined by their distinct trophic (δ15N) and spatial (δ13C) position. The only non-breeding destination in which the two dietary clusters co-occurred was off the coast of northwest Africa. The majority of individuals adopted a consistent foraging strategy, as they remained within the same dietary cluster across years, with little variation in body mass corrected for size among these consistent individuals. In contrast, the few individuals that switched clusters between years were in better condition relative to the rest of the population, suggesting there may be benefits to flexibility during the non-breeding period. Our results indicate that a consistent migratory strategy can be effective regardless of wintering region or diet, but that there may be benefits to those individuals able to display flexibility. This appears to be an important behavioral strategy that may enhance individual condition.

Published
2019
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8. Biological Earth observation with animal sensors

Author

Walter Jetz, Grigori Tertitski, Roland Kays, Uschi Mueller, Martin Wikelski, Susanne Åkesson, Yury Anisimov, Aleksey Antonov, Walter Arnold, Franz Bairlein, Oriol Baltà, Diane Baum, Mario Beck, Olga Belonovich, Mikhail Belyaev, Matthias Berger, Peter Berthold, Steffen Bittner, Stephen Blake, Barbara Block, Daniel Bloche, Katrin Boehning-Gaese, Gil Bohrer, Julia Bojarinova, Gerhard Bommas, Oleg Bourski, Albert Bragin, Alexandr Bragin, Rachel Bristol, Vojtěch Brlík, Victor Bulyuk, Francesca Cagnacci, Ben Carlson, Taylor K. Chapple, Kalkidan F. Chefira, Yachang Cheng, Nikita Chernetsov, Grzegorz Cierlik, Simon S. Christiansen, Oriol Clarabuch, William Cochran, Jamie Margaret Cornelius, Iain Couzin, Margret C. Crofoot, Sebastian Cruz, Alexander Davydov, Sarah Davidson, Stefan Dech, Dina Dechmann, Ekaterina Demidova, Jan Dettmann, Sven Dittmar, Dmitry Dorofeev, Detlev Drenckhahn, Vladimir Dubyanskiy, Nikolay Egorov, Sophie Ehnbom, Diego Ellis-Soto, Ralf Ewald, Chris Feare, Igor Fefelov, Péter Fehérvári, Wolfgang Fiedler, Andrea Flack, Magnus Froböse, Ivan Fufachev, Pavel Futoran, Vyachaslav Gabyshev, Anna Gagliardo, Stefan Garthe, Sergey Gashkov, Luke Gibson, Wolfgang Goymann, Gerd Gruppe, Chris Guglielmo, Phil Hartl, Anders Hedenström, Arne Hegemann, Georg Heine, Mäggi Hieber Ruiz, Heribert Hofer, Felix Huber, Edward Hurme, Fabiola Iannarilli, Marc Illa, Arkadiy Isaev, Bent Jakobsen, Lukas Jenni, Susi Jenni-Eiermann, Brett Jesmer, Frédéric Jiguet, Tatiana Karimova, N. Jeremy Kasdin, Fedor Kazansky, Ruslan Kirillin, Thomas Klinner, Andreas Knopp, Andrea Kölzsch, Alexander Kondratyev, Marco Krondorf, Pavel Ktitorov, Olga Kulikova, R. Suresh Kumar, Claudia Künzer, Anatoliy Larionov, Christine Larose, Felix Liechti, Nils Linek, Ashley Lohr, Anna Lushchekina, Kate Mansfield, Maria Matantseva, Mikhail Markovets, Peter Marra, Juan F. Masello, Jörg Melzheimer, Myles H.M. Menz, Stephen Menzie, Swetlana Meshcheryagina, Dale Miquelle, Vladimir Morozov, Andrey Mukhin, Inge Müller, Thomas Mueller, Juan G. Navedo, Ran Nathan, Luke Nelson, Zoltán Németh, Scott Newman, Ryan Norris, Olivier Nsengimana, Innokentiy Okhlopkov, Wioleta Oleś, Ruth Oliver, Teague O’Mara, Peter Palatitz, Jesko Partecke, Ryan Pavlick, Anastasia Pedenko, Alys Perry, Julie Pham, Daniel Piechowski, Allison Pierce, Theunis Piersma, Wolfgang Pitz, Dirk Plettemeier, Irina Pokrovskaya, Liya Pokrovskaya, Ivan Pokrovsky, Morrison Pot, Petr Procházka, Petra Quillfeldt, Eldar Rakhimberdiev, Marilyn Ramenofsky, Ajay Ranipeta, Jan Rapczyński, Magdalena Remisiewicz, Viatcheslav Rozhnov, Froukje Rienks, Vyacheslav Rozhnov, Christian Rutz, Vital Sakhvon, Nir Sapir, Kamran Safi, Friedrich Schäuffelhut, David Schimel, Andreas Schmidt, Judy Shamoun-Baranes, Alexander Sharikov, Laura Shearer, Evgeny Shemyakin, Sherub Sherub, Ryan Shipley, Yanina Sica, Thomas B. Smith, Sergey Simonov, Katherine Snell, Aleksandr Sokolov, Vasiliy Sokolov, Olga Solomina, Mikhail Soloviev, Fernando Spina, Kamiel Spoelstra, Martin Storhas, Tatiana Sviridova, George Swenson, Phil Taylor, Kasper Thorup, Arseny Tsvey, Marlee Tucker, Sophie Tuppen, Woody Turner, Innocent Twizeyimana, Henk van der Jeugd, Louis van Schalkwyk, Mariëlle van Toor, Pauli Viljoen, Marcel E. Visser, Tamara Volkmer, Andrei Volkov, Sergey Volkov, Oleg Volkov, Jan A.C. von Rönn, Bernd Vorneweg, Bettina Wachter, Jonas Waldenström, Natalie Weber, Martin Wegmann, Aloysius Wehr, Rolf Weinzierl, Johannes Weppler, David Wilcove, Timm Wild, Hannah J. Williams, John Wilshire, John Wingfield, Michael Wunder, Anna Yachmennikova, Scott Yanco, Elisabeth Yohannes, Amelie Zeller, Christian Ziegler, Anna Zięcik, Cheryl Zook, University of St Andrews. School of Biology, University of St Andrews. Centre for Biological Diversity, University of St Andrews. Centre for Social Learning & Cognitive Evolution, Piersma group, Animal Ecology (AnE), Dutch Centre for Avian Migration & Demography, and Netherlands Institute of Ecology (NIOO)

Subjects
Conservation of Natural Resources, сбор данных, GE, Earth, Planet, QH301 Biology, Movement, T-NDAS, биологические наблюдения, Земля, планета, Animal sensors, Animal tracking-based Earth observation, QH301, SDG 3 - Good Health and Well-being, дистанционное зондирование, Settore BIO/07 - ECOLOGIA, ddc:570, животные, Animals, Movement [MeSH], Ecology, Evolution, Behavior and Systematics, Conservation of Natural Resources [MeSH], Ecosystem [MeSH], Animals [MeSH], Earth, Planet [MeSH], датчики, Ecosystem, GE Environmental Sciences
Abstract

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmental change. Publisher PDF

Published
2022

9. Certainty and integration of options in animal movement

Author

Kamran Safi and Hannah J. Williams

Subjects
Movement (music), business.industry, Computer science, Movement, Energy (esotericism), media_common.quotation_subject, Sampling (statistics), Energy landscape, Distribution (economics), Fear, Certainty, optimal movement theory, decision making, movement ecology, social information, Bayesian theory, physical intelligence, Microeconomics, Resource (project management), ddc:570, Animals, Social information, business, Ecosystem, Ecology, Evolution, Behavior and Systematics, media_common
Abstract

Physical energy defines the energy landscape and determines the species-specific cost of movement, thus influencing movement decisions. In unpredictable and dynamic environments, observing the locomotion of others increases individual certainty in the distribution of physical energy to increase movement efficiency. Beyond the physical energy landscape, social sampling increases certainty in all ecological landscapes that influence animal movement (including fear and resource landscapes), and individuals use energy to express each of these. We call for the development of an 'optimal movement theory' (OMT) that integrates the multidimensional reality of movement decisions by combining ecological landscapes according to a single expectation of energy cost-benefit, where social sampling provides up-to-date information under uncertain conditions. This mechanistic framework has implications for predicting individual movement patterns and for investigating the emergence of aggregations. published

Published
2021

10. Optimizing the use of biologgers for movement ecology research

Author

Holly M. English, Juan M. Morales, Urška Demšar, Alice M. Trevail, Agustina Gómez-Laich, Rachael C Griffiths, Anouk Spelt, Rory P. Wilson, Thomas A. Clay, William P. Kay, Allert I. Bijleveld, Lucy A. Taylor, Christian Rutz, Luca Börger, Jonathan R. Potts, Katharine F Rogerson, Hannah J. Williams, Simon Benhamou, Sophie de Grissac, Novella Franconi, Department of Biosciences, Swansea University, University of Oxford [Oxford], Save the Elephants, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Royal Netherlands Institute for Sea Research (NIOZ), School of Environmental Sciences [Liverpool], University of Liverpool, University of St Andrews [Scotland], Instituto de Biología de Organismos Marinos [Chubut] (IBIOMAR), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET), Universidad Nacional del Comahue [Neuquén] (UNCOMA), Instituto Nacional de Investigaciones en Biodiversidad y Medioambiente [Bariloche] (INIBIOMA-CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET)-Universidad Nacional del Comahue [Neuquén] (UNCOMA), School of Mathematics and Statistics [Sheffield] (SoMaS), University of Sheffield [Sheffield], School of Environmental and Life Sciences, University of East Anglia [Norwich] (UEA), Department of Aerospace Engineering [Bristol], University of Bristol [Bristol], University of Oxford, Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), The Leverhulme Trust, University of St Andrews. School of Geography & Sustainable Development, University of St Andrews. Bell-Edwards Geographic Data Institute, University of St Andrews. Centre for Biological Diversity, University of St Andrews. Centre for Social Learning & Cognitive Evolution, and University of St Andrews. School of Biology

Subjects
0106 biological sciences, Matching (statistics), BIG DATA, Computer science, Biologging, QH301 Biology, Ecology (disciplines), Movement, GPS, Big data, NDAS, 010603 evolutionary biology, 01 natural sciences, Article, purl.org/becyt/ford/1 [https], Ciencias Biológicas, QH301, Data visualization, ACCELEROMETER, Multidisciplinary approach, MOVEMENT ECOLOGY, Multidimensional visualization, Animals, DATA VISUALIZATION, Set (psychology), purl.org/becyt/ford/1.6 [https], Ecology, Evolution, Behavior and Systematics, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Ecology, business.industry, 010604 marine biology & hydrobiology, INTEGRATED BIOLOGGING FRAMEWORK, Statistical model, Ecología, MULTIDISCIPLINARY COLLABORATION, 13. Climate action, Animal Science and Zoology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, CIENCIAS NATURALES Y EXACTAS, MULTISENSOR APPROACH
Abstract

The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models. Fil: Williams, Hannah J.. Swansea University; Reino Unido Fil: Taylor, Lucy. University of Oxford; Reino Unido. Save The Elephants; Kenia Fil: Benhamou, Simon. Centre National de la Recherche Scientifique; Francia Fil: Bijleveld, Allert. Utrecht University; Países Bajos Fil: Clay, Thomas. University of Liverpool; Reino Unido Fil: de Grissac, Sophie. Swansea University; Reino Unido Fil: Demsar, Urska. University of St. Andrews; Reino Unido Fil: English, Holly M.. Swansea University; Reino Unido Fil: Franconi, Novella. Swansea University; Reino Unido Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Griffiths, Rachael. Swansea University; Reino Unido Fil: Kay, William P.. Swansea University; Reino Unido Fil: Morales, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina Fil: Potts, Jonathan. University of Sheffield; Reino Unido Fil: Rogerson, Katharine F.. University of East Anglia; Reino Unido Fil: Rutz, Christian. University of St. Andrews; Reino Unido Fil: Spelt, Anouk. University of Bristol; Reino Unido Fil: Trevail, Alice. University of Liverpool; Reino Unido Fil: Wilson, Rory P.. Swansea University; Reino Unido Fil: Börger, Luca. Swansea University; Reino Unido

Published
2020

11. Estimates for energy expenditure in free‐living animals using acceleration proxies; a reappraisal

Author

Nathan R. Geraldi, Agustina Gómez-Laich, Hannah J. Williams, Frank Rosell, Nikki J. Marks, Richard Gunner, Flavio Quintana, Patricia Maria Graf, Carlos M. Duarte, Rebecca Scott, Mark D. Holton, Emily L. C. Shepard, Hermina Robotka, Michael S. Strano, Lloyd W. Hopkins, Andreas Fahlman, Rory P. Wilson, Christophe Eizaguirre, D. Michael Scantlebury, and Luca Börger

Subjects
0106 biological sciences, Computer science, Movement, Acceleration, WILD ANIMALS, 010603 evolutionary biology, 01 natural sciences, Proxy (climate), Article, purl.org/becyt/ford/1 [https], Ciencias Biológicas, MOVEMENT COSTS, Econometrics, Animals, Total energy, purl.org/becyt/ford/1.6 [https], Ecology, Evolution, Behavior and Systematics, Metabolic power, DOUBLY LABELLED WATER, 010604 marine biology & hydrobiology, DYNAMIC BODY ACCELERATION, Zoología, Ornitología, Entomología, Etología, ENERGY EXPENDITURE, Energy expenditure, Metabolic rate, Animal Science and Zoology, Energy Metabolism, CIENCIAS NATURALES Y EXACTAS
Abstract

It is fundamentally important for many animal ecologists to quantify the costs of animal activities, although it is not straightforward to do so. The recording of triaxial acceleration by animal-attached devices has been proposed as a way forward for this, with the specific suggestion that dynamic body acceleration (DBA) be used as a proxy for movement-based power. Dynamic body acceleration has now been validated frequently, both in the laboratory and in the field, although the literature still shows that some aspects of DBA theory and practice are misunderstood. Here, we examine the theory behind DBA and employ modelling approaches to assess factors that affect the link between DBA and energy expenditure, from the deployment of the tag, through to the calibration of DBA with energy use in laboratory and field settings. Using data from a range of species and movement modes, we illustrate that vectorial and additive DBA metrics are proportional to each other. Either can be used as a proxy for energy and summed to estimate total energy expended over a given period, or divided by time to give a proxy for movement-related metabolic power. Nonetheless, we highlight how the ability of DBA to predict metabolic rate declines as the contribution of non-movement-related factors, such as heat production, increases. Overall, DBA seems to be a substantive proxy for movement-based power but consideration of other movement-related metrics, such as the static body acceleration and the rate of change of body pitch and roll, may enable researchers to refine movement-based metabolic costs, particularly in animals where movement is not characterized by marked changes in body acceleration. Fil: Wilson, Rory P.. Swansea University; Reino Unido Fil: Börger, Luca. Swansea University; Reino Unido Fil: Holton, Mark. Swansea University; Reino Unido Fil: Scantlebury, D. Michael. The Queens University of Belfast; Irlanda Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Rosell, Frank. University of South‐Eastern Norway; Noruega Fil: Graf, Patricia M.. Universitat Fur Bodenkultur Wien; Austria. University of Ljubljana; Eslovenia Fil: Williams, Hannah. Swansea University; Reino Unido Fil: Gunner, Richard. Swansea University; Reino Unido Fil: Hopkins, Lloyd. Swansea University; Reino Unido Fil: Marks, Nikki. The Queens University of Belfast; Irlanda Fil: Geraldi, Nathan R.. King Abdullah University of Science and Technology; Arabia Saudita Fil: Duarte, Carlos M.. King Abdullah University of Science and Technology; Arabia Saudita Fil: Scott, Rebecca. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: Strano, Michael S.. Massachusetts Institute of Technology; Estados Unidos Fil: Robotka, Hermina. Max Planck Institute für Ornithologie; Alemania Fil: Eizaguirre, Christophe. , Queen Mary University of London; Reino Unido Fil: Fahlman, Andreas. undación Oceanogràfic de la Comunidad Valenciana; España Fil: Shepard, Emily L. C.. Swansea University; Reino Unido. Max Planck Institute für Ornithologie; Alemania

Published
2020

12. Consistency and flexibility in migration

Author

Ian R. Cleasby, Thomas W. Bodey, Stephen C. Votier, Richard A. Phillips, Hannah J. Williams, Keith C. Hamer, Ewan D. Wakefield, Jason Newton, W. James Grecian, Stuart Bearhop, David Grémillet, Amélie Lescroël, Mélanie Le Nuz, Samantha C. Patrick, Marine Biology and Ecology Research Centre, Plymouth University, Institut d'histoire moderne et contemporaine (IHMC), Université Paris 1 Panthéon-Sorbonne (UP1)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Environment and Sustainability Institute [Penryn, UK], University of Exeter, Centre for Ecology and Conservation, University of Exeter, Cornwall Campus, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), FitzPatrick Institute of African Ornithology, University of Cape Town-DST-NRF Centre of Excellence, Réserve Naturelle des Sept-Iles Station, LPO, Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), PRBO Conservation Science, British Antarctic Survey (BAS), Natural Environment Research Council (NERC), Institute of Biodiversity, Animal Health and Comparative Medicine, School of Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)-Université Panthéon-Sorbonne (UP1), Environment and Sustainability Institute, Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Paul-Valéry - Montpellier 3 (UM3), Centre d'études biologiques de Chizé (CEBC), Centre National de la Recherche Scientifique (CNRS), Université Paris 1 Panthéon-Sorbonne (UP1)-École normale supérieure - Paris (ENS-PSL), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), University of St Andrews. School of Biology, University of St Andrews. Scottish Oceans Institute, and University of St Andrews. Sea Mammal Research Unit

Subjects
0106 biological sciences, 0301 basic medicine, stable isotope analysis (SIA), Individual variation, Geolocator (GLS), Range (biology), QH301 Biology, Population, Foraging, lcsh:Evolution, Animal migration, Biology, 010603 evolutionary biology, 01 natural sciences, Latitude, QH301, 03 medical and health sciences, lcsh:QH540-549.5, lcsh:QH359-425, Carry-over effects, 14. Life underwater, education, Ecology, Evolution, Behavior and Systematics, ComputingMilieux_MISCELLANEOUS, Trophic level, animal migration, education.field_of_study, Ecology, δ13C, individual variation, Stable isotope analysis (SIA), Flexibility (personality), DAS, Geolocation, carry-over effects, 030104 developmental biology, [SDE]Environmental Sciences, lcsh:Ecology
Abstract

Funding: UK Natural Environment Research Council (NERC Standard Grant NE/H007466/1). SB is currently funded by an ERC consolidator’s grant (310820: STATEMIG). EW is funded by the NERC (Independent Research Fellowship NE/M017990/1). Migration is a fundamental behavioral process prevalent among a wide variety of animal taxa. As individuals are increasingly shown to present consistent responses to environmental cues for breeding or foraging, it may be expected that approaches to migration would present similar among-individual consistencies. Seabirds frequently show consistent individual differences in a range of traits related to foraging and space-use during both the breeding and non-breeding seasons, but the causes and consequences of this consistency are poorly understood. In this study, we combined analysis of geolocation and stable isotope data across multiple years to investigate individual variation in the non-breeding movements and diets of northern gannets Morus bassanus, and the consequences for changes in body condition. We found that individuals were highly repeatable in their non-breeding destination over consecutive years even though the population-level non-breeding distribution spanned >35° of latitude. Isotopic signatures were also strongly repeatable, with individuals assigned to one of two dietary clusters defined by their distinct trophic (δ15N) and spatial (δ13C) position. The only non-breeding destination in which the two dietary clusters co-occurred was off the coast of northwest Africa. The majority of individuals adopted a consistent foraging strategy, as they remained within the same dietary cluster across years, with little variation in body mass corrected for size among these consistent individuals. In contrast, the few individuals that switched clusters between years were in better condition relative to the rest of the population, suggesting there may be benefits to flexibility during the non-breeding period. Our results indicate that a consistent migratory strategy can be effective regardless of wintering region or diet, but that there may be benefits to those individuals able to display flexibility. This appears to be an important behavioral strategy that may enhance individual condition. Publisher PDF

Published
2019

13. Give the machine a hand: A Boolean time-based decision-tree template for rapidly finding animal behaviours in multisensor data

Author

Agustina di Virgilio, Rory P. Wilson, D. Michael Scantlebury, Eun Sun Lee, Carlos M. Duarte, Juan Emilio Sala, Hannah J. Williams, Sergio A. Lambertucci, Flavio Quintana, Mani Srivastava, Bharathan Balaji, Mark D. Holton, and Emily L. C. Shepard

Subjects
0106 biological sciences, business.industry, Computer science, 010604 marine biology & hydrobiology, Ecological Modeling, BIOINFORMATICS, Decision tree, BEHAVIOUR, SOFTWARE, Ecología, Machine learning, computer.software_genre, Accelerometer, Time based, 010603 evolutionary biology, 01 natural sciences, Ciencias Biológicas, Software, ACCELEROMETER, Artificial intelligence, business, computer, Ecology, Evolution, Behavior and Systematics, BEHAVIOUR IDENTIFICATION, CIENCIAS NATURALES Y EXACTAS
Abstract

The development of multisensor animal-attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. The high volumes of data, are pushing us towards machine-learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag-equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. Overall behaviour recognition using our new approach was better than most other methods due to; (1) its ability to deal with behavioural variation and (2) the speed with which the task was completed because extraneous data are avoided in the process. We suggest that this approach is a promising way forward in an increasingly data-rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work. Fil: Wilson, Rory P.. Swansea University; Reino Unido Fil: Holton, Mark. Swansea University; Reino Unido Fil: Di Virgilio, Agustina Soledad. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; Argentina Fil: Williams, Hannah. Swansea University; Reino Unido Fil: Shepard, Emily L. C.. Swansea University; Reino Unido Fil: Lambertucci, Sergio Agustin. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche; Argentina Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Sala, Juan Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Biología de Organismos Marinos; Argentina Fil: Balaji, Bharathan. University of California at Los Angeles; Estados Unidos Fil: Lee, Eun Sun. University of California at Los Angeles; Estados Unidos Fil: Srivastava, Mani. University of California at Los Angeles; Estados Unidos Fil: Scantlebury, D. Michael. The Queens University of Belfast; Irlanda Fil: Duarte, Carlos M.. King Abdullah University Of Science And Technology

Published
2018

14. Can accelerometry be used to distinguish between flight types in soaring birds?

Author

Emily L. C. Shepard, Sergio A. Lambertucci, Hannah J. Williams, Olivier Duriez, Swansea University, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Instituto Nacional de Investigaciones en Biodiversidad y Medioambiente [Bariloche] (INIBIOMA-CONICET), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET)-Universidad Nacional del Comahue [Neuquén] (UNCOMA), Institut de Recherche pour le Développement (IRD [France-Sud])-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université Paul-Valéry - Montpellier 3 (UM3), Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
Heading (navigation), Computer Networks and Communications, Airspeed, Acceleration, KNN, Magnetometry, Daily diary, Accelerometer, purl.org/becyt/ford/1 [https], Ciencias Biológicas, Centripetal acceleration, Range (aeronautics), Thermal, purl.org/becyt/ford/1.6 [https], Instrumentation, Simulation, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Daily Diary, [SDV.BA]Life Sciences [q-bio]/Animal biology, Pulling-g, Ecología, Geodesy, Centripetal force, Soaring flight, Signal Processing, Flapping, Animal Science and Zoology, Geology, CIENCIAS NATURALES Y EXACTAS
Abstract

Background: Accelerometry has been used to identify behaviours through the quantification of body posture and motion for a range of species moving in different media. This technique has not been applied to flight behaviours to the same degree, having only been used to distinguish flapping from soaring flight, even though identifying the type of soaring flight could provide important insights into the factors underlying movement paths in soaring birds. This may be due to the complexities of interpreting acceleration data, as movement in the aerial environment may be influenced by phenomena such as centripetal acceleration (pulling-g). This study used high-resolution movement data on the flight of free-living Andean condors (Vultur gryphus) and a captive Eurasian griffon vulture (Gyps fulvus) to examine the influence of gravitational, dynamic and centripetal acceleration in different flight types. Flight behaviour was categorised as thermal soaring, slope soaring, gliding and flapping, using changes in altitude and heading from magnetometry data. We examined the ability of the k-nearest neighbour (KNN) algorithm to distinguish between these behaviours using acceleration data alone. Results: Values of the vectorial static body acceleration (VeSBA) suggest that these birds experience relatively little centripetal acceleration in flight, though this varies between flight types. Centripetal acceleration appears to be of most influence during thermal soaring; consequently, it is not possible to derive bank angle from smoothed values of lateral acceleration. In contrast, the smoothed acceleration values in the dorso-ventral axis provide insight into body pitch, which varied linearly with airspeed. Classification of passive flight types via KNN was limited, with low accuracy and precision for soaring and gliding. Conclusion: The importance of soaring was evident in the high proportion of time each bird spent in this flight mode (52.17–84.00 %). Accelerometry alone was limited in its ability to distinguish between passive flight types, though smoothed values in the dorso-ventral axis did vary with airspeed. Other sensors, in particular the magnetometer, provided powerful methods of identifying flight behaviour and these data may be better suited for automated behavioural identification. This should provide further insight into the type and strength of updraughts available to soaring birds. Fil: Williams, H. J.. Swansea University; Reino Unido Fil: Shepard, E. L. C.. Swansea University; Reino Unido Fil: Duriez, O.. Universite Montpellier Ii; Francia Fil: Lambertucci, Sergio Agustin. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación en Biodiversidad y Medioambiente; Argentina

Published
2015

15. Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags

Author

Mark W. Jones, Rory P. Wilson, James Walker, Elizabeth Magowan, Mark D. Holton, Agustina di Virgilio, Emily L. C. Shepard, Owen R. Bidder, D. Michael Scantlebury, Robert S. Laramee, Hannah J. Williams, Iain E. Maguire, and Nikki J. Marks

Subjects
Complex data type, Software suite, ANIMAL MOVEMENT, business.industry, Computer science, FRAMEWORK4, purl.org/becyt/ford/1.2 [https], ANIMAL BEHAVIOR, DAILY DIARY, Data science, Software Article, Environmental data, purl.org/becyt/ford/1 [https], Data set, Annotation, Software, Animal ecology, Ciencias de la Computación e Información, Key (cryptography), business, Otras Ciencias de la Computación e Información, CIENCIAS NATURALES Y EXACTAS, Ecology, Evolution, Behavior and Systematics
Abstract

Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals operate. However, interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. This work presents a single program, FRAMEWORK 4, that uses a particular sensor constellation described in the?Daily Diary? tag (recording tri-axial acceleration, tri-axial magnetic field intensity, pressure and e.g. temperature and light intensity) to determine the 4 key elements considered pivotal within the conception of the tag. These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal operates. The program takes the original data recorded by the Daily Dairy and transforms it into dead-reckoned movements,template-matched behaviours, dynamic body acceleration-derived energetics and positionlinked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space. Fil: Walker, James S.. Swansea University. College Of Sciences; Reino Unido Fil: Jones, Mark W.. Swansea University. College Of Sciences; Reino Unido Fil: Laramee, Robert S.. Swansea University. College Of Sciences; Reino Unido Fil: Holton, Mark D.. Swansea University; Reino Unido Fil: Shepard, Emily L. C.. Swansea University. College Of Sciences; Reino Unido Fil: Williams, Hannah J.. Swansea University. College Of Sciences; Reino Unido Fil: Scantlebury, D. Michael. The Queens University Of Belfast; Irlanda Fil: Marks, Nikki, J.. The Queens University Of Belfast; Irlanda Fil: Magowan, Elizabeth A.. The Queens University Of Belfast; Irlanda Fil: Maguire, Iain E.. The Queens University Of Belfast; Irlanda Fil: Grundy, Ed. Swansea University. College Of Sciences; Reino Unido Fil: Di Virgilio, Agustina Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue; Argentina Fil: Wilson, Rory P.. Swansea University. College Of Sciences; Reino Unido

Published
2015

16. Identification of animal movement patterns using tri-axial magnetometry

Author

Elizabeth Magowan, Mark D. Holton, Brad Norman, Emily L. C. Shepard, Rory P. Wilson, Olivier Duriez, Peter G. Ryan, Nicola Largey, Michael Scantlebury, Hannah J. Williams, Flavio Quintana, Nigel C. Bennett, Nikki J. Marks, Abdulaziz N. Alagaili, Swansea University, ECOCEAN Inc., ECOCEAN, University of Cape Town, Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Queen's University [Belfast] (QUB), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET), King Saud University [Riyadh] (KSU), University of Pretoria [South Africa], FitzPatrick Institute of African Ornithology, Faculty of Science, Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
0106 biological sciences, Computer science, Accelerometer, Rotation, 01 natural sciences, law.invention, purl.org/becyt/ford/1 [https], law, BEHAVIOURAL CONSISTENCY, Computer vision, lcsh:QH301-705.5, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Movement (music), Methodology Article, [SDV.BA]Life Sciences [q-bio]/Animal biology, Animal behaviour, Magnetometer, ANIMAL BEHAVIOUR, ANGULAR VELOCITY, Normal operational plane, CIENCIAS NATURALES Y EXACTAS, Heading (navigation), Otras Ciencias Biológicas, Angular velocity, MAGNETIC FIELD, 010603 evolutionary biology, Ciencias Biológicas, Acceleration, ACCELEROMETER, purl.org/becyt/ford/1.6 [https], Ecology, Evolution, Behavior and Systematics, MAGNETOMETER, info:ar-repo/semantics/artículo, Bio-logging, business.industry, 010604 marine biology & hydrobiology, NORMAL OPERATIONAL PLANE, Behavioural consistency, Magnetic field, BIO-LOGGING, lcsh:Biology (General), Animal ecology, Artificial intelligence, business
Abstract

Background: Accelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers. Methods: We calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour.Results: Tri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading. Conclusion: Magnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry. Fil: Williams, Hannah J.. Swansea University; Reino Unido Fil: Holton, Mark D.. Swansea University; Reino Unido Fil: Shepard, Emily L. C.. Swansea University; Reino Unido Fil: Largey, Nicola. Swansea University; Reino Unido Fil: Norman, Brad. Ecocean Inc. (aust.); Australia Fil: Ryan, Peter G.. University of Cape Town; Sudáfrica Fil: Duriez, Olivier. Université Paul-Valéry Montpellier; Francia Fil: Scantlebury, Michael. The Queens University of Belfast; Irlanda Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Magowan, Elizabeth A.. The Queens University of Belfast; Irlanda Fil: Marks, Nikki J.. The Queens University of Belfast; Irlanda Fil: Alagaili, Abdulaziz N.. King Saud University; Arabia Saudita Fil: Bennett, Nigel C.. University of Pretoria; Sudáfrica Fil: Wilson, Rory P.. Swansea University; Reino Unido

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17. Sensory collectives in natural systems

Author

Hannah J Williams, Vivek H Sridhar, Edward Hurme, Gabriella EC Gall, Natalia Borrego, Genevieve E Finerty, Iain D Couzin, C Giovanni Galizia, Nathaniel J Dominy, Hannah M Rowland, Mark E Hauber, James P Higham, Ariana Strandburg-Peshkin, and Amanda D Melin

Subjects
umwelt, perception, collective movement, sensory ecology, animal behaviour, complex systems, Medicine, Science, Biology (General), QH301-705.5
Abstract

Groups of animals inhabit vastly different sensory worlds, or umwelten, which shape fundamental aspects of their behaviour. Yet the sensory ecology of species is rarely incorporated into the emerging field of collective behaviour, which studies the movements, population-level behaviours, and emergent properties of animal groups. Here, we review the contributions of sensory ecology and collective behaviour to understanding how animals move and interact within the context of their social and physical environments. Our goal is to advance and bridge these two areas of inquiry and highlight the potential for their creative integration. To achieve this goal, we organise our review around the following themes: (1) identifying the promise of integrating collective behaviour and sensory ecology; (2) defining and exploring the concept of a ‘sensory collective’; (3) considering the potential for sensory collectives to shape the evolution of sensory systems; (4) exploring examples from diverse taxa to illustrate neural circuits involved in sensing and collective behaviour; and (5) suggesting the need for creative conceptual and methodological advances to quantify ‘sensescapes’. In the final section, (6) applications to biological conservation, we argue that these topics are timely, given the ongoing anthropogenic changes to sensory stimuli (e.g. via light, sound, and chemical pollution) which are anticipated to impact animal collectives and group-level behaviour and, in turn, ecosystem composition and function. Our synthesis seeks to provide a forward-looking perspective on how sensory ecologists and collective behaviourists can both learn from and inspire one another to advance our understanding of animal behaviour, ecology, adaptation, and evolution.

Published
2023
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