Biologging Special Feature

Abstract

Imagine yourself, as an ecologist during field work, deep in the woods. Eerily silent was the forest, when loudly from the tree above a wren started to sing. A quick, skilful use of the binoculars showed it was the male ringed last week, but swiftly the bird disappeared again among the leaves. Similar difficulties in reliably observing the behaviour of the study species will be familiar to many ecologists and can strongly affect the choice of the study species; for example, the ethologist and zoologist Nikolaas Tinbergen mentioned ease of observation as a motivation to study seabirds instead of forest birds (Tinbergen, 1939). While certainly smart choices of the study species are key to successful research, typified by the Krogh principle: “for a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied” (Krogh, 1929), most terrestrial, aquatic and aerial species cannot be well observed in the field. Technological solutions to record the movements, behaviour and physiology of animals, and associated methodological advancements for analysing the data collected, have revolutionized research in animal ecology and beyond (Brisson-Curadeau, Patterson, Whelan, Lazarus, & Elliott, 2017; Kenward, 2001; Ropert-Coudert, Beaulieu, Hanuise, & Kato, 2009; Ropert-Coudert & Wilson, 2005; Weimerskirch, 2009). The general term for this technological approach to study animals is called Biologging—‘the use of miniaturized animal-attached tags for logging and/or relaying data about an animal's movements, behaviour, physiology, and/or environment’ (Rutz & Hays, 2009). It is closely related to and comprises the field of Biotelemetry—the remote measurement of the physiological conditions and activity/behavioural state of animals (Cooke et al., 2004), including biomedical applications in humans. The use of electronic loggers and transmitters offers unprecedented opportunities for uncovering the ‘hidden lives’ of animals and achieve a more mechanistic understanding of their ecology, and indeed the first ‘Virtual Issue’ (an online collection of papers published on a specific topic) published by the Journal of Animal Ecology was on ‘Biotelemetry and Biologging’ (Hays, 2008). Progress in this broad field has been exceptional in the last decade (Baratchi, Meratnia, Havinga, Skidmore, & Toxopeus, 2013; Hussey et al., 2015; Kays, Crofoot, Jetz, & Wikelski, 2015; Wilmers et al., 2015; Brisson-Curadeau et al., 2017; Tibbetts, 2017; Harcourt et al., 2019; Lowerre-Barbieri, Kays, Thorson, & Wikelski, 2019), with exciting ongoing developments often occurring outside the field of animal ecology, including in different disciplines such as engineering, physics or computer science. As such, the Journal of Animal Ecology issued an Open Call in 2018 for a Special Feature on ‘Biologging’, with the aim to showcase the novel developments in the field and the range of ecological questions which can now be addressed. The call resulted in the largest number of submitted manuscripts to any Special Feature in the Journal so far, which is a further indication of the interest in the topic. In this Editorial for the Special Feature, we discuss the papers and topics covered and conclude with a brief outlook on ongoing and future developments. This Special Feature comprises 18 contributions, of which 13 present novel analyses and approaches, three are reviews, one is a meta-analysis and one is a ‘How to’ paper. Overall, the papers cover a broad range of biologging technologies used to address a variety of fundamental questions in animal ecology, in aquatic, terrestrial and aerial species. Three papers use light-level geolocator tags—miniature light-weight tags which measure ambient light levels to determine sunrise and sunset times, and hence estimate the approximate location of the animal (Bridge et al., 2011; Wilson, Ducamp, Rees, Culik, & Niekamp, 1992)—to investigate the ontogeny of migratory behaviour in a long-lived seabird species (Campioni, Dias, Granadeiro, & Catry, 2020), quantify effects of biologgers on the survival of tagged birds (Brlík et al., 2020) and provide a practical guide for the effective application of geolocator tags to track animals (Lisovski et al., 2020). Seven papers use GPS loggers (for a review of GPS technology, see Tomkiewicz, Fuller, Kie, & Bates, 2010) often combined with other sensor technologies such as accelerometers (see Shepard et al., 2008 for a review of the technology) and/or complementary methods including stable isotopes (see Hobson & Wassenaar, 2008 for information about the method) and behavioural observations (see Altmann, 1974 about observational methods to study animal behaviour). These GPS-based papers investigate predator–prey spatiotemporal interactions among elk Cervus canadensis and wolf Canis lupus (Cusack et al., 2020), quantify foraging niche overlap between sympatric seabird species (Dehnhard et al., 2020), or assess effects of personality on the consistency and repeatability of foraging trips in black-legged kittiwakes Rissa tridactyla (Harris et al., 2020). Other contributions present novel statistical methods to estimate individual variation in habitat selection (Muff, Signer, & Fieberg, 2020) or to identify different movement modes in movement tracks (Patin, Etienne, Lebarbier, Chamaillé-Jammes, & Benhamou, 2020), whereas other studies use fine-scale movement data to quantify the impact of wind turbines on functional habitat loss of a soaring terrestrial bird, the black kite Milvus migrans (Marques et al., 2020), or identify mating tactics of male African elephants Loxodonta africana (Taylor et al., 2020). Seven papers primarily use other biologging sensors, alone or in combination with GPS tags, including inertial measurement unit sensors (see Baratchi et al., 2013 for information on the technology) such as accelerometers (Shepard et al., 2008) and magnetometers (see Williams et al., 2017 for information on magnetometers), or wet–dry and pressure and depth sensors (for a review see Ropert-Coudert et al., 2009), to markedly enhance the quantity of information on animal behaviour, individual state and performance that can be obtained from the tagged animals. In particular, Wilson et al. (2020) critically assesses the use of metrics derived from accelerometers as a proxy for movement-related metabolic energy expenditure, with Benoit et al. (2020) using such metrics to quantify the cost of dispersal in roe deer Capreolus capreolus, and Corbeau, Prudor, Kato, and Weimerskirch (2020) to quantify and compare average energy expenditure during different flight phases (soaring and flapping flight) in juvenile and adult great frigatebirds Fregata minor during their foraging trips, to study the ontogeny of flight and foraging behaviour. Bonnot et al. (2020) use activity sensors in roe deer to disentangle the contrasting effects of predator density and human disturbance on diel activity patterns, whereas Nuijten, Gerrits, Shamoun-Baranes and Nolet (2020) present a new data compression approach for accelerometer data to overcome limitations in storage and energy capacity of loggers and aid data transmission while preserving the behavioural signal in the data. Barkley et al. (2020) develop a novel multi-sensor biologging package, combined with a new statistical modelling approach, to detect and record sub-surface interactions among aquatic animals and ensuing movement-related behavioural responses, and apply it to Greenland sharks Somniosus microcephalus. More generally, Williams et al. (2020) review a large set of biologging sensors and address the question of how to select the most appropriate type or combination of devices for different biological questions. Finally, Joo et al. (2020) review an astonishing number of 58 different R packages which have become available in the last few years for analysing movement and biologging data, to act as a road map for ecologists and software developers. We now describe in more detail the questions and topics addressed by the papers of this Special Feature. We structure this section around the diverse research questions and themes addressed by these article—ranging from topics in Behavioural Ecology, Community Ecology, Statistical Ecology and Functional Ecology, to methodological approaches, with some papers linking multiple research fields. Understanding how behaviour arises is a key question in behavioural ecology. An adaptive behaviour can be informed by genetically controlled (innate) or learned components, but while some seem to be mostly programmed from birth, such as pecking in young domestic chicks (Dawkins, 1968), others, like the chaffinch song, have an innate basis but require the animal to practise and even learn from others (Thorpe, 1958). The scope for learnt behaviours may be particularly important in long-lived species, whose long lifespan increases the opportunity to practise and learn. In fact, the breeding deferral observed in many long-lived species is thought to be driven by high costs of early breeding (Lack, 1968), which could be caused by an incomplete set of skills (Daunt, Afanasyev, Adam, Croxall, & Wanless, 2007). Thanks to ever smaller loggers which can record an animal's behaviour for ever longer periods of time, biologging is now allowing researchers to study with unprecedented detail how behaviours develop in slow-maturing animals. In this Special Feature, two papers push the boundaries of this emerging field and highlight the potential of biologging to advance our understanding of the ontogeny of animal behaviour. Corbeau et al. (2020) demonstrate how juvenile great frigatebirds progressively improve their flight skills in the first few months following their first flight. Combining GPS and accelerometers to distinguish between different flight behaviours (e.g. flapping, gliding, soaring), they show that juveniles’ flight skills, initially inferior, improve gradually until becoming comparable to adults’. Interestingly, juveniles outperformed adults in some aspects, likely due to their morphology, and this may explain their remarkable months-long dispersive flights (Weimerskirch, Bishop, Jeanniard-du-Dot, Prudor, & Sachs, 2016). These findings provide one of the first insights into the development of flight in long-lived birds (Rotics et al., 2016; Yoda, Kohno, & Yasuhiko, 2004), and highlight the importance of early-life learning for the acquisition of physical skills. Campioni et al. (2020) focus on another behaviour whose ontogeny is poorly understood: migration. Some animals learn their migration routes by following older conspecifics (Mueller, O’Hara, Converse, Urbanek, & Fagan, 2013), while others follow an innate migratory distance and direction (Liedvogel, Åkesson, & Bensch, 2011). Campioni et al. (2020) provide the first robust evidence for a third mechanism by which long-lived animals may acquire a migratory strategy. In an impressive long-term study tracking the migration of Cory's shearwaters Calonectris borealis across ages, from immatures to established breeders, they show that young birds follow more exploratory routes, and as they aged they gradually advance their migration timings and shorten their migration route. These findings show that learning, memory and experience can play a key role in the development of migration behaviour in long-lived species, and provide support for the exploration-refinement hypothesis (Guilford et al., 2011) as another mechanism for the development of migration behaviour in long-lived animals (Fayet, accepted). Animal movements are fundamentally characterized by between movement modes et al., 2008) and many methods have been to identify and movement into different behavioural & Benhamou, & 2013; Signer, & 2016; et al., 2015; et al., 2017; & 2019; 2019), of and the between and movements are of importance et al. (2020) to this by the approach of to identify in of biologging data more any and into on in data This a to established but often methods (e.g. for across data. the that in some such can these more and application to and biologging data they demonstrate that their approach is and may be to many questions. An to using statistical methods to identify different movement modes is to the behaviour and state of tagged the movement with the observed behaviour or state from the a set of to distinguish different individual or behaviour modes from the of the movement and use these to identify in state or movement from tagged animals which been et al. (2020) a novel use of to identify different of behaviour in male African elephants Loxodonta africana as a of their The study that the activity and range of elephants with male and and as such an exceptional opportunity to reliably estimate metrics from movement The further discuss the for the and of as well as the opportunities of long-term biologging of for linking movement to While an of research has the impact of individual in behavioural called animal or behavioural et al., & 2004), on foraging behaviour, exploratory movements and other behaviours et al., & et al., & & Wilson & the important between animal personality and foraging has been in et al. (2020) GPS tagged breeding kittiwakes Rissa tridactyla across in and used a robust type of novel to measure the personality of the tagged to identify the foraging and the repeatability of foraging show that individual in can be driven by in individual with birds more foraging trips and a of during the This has important for studies on individual in foraging behaviour and movements, that in to and or personality such as will to be A key aim of movement ecology research is to quantify and selection by animals & & et al., 2015; & & individual movements to the of habitat selection and use & & and in habitat use between may be caused by in the individual state et al., or the et al., 2008). individual in behaviour is a key focus of ecological research et al., and for selection have as early as et al. and and occurring more for example, et al. and et al. solutions for these solutions for selection et al., or selection analyses & 2016). et al. (2020) this and present new statistical methods to estimate individual variation in habitat The approach from the between and have been as a which used in to a set of available and the more understanding that selection and are a & The on this between and and develop an approach on to estimate for and habitat selection for and using and approaches, and the approach using and This methodological advance a new for and habitat selection studies and researchers to estimate individual an unprecedented opportunity to questions of in the ecology of habitat and the habitat used by animals is critically important for questions. the for energy and the on will habitat tracking movement analyses and et al. (2020) showed that soaring black turbines during migration. a loss of to of habitat for these the highlight that the of wind turbines is and to that soaring A fundamental of the movement ecology is that the interactions between individual conditions and the and of the the structure and of movement et al., 2008). Thanks to the in biologging technology, there has been a in the movements and behaviour or survival of multiple from species. et al. (2020) use a large tracking GPS and wet–dry sensors, to investigate and niche overlap in three breeding and closely related species of stable to investigate GPS data and to identify foraging in the tracking data. a high of and overlap in foraging in and with niche and individual of foraging location or The study novel evidence that foraging may be in even from and a contrasting to niche by by other seabirds et al., 2019; & et al. (2020) combined movement and data from a predator–prey and in the investigate the question how use can a set of data, combined with a the three in the of of robust and obtained spatiotemporal show an of of to Bonnot et al. (2020) of predator on the use activity sensor and accelerometer data from GPS that to from the on of roe to in activity in to disturbance and predator by human and deer in by their in to human with the human and this is when with human the in roe deer activity a when to the of a how human may with predator–prey More generally, the is an of how technological in biologging may researchers to large a biologging Finally, biologging new opportunities to interactions for and species. Barkley et al. (2020) demonstrate this by and a novel multi-sensor biologging of a combined and a with a accelerometer and a a to the and on a the Greenland This is to an and statistical methods to estimate the of animal interactions on and of between tagged The use these sensors to assess behavioural in and depth during and following and they discuss how this may be and to many species, with exciting potential for future energy of animals are mostly by the of and obtained from & the and energy of the foraging behaviour and habitat use of animals et al., these research has the to overcome to data on and energy in animals et al., 2016). of the in the is the observations to estimate energy Wilson et al. (2020) this by critically the use of metrics derived from accelerometers as a proxy for movement-related metabolic energy biologging Benoit et al. (2020) to quantify energy expenditure, and distance as a proxy for to the costs of that or to that is a fundamental question in how we animal movement with for and mating & 2009). roe Benoit et al. (2020) that the of dispersal is markedly more that these energy costs become more in by and that these costs are primarily so many behavioural are between energy and energy biologging quantify and these Corbeau et al. (2020) GPS with of and to identify different flight behaviours (e.g. in great frigatebirds and quantify energy expenditure during This to compare the flapping and of soaring or between and to the hypothesis that juvenile birds have flight skills but that they learn how to improve their skills (see above in the Behavioural Ecology is a general that biologging has our understanding of and species & Wilson, 2005; Wilmers et al., It is that the of biologgers to animals & Wilson, Wilson & for including the location of the devices on the and their (e.g. and few studies the behavioural to (e.g. & 2019; et al., 2017; et al., Williams et al. (2020) discuss how many of these have been addressed In particular, the highlight the for more information on physical (e.g. to the and long-term effects for animals. potential this a from et al. (2020) that meta-analysis to review the to effects of geolocator on bird species. findings that the may to a potential on the survival of tagged Overall, are with their on the of and to to the application of biologging technologies to ecological research is the of the devices and of the of data they It is for a to of observations on a et al., which storage the during data collection and in data These are by to animal such that affect the animal the of data. are such covered in great detail in the ecological and a of this Special Feature is in and for et al. (2020) provide a practical on the effective use of and how data be and for This multiple online in an the of linking to the provide data and to of geolocator studies and data to data and between which may this of data strongly data for such to the data storage and transmission of biologgers methods to and the data and the data et al., & 2019), or the use of to the sensors to record data when the animals the behaviour of interest et al., et al. (2020) present a new data compression approach for accelerometer data to storage capacity and for the data while the data from tagged the compare the information from of accelerometer data and from in a in data and energy use while the in behaviour and The in use and storage can hence be used to or to the and a more of the of tagged the use of biologging sensors, a of the of the many different sensors Interestingly, in the to the on and & the the to researchers from contrasting development of ever more is among between some of is this more between and and with a it can be years the importance of is as important as ever to of the opportunities by the biologging as Williams et al. (2020) highlight in a review of the field in this Special Feature. the identify sensors, data and and it into an biologging to aid for ecologists to the use of biologging technologies for ecological questions. on the the address in detail the question of how to biological questions with the most appropriate type and combination of biologging sensors, as well as how to the and how to and biologging data, and conclude with an outlook of the most future developments for the use of Finally, as the and of movement and data has so has the number of statistical and methods to movement data, as well as the number of software packages for movement researchers are of the number and of software packages available for movement often the to select the most appropriate and software for the question addressed. Joo et al. (2020) provide the first of the field and review a 58 different packages available for analysing movement and biologging data in the R the first set a for the of tracking data, three key which they use to the software packages by the package, including the of the available the use to assess the between the and development of the and provide a road map for ecologists and software to the most appropriate for a research question and improve the of software the the movements and behaviour of most animal species cannot be using or technological smaller sensors, smaller novel and methods and their to their be to advance research in animal ecology and will be These could to insights into a range of research for of in (e.g. et al., 2011; et al., et al., of the and of species & a understanding of the terrestrial and aquatic species in and their (Cooke et al., and the effects of and in habitat foraging behaviour and acquisition in and their et al., ongoing developments the of GPS but the development of that smaller tracking devices et al., 2009; & 2019; & and biologgers with sensors to measure the impact of devices on tagged animals, and novel for and remote transmission of data et al., 2020). a of the and of movement ecology, combined with will be to of the and and of data now by biologging will be key to achieve future ecologists will to to the and to and the and in data and and and and statistical The papers in this Special Feature an exciting set of the and methodological which can be and an of how more may be with biologging We hence that by the and has the capacity to ecology as as and GPS years It is our that this Special Feature the many insights by the application of biologging to animal ecology and the of to for their

Topics & Concepts