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Valle D, Attias N, Cullen JA, Hooten MB, Giroux A, Oliveira-Santos LGR, Desbiez ALJ, Fletcher RJ. Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model. MOVEMENT ECOLOGY 2024; 12:19. [PMID: 38429836 PMCID: PMC10908110 DOI: 10.1186/s40462-024-00461-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND Understanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes. METHODS We propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil. RESULTS The time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats. CONCLUSIONS The proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.
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Affiliation(s)
- Denis Valle
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA.
| | - Nina Attias
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USA
- Instituto de Conservação de Animais Silvestres, Campo Grande, Mato Grosso do Sul, Brazil
| | - Joshua A Cullen
- Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA
| | - Mevin B Hooten
- Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USA
| | - Aline Giroux
- Ecology Department, Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil
| | | | - Arnaud L J Desbiez
- Instituto de Conservação de Animais Silvestres, Campo Grande, Mato Grosso do Sul, Brazil
- Royal Zoological Society of Scotland, Murrayfield, Edinburgh, UK
- Instituto de Pesquisas Ecologicas, Nazare Paulista, Sao Paulo, Brazil
| | - Robert J Fletcher
- Department of Wildlife Ecology and Conservation, University of Florida, P.O. Box 110410, Gainesville, FL, USA
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2
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Pekarsky S, Shohami D, Horvitz N, Bowie RCK, Kamath PL, Markin Y, Getz WM, Nathan R. Cranes soar on thermal updrafts behind cold fronts as they migrate across the sea. Proc Biol Sci 2024; 291:20231243. [PMID: 38229520 DOI: 10.1098/rspb.2023.1243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 12/11/2023] [Indexed: 01/18/2024] Open
Abstract
Thermal soaring conditions above the sea have long been assumed absent or too weak for terrestrial migrating birds, forcing obligate soarers to take long detours and avoid sea-crossing, and facultative soarers to cross exclusively by costly flapping flight. Thus, while atmospheric convection does develop at sea and is used by some seabirds, it has been largely ignored in avian migration research. Here, we provide direct evidence for routine thermal soaring over open sea in the common crane, the heaviest facultative soarer known among terrestrial migrating birds. Using high-resolution biologging from 44 cranes tracked across their transcontinental migration over 4 years, we show that soaring performance was no different over sea than over land in mid-latitudes. Sea-soaring occurred predominantly in autumn when large water-air temperature difference followed mid-latitude cyclones. Our findings challenge a fundamental migration research paradigm and suggest that obligate soarers avoid sea-crossing not due to the absence or weakness of thermals but due to their low frequency, for which they cannot compensate with prolonged flapping. Conversely, facultative soarers other than cranes should also be able to use thermals over the sea. Marine cold air outbreaks, imperative to global energy budget and climate, may also be important for bird migration.
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Affiliation(s)
- Sasha Pekarsky
- Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - David Shohami
- Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Nir Horvitz
- Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Rauri C K Bowie
- Museum of Vertebrate Zoology, University of California, Berkeley, CA 94720, USA
- Department of Integrative Biology, University of California, Berkeley, CA 94720-3114, USA
| | - Pauline L Kamath
- School of Food and Agriculture, University of Maine, Orono, ME 04469, USA
| | - Yuri Markin
- Oksky State Reserve, pos. Brykin Bor, Spassky raion, Ryazanskaya oblast 391072, Russia
| | - Wayne M Getz
- School Mathematical Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Ran Nathan
- Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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English HM, Börger L, Kane A, Ciuti S. Advances in biologging can identify nuanced energetic costs and gains in predators. MOVEMENT ECOLOGY 2024; 12:7. [PMID: 38254232 PMCID: PMC10802026 DOI: 10.1186/s40462-024-00448-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
Foraging is a key driver of animal movement patterns, with specific challenges for predators which must search for mobile prey. These patterns are increasingly impacted by global changes, principally in land use and climate. Understanding the degree of flexibility in predator foraging and social strategies is pertinent to wildlife conservation under global change, including potential top-down effects on wider ecosystems. Here we propose key future research directions to better understand foraging strategies and social flexibility in predators. In particular, rapid continued advances in biologging technology are helping to record and understand dynamic behavioural and movement responses of animals to environmental changes, and their energetic consequences. Data collection can be optimised by calibrating behavioural interpretation methods in captive settings and strategic tagging decisions within and between social groups. Importantly, many species' social systems are increasingly being found to be more flexible than originally described in the literature, which may be more readily detectable through biologging approaches than behavioural observation. Integrating the effects of the physical landscape and biotic interactions will be key to explaining and predicting animal movements and energetic balance in a changing world.
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Affiliation(s)
- Holly M English
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland.
| | - Luca Börger
- Department of Biosciences, Swansea University, Swansea, UK
| | - Adam Kane
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland
| | - Simone Ciuti
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland
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4
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Angelakis N, Goldsworthy SD, Connell SD, Durante LM. A novel method for identifying fine-scale bottom-use in a benthic-foraging pinniped. MOVEMENT ECOLOGY 2023; 11:34. [PMID: 37296462 PMCID: PMC10257308 DOI: 10.1186/s40462-023-00386-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/16/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND For diving, marine predators, accelerometer and magnetometer data provides critical information on sub-surface foraging behaviours that cannot be identified from location or time-depth data. By measuring head movement and body orientation, accelerometers and magnetometers can help identify broad shifts in foraging movements, fine-scale habitat use and energy expenditure of terrestrial and marine species. Here, we use accelerometer and magnetometer data from tagged Australian sea lions and provide a new method to identify key benthic foraging areas. As Australian sea lions are listed as endangered by the IUCN and Australian legislation, identifying key areas for the species is vital to support targeted management of populations. METHODS Firstly, tri-axial magnetometer and accelerometer data from adult female Australian sea lions is used in conjunction with GPS and dive data to dead-reckon their three-dimensional foraging paths. We then isolate all benthic phases from their foraging trips and calculate a range of dive metrics to characterise their bottom usage. Finally, k-means cluster analysis is used to identify core benthic areas utilised by sea lions. Backwards stepwise regressions are then iteratively performed to identify the most parsimonious model for describing bottom usage and its included predictor variables. RESULTS Our results show distinct spatial partitioning in benthic habitat-use by Australian sea lions. This method has also identified individual differences in benthic habitat-use. Here, the application of high-resolution magnetometer/accelerometer data has helped reveal the tortuous foraging movements Australian sea lions use to exploit key benthic marine habitats and features. CONCLUSIONS This study has illustrated how magnetometer and accelerometer data can provide a fine-scale description of the underwater movement of diving species, beyond GPS and depth data alone, For endangered species like Australian sea lions, management of populations must be spatially targeted. Here, this method demonstrates a fine-scale analysis of benthic habitat-use which can help identify key areas for both marine and terrestrial species. Future integration of this method with concurrent habitat and prey data would further augment its power as a tool for understanding the foraging behaviours of species.
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Affiliation(s)
- Nathan Angelakis
- University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
| | - Simon D Goldsworthy
- University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia
- South Australian Research and Development Institute (SARDI) (Aquatic Sciences), 2 Hamra Avenue, West Beach, SA, 5024, Australia
| | - Sean D Connell
- University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia
| | - Leonardo M Durante
- South Australian Research and Development Institute (SARDI) (Aquatic Sciences), 2 Hamra Avenue, West Beach, SA, 5024, Australia
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5
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Redpath SHA, Marks NJ, Menzies FD, O'Hagan MJH, Wilson RP, Smith S, Magowan EA, McClune DW, Collins SF, McCormick CM, Scantlebury DM. Impact of test, vaccinate or remove protocol on home ranges and nightly movements of badgers a medium density population. Sci Rep 2023; 13:2592. [PMID: 36788237 PMCID: PMC9929337 DOI: 10.1038/s41598-023-28620-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/20/2023] [Indexed: 02/16/2023] Open
Abstract
In the British Isles, the European badger (Meles meles) is thought to be the primary wildlife reservoir of bovine tuberculosis (bTB), an endemic disease in cattle. Test, vaccinate or remove ('TVR') of bTB test-positive badgers, has been suggested to be a potentially useful protocol to reduce bTB incidence in cattle. However, the practice of removing or culling badgers is controversial both for ethical reasons and because there is no consistent observed effect on bTB levels in cattle. While removing badgers reduces population density, it may also result in disruption of their social behaviour, increase their ranging, and lead to greater intra- and inter-species bTB transmission. This effect has been recorded in high badger density areas, such as in southwest England. However, little is known about how TVR affects the behaviour and movement of badgers within a medium density population, such as those that occur in Northern Ireland (NI), which the current study aimed to examine. During 2014-2017, badger ranging behaviours were examined prior to and during a TVR protocol in NI. Nightly distances travelled by 38 individuals were determined using Global Positioning System (GPS) measurements of animal tracks and GPS-enhanced dead-reckoned tracks. The latter was calculated using GPS, tri-axial accelerometer and tri-axial magnetometer data loggers attached to animals. Home range and core home range size were measured using 95% and 50% autocorrelated kernel density estimates, respectively, based on location fixes. TVR was not associated with measured increases in either distances travelled per night (mean = 3.31 ± 2.64 km) or home range size (95% mean = 1.56 ± 0.62 km2, 50% mean = 0.39 ± 0.62 km2) over the four years of study. However, following trapping, mean distances travelled per night increased by up to 44% eight days post capture. Findings differ from those observed in higher density badger populations in England, in which badger ranging increased following culling. Whilst we did not assess behaviours of individual badgers, possible reasons why no differences in home range size were observed include higher inherent 'social fluidity' in Irish populations whereby movements are less restricted by habitat saturation and/or that the numbers removed did not reach a threshold that might induce increases in ranging behaviour. Nevertheless, short-term behavioural disruption from trapping was observed, which led to significant increases in the movements of individual animals within their home range. Whether or not TVR may alter badger behaviours remains to be seen, but it would be better to utilise solutions such as oral vaccination of badgers and/or cattle as well as increased biosecurity to limit bTB transmission, which may be less likely to cause interference and thereby reduce the likelihood of bTB transmission.
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Affiliation(s)
- Sophie H A Redpath
- School of Biological Sciences, Queens' University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland
- Veterinary Sciences Division, Agri-Food and Biosciences Institute, Belfast, BT4 3SD, Northern Ireland
| | - Nikki J Marks
- School of Biological Sciences, Queens' University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland
| | - Fraser D Menzies
- Department of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, BT4 3SB, Northern Ireland
| | - Maria J H O'Hagan
- Department of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, BT4 3SB, Northern Ireland
| | - Rory P Wilson
- Department of Biological Sciences, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales
| | - Sinéad Smith
- School of Biological Sciences, Queens' University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland
| | - Elizabeth A Magowan
- School of Biological Sciences, Queens' University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland
| | - David W McClune
- School of Biological Sciences, Queens' University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland
| | - Shane F Collins
- Department of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, BT4 3SB, Northern Ireland
| | - Carl M McCormick
- Veterinary Sciences Division, Agri-Food and Biosciences Institute, Belfast, BT4 3SD, Northern Ireland
| | - D Michael Scantlebury
- School of Biological Sciences, Queens' University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland.
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6
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Wijers M, Trethowan P, du Preez B, Loveridge AJ, Markham A, Macdonald DW, Montgomery RA. Something in the wind: the influence of wind speed and direction on African lion movement behavior. Behav Ecol 2022. [DOI: 10.1093/beheco/arac087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Olfaction is a key sense, enabling animals to locate forage, select mates, navigate their environment, and avoid predation. Wind is an important abiotic factor that modulates the strength of olfactory information detected by animals. In theory, when airflow is unidirectional, an animal can increase odor detection probability and maximize the amount of olfactory information gained by moving crosswind. Given energetic costs inherent to activity and locomotion, behavioral search strategies that optimize the benefit-cost ratio should be advantageous. We tested whether African lions (Panthera leo) modify their movement directionality and distance according to wind speed and direction during hours of darkness when they are most active. We tracked 29 lions in southern Zimbabwe using GPS collars and deployed a weather station to collect detailed abiotic data. We found that when wind speeds increased lions were more likely to move crosswind. We also found that female lions, which tend to hunt more often than males, traveled farther when wind speeds were stronger. The results of our analysis suggest that lions adjust their movement behavior according to wind speed and direction. We inferred that this was a behavioral decision to maximize the amount of olfactory information gained per unit of energy spent. Our findings not only offer one of the first detailed insights on large carnivore anemotaxis (movement direction relative to wind) but also make an important contribution towards understanding the influence of wind on predator ecology in general which remains understudied to date.
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Affiliation(s)
- Matthew Wijers
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Recanati-Kaplan Centre , Abingdon Road, Tubney , United Kingdom
| | - Paul Trethowan
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Recanati-Kaplan Centre , Abingdon Road, Tubney , United Kingdom
| | - Byron du Preez
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Recanati-Kaplan Centre , Abingdon Road, Tubney , United Kingdom
| | - Andrew J Loveridge
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Recanati-Kaplan Centre , Abingdon Road, Tubney , United Kingdom
| | - Andrew Markham
- Department of Computer Science, University of Oxford , Parks Road, Oxford , United Kingdom
| | - David W Macdonald
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Recanati-Kaplan Centre , Abingdon Road, Tubney , United Kingdom
| | - Robert A Montgomery
- Wildlife Conservation Research Unit, Department of Biology, University of Oxford, Recanati-Kaplan Centre , Abingdon Road, Tubney , United Kingdom
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7
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Harel R, Alavi S, Ashbury AM, Aurisano J, Berger-Wolf T, Davis GH, Hirsch BT, Kalbitzer U, Kays R, Mclean K, Núñez CL, Vining A, Walton Z, Havmøller RW, Crofoot MC. Life in 2.5D: Animal Movement in the Trees. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.801850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The complex, interconnected, and non-contiguous nature of canopy environments present unique cognitive, locomotor, and sensory challenges to their animal inhabitants. Animal movement through forest canopies is constrained; unlike most aquatic or aerial habitats, the three-dimensional space of a forest canopy is not fully realized or available to the animals within it. Determining how the unique constraints of arboreal habitats shape the ecology and evolution of canopy-dwelling animals is key to fully understanding forest ecosystems. With emerging technologies, there is now the opportunity to quantify and map tree connectivity, and to embed the fine-scale horizontal and vertical position of moving animals into these networks of branching pathways. Integrating detailed multi-dimensional habitat structure and animal movement data will enable us to see the world from the perspective of an arboreal animal. This synthesis will shed light on fundamental aspects of arboreal animals’ cognition and ecology, including how they navigate landscapes of risk and reward and weigh energetic trade-offs, as well as how their environment shapes their spatial cognition and their social dynamics.
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8
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Magowan EA, Maguire IE, Smith S, Redpath S, Marks NJ, Wilson RP, Menzies F, O’Hagan M, Scantlebury DM. Dead-reckoning elucidates fine-scale habitat use by European badgers Meles meles. ANIMAL BIOTELEMETRY 2022; 10:10. [PMID: 37521810 PMCID: PMC8908954 DOI: 10.1186/s40317-022-00282-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/23/2022] [Indexed: 05/03/2023]
Abstract
Background Recent developments in both hardware and software of animal-borne data loggers now enable large amounts of data to be collected on both animal movement and behaviour. In particular, the combined use of tri-axial accelerometers, tri-axial magnetometers and GPS loggers enables animal tracks to be elucidated using a procedure of 'dead-reckoning'. Although this approach was first suggested 30 years ago by Wilson et al. (1991), surprisingly few measurements have been made in free-ranging terrestrial animals. The current study examines movements, interactions with habitat features, and home-ranges calculated from just GPS data and also from dead-reckoned data in a model terrestrial mammal, the European badger (Meles meles). Methods Research was undertaken in farmland in Northern Ireland. Two badgers (one male, one female) were live-trapped and fitted with a GPS logger, a tri-axial accelerometer, and a tri-axial magnetometer. Thereafter, the badgers' movement paths over 2 weeks were elucidated using just GPS data and GPS-enabled dead-reckoned data, respectively. Results Badgers travelled further using data from dead-reckoned calculations than using the data from only GPS data. Whilst once-hourly GPS data could only be represented by straight-line movements between sequential points, the sub-second resolution dead-reckoned tracks were more tortuous. Although there were no differences in Minimum Convex Polygon determinations between GPS- and dead-reckoned data, Kernel Utilisation Distribution determinations of home-range size were larger using the former method. This was because dead-reckoned data more accurately described the particular parts of landscape constituting most-visited core areas, effectively narrowing the calculation of habitat use. Finally, the dead-reckoned data showed badgers spent more time near to field margins and hedges than simple GPS data would suggest. Conclusion Significant differences emerge when analyses of habitat use and movements are compared between calculations made using just GPS data or GPS-enabled dead-reckoned data. In particular, use of dead-reckoned data showed that animals moved 2.2 times farther, had better-defined use of the habitat (revealing clear core areas), and made more use of certain habitats (field margins, hedges). Use of dead-reckoning to provide detailed accounts of animal movement and highlight the minutiae of interactions with the environment should be considered an important technique in the ecologist's toolkit.
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Affiliation(s)
- E. A. Magowan
- School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK
- Randox Laboratories Ltd. Crumlin, Antrim, Northern Ireland UK
| | - I. E. Maguire
- School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK
- Randox Laboratories Ltd. Crumlin, Antrim, Northern Ireland UK
| | - S. Smith
- School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK
| | - S. Redpath
- School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK
| | - N. J. Marks
- School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK
| | - R. P. Wilson
- Department of Biological Sciences, Institute of Environmental Sustainability, Swansea University, Swansea, UK
| | - F. Menzies
- Department of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, UK
| | - M. O’Hagan
- Department of Agriculture, Environment and Rural Affairs, Veterinary Epidemiology Unit, Belfast, UK
| | - D. M. Scantlebury
- School of Biological Sciences, Queen’s University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL Northern Ireland UK
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9
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Santini G, Abolaffio M, Ossi F, Franzetti B, Cagnacci F, Focardi S. Population assessment without individual identification using camera-traps: a comparison of four methods. Basic Appl Ecol 2022. [DOI: 10.1016/j.baae.2022.03.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Nathan R, Monk CT, Arlinghaus R, Adam T, Alós J, Assaf M, Baktoft H, Beardsworth CE, Bertram MG, Bijleveld AI, Brodin T, Brooks JL, Campos-Candela A, Cooke SJ, Gjelland KØ, Gupte PR, Harel R, Hellström G, Jeltsch F, Killen SS, Klefoth T, Langrock R, Lennox RJ, Lourie E, Madden JR, Orchan Y, Pauwels IS, Říha M, Roeleke M, Schlägel UE, Shohami D, Signer J, Toledo S, Vilk O, Westrelin S, Whiteside MA, Jarić I. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 2022; 375:eabg1780. [PMID: 35175823 DOI: 10.1126/science.abg1780] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.
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Affiliation(s)
- Ran Nathan
- Movement Ecology Lab, A. Silberman Institute of Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel.,Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Christopher T Monk
- Institute of Marine Research, His, Norway.,Centre for Coastal Research (CCR), Department of Natural Sciences, University of Agder, Kristiansand, Norway.,Department of Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Robert Arlinghaus
- Department of Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.,Division of Integrative Fisheries Management, Faculty of Life Sciences and Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin, Germany
| | - Timo Adam
- Centre for Research into Ecological and Environmental Modelling, School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
| | - Josep Alós
- Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), Esporles, Spain
| | - Michael Assaf
- Racah Institute of Physics, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Henrik Baktoft
- National Institute of Aquatic Resources, Section for Freshwater Fisheries and Ecology, Technical University of Denmark, Silkeborg, Denmark
| | - Christine E Beardsworth
- NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Den Burg, The Netherlands.,Centre for Research in Animal Behaviour, Psychology, University of Exeter, Exeter, UK
| | - Michael G Bertram
- Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Allert I Bijleveld
- NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Den Burg, The Netherlands
| | - Tomas Brodin
- Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Jill L Brooks
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, ON, Canada
| | - Andrea Campos-Candela
- Department of Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.,Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), Esporles, Spain
| | - Steven J Cooke
- Fish Ecology and Conservation Physiology Laboratory, Department of Biology, Carleton University, Ottawa, ON, Canada
| | | | - Pratik R Gupte
- NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Den Burg, The Netherlands.,Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - Roi Harel
- Movement Ecology Lab, A. Silberman Institute of Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel.,Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Gustav Hellström
- Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Florian Jeltsch
- Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.,Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
| | - Shaun S Killen
- Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Glasgow UK
| | - Thomas Klefoth
- Ecology and Conservation, Faculty of Nature and Engineering, Hochschule Bremen, City University of Applied Sciences, Bremen, Germany
| | - Roland Langrock
- Department of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Robert J Lennox
- NORCE Norwegian Research Centre, Laboratory for Freshwater Ecology and Inland Fisheries, Bergen, Norway
| | - Emmanuel Lourie
- Movement Ecology Lab, A. Silberman Institute of Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel.,Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joah R Madden
- Centre for Research in Animal Behaviour, Psychology, University of Exeter, Exeter, UK
| | - Yotam Orchan
- Movement Ecology Lab, A. Silberman Institute of Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel.,Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ine S Pauwels
- Research Institute for Nature and Forest (INBO), Brussels, Belgium
| | - Milan Říha
- Biology Centre of the Czech Academy of Sciences, Institute of Hydrobiology, České Budějovice, Czech Republic
| | - Manuel Roeleke
- Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Ulrike E Schlägel
- Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - David Shohami
- Movement Ecology Lab, A. Silberman Institute of Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel.,Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Johannes Signer
- Wildlife Sciences, Faculty of Forest Sciences and Forest Ecology, University of Goettingen, Göttingen, Germany
| | - Sivan Toledo
- Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel.,Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Ohad Vilk
- Movement Ecology Lab, A. Silberman Institute of Life Sciences, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel.,Minerva Center for Movement Ecology, The Hebrew University of Jerusalem, Jerusalem, Israel.,Racah Institute of Physics, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Samuel Westrelin
- INRAE, Aix Marseille Univ, Pôle R&D ECLA, RECOVER, Aix-en-Provence, France
| | - Mark A Whiteside
- Centre for Research in Animal Behaviour, Psychology, University of Exeter, Exeter, UK.,School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth, UK
| | - Ivan Jarić
- Biology Centre of the Czech Academy of Sciences, Institute of Hydrobiology, České Budějovice, Czech Republic.,University of South Bohemia, Faculty of Science, Department of Ecosystem Biology, České Budějovice, Czech Republic
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11
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McGowan NE, Marks NJ, Maule AG, Schmidt-Küntzel A, Marker LL, Scantlebury DM. Categorising cheetah behaviour using tri-axial accelerometer data loggers: a comparison of model resolution and data logger performance. MOVEMENT ECOLOGY 2022; 10:7. [PMID: 35123592 PMCID: PMC8818224 DOI: 10.1186/s40462-022-00305-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Extinction is one of the greatest threats to the living world, endangering organisms globally, advancing conservation to the forefront of species research. To maximise the efficacy of conservation efforts, understanding the ecological, physiological, and behavioural requirements of vulnerable species is vital. Technological advances, particularly in remote sensing, enable researchers to continuously monitor movement and behaviours of multiple individuals simultaneously with minimal human intervention. Cheetahs, Acinonyx jubatus, constitute a "vulnerable" species for which only coarse behaviours have been elucidated. The aims of this study were to use animal-attached accelerometers to (1) determine fine-scale behaviours in cheetahs, (2) compare the performances of different devices in behaviour categorisation, and (3) provide a behavioural categorisation framework. METHODS Two different accelerometer devices (CEFAS, frequency: 30 Hz, maximum capacity: ~ 2 g; GCDC, frequency: 50 Hz, maximum capacity: ~ 8 g) were mounted onto collars, fitted to five individual captive cheetahs. The cheetahs chased a lure around a track, during which time their behaviours were videoed. Accelerometer data were temporally aligned with corresponding video footage and labelled with one of 17 behaviours. Six separate random forest models were run (three per device type) to determine the categorisation accuracy for behaviours at a fine, medium, and coarse resolution. RESULTS Fine- and medium-scale models had an overall categorisation accuracy of 83-86% and 84-88% respectively. Non-locomotory behaviours were best categorised on both loggers with GCDC outperforming CEFAS devices overall. On a coarse scale, both devices performed well when categorising activity (86.9% (CEFAS) vs. 89.3% (GCDC) accuracy) and inactivity (95.5% (CEFAS) vs. 95.0% (GCDC) accuracy). This study defined cheetah behaviour beyond three categories and accurately determined stalking behaviours by remote sensing. We also show that device specification and configuration may affect categorisation accuracy, so we recommend deploying several different loggers simultaneously on the same individual. CONCLUSION The results of this study will be useful in determining wild cheetah behaviour. The methods used here allowed broad-scale (active/inactive) as well as fine-scale (e.g. stalking) behaviours to be categorised remotely. These findings and methodological approaches will be useful in monitoring the behaviour of wild cheetahs and other species of conservation interest.
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Affiliation(s)
- Natasha E McGowan
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
| | - Nikki J Marks
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
| | - Aaron G Maule
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK
| | | | - Laurie L Marker
- Cheetah Conservation Fund, PO Box 1755, Otjiwarongo, Namibia
| | - David M Scantlebury
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, UK.
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12
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Wild TA, Wikelski M, Tyndel S, Alarcón‐Nieto G, Klump BC, Aplin LM, Meboldt M, Williams HJ. Internet on animals: Wi‐Fi‐enabled devices provide a solution for big data transmission in biologging. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13798] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
- Timm A. Wild
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Department of Biology University of Konstanz Konstanz Germany
- Product Development Group Zurich (pd z) ETH Zürich Zürich Switzerland
| | - Martin Wikelski
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Department of Biology University of Konstanz Konstanz Germany
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany
| | - Stephen Tyndel
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Gustavo Alarcón‐Nieto
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Barbara C. Klump
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Lucy M. Aplin
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Konstanz Germany
- Cognitive and Cultural Ecology Research Group Max Planck Institute of Animal Behavior Radolfzell Germany
| | - Mirko Meboldt
- Product Development Group Zurich (pd z) ETH Zürich Zürich Switzerland
| | - Hannah J. Williams
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Department of Biology University of Konstanz Konstanz Germany
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13
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Gunner RM, Wilson RP, Holton MD, Hopkins P, Bell SH, Marks NJ, Bennett NC, Ferreira S, Govender D, Viljoen P, Bruns A, van Schalkwyk OL, Bertelsen MF, Duarte CM, van Rooyen MC, Tambling CJ, Göppert A, Diesel D, Scantlebury DM. Decision rules for determining terrestrial movement and the consequences for filtering high-resolution global positioning system tracks: a case study using the African lion ( Panthera leo). J R Soc Interface 2022; 19:20210692. [PMID: 35042386 PMCID: PMC8767188 DOI: 10.1098/rsif.2021.0692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/08/2021] [Indexed: 01/18/2023] Open
Abstract
The combined use of global positioning system (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequencies. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy being affected by signal reception. We hypothesized that animal behaviour was the main factor affecting fix inaccuracy, with inherent GPS positional noise (jitter) being most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A movement-verified filtering (MVF) protocol was constructed to compare GPS-derived speed data with dynamic body acceleration, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions (Panthera leo) fitted with collar-mounted GPS units and tri-axial motion sensors recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by greater than 80% prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high-resolution GPS datasets and demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement.
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Affiliation(s)
- Richard M. Gunner
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
- Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany
| | - Rory P. Wilson
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
| | - Mark D. Holton
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
| | - Phil Hopkins
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
| | - Stephen H. Bell
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - Nikki J. Marks
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - Nigel C. Bennett
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria 002, South Africa
| | - Sam Ferreira
- Savanna and Grassland Research Unit, South African National Parks, Scientific Services Skukuza, Kruger National Park, Skukuza 1350, South Africa
| | - Danny Govender
- Savanna and Grassland Research Unit, South African National Parks, Scientific Services Skukuza, Kruger National Park, Skukuza 1350, South Africa
| | - Pauli Viljoen
- Savanna and Grassland Research Unit, South African National Parks, Scientific Services Skukuza, Kruger National Park, Skukuza 1350, South Africa
| | - Angela Bruns
- Veterinary Wildlife Services, South African National Parks, 97 Memorial Road, Old Testing Grounds, 8301 Kimberley, South Africa
| | - O. Louis van Schalkwyk
- Department of Agriculture, Forestry and Fisheries, Government of South Africa, Skukuza, South Africa
- Department of Migration, Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany
| | - Mads F. Bertelsen
- Center for Zoo and Wild Animal Health, Copenhagen Zoo, Roskildevej 38, 2000 Frederiksberg, Denmark
| | - Carlos M. Duarte
- Red Sea Research Centre, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Martin C. van Rooyen
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria 002, South Africa
| | - Craig J. Tambling
- Department of Zoology and Entomology, University of Fort Hare Alice Campus, Ring Road, Alice 5700, South Africa
| | - Aoife Göppert
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - Delmar Diesel
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - D. Michael Scantlebury
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
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14
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Abstract
We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.
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15
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Gunner RM, Holton MD, Scantlebury DM, Hopkins P, Shepard ELC, Fell AJ, Garde B, Quintana F, Gómez-Laich A, Yoda K, Yamamoto T, English H, Ferreira S, Govender D, Viljoen P, Bruns A, van Schalkwyk OL, Cole NC, Tatayah V, Börger L, Redcliffe J, Bell SH, Marks NJ, Bennett NC, Tonini MH, Williams HJ, Duarte CM, van Rooyen MC, Bertelsen MF, Tambling CJ, Wilson RP. How often should dead-reckoned animal movement paths be corrected for drift? ANIMAL BIOTELEMETRY 2021; 9:43. [PMID: 34900262 PMCID: PMC7612089 DOI: 10.1186/s40317-021-00265-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/25/2021] [Indexed: 05/19/2023]
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.
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Affiliation(s)
- Richard M. Gunner
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
| | - Mark D. Holton
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
| | - David M. Scantlebury
- School of Biological Sciences, Queen’s University Belfast, Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, Northern Ireland, UK
| | - Phil Hopkins
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
| | - Emily L. C. Shepard
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
| | - Adam J. Fell
- Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, Scotland, UK
| | - Baptiste Garde
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
| | - Flavio Quintana
- Instituto de Biología de Organismos Marinos (IBIOMAR), CONICET. Boulevard Brown, 2915, U9120ACD Puerto Madryn, Chubut, Argentina
| | - Agustina Gómez-Laich
- Departamento de Ecología, Genética y Evolución & Instituto de Ecología, Genética Y Evolución de Buenos Aires (IEGEBA), CONICET, Pabellón II Ciudad Universitaria, C1428EGA Buenos Aires, Argentina
| | - Ken Yoda
- Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Takashi Yamamoto
- Organization for the Strategic Coordination of Research and Intellectual Properties, Meiji University, Nakano, Tokyo, Japan
| | - Holly English
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland
| | - Sam Ferreira
- Savanna and Grassland Research Unit, Scientific Services Skukuza, South African National Parks, Kruger National Park, Skukuza 1350, South Africa
| | - Danny Govender
- Savanna and Grassland Research Unit, Scientific Services Skukuza, South African National Parks, Kruger National Park, Skukuza 1350, South Africa
| | - Pauli Viljoen
- Savanna and Grassland Research Unit, Scientific Services Skukuza, South African National Parks, Kruger National Park, Skukuza 1350, South Africa
| | - Angela Bruns
- Veterinary Wildlife Services, South African National Parks, 97 Memorial Road, Old Testing Grounds, Kimberley 8301, South Africa
| | - O. Louis van Schalkwyk
- Department of Agriculture, Government of South Africa, Land Reform and Rural Development, Pretoria 001, South Africa
- Department of Migration, Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany
- Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Onderstepoort 0110, South Africa
| | - Nik C. Cole
- Durrell Wildlife Conservation Trust, Les Augrès Manor, Channel Islands, Trinity JE3 5BP, Jersey, UK
- Mauritian Wildlife Foundation, Grannum Road, Indian Ocean, Vacoas, Mauritius
| | - Vikash Tatayah
- Mauritian Wildlife Foundation, Grannum Road, Indian Ocean, Vacoas, Mauritius
| | - Luca Börger
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
- Centre for Biomathematics, Swansea University, Swansea SA2 8PP, UK
| | - James Redcliffe
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
| | - Stephen H. Bell
- School of Biological Sciences, Queen’s University Belfast, Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, Northern Ireland, UK
| | - Nikki J. Marks
- School of Biological Sciences, Queen’s University Belfast, Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, Northern Ireland, UK
| | - Nigel C. Bennett
- Mammal Research Institute. Department of Zoology and Entomology, University of Pretoria, Pretoria 002., South Africa
| | - Mariano H. Tonini
- Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales, Grupo GEA, IPATEC-UNCO-CONICET, San Carlos de Bariloche, Río Negro, Argentina
| | - Hannah J. Williams
- Department of Migration, Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany
| | - Carlos M. Duarte
- Red Sea Research Centre, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Martin C. van Rooyen
- Mammal Research Institute. Department of Zoology and Entomology, University of Pretoria, Pretoria 002., South Africa
| | - Mads F. Bertelsen
- Center for Zoo and Wild Animal Health, Copenhagen Zoo, Roskildevej 38, DK-2000 Frederiksberg, Denmark
| | - Craig J. Tambling
- Department of Zoology and Entomology, University of Fort Hare, Alice Campus, Ring Road, Alice 5700, South Africa
| | - Rory P. Wilson
- Swansea Lab for Animal Movement, Department of Biosciences, Swansea University, Singleton Park, Swansea SA2 8PP, Wales, UK
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16
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Kashetsky T, Avgar T, Dukas R. The Cognitive Ecology of Animal Movement: Evidence From Birds and Mammals. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.724887] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Cognition, defined as the processes concerned with the acquisition, retention and use of information, underlies animals’ abilities to navigate their local surroundings, embark on long-distance seasonal migrations, and socially learn information relevant to movement. Hence, in order to fully understand and predict animal movement, researchers must know the cognitive mechanisms that generate such movement. Work on a few model systems indicates that most animals possess excellent spatial learning and memory abilities, meaning that they can acquire and later recall information about distances and directions among relevant objects. Similarly, field work on several species has revealed some of the mechanisms that enable them to navigate over distances of up to several thousand kilometers. Key behaviors related to movement such as the choice of nest location, home range location and migration route are often affected by parents and other conspecifics. In some species, such social influence leads to the formation of aggregations, which in turn may lead to further social learning about food locations or other resources. Throughout the review, we note a variety of topics at the interface of cognition and movement that invite further investigation. These include the use of social information embedded in trails, the likely important roles of soundscapes and smellscapes, the mechanisms that large mammals rely on for long-distance migration, and the effects of expertise acquired over extended periods.
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17
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Papastamatiou YP, Iosilevskii G, Di Santo V, Huveneers C, Hattab T, Planes S, Ballesta L, Mourier J. Sharks surf the slope: Current updrafts reduce energy expenditure for aggregating marine predators. J Anim Ecol 2021; 90:2302-2314. [DOI: 10.1111/1365-2656.13536] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 05/14/2021] [Indexed: 12/01/2022]
Affiliation(s)
- Yannis P. Papastamatiou
- Institute of the Environment Department of Biological Sciences Florida International University North Miami FL USA
| | | | - Valentina Di Santo
- Division of Functional Morphology Department of Zoology Stockholm University Stockholm Sweden
| | - Charlie Huveneers
- College of Science and Engineering Flinders University Bedford Park South Australia Australia
| | - Tarek Hattab
- MARBECUniv MontpellierCNRSIFREMERIRD Sète France
| | - Serge Planes
- PSL Research UniversityEPHE‐UPVD‐CNRSUSR 3278 CRIOBE Perpignan France
- Laboratoire d'Excellence “CORAIL” USR 3278 CNRS‐EPHE‐UPVD CRIOBE Perpignan France
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18
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Ellis-Soto D, Ferraro KM, Rizzuto M, Briggs E, Monk JD, Schmitz OJ. A methodological roadmap to quantify animal-vectored spatial ecosystem subsidies. J Anim Ecol 2021; 90:1605-1622. [PMID: 34014558 DOI: 10.1111/1365-2656.13538] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/04/2021] [Indexed: 12/31/2022]
Abstract
Energy, nutrients and organisms move over landscapes, connecting ecosystems across space and time. Meta-ecosystem theory investigates the emerging properties of local ecosystems coupled spatially by these movements of organisms and matter, by explicitly tracking exchanges of multiple substances across ecosystem borders. To date, meta-ecosystem research has focused mostly on abiotic flows-neglecting biotic nutrient flows. However, recent work has indicated animals act as spatial nutrient vectors when they transport nutrients across landscapes in the form of excreta, egesta and their own bodies. Partly due to its high level of abstraction, there are few empirical tests of meta-ecosystem theory. Furthermore, while animals may be viewed as important mediators of ecosystem functions, better integration of tools is needed to develop predictive insights of their relative roles and impacts on diverse ecosystems. We present a methodological roadmap that explains how to do such integration by discussing how to combine insights from movement, foraging and ecosystem ecology to develop a coherent understanding of animal-vectored nutrient transport on meta-ecosystems processes. We discuss how the slate of newly developed technologies and methods-tracking devices, mechanistic movement models, diet reconstruction techniques and remote sensing-that when integrated have the potential to advance the quantification of animal-vectored nutrient flows and increase the predictive power of meta-ecosystem theory. We demonstrate that by integrating novel and established tools of animal ecology, ecosystem ecology and remote sensing, we can begin to identify and quantify animal-mediated nutrient translocation by large animals. We also provide conceptual examples that show how our proposed integration of methodologies can help investigate ecosystem impacts of large animal movement. We conclude by describing practical advancements to understanding cross-ecosystem contributions of animals on the move. Understanding the mechanisms by which animals shape ecosystem dynamics is important for ongoing conservation, rewilding and restoration initiatives around the world, and for developing more accurate models of ecosystem nutrient budgets. Our roadmap will enable ecologists to better qualify and quantify animal-mediated nutrient translocation for animals on the move.
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Affiliation(s)
- Diego Ellis-Soto
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.,Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA
| | | | - Matteo Rizzuto
- Department of Biology, Memorial University of Newfoundland, St. John's, Canada
| | - Emily Briggs
- School of the Environment, Yale University, New Haven, CT, USA.,Department of Anthropology, Yale University, New Haven, CT, USA
| | - Julia D Monk
- School of the Environment, Yale University, New Haven, CT, USA
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19
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Munden R, Börger L, Wilson RP, Redcliffe J, Brown R, Garel M, Potts JR. Why did the animal turn? Time‐varying step selection analysis for inference between observed turning‐points in high frequency data. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13574] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rhys Munden
- School of Mathematics and Statistics University of Sheffield Sheffield UK
| | - Luca Börger
- Department of Biosciences College of Science Swansea University Swansea UK
- Centre for Biomathematics College of Science Swansea University Swansea UK
| | - Rory P. Wilson
- Department of Biosciences College of Science Swansea University Swansea UK
| | - James Redcliffe
- Department of Biosciences College of Science Swansea University Swansea UK
| | - Rowan Brown
- College of Engineering Swansea UniversityBay Campus Wales UK
| | - Mathieu Garel
- Office Français de la BiodiversitéUnité Ongulés Sauvages Gières France
| | - Jonathan R. Potts
- School of Mathematics and Statistics University of Sheffield Sheffield UK
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20
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Yu H, Deng J, Nathan R, Kröschel M, Pekarsky S, Li G, Klaassen M. An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers. MOVEMENT ECOLOGY 2021; 9:15. [PMID: 33785056 PMCID: PMC8011142 DOI: 10.1186/s40462-021-00245-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 02/14/2021] [Indexed: 05/16/2023]
Abstract
BACKGROUND Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission. METHODS We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus). RESULTS Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. CONCLUSIONS Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.
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Affiliation(s)
- Hui Yu
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, Australia
- Druid Technology Co., Ltd, Chengdu, Sichuan, China
| | - Jian Deng
- Druid Technology Co., Ltd, Chengdu, Sichuan, China
| | - Ran Nathan
- The Movement Ecology Laboratory, Department of Evolution, Systematics, and Ecology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Max Kröschel
- Department of Wildlife Ecology, Forest Research Institute of Baden-Württemberg, Freiburg, Germany
- Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, 79106, Freiburg, Germany
| | - Sasha Pekarsky
- The Movement Ecology Laboratory, Department of Evolution, Systematics, and Ecology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Guozheng Li
- Druid Technology Co., Ltd, Chengdu, Sichuan, China.
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu, China.
| | - Marcel Klaassen
- Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, Australia
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Conners MG, Michelot T, Heywood EI, Orben RA, Phillips RA, Vyssotski AL, Shaffer SA, Thorne LH. Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species. MOVEMENT ECOLOGY 2021; 9:7. [PMID: 33618773 PMCID: PMC7901071 DOI: 10.1186/s40462-021-00243-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/03/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors. METHODS We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: 'flapping flight', 'soaring flight', and 'on-water'. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data. RESULTS HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for 'flapping flight', 'soaring flight' and 'on-water', respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale. CONCLUSIONS The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.
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Affiliation(s)
- Melinda G Conners
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA.
| | - Théo Michelot
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, KY169LZ, UK
| | - Eleanor I Heywood
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Rachael A Orben
- Department of Fisheries and Wildlife, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Dr., Newport, OR, 97365, USA
| | - Richard A Phillips
- British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge, CB3 0ET, UK
| | - Alexei L Vyssotski
- Institute of Neuroinformatics, University of Zurich and Swiss Federal Institute of Technology (ETH), 8057, Zurich, Switzerland
| | - Scott A Shaffer
- Department of Biological Sciences, San Jose State University, San Jose, CA, 95192-0100, USA
| | - Lesley H Thorne
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
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22
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A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives. Neuron 2020; 108:44-65. [DOI: 10.1016/j.neuron.2020.09.017] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/02/2020] [Accepted: 09/10/2020] [Indexed: 11/21/2022]
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Gunner RM, Wilson RP, Holton MD, Scott R, Hopkins P, Duarte CM. A new direction for differentiating animal activity based on measuring angular velocity about the yaw axis. Ecol Evol 2020; 10:7872-7886. [PMID: 32760571 PMCID: PMC7391348 DOI: 10.1002/ece3.6515] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 06/08/2020] [Accepted: 06/09/2020] [Indexed: 12/11/2022] Open
Abstract
The use of animal-attached data loggers to quantify animal movement has increased in popularity and application in recent years. High-resolution tri-axial acceleration and magnetometry measurements have been fundamental in elucidating fine-scale animal movements, providing information on posture, traveling speed, energy expenditure, and associated behavioral patterns. Heading is a key variable obtained from the tandem use of magnetometers and accelerometers, although few field investigations have explored fine-scale changes in heading to elucidate differences in animal activity (beyond the notable exceptions of dead-reckoning).This paper provides an overview of the value and use of animal heading and a prime derivative, angular velocity about the yaw axis, as an important element for assessing activity extent with potential to allude to behaviors, using "free-ranging" Loggerhead turtles (Caretta caretta) as a model species.We also demonstrate the value of yaw rotation for assessing activity extent, which varies over the time scales considered and show that various scales of body rotation, particularly rate of change of yaw, can help resolve differences between fine-scale behavior-specific movements. For example, oscillating yaw movements about a central point of the body's arc implies bouts of foraging, while unusual circling behavior, indicative of conspecific interactions, could be identified from complete revolutions of the longitudinal axis.We believe this approach should help identification of behaviors and "space-state" approaches to enhance our interpretation of behavior-based movements, particularly in scenarios where acceleration metrics have limited value, such as for slow-moving animals.
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Affiliation(s)
- Richard M. Gunner
- Swansea Lab for Animal Movement, BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Rory P. Wilson
- Swansea Lab for Animal Movement, BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Mark D. Holton
- Swansea Lab for Animal Movement, BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Rebecca Scott
- Future Ocean Cluster of ExcellenceGEOMAR Helmholtz Centre for Ocean ResearchKielGermany
- Natural Environmental Research Council, Polaris HouseSwindonUK
| | - Phil Hopkins
- Swansea Lab for Animal Movement, BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Carlos M. Duarte
- Red Sea Research CentreKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
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24
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A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10113803] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Indoor pedestrian localization measurement is a hot topic and is widely used in indoor navigation and unmanned devices. PDR (Pedestrian Dead Reckoning) is a low-cost and independent indoor localization method, estimating position of pedestrians independently and continuously. PDR fuses the accelerometer, gyroscope and magnetometer to calculate relative distance from starting point, which is mainly composed of three modules: step detection, stride length estimation and heading calculation. However, PDR is affected by cumulative error and can only work in two-dimensional planes, which makes it limited in practical applications. In this paper, a novel localization method V-PDR is presented, which combines VPR (Visual Place Recognition) and PDR in a loosely coupled way. When there is error between the localization result of PDR and VPR, the algorithm will correct the localization of PDR, which significantly reduces the cumulative error. In addition, VPR recognizes scenes on different floors to correct floor localization due to vertical movement, which extends application scene of PDR from two-dimensional planes to three-dimensional spaces. Extensive experiments were conducted in our laboratory building to verify the performance of the proposed method. The results demonstrate that the proposed method outperforms general PDR method in accuracy and can work in three-dimensional space.
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25
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Wilson RP, Williams HJ, Holton MD, di Virgilio A, Börger L, Potts JR, Gunner R, Arkwright A, Fahlman A, Bennett NC, Alagaili A, Cole NC, Duarte CM, Scantlebury DM. An "orientation sphere" visualization for examining animal head movements. Ecol Evol 2020; 10:4291-4302. [PMID: 32489597 PMCID: PMC7246194 DOI: 10.1002/ece3.6197] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 02/06/2020] [Accepted: 02/24/2020] [Indexed: 11/09/2022] Open
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.
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Affiliation(s)
- Rory P. Wilson
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | | | - Mark D. Holton
- Department of Computing ScienceCollege of ScienceSwansea UniversitySwanseaUK
| | - Agustina di Virgilio
- Grupo de Biología de la ConservaciónLaboratorio EcotonoINIBIOMA (CONICET‐Universidad Nacional del Comahue)BarilocheArgentina
- Grupo de Ecología CuantitativaINIBIOMA (CONICET‐Universidad Nacional del Comahue)BarilocheArgentina
| | - Luca Börger
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Jonathan R. Potts
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUK
| | - Richard Gunner
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Alex Arkwright
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
- Fundación Oceanogràfic de la Comunitat ValencianaValenciaSpain
| | - Andreas Fahlman
- Fundación Oceanogràfic de la Comunitat ValencianaValenciaSpain
| | - Nigel C. Bennett
- Department of Zoology and EntomologyMammal Research InstituteUniversity of PretoriaPretoriaSouth Africa
| | | | - Nik C. Cole
- Mauritian Wildlife FoundationVacoasMauritius
- Durrell Wildlife Conservation TrustJerseyChannel Islands
| | - Carlos M. Duarte
- Red Sea Research CentreKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
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Arkwright AC, Archibald E, Fahlman A, Holton MD, Crespo-Picazo JL, Cabedo VM, Duarte CM, Scott R, Webb S, Gunner RM, Wilson RP. Behavioral Biomarkers for Animal Health: A Case Study Using Animal-Attached Technology on Loggerhead Turtles. Front Ecol Evol 2020. [DOI: 10.3389/fevo.2019.00504] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Comer S, Clausen L, Cowen S, Pinder J, Thomas A, Burbidge AH, Tiller C, Algar D, Speldewinde P. Integrating feral cat (Felis catus) control into landscape-scale introduced predator management to improve conservation prospects for threatened fauna: a case study from the south coast of Western Australia. WILDLIFE RESEARCH 2020. [DOI: 10.1071/wr19217] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Abstract
ContextFeral cat predation has had a significant impact on native Australian fauna in the past 200 years. In the early 2000s, population monitoring of the western ground parrot showed a dramatic decline from the pre-2000 range, with one of three meta-populations declining to very low levels and a second becoming locally extinct. We review 8 years of integrated introduced predator control, which trialled the incorporation of the feral cat bait Eradicat® into existing fox baiting programs.
AimsTo test the efficacy of integrating feral cat control into an existing introduced predator control program in an adaptive management framework conducted in response to the decline of native species. The objective was to protect the remaining western ground parrot populations and other threatened fauna on the south coast of Western Australia.
MethodsA landscape-scale feral cat and fox baiting program was delivered across south coast reserves that were occupied by western ground parrots in the early 2000s. Up to 500000ha of national parks and natures reserves were baited per annum. Monitoring was established to evaluate both the efficacy of landscape-scale baiting in management of feral cat populations, and the response of several native fauna species, including the western ground parrot, to an integrated introduced predator control program.
Key resultsOn average, 28% of radio-collared feral cats died from Eradicat® baiting each year, over a 5-year period. The results varied from 0% to 62% between years. Changes in site occupancy by feral cats, as measured by detection on camera traps, was also variable, with significant declines detected after baiting in some years and sites. Trends in populations of native fauna, including the western ground parrot and chuditch, showed positive responses to integrated control of foxes and cats.
ImplicationsLandscape-scale baiting of feral cats in ecosystems on the south coast of Western Australia had varying success when measured by direct knockdown of cats and site occupancy as determined by camera trapping; however, native species appeared to respond favourably to integrated predator control. For the protection of native species, we recommend ongoing baiting for both foxes and feral cats, complemented by post-bait trapping of feral cats. We advocate monitoring baiting efficacy in a well designed adaptive management framework to deliver long-term recovery of threatened species that have been impacted by cats.
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Fahlbusch JA, Harrington KJ. A low-cost, open-source inertial movement GPS logger for eco-physiology applications. ACTA ACUST UNITED AC 2019; 222:jeb.211136. [PMID: 31753906 DOI: 10.1242/jeb.211136] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/15/2019] [Indexed: 11/20/2022]
Abstract
Open-source technology has been increasingly used for developing low-cost animal-borne bio-loggers; however, a gap remains for a bio-logger that records both inertial movement and GPS positions. We address this need with the Tapered Wings Logger (TWLogger), an archival bio-logger that records high-resolution (e.g. 50 Hz) tri-axial accelerometry and magnetometry, temperature and GPS. The TWLogger can be built for 90 USD, accepts user-defined sampling parameters, and with a 500 mA h battery weighs 25 g. We provide publicly available build instructions and custom analysis scripts. Bench tests recorded 50 Hz inertial movement and 2 min GPS for 31.8±2.2 h (mean±s.d., n=6) with GPS accuracy within 10.9±13.6 m. Field deployments on a medium-sized bird of prey in the wild achieved similar results (n=13). The customizable TWLogger has wide-ranging application across systems and thus offers a practical solution for eco-physiology applications.
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Affiliation(s)
- James A Fahlbusch
- Hopkins Marine Station, Stanford University, 120 Ocean View Blvd, Pacific Grove, CA 93950, USA
| | - Katie J Harrington
- Acopian Center for Conservation Learning, Hawk Mountain Sanctuary, 410 Summer Valley Road, Orwigsburg, PA 17961, USA
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29
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Joo R, Boone ME, Clay TA, Patrick SC, Clusella-Trullas S, Basille M. Navigating through the r packages for movement. J Anim Ecol 2019; 89:248-267. [PMID: 31587257 DOI: 10.1111/1365-2656.13116] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 09/23/2019] [Indexed: 11/28/2022]
Abstract
The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community.
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Affiliation(s)
- Rocío Joo
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, Fort Lauderdale, FL, USA
| | - Matthew E Boone
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, Fort Lauderdale, FL, USA
| | - Thomas A Clay
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Samantha C Patrick
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Susana Clusella-Trullas
- Department of Botany and Zoology and Centre for Invasion Biology, Stellenbosch University, Stellenbosch, South Africa
| | - Mathieu Basille
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, Fort Lauderdale, FL, USA
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Williams HJ, Taylor LA, Benhamou S, Bijleveld AI, Clay TA, de Grissac S, Demšar U, English HM, Franconi N, Gómez-Laich A, Griffiths RC, Kay WP, Morales JM, Potts JR, Rogerson KF, Rutz C, Spelt A, Trevail AM, Wilson RP, Börger L. Optimizing the use of biologgers for movement ecology research. J Anim Ecol 2019; 89:186-206. [PMID: 31424571 DOI: 10.1111/1365-2656.13094] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Accepted: 08/08/2019] [Indexed: 10/26/2022]
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.
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Affiliation(s)
- Hannah J Williams
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Lucy A Taylor
- Save the Elephants, Nairobi, Kenya.,Department of Zoology, University of Oxford, Oxford, UK
| | - Simon Benhamou
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS Montpellier, Montpellier, France
| | - Allert I Bijleveld
- NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Utrecht University, Den Burg, The Netherlands
| | - Thomas A Clay
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Sophie de Grissac
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Urška Demšar
- School of Geography & Sustainable Development, University of St Andrews, St Andrews, UK
| | - Holly M English
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Novella Franconi
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Agustina Gómez-Laich
- Instituto de Biología de Organismos Marinos (IBIOMAR), CONICET, Puerto Madryn, Chubut, Argentina
| | - Rachael C Griffiths
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - William P Kay
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Juan Manuel Morales
- Grupo de Ecología Cuantitativa, INIBIOMA-Universidad Nacional del Comahue, CONICET, Bariloche, Argentina
| | - Jonathan R Potts
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
| | | | - Christian Rutz
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK
| | - Anouk Spelt
- Department of Aerospace Engineering, University of Bristol, University Walk, UK
| | - Alice M Trevail
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Rory P Wilson
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Luca Börger
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
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High frequency GPS bursts and path-level analysis reveal linear feature tracking by red foxes. Sci Rep 2019; 9:8849. [PMID: 31221989 PMCID: PMC6586955 DOI: 10.1038/s41598-019-45150-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/31/2019] [Indexed: 12/05/2022] Open
Abstract
There is a need to quantify and better understand how wildlife interact with linear features, as these are integral elements of most landscapes. One potentially important aspect is linear feature tracking (LFT), yet studies rarely succeed in directly revealing or quantifying this behavior. In a proof-of-concept study, we employed short-term intensive GPS monitoring of red foxes (Vulpes vulpes) in a multiple-use landscape in southern Norway. Using periodic bursts of high frequency GPS position fixes, we performed modified path selection analyses to estimate the propensity of foxes to track natural and man-made linear features (roads, forest edges, and streams) once they are encountered. Foxes in our study tracked primarily forest edges and roads. Forty-three percent of bursts that encountered any linear feature resulted in LFT. LFT, although prominent, was manifested as a short-lived behavior, with overall median times to linear feature abandonment around two minutes. Movement speeds were highest along roads, perhaps due to greater ease of travel or higher perceived risk. In the highly heterogeneous habitats that characterize human-dominated landscapes, LFT may be manifested at such a fine spatio-temporal scale that it would remain hidden during telemetry studies employing conventional position fix frequencies. The approach described here may aid others studying spatial behaviors that are manifested over very short durations, yet are biologically significant.
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Right on track? Performance of satellite telemetry in terrestrial wildlife research. PLoS One 2019; 14:e0216223. [PMID: 31071155 PMCID: PMC6508664 DOI: 10.1371/journal.pone.0216223] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 04/15/2019] [Indexed: 11/19/2022] Open
Abstract
Satellite telemetry is an increasingly utilized technology in wildlife research, and current devices can track individual animal movements at unprecedented spatial and temporal resolutions. However, as we enter the golden age of satellite telemetry, we need an in-depth understanding of the main technological, species-specific and environmental factors that determine the success and failure of satellite tracking devices across species and habitats. Here, we assess the relative influence of such factors on the ability of satellite telemetry units to provide the expected amount and quality of data by analyzing data from over 3,000 devices deployed on 62 terrestrial species in 167 projects worldwide. We evaluate the success rate in obtaining GPS fixes as well as in transferring these fixes to the user and we evaluate failure rates. Average fix success and data transfer rates were high and were generally better predicted by species and unit characteristics, while environmental characteristics influenced the variability of performance. However, 48% of the unit deployments ended prematurely, half of them due to technical failure. Nonetheless, this study shows that the performance of satellite telemetry applications has shown improvements over time, and based on our findings, we provide further recommendations for both users and manufacturers.
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Pagano AM, Williams TM. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol Evol 2019; 9:4210-4219. [PMID: 31015999 PMCID: PMC6468055 DOI: 10.1002/ece3.5053] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Revised: 02/13/2019] [Accepted: 02/19/2019] [Indexed: 01/27/2023] Open
Abstract
Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free-ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free-ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free-ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer-based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer-based measures of energy expenditure strongly correlated (r 2 = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free-ranging animals.
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Affiliation(s)
- Anthony M. Pagano
- Alaska Science CenterU.S. Geological SurveyAnchorageAlaska
- Present address:
Institute for Conservation ResearchSan Diego Zoo GlobalSan DiegoCalifornia
| | - Terrie M. Williams
- Department of Ecology & Evolutionary BiologyUniversity of California, Santa CruzSanta CruzCalifornia
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Munden R, Börger L, Wilson RP, Redcliffe J, Loison A, Garel M, Potts JR. Making sense of ultrahigh-resolution movement data: A new algorithm for inferring sites of interest. Ecol Evol 2019; 9:265-274. [PMID: 30680112 PMCID: PMC6342090 DOI: 10.1002/ece3.4721] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/30/2018] [Accepted: 09/03/2018] [Indexed: 11/08/2022] Open
Abstract
Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high-resolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting high-resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte-Lambert (2012). Adapting it for use with high-resolution data. The data's resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm's speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of "high confidence" paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, high-resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point-to-point analysis techniques such as optimal foraging theory. It is encoded into an R package called SitesInterest, so should serve as a valuable tool for making sense of these increasingly large data streams.
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Affiliation(s)
- Rhys Munden
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUK
| | - Luca Börger
- Department of Biosciences, College of ScienceSwansea UniversitySwanseaWalesUK
| | - Rory P. Wilson
- Department of Biosciences, College of ScienceSwansea UniversitySwanseaWalesUK
| | - James Redcliffe
- Department of Biosciences, College of ScienceSwansea UniversitySwanseaWalesUK
| | - Anne Loison
- Laboratoire d’Ecologie Alpine, UMR CNRS 5553Université de SavoieLe Bourget‐du‐LacFrance
| | - Mathieu Garel
- Office National de la Chasse et de la Faune Sauvage, Unité Ongulés SauvagesGièresFrance
| | - Jonathan R. Potts
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUK
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Williams HJ, Duriez O, Holton MD, Dell'Omo G, Wilson RP, Shepard ELC. Vultures respond to challenges of near-ground thermal soaring by varying bank angle. ACTA ACUST UNITED AC 2018; 221:jeb.174995. [PMID: 30337356 DOI: 10.1242/jeb.174995] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 10/11/2018] [Indexed: 11/20/2022]
Abstract
Many large birds rely on thermal soaring flight to travel cross-country. As such, they are under selective pressure to minimise the time spent gaining altitude in thermal updrafts. Birds should be able to maximise their climb rates by maintaining a position close to the thermal core through careful selection of bank angle and airspeed; however, there have been few direct measurements of either parameter. Here, we apply a novel methodology to quantify the bank angles selected by soaring birds using on-board magnetometers. We couple these data with airspeed measurements to parameterise the soaring envelope of two species of Gyps vulture, from which it is possible to predict 'optimal' bank angles. Our results show that these large birds respond to the challenges of gaining altitude in the initial phase of the climb, where thermal updrafts are weak and narrow, by adopting relatively high, and conserved, bank angles (25-35 deg). The bank angle decreased with increasing altitude, in a manner that was broadly consistent with a strategy of maximising the rate of climb. However, the lift coefficients estimated in our study were lower than those predicted by theoretical models and wind-tunnel studies. Overall, our results highlight how the relevant currency for soaring performance changes within individual climbs: when thermal radius is limiting, birds vary bank angle and maintain a constant airspeed, but speed increases later in the climb in order to respond to decreasing air density.
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Affiliation(s)
- Hannah J Williams
- Department of Biosciences, College of Science, Swansea University, Swansea SA2 8PP, UK
| | - Olivier Duriez
- CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 route de Mende, 34293 Montpellier Cedex 5, France
| | - Mark D Holton
- Department of Biosciences, College of Science, Swansea University, Swansea SA2 8PP, UK.,Computational Foundry, College of Science, Swansea University, Swansea SA2 8PP, UK
| | | | - Rory P Wilson
- Department of Biosciences, College of Science, Swansea University, Swansea SA2 8PP, UK
| | - Emily L C Shepard
- Department of Biosciences, College of Science, Swansea University, Swansea SA2 8PP, UK
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Hirakawa T, Yamashita T, Tamaki T, Fujiyoshi H, Umezu Y, Takeuchi I, Matsumoto S, Yoda K. Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning. Ecosphere 2018. [DOI: 10.1002/ecs2.2447] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Tsubasa Hirakawa
- Department of Computer Science; Chubu University; 1200 Matsumoto Kasugai Aichi 487-0027 Japan
| | - Takayoshi Yamashita
- Department of Computer Science; Chubu University; 1200 Matsumoto Kasugai Aichi 487-0027 Japan
| | - Toru Tamaki
- Graduate School of Engineering; Hiroshima University; 1-4-1 Kagamiyama Higashi Hiroshima Hiroshima 739-8527 Japan
| | - Hironobu Fujiyoshi
- Department of Computer Science; Chubu University; 1200 Matsumoto Kasugai Aichi 487-0027 Japan
| | - Yuta Umezu
- Department of Computer Science; Nagoya Institute of Technology; Gokiso-cho Showa-ku Nagoya 466-8555 Japan
| | - Ichiro Takeuchi
- Department of Computer Science; Nagoya Institute of Technology; Gokiso-cho Showa-ku Nagoya 466-8555 Japan
- RIKEN Center for Advanced Intelligence Project; 1-4-1 Nihonbashi Chuo-ku Tokyo 103-0027 Japan
- Center for Materials Research by Information Integration; National Institute for Materials Science; 1-2-1 Sengen Tsukuba 305-0047 Japan
| | - Sakiko Matsumoto
- Graduate School of Environmental Studies; Nagoya University; Furo Chikusa Nagoya 464-8601 Japan
| | - Ken Yoda
- Graduate School of Environmental Studies; Nagoya University; Furo Chikusa Nagoya 464-8601 Japan
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Wilson RP, Holton MD, Virgilio A, Williams H, Shepard ELC, Lambertucci S, Quintana F, Sala JE, Balaji B, Lee ES, Srivastava M, Scantlebury DM, Duarte CM. Give the machine a hand: A Boolean time‐based decision‐tree template for rapidly finding animal behaviours in multisensor data. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13069] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Rory P. Wilson
- Department of BiosciencesCollege of ScienceSwansea University Swansea UK
| | - Mark D. Holton
- Department of Computing ScienceCollege of ScienceSwansea University Swansea UK
| | - Agustina Virgilio
- Grupo de Biología de la ConservaciónLaboratorio EcotonoINIBIOMA (CONICET‐Universidad Nacional del Comahue) Bariloche Argentina
- Grupo de Ecología CuantitativaINIBIOMA (CONICET‐Universidad Nacional del Comahue) Bariloche Argentina
| | - Hannah Williams
- Department of BiosciencesCollege of ScienceSwansea University Swansea UK
| | | | - Sergio Lambertucci
- Grupo de Biología de la ConservaciónLaboratorio EcotonoINIBIOMA (CONICET‐Universidad Nacional del Comahue) Bariloche Argentina
| | - Flavio Quintana
- Instituto de Biologia de Organismos Marinos IBIOMAR‐CONICET (9120) Puerto Madryn Chubut Argentina
| | - Juan E. Sala
- Instituto de Biologia de Organismos Marinos IBIOMAR‐CONICET (9120) Puerto Madryn Chubut Argentina
| | - Bharathan Balaji
- Department of Electrical and Computer EngineeringUniversity of California, Los Angeles Los Angeles California
| | - Eun Sun Lee
- Department of Electrical and Computer EngineeringUniversity of California, Los Angeles Los Angeles California
| | - Mani Srivastava
- Department of Electrical and Computer EngineeringUniversity of California, Los Angeles Los Angeles California
| | - D. Michael Scantlebury
- School of Biological SciencesInstitute for Global Food SecurityQueen's University Belfast Belfast UK
| | - Carlos M. Duarte
- Red Sea Research CentreKing Abdullah University of Science and Technology Thuwal Saudi Arabia
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Potts JR, Börger L, Scantlebury DM, Bennett NC, Alagaili A, Wilson RP. Finding turning‐points in ultra‐high‐resolution animal movement data. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13056] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Jonathan R. Potts
- School of Mathematics and StatisticsUniversity of Sheffield Sheffield UK
| | - Luca Börger
- Department of Biosciences, College of ScienceSwansea University Swansea UK
| | - D. Michael Scantlebury
- School of Biological Sciences, Institute for Global Food SecurityQueens University Belfast Belfast UK
| | - Nigel C. Bennett
- Department of Zoology and EntomologyUniversity of Pretoria Pretoria South Africa
- Zoology DepartmentKing Saud University Riyadh Saudi Arabia
| | - Abdulaziz Alagaili
- KSU Mammals Research ChairZoology Department, King Saud University Riyadh Saudi Arabia
| | - Rory P. Wilson
- Department of Biosciences, College of ScienceSwansea University Swansea UK
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di Virgilio A, Morales JM, Lambertucci SA, Shepard EL, Wilson RP. Multi-dimensional Precision Livestock Farming: a potential toolbox for sustainable rangeland management. PeerJ 2018; 6:e4867. [PMID: 29868276 PMCID: PMC5984589 DOI: 10.7717/peerj.4867] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 05/09/2018] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Precision Livestock Farming (PLF) is a promising approach to minimize the conflicts between socio-economic activities and landscape conservation. However, its application on extensive systems of livestock production can be challenging. The main difficulties arise because animals graze on large natural pastures where they are exposed to competition with wild herbivores for heterogeneous and scarce resources, predation risk, adverse weather, and complex topography. Considering that the 91% of the world's surface devoted to livestock production is composed of extensive systems (i.e., rangelands), our general aim was to develop a PLF methodology that quantifies: (i) detailed behavioural patterns, (ii) feeding rate, and (iii) costs associated with different behaviours and landscape traits. METHODS For this, we used Merino sheep in Patagonian rangelands as a case study. We combined data from an animal-attached multi-sensor tag (tri-axial acceleration, tri-axial magnetometry, temperature sensor and Global Positioning System) with landscape layers from a Geographical Information System to acquire data. Then, we used high accuracy decision trees, dead reckoning methods and spatial data processing techniques to show how this combination of tools could be used to assess energy balance, predation risk and competition experienced by livestock through time and space. RESULTS The combination of methods proposed here are a useful tool to assess livestock behaviour and the different factors that influence extensive livestock production, such as topography, environmental temperature, predation risk and competition for heterogeneous resources. We were able to quantify feeding rate continuously through time and space with high accuracy and show how it could be used to estimate animal production and the intensity of grazing on the landscape. We also assessed the effects of resource heterogeneity (inferred through search times), and the potential costs associated with predation risk, competition, thermoregulation and movement on complex topography. DISCUSSION The quantification of feeding rate and behavioural costs provided by our approach could be used to estimate energy balance and to predict individual growth, survival and reproduction. Finally, we discussed how the information provided by this combination of methods can be used to develop wildlife-friendly strategies that also maximize animal welfare, quality and environmental sustainability.
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Affiliation(s)
- Agustina di Virgilio
- Grupo de Ecología Cuantitativa, INIBIOMA, CONICET-UNCO, Bariloche, Río Negro, Argentina
- Grupo de Investigaciones en Biología de la Conservación, INIBIOMA, CONICET-UNCO, Bariloche, Río Negro, Argentina
| | - Juan M. Morales
- Grupo de Ecología Cuantitativa, INIBIOMA, CONICET-UNCO, Bariloche, Río Negro, Argentina
| | - Sergio A. Lambertucci
- Grupo de Investigaciones en Biología de la Conservación, INIBIOMA, CONICET-UNCO, Bariloche, Río Negro, Argentina
| | | | - Rory P. Wilson
- Department of Biosciences, University of Wales, Swansea, United Kingdom
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Hughey LF, Hein AM, Strandburg-Peshkin A, Jensen FH. Challenges and solutions for studying collective animal behaviour in the wild. Philos Trans R Soc Lond B Biol Sci 2018; 373:20170005. [PMID: 29581390 PMCID: PMC5882975 DOI: 10.1098/rstb.2017.0005] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/15/2017] [Indexed: 01/24/2023] Open
Abstract
Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data.This article is part of the theme issue 'Collective movement ecology'.
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Affiliation(s)
- Lacey F Hughey
- Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA 93106, USA
| | - Andrew M Hein
- Southwest Fisheries Science Center, National Oceanographic and Atmospheric Administration, Santa Cruz, CA 95060, USA
- Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Ariana Strandburg-Peshkin
- Department of Migration and Immuno-Ecology, Max Planck Institute for Ornithology, Am Obstberg 1, 78315 Radolfzell, Germany
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurstrasse 190, 8057 Zurich, Switzerland
| | - Frants H Jensen
- Aarhus Institute of Advanced Studies, Aarhus University, Høegh-Guldbergs Gade 6B, 8000 Aarhus C, Denmark
- Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
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41
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Street GM, Avgar T, Börger L. Net displacement and temporal scaling: Model fitting, interpretation and implementation. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12978] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Garrett M. Street
- Department of Wildlife, Fisheries, and AquacultureMississippi State University Mississippi State MS USA
| | - Tal Avgar
- Department of Integrative BiologyUniversity of Guelph Guelph ON Canada
| | - Luca Börger
- Department of BiosciencesCollege of ScienceSwansea University Swansea UK
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A Geometric Framework for Detection of Critical Points in a Trajectory Using Convex Hulls. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7010014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Williams HJ, Holton MD, Shepard ELC, Largey N, Norman B, Ryan PG, Duriez O, Scantlebury M, Quintana F, Magowan EA, Marks NJ, Alagaili AN, Bennett NC, Wilson RP. Identification of animal movement patterns using tri-axial magnetometry. MOVEMENT ECOLOGY 2017; 5:6. [PMID: 28357113 PMCID: PMC5367006 DOI: 10.1186/s40462-017-0097-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 02/27/2017] [Indexed: 05/24/2023]
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.
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Affiliation(s)
- Hannah J. Williams
- Department of Biosciences, College of Science, Swansea University, Swansea, SA2 8PP UK
| | - Mark D. Holton
- Department of Biosciences, College of Science, Swansea University, Swansea, SA2 8PP UK
| | - Emily L. C. Shepard
- Department of Biosciences, College of Science, Swansea University, Swansea, SA2 8PP UK
| | - Nicola Largey
- Department of Biosciences, College of Science, Swansea University, Swansea, SA2 8PP UK
| | - Brad Norman
- ECOCEAN Inc. (Aust.), ECOCEAN (USA), C/o 68a Railway Street, Perth, WA 6011 Australia
| | - Peter G. Ryan
- FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, University of Cape Town, Rondebosch, 7701 South Africa
| | - Olivier Duriez
- CEFE UMR 5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, 1919 route de Mende, 34293 Montpellier Cedex 5, France
| | - Michael Scantlebury
- School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, Ireland
| | - Flavio Quintana
- Centro Nacional Patagónico, CONICET (9120), Puerto Madryn, Chubut Argentina
| | - Elizabeth A. Magowan
- School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, Ireland
| | - Nikki J. Marks
- School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, Ireland
| | - Abdulaziz N. Alagaili
- KSU Mammals Research Chair, Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451 Saudi Arabia
| | - Nigel C. Bennett
- Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
| | - Rory P. Wilson
- Department of Biosciences, College of Science, Swansea University, Swansea, SA2 8PP UK
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Actogram analysis of free-flying migratory birds: new perspectives based on acceleration logging. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2017; 203:543-564. [PMID: 28343237 PMCID: PMC5522517 DOI: 10.1007/s00359-017-1165-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 03/06/2017] [Accepted: 03/07/2017] [Indexed: 11/24/2022]
Abstract
The use of accelerometers has become an important part of biologging techniques for large-sized birds with accelerometer data providing information about flight mode, wing-beat pattern, behaviour and energy expenditure. Such data show that birds using much energy-saving soaring/gliding flight like frigatebirds and swifts can stay airborne without landing for several months. Successful accelerometer studies have recently been conducted also for free-flying small songbirds during their entire annual cycle. Here we review the principles and possibilities for accelerometer studies in bird migration. We use the first annual actograms (for red-backed shrike Lanius collurio) to explore new analyses and insights that become possible with accelerometer data. Actogram data allow precise estimates of numbers of flights, flight durations as well as departure/landing times during the annual cycle. Annual and diurnal rhythms of migratory flights, as well as prolonged nocturnal flights across desert barriers are illustrated. The shifting balance between flight, rest and different intensities of activity throughout the year as revealed by actogram data can be used to analyse exertion levels during different phases of the life cycle. Accelerometer recording of the annual activity patterns of individual birds will open up a new dimension in bird migration research.
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Wright BM, Ford JKB, Ellis GM, Deecke VB, Shapiro AD, Battaile BC, Trites AW. Fine-scale foraging movements by fish-eating killer whales ( Orcinus orca) relate to the vertical distributions and escape responses of salmonid prey ( Oncorhynchus spp.). MOVEMENT ECOLOGY 2017; 5:3. [PMID: 28239473 PMCID: PMC5319153 DOI: 10.1186/s40462-017-0094-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 01/30/2017] [Indexed: 05/19/2023]
Abstract
BACKGROUND We sought to quantitatively describe the fine-scale foraging behavior of northern resident killer whales (Orcinus orca), a population of fish-eating killer whales that feeds almost exclusively on Pacific salmon (Oncorhynchus spp.). To reconstruct the underwater movements of these specialist predators, we deployed 34 biologging Dtags on 32 individuals and collected high-resolution, three-dimensional accelerometry and acoustic data. We used the resulting dive paths to compare killer whale foraging behavior to the distributions of different salmonid prey species. Understanding the foraging movements of these threatened predators is important from a conservation standpoint, since prey availability has been identified as a limiting factor in their population dynamics and recovery. RESULTS Three-dimensional dive tracks indicated that foraging (N = 701) and non-foraging dives (N = 10,618) were kinematically distinct (Wilks' lambda: λ16 = 0.321, P < 0.001). While foraging, killer whales dove deeper, remained submerged longer, swam faster, increased their dive path tortuosity, and rolled their bodies to a greater extent than during other activities. Maximum foraging dive depths reflected the deeper vertical distribution of Chinook (compared to other salmonids) and the tendency of Pacific salmon to evade predators by diving steeply. Kinematic characteristics of prey pursuit by resident killer whales also revealed several other escape strategies employed by salmon attempting to avoid predation, including increased swimming speeds and evasive maneuvering. CONCLUSIONS High-resolution dive tracks reconstructed using data collected by multi-sensor accelerometer tags found that movements by resident killer whales relate significantly to the vertical distributions and escape responses of their primary prey, Pacific salmon.
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Affiliation(s)
- Brianna M. Wright
- Marine Mammal Research Unit, Institute for the Oceans and Fisheries, University of British Columbia, AERL Building, Room 247 - 2202 Main Mall, Vancouver, BC V6T 1Z4 Canada
- Department of Zoology, University of British Columbia, #4200 - 6270 University Blvd., Vancouver, BC V6T 1Z4 Canada
- Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 1K6 Canada
| | - John K. B. Ford
- Department of Zoology, University of British Columbia, #4200 - 6270 University Blvd., Vancouver, BC V6T 1Z4 Canada
- Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 1K6 Canada
| | - Graeme M. Ellis
- Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 1K6 Canada
| | - Volker B. Deecke
- Centre for Wildlife Conservation, University of Cumbria, Rydal Road, Ambleside, Cumbria L22 9BB UK
| | - Ari Daniel Shapiro
- Woods Hole Oceanographic Institution, 266 Woods Hole Road, Woods Hole, MA 02543-1050 USA
| | - Brian C. Battaile
- Marine Mammal Research Unit, Institute for the Oceans and Fisheries, University of British Columbia, AERL Building, Room 247 - 2202 Main Mall, Vancouver, BC V6T 1Z4 Canada
- Department of Zoology, University of British Columbia, #4200 - 6270 University Blvd., Vancouver, BC V6T 1Z4 Canada
| | - Andrew W. Trites
- Marine Mammal Research Unit, Institute for the Oceans and Fisheries, University of British Columbia, AERL Building, Room 247 - 2202 Main Mall, Vancouver, BC V6T 1Z4 Canada
- Department of Zoology, University of British Columbia, #4200 - 6270 University Blvd., Vancouver, BC V6T 1Z4 Canada
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Clemente CJ, Cooper CE, Withers PC, Freakley C, Singh S, Terrill P. The private life of echidnas: using accelerometry and GPS to examine field biomechanics and assess the ecological impact of a widespread, semi-fossorial monotreme. J Exp Biol 2016; 219:3271-3283. [DOI: 10.1242/jeb.143867] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 08/05/2016] [Indexed: 11/20/2022]
Abstract
ABSTRACT
The short-beaked echidna (Tachyglossus aculeatus) is a monotreme and therefore provides a unique combination of phylogenetic history, morphological differentiation and ecological specialisation for a mammal. The echidna has a unique appendicular skeleton, a highly specialised myrmecophagous lifestyle and a mode of locomotion that is neither typically mammalian nor reptilian, but has aspects of both lineages. We therefore were interested in the interactions of locomotor biomechanics, ecology and movements for wild, free-living short-beaked echidnas. To assess locomotion in its complex natural environment, we attached both GPS and accelerometer loggers to the back of echidnas in both spring and summer. We found that the locomotor biomechanics of echidnas is unique, with lower stride length and stride frequency than reported for similar-sized mammals. Speed modulation is primarily accomplished through changes in stride frequency, with a mean of 1.39 Hz and a maximum of 2.31 Hz. Daily activity period was linked to ambient air temperature, which restricted daytime activity during the hotter summer months. Echidnas had longer activity periods and longer digging bouts in spring compared with summer. In summer, echidnas had higher walking speeds than in spring, perhaps because of the shorter time suitable for activity. Echidnas spent, on average, 12% of their time digging, which indicates their potential to excavate up to 204 m3 of soil a year. This information highlights the important contribution towards ecosystem health, via bioturbation, of this widespread Australian monotreme.
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Affiliation(s)
- Christofer J. Clemente
- School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
- School of Biological Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Christine E. Cooper
- Department of Environment and Agriculture, Curtin University, Perth, WA 6102, Australia
- Zoology, School of Animal Biology M092, University of Western Australia, Perth, WA 6009, Australia
| | - Philip C. Withers
- Department of Environment and Agriculture, Curtin University, Perth, WA 6102, Australia
- Zoology, School of Animal Biology M092, University of Western Australia, Perth, WA 6009, Australia
| | - Craig Freakley
- School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Surya Singh
- School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Philip Terrill
- School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD 4072, Australia
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Dewhirst OP, Evans HK, Roskilly K, Harvey RJ, Hubel TY, Wilson AM. Improving the accuracy of estimates of animal path and travel distance using GPS drift-corrected dead reckoning. Ecol Evol 2016; 6:6210-22. [PMID: 27648238 PMCID: PMC5016644 DOI: 10.1002/ece3.2359] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 07/05/2016] [Accepted: 07/11/2016] [Indexed: 11/17/2022] Open
Abstract
Route taken and distance travelled are important parameters for studies of animal locomotion. They are often measured using a collar equipped with GPS. Collar weight restrictions limit battery size, which leads to a compromise between collar operating life and GPS fix rate. In studies that rely on linear interpolation between intermittent GPS fixes, path tortuosity will often lead to inaccurate path and distance travelled estimates. Here, we investigate whether GPS-corrected dead reckoning can improve the accuracy of localization and distance travelled estimates while maximizing collar operating life. Custom-built tracking collars were deployed on nine freely exercising domestic dogs to collect high fix rate GPS data. Simulations were carried out to measure the extent to which combining accelerometer-based speed and magnetometer heading estimates (dead reckoning) with low fix rate GPS drift correction could improve the accuracy of path and distance travelled estimates. In our study, median 2-dimensional root-mean-squared (2D-RMS) position error was between 158 and 463 m (median path length 16.43 km) and distance travelled was underestimated by between 30% and 64% when a GPS position fix was taken every 5 min. Dead reckoning with GPS drift correction (1 GPS fix every 5 min) reduced 2D-RMS position error to between 15 and 38 m and distance travelled to between an underestimation of 2% and an overestimation of 5%. Achieving this accuracy from GPS alone would require approximately 12 fixes every minute and result in a battery life of approximately 11 days; dead reckoning reduces the number of fixes required, enabling a collar life of approximately 10 months. Our results are generally applicable to GPS-based tracking studies of quadrupedal animals and could be applied to studies of energetics, behavioral ecology, and locomotion. This low-cost approach overcomes the limitation of low fix rate GPS and enables the long-term deployment of lightweight GPS collars.
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Affiliation(s)
- Oliver P. Dewhirst
- Structure & Motion LaboratoryThe Royal Veterinary College, University of LondonHatfieldAL9 7TAUK
| | - Hannah K. Evans
- Structure & Motion LaboratoryThe Royal Veterinary College, University of LondonHatfieldAL9 7TAUK
| | - Kyle Roskilly
- Structure & Motion LaboratoryThe Royal Veterinary College, University of LondonHatfieldAL9 7TAUK
| | - Richard J. Harvey
- Structure & Motion LaboratoryThe Royal Veterinary College, University of LondonHatfieldAL9 7TAUK
| | - Tatjana Y. Hubel
- Structure & Motion LaboratoryThe Royal Veterinary College, University of LondonHatfieldAL9 7TAUK
| | - Alan M. Wilson
- Structure & Motion LaboratoryThe Royal Veterinary College, University of LondonHatfieldAL9 7TAUK
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48
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Edelhoff H, Signer J, Balkenhol N. Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns. MOVEMENT ECOLOGY 2016; 4:21. [PMID: 27595001 PMCID: PMC5010771 DOI: 10.1186/s40462-016-0086-5] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 08/09/2016] [Indexed: 05/07/2023]
Abstract
Increased availability of high-resolution movement data has led to the development of numerous methods for studying changes in animal movement behavior. Path segmentation methods provide basics for detecting movement changes and the behavioral mechanisms driving them. However, available path segmentation methods differ vastly with respect to underlying statistical assumptions and output produced. Consequently, it is currently difficult for researchers new to path segmentation to gain an overview of the different methods, and choose one that is appropriate for their data and research questions. Here, we provide an overview of different methods for segmenting movement paths according to potential changes in underlying behavior. To structure our overview, we outline three broad types of research questions that are commonly addressed through path segmentation: 1) the quantitative description of movement patterns, 2) the detection of significant change-points, and 3) the identification of underlying processes or 'hidden states'. We discuss advantages and limitations of different approaches for addressing these research questions using path-level movement data, and present general guidelines for choosing methods based on data characteristics and questions. Our overview illustrates the large diversity of available path segmentation approaches, highlights the need for studies that compare the utility of different methods, and identifies opportunities for future developments in path-level data analysis.
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Affiliation(s)
- Hendrik Edelhoff
- Department of Wildlife Sciences, University of Göttingen, Büsgenweg 3, 37077 Göttingen, Germany
| | - Johannes Signer
- Department of Wildlife Sciences, University of Göttingen, Büsgenweg 3, 37077 Göttingen, Germany
| | - Niko Balkenhol
- Department of Wildlife Sciences, University of Göttingen, Büsgenweg 3, 37077 Göttingen, Germany
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Vaccario G, Antoine C, Talbot J. First-Passage Times in d-Dimensional Heterogeneous Media. PHYSICAL REVIEW LETTERS 2015; 115:240601. [PMID: 26705617 DOI: 10.1103/physrevlett.115.240601] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Indexed: 06/05/2023]
Abstract
Although there are many theoretical studies of the mean first-passage time (MFPT), most neglect the diffusive heterogeneity of real systems. We present exact analytical expressions for the MFPT and residence times of a pointlike particle diffusing in a spherically symmetric d-dimensional heterogeneous system composed of two concentric media with different diffusion coefficients with an absorbing inner boundary (target) and a reflecting outer boundary. By varying the convention, e.g., Itō, Stratonovich, or isothermal, chosen to interpret the overdamped Langevin equation with multiplicative noise describing the diffusion process, we find different predictions and counterintuitive results for the residence time in the outer region and hence for the MFPT, while the residence time in the inner region is independent of the convention. This convention dependence of residence times and the MFPT could provide insights about the heterogeneous diffusion in a cell or in a tumor, or for animal and insect searches inside their home range.
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Affiliation(s)
- G Vaccario
- Laboratoire de Physique Théorique de la Matiére Condensée, UPMC, CNRS UMR 7600, Sorbonne Universités, 4, Place Jussieu, 75252 Paris, Cedex 05, France
| | - C Antoine
- Laboratoire de Physique Théorique de la Matiére Condensée, UPMC, CNRS UMR 7600, Sorbonne Universités, 4, Place Jussieu, 75252 Paris, Cedex 05, France
| | - J Talbot
- Laboratoire de Physique Théorique de la Matiére Condensée, UPMC, CNRS UMR 7600, Sorbonne Universités, 4, Place Jussieu, 75252 Paris, Cedex 05, France
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Walker JS, Jones MW, Laramee RS, Holton MD, Shepard ELC, Williams HJ, Scantlebury DM, Marks NJ, Magowan EA, Maguire IE, Bidder OR, Di Virgilio A, Wilson RP. Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags. MOVEMENT ECOLOGY 2015; 3:29. [PMID: 26392863 PMCID: PMC4576376 DOI: 10.1186/s40462-015-0056-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 09/06/2015] [Indexed: 05/02/2023]
Abstract
BACKGROUND 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 behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. DESCRIPTION This work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked 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. CONCLUSIONS Framework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.
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Affiliation(s)
- James S. Walker
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Mark W. Jones
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Robert S. Laramee
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Mark D. Holton
- />College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Emily LC Shepard
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Hannah J. Williams
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - D. Michael Scantlebury
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Nikki, J. Marks
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Elizabeth A. Magowan
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Iain E. Maguire
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Owen R. Bidder
- />Institut für Terrestrische und Aquatische Wildtierforschung, Stiftung Tierärztliche Hochschule, Werfstr. 6, 25761, Hannover, Büsum Germany
| | - Agustina Di Virgilio
- />Laboratorio Ecotono, INIBIOMA-CONICET, National University of Comahue, Quintral 1250, (8400), Bariloche, Argentina
| | - Rory P. Wilson
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
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