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Guillermo‐Ferreira R, Filippov AE, Kovalev A, Gorb SN. Voronoi diagrams and Delaunay triangulation for modelling animal territorial behaviour. Ecol Evol 2024; 14:e11715. [PMID: 39045500 PMCID: PMC11263813 DOI: 10.1002/ece3.11715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 06/19/2024] [Accepted: 06/26/2024] [Indexed: 07/25/2024] Open
Abstract
We explore the use of movable automata in numerical modelling of male competition for territory. We used territorial dragonflies as our biological inspiration for the model, assuming two types of competing males: (a) faster and larger males that adopt a face-off strategy and repulse other males; (b) slower and smaller males that adopt a non-aggressive strategy. The faster and larger males have higher noise intensity, leading to faster motion and longer conservation of motion direction. The velocity distributions resemble the Maxwell distributions of velocity, expected in Brownian dynamics, with two probable velocities and distribution widths for the two animal subpopulations. The fast animals' trajectories move between visually fixed density folds of the slower animal subpopulation. A correlation is found between individual velocity and individual area distribution, with smaller animals concentrated in a region of small velocities and areas. Attraction between animals results in a modification of the system behaviour, with larger animals spending more time being surrounded by smaller animals and being slowed down by their interaction with the surroundings. Overall, the study provides insights into the dynamics of animal competition for territory and the impact of attraction between animals.
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Affiliation(s)
| | - Alexander E. Filippov
- Donetsk Institute for Physics and EngineeringNational Academy of Sciences of UkraineDonetskUkraine
- Functional Morphology and Biomechanics, Zoological InstituteKiel UniversityKielGermany
| | - Alexander Kovalev
- Functional Morphology and Biomechanics, Zoological InstituteKiel UniversityKielGermany
| | - Stanislav N. Gorb
- Functional Morphology and Biomechanics, Zoological InstituteKiel UniversityKielGermany
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2
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Subramanian A, Germain RM. Landscape use by large grazers in a grassland is restructured by wildfire. PLoS One 2024; 19:e0297290. [PMID: 38349917 PMCID: PMC10863880 DOI: 10.1371/journal.pone.0297290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 01/02/2024] [Indexed: 02/15/2024] Open
Abstract
Animals navigate landscapes based on perceived risks vs. rewards, as inferred from features of the landscape. In the wild, knowing how strongly animal movement is directed by landscape features is difficult to ascertain but widespread disturbances such as wildfires can serve as natural experiments. We tested the hypothesis that wildfires homogenize the risk/reward landscape, causing movement to become less directed, given that fires reduce landscape complexity as habitat structures (e.g., tree cover, dense brush) are burned. We used satellite imagery of a research reserve in Northern California to count and categorize paths made primarily by mule deer (Odocoileus hemionus) in grasslands. Specifically, we compared pre-wildfire (August 2014) and post-wildfire (September 2018) image history layers among locations that were or were not impacted by wildfire (i.e., a Before/After Control/Impact design). Wildfire significantly altered spatial patterns of deer movement: more new paths were gained and more old paths were lost in areas of the reserve that were impacted by wildfire; movement patterns became less directed in response to fire, suggesting that the risk/reward landscape became more homogenous, as hypothesized. We found evidence to suggest that wildfire affects deer populations at spatial scales beyond their scale of direct impact and raises the interesting possibility that deer perceive risks and rewards at different spatial scales. In conclusion, our study provides an example of how animals integrate spatial information from the environment to make movement decisions, setting the stage for future work on the broader ecological implications for populations, communities, and ecosystems, an emerging interest in ecology.
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Affiliation(s)
- Aishwarya Subramanian
- Department of Biology, Irving K. Barber Faculty of Science, University of British Columbia Okanagan, Kelowna, BC, Canada
- Department of Zoology, University of British Columbia, Vancouver, BC, Canada
| | - Rachel M. Germain
- Department of Zoology, University of British Columbia, Vancouver, BC, Canada
- Biodiversity Research Centre, University of British Columbia, Vancouver, BC, Canada
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3
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Chopra K, Enticott G, Codling EA. Where did my dog go? A pilot study exploring the movement ecology of farm dogs. Front Vet Sci 2024; 10:1325609. [PMID: 38260201 PMCID: PMC10800614 DOI: 10.3389/fvets.2023.1325609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/21/2023] [Indexed: 01/24/2024] Open
Abstract
Movement ecology is important for advancing our comprehension of animal behavior, but its application is yet to be applied to farm dogs. This pilot study uses combined GPS and accelerometer technology to explore the spatial patterns and activity levels of free roaming farm dogs, Canis familiaris (n = 3). Space-use distributions and range sizes were determined to compare locations visited across days and between individuals, as well as in relation to specific areas of interest. Individual activity levels were analyzed and compared within and between dogs. Space-use patterns and range sizes showed variation among the dogs, although substantial similarity in overall spatial distributions were observed between each pair. Among the dogs, the extent of spatial distribution overlap between days varied, with some individuals exhibiting more overlap than others. The dogs allocated different amounts of their time close to landscape features, and to slow-, medium-, and fast movements. This study demonstrates the potential of using automated tracking technology to monitor space-use and interactions between dogs, livestock, and wildlife. By understanding and managing the free ranging behavior of their farm dogs, farmers could potentially take steps to improve the health and wellbeing of both their dogs and their livestock, limiting disease spread, and reducing the possibility of related economic losses.
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Affiliation(s)
- Kareemah Chopra
- School of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, United Kingdom
| | - Gareth Enticott
- School of Geography and Planning, University of Cardiff, Cardiff, United Kingdom
| | - Edward A. Codling
- School of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, United Kingdom
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4
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St Clair CC, Raymond S. Mammals responded to reduced road traffic. Science 2023; 380:1008-1009. [PMID: 37289891 DOI: 10.1126/science.add9662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
COVID-19 restrictions in 2020 reduced traffic worldwide and altered animal movement.
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Affiliation(s)
| | - Sage Raymond
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
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5
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Koger B, Deshpande A, Kerby JT, Graving JM, Costelloe BR, Couzin ID. Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision. J Anim Ecol 2023. [PMID: 36945122 DOI: 10.1111/1365-2656.13904] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 02/07/2023] [Indexed: 03/23/2023]
Abstract
Methods for collecting animal behaviour data in natural environments, such as direct observation and biologging, are typically limited in spatiotemporal resolution, the number of animals that can be observed and information about animals' social and physical environments. Video imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals' locations, into geographical coordinates. We demonstrate a new system for studying behaviour in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals' body postures (poses) and extract environmental features, including topography of the landscape and animal trails. By quantifying animal movement and posture while reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments.
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Affiliation(s)
- Benjamin Koger
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Adwait Deshpande
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Jeffrey T Kerby
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
- Neukom Institute for Computational Science, Dartmouth College, Hanover, New Hampshire, USA
- Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark
| | - Jacob M Graving
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
- Advanced Research Technology Unit, Max Planck Institute of Animal Behaviour, Konstanz, Germany
| | - Blair R Costelloe
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Iain D Couzin
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
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6
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Neumann LK, Davis CA, Fuhlendorf SD, Elmore RD. Does weather drive habitat use and movement of a nonmigratory bird? Ecosphere 2023. [DOI: 10.1002/ecs2.4407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Affiliation(s)
- L. K. Neumann
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - C. A. Davis
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - S. D. Fuhlendorf
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - R. D. Elmore
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
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7
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Saha K, Prakash H, Mohapatra PP, Balakrishnan R. Is flying riskier for female katydids than for males? Behav Ecol Sociobiol 2023. [DOI: 10.1007/s00265-023-03298-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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8
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Popp S, Dornhaus A. Ants combine systematic meandering and correlated random walks when searching for unknown resources. iScience 2023; 26:105916. [PMID: 36866038 PMCID: PMC9971824 DOI: 10.1016/j.isci.2022.105916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 11/07/2022] [Accepted: 12/29/2022] [Indexed: 01/31/2023] Open
Abstract
Animal search movements are typically assumed to be mostly random walks, although non-random elements may be widespread. We tracked ants (Temnothorax rugatulus) in a large empty arena, resulting in almost 5 km of trajectories. We tested for meandering by comparing the turn autocorrelations for empirical ant tracks and simulated, realistic Correlated Random Walks. We found that 78% of ants show significant negative autocorrelation around 10 mm (3 body lengths). This means that turns in one direction are likely followed by turns in the opposite direction after this distance. This meandering likely makes the search more efficient, as it allows ants to avoid crossing their own paths while staying close to the nest, avoiding return-travel time. Combining systematic search with stochastic elements may make the strategy less vulnerable to directional inaccuracies. This study is the first to find evidence for efficient search by regular meandering in a freely searching animal.
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Affiliation(s)
- Stefan Popp
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA,Corresponding author
| | - Anna Dornhaus
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA
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9
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Neumann LK, Davis CA, Fuhlendorf SD, Andersson K, Elmore RD, Goodman LE. Understanding how diel and seasonal rhythms affect the movements of a small non‐migratory bird. Ecosphere 2022. [DOI: 10.1002/ecs2.4149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- L. K. Neumann
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - C. A. Davis
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - S. D. Fuhlendorf
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - K. Andersson
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - R. D. Elmore
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
| | - L. E. Goodman
- Natural Resource Ecology and Management Oklahoma State University Stillwater Oklahoma USA
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10
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Joo R, Picardi S, Boone ME, Clay TA, Patrick SC, Romero-Romero VS, Basille M. Recent trends in movement ecology of animals and human mobility. MOVEMENT ECOLOGY 2022; 10:26. [PMID: 35614458 PMCID: PMC9134608 DOI: 10.1186/s40462-022-00322-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
Movement is fundamental to life, shaping population dynamics, biodiversity patterns, and ecosystem structure. In 2008, the movement ecology framework (MEF Nathan et al. in PNAS 105(49):19052-19059, 2008) introduced an integrative theory of organismal movement-linking internal state, motion capacity, and navigation capacity to external factors-which has been recognized as a milestone in the field. Since then, the study of movement experienced a technological boom, which provided massive quantities of tracking data of both animal and human movement globally and at ever finer spatio-temporal resolutions. In this work, we provide a quantitative assessment of the state of research within the MEF, focusing on animal movement, including humans and invertebrates, and excluding movement of plants and microorganisms. Using a text mining approach, we digitally scanned the contents of [Formula: see text] papers from 2009 to 2018 available online, identified tools and methods used, and assessed linkages between all components of the MEF. Over the past decade, the publication rate has increased considerably, along with major technological changes, such as an increased use of GPS devices and accelerometers and a majority of studies now using the R software environment for statistical computing. However, animal movement research still largely focuses on the effect of environmental factors on movement, with motion and navigation continuing to receive little attention. A search of topics based on words featured in abstracts revealed a clustering of papers among marine and terrestrial realms, as well as applications and methods across taxa. We discuss the potential for technological and methodological advances in the field to lead to more integrated and interdisciplinary research and an increased exploration of key movement processes such as navigation, as well as the evolutionary, physiological, and life-history consequences of movement.
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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
- Global Fishing Watch, Washington DC, USA
| | - Simona Picardi
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, Fort Lauderdale, FL USA
- Jack H. Berryman Institute and Department of Wildland Resources, S.J. & Jessie E. Quinney College of Natural Resources, Utah State University, Logan, UT 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
- Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, CA USA
| | | | - Vilma S. Romero-Romero
- Systems Engineering, Faculty of Engineering and Architecture, University of Lima, Lima, Peru
| | - 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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11
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Thorstensen MJ, Vandervelde CA, Bugg WS, Michaleski S, Vo L, Mackey TE, Lawrence MJ, Jeffries KM. Non-Lethal Sampling Supports Integrative Movement Research in Freshwater Fish. Front Genet 2022; 13:795355. [PMID: 35547248 PMCID: PMC9081360 DOI: 10.3389/fgene.2022.795355] [Citation(s) in RCA: 4] [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: 10/14/2021] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Freshwater ecosystems and fishes are enormous resources for human uses and biodiversity worldwide. However, anthropogenic climate change and factors such as dams and environmental contaminants threaten these freshwater systems. One way that researchers can address conservation issues in freshwater fishes is via integrative non-lethal movement research. We review different methods for studying movement, such as with acoustic telemetry. Methods for connecting movement and physiology are then reviewed, by using non-lethal tissue biopsies to assay environmental contaminants, isotope composition, protein metabolism, and gene expression. Methods for connecting movement and genetics are reviewed as well, such as by using population genetics or quantitative genetics and genome-wide association studies. We present further considerations for collecting molecular data, the ethical foundations of non-lethal sampling, integrative approaches to research, and management decisions. Ultimately, we argue that non-lethal sampling is effective for conducting integrative, movement-oriented research in freshwater fishes. This research has the potential for addressing critical issues in freshwater systems in the future.
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Affiliation(s)
- Matt J. Thorstensen
- Department of Biological Sciences, University of Manitoba, Winnipeg, MB, Canada
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12
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Neumann LK, Fuhlendorf SD, Davis CD, Wilder SM. Climate alters the movement ecology of a non-migratory bird. Ecol Evol 2022; 12:e8869. [PMID: 35475174 PMCID: PMC9034450 DOI: 10.1002/ece3.8869] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/01/2022] [Accepted: 04/08/2022] [Indexed: 11/11/2022] Open
Abstract
Global climate change is causing increased climate extremes threatening biodiversity and altering ecosystems. Climate is comprised of many variables including air temperature, barometric pressure, solar radiation, wind, relative humidity, and precipitation that interact with each other. As movement connects various aspects of an animal's life, understanding how climate influences movement at a fine-temporal scale will be critical to the long-term conservation of species impacted by climate change. The sedentary nature of non-migratory species could increase some species risk of extirpation caused by climate change. We used Northern Bobwhite (Colinus virginianus; hereafter bobwhite) as a model to better understand the relationship between climate and the movement ecology of a non-migratory species at a fine-temporal scale. We collected movement data on bobwhite from across western Oklahoma during 2019-2020 and paired these data with meteorological data. We analyzed movement in three different ways (probability of movement, hourly distance moved, and sinuosity) using two calculated movement metrics: hourly movement (displacement between two consecutive fixes an hour apart) and sinuosity (a form of tortuosity that determines the amount of curvature of a random search path). We used generalized linear-mixed models to analyze probability of movement and hourly distance moved, and used linear-mixed models to analyze sinuosity. The interaction between air temperature and solar radiation affected probability of movement and hourly distance moved. Bobwhite movement increased as air temperature increased beyond 10°C during low solar radiation. During medium and high solar radiation, bobwhite moved farther as air temperature increased until 25-30°C when hourly distance moved plateaued. Bobwhite sinuosity increased as solar radiation increased. Our results show that specific climate variables alter the fine-scale movement of a non-migratory species. Understanding the link between climate and movement is important to determining how climate change may impact a species' space use and fitness now and in the future.
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Affiliation(s)
- Landon K. Neumann
- Oklahoma State UniversityStillwaterOklahomaUSA,Natural Resource Ecology and ManagementOklahoma State UniversityStillwaterOklahomaUSA
| | - Samuel D. Fuhlendorf
- Natural Resource Ecology and ManagementOklahoma State UniversityStillwaterOklahomaUSA
| | - Craig D. Davis
- Natural Resource Ecology and ManagementOklahoma State UniversityStillwaterOklahomaUSA
| | - Shawn M. Wilder
- Department of Integrative BiologyOklahoma State UniversityStillwaterOklahomaUSA
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13
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Dhanjal-Adams KL, Willener AST, Liechti F. sspamlr: a toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R. J Anim Ecol 2022; 91:1345-1360. [PMID: 35362103 PMCID: PMC9542251 DOI: 10.1111/1365-2656.13695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 03/09/2022] [Indexed: 11/28/2022]
Abstract
Light‐level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large‐scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights. Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k‐means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events). Finally, we highlight some of the limitations, implications and opportunities of using these methods.
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Affiliation(s)
- Kiran L Dhanjal-Adams
- Swiss ornithological institute, Seerose 1, 6204, Sempach, Switzerland.,Centre for the Advanced Study of Collective Behavior, University of Konstanz, Universitätsstraße 10, 78464, Konstanz, Germany.,Max Planck Institute of Animal Behavior, Bücklestraße 5, 78467, Konstanz, Germany
| | | | - Felix Liechti
- Swiss ornithological institute, Seerose 1, 6204, Sempach, Switzerland
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14
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Deep inference of seabird dives from GPS-only records: Performance and generalization properties. PLoS Comput Biol 2022; 18:e1009890. [PMID: 35275918 PMCID: PMC8942281 DOI: 10.1371/journal.pcbi.1009890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 03/23/2022] [Accepted: 02/02/2022] [Indexed: 12/02/2022] Open
Abstract
At-sea behaviour of seabirds have received significant attention in ecology over the last decades as it is a key process in the ecology and fate of these populations. It is also, through the position of top predator that these species often occupy, a relevant and integrative indicator of the dynamics of the marine ecosystems they rely on. Seabird trajectories are recorded through the deployment of GPS, and a variety of statistical approaches have been tested to infer probable behaviours from these location data. Recently, deep learning tools have shown promising results for the segmentation and classification of animal behaviour from trajectory data. Yet, these approaches have not been widely used and investigation is still needed to identify optimal network architecture and to demonstrate their generalization properties. From a database of about 300 foraging trajectories derived from GPS data deployed simultaneously with pressure sensors for the identification of dives, this work has benchmarked deep neural network architectures trained in a supervised manner for the prediction of dives from trajectory data. It first confirms that deep learning allows better dive prediction than usual methods such as Hidden Markov Models. It also demonstrates the generalization properties of the trained networks for inferring dives distribution for seabirds from other colonies and ecosystems. In particular, convolutional networks trained on Peruvian boobies from a specific colony show great ability to predict dives of boobies from other colonies and from distinct ecosystems. We further investigate accross-species generalization using a transfer learning strategy known as ‘fine-tuning’. Starting from a convolutional network pre-trained on Guanay cormorant data reduced by two the size of the dataset needed to accurately predict dives in a tropical booby from Brazil. We believe that the networks trained in this study will provide relevant starting point for future fine-tuning works for seabird trajectory segmentation. Over the last decades, the use of miniaturized electronic devices enabled the tracking of many wide-ranging animal species. The deployment of GPS has notably informed on migratory, habitat and foraging strategies of numerous seabird species. A key challenge in movement ecology is to identify specific behavioural patterns (e.g. travelling, resting, foraging) through the observed movement data. In this work, we address the inference of seabird diving behaviour from GPS data using deep learning methods. We demonstrate the performance of deep networks to accurately identify movement patterns from GPS data over state-of-the-art tools, and we illustrate their great accross-species generalization properties (i.e. the ability to generalize prediction from one seabird species to aother). Our results further supports the relevance of deep learning schemes as ‘ready-to-use’ tools which could be used by ecologists to segmentate animal trajectories on new (small) datasets, including when these datasets do not include groundtruthed labelled data for a supervised training.
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15
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Chan AN, Wittemyer G, McEvoy J, Williams AC, Cox N, Soe P, Grindley M, Shwe NM, Chit AM, Oo ZM, Leimgruber P. Landscape characteristics influence ranging behavior of Asian elephants at the human-wildlands interface in Myanmar. MOVEMENT ECOLOGY 2022; 10:6. [PMID: 35123584 PMCID: PMC8818246 DOI: 10.1186/s40462-022-00304-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
CONTEXT Asian elephant numbers are declining across much of their range driven largely by serious threats from land use change resulting in habitat loss and fragmentation. Myanmar, holding critical range for the species, is undergoing major developments due to recent sociopolitical changes. To effectively manage and conserve the remaining populations of endangered elephants in the country, it is crucial to understand their ranging behavior. OBJECTIVES Our objectives were to (1) estimate the sizes of dry, wet, and annual ranges of wild elephants in Myanmar; and quantify the relationship between dry season (the period when human-elephant interactions are the most likely to occur) range size and configurations of agriculture and natural vegetation within the range, and (2) evaluate how percentage of agriculture within dry core range (50% AKDE range) of elephants relates to their daily distance traveled. METHODS We used autocorrelated kernel density estimator (AKDE) based on a continuous-time movement modeling (ctmm) framework to estimate dry season (26 ranges from 22 different individuals), wet season (12 ranges from 10 different individuals), and annual range sizes (8 individuals), and reported the 95%, 50% AKDE, and 95% Minimum Convex Polygon (MCP) range sizes. We assessed how landscape characteristics influenced range size based on a broad array of 48 landscape metrics characterizing aspects of vegetation, water, and human features and their juxtaposition in the study areas. To identify the most relevant landscape metrics and simplify our candidate set of informative metrics, we relied on exploratory factor analysis and Spearman's rank correlation coefficient. Based on this analysis we adopted a final set of metrics into our regression analysis. In a multiple regression framework, we developed candidate models to explain the variation in AKDE dry season range sizes based on the previously identified, salient metrics of landscape composition. RESULTS Elephant dry season ranges were highly variable averaging 792.0 km2 and 184.2 km2 for the 95% and 50% AKDE home ranges, respectively. We found both the shape and spatial configuration of agriculture and natural vegetation patches within an individual elephant's range play a significant role in determining the size of its range. We also found that elephants are moving more (larger energy expenditure) in ranges with higher percentages of agricultural area. CONCLUSION Our results provide baseline information on elephant spatial requirements and the factors affecting them in Myanmar. This information is important for advancing future land use planning that takes into account space-use requirements for elephants. Failing to do so may further endanger already declining elephant populations in Myanmar and across the species' range.
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Affiliation(s)
- A N Chan
- Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, 22630, USA.
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, 80523, USA.
- WWF-Myanmar, Yangon, Myanmar.
- Fauna and Flora International, Yangon, Myanmar.
| | - G Wittemyer
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO, 80523, USA
| | - J McEvoy
- Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, 22630, USA
| | | | - N Cox
- WWF-Myanmar, Yangon, Myanmar
| | - P Soe
- WWF-Myanmar, Yangon, Myanmar
| | - M Grindley
- Fauna and Flora International, Yangon, Myanmar
| | - N M Shwe
- Fauna and Flora International, Yangon, Myanmar
| | - A M Chit
- Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, 22630, USA
| | - Z M Oo
- Ministry of Natural Resources and Environmental Conservation, Myanma Timber Enterprise, Alone, Yangon, Myanmar
| | - P Leimgruber
- Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, 22630, USA
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16
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Gupte PR, Beardsworth CE, Spiegel O, Lourie E, Toledo S, Nathan R, Bijleveld AI. A guide to pre-processing high-throughput animal tracking data. J Anim Ecol 2022; 91:287-307. [PMID: 34657296 PMCID: PMC9299236 DOI: 10.1111/1365-2656.13610] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 10/14/2021] [Indexed: 11/29/2022]
Abstract
Modern, high-throughput animal tracking increasingly yields 'big data' at very fine temporal scales. At these scales, location error can exceed the animal's step size, leading to mis-estimation of behaviours inferred from movement. 'Cleaning' the data to reduce location errors is one of the main ways to deal with position uncertainty. Although data cleaning is widely recommended, inclusive, uniform guidance on this crucial step, and on how to organise the cleaning of massive datasets, is relatively scarce. A pipeline for cleaning massive high-throughput datasets must balance ease of use and computationally efficiency, in which location errors are rejected while preserving valid animal movements. Another useful feature of a pre-processing pipeline is efficiently segmenting and clustering location data for statistical methods while also being scalable to large datasets and robust to imperfect sampling. Manual methods being prohibitively time-consuming, and to boost reproducibility, pre-processing pipelines must be automated. We provide guidance on building pipelines for pre-processing high-throughput animal tracking data to prepare it for subsequent analyses. We apply our proposed pipeline to simulated movement data with location errors, and also show how large volumes of cleaned data can be transformed into biologically meaningful 'residence patches', for exploratory inference on animal space use. We use tracking data from the Wadden Sea ATLAS system (WATLAS) to show how pre-processing improves its quality, and to verify the usefulness of the residence patch method. Finally, with tracks from Egyptian fruit bats Rousettus aegyptiacus, we demonstrate the pre-processing pipeline and residence patch method in a fully worked out example. To help with fast implementation of standardised methods, we developed the R package atlastools, which we also introduce here. Our pre-processing pipeline and atlastools can be used with any high-throughput animal movement data in which the high data-volume combined with knowledge of the tracked individuals' movement capacity can be used to reduce location errors. atlastools is easy to use for beginners while providing a template for further development. The common use of simple yet robust pre-processing steps promotes standardised methods in the field of movement ecology and leads to better inferences from data.
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Affiliation(s)
- Pratik Rajan Gupte
- Groningen Institute for Evolutionary Life SciencesUniversity of GroningenGroningenThe Netherlands
- Department of Coastal SystemsNIOZ Royal Netherlands Institute for Sea ResearchDen BurgThe Netherlands
| | - Christine E. Beardsworth
- Department of Coastal SystemsNIOZ Royal Netherlands Institute for Sea ResearchDen BurgThe Netherlands
| | - Orr Spiegel
- School of ZoologyFaculty of Life SciencesTel Aviv UniversityTel AvivIsrael
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
| | - Emmanuel Lourie
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
- Movement Ecology LabDepartment of Ecology, Evolution, and BehaviorAlexander Silberman Institute of Life SciencesThe Hebrew University of JerusalemJerusalemIsrael
| | - Sivan Toledo
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
- Blavatnik School of Computer ScienceTel Aviv UniversityTel AvivIsrael
| | - Ran Nathan
- Minerva Center for Movement EcologyThe Hebrew University of JerusalemJerusalemIsrael
- Movement Ecology LabDepartment of Ecology, Evolution, and BehaviorAlexander Silberman Institute of Life SciencesThe Hebrew University of JerusalemJerusalemIsrael
| | - Allert I. Bijleveld
- Department of Coastal SystemsNIOZ Royal Netherlands Institute for Sea ResearchDen BurgThe Netherlands
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17
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Hart AG, Dawson M, Fourie R, MacTavish L, Goodenough AE. Comparing the effectiveness of camera trapping, driven transects and ad hoc records for surveying nocturnal mammals against a known species assemblage. COMMUNITY ECOL 2022. [DOI: 10.1007/s42974-021-00070-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Michelangeli M, Payne E, Spiegel O, Sinn DL, Leu ST, Gardner MG, Sih A. Personality, spatiotemporal ecological variation and resident/explorer movement syndromes in the sleepy lizard. J Anim Ecol 2021; 91:210-223. [PMID: 34679184 DOI: 10.1111/1365-2656.13616] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 10/04/2021] [Indexed: 01/26/2023]
Abstract
Individual variation in movement is profoundly important for fitness and offers key insights into the spatial and temporal dynamics of populations and communities. Nonetheless, individual variation in fine-scale movement behaviours is rarely examined even though animal tracking devices offer the long-term, high-resolution, repeatable data in natural conditions that are ideal for studying this variation. Furthermore, of the few studies that consider individual variation in movement, even fewer also consider the internal traits and environmental factors that drive movement behaviour which are necessary for contextualising individual differences in movement patterns. In this study, we GPS tracked a free-ranging population of sleepy lizards Tiliqua rugosa, each Austral spring over 5 years to examine consistent among-individual variation in movement patterns, as well as how these differences were mediated by key internal and ecological factors. We found that individuals consistently differed in a suite of weekly movement traits, and that these traits strongly covaried among-individuals, forming movement syndromes. Lizards fell on a primary movement continuum, from 'residents' that spent extended periods of time residing within smaller core areas of their home range, to 'explorers' that moved greater distances and explored vaster areas of the environment. Importantly, we also found that these consistent differences in lizard movement were related to two ecologically important animal personality traits (boldness and aggression), their sex, key features of the environment (including food availability, and a key water resource), habitat type and seasonal variation (cool/moist vs. hot/drier) in environmental conditions. Broadly, these movement specialisations likely reflect variation in life-history tactics including foraging and mating tactics that ultimately underlie key differences in space use. Such information can be used to connect phenotypic population structure to key ecological and evolutionary processes, for example social networks and disease-transmission pathways, further highlighting the value of examining individual variation in movement behaviour.
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Affiliation(s)
- Marcus Michelangeli
- Department of Environmental Science and Policy, University of California, Davis, CA, USA.,School of Biological Sciences, Monash University, Melbourne, Vic., Australia.,Department of Wildlife, Fish and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - Eric Payne
- Department of Environmental Science and Policy, University of California, Davis, CA, USA
| | - Orr Spiegel
- Department of Environmental Science and Policy, University of California, Davis, CA, USA.,The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - David L Sinn
- Department of Environmental Science and Policy, University of California, Davis, CA, USA
| | - Stephan T Leu
- School of Animal and Veterinary Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Michael G Gardner
- College of Science and Engineering, Flinders University, Adelaide, SA, Australia.,Evolutionary Biology Unit, South Australian Museum, North Terrace, Adelaide, SA, Australia
| | - Andrew Sih
- Department of Environmental Science and Policy, University of California, Davis, CA, USA
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19
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Rolland E, Trull S. Spatial mapping memory: methods used to determine the existence and type of cognitive maps in arboreal mammals. Mamm Rev 2021. [DOI: 10.1111/mam.12272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Eléonore Rolland
- 12 rue Pierre Viorrain Bagnères de Bigorre65200France
- Max Planck Institute for Evolutionary Anthropology Deutscher Pl. 6 Leipzig04103Germany
| | - Sam Trull
- The Sloth Institute at Tulemar Gardens Manuel Antonio, Puntarenas60601Costa Rica
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20
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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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21
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Humphreys JM, Douglas DC, Ramey AM, Mullinax JM, Soos C, Link P, Walther P, Prosser DJ. The spatial–temporal relationship of blue‐winged teal to domestic poultry: Movement state modelling of a highly mobile avian influenza host. J Appl Ecol 2021. [DOI: 10.1111/1365-2664.13963] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- John M. Humphreys
- Agricultural Research Service U.S. Department of Agriculture Sidney MT USA
- Eastern Ecological Science Center at the Patuxent Research RefugeU.S. Geological Survey Laurel MD USA
| | | | - Andrew M. Ramey
- Alaska Science Center U.S. Geological Survey Anchorage AK USA
| | | | - Catherine Soos
- Ecotoxicology and Wildlife Health Division Environment and Climate Change Canada, Saskatoon Saskatchewan CA USA
| | - Paul Link
- Louisiana Department of Wildlife and Fisheries Baton Rouge LA USA
| | - Patrick Walther
- Texas Chenier Plain Refuge Complex U.S. Fish and Wildlife Service Anahuac TX USA
| | - Diann J. Prosser
- Eastern Ecological Science Center at the Patuxent Research RefugeU.S. Geological Survey Laurel MD USA
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22
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Gallagher CA, Chudzinska M, Larsen-Gray A, Pollock CJ, Sells SN, White PJC, Berger U. From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models. Biol Rev Camb Philos Soc 2021; 96:1868-1888. [PMID: 33978325 DOI: 10.1111/brv.12729] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 01/21/2023]
Abstract
To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.
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Affiliation(s)
- Cara A Gallagher
- Department of Plant Ecology and Conservation Biology, University of Potsdam, Am Mühlenberg 3, Potsdam, 14469, Germany.,Department of Bioscience, Aarhus University, Frederiksborgvej 399, Roskilde, 4000
| | - Magda Chudzinska
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, KY16 9ST, U.K
| | - Angela Larsen-Gray
- Department of Integrative Biology, University of Wisconsin-Madison, 250 N. Mills St., Madison, WI, 53706, U.S.A
| | | | - Sarah N Sells
- Montana Cooperative Wildlife Research Unit, The University of Montana, 205 Natural Sciences, Missoula, MT, 59812, U.S.A
| | - Patrick J C White
- School of Applied Sciences, Edinburgh Napier University, 9 Sighthill Ct., Edinburgh, EH11 4BN, U.K
| | - Uta Berger
- Institute of Forest Growth and Computer Science, Technische Universität Dresden, Dresden, 01062, Germany
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23
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24
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Eikelboom JAJ, de Knegt HJ, Klaver M, van Langevelde F, van der Wal T, Prins HHT. Inferring an animal's environment through biologging: quantifying the environmental influence on animal movement. MOVEMENT ECOLOGY 2020; 8:40. [PMID: 33088572 PMCID: PMC7574229 DOI: 10.1186/s40462-020-00228-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 10/12/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Animals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis. METHODS We propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms. RESULTS We show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows' neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement. CONCLUSIONS Not only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment. SUPPLEMENTARY INFORMATION Supplementary information accompanies this paper at 10.1186/s40462-020-00228-4.
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Affiliation(s)
- J. A. J. Eikelboom
- Wildlife Ecology and Conservation Group, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - H. J. de Knegt
- Wildlife Ecology and Conservation Group, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - M. Klaver
- Wildlife Ecology and Conservation Group, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - F. van Langevelde
- Wildlife Ecology and Conservation Group, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
- School of Life Sciences, Westville Campus, University of KwaZulu-Natal, Durban, 4000 South Africa
| | - T. van der Wal
- Spatial Knowledge Systems, Wageningen Environmental Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
| | - H. H. T. Prins
- Wildlife Ecology and Conservation Group, Wageningen University and Research, Droevendaalsesteeg 3a, 6708 PB Wageningen, Netherlands
- Department of Animal Sciences, Wageningen University and Research, De Elst 1, 6708 WD Wageningen, Netherlands
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25
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Albery GF, Kirkpatrick L, Firth JA, Bansal S. Unifying spatial and social network analysis in disease ecology. J Anim Ecol 2020; 90:45-61. [DOI: 10.1111/1365-2656.13356] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/24/2020] [Indexed: 01/18/2023]
Affiliation(s)
| | | | - Josh A. Firth
- Department of Zoology Edward Grey Institute University of Oxford Oxford UK
- Merton College Oxford University Oxford UK
| | - Shweta Bansal
- Department of Biology Georgetown University Washington DC USA
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26
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Sotillo A, Baert JM, Müller W, Stienen EWM, Soares AMVM, Lens L. Time and energy costs of different foraging choices in an avian generalist species. MOVEMENT ECOLOGY 2019; 7:41. [PMID: 31908778 PMCID: PMC6937837 DOI: 10.1186/s40462-019-0188-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 12/18/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Animals can obtain a higher foraging yield by optimizing energy expenditure or minimizing time costs. In this study, we assessed how individual variation in the relative use of marine and terrestrial foraging habitats relates to differences in the energy and time investments of an avian generalistic feeder (the Lesser Black-backed Gull, Larus fuscus), and how this changes during the course of the chick-rearing period. METHODS We analyzed 5 years of GPS tracking data collected at the colony of Zeebrugge (Belgium). Cost proxies for energy expenditure (overall dynamic body acceleration) and time costs (trip durations and time spent away from the colony), together with trip frequency, were analyzed against the relative use of the marine and terrestrial habitats. RESULTS The marine habitat was most often used by males and outside weekends, when fisheries are active. Marine trips implied higher energetic costs and lower time investments. As chicks became older, terrestrial trips became more prevalent, and trip frequency reached a peak towards 20 days after hatching of the first egg. Over a full chick rearing period, energy costs varied widely between individuals, but no trends were found across the marine foraging gradient. Conversely, a higher use of marine foraging implied lower overall amounts of time spent away from the colony. CONCLUSIONS Foraging habitat choice was related to overall time costs incurred by gulls, but not to energy costs. The effect of chick age on foraging habitat choice and effort may be driven by energy expenditure constraints on the amount of marine foraging that can be performed. If time is less constraining to them, Lesser Black-backed Gulls may meet the increasing chick demand for food by switching from high to low energy demanding foraging strategies.
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Affiliation(s)
- Alejandro Sotillo
- Department of Biology, Terrestrial Ecology Unit, Ghent University, K.L. Ledeganckstraat 35, 9000 Ghent, Belgium
- Department of Biology & CESAM - Centre for Environmental and Marine Studies, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal
| | - Jan M. Baert
- Department of Biology, Terrestrial Ecology Unit, Ghent University, K.L. Ledeganckstraat 35, 9000 Ghent, Belgium
- Department of Biology – Behavioural Ecology and Ecophysiology Group, University of Antwerp, Campus Drie Eiken, Universiteitsplein 1, 2610 Wilrijk, Antwerp Belgium
| | - Wendt Müller
- Department of Biology – Behavioural Ecology and Ecophysiology Group, University of Antwerp, Campus Drie Eiken, Universiteitsplein 1, 2610 Wilrijk, Antwerp Belgium
| | - Eric W. M. Stienen
- Research Institute for Nature and Forest (INBO), Herman Teirlinckgebouw, Havenlaan 88, 1000 Brussels, Belgium
| | - Amadeu M. V. M. Soares
- Department of Biology & CESAM - Centre for Environmental and Marine Studies, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal
| | - Luc Lens
- Department of Biology, Terrestrial Ecology Unit, Ghent University, K.L. Ledeganckstraat 35, 9000 Ghent, Belgium
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27
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Using time-series similarity measures to compare animal movement trajectories in ecology. Behav Ecol Sociobiol 2019. [DOI: 10.1007/s00265-019-2761-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Abstract
Identifying and understanding patterns in movement data are amongst the principal aims of movement ecology. By quantifying the similarity of movement trajectories, inferences can be made about diverse processes, ranging from individual specialisation to the ontogeny of foraging strategies. Movement analysis is not unique to ecology however, and methods for estimating the similarity of movement trajectories have been developed in other fields but are currently under-utilised by ecologists. Here, we introduce five commonly used measures of trajectory similarity: dynamic time warping (DTW), longest common subsequence (LCSS), edit distance for real sequences (EDR), Fréchet distance and nearest neighbour distance (NND), of which only NND is routinely used by ecologists. We investigate the performance of each of these measures by simulating movement trajectories using an Ornstein-Uhlenbeck (OU) model in which we varied the following parameters: (1) the point of attraction, (2) the strength of attraction to this point and (3) the noise or volatility added to the movement process in order to determine which measures were most responsive to such changes. In addition, we demonstrate how these measures can be applied using movement trajectories of breeding northern gannets (Morus bassanus) by performing trajectory clustering on a large ecological dataset. Simulations showed that DTW and Fréchet distance were most responsive to changes in movement parameters and were able to distinguish between all the different parameter combinations we trialled. In contrast, NND was the least sensitive measure trialled. When applied to our gannet dataset, the five similarity measures were highly correlated despite differences in their underlying calculation. Clustering of trajectories within and across individuals allowed us to easily visualise and compare patterns of space use over time across a large dataset. Trajectory clusters reflected the bearing on which birds departed the colony and highlighted the use of well-known bathymetric features. As both the volume of movement data and the need to quantify similarity amongst animal trajectories grow, the measures described here and the bridge they provide to other fields of research will become increasingly useful in ecology.
Significance statement
As the use of tracking technology increases, there is a need to develop analytical techniques to process such large volumes of data. One area in which this would be useful is the comparison of individual movement trajectories. In response, a variety of measures of trajectory similarity have been developed within the information sciences. However, such measures are rarely used by ecologists who may be unaware of them. To remedy this, we apply five common measures of trajectory similarity to both simulated data and real ecological dataset comprising of movement trajectories of breeding northern gannets. Dynamic time warping and Fréchet distance performed best on simulated data. Using trajectory similarity measures on our gannet dataset, we identified distinct foraging clusters centred on different bathymetric features, demonstrating one application of such similarity measures. As new technology and analysis techniques proliferate across ecology and the information sciences, closer ties between these fields promise further innovative analysis of movement data.
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Santos EAP, Silva ACCD, Sforza R, Oliveira FLC, Weber MI, Castilhos JC, López-Mendilaharsu M, Marcovaldi MAAG, Ramos RMA, DiMatteo A. Olive ridley inter-nesting and post-nesting movements along the Brazilian coast and Atlantic Ocean. ENDANGER SPECIES RES 2019. [DOI: 10.3354/esr00985] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Gottwald J, Zeidler R, Friess N, Ludwig M, Reudenbach C, Nauss T. Introduction of an automatic and open‐source radio‐tracking system for small animals. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13294] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jannis Gottwald
- Faculty of Geography Philipps‐University Marburg Marburg Germany
| | - Ralf Zeidler
- Freies Institut für Datenanalyse – Dipl.‐Phys. Ralf Zeidler Freiburg Germany
| | - Nicolas Friess
- Faculty of Geography Philipps‐University Marburg Marburg Germany
| | - Marvin Ludwig
- Faculty of Geography Philipps‐University Marburg Marburg Germany
| | | | - Thomas Nauss
- Faculty of Geography Philipps‐University Marburg Marburg Germany
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Miller HJ, Dodge S, Miller J, Bohrer G. Towards an Integrated Science of Movement: Converging Research on Animal Movement Ecology and Human Mobility Science. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE : IJGIS 2019; 33:855-876. [PMID: 33013182 PMCID: PMC7531019 DOI: 10.1080/13658816.2018.1564317] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 12/25/2018] [Indexed: 05/29/2023]
Abstract
There is long-standing scientific interest in understanding purposeful movement by animals and humans. Traditionally, collecting data on individual moving entities was difficult and time-consuming, limiting scientific progress. The growth of location-aware and other geospatial technologies for capturing, managing and analyzing moving objects data are shattering these limitations, leading to revolutions in animal movement ecology and human mobility science. Despite parallel transitions towards massive individual-level data collected automatically via sensors, there is little scientific cross-fertilization across the animal and human divide. There are potential synergies from converging these separate domains towards an integrated science of movement. This paper discusses the data-driven revolutions in the animal movement ecology and human mobility science, their contrasting worldviews and, as examples of complementarity, transdisciplinary questions that span both fields. We also identify research challenges that should be met to develop an integrated science of movement trajectories.
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Affiliation(s)
- Harvey J Miller
- Department of Geography and Center for Urban and Regional Analysis (CURA), The Ohio State University
| | - Somayeh Dodge
- Department of Geography, Environment and Society, University of Minnesota
| | - Jennifer Miller
- Department of Geography and the Environment, The University of Texas at Austin
| | - Gil Bohrer
- Department of Civil, Environmental and Geodetic Engineering, The Ohio State University
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