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Rieber CJ, Hefley TJ, Haukos DA. Treed Gaussian processes for animal movement modeling. Ecol Evol 2024; 14:e11447. [PMID: 38832142 PMCID: PMC11144715 DOI: 10.1002/ece3.11447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 04/30/2024] [Accepted: 05/07/2024] [Indexed: 06/05/2024] Open
Abstract
Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous-time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie-chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie-chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions.
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Affiliation(s)
- Camille J. Rieber
- Department of Statistics and Kansas Cooperative Fish and Wildlife Research UnitKansas State UniversityManhattanKansasUSA
| | - Trevor J. Hefley
- Department of StatisticsKansas State UniversityManhattanKansasUSA
| | - David A. Haukos
- U.S. Geological Survey, Kansas Cooperative Fish and Wildlife Research UnitKansas State UniversityManhattanKansasUSA
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2
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Jreidini N, Green DM. Study methodology impacts density-dependent dispersal observations: a systematic review. MOVEMENT ECOLOGY 2024; 12:39. [PMID: 38773669 PMCID: PMC11107046 DOI: 10.1186/s40462-024-00478-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 05/08/2024] [Indexed: 05/24/2024]
Abstract
The relationship between animal dispersal and conspecific density has been explored in various study systems but results in terms of both the magnitude and the direction of density dependence are inconsistent. We conducted a thorough review of the literature (2000-2023) and found k = 97 empirical studies of birds, fishes, herpetofauna (amphibians and reptiles), invertebrates, or mammals that had tested for a correlation between conspecific density and animal dispersal. We extracted categorical variables for taxonomic group, sex, age, migratory behavior, study design, dispersal metric, density metric and variable type, as well as temporal and spatial scale, to test each of their correlation with the effect of density on dispersal (Pearson's r) using linear regressions and multilevel mixed-effect modelling. We found certain biases in the published literature, highlighting that the impact of conspecific density on dispersal is not as widespread as it is thought to be. We also found no predominant trend for density-dependent dispersal across taxonomic groups. Instead, results show that the scale and metrics of empirical observations significantly affected analytical results, and heterogeneity measures were high within taxonomic groups. Therefore, the direction and magnitude of the interaction between density and dispersal in empirical studies could partially be attributed to the data collection method involved. We suggest that the contradictory observations for density-dependent dispersal could be explained by dispersal-dependent density, where density is driven by movement instead, and urge researchers to either test this interaction when applicable or consider this perspective when reporting results.
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Affiliation(s)
| | - David M Green
- Redpath Museum, McGill University, Montreal, QC, Canada
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3
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Taub M, Goldshtein A, Boonman A, Eitan O, Hurme E, Greif S, Yovel Y. What determines the information update rate in echolocating bats. Commun Biol 2023; 6:1187. [PMID: 37989853 PMCID: PMC10663583 DOI: 10.1038/s42003-023-05563-x] [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: 08/03/2023] [Accepted: 11/09/2023] [Indexed: 11/23/2023] Open
Abstract
The rate of sensory update is one of the most important parameters of any sensory system. The acquisition rate of most sensory systems is fixed and has been optimized by evolution to the needs of the animal. Echolocating bats have the ability to adjust their sensory update rate which is determined by the intervals between emissions - the inter-pulse intervals (IPI). The IPI is routinely adjusted, but the exact factors driving its regulation are unknown. We use on-board audio recordings to determine how four species of echolocating bats with different foraging strategies regulate their sensory update rate during commute flights. We reveal strong correlations between the IPI and various echolocation and movement parameters. Specifically, the update rate increases when the signals' peak-energy frequency and intensity increases while the update rate decreases when flight speed and altitude increases. We suggest that bats control their information update rate according to the behavioral mode they are engaged in, while always maintaining sensory continuity. Specifically, we suggest that bats apply two modes of attention during commute flights. Our data moreover suggests that bats emit echolocation signals at accurate intervals without the need for external feedback.
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Affiliation(s)
- Mor Taub
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Aya Goldshtein
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
- Department of Collective Behaviour, Max Planck Institute of Animal Behaviour, Konstanz, 78464, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Arjan Boonman
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Ofri Eitan
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Edward Hurme
- Department of Migration, Max Planck Institute of Animal Behavior, Radolfzell, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| | - Stefan Greif
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Yossi Yovel
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, 6997801, Israel.
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4
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Egert-Berg K, Handel M, Goldshtein A, Eitan O, Borissov I, Yovel Y. Fruit bats adjust their foraging strategies to urban environments to diversify their diet. BMC Biol 2021; 19:123. [PMID: 34134697 PMCID: PMC8210355 DOI: 10.1186/s12915-021-01060-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 05/30/2021] [Indexed: 11/18/2022] Open
Abstract
Background Urbanization is one of the most influential processes on our globe, putting a great number of species under threat. Some species learn to cope with urbanization, and a few even benefit from it, but we are only starting to understand how they do so. In this study, we GPS tracked Egyptian fruit bats from urban and rural populations to compare their movement and foraging in urban and rural environments. Because fruit trees are distributed differently in these two environments, with a higher diversity in urban environments, we hypothesized that foraging strategies will differ too. Results When foraging in urban environments, bats were much more exploratory than when foraging in rural environments, visiting more sites per hour and switching foraging sites more often on consecutive nights. By doing so, bats foraging in settlements diversified their diet in comparison to rural bats, as was also evident from their choice to often switch fruit species. Interestingly, the location of the roost did not dictate the foraging grounds, and we found that many bats choose to roost in the countryside but nightly commute to and forage in urban environments. Conclusions Bats are unique among small mammals in their ability to move far rapidly. Our study is an excellent example of how animals adjust to environmental changes, and it shows how such mobile mammals might exploit the new urban fragmented environment that is taking over our landscape. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01060-x.
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Affiliation(s)
- Katya Egert-Berg
- Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Michal Handel
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Aya Goldshtein
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Ofri Eitan
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Ivailo Borissov
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, 6997801, Tel Aviv, Israel. .,School of Zoology, Faculty of Life Sciences, Tel Aviv University, 6997801, Tel Aviv, Israel. .,Wissenschaftskolleg zu Berlin, Berlin, Germany.
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5
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Egert-Berg K, Hurme ER, Greif S, Goldstein A, Harten L, Herrera M LG, Flores-Martínez JJ, Valdés AT, Johnston DS, Eitan O, Borissov I, Shipley JR, Medellin RA, Wilkinson GS, Goerlitz HR, Yovel Y. Resource Ephemerality Drives Social Foraging in Bats. Curr Biol 2018; 28:3667-3673.e5. [PMID: 30393034 DOI: 10.1016/j.cub.2018.09.064] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 08/08/2018] [Accepted: 09/26/2018] [Indexed: 11/18/2022]
Abstract
Observations of animals feeding in aggregations are often interpreted as events of social foraging, but it can be difficult to determine whether the animals arrived at the foraging sites after collective search [1-4] or whether they found the sites by following a leader [5, 6] or even independently, aggregating as an artifact of food availability [7, 8]. Distinguishing between these explanations is important, because functionally, they might have very different consequences. In the first case, the animals could benefit from the presence of conspecifics, whereas in the second and third, they often suffer from increased competition [3, 9-13]. Using novel miniature sensors, we recorded GPS tracks and audio of five species of bats, monitoring their movement and interactions with conspecifics, which could be inferred from the audio recordings. We examined the hypothesis that food distribution plays a key role in determining social foraging patterns [14-16]. Specifically, this hypothesis predicts that searching for an ephemeral resource (whose distribution in time or space is hard to predict) is more likely to favor social foraging [10, 13-15] than searching for a predictable resource. The movement and social interactions differed between bats foraging on ephemeral versus predictable resources. Ephemeral species changed foraging sites and showed large temporal variation nightly. They aggregated with conspecifics as was supported by playback experiments and computer simulations. In contrast, predictable species were never observed near conspecifics and showed high spatial fidelity to the same foraging sites over multiple nights. Our results suggest that resource (un)predictability influences the costs and benefits of social foraging.
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Affiliation(s)
- Katya Egert-Berg
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Edward R Hurme
- Department of Biology, University of Maryland, College Park, MD 20742, USA
| | - Stefan Greif
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel; Acoustic and Functional Ecology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Aya Goldstein
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Lee Harten
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Luis Gerardo Herrera M
- Estación de Biología de Chamela, Instituto de Biología, Universidad Nacional Autónoma de México, San Patricio, Jalisco 48980, México
| | - José Juan Flores-Martínez
- Laboratorio de Sistemas de Información Geográfica, Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México 04510, México
| | - Andrea T Valdés
- Posgrado en Ciencias Biológicas, Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México 04510, México
| | - Dave S Johnston
- H.T. Harvey & Associates, Los Gatos, CA, USA; Department of Biology, San Jose State University, San Jose, CA, USA
| | - Ofri Eitan
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Ivo Borissov
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Jeremy Ryan Shipley
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
| | - Rodrigo A Medellin
- Departamento de Ecología de la Biodiversidad, Instituto de Ecología, Universidad, Nacional Autónoma de México, Ciudad de México 04510, México
| | - Gerald S Wilkinson
- Department of Biology, University of Maryland, College Park, MD 20742, USA
| | - Holger R Goerlitz
- Acoustic and Functional Ecology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Yossi Yovel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel; School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
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6
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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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7
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Hooten MB, Scharf HR, Hefley TJ, Pearse AT, Weegman MD. Animal movement models for migratory individuals and groups. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Mevin B. Hooten
- U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research UnitDepartment of Fish, Wildlife, and ConservationDepartment of Fish, Wildlife, and ConservationColorado State University Fort Collins Colorado
- Department of StatisticsColorado State University Fort Collins Colorado
| | - Henry R. Scharf
- Department of StatisticsColorado State University Fort Collins Colorado
| | - Trevor J. Hefley
- Department of StatisticsKansas State University Manhattan Kansas
| | - Aaron T. Pearse
- U.S. Geological SurveyNorthern Prairie Wildlife Research Center Jamestown North Dakota
| | - Mitch D. Weegman
- School of Natural ResourcesUniversity of Missouri Columbia Missouri
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8
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Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7020078] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Walden-Schreiner C, Leung YF, Kuhn T, Newburger T. Integrating direct observation and GPS tracking to monitor animal behavior for resource management. ENVIRONMENTAL MONITORING AND ASSESSMENT 2018; 190:75. [PMID: 29322276 DOI: 10.1007/s10661-018-6463-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 01/01/2018] [Indexed: 06/07/2023]
Abstract
Monitoring the behavior of pack animals in protected areas informs management about use patterns and the potential associated negative impacts. However, systematic assessments of behavior are uncommon due to methodological and logistical constraints. This study integrated behavior mapping with GPS tracking, and applied behavior change point analysis, as an approach to monitor the behaviors of pack animals during overnight periods. The integrated approach identified multiple grazing patterns (i.e., locally intense grazing, ambulatory grazing) not feasible through a single methodology alone. Monitoring behavior and corresponding environmental conditions aid managers in implementing strategies designed to mitigate impacts associated with pack animals in natural areas. Results also contrast the influence of temporal scale on behavior segmentation to inform decisions for further monitoring and management of domestic animal use and impacts in natural areas. This integrated approach reduced time and logistical constraints of each method individually to promote ongoing monitoring and highlight how multiple management tactics could reduce impacts to sensitive habitats.
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Affiliation(s)
- Chelsey Walden-Schreiner
- Department of Parks, Recreation, and Tourism Management, North Carolina State University, CB 8004, Raleigh, NC, 27695, USA
| | - Yu-Fai Leung
- Department of Parks, Recreation, and Tourism Management, North Carolina State University, CB 8004, Raleigh, NC, 27695, USA.
| | - Tim Kuhn
- Division of Resources Management and Science, U.S. National Park Service, Yosemite National Park, El Portal, CA, 95318, USA
| | - Todd Newburger
- Division of Resources Management and Science, U.S. National Park Service, Yosemite National Park, El Portal, CA, 95318, USA
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10
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Ironside KE, Mattson DJ, Theimer T, Jansen B, Holton B, Arundel T, Peters M, Sexton JO, Edwards TC. Quantifying animal movement for caching foragers: the path identification index (PII) and cougars, Puma concolor. MOVEMENT ECOLOGY 2017; 5:24. [PMID: 29201376 PMCID: PMC5700564 DOI: 10.1186/s40462-017-0115-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 11/14/2017] [Indexed: 06/02/2023]
Abstract
BACKGROUND Many studies of animal movement have focused on directed versus area-restricted movement, which rely on correlations between step-length and turn-angles and on stationarity through time to define behavioral states. Although these approaches might apply well to grazing in patchy landscapes, species that either feed for short periods on large, concentrated food sources or cache food exhibit movements that are difficult to model using the traditional metrics of turn-angle and step-length alone. RESULTS We used GPS telemetry collected from a prey-caching predator, the cougar (Puma concolor, Linnaeus), to test whether combining metrics of site recursion, spatiotemporal clustering, speed, and turning into an index of movement using partial sums, improves the ability to identify caching behavior. The index was used to identify changes in movement characteristics over time and segment paths into behavioral classes. The identification of behaviors from the Path Identification Index (PII) was evaluated using field investigations of cougar activities at GPS locations. We tested for statistical stationarity across behaviors for use of topographic view-sheds. Changes in the frequency and duration of PII were useful for identifying seasonal activities such as migration, gestation, and denning. The comparison of field investigations of cougar activities to behavioral PII classes resulted in an overall classification accuracy of 81%. CONCLUSIONS Changes in behaviors were reflected in cougars' use of topographic view-sheds, resulting in statistical nonstationarity over time, and revealed important aspects of hunting behavior. Incorporating metrics of site recursion and spatiotemporal clustering revealed the temporal structure in movements of a caching forager. The movement index PII, shows promise for identifying behaviors in species that frequently return to specific locations such as food caches, watering holes, or dens, and highlights the potential role memory and cognitive abilities play in determining animal movements.
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Affiliation(s)
- Kirsten E. Ironside
- U.S. Geological Survey, Southwest Biological Science Center, 2255 N. Gemini Dr, Flagstaff, AZ 86001 USA
| | - David J. Mattson
- U.S. Geological Survey, Southwest Biological Science Center, 2255 N. Gemini Dr, Flagstaff, AZ 86001 USA
| | - Tad Theimer
- Biological Sciences Department, Northern Arizona University, Flagstaff, AZ 86011 USA
| | - Brian Jansen
- U.S. Geological Survey, Southwest Biological Science Center, 2255 N. Gemini Dr, Flagstaff, AZ 86001 USA
| | - Brandon Holton
- National Park Service, Grand Canyon National Park, Science and Resource Center, Grand Canyon, AZ 86023 USA
| | - Terence Arundel
- U.S. Geological Survey, Southwest Biological Science Center, 2255 N. Gemini Dr, Flagstaff, AZ 86001 USA
| | | | - Joseph O. Sexton
- Global Land Cover Facility, Department of Geographical Sciences, University of Maryland, 4231 Hartwick Road, College Park, MD 20742 USA
| | - Thomas C. Edwards
- U.S. Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, Utah State University, 5230 Old Main Hill, Logan, UT 84322-5230 USA
- Department of Wildland Resources, Utah State University, 5230 Old Main Hill, Logan, UT 84322-5230 USA
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11
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Browning E, Bolton M, Owen E, Shoji A, Guilford T, Freeman R. Predicting animal behaviour using deep learning:
GPS
data alone accurately predict diving in seabirds. Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12926] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ella Browning
- Centre for Biodiversity and Environment ResearchUniversity College London London UK
- Institute of ZoologyZoological Society of London London UK
| | - Mark Bolton
- RSPB Centre for Conservation Science Sandy Bedfordshire UK
| | - Ellie Owen
- RSPB Centre for Conservation Science Inverness UK
| | - Akiko Shoji
- Department of ZoologyOxford University Oxford UK
| | - Tim Guilford
- Department of ZoologyOxford University Oxford UK
| | - Robin Freeman
- Institute of ZoologyZoological Society of London London UK
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12
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Soleymani A, Pennekamp F, Dodge S, Weibel R. Characterizing change points and continuous transitions in movement behaviours using wavelet decomposition. Methods Ecol Evol 2017. [DOI: 10.1111/2041-210x.12755] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ali Soleymani
- Department of Geography University of Zurich Zurich Switzerland
| | - Frank Pennekamp
- Institute of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland
| | - Somayeh Dodge
- Department of Geography, Environment, and Society University of Minnesota Twin Cities MN USA
| | - Robert Weibel
- Department of Geography University of Zurich Zurich Switzerland
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13
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Torres LG, Orben RA, Tolkova I, Thompson DR. Classification of Animal Movement Behavior through Residence in Space and Time. PLoS One 2017; 12:e0168513. [PMID: 28045906 PMCID: PMC5207689 DOI: 10.1371/journal.pone.0168513] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 12/01/2016] [Indexed: 11/18/2022] Open
Abstract
Identification and classification of behavior states in animal movement data can be complex, temporally biased, time-intensive, scale-dependent, and unstandardized across studies and taxa. Large movement datasets are increasingly common and there is a need for efficient methods of data exploration that adjust to the individual variability of each track. We present the Residence in Space and Time (RST) method to classify behavior patterns in movement data based on the concept that behavior states can be partitioned by the amount of space and time occupied in an area of constant scale. Using normalized values of Residence Time and Residence Distance within a constant search radius, RST is able to differentiate behavior patterns that are time-intensive (e.g., rest), time & distance-intensive (e.g., area restricted search), and transit (short time and distance). We use grey-headed albatross (Thalassarche chrysostoma) GPS tracks to demonstrate RST’s ability to classify behavior patterns and adjust to the inherent scale and individuality of each track. Next, we evaluate RST’s ability to discriminate between behavior states relative to other classical movement metrics. We then temporally sub-sample albatross track data to illustrate RST’s response to less resolved data. Finally, we evaluate RST’s performance using datasets from four taxa with diverse ecology, functional scales, ecosystems, and data-types. We conclude that RST is a robust, rapid, and flexible method for detailed exploratory analysis and meta-analyses of behavioral states in animal movement data based on its ability to integrate distance and time measurements into one descriptive metric of behavior groupings. Given the increasing amount of animal movement data collected, it is timely and useful to implement a consistent metric of behavior classification to enable efficient and comparative analyses. Overall, the application of RST to objectively explore and compare behavior patterns in movement data can enhance our fine- and broad- scale understanding of animal movement ecology.
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Affiliation(s)
- Leigh G. Torres
- Department of Fisheries and Wildlife, Marine Mammal Institute, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America
- * E-mail:
| | - Rachael A. Orben
- Department of Fisheries and Wildlife, Marine Mammal Institute, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America
| | - Irina Tolkova
- Applied Math and Computer Science Departments, University of Washington, Seattle, Washington, United States of America
| | - David R. Thompson
- National Institute of Water and Atmospheric Research Ltd., Hataitai, Wellington, New Zealand
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14
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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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Zhang J, O’Reilly KM, Perry GLW, Taylor GA, Dennis TE. Extending the Functionality of Behavioural Change-Point Analysis with k-Means Clustering: A Case Study with the Little Penguin (Eudyptula minor). PLoS One 2015; 10:e0122811. [PMID: 25922935 PMCID: PMC4414459 DOI: 10.1371/journal.pone.0122811] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 02/14/2015] [Indexed: 11/19/2022] Open
Abstract
We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known ‘artificial behaviours’ comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.
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Affiliation(s)
- Jingjing Zhang
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - Kathleen M. O’Reilly
- Department of Biology, University of Portland, Portland, Oregon, United States of America
| | - George L. W. Perry
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- School of Environment, University of Auckland, Auckland, New Zealand
| | | | - Todd E. Dennis
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
- * E-mail:
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Using a partial sum method and GPS tracking data to identify area restricted search by artisanal fishers at moored fish aggregating devices in the Commonwealth of Dominica. PLoS One 2015; 10:e0115552. [PMID: 25647288 PMCID: PMC4315603 DOI: 10.1371/journal.pone.0115552] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Accepted: 11/25/2014] [Indexed: 11/19/2022] Open
Abstract
Foragers must often travel from a central place to exploit aggregations of prey. These patches can be identified behaviorally when a forager shifts from travel to area restricted search, identified by a decrease in speed and an increase in sinuosity of movement. Faster, more directed movement is associated with travel. Differentiating foraging behavior at patches from travel to patches is important for a variety of research questions and has now been made easier by the advent of small, GPS devices that can track forager movement with high resolution. In the summer and fall of 2012, movement data were collected from GPS devices placed on foraging trips originating in the artisanal fishing village of Desa Ikan (pseudonym), on the east coast of the Caribbean island nation of the Commonwealth Dominica. Moored FADs are human-made structures anchored to the ocean floor with fish attraction material on or near the surface designed to effectively create a resource patch. The ultimate goal of the research is to understand how property rights are emerging after the introduction of fish aggregating device (FAD) technology at the site in 1999. This paper reports on research to identify area-restricted search foraging behavior at FAD patches. For 22 foraging trips simultaneous behavioral observations were made to ground-truth the GPS movement data. Using a cumulative sum method, area restricted search was identified as negative deviations from the mean travel speed and the method was able to correctly identify FAD patches in every case.
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17
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Liu X, Xu N, Jiang A. Tortuosity entropy: a measure of spatial complexity of behavioral changes in animal movement. J Theor Biol 2014; 364:197-205. [PMID: 25261731 DOI: 10.1016/j.jtbi.2014.09.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Revised: 09/16/2014] [Accepted: 09/17/2014] [Indexed: 11/25/2022]
Abstract
The goal of animal movement analysis is to understand how organisms explore and exploit complex and varying environments. Animals usually exhibit varied and complicated movements, from apparently deterministic behaviours to highly random behaviours. It has been a common method to assess movement efficiency and foraging strategies by means of quantifying and analyzing movement trajectories. Here we introduce a tortuosity entropy (TorEn), a simple measure for quantifying the behavioral change in animal movement data. In our approach, the differences between pairwise successive track points are transformed into symbolic sequences, then we map these symbols into a group of pattern vectors and calculate the information entropy of pattern vectors. We test the algorithm on both simulated trajectories and real trajectories to show that it can accurately identify not only the mixed segments in simulated data, but also the different phases in real movement data. Tortuosity entropy can be easily applied to arbitrary real-world data, whether deterministic or stochastic, stationary or non-stationary. It could be a promising tool to reveal behavioral mechanism in movement data.
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Affiliation(s)
- Xiaofeng Liu
- College of IoT Engineering, Hohai University, Changzhou 213022, China; Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China.
| | - Ning Xu
- College of IoT Engineering, Hohai University, Changzhou 213022, China; Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China
| | - Aimin Jiang
- College of IoT Engineering, Hohai University, Changzhou 213022, China; Changzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, China
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18
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Benhamou S. Of scales and stationarity in animal movements. Ecol Lett 2013; 17:261-72. [DOI: 10.1111/ele.12225] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2013] [Revised: 10/11/2013] [Accepted: 10/30/2013] [Indexed: 12/19/2022]
Affiliation(s)
- Simon Benhamou
- Centre d’Écologie Fonctionnelle et Évolutive; CNRS UMR 5175 Montpellier France
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19
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Thiebault A, Tremblay Y. Splitting animal trajectories into fine-scale behaviorally consistent movement units: breaking points relate to external stimuli in a foraging seabird. Behav Ecol Sociobiol 2013. [DOI: 10.1007/s00265-013-1546-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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