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On random walk models as a baseline for animal movement in three-dimensional space. Ecol Modell 2022. [DOI: 10.1016/j.ecolmodel.2022.110169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Gurarie E, Potluri S, Cosner GC, Cantrell RS, Fagan WF. Memories of Migrations Past: Sociality and Cognition in Dynamic, Seasonal Environments. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.742920] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Seasonal migrations are a widespread and broadly successful strategy for animals to exploit periodic and localized resources over large spatial scales. It remains an open and largely case-specific question whether long-distance migrations are resilient to environmental disruptions. High levels of mobility suggest an ability to shift ranges that can confer resilience. On the other hand, a conservative, hard-wired commitment to a risky behavior can be costly if conditions change. Mechanisms that contribute to migration include identification and responsiveness to resources, sociality, and cognitive processes such as spatial memory and learning. Our goal was to explore the extent to which these factors interact not only to maintain a migratory behavior but also to provide resilience against environmental changes. We develop a diffusion-advection model of animal movement in which an endogenous migratory behavior is modified by recent experiences via a memory process, and animals have a social swarming-like behavior over a range of spatial scales. We found that this relatively simple framework was able to adapt to a stable, seasonal resource dynamic under a broad range of parameter values. Furthermore, the model was able to acquire an adaptive migration behavior with time. However, the resilience of the process depended on all the parameters under consideration, with many complex trade-offs. For example, the spatial scale of sociality needed to be large enough to capture changes in the resource, but not so large that the acquired collective information was overly diluted. A long-term reference memory was important for hedging against a highly stochastic process, but a higher weighting of more recent memory was needed for adapting to directional changes in resource phenology. Our model provides a general and versatile framework for exploring the interaction of memory, movement, social and resource dynamics, even as environmental conditions globally are undergoing rapid change.
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Ahmed DA, Benhamou S, Bonsall MB, Petrovskii SV. Three-dimensional random walk models of individual animal movement and their application to trap counts modelling. J Theor Biol 2021; 524:110728. [PMID: 33895179 DOI: 10.1016/j.jtbi.2021.110728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 04/11/2021] [Accepted: 04/15/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Random walks (RWs) have proved to be a powerful modelling tool in ecology, particularly in the study of animal movement. An application of RW concerns trapping which is the predominant sampling method to date in insect ecology and agricultural pest management. A lot of research effort has been directed towards modelling ground-dwelling insects by simulating their movement in 2D, and computing pitfall trap counts, but comparatively very little for flying insects with 3D elevated traps. METHODS We introduce the mathematics behind 3D RWs and present key metrics such as the mean squared displacement (MSD) and path sinuosity, which are already well known in 2D. We develop the mathematical theory behind the 3D correlated random walk (CRW) which involves short-term directional persistence and the 3D Biased random walk (BRW) which introduces a long-term directional bias in the movement so that there is an overall preferred movement direction. In this study, we focus on the geometrical aspects of the 3D trap and thus consider three types of shape; a spheroidal trap, a cylindrical trap and a rectangular cuboidal trap. By simulating movement in 3D space, we investigated the effect of 3D trap shapes and sizes and of movement diffusion on trapping efficiency. RESULTS We found that there is a non-linear dependence of trap counts on the trap surface area or volume, but the effect of volume appeared to be a simple consequence of changes in area. Nevertheless, there is a slight but clear hierarchy of trap shapes in terms of capture efficiency, with the spheroidal trap retaining more counts than a cylinder, followed by the cuboidal type for a given area. We also showed that there is no effect of short-term persistence when diffusion is kept constant, but trap counts significantly decrease with increasing diffusion. CONCLUSION Our results provide a better understanding of the interplay between the movement pattern, trap geometry and impacts on trapping efficiency, which leads to improved trap count interpretations, and more broadly, has implications for spatial ecology and population dynamics.
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Affiliation(s)
- D A Ahmed
- Center for Applied Mathematics and Bioinformatics (CAMB), Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, P.O. Box 7207, Hawally 32093, Kuwait
| | - S Benhamou
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, Cogitamus Lab, Montpellier, France
| | - M B Bonsall
- Mathematical Ecology Research Group, Department of Zoology, University of Oxford, Mansfield Road, OX1 3SZ Oxford, UK
| | - S V Petrovskii
- School of Mathematics and Actuarial Science, University of Leicester, University Road, Leicester LE1 7RH, UK; Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St, Moscow 117198, Russian Federation
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Mercker M, Schwemmer P, Peschko V, Enners L, Garthe S. Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method? MOVEMENT ECOLOGY 2021; 9:20. [PMID: 33892815 PMCID: PMC8063450 DOI: 10.1186/s40462-021-00260-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 04/04/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. METHODS We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. RESULTS We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. CONCLUSIONS Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.
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Affiliation(s)
- Moritz Mercker
- Bionum GmbH - Consultants in Biostatistics, Hamburg, Finkenwerder Norderdeich 15 A, Hamburg, Germany
- Research and Technology Centre (FTZ) Kiel University, Hafentörn 1, Büsum, 25761 Germany
| | - Philipp Schwemmer
- Institute of Applied Mathematics (IAM) Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, 69120 Germany
| | - Verena Peschko
- Institute of Applied Mathematics (IAM) Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, 69120 Germany
| | - Leonie Enners
- Institute of Applied Mathematics (IAM) Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, 69120 Germany
| | - Stefan Garthe
- Institute of Applied Mathematics (IAM) Heidelberg University, Im Neuenheimer Feld 205, Heidelberg, 69120 Germany
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Bailey JD, King AJ, Codling EA, Short AM, Johns GI, Fürtbauer I. "Micropersonality" traits and their implications for behavioral and movement ecology research. Ecol Evol 2021; 11:3264-3273. [PMID: 33841782 PMCID: PMC8019044 DOI: 10.1002/ece3.7275] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 12/23/2020] [Accepted: 01/18/2021] [Indexed: 11/06/2022] Open
Abstract
Many animal personality traits have implicit movement-based definitions and can directly or indirectly influence ecological and evolutionary processes. It has therefore been proposed that animal movement studies could benefit from acknowledging and studying consistent interindividual differences (personality), and, conversely, animal personality studies could adopt a more quantitative representation of movement patterns.Using high-resolution tracking data of three-spined stickleback fish (Gasterosteus aculeatus), we examined the repeatability of four movement parameters commonly used in the analysis of discrete time series movement data (time stationary, step length, turning angle, burst frequency) and four behavioral parameters commonly used in animal personality studies (distance travelled, space use, time in free water, and time near objects).Fish showed repeatable interindividual differences in both movement and behavioral parameters when observed in a simple environment with two, three, or five shelters present. Moreover, individuals that spent less time stationary, took more direct paths, and less commonly burst travelled (movement parameters), were found to travel farther, explored more of the tank, and spent more time in open water (behavioral parameters).Our case study indicates that the two approaches-quantifying movement and behavioral parameters-are broadly equivalent, and we suggest that movement parameters can be viewed as "micropersonality" traits that give rise to broad-scale consistent interindividual differences in behavior. This finding has implications for both personality and movement ecology research areas. For example, the study of movement parameters may provide a robust way to analyze individual personalities in species that are difficult or impossible to study using standardized behavioral assays.
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Affiliation(s)
- Joseph D. Bailey
- Department of Mathematical SciencesUniversity of EssexColchesterUK
| | - Andrew J. King
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | | | - Ashley M. Short
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Gemma I. Johns
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
| | - Ines Fürtbauer
- Department of BiosciencesCollege of ScienceSwansea UniversitySwanseaUK
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Genoud AP, Torsiello J, Belson M, Thomas BP. Entomological photonic sensors: Estimating insect population density, its uncertainty and temporal resolution from transit data. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2020.101186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abstract
Landscape connectivity is increasingly promoted as a conservation tool to combat the negative effects of habitat loss, fragmentation, and climate change. Given its importance as a key conservation strategy, connectivity science is a rapidly growing discipline. However, most landscape connectivity models consider connectivity for only a single snapshot in time, despite the widespread recognition that landscapes and ecological processes are dynamic. In this paper, we discuss the emergence of dynamic connectivity and the importance of including dynamism in connectivity models and assessments. We outline dynamic processes for both structural and functional connectivity at multiple spatiotemporal scales and provide examples of modeling approaches at each of these scales. We highlight the unique challenges that accompany the adoption of dynamic connectivity for conservation management and planning in the context of traditional conservation prioritization approaches. With the increased availability of time series and species movement data, computational capacity, and an expanding number of empirical examples in the literature, incorporating dynamic processes into connectivity models is more feasible than ever. Here, we articulate how dynamism is an intrinsic component of connectivity and integral to the future of connectivity science.
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Noonan MJ, Fleming CH, Tucker MA, Kays R, Harrison A, Crofoot MC, Abrahms B, Alberts SC, Ali AH, Altmann J, Antunes PC, Attias N, Belant JL, Beyer DE, Bidner LR, Blaum N, Boone RB, Caillaud D, de Paula RC, de la Torre JA, Dekker J, DePerno CS, Farhadinia M, Fennessy J, Fichtel C, Fischer C, Ford A, Goheen JR, Havmøller RW, Hirsch BT, Hurtado C, Isbell LA, Janssen R, Jeltsch F, Kaczensky P, Kaneko Y, Kappeler P, Katna A, Kauffman M, Koch F, Kulkarni A, LaPoint S, Leimgruber P, Macdonald DW, Markham AC, McMahon L, Mertes K, Moorman CE, Morato RG, Moßbrucker AM, Mourão G, O'Connor D, Oliveira‐Santos LGR, Pastorini J, Patterson BD, Rachlow J, Ranglack DH, Reid N, Scantlebury DM, Scott DM, Selva N, Sergiel A, Songer M, Songsasen N, Stabach JA, Stacy‐Dawes J, Swingen MB, Thompson JJ, Ullmann W, Vanak AT, Thaker M, Wilson JW, Yamazaki K, Yarnell RW, Zieba F, Zwijacz‐Kozica T, Fagan WF, Mueller T, Calabrese JM. Effects of body size on estimation of mammalian area requirements. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2020; 34:1017-1028. [PMID: 32362060 PMCID: PMC7496598 DOI: 10.1111/cobi.13495] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 11/27/2019] [Accepted: 12/24/2019] [Indexed: 06/08/2023]
Abstract
Accurately quantifying species' area requirements is a prerequisite for effective area-based conservation. This typically involves collecting tracking data on species of interest and then conducting home-range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home-range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross-validation to quantify bias in empirical home-range estimates. Area requirements of mammals <10 kg were underestimated by a mean approximately15%, and species weighing approximately100 kg were underestimated by approximately50% on average. Thus, we found area estimation was subject to autocorrelation-induced bias that was worse for large species. Combined with the fact that extinction risk increases as body mass increases, the allometric scaling of bias we observed suggests the most threatened species are also likely to be those with the least accurate home-range estimates. As a correction, we tested whether data thinning or autocorrelation-informed home-range estimation minimized the scaling effect of autocorrelation on area estimates. Data thinning required an approximately93% data loss to achieve statistical independence with 95% confidence and was, therefore, not a viable solution. In contrast, autocorrelation-informed home-range estimation resulted in consistently accurate estimates irrespective of mass. When relating body mass to home range size, we detected that correcting for autocorrelation resulted in a scaling exponent significantly >1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum.
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Affiliation(s)
- Michael J. Noonan
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
- Department of BiologyUniversity of MarylandCollege ParkMD20742U.S.A.
| | - Christen H. Fleming
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
- Department of BiologyUniversity of MarylandCollege ParkMD20742U.S.A.
| | - Marlee A. Tucker
- Senckenberg Biodiversity and Climate Research CentreSenckenberg Gesellschaft für NaturforschungSenckenberganlage 25Frankfurt (Main)60325Germany
- Department of Biological SciencesGoethe UniversityMax‐von‐Laue‐Straße 9Frankfurt (Main)60438Germany
- Department of Environmental ScienceInstitute for Wetland and Water ResearchRadboud UniversityP.O. Box 9010NijmegenGLNL‐6500The Netherlands
| | - Roland Kays
- North Carolina Museum of Natural SciencesBiodiversity LabRaleighNC27601U.S.A.
- Fisheries, Wildlife, and Conservation Biology Program, College of Natural Resources Campus Box 8001North Carolina State UniversityRaleighNC27695U.S.A.
| | - Autumn‐Lynn Harrison
- Migratory Bird CenterSmithsonian Conservation Biology InstituteWashingtonD.C.20013U.S.A.
| | - Margaret C. Crofoot
- Department of AnthropologyUniversity of California, DavisDavisCA95616U.S.A.
- Smithsonian Tropical Research InstituteBalboa Ancon0843‐03092Republic of Panama
| | - Briana Abrahms
- Environmental Research DivisionNOAA Southwest Fisheries Science CenterMontereyCA93940U.S.A.
| | - Susan C. Alberts
- Departments of Biology and Evolutionary AnthropologyDuke UniversityDurhamNC27708U.S.A.
| | | | - Jeanne Altmann
- Department of Ecology and EvolutionPrinceton University106A Guyot HallPrincetonNJ08544U.S.A.
| | - Pamela Castro Antunes
- Department of EcologyFederal University of Mato Grosso do SulCampo GrandeMS79070–900Brazil
| | - Nina Attias
- Programa de Pós‐Graduaçao em Biologia Animal, Universidade Federal do Mato Grosso do SulCidade UniversitáriaAv. Costa e SilvaCampo GrandeMato Grosso do Sul79070‐900Brazil
| | - Jerrold L. Belant
- Camp Fire Program in Wildlife Conservation, State University of New YorkCollege of Environmental Science and ForestrySyracuseNY13210U.S.A.
| | - Dean E. Beyer
- Michigan Department of Natural Resources1990 U.S. 41 SouthMarquetteMI49855U.S.A.
| | - Laura R. Bidner
- Department of AnthropologyUniversity of California, DavisDavisCA95616U.S.A.
- Mpala Research CentreNanyuki555–104000Kenya
| | - Niels Blaum
- University of Potsdam, Plant Ecology and Nature ConservationAm Mühlenberg 3Potsdam14476Germany
| | - Randall B. Boone
- Natural Resource Ecology LaboratoryColorado State UniversityFort CollinsCO80523U.S.A.
- Department of Ecosystem Science and SustainabilityColorado State UniversityFort CollinsCO80523U.S.A.
| | - Damien Caillaud
- Department of AnthropologyUniversity of California, DavisDavisCA95616U.S.A.
| | - Rogerio Cunha de Paula
- National Research Center for Carnivores ConservationChico Mendes Institute for the Conservation of BiodiversityEstrada Municipal Hisaichi Takebayashi 8600AtibaiaSP12952‐011Brazil
| | - J. Antonio de la Torre
- Instituto de Ecología, Universidad Nacional Autónoma de Mexico and CONACyTCiudad UniversitariaMexicoD.F.04318Mexico
| | - Jasja Dekker
- Jasja Dekker DierecologieEnkhuizenstraat 26ArnhemWZ6843The Netherlands
| | - Christopher S. DePerno
- Fisheries, Wildlife, and Conservation Biology Program, College of Natural Resources Campus Box 8001North Carolina State UniversityRaleighNC27695U.S.A.
| | - Mohammad Farhadinia
- Wildlife Conservation Research Unit, Department of ZoologyUniversity of OxfordTubney House, OxfordshireOxfordOX13 5QLU.K.
- Future4Leopards FoundationTehranIran
| | | | - Claudia Fichtel
- German Primate CenterBehavioral Ecology & Sociobiology UnitKellnerweg 4Göttingen37077Germany
| | - Christina Fischer
- Restoration Ecology, Department of Ecology and Ecosystem ManagementTechnische Universität MünchenEmil‐Ramann‐Straße 6Freising85354Germany
| | - Adam Ford
- The Irving K. Barber School of Arts and Sciences, Unit 2: BiologyThe University of British ColumbiaOkanagan Campus, SCI 109, 1177 Research RoadKelownaBCV1V 1V7Canada
| | - Jacob R. Goheen
- Department of Zoology and PhysiologyUniversity of WyomingLaramieWY82071U.S.A.
| | | | - Ben T. Hirsch
- Zoology and Ecology, College of Science and EngineeringJames Cook UniversityTownsvilleQLD4811Australia
| | - Cindy Hurtado
- Museo de Historia NaturalUniversidad Nacional Mayor de San MarcosLima15072Peru
- Department of Forest Resources ManagementThe University of British ColumbiaVancouverBCV6T 1Z4Canada
| | - Lynne A. Isbell
- Department of AnthropologyUniversity of California, DavisDavisCA95616U.S.A.
- Mpala Research CentreNanyuki555–104000Kenya
| | - René Janssen
- Bionet NatuuronderzoekValderstraat 39Stein6171ELThe Netherlands
| | - Florian Jeltsch
- University of Potsdam, Plant Ecology and Nature ConservationAm Mühlenberg 3Potsdam14476Germany
| | - Petra Kaczensky
- Norwegian Institute for Nature Research — NINASluppenTrondheimNO‐7485Norway
- Research Institute of Wildlife Ecology, University of Veterinary MedicineSavoyenstraße 1ViennaA‐1160Austria
| | - Yayoi Kaneko
- Tokyo University of Agriculture and TechnologyTokyo183–8509Japan
| | - Peter Kappeler
- German Primate CenterBehavioral Ecology & Sociobiology UnitKellnerweg 4Göttingen37077Germany
| | - Anjan Katna
- Ashoka Trust for Research in Ecology and the Environment (ATREE)BangaloreKarnataka560064India
- Manipal Academy of Higher EducationManipalKarnataka576104India
| | - Matthew Kauffman
- U.S. Geological Survey, Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and PhysiologyUniversity of WyomingLaramieWY82071U.S.A.
| | - Flavia Koch
- German Primate CenterBehavioral Ecology & Sociobiology UnitKellnerweg 4Göttingen37077Germany
| | - Abhijeet Kulkarni
- Ashoka Trust for Research in Ecology and the Environment (ATREE)BangaloreKarnataka560064India
| | - Scott LaPoint
- Max Planck Institute for OrnithologyVogelwarte RadolfzellAm Obstberg 1RadolfzellD‐78315Germany
- Black Rock Forest65 Reservoir RoadCornwallNY12518U.S.A.
| | - Peter Leimgruber
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
| | - David W. Macdonald
- Wildlife Conservation Research Unit, Department of ZoologyUniversity of OxfordTubney House, OxfordshireOxfordOX13 5QLU.K.
| | | | - Laura McMahon
- Office of Applied ScienceDepartment of Natural ResourcesRhinelanderWI54501U.S.A.
| | - Katherine Mertes
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
| | - Christopher E. Moorman
- Fisheries, Wildlife, and Conservation Biology Program, College of Natural Resources Campus Box 8001North Carolina State UniversityRaleighNC27695U.S.A.
| | - Ronaldo G. Morato
- National Research Center for Carnivores ConservationChico Mendes Institute for the Conservation of BiodiversityEstrada Municipal Hisaichi Takebayashi 8600AtibaiaSP12952‐011Brazil
- Institute for the Conservation of Neotropical Carnivores – Pró‐CarnívorosAtibaiaSao Paulo12945‐010Brazil
| | | | - Guilherme Mourão
- Embrapa PantanalRua 21 de setembro 1880Corumb´aMS79320–900Brazil
| | - David O'Connor
- Department of Biological SciencesGoethe UniversityMax‐von‐Laue‐Straße 9Frankfurt (Main)60438Germany
- San Diego Zoo Institute of Conservation Research15600 San Pasqual Valley RoadEscondidoCA92027U.S.A.
- National Geographic Partners1145 17th Street NWWashingtonD.C.20036U.S.A.
| | | | - Jennifer Pastorini
- Centre for Conservation and Research26/7 C2 Road, KodigahawewaJulpallamaTissamaharama82600Sri Lanka
- Anthropologisches InstitutUniversität ZürichWinterthurerstrasse 190Zurich8057Switzerland
| | - Bruce D. Patterson
- Integrative Research CenterField Museum of Natural HistoryChicagoIL60605U.S.A.
| | - Janet Rachlow
- Department of Fish and Wildlife SciencesUniversity of Idaho875 Perimeter Drive MS 1136MoscowID83844‐1136U.S.A.
| | - Dustin H. Ranglack
- Department of BiologyUniversity of Nebraska at KearneyKearneyNE68849U.S.A.
| | - Neil Reid
- Institute for Global Food Security (IGFS), School of Biological SciencesQueen's University BelfastBelfastBT9 5DLU.K.
| | - David M. Scantlebury
- School of Biological SciencesQueen's University Belfast19 Chlorine GardensBelfastNorthern IrelandBT9 5DLU.K.
| | - Dawn M. Scott
- School of Life SciencesKeele UniversityKeeleStaffordshireST5 5BGU.K.
| | - Nuria Selva
- Institute of Nature ConservationPolish Academy of SciencesMickiewicza 33Krakow31–120Poland
| | - Agnieszka Sergiel
- Institute of Nature ConservationPolish Academy of SciencesMickiewicza 33Krakow31–120Poland
| | - Melissa Songer
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
| | - Nucharin Songsasen
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
| | - Jared A. Stabach
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
| | - Jenna Stacy‐Dawes
- San Diego Zoo Institute of Conservation Research15600 San Pasqual Valley RoadEscondidoCA92027U.S.A.
| | - Morgan B. Swingen
- Fisheries, Wildlife, and Conservation Biology Program, College of Natural Resources Campus Box 8001North Carolina State UniversityRaleighNC27695U.S.A.
- 1854 Treaty Authority4428 Haines RoadDuluthMN55811U.S.A.
| | - Jeffrey J. Thompson
- Asociación Guyra Paraguay – CONACYTParque Ecológico Asunción VerdeAsuncion1101Paraguay
- Instituto SaiteCoronel Felix Cabrera 166Asuncion1101Paraguay
| | - Wiebke Ullmann
- University of Potsdam, Plant Ecology and Nature ConservationAm Mühlenberg 3Potsdam14476Germany
| | - Abi Tamim Vanak
- Ashoka Trust for Research in Ecology and the Environment (ATREE)BangaloreKarnataka560064India
- Wellcome Trust/DBT India AllianceHyderabad500034India
- School of Life SciencesUniversity of KwaZulu‐NatalWestvilleDurban4041South Africa
| | - Maria Thaker
- Centre for Ecological SciencesIndian Institute of ScienceBangalore560012India
| | - John W. Wilson
- Department of Zoology & EntomologyUniversity of PretoriaPretoria0002South Africa
| | - Koji Yamazaki
- Ibaraki Nature MuseumZoological Laboratory700 OsakiBando‐cityIbaraki306–0622Japan
- Forest Ecology LaboratoryDepartment of Forest ScienceTokyo University of Agriculture1‐1‐1 SakuragaokaSetagaya‐KuTokyo156–8502Japan
| | - Richard W. Yarnell
- School of Animal, Rural and Environmental SciencesNottingham Trent UniversityBrackenhurst CampusSouthwellNG25 0QFU.K.
| | - Filip Zieba
- Tatra National ParkKúznice 1Zakopane34–500Poland
| | | | - William F. Fagan
- Department of BiologyUniversity of MarylandCollege ParkMD20742U.S.A.
| | - Thomas Mueller
- Senckenberg Biodiversity and Climate Research CentreSenckenberg Gesellschaft für NaturforschungSenckenberganlage 25Frankfurt (Main)60325Germany
- Department of Biological SciencesGoethe UniversityMax‐von‐Laue‐Straße 9Frankfurt (Main)60438Germany
| | - Justin M. Calabrese
- Smithsonian Conservation Biology InstituteNational Zoological Park1500 Remount RoadFront RoyalVA22630U.S.A.
- Department of BiologyUniversity of MarylandCollege ParkMD20742U.S.A.
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Matthiopoulos J, Fieberg J, Aarts G, Barraquand F, Kendall BE. Within Reach? Habitat Availability as a Function of Individual Mobility and Spatial Structuring. Am Nat 2020; 195:1009-1026. [PMID: 32469662 DOI: 10.1086/708519] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Organisms need access to particular habitats for their survival and reproduction. However, even if all necessary habitats are available within the broader environment, they may not all be easily reachable from the position of a single individual. Many species distribution models consider populations in environmental (or niche) space, hence overlooking this fundamental aspect of geographical accessibility. Here, we develop a formal way of thinking about habitat availability in environmental spaces by describing how limitations in accessibility can cause animals to experience a more limited or simply different mixture of habitats than those more broadly available. We develop an analytical framework for characterizing constrained habitat availability based on the statistical properties of movement and environmental autocorrelation. Using simulation experiments, we show that our general statistical representation of constrained availability is a good approximation of habitat availability for particular realizations of landscape-organism interactions. We present two applications of our approach, one to the statistical analysis of habitat preference (using step-selection functions to analyze harbor seal telemetry data) and a second that derives theoretical insights about population viability from knowledge of the underlying environment. Analytical expressions for habitat availability, such as those we develop here, can yield gains in analytical speed, biological realism, and conceptual generality by allowing us to formulate models that are habitat sensitive without needing to be spatially explicit.
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10
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How range residency and long-range perception change encounter rates. J Theor Biol 2020; 498:110267. [PMID: 32275984 DOI: 10.1016/j.jtbi.2020.110267] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 03/18/2020] [Accepted: 04/02/2020] [Indexed: 11/22/2022]
Abstract
Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are often capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home-range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can lead to systematic, non-negligible biases in encounter-rate predictions.
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Ruiz-Suarez S, Leos-Barajas V, Alvarez-Castro I, Morales JM. Using approximate Bayesian inference for a "steps and turns" continuous-time random walk observed at regular time intervals. PeerJ 2020; 8:e8452. [PMID: 32095333 PMCID: PMC7020826 DOI: 10.7717/peerj.8452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/23/2019] [Indexed: 11/20/2022] Open
Abstract
The study of animal movement is challenging because movement is a process modulated by many factors acting at different spatial and temporal scales. In order to describe and analyse animal movement, several models have been proposed which differ primarily in the temporal conceptualization, namely continuous and discrete time formulations. Naturally, animal movement occurs in continuous time but we tend to observe it at fixed time intervals. To account for the temporal mismatch between observations and movement decisions, we used a state-space model where movement decisions (steps and turns) are made in continuous time. That is, at any time there is a non-zero probability of making a change in movement direction. The movement process is then observed at regular time intervals. As the likelihood function of this state-space model turned out to be intractable yet simulating data is straightforward, we conduct inference using different variations of Approximate Bayesian Computation (ABC). We explore the applicability of this approach as a function of the discrepancy between the temporal scale of the observations and that of the movement process in a simulation study. Simulation results suggest that the model parameters can be recovered if the observation time scale is moderately close to the average time between changes in movement direction. Good estimates were obtained when the scale of observation was up to five times that of the scale of changes in direction. We demonstrate the application of this model to a trajectory of a sheep that was reconstructed in high resolution using information from magnetometer and GPS devices. The state-space model used here allowed us to connect the scales of the observations and movement decisions in an intuitive and easy to interpret way. Our findings underscore the idea that the time scale at which animal movement decisions are made needs to be considered when designing data collection protocols. In principle, ABC methods allow to make inferences about movement processes defined in continuous time but in terms of easily interpreted steps and turns.
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Affiliation(s)
- Sofia Ruiz-Suarez
- INIBIOMA (CONICET-Universidad Nacional del Comahue), Rio Negro, Argentina
- Facultad de Ciencias Económicas, Universidad Nacional de Rosario, Rosario, Argentina
| | - Vianey Leos-Barajas
- Department of Statistics, North Carolina State University, Raleigh, United States of America
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, United States of America
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Mertes K, Stabach JA, Songer M, Wacher T, Newby J, Chuven J, Al Dhaheri S, Leimgruber P, Monfort S. Management Background and Release Conditions Structure Post-release Movements in Reintroduced Ungulates. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00470] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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13
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Noonan MJ, Fleming CH, Akre TS, Drescher-Lehman J, Gurarie E, Harrison AL, Kays R, Calabrese JM. Scale-insensitive estimation of speed and distance traveled from animal tracking data. MOVEMENT ECOLOGY 2019; 7:35. [PMID: 31788314 PMCID: PMC6858693 DOI: 10.1186/s40462-019-0177-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 10/01/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Speed and distance traveled provide quantifiable links between behavior and energetics, and are among the metrics most routinely estimated from animal tracking data. Researchers typically sum over the straight-line displacements (SLDs) between sampled locations to quantify distance traveled, while speed is estimated by dividing these displacements by time. Problematically, this approach is highly sensitive to the measurement scale, with biases subject to the sampling frequency, the tortuosity of the animal's movement, and the amount of measurement error. Compounding the issue of scale-sensitivity, SLD estimates do not come equipped with confidence intervals to quantify their uncertainty. METHODS To overcome the limitations of SLD estimation, we outline a continuous-time speed and distance (CTSD) estimation method. An inherent property of working in continuous-time is the ability to separate the underlying continuous-time movement process from the discrete-time sampling process, making these models less sensitive to the sampling schedule when estimating parameters. The first step of CTSD is to estimate the device's error parameters to calibrate the measurement error. Once the errors have been calibrated, model selection techniques are employed to identify the best fit continuous-time movement model for the data. A simulation-based approach is then employed to sample from the distribution of trajectories conditional on the data, from which the mean speed estimate and its confidence intervals can be extracted. RESULTS Using simulated data, we demonstrate how CTSD provides accurate, scale-insensitive estimates with reliable confidence intervals. When applied to empirical GPS data, we found that SLD estimates varied substantially with sampling frequency, whereas CTSD provided relatively consistent estimates, with often dramatic improvements over SLD. CONCLUSIONS The methods described in this study allow for the computationally efficient, scale-insensitive estimation of speed and distance traveled, without biases due to the sampling frequency, the tortuosity of the animal's movement, or the amount of measurement error. In addition to being robust to the sampling schedule, the point estimates come equipped with confidence intervals, permitting formal statistical inference. All the methods developed in this study are now freely available in the ctmmR package or the ctmmweb point-and-click web based graphical user interface.
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Affiliation(s)
- Michael J. Noonan
- Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd, Front Royal, 22630 USA
- Department of Biology, University of Maryland, College Park, 20742 USA
| | - Christen H. Fleming
- Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd, Front Royal, 22630 USA
- Department of Biology, University of Maryland, College Park, 20742 USA
| | - Thomas S. Akre
- Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd, Front Royal, 22630 USA
| | - Jonathan Drescher-Lehman
- Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd, Front Royal, 22630 USA
- Department of Biology, George Mason University, 4400 University Drive, Fairfax, 22030 USA
| | - Eliezer Gurarie
- Department of Biology, University of Maryland, College Park, 20742 USA
| | - Autumn-Lynn Harrison
- Migratory Bird Center, Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC, 20008 USA
| | - Roland Kays
- North Carolina Museum of Natural Sciences, Biodiversity Lab, Raleigh, 27601 USA
- Department of Forestry & Environmental Resources, North Carolina State University, 4400 University Drive, Raleigh, 27695 USA
| | - Justin M. Calabrese
- Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd, Front Royal, 22630 USA
- Department of Biology, University of Maryland, College Park, 20742 USA
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14
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Dinsdale D, Salibian-Barrera M. Modelling ocean temperatures from bio-probes under preferential sampling. Ann Appl Stat 2019. [DOI: 10.1214/18-aoas1217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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15
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Calabrese JM, Fleming CH, Fagan WF, Rimmler M, Kaczensky P, Bewick S, Leimgruber P, Mueller T. Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data. Philos Trans R Soc Lond B Biol Sci 2019; 373:rstb.2017.0007. [PMID: 29581392 DOI: 10.1098/rstb.2017.0007] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 10/31/2017] [Indexed: 11/12/2022] Open
Abstract
While many animal species exhibit strong conspecific interactions, movement analyses of wildlife tracking datasets still largely focus on single individuals. Multi-individual wildlife tracking studies provide new opportunities to explore how individuals move relative to one another, but such datasets are frequently too sparse for the detailed, acceleration-based analytical methods typically employed in collective motion studies. Here, we address the methodological gap between wildlife tracking data and collective motion by developing a general method for quantifying movement correlation from sparsely sampled data. Unlike most existing techniques for studying the non-independence of individual movements with wildlife tracking data, our approach is derived from an analytically tractable stochastic model of correlated movement. Our approach partitions correlation into a deterministic tendency to move in the same direction termed 'drift correlation' and a stochastic component called 'diffusive correlation'. These components suggest the mechanisms that coordinate movements, with drift correlation indicating external influences, and diffusive correlation pointing to social interactions. We use two case studies to highlight the ability of our approach both to quantify correlated movements in tracking data and to suggest the mechanisms that generate the correlation. First, we use an abrupt change in movement correlation to pinpoint the onset of spring migration in barren-ground caribou. Second, we show how spatial proximity mediates intermittently correlated movements among khulans in the Gobi desert. We conclude by discussing the linkages of our approach to the theory of collective motion.This article is part of the theme issue 'Collective movement ecology'.
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Affiliation(s)
- Justin M Calabrese
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, USA
| | - Christen H Fleming
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, USA.,Department of Biology, University of Maryland, College Park, MD, USA
| | - William F Fagan
- Department of Biology, University of Maryland, College Park, MD, USA
| | - Martin Rimmler
- Department of Biology, University of Stuttgart, Stuttgart, Germany
| | | | - Sharon Bewick
- Department of Biology, University of Maryland, College Park, MD, USA
| | - Peter Leimgruber
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, USA
| | - Thomas Mueller
- Senckenberg Biodiversity and Climate Research Centre, Frankfurt, Germany.,Department of Biological Sciences, University Frankfurt, Frankfurt, Germany
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16
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Noonan MJ, Tucker MA, Fleming CH, Akre TS, Alberts SC, Ali AH, Altmann J, Antunes PC, Belant JL, Beyer D, Blaum N, Böhning‐Gaese K, Cullen L, Paula RC, Dekker J, Drescher‐Lehman J, Farwig N, Fichtel C, Fischer C, Ford AT, Goheen JR, Janssen R, Jeltsch F, Kauffman M, Kappeler PM, Koch F, LaPoint S, Markham AC, Medici EP, Morato RG, Nathan R, Oliveira‐Santos LGR, Olson KA, Patterson BD, Paviolo A, Ramalho EE, Rösner S, Schabo DG, Selva N, Sergiel A, Xavier da Silva M, Spiegel O, Thompson P, Ullmann W, Zięba F, Zwijacz‐Kozica T, Fagan WF, Mueller T, Calabrese JM. A comprehensive analysis of autocorrelation and bias in home range estimation. ECOL MONOGR 2019. [DOI: 10.1002/ecm.1344] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Michael J. Noonan
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Road Front Royal Virginia 22630 USA
- Department of Biology University of Maryland College Park Maryland 20742 USA
| | - Marlee A. Tucker
- Senckenberg Biodiversity and Climate Research Centre Senckenberg Gesellschaft für Naturforschung Senckenberganlage 25 60325 Frankfurt (Main) Germany
- Department of Biological Sciences Goethe University Max‐von‐Laue‐Straße 9 60438 Frankfurt (Main) Germany
| | - Christen H. Fleming
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Road Front Royal Virginia 22630 USA
- Department of Biology University of Maryland College Park Maryland 20742 USA
| | - Thomas S. Akre
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Road Front Royal Virginia 22630 USA
| | - Susan C. Alberts
- Departments of Biology and Evolutionary Anthropology Duke University Durham North Carolina 27708 USA
| | | | - Jeanne Altmann
- Department of Ecology and Evolution Princeton University Princeton New Jersey 08544 USA
| | - Pamela Castro Antunes
- Department of Ecology Federal University of Mato Grosso do Sul Campo Grande MS 79070‐900 Brazil
| | - Jerrold L. Belant
- Camp Fire Program in Wildlife Conservation College of Environmental Science and Forestry State University of New York Syracuse New York 13210 USA
| | - Dean Beyer
- Conservation Ecology Faculty of Biology Philipps‐University Marburg Karl‐von‐Frisch Straße 8 Marburg D‐35043 Germany
| | - Niels Blaum
- Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 14476 Potsdam Germany
| | - Katrin Böhning‐Gaese
- Senckenberg Biodiversity and Climate Research Centre Senckenberg Gesellschaft für Naturforschung Senckenberganlage 25 60325 Frankfurt (Main) Germany
- Department of Biological Sciences Goethe University Max‐von‐Laue‐Straße 9 60438 Frankfurt (Main) Germany
| | - Laury Cullen
- Instituto de Pesquisas Ecológicas Nazare Paulista Rod. Dom Pedro I, km 47 Caixa Postal 47 ‐ 12960‐000 Nazaré Paulista SP Brazil
| | - Rogerio Cunha Paula
- National Research Center for Carnivores Conservation Chico Mendes Institute for the Conservation of Biodiversity Estrada Municipal Hisaichi Takebayashi 8600 Atibaia SP 12952‐011 Brazil
| | - Jasja Dekker
- Jasja Dekker Dierecologie Enkhuizenstraat 26 6843 WZ Arnhem The Netherlands
| | - Jonathan Drescher‐Lehman
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Road Front Royal Virginia 22630 USA
- Department of Biology George Mason University 4400 University Drive Fairfax Virginia 22030 USA
| | - Nina Farwig
- Michigan Department of Natural Resources 1990 U.S. 41 South Marquette Michigan 49855 USA
| | - Claudia Fichtel
- Behavioral Ecology & Sociobiology Unit German Primate Center Kellnerweg 4 37077 Göttingen Germany
| | - Christina Fischer
- Restoration Ecology Department of Ecology and Ecosystem Management Technische Universität München Emil‐Ramann‐Straße 6 85354 Freising Germany
| | - Adam T. Ford
- Department of Biology University of British Columbia 1177 Research Road Kelowna British Columbia V1V 1V7 Canada
| | - Jacob R. Goheen
- Department of Zoology and Physiology University of Wyoming Laramie Wyoming 82071 USA
| | - René Janssen
- Bionet Natuuronderzoek Valderstraat 39 6171EL Stein The Netherlands
| | - Florian Jeltsch
- Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 14476 Potsdam Germany
| | - Matthew Kauffman
- U.S. Geological Survey Wyoming Cooperative Fish and Wildlife Research Unit Department of Zoology and Physiology University of Wyoming Laramie Wyoming 82071 USA
| | - Peter M. Kappeler
- Behavioral Ecology & Sociobiology Unit German Primate Center Kellnerweg 4 37077 Göttingen Germany
| | - Flávia Koch
- Behavioral Ecology & Sociobiology Unit German Primate Center Kellnerweg 4 37077 Göttingen Germany
| | - Scott LaPoint
- Max Planck Institute for Ornithology, Vogelwarte Radolfzell Am Obstberg 1 D‐78315 Radolfzell Germany
- Lamont‐Doherty Earth Observatory Columbia University Palisades New York 10964 USA
| | - A. Catherine Markham
- Department of Anthropology Stony Brook University Stony Brook New York 11794 USA
| | - Emilia Patricia Medici
- Lowland Tapir Conservation Initiative (LTCI) Instituto de Pesquisas Ecologicas (IPE) & IUCN SSC Tapir Specialist Group (TSG) Rua Licuala 622, Damha 1, CEP: 79046‐150 Campo Grande Mato Grosso do Sul Brazil
| | - Ronaldo G. Morato
- National Research Center for Carnivores Conservation Chico Mendes Institute for the Conservation of Biodiversity Estrada Municipal Hisaichi Takebayashi 8600 Atibaia SP 12952‐011 Brazil
- Institute for the Conservation of Neotropical Carnivores – Pro‐Carnívoros Atibaia SP 12945‐010 Brazil
| | - Ran Nathan
- Movement Ecology Laboratory Department of Ecology, Evolution and Behavior Alexander Silberman Institute of Life Sciences The Hebrew University of Jerusalem Edmond J. Safra Campus Jerusalem 91904 Israel
| | | | - Kirk A. Olson
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Road Front Royal Virginia 22630 USA
- Wildlife Conservation Society Mongolia Program 201 San Business Center Amar Street 29, Small Ring Road, Sukhbaatar District Post 20A, Box‐21 Ulaanbaatar Mongolia
| | - Bruce D. Patterson
- Integrative Research Center Field Museum of Natural History Chicago Illinois 60605 USA
| | - Agustin Paviolo
- Instituto de Biología Subtropical Universidad Nacional de Misiones and CONICET Bertoni 85 3370 Puerto Iguazú Misiones Argentina
| | - Emiliano Esterci Ramalho
- Institute for the Conservation of Neotropical Carnivores – Pro‐Carnívoros Atibaia SP 12945‐010 Brazil
- Instituto de Desenvolvimento Sustentável Mamirauá Estrada do Bexiga, 2.584 Bairro Fonte Boa Caixa Postal 38 69.553‐225 Tefé Amazonas Brazil
| | - Sascha Rösner
- Michigan Department of Natural Resources 1990 U.S. 41 South Marquette Michigan 49855 USA
| | - Dana G. Schabo
- Michigan Department of Natural Resources 1990 U.S. 41 South Marquette Michigan 49855 USA
| | - Nuria Selva
- Institute of Nature Conservation Polish Academy of Sciences Mickiewicza 33 31‐120 Krakow Poland
| | - Agnieszka Sergiel
- Institute of Nature Conservation Polish Academy of Sciences Mickiewicza 33 31‐120 Krakow Poland
| | - Marina Xavier da Silva
- Projeto Carnívoros do Iguaçu Parque Nacional do Iguaçu BR‐469, Km 22.5, CEP 85851‐970 Foz do Iguaçu PR Brazil
| | - Orr Spiegel
- School of Zoology Faculty of Life Sciences Tel Aviv University Tel Aviv 69978 Israel
| | - Peter Thompson
- Department of Biology University of Maryland College Park Maryland 20742 USA
| | - Wiebke Ullmann
- Plant Ecology and Nature Conservation University of Potsdam Am Mühlenberg 3 14476 Potsdam Germany
| | - Filip Zięba
- Tatra National Park Kuźnice 1 34‐500 Zakopane Poland
| | | | - William F. Fagan
- Department of Biology University of Maryland College Park Maryland 20742 USA
| | - Thomas Mueller
- Senckenberg Biodiversity and Climate Research Centre Senckenberg Gesellschaft für Naturforschung Senckenberganlage 25 60325 Frankfurt (Main) Germany
- Department of Biological Sciences Goethe University Max‐von‐Laue‐Straße 9 60438 Frankfurt (Main) Germany
| | - Justin M. Calabrese
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Road Front Royal Virginia 22630 USA
- Department of Biology University of Maryland College Park Maryland 20742 USA
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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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18
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Ahearn SC, Dodge S. Recursive multi‐frequency segmentation of movement trajectories (ReMuS). Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.12958] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sean C. Ahearn
- Center for Advanced Research of Spatial Information (CARSI)Hunter College – CUNY New York NY USA
| | - Somayeh Dodge
- Department of Geography, Environment and SocietyUniversity of Minnesota Twin Cities MN USA
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19
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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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A random acceleration model of individual animal movement allowing for diffusive, superdiffusive and superballistic regimes. Sci Rep 2017; 7:14364. [PMID: 29085003 PMCID: PMC5662607 DOI: 10.1038/s41598-017-14511-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 10/06/2017] [Indexed: 12/02/2022] Open
Abstract
Patterns of individual animal movement attracted considerable attention over the last two decades. In particular, question as to whether animal movement is predominantly diffusive or superdiffusive has been a focus of discussion and controversy. We consider this problem using a theory of stochastic motion based on the Langevin equation with non-Wiener stochastic forcing that originates in animal’s response to environmental noise. We show that diffusive and superdiffusive types of motion are inherent parts of the same general movement process that arises as interplay between the force exerted by animals (essentially, by animal’s muscles) and the environmental drag. The movement is superballistic with the mean square displacement growing with time as \documentclass[12pt]{minimal}
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\begin{document}$$\langle {x}^{2}(t)\rangle \sim {t}^{4}$$\end{document}〈x2(t)〉∼t4 at the beginning and eventually slowing down to the diffusive spread \documentclass[12pt]{minimal}
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\begin{document}$$\langle {x}^{2}(t)\rangle \sim t$$\end{document}〈x2(t)〉∼t. We show that the duration of the superballistic and superdiffusive stages can be long depending on the properties of the environmental noise and the intensity of drag. Our findings demonstrate theoretically how the movement pattern that includes diffusive and superdiffusive/superballistic motion arises naturally as a result of the interplay between the dissipative properties of the environment and the animal’s biological traits such as the body mass, typical movement velocity and the typical duration of uninterrupted movement.
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Fleming CH, Sheldon D, Gurarie E, Fagan WF, LaPoint S, Calabrese JM. Kálmán filters for continuous-time movement models. ECOL INFORM 2017. [DOI: 10.1016/j.ecoinf.2017.04.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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22
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Karelus DL, McCown JW, Scheick BK, van de Kerk M, Bolker BM, Oli MK. Effects of environmental factors and landscape features on movement patterns of Florida black bears. J Mammal 2017. [DOI: 10.1093/jmammal/gyx066] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
A greater understanding of how environmental factors and anthropogenic landscape features influence animal movements can inform management and potentially aid in mitigating human–wildlife conflicts. We investigated the movement patterns of 16 Florida black bears (Ursus americanus floridanus; 6 females, 10 males) in north-central Florida at multiple temporal scales using GPS data collected from 2011 to 2014. We calculated bi-hourly step-lengths and directional persistence, as well as daily and weekly observed displacements and expected displacements. We used those movement metrics as response variables in linear mixed models and tested for effects of sex, season, and landscape features. We found that step-lengths of males were generally longer than step-lengths of females, and both sexes had the shortest step-lengths during the daytime. Bears moved more slowly (shorter step-lengths) and exhibited less directed movement when near creeks, in forested wetlands, and in marsh habitats, possibly indicating foraging behavior. In urban areas, bears moved more quickly (longer step-lengths) and along more directed paths. The results were similar across all temporal scales. Major roads tended to act as a semipermeable barrier to bear movement. Males crossed major roads more frequently than females but both sexes crossed major roads much less frequently than minor roads. Our findings regarding the influence of landscape and habitat features on movement patterns of Florida black bears could be useful for planning effective wildlife corridors and understanding how future residential or commercial development and road expansions may affect animal movement.
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Affiliation(s)
- Dana L Karelus
- Department of Wildlife Ecology and Conservation, and School of Natural Resources and Environment, Department of Wildlife Ecology and Conservation, University of Florida, 110 Newins-Ziegler Hall, Gainesville, FL 32611, USA (DLK, MK, MKO)
| | - J Walter McCown
- Florida Fish and Wildlife Conservation Commission, 4005 S. Main St., Gainesville, FL 32601, USA (JWM, BKS)
| | - Brian K Scheick
- Florida Fish and Wildlife Conservation Commission, 4005 S. Main St., Gainesville, FL 32601, USA (JWM, BKS)
| | - Madelon van de Kerk
- Department of Wildlife Ecology and Conservation, and School of Natural Resources and Environment, Department of Wildlife Ecology and Conservation, University of Florida, 110 Newins-Ziegler Hall, Gainesville, FL 32611, USA (DLK, MK, MKO)
| | - Benjamin M Bolker
- Departments of Mathematics & Statistics and Biology, McMaster University, 314 Hamilton Hall, Hamilton, Ontario L8S 4K1, Canada (BMB)
| | - Madan K Oli
- Department of Wildlife Ecology and Conservation, and School of Natural Resources and Environment, Department of Wildlife Ecology and Conservation, University of Florida, 110 Newins-Ziegler Hall, Gainesville, FL 32611, USA (DLK, MK, MKO)
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Gurarie E, Cagnacci F, Peters W, Fleming CH, Calabrese JM, Mueller T, Fagan WF. A framework for modelling range shifts and migrations: asking when, whither, whether and will it return. J Anim Ecol 2017; 86:943-959. [DOI: 10.1111/1365-2656.12674] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 03/12/2017] [Indexed: 11/28/2022]
Affiliation(s)
- Eliezer Gurarie
- Department of Biology University of Maryland College Park MD 20742 USA
| | - Francesca Cagnacci
- Biodiversity and Molecular Ecology Department IASMA Research and Innovation Centre Fondazione Edmund Mach San Michele all’Adige Italy
- Department of Organismic and Evolutionary Biology Harvard University Cambridge MA USA
| | - Wibke Peters
- Biodiversity and Molecular Ecology Department IASMA Research and Innovation Centre Fondazione Edmund Mach San Michele all’Adige Italy
- Wildlife Biology Program College of Forestry and Conservation University of Montana Missoula MT USA
| | - Christen H. Fleming
- Department of Biology University of Maryland College Park MD 20742 USA
- Conservation Ecology Center Smithsonian Conservation Biology Institute National Zoological Park Front Royal VA USA
| | - Justin M. Calabrese
- Department of Biology University of Maryland College Park MD 20742 USA
- Conservation Ecology Center Smithsonian Conservation Biology Institute National Zoological Park Front Royal VA USA
| | - Thomas Mueller
- Biodiversity and Climate Research Centre Senckenberg Gesellschaft für Naturforschung Frankfurt Germany
- Department of Biological Sciences Goethe University Frankfurt Frankfurt Germany
| | - William F. Fagan
- Department of Biology University of Maryland College Park MD 20742 USA
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Péron G, Fleming CH, de Paula RC, Mitchell N, Strohbach M, Leimgruber P, Calabrese JM. Periodic continuous-time movement models uncover behavioral changes of wild canids along anthropization gradients. ECOL MONOGR 2017. [DOI: 10.1002/ecm.1260] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Guillaume Péron
- Smithsonian Conservation Biology Institute; National Zoological Park Front Royal Virginia 22630 USA
- Univ Lyon; Laboratoire de Biométrie et Biologie Evolutive UMR5558; CNRS; Université Lyon 1; F-69622 Villeurbanne France
| | - Christen H. Fleming
- Smithsonian Conservation Biology Institute; National Zoological Park Front Royal Virginia 22630 USA
- Department of Biology; University of Maryland; College Park Maryland 20742 USA
| | - Rogerio C. de Paula
- National Research Center for Carnivore Conservation (CENAP/ICMBio); Atibaia Sao Paulo Brazil
| | - Numi Mitchell
- The Conservation Agency; 67 Howland Avenue Jamestown Rhode Island 02835 USA
| | - Michael Strohbach
- Landscape Ecology and Environmental Systems Analysis; Institute of Geoecology; Technische Universität Braunschweig; Braunschweig Germany
| | - Peter Leimgruber
- Smithsonian Conservation Biology Institute; National Zoological Park Front Royal Virginia 22630 USA
| | - Justin M. Calabrese
- Smithsonian Conservation Biology Institute; National Zoological Park Front Royal Virginia 22630 USA
- Univ Lyon; Laboratoire de Biométrie et Biologie Evolutive UMR5558; CNRS; Université Lyon 1; F-69622 Villeurbanne France
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25
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Gurarie E, Fleming CH, Fagan WF, Laidre KL, Hernández-Pliego J, Ovaskainen O. Correlated velocity models as a fundamental unit of animal movement: synthesis and applications. MOVEMENT ECOLOGY 2017; 5:13. [PMID: 28496983 PMCID: PMC5424322 DOI: 10.1186/s40462-017-0103-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 03/27/2017] [Indexed: 05/08/2023]
Abstract
BACKGROUND Continuous time movement models resolve many of the problems with scaling, sampling, and interpretation that affect discrete movement models. They can, however, be challenging to estimate, have been presented in inconsistent ways, and are not widely used. METHODS We review the literature on integrated Ornstein-Uhlenbeck velocity models and propose four fundamental correlated velocity movement models (CVM's): random, advective, rotational, and rotational-advective. The models are defined in terms of biologically meaningful speeds and time scales of autocorrelation. We summarize several approaches to estimating the models, and apply these tools for the higher order task of behavioral partitioning via change point analysis. RESULTS An array of simulation illustrate the precision and accuracy of the estimation tools. An analysis of a swimming track of a bowhead whale (Balaena mysticetus) illustrates their robustness to irregular and sparse sampling and identifies switches between slower and faster, and directed vs. random movements. An analysis of a short flight of a lesser kestrel (Falco naumanni) identifies exact moments when switches occur between loopy, thermal soaring and directed flapping or gliding flights. CONCLUSIONS We provide tools to estimate parameters and perform change point analyses in continuous time movement models as an R package (smoove). These resources, together with the synthesis, should facilitate the wider application and development of correlated velocity models among movement ecologists.
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Affiliation(s)
- Eliezer Gurarie
- Department of Biology, University of Maryland, College Park, MD, 20742 USA
| | - Christen H. Fleming
- Department of Biology, University of Maryland, College Park, MD, 20742 USA
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, USA
| | - William F. Fagan
- Department of Biology, University of Maryland, College Park, MD, 20742 USA
| | - Kristin L. Laidre
- Polar Science Center, Applied Physics Laboratory, University of Washington, Seattle, 98195 WA USA
| | - Jesús Hernández-Pliego
- Department of Wetland Ecology, Estación Biológica de Doñana (EBD-CSIC), c/ Américo Vespucio s/n, Seville, 41092 Spain
| | - Otso Ovaskainen
- Department of Biosciences, University of Helsinki, Helsinki, 00014 Finland
- Centre for Biodiversity Dynamics, Department of Biology, University of Science and Technology, Trondheim, N-7491 Norway
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26
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Signer J, Ovaskainen O. Detecting the influence of environmental covariates on animal movement: a semivariance approach. Methods Ecol Evol 2016. [DOI: 10.1111/2041-210x.12692] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Johannes Signer
- Department of Wildlife Sciences University of Göttingen Büsgenweg 3 37077 Göttingen Germany
| | - Otso Ovaskainen
- Metapopulation Research Centre, Department of Biosciences University of Helsinki P.O. Box 65 FI‐00014 Finland
- Centre for Biodiversity Dynamics, Department of Biology Norwegian University of Science and Technology N‐7491 Trondheim Norway
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27
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Calabrese JM, Fleming CH, Gurarie E. ctmm: an
r
package for analyzing animal relocation data as a continuous‐time stochastic process. Methods Ecol Evol 2016. [DOI: 10.1111/2041-210x.12559] [Citation(s) in RCA: 238] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Justin M. Calabrese
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Rd. Front Royal VA 22630 USA
- Department of Biology University of Maryland College Park MD 20742 USA
| | - Chris H. Fleming
- Smithsonian Conservation Biology Institute National Zoological Park 1500 Remount Rd. Front Royal VA 22630 USA
- Department of Biology University of Maryland College Park MD 20742 USA
| | - Eliezer Gurarie
- Department of Biology University of Maryland College Park MD 20742 USA
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28
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Zattara EE, Turlington KW, Bely AE. Long-term time-lapse live imaging reveals extensive cell migration during annelid regeneration. BMC DEVELOPMENTAL BIOLOGY 2016; 16:6. [PMID: 27006129 PMCID: PMC4804569 DOI: 10.1186/s12861-016-0104-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 02/10/2016] [Indexed: 01/09/2023]
Abstract
BACKGROUND Time-lapse imaging has proven highly valuable for studying development, yielding data of much finer resolution than traditional "still-shot" studies and allowing direct examination of tissue and cell dynamics. A major challenge for time-lapse imaging of animals is keeping specimens immobile yet healthy for extended periods of time. Although this is often feasible for embryos, the difficulty of immobilizing typically motile juvenile and adult stages remains a persistent obstacle to time-lapse imaging of post-embryonic development. RESULTS Here we describe a new method for long-duration time-lapse imaging of adults of the small freshwater annelid Pristina leidyi and use this method to investigate its regenerative processes. Specimens are immobilized with tetrodotoxin, resulting in irreversible paralysis yet apparently normal regeneration, and mounted in agarose surrounded by culture water or halocarbon oil, to prevent dehydration but allowing gas exchange. Using this method, worms can be imaged continuously and at high spatial-temporal resolution for up to 5 days, spanning the entire regeneration process. We performed a fine-scale analysis of regeneration growth rate and characterized cell migration dynamics during early regeneration. Our studies reveal the migration of several putative cell types, including one strongly resembling published descriptions of annelid neoblasts, a cell type suggested to be migratory based on "still-shot" studies and long hypothesized to be linked to regenerative success in annelids. CONCLUSIONS Combining neurotoxin-based paralysis, live mounting techniques and a starvation-tolerant study system has allowed us to obtain the most extensive high-resolution longitudinal recordings of full anterior and posterior regeneration in an invertebrate, and to detect and characterize several cell types undergoing extensive migration during this process. We expect the tetrodotoxin paralysis and time-lapse imaging methods presented here to be broadly useful in studying other animals and of particular value for studying post-embryonic development.
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Affiliation(s)
- Eduardo E. Zattara
- Department of Biology, University of Maryland, College Park, MD 20740 USA
| | - Kate W. Turlington
- Department of Biology, University of Maryland, College Park, MD 20740 USA
| | - Alexandra E. Bely
- Department of Biology, University of Maryland, College Park, MD 20740 USA
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29
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Ecological influences on the local movement dynamics of the blackspotted topminnow, Fundulus olivaceus. Behav Ecol Sociobiol 2016. [DOI: 10.1007/s00265-016-2073-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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30
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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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31
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van Beest FM, Aars J, Routti H, Lie E, Andersen M, Pavlova V, Sonne C, Nabe-Nielsen J, Dietz R. Spatiotemporal variation in home range size of female polar bears and correlations with individual contaminant load. Polar Biol 2015. [DOI: 10.1007/s00300-015-1876-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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32
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Tilles PFC, Petrovskii SV. How animals move along? Exactly solvable model of superdiffusive spread resulting from animal's decision making. J Math Biol 2015; 73:227-55. [PMID: 26650504 DOI: 10.1007/s00285-015-0947-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2014] [Revised: 11/15/2015] [Indexed: 11/26/2022]
Abstract
Patterns of individual animal movement have been a focus of considerable attention recently. Of particular interest is a question how different macroscopic properties of animal dispersal result from the stochastic processes occurring on the microscale of the individual behavior. In this paper, we perform a comprehensive analytical study of a model where the animal changes the movement velocity as a result of its behavioral response to environmental stochasticity. The stochasticity is assumed to manifest itself through certain signals, and the animal modifies its velocity as a response to the signals. We consider two different cases, i.e. where the change in the velocity is or is not correlated to its current value. We show that in both cases the early, transient stage of the animal movement is super-diffusive, i.e. ballistic. The large-time asymptotic behavior appears to be diffusive in the uncorrelated case but super-ballistic in the correlated case. We also calculate analytically the dispersal kernel of the movement and show that, whilst it converge to a normal distribution in the large-time limit, it possesses a fatter tail during the transient stage, i.e. at early and intermediate time. Since the transients are known to be highly relevant in ecology, our findings may indicate that the fat tails and superdiffusive spread that are sometimes observed in the movement data may be a feature of the transitional dynamics rather than an inherent property of the animal movement.
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Affiliation(s)
- Paulo F C Tilles
- Universidade Estadual de Londrina, Londrina, Parana, Brazil
- Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK
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33
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Martin J, Sabatier Q, Gowan TA, Giraud C, Gurarie E, Calleson CS, Ortega‐Ortiz JG, Deutsch CJ, Rycyk A, Koslovsky SM. A quantitative framework for investigating risk of deadly collisions between marine wildlife and boats. Methods Ecol Evol 2015. [DOI: 10.1111/2041-210x.12447] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Julien Martin
- Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute St Petersburg FL 33701 USA
- Southeast Ecological Science Center U.S. Geological Survey 7920 NW 71st Street Gainesville, FL 32653 USA
| | - Quentin Sabatier
- Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute St Petersburg FL 33701 USA
- Ecole Polytechnique CMAP, UMR CNRS 7641 91128 Palaiseau Cedex France
- Department of Wildlife Ecology and Conservation University of Florida Gainesville FL 32611 USA
| | - Timothy A. Gowan
- Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute St Petersburg FL 33701 USA
| | - Christophe Giraud
- Laboratoire de Mathématiques d'Orsay UMR 8628, Université Paris‐Sud F‐91405 Orsay Cedex France
| | - Eliezer Gurarie
- Department of Biology University of Maryland College Park MD 20742 USA
| | - Charles Scott Calleson
- Florida Fish and Wildlife Conservation Commission Imperiled Species Management Section Tallahassee FL 32399 USA
| | - Joel G. Ortega‐Ortiz
- Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute St Petersburg FL 33701 USA
- College of Marine Science University of South Florida St Petersburg FL 33701 USA
| | - Charles J. Deutsch
- Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute Gainesville FL 32601 USA
| | - Athena Rycyk
- Department of Oceanography Florida State University Tallahassee FL 32306 USA
| | - Stacie M. Koslovsky
- Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute St Petersburg FL 33701 USA
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Abstract
An individual’s choices are shaped by its experience, a fundamental property of behavior important to understanding complex processes. Learning and memory are observed across many taxa and can drive behaviors, including foraging behavior. To explore the conditions under which memory provides an advantage, we present a continuous-space, continuous-time model of animal movement that incorporates learning and memory. Using simulation models, we evaluate the benefit memory provides across several types of landscapes with variable-quality resources and compare the memory model within a nested hierarchy of simpler models (behavioral switching and random walk). We find that memory almost always leads to improved foraging success, but that this effect is most marked in landscapes containing sparse, contiguous patches of high-value resources that regenerate relatively fast and are located in an otherwise devoid landscape. In these cases, there is a large payoff for finding a resource patch, due to size, value, or locational difficulty. While memory-informed search is difficult to differentiate from other factors using solely movement data, our results suggest that disproportionate spatial use of higher value areas, higher consumption rates, and consumption variability all point to memory influencing the movement direction of animals in certain ecosystems.
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35
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Gurarie E, Bracis C, Delgado M, Meckley TD, Kojola I, Wagner CM. What is the animal doing? Tools for exploring behavioural structure in animal movements. J Anim Ecol 2015; 85:69-84. [DOI: 10.1111/1365-2656.12379] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Accepted: 04/07/2015] [Indexed: 11/29/2022]
Affiliation(s)
- Eliezer Gurarie
- Department of Biology University of Maryland College Park MD 20742 USA
- School of Environmental and Forest Sciences University of Washington Seattle WA 98195 USA
| | - Chloe Bracis
- Quantitative Ecology and Resource Management University of Washington Seattle WA 98195 USA
| | - Maria Delgado
- Department of Biosciences University of Helsinki 00014Helsinki Finland
- Research Unit of Biodiversity (UMIB, UO‐CSIC‐PA) Oviedo University – Campus Mieres 33600Mieres Spain
| | - Trevor D. Meckley
- Department of Fisheries and Wildlife Michigan State University East Lansing MI 48824 USA
| | - Ilpo Kojola
- Natural Resources Institute Box 16 FI‐96301Rovaniemi Finland
| | - C. Michael Wagner
- Department of Fisheries and Wildlife Michigan State University East Lansing MI 48824 USA
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36
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Fleming CH, Subaşı Y, Calabrese JM. Maximum-entropy description of animal movement. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2015; 91:032107. [PMID: 25871054 DOI: 10.1103/physreve.91.032107] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2014] [Indexed: 05/08/2023]
Abstract
We introduce a class of maximum-entropy states that naturally includes within it all of the major continuous-time stochastic processes that have been applied to animal movement, including Brownian motion, Ornstein-Uhlenbeck motion, integrated Ornstein-Uhlenbeck motion, a recently discovered hybrid of the previous models, and a new model that describes central-place foraging. We are also able to predict a further hierarchy of new models that will emerge as data quality improves to better resolve the underlying continuity of animal movement. Finally, we also show that Langevin equations must obey a fluctuation-dissipation theorem to generate processes that fall from this class of maximum-entropy distributions when the constraints are purely kinematic.
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Affiliation(s)
- Chris H Fleming
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd., Front Royal, Virginia 22630, USA
- Department of Biology, University of Maryland, College Park, College Park, Maryland 20742, USA
| | - Yiğit Subaşı
- Department of Chemistry and Biochemistry, University of Maryland, College Park, College Park, Maryland 20742, USA
| | - Justin M Calabrese
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, 1500 Remount Rd., Front Royal, Virginia 22630, USA
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37
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Asymptotic Analysis of First Passage Time Problems Inspired by Ecology. Bull Math Biol 2014; 77:83-125. [DOI: 10.1007/s11538-014-0053-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 12/08/2014] [Indexed: 01/31/2023]
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38
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Fleming CH, Calabrese JM, Mueller T, Olson KA, Leimgruber P, Fagan WF. Non-Markovian maximum likelihood estimation of autocorrelated movement processes. Methods Ecol Evol 2014. [DOI: 10.1111/2041-210x.12176] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Christen H. Fleming
- Conservation Ecology Center; Smithsonian Conservation Biology Institute; National Zoological Park; 1500 Remount Road Front Royal VA 22630 USA
- Department of Biology; University of Maryland; College Park MD 20742 USA
| | - Justin M. Calabrese
- Conservation Ecology Center; Smithsonian Conservation Biology Institute; National Zoological Park; 1500 Remount Road Front Royal VA 22630 USA
| | - Thomas Mueller
- Conservation Ecology Center; Smithsonian Conservation Biology Institute; National Zoological Park; 1500 Remount Road Front Royal VA 22630 USA
- Department of Biology; University of Maryland; College Park MD 20742 USA
| | - Kirk A. Olson
- Conservation Ecology Center; Smithsonian Conservation Biology Institute; National Zoological Park; 1500 Remount Road Front Royal VA 22630 USA
| | - Peter Leimgruber
- Conservation Ecology Center; Smithsonian Conservation Biology Institute; National Zoological Park; 1500 Remount Road Front Royal VA 22630 USA
| | - William F. Fagan
- Department of Biology; University of Maryland; College Park MD 20742 USA
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39
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Fleming CH, Calabrese JM, Mueller T, Olson KA, Leimgruber P, Fagan WF. From fine-scale foraging to home ranges: a semivariance approach to identifying movement modes across spatiotemporal scales. Am Nat 2014; 183:E154-67. [PMID: 24739204 DOI: 10.1086/675504] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking. Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous-space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales.
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Affiliation(s)
- Chris H Fleming
- Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, Virginia 22630
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40
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McClintock BT, Johnson DS, Hooten MB, Ver Hoef JM, Morales JM. When to be discrete: the importance of time formulation in understanding animal movement. MOVEMENT ECOLOGY 2014; 2:21. [PMID: 25709830 PMCID: PMC4337762 DOI: 10.1186/s40462-014-0021-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Accepted: 09/29/2014] [Indexed: 05/21/2023]
Abstract
Animal movement is essential to our understanding of population dynamics, animal behavior, and the impacts of global change. Coupled with high-resolution biotelemetry data, exciting new inferences about animal movement have been facilitated by various specifications of contemporary models. These approaches differ, but most share common themes. One key distinction is whether the underlying movement process is conceptualized in discrete or continuous time. This is perhaps the greatest source of confusion among practitioners, both in terms of implementation and biological interpretation. In general, animal movement occurs in continuous time but we observe it at fixed discrete-time intervals. Thus, continuous time is conceptually and theoretically appealing, but in practice it is perhaps more intuitive to interpret movement in discrete intervals. With an emphasis on state-space models, we explore the differences and similarities between continuous and discrete versions of mechanistic movement models, establish some common terminology, and indicate under which circumstances one form might be preferred over another. Counter to the overly simplistic view that discrete- and continuous-time conceptualizations are merely different means to the same end, we present novel mathematical results revealing hitherto unappreciated consequences of model formulation on inferences about animal movement. Notably, the speed and direction of movement are intrinsically linked in current continuous-time random walk formulations, and this can have important implications when interpreting animal behavior. We illustrate these concepts in the context of state-space models with multiple movement behavior states using northern fur seal (Callorhinus ursinus) biotelemetry data.
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Affiliation(s)
- Brett T McClintock
- />National Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, WA 98115 USA
| | - Devin S Johnson
- />National Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Seattle, WA 98115 USA
| | - Mevin B Hooten
- />U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Department of Statistics, Colorado State University, Fort Collins, CO 80523 USA
| | - Jay M Ver Hoef
- />National Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center, Fairbanks, AK 99775 USA
| | - Juan M Morales
- />Ecotono, INIBIOMA–CONICET, Universidad Nacional del Comahue, Quintral 1250, Bariloche, 8400 Argentina
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41
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Kouba M, Bartoš L, Štastný K. Differential movement patterns of juvenile Tengmalms owls (Aegolius funereus) during the post-fledging dependence period in two years with contrasting prey abundance. PLoS One 2013; 8:e67034. [PMID: 23843981 PMCID: PMC3700927 DOI: 10.1371/journal.pone.0067034] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2013] [Accepted: 05/13/2013] [Indexed: 11/29/2022] Open
Abstract
Fledgling behaviour and movement patterns throughout the post-fledging dependence period (PFDP), especially in relation to changing environmental conditions, have been rarely studied, despite the fact that this period is recognized as of crucial significance in terms of high mortality of juveniles. The PFDP can extend over quite a protracted period, particularly in birds of prey, and a knowledge of the movement patterns of individuals is fundamental for understanding mechanisms underlying survival, habitat use and dispersion. We radiotracked 39 fledglings of the Tengmalm’s owl (Aegolius funereus) in two years with different availability of prey: 2010 (n = 29) and 2011 (n = 10) and obtained 1455 daily locations. Fledglings reached independence on average in 45 days after fledging in 2010 (n = 22) and 57 days in 2011 (n = 6). Within years, the most important measures influencing the distance moved from the nest box were age of fledglings and number of surviving siblings present. Individual home range size and duration of PFDP in particular were dependent on maximal number of siblings seen outside the nest box. In the season with low prey availability fledglings were observed at greater distances from the nest box than in the year with higher prey availability (mean distance: 350 m in 2010 and 650 m in 2011) and occupied larger home ranges (mean: 30.3 ha in 2010 and 57.7 ha in 2011). The main factor causing these differences between years was probably the different availability of prey in these two years, affecting breeding success and post-fledging survivorship of the Tengmalm’s owls.
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Affiliation(s)
- Marek Kouba
- Department of Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic.
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Nams VO. Sampling animal movement paths causes turn autocorrelation. Acta Biotheor 2013; 61:269-84. [PMID: 23463145 DOI: 10.1007/s10441-013-9182-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2012] [Accepted: 02/27/2013] [Indexed: 10/27/2022]
Abstract
Animal movement models allow ecologists to study processes that operate over a wide range of scales. In order to study them, continuous movements of animals are translated into discrete data points, and then modelled as discrete models. This discretization can bias the representation of the movement path. This paper shows that discretizing correlated random movement paths creates a biased path by creating correlations between successive turning angles. The discretization also biases statistical tests for correlated random walks (CRW) and causes an overestimate in distances travelled; a correction is given for these biases. This effect suggests that there is a natural scale to CRWs, but that distance-discretized CRWs are in a sense, scale invariant. Perhaps a new null model for continuous movement paths is needed. Authors need to be aware of the biases caused by discretizing correlated random walks, and deal with them appropriately.
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Laidre KL, Born EW, Gurarie E, Wiig Ø, Dietz R, Stern H. Females roam while males patrol: divergence in breeding season movements of pack-ice polar bears (Ursus maritimus). Proc Biol Sci 2013; 280:20122371. [PMID: 23222446 PMCID: PMC3574305 DOI: 10.1098/rspb.2012.2371] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 11/09/2012] [Indexed: 11/12/2022] Open
Abstract
Intraspecific differences in movement behaviour reflect different tactics used by individuals or sexes to favour strategies that maximize fitness. We report movement data collected from n = 23 adult male polar bears with novel ear-attached transmitters in two separate pack ice subpopulations over five breeding seasons. We compared movements with n = 26 concurrently tagged adult females, and analysed velocities, movement tortuosity, range sizes and habitat selection with respect to sex, reproductive status and body mass. There were no differences in 4-day displacements or sea ice habitat selection for sex or population. By contrast, adult females in all years and both populations had significantly more linear movements and significantly larger breeding range sizes than males. We hypothesized that differences were related to encounter rates, and used observed movement metrics to parametrize a simulation model of male-male and male-female encounter. The simulation showed that the more tortuous movement of males leads to significantly longer times to male-male encounter, while having little impact on male-female encounter. By contrast, linear movements of females are consistent with a prioritized search for sparsely distributed prey. These results suggest a possible mechanism for explaining the smaller breeding range sizes of some solitary male carnivores compared to females.
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Affiliation(s)
- Kristin L Laidre
- Polar Science Center, APL, University of Washington, Seattle, WA 98105, USA.
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Shimatani IK, Yoda K, Katsumata N, Sato K. Toward the quantification of a conceptual framework for movement ecology using circular statistical modeling. PLoS One 2012; 7:e50309. [PMID: 23226261 PMCID: PMC3511459 DOI: 10.1371/journal.pone.0050309] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 10/22/2012] [Indexed: 11/21/2022] Open
Abstract
To analyze an animal’s movement trajectory, a basic model is required that satisfies the following conditions: the model must have an ecological basis and the parameters used in the model must have ecological interpretations, a broad range of movement patterns can be explained by that model, and equations and probability distributions in the model should be mathematically tractable. Random walk models used in previous studies do not necessarily satisfy these requirements, partly because movement trajectories are often more oriented or tortuous than expected from the models. By improving the modeling for turning angles, this study aims to propose a basic movement model. On the basis of the recently developed circular auto-regressive model, we introduced a new movement model and extended its applicability to capture the asymmetric effects of external factors such as wind. The model was applied to GPS trajectories of a seabird (Calonectris leucomelas) to demonstrate its applicability to various movement patterns and to explain how the model parameters are ecologically interpreted under a general conceptual framework for movement ecology. Although it is based on a simple extension of a generalized linear model to circular variables, the proposed model enables us to evaluate the effects of external factors on movement separately from the animal’s internal state. For example, maximum likelihood estimates and model selection suggested that in one homing flight section, the seabird intended to fly toward the island, but misjudged its navigation and was driven off-course by strong winds, while in the subsequent flight section, the seabird reset the focal direction, navigated the flight under strong wind conditions, and succeeded in approaching the island.
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Bocedi G, Pe’er G, Heikkinen RK, Matsinos Y, Travis JMJ. Projecting species’ range expansion dynamics: sources of systematic biases when scaling up patterns and processes. Methods Ecol Evol 2012. [DOI: 10.1111/j.2041-210x.2012.00235.x] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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