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Research Article

Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement

, , , , &
Pages 1505-1522 | Received 12 Jan 2017, Accepted 21 Dec 2017, Published online: 03 Jan 2018

References

  • Amstrup, S.C., Mcdonald, T.L., and Durner, G.M., 2004. Using satellite radiotelemetry data to delineate and manage wildlife populations. Wildlife Society Bulletin, 32 (3), 661–679. doi:10.2193/0091-7648(2004)032[0661:USRDTD]2.0.CO;2
  • Anderson, J.H., et al. 2017. Agent-based simulation of Muscovy duck movements using observed habitat transition and distance frequencies. Computers, Environment and Urban Systems, 61 (Part A), 49–55. doi:10.1016/j.compenvurbsys.2016.09.002
  • Benenson, I., 2013. Agent-based models of geographical systems. International Journal of Geographical Information Science, 27 (5), 1047–1053. doi:10.1080/13658816.2013.763946
  • Bennett, D.A. and Tang, W., 2006. Modelling adaptive, spatially aware, and mobile agents: elk migration in Yellowstone. International Journal of Geographical Information Science, 20 (9), 1039–1066. doi:10.1080/13658810600830806
  • Beyer, H.L. 2012. Geospatial Modelling Environment (Version 0.6.0.0) (software). Available from: http://www.spatialecology.com/gme.
  • Blundell, G.M., Maier, J.A.K., and Debevec, E.M., 2001. Linear home ranges: effects of smoothing, sample size, and autocorrelation on kernel estimates. Ecological Monographs, 71 (3), 469–489. doi:10.1890/0012-9615(2001)071[0469:LHREOS]2.0.CO;2
  • Borger, L., et al. 2006. Effects of sampling regime on the mean and variance of home range size estimates. Journal of Animal Ecology, 75 (6), 1493–1505. doi:10.1111/j.1365-2656.2006.01164.x
  • Breed, G.A., Golson, E.A., and Tinker, M.T., 2017. Predicting animal home-range structure and transitions using a multistate Ornstein-Uhlenbeck biased random walk. Ecology, 98 (1), 32–47. doi:10.1002/ecy.1615
  • Brown, D.G. and Xie, Y.C., 2006. Spatial agent-based modelling. International Journal of Geographical Information Science, 20 (9), 941–943. doi:10.1080/13658810600830491
  • Costa, D.P., et al. 2010. Accuracy of ARGOS Locations of pinnipeds at-sea estimated using Fastloc GPS. PLoS ONE, 5 (1), e8677. doi:10.1371/journal.pone.0008677
  • Cressie, N., 1993. Statistics for spatial data. NY: Wiley.
  • Dodge, S., et al. 2016. Analysis of movement data. International Journal of Geographical Information Science, 30 (5), 825–834. doi:10.1080/13658816.2015.1132424
  • Dodge, S., Weibel, R., and Lautenschutz, A.K., 2008. Towards a taxonomy of movement patterns. Information Visualization, 7 (3–4), 240–252. doi:10.1057/PALGRAVE.IVS.9500182
  • Dougherty, E.R., et al. 2017. A cross-validation-based approach for delimiting reliable home range estimates. Movement Ecology, 5 (1), 19. doi:10.1186/s40462-017-0110-4
  • Downs, J.A., 2010. Time-geographic density estimation for analysing moving point objects. Lecture Notes in Computer Science, 6292, 16–26.
  • Downs, J.A., et al. 2012. Accuracy of home range estimators for homogeneous and inhomogeneous point patterns. Ecological Modelling, 225, 66–73. doi:10.1016/j.ecolmodel.2011.11.010
  • Downs, J.A., et al. 2014. Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use. International Journal of Geographical Information Science, 28 (5), 875–890. doi:10.1080/13658816.2013.850170
  • Downs, J.A., 2016. Mapping sex offender activity spaces relative to crime using time-geographic methods. Annals of GIS, 22 (2), 141–150. doi:10.1080/19475683.2016.1147495
  • Downs, J.A. and Horner, M.W., 2008. Effects of point pattern shape on home-range estimates. Journal of Wildlife Management, 72 (8), 1813–1818. doi:10.2193/2007-454
  • Downs, J.A. and Horner, M.W., 2009. A characteristic-hull based method for home range estimation. Transactions in GIS, 13 (5–6), 527–537. doi:10.1111/tgis.2009.13.issue-5-6
  • Downs, J.A. and Horner, M.W., 2012. Probabilistic potential path trees for visualizing and analyzing vehicle tracking data. Journal of Transport Geography, 23, 72–80. doi:10.1016/j.jtrangeo.2012.03.017
  • Downs, J.A., and Horner, M.W., 2014. Adaptive-velocity time-geographic density estimation for mapping the potential and probable locations of mobile objects. Environment and Planning B: Planning and Design, 41 (6), 1006-1021. doi:10.1068/b130065p
  • Downs, J.A., Horner, M.W., and Tucker, A., 2011. Time-geographic density estimation for home range analysis. Annals of GIS, 17 (163–171). doi:10.1080/19475683.2011.602023
  • Duckham, M., et al. 2008. Efficient generation of simple polygons for characterizing the shape of a set of points in the plane. Pattern Recognition, 41 (10), 3224–3236. doi:10.1016/j.patcog.2008.03.023
  • Fleming, C.H. and Calabrese, J.M., 2017. A new kernel density estimator for accurate home‐range and species‐range area estimation. Methods in Ecology and Evolution, 8 (5), 571–579. doi:10.1111/2041-210X.12673
  • Gagliardo, A., et al. 2011. Homing pigeons only navigate in air with intact environmental odours: a test of the olfactory activation hypothesis with GPS data loggers. PLoS ONE, 6 (8), 14. doi:10.1371/journal.pone.0022385
  • Ghavami, S.M., Taleai, M., and Arentze, T., 2017. An intelligent spatial land use planning support system using socially rational agents. International Journal of Geographical Information Science, 31 (5), 1022–1041. doi:10.1080/13658816.2016.1263306
  • Gitzen, R.A., Millspaugh, J.J., and Kernohan, B.J., 2006. Bandwidth selection for fixed-kernel analysis of animal utilization distributions. Journal of Wildlife Management, 70 (5), 1334–1344. doi:10.2193/0022-541X(2006)70[1334:BSFFAO]2.0.CO;2
  • Hemson, G., et al. 2005. Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home-range analyses with least-squares cross-validation. Journal of Animal Ecology, 74 (3), 455–463. doi:10.1111/j.1365-2656.2005.00944.x
  • Holloway, P. and Miller, J., 2014. Uncertainty analysis of step-selection functions: the effect of model parameters on inferences about the relationship between animal movement and the environment. In: M. Duckham, E. Pebesma, K. Stewart and A. Frank, eds.. Geographic information science. Cham, Switzerland: Springer International Publishing, 48–63.
  • Horne, J.S. and Garton, E.O., 2006. Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis. Journal of Wildlife Management, 70 (3), 641–648. doi:10.2193/0022-541X(2006)70[641:LCVLSC]2.0.CO;2
  • Horner, M.W. and Downs, J.A., 2014. Integrating people and place: A density-based measure for assessing accessibility to opportunities. Journal of Transport and Land Use, 7 (2), 23–40. doi:10.5198/jtlu.v7i2
  • Jose-Dominguez, J.M., Savini, T., and Asensio, N., 2015. Ranging and site fidelity in northern pigtailed macaques (Macaca leonina) over different temporal scales. American Journal of Primatology, 77 (8), 841–853. doi:10.1002/ajp.22409
  • Juang, P., et al. 2002. Energy-efficient computing for wildlife tracking: design tradeoffs and early experiences with ZebraNet. Acm Sigplan Notices, 37 (10), 96–107. doi:10.1145/605432.605408
  • Kranstauber, B., et al. 2011. The Movebank data model for animal tracking. Environmental Modelling & Software, 26 (6), 834–835. doi:10.1016/j.envsoft.2010.12.005
  • Laube, P. and Purves, R.S., 2011. How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15 (3), 401–418. doi:10.1111/j.1467-9671.2011.01256.x
  • Long, J. and Nelson, T., 2015. Home range and habitat analysis using dynamic time geography. Journal of Wildlife Management, 79 (3), 481–490. doi:10.1002/jwmg.845
  • Long, J.A., et al. 2014. A critical examination of indices of dynamic interaction for wildlife telemetry studies. Journal of Animal Ecology, 83 (5), 1216–1233. doi:10.1111/1365-2656.12198
  • Long, J.A., 2015. Quantifying spatial-temporal interactions from wildlife tracking data: issues of space, time, and statistical significance. In: A. Stein and D. Allard, eds.. Spatial statistics conference 2015, part 1, 3–10. doi:10.1016/j.proenv.2015.05.004.
  • Long, J.A. and Nelson, T.A., 2013. A review of quantitative methods for movement data. International Journal of Geographical Information Science, 27 (2), 292–318. doi:10.1080/13658816.2012.682578
  • Mao, L. and Bian, L., 2011. Agent-based simulation for a dual-diffusion process of influenza and human preventive behavior. International Journal of Geographical Information Science, 25 (9), 1371–1388. doi:10.1080/13658816.2011.556121
  • Miller, H.J., 2005. A measurement theory for time geography. Geographical Analysis, 37 (1), 17–45. doi:10.1111/gean.2005.37.issue-1
  • Miller, J.A., 2012. Using spatially explicit simulated data to analyze animal interactions: a case study with Brown Hyenas in Northern Botswana. Transactions in GIS, 16 (3), 271–291. doi:10.1111/tgis.2012.16.issue-3
  • Miller, J.A., 2015. Towards a better understanding of dynamic interaction metrics for wildlife: a null model approach. Transactions in GIS, 19 (3), 342–361. doi:10.1111/tgis.2015.19.issue-3
  • Morrison, A.E. and Allen, M.S., 2017. Agent-based modelling, molluscan population dynamics, and archaeomalacology. Quaternary International, 427, 170–183. doi:10.1016/j.quaint.2015.09.004
  • Murakami, H., Niizato, T., and Gunji, Y.P., 2017. Emergence of a coherent and cohesive swarm based on mutual anticipation. Scientific Reports, 7, 9. doi:10.1038/srep46447
  • Nams, V.O., 2013. Sampling Animal movement paths causes turn autocorrelation. Acta Biotheoretica, 61, 269–284. doi:10.1007/s10441-013-9182-8
  • Olsen, J., et al. 2011. HOME-range size and territorial calling of southern boobooks (Ninox novaeseelandiae) in adjacent territories. Journal of Raptor Research, 45 (2), 136–142. doi:10.3356/JRR-10-92.1
  • Plank, M.J., Auger-Methe, M., and Codling, E.A., 2013. Levy or not? analysing positional data from animal movement paths. In: M.A. Lewis, P.K. Maini, and S.V. Petrovskii, eds. Dispersal, individual movement and spatial ecology: a mathematical perspective. BerlIn: Springer-Verlag Berlin, 33–52.
  • Plank, M.J. and Codling, E.A., 2009. Sampling rate and misidentification of Levy and non-Levy movement paths. Ecology, 90 (12), 3546–3553. doi:10.1890/09-0079.1
  • Purves, R.S., et al., 2014. Moving beyond the point: an agenda for research in movement analysis with real data. Computers Environment and Urban Systems, 47, 1–4. doi:10.1016/j.compenvurbsys.2014.06.003
  • Row, J.R. and Blouin-Demers, G., 2006. Kernels are not accurate estimators of home-range size for herpetofauna. Copeia, (4), 797–802. doi:10.1643/0045-8511(2006)6[797:KANAEO]2.0.CO;2
  • Scott, D.W., 2012. Multivariate density estimation and visualization. In: J.E. Gentle, W.K. Härdle, and Y. Mori, eds. Handbook of computational statistics: concepts and methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 549–569.
  • Silverman, B.W., 1986. Density estimation for statistics and data analysis. London: Chapman Hall.
  • Tan, L., Wu, L., and Lin, H., 2015. An individual cognitive evacuation behaviour model for agent-based simulation: a case study of a large outdoor event. International Journal of Geographical Information Science, 29 (9), 1552–1568. doi:10.1080/13658816.2015.1030751
  • Tang, W.W. and Bennett, D.A., 2010. The explicit representation of context in agent-based models of complex adaptive spatial systems. Annals of the Association of American Geographers, 100 (5), 1128–1155. doi:10.1080/00045608.2010.517739
  • Vermeiren, K., et al. 2016. ASSURE: a model for the simulation of urban expansion and intra-urban social segregation. International Journal of Geographical Information Science, 30 (12), 2377–2400. doi:10.1080/13658816.2016.1177641
  • Wallentin, G. and Neuwirth, C., 2017. Dynamic hybrid modelling: switching between AB and SD designs of a predator-prey model. Ecological Modelling, 345, 165–175. doi:10.1016/j.ecolmodel.2016.11.007
  • Worton, B.J., 1987. A review of models of home range for animal movement. Ecological Modelling, 38 (3–4), 277–298. doi:10.1016/0304-3800(87)90101-3

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