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Simulating animal movements to predict wildlife-vehicle collisions: illustrating an application of the novel R package SiMRiv

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Abstract

In conservation, there is a high demand for methods to predict how animals respond to human infrastructure, such as estimating the location of road mortalities and evaluating the effectiveness of mitigation measures. Computer-based simulation models have emerged as an important tool in understanding wildlife-infrastructure interactions. Such models, however, often assume animal omniscience of the landscape yielding unrealistic movements, focus more on genetic connectivity than actual movement paths, or are case-specific and mathematically/computationally challenging to apply. Here, we illustrate the potential of SiMRiv, a novel R package for simulating spatially explicit, individual multistate (Markovian) movements incorporating landscape heterogeneity, in the subject of road ecology. In particular, we used SiMRiv to reproduce wildlife movement patterns and predict high-risk areas for road-kill, using Eurasian otters (Lutra lutra) as a model species. We compared the number of road crossings in real otter movements and null models (simulated, multistate Markovian movements) incorporating the effect of the landscape structure (here, water dependence). The number of road crossings in real and simulated movements was remarkably similar, and available limited real road-kill data supported SiMRiv’s road-kill risk predictions. Further, other emergent movement properties were also very similar in real and simulated movements. Overall, results show that SiMRiv has potential for reconstructing real wildlife movement patterns, as well as for predicting road-kill risk areas. SiMRiv constitutes a flexible, powerful, and intuitive tool to help biologists and managers to test mechanistic hypotheses on wildlife movement ecology, including those related to wildlife-vehicle interactions.

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References

  • Allen CH, Parrott L, Kyle C (2016) An individual-based modelling approach to estimate landscape connectivity for bighorn sheep (Oviscanadensis). PeerJ 4:e2001. https://doi.org/10.7717/peerj.2001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bastille-Rousseau G, Murray DL, Schaefer JA, Lewis MA, Mahoney SP, Potts JR (2018) Spatial scales of habitat selection decisions: implications for telemetry-based movement modelling. Ecography 41:437–443

    Google Scholar 

  • Beyer HL, Gurarie E, Börger L, Panzacchi M, Basille M, Herfindal I, Van Moorter B, Lele SR, Matthiopoulos J (2016) ‘You shall not pass!’: quantifying barrier permeability and proximity avoidance by animals. J Anim Ecol 85:43–53

    PubMed  Google Scholar 

  • Chetkiewicz CLB, Boyce MS (2009) Use of resource selection functions to identify conservation corridors. J Appl Ecol 46:1036–1047

    Google Scholar 

  • Clevenger AP, Barrueto M, Gunson KE, Caryl FM, Ford AT (2015) Context-dependent effects on spatial variation in deer-vehicle collisions. Ecosphere 6:1–20

    Google Scholar 

  • Cushman SA, Lewis JS (2010) Movement behavior explains genetic differentiation in American black bears. Landsc Ecol 25:1613–1625

    Google Scholar 

  • Davy CM, Ford AT, Fraser KC (2017) Aeroconservation for the fragmented skies. Conserv Lett 10:773–780

    Google Scholar 

  • Dormann CF, Schymanski SJ, Cabral J, Chuine I, Graham C, Hartig F, Kearney M, Morin X, Römermann C, Schröder B, Singer A (2012) Correlation and process in species distribution models: bridging a dichotomy. J Biogeogr 39:2119–2131

    Google Scholar 

  • Fahrig L (2007) Non-optimal animal movement in human-altered landscapes. Funct Ecol 21:1003–1015

    Google Scholar 

  • Fortin D, Beyer HL, Boyce MS, Smith DW, Duchesne T, Mao JS (2005) Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park. Ecology 86:1320–1330

    Google Scholar 

  • Gotelli NJ, Graves GR (1996) Null models in ecology. Smithsonian Institution Press

  • Grant TJ, Parry HR, Zalucki MP, Bradbury SP (2018) Predicting monarch butterfly (Danausplexippus) movement and egg-laying with a spatially-explicit agent-based model: the role of monarch perceptual range and spatial memory. Ecol Model 374:37–50

    Google Scholar 

  • Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke H-H, Weiner J, Wiegand T, DeAngelis DL (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987–991

    PubMed  Google Scholar 

  • Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198:115–126

    Google Scholar 

  • Hartig F, Calabrese JM, Reineking B, Wiegand T, Huth A (2011) Statistical inference for stochastic simulation models–theory and application. Ecol Lett 14:816–827

    PubMed  Google Scholar 

  • Jaeger JA, Fahrig L (2004) Effects of road fencing on population persistence. Conserv Biol 18:1651–1657

    Google Scholar 

  • Kanagaraj R, Wiegand T, Kramer-Schadt S, Goyal SP (2013) Using individual-based movement models to assess inter-patch connectivity for large carnivores in fragmented landscapes. Biol Conserv 167:298–309

    Google Scholar 

  • Koen EL, Bowman J, Sadowski C, Walpole AA (2014) Landscape connectivity for wildlife: development and validation of multispecies linkage maps. Methods Ecol Evol 5:626–633

    Google Scholar 

  • Kool JT, Moilanen A, Treml EA (2013) Population connectivity: recent advances and new perspectives. Landsc Ecol 28:165–185

    Google Scholar 

  • Landguth EL, Hand BK, Glassy J, Cushman SA, Sawaya MA (2012) UNICOR: a species connectivity and corridor network simulator. Ecography 35:9–14

    Google Scholar 

  • Landguth EL, Bearlin A, Day CC, Dunham J (2017) CDMetaPOP: an individual-based, eco-evolutionary model for spatially explicit simulation of landscape demogenetics. Methods Ecol Evol 8:4–11

    Google Scholar 

  • Lima SL, Zollner PA (1996) Towards a behavioral ecology of ecological landscapes. Trends Ecol Evol 11:131–135

    CAS  PubMed  Google Scholar 

  • Malishev M, Bull CM, Kearney MR (2018) An individual-based model of ectotherm movement integrating metabolic and microclimatic constraints. Methods Ecol Evol 9:472–489

    Google Scholar 

  • McClure ML, Dickson BG, Nicholson KL (2017) Modeling connectivity to identify current and future anthropogenic barriers to movement of large carnivores: a case study in the American Southwest. Ecol Evol 7:3762–3772

    PubMed  PubMed Central  Google Scholar 

  • McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci 104:19885–19890

    CAS  PubMed  Google Scholar 

  • McRae BH, Kavanagh DM (2011) Linkage mapper connectivity analysis software. The Nature Conservancy. http://www.circuitscape.org/linkagemapper

  • McRae BH, Shah VB, Mohapatra TK (2013) Circuitscape 4 User Guide. The Nature Conservancy. http://www.circuitscape.org.

  • Michelot T, Langrock R, Patterson TA (2016) moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol Evol 7:1308–1315

    Google Scholar 

  • Moorcroft PR, Lewis MA (2006) Mechanistic home range analysis. Princeton University Press

  • Morales JM, Haydon DT, Frair J, Holsinger KE, Fryxell JM (2004) Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 85:2436–2445

    Google Scholar 

  • Mueller T, Fagan WF (2008) Search and navigation in dynamic environments–from individual behaviors to population distributions. Oikos 117:654–664

    Google Scholar 

  • Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE (2008) A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci 105:19052–19059

    CAS  PubMed  Google Scholar 

  • Palmer SC, Coulon A, Travis JM (2011) Introducing a ‘stochastic movement simulator’ for estimating habitat connectivity. Methods Ecol Evol 2:258–268

    Google Scholar 

  • Porto M, Quaglietta L (2017) ‘SiMRiv’ (version 1.0.1) An R package for simulation and analysis of spatially-explicit individual multi-state (animal) movements in any landscape. https://cran.r-903/project.org/web/packages/SiMRiv/vignettes/SiMRiv.pdf. 2017.

  • Potts JR, Lewis MA (2014) How do animal territories form and change? Lessons from 20 years of mechanistic modelling. Proc R Soc Lond B Biol Sci 281(472):20140231

    Google Scholar 

  • Potts JR, Bastille-Rousseau G, Murray DL, Schaefer JA, Lewis MA (2014) Predicting local and non-local effects of resources on animal space use using a mechanistic step selection model. Methods Ecol Evol 5:253–262

    PubMed  PubMed Central  Google Scholar 

  • Proctor MF, Nielsen SE, Kasworm WF, Servheen C, Radandt TG, Machutchon AG, Boyce MS (2015) Grizzly bear connectivity mapping in the Canada–United States trans-border region. J Wildl Manag 79:544–558

    Google Scholar 

  • Quaglietta L (2011) Ecology and behaviour of the Eurasian otter (Lutralutra) in a Mediterranean area (Alentejo, Portugal). PhD Thesis, University of Roma “La Sapienza” [in Italian].

  • Quaglietta L, Porto M (2018) SiMRiv: individual-based, spatially-explicit simulation and analysis of multi-state movements in river networks and heterogeneous landscapes. R package version 1.0.3. https://CRAN.R-project.org/package = SiMRiv

  • Quaglietta L, Porto M (2019) SiMRiv: an R package for mechanistic, high-frequency simulation of individual multistate movements in rivers, heterogeneous and homogeneous spaces incorporating landscape bias. Mov Ecol 7:1–9. https://doi.org/10.1186/s40462-019-0154-8

    Article  Google Scholar 

  • Quaglietta L, Martins BH, de Jongh A, Mira A, Boitani L (2012) A low-cost GPS GSM/GPRS telemetry system: performance in stationary field tests and preliminary data on wild otters (Lutralutra). PLoS One 7:e29235

    CAS  PubMed  PubMed Central  Google Scholar 

  • Quaglietta L, Fonseca VC, Mira A, Boitani L (2014) Sociospatial organization of a solitary carnivore, the Eurasian otter (Lutralutra). J Mammal 95:140–150

    Google Scholar 

  • Quaglietta L, Hájková P, Mira A, Boitani L (2015) Eurasian otter (Lutra lutra) density estimate based on radio tracking and other data sources. Mammal Research 60:127–137

    Google Scholar 

  • Quaglietta L, Mira A, Boitani L (2018) Extrinsic and intrinsic factors affecting the daily rhythms of a semiaquatic carnivore in a Mediterranean environment. Hystrix Ital J Zool. https://doi.org/10.4404/hystrix-00022-2017

  • Ribeiro JW, Silveira dos Santos J, Dodonov P, Martello F, Brandão Niebuhr B, Ribeiro MC (2017) Landscape Corridors (LSCorridors): a new software package for modeling ecological corridors based on landscape patterns and species requirements. Methods Ecol Evol 8(952):1425–1432

    Google Scholar 

  • Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40:99–121

    Google Scholar 

  • Rytwinski T, van der Ree R, Cunnington GM, Fahrig L, Findlay CS, Houlahan J, Jaeger JAG, Soanes K, van der Grift EA (2015) Experimental study designs to improve the evaluation of road mitigation measures for wildlife. J Environ Manag 154:48–64

    Google Scholar 

  • Santos SM, Lourenço R, Mira A, Beja P (2013) Relative effects of road risk, habitat suitability, and connectivity on wildlife roadkills: the case of tawny owls (Strixaluco). PLoS One 8:e79967

    PubMed  PubMed Central  Google Scholar 

  • Shirabe T (2018) Buffered or bundled, least-cost paths are not least-cost corridors: computational experiments on path-based and wide-path-based models for conservation corridor design and effective distance estimation. Ecol Inform 44:109–116

    Google Scholar 

  • Spear SF, Balkenhol N, Fortin MJ, McRae BH, Scribner KIM (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591

    PubMed  Google Scholar 

  • Sutherland C, Fuller AK, Royle JA (2015) Modelling non-Euclidean movement and landscape connectivity in highly structured ecological networks. Methods Ecol Evol 6:169–177

    Google Scholar 

  • Tang W, Bennett DA (2010) Agent-based modeling of animal movement: a review. Geogr Compass 4:682–700

    Google Scholar 

  • Taylor PD, Fahrig L, With KA (2006) Landscape connectivity: a return to the basics. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, Cambridge, pp 29–43

    Google Scholar 

  • Theobald DM, Reed SE, Fields K, Soule M (2012) Connecting natural landscapes using a landscape permeability model to prioritize conservation activities in the United States. Conserv Lett 5:123–133

    Google Scholar 

  • Thiele JC, Kurth W, Grimm V (2012) RNETLOGO: an R package for running and exploring individual-based models implemented in NETLOGO. Methods Ecol Evol 3:480–483

    Google Scholar 

  • Thiele JC, Kurth W, Grimm V (2014) Facilitating parameter estimation and sensitivity analysis of agent-based models: A cookbook using NetLogo and R. Journal of Artificial Societies and Social Simulation 17:11

  • Turchin P (1998) Quantitative analysis of movement: measuring and modeling population redistribution in plants and animals. Sinauer Associates

  • van der Grift EA, van der Ree R, Fahrig L, Findlay S, Houlahan J, Jaeger JA, Klar N, Madriñan LF, Olson L (2013) Evaluating the effectiveness of road mitigation measures. Biodivers Conserv 22:425–448

    Google Scholar 

  • Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York ISBN 0-387-95457-0

    Google Scholar 

  • Wilensky U (1999) NetLogo. Evanston, IL: Center for connected learning and computer-based modeling, Northwestern University. http://ccl.northwestern.edu/netlogo.

  • Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797

    Google Scholar 

  • Zeller KA, McGarigal K, Cushman SA, Beier P, Vickers TW, Boyce WM (2017) Sensitivity of resource selection and connectivity models to landscape definition. Landsc Ecol 32:835–855

    Google Scholar 

  • Zimmermann Teixeira F, Kindel A, Hartz SM, Mitchell S, Fahrig L (2017) When road-kill hotspots do not indicate the best sites for road-kill mitigation. J Appl Ecol 54:1544–1551

    Google Scholar 

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Acknowledgments

We thank the collaborators who helped with field tracking data collection, António Mira and the “Move” project for their help with the otter carcass collection and the collaboration and support to LQ’s Ph.D., the veterinarians J. Potes and J. Reis (Évora University) who surgically implanted the radio-transmitters into the otters, A. Bokkasa for his help with the proof reading, and three anonymous referees for their useful suggestions.

Funding

In this work, we used a subsample of field tracking data collected within the framework of the Ph.D. project of LQ, for which limited funding was provided by Fundação Luis de Molina (Évora University) and supplemented by LQ’s Ph.D. fellowship. We had no specific funding for this project. ATF is supported by the National Science and Engineering Research Council and the Canada Research Chairs Program.

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Authors and Affiliations

  1. CIBIO/InBio – Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, Vairão, 4485-661, Vairão, Portugal

    Lorenzo Quaglietta & Miguel Porto

  2. CEABN/InBIO, Centro de Ecologia Aplicada “Prof Baeta Neves”, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisbon, Portugal

    Lorenzo Quaglietta & Miguel Porto

  3. Department of Biology, University of British Columbia, 1177 Research Road, Kelowna, BC, V1V 1V7, Canada

    Adam T. Ford

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  1. Lorenzo Quaglietta
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Correspondence to Lorenzo Quaglietta.

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Quaglietta, L., Porto, M. & Ford, A.T. Simulating animal movements to predict wildlife-vehicle collisions: illustrating an application of the novel R package SiMRiv. Eur J Wildl Res 65, 100 (2019). https://doi.org/10.1007/s10344-019-1333-z

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