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A General Approach to Model Movement in (Highly) Fragmented Patch Networks

Author

Listed:
  • Juan Manuel Morales

    (Grupo de Ecología Cuantitativa. INIBIOMA-CRUB)

  • Agustina Virgilio

    (Grupo de Ecología Cuantitativa. INIBIOMA-CRUB)

  • María Delgado

    (Oviedo University - Campus Mieres)

  • Otso Ovaskainen

    (University of Helsinki
    Centre for Biodiversity Dynamics, Norwegian University of Science and Technology)

Abstract

Landscape heterogeneity can often be represented as a series of discrete habitat or resource patches surrounded by a matrix of non-habitat. Understanding how animals move in such networks of patches is important for many theoretical and applied questions. The probability of going from one patch to another is affected in a non-trivial way by the characteristics and location of other patches in the network. Nearby patches can compete as possible destinations, and a particular patch can be shadowed by neighboring patches. We present a way to account for the effects of the spatial configuration of patches in models of space use where individuals alternate between spending time in a patch and moving to other patches in the network. The approach is based on the original derivation of Ovaskainen and Cornell (J Appl Probab 40:557–580, 2003) for a diffusion model that considered all possible ways in which an individual leaving a particular patch can eventually reach another patch before dying or leaving the patch network. By replacing the theoretical results of Ovaskainen and Cornell by other appropriate functions, we provide generality and thus make their approach useful in contexts where diffusion is not a good approximation of movement. Furthermore, we provide ways to estimate time spent in the non-habitat matrix when going from patch to patch and implement a method to incorporate the effect of the history of previous visits on future patch use. We present an MCMC way to fit these models to data and illustrate the approach with both simulated data and data from sheep moving among seasonally flooded meadows in northern Patagonia.Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Juan Manuel Morales & Agustina Virgilio & María Delgado & Otso Ovaskainen, 2017. "A General Approach to Model Movement in (Highly) Fragmented Patch Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 393-412, September.
    • Handle: RePEc:spr:jagbes:v:22:y:2017:i:3:d:10.1007_s13253-017-0298-1
    DOI: 10.1007/s13253-017-0298-1
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    References listed on IDEAS

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    1. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    2. van der Vaart, Elske & Beaumont, Mark A. & Johnston, Alice S.A. & Sibly, Richard M., 2015. "Calibration and evaluation of individual-based models using Approximate Bayesian Computation," Ecological Modelling, Elsevier, vol. 312(C), pages 182-190.
    3. D. L. Borchers & M. G. Efford, 2008. "Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 377-385, June.
    4. Zheng, Chaozhi & Pennanen, Juho & Ovaskainen, Otso, 2009. "Modelling dispersal with diffusion and habitat selection: Analytical results for highly fragmented landscapes," Ecological Modelling, Elsevier, vol. 220(12), pages 1495-1505.
    5. Ovaskainen, Otso, 2008. "Analytical and numerical tools for diffusion-based movement models," Theoretical Population Biology, Elsevier, vol. 73(2), pages 198-211.
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    Cited by:

    1. Mevin B. Hooten & Ruth King & Roland Langrock, 2017. "Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 224-231, September.

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