IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v209y2007i2p314-322.html
   My bibliography  Save this article

Evaluating least-cost model predictions with empirical dispersal data: A case-study using radiotracking data of hedgehogs (Erinaceus europaeus)

Author

Listed:
  • Driezen, Kassandra
  • Adriaensen, Frank
  • Rondinini, Carlo
  • Doncaster, C. Patrick
  • Matthysen, Erik

Abstract

Habitat fragmentation and habitat loss are widely recognized as major threats to biodiversity on a regional as well as on a global scale. To restrict its effects, ecological networks such as the trans-European network NATURA2000 are being developed based on the assumption that structural connections between habitat fragments lead to increased exchange through dispersal and a higher viability of (meta)populations. However, there is a great need for techniques that translate these networks and/or structural characteristics of landscapes into functional connectivity for specific organisms. Least-cost analysis has the capacities to fulfill these needs, but has never been validated against actual observations of dispersal paths. Here we present a method to validate the results of a least-cost analysis by comparing realized movement paths of hedgehogs in unfamiliar areas, obtained by radiotracking, with statistics on landscape-wide distribution of cost values. The degree of correspondence between empirical dispersal paths and the output of a least-cost analysis can be visualized and quantified, and least-cost scenarios can be statistically compared. We show that hedgehogs moved along paths with significantly lower cost values than the average landscape, implying that they took better than random routes, but performance was relatively poor. We attribute this to the relatively generalistic habitat use of the model species and the rather homogeneous landscapes. We conclude that this approach can be useful for further validation of the least-cost model and allows a direct comparison of model performance among different taxa and/or landscapes.

Suggested Citation

  • Driezen, Kassandra & Adriaensen, Frank & Rondinini, Carlo & Doncaster, C. Patrick & Matthysen, Erik, 2007. "Evaluating least-cost model predictions with empirical dispersal data: A case-study using radiotracking data of hedgehogs (Erinaceus europaeus)," Ecological Modelling, Elsevier, vol. 209(2), pages 314-322.
  • Handle: RePEc:eee:ecomod:v:209:y:2007:i:2:p:314-322
    DOI: 10.1016/j.ecolmodel.2007.07.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380007003468
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2007.07.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dayanand Naik & Shantha Rao, 2001. "Analysis of multivariate repeated measures data with a Kronecker product structured covariance matrix," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(1), pages 91-105.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Tianxiang & Jing, Dong & Wang, Shoubing, 2015. "Applying and exploring a new modeling approach of functional connectivity regarding ecological network: A case study on the dynamic lines of space syntax," Ecological Modelling, Elsevier, vol. 318(C), pages 126-137.
    2. Rong Guo & Yujing Bai, 2019. "Simulation of an Urban-Rural Spatial Structure on the Basis of Green Infrastructure Assessment: The Case of Harbin, China," Land, MDPI, vol. 8(12), pages 1-21, December.
    3. Brendan Hoover & Richard S. Middleton & Sean Yaw, 2019. "CostMAP: An open-source software package for developing cost surfaces," Papers 1906.08872, arXiv.org.
    4. Junga Lee & Christopher D. Ellis & Yun Eui Choi & Soojin You & Jinhyung Chon, 2015. "An Integrated Approach to Mitigation Wetland Site Selection: A Case Study in Gwacheon, Korea," Sustainability, MDPI, vol. 7(3), pages 1-28, March.
    5. J Nevil Amos & Andrew F Bennett & Ralph Mac Nally & Graeme Newell & Alexandra Pavlova & James Q Radford & James R Thomson & Matt White & Paul Sunnucks, 2012. "Predicting Landscape-Genetic Consequences of Habitat Loss, Fragmentation and Mobility for Multiple Species of Woodland Birds," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-12, February.
    6. Finn, J.T. & Brownscombe, J.W. & Haak, C.R. & Cooke, S.J. & Cormier, R. & Gagne, T. & Danylchuk, A.J., 2014. "Applying network methods to acoustic telemetry data: Modeling the movements of tropical marine fishes," Ecological Modelling, Elsevier, vol. 293(C), pages 139-149.
    7. Etienne Lalechère & Laurent Bergès, 2021. "A Validation Procedure for Ecological Corridor Locations," Land, MDPI, vol. 10(12), pages 1-18, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kohli, Priya & Garcia, Tanya P. & Pourahmadi, Mohsen, 2016. "Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 87-100.
    2. Kim, Chulmin & Zimmerman, Dale L., 2012. "Unconstrained models for the covariance structure of multivariate longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 104-118.
    3. Feng, Sanying & Lian, Heng & Xue, Liugen, 2016. "A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 98-109.
    4. Wojciech Łukaszonek, 2017. "A Multidimensional And Dynamised Classification Of Polish Provinces Based On Selected Features Of Higher Education In 2002–2013," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 271-290, June.
    5. Filipiak, Katarzyna & Klein, Daniel & Roy, Anuradha, 2016. "Score test for a separable covariance structure with the first component as compound symmetric correlation matrix," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 105-124.
    6. Lingzhe Guo & Reza Modarres, 2020. "Testing the equality of matrix distributions," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 289-307, June.
    7. Sean L Simpson & Lloyd J Edwards & Martin A Styner & Keith E Muller, 2014. "Kronecker Product Linear Exponent AR(1) Correlation Structures for Multivariate Repeated Measures," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    8. Viroli, Cinzia, 2012. "On matrix-variate regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 296-309.
    9. Glanz, Hunter & Carvalho, Luis, 2018. "An expectation–maximization algorithm for the matrix normal distribution with an application in remote sensing," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 31-48.
    10. Filipiak, Katarzyna & Klein, Daniel, 2017. "Estimation of parameters under a generalized growth curve model," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 73-86.
    11. Lee, Keunbaik & Lee, Chang-Hoon & Kwak, Min-Sun & Jang, Eun Jin, 2021. "Analysis of multivariate longitudinal data using ARMA Cholesky and hypersphere decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    12. Manceur, A.M. & Dutilleul, P., 2013. "Unbiased modified likelihood ratio tests for simple and double separability of a variance–covariance structure," Statistics & Probability Letters, Elsevier, vol. 83(2), pages 631-636.
    13. Katarzyna Filipiak & Daniel Klein & Anuradha Roy, 2015. "Score test for a separable covariance structure with the first component as compound symmetric correlation matrix," Working Papers 0148mss, College of Business, University of Texas at San Antonio.
    14. Lu, Nelson & Zimmerman, Dale L., 2005. "The likelihood ratio test for a separable covariance matrix," Statistics & Probability Letters, Elsevier, vol. 73(4), pages 449-457, July.
    15. Mirosław Krzyśko & Wojciech Łukaszonek & Waldemar Wołyński, 2018. "Canonical Correlation Analysis In The Case Of Multivariate Repeated Measures Data," Statistics in Transition New Series, Polish Statistical Association, vol. 19(1), pages 75-85, March.
    16. Anestis Touloumis & Simon Tavaré & John C. Marioni, 2015. "Testing the mean matrix in high-dimensional transposable data," Biometrics, The International Biometric Society, vol. 71(1), pages 157-166, March.
    17. Martin Ohlson & Zhanna Andrushchenko & Dietrich Rosen, 2011. "Explicit estimators under m-dependence for a multivariate normal distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(1), pages 29-42, February.
    18. Anuradha Roy & Ricardo Leiva, 2008. "Testing of a Structures Covariance Matrix for Three-Level Repeated Measures Data," Working Papers 0037, College of Business, University of Texas at San Antonio.
    19. Geoffrey Colin L. Peterson & Dong Li & Brian J. Reich & Donald Brenner, 2017. "Spatial prediction of crystalline defects observed in molecular dynamic simulations of plastic damage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1761-1784, July.
    20. Hao, Chengcheng & Liang, Yuli & Mathew, Thomas, 2016. "Testing variance parameters in models with a Kronecker product covariance structure," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 182-189.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:209:y:2007:i:2:p:314-322. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.