IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v220y2009i24p3499-3511.html
   My bibliography  Save this article

Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging

Author

Listed:
  • Hengl, Tomislav
  • Sierdsema, Henk
  • Radović, Andreja
  • Dilo, Arta

Abstract

A computational framework to map species’ distributions (realized density) using occurrence-only data and environmental predictors is presented and illustrated using a textbook example and two case studies: distribution of root vole (Microtes oeconomus) in the Netherlands, and distribution of white-tailed eagle nests (Haliaeetus albicilla) in Croatia. The framework combines strengths of point pattern analysis (kernel smoothing), Ecological Niche Factor Analysis (ENFA) and geostatistics (logistic regression-kriging), as implemented in the spatstat, adehabitat and gstat packages of the R environment for statistical computing. A procedure to generate pseudo-absences is proposed. It uses Habitat Suitability Index (HSI, derived through ENFA) and distance from observations as weight maps to allocate pseudo-absence points. This design ensures that the simulated pseudo-absences fall further away from the occurrence points in both feature and geographical spaces. The simulated pseudo-absences can then be combined with occurrence locations and used to build regression-kriging prediction models. The output of prediction are either probabilitiesy of species’ occurrence or density measures. Addition of the pseudo-absence locations has proven effective — the adjusted R-square increased from 0.71 to 0.80 for root vole (562 records), and from 0.69 to 0.83 for white-tailed eagle (135 records) respectively; pseudo-absences improve spreading of the points in feature space and ensure consistent mapping over the whole area of interest. Results of cross validation (leave-one-out method) for these two species showed that the model explains 98% of the total variability in the density values for the root vole, and 94% of the total variability for the white-tailed eagle. The framework could be further extended to Generalized multivariate Linear Geostatistical Models and spatial prediction of multiple species. A copy of the R script and step-by-step instructions to run such analysis are available via contact author’s website.

Suggested Citation

  • Hengl, Tomislav & Sierdsema, Henk & Radović, Andreja & Dilo, Arta, 2009. "Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging," Ecological Modelling, Elsevier, vol. 220(24), pages 3499-3511.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:24:p:3499-3511
    DOI: 10.1016/j.ecolmodel.2009.06.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009004438
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.06.038?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. Calenge, Clément, 2007. "Exploring Habitat Selection by Wildlife with adehabitat," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 22(i06).
    2. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(5), pages 777-788, October.
    3. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(1), pages 151-160, February.
    4. Miller, Jennifer & Franklin, Janet & Aspinall, Richard, 2007. "Incorporating spatial dependence in predictive vegetation models," Ecological Modelling, Elsevier, vol. 202(3), pages 225-242.
    5. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(4), pages 629-637, August.
    6. Chefaoui, Rosa M. & Lobo, Jorge M., 2008. "Assessing the effects of pseudo-absences on predictive distribution model performance," Ecological Modelling, Elsevier, vol. 210(4), pages 478-486.
    7. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(3), pages 427-432, June.
    8. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
    9. Baddeley, Adrian & Turner, Rolf, 2005. "spatstat: An R Package for Analyzing Spatial Point Patterns," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 12(i06).
    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. Ranjitkar, Sailesh & Xu, Jianchu & Shrestha, Krishna Kumar & Kindt, Roeland, 2014. "Ensemble forecast of climate suitability for the Trans-Himalayan Nyctaginaceae species," Ecological Modelling, Elsevier, vol. 282(C), pages 18-24.
    2. Mendes, Poliana & Velazco, Santiago José Elías & Andrade, André Felipe Alves de & De Marco, Paulo, 2020. "Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy," Ecological Modelling, Elsevier, vol. 431(C).
    3. Bevan, Andrew & Conolly, James, 2011. "Terraced fields and Mediterranean landscape structure: An analytical case study from Antikythera, Greece," Ecological Modelling, Elsevier, vol. 222(7), pages 1303-1314.
    4. Schickele, Alexandre & Leroy, Boris & Beaugrand, Gregory & Goberville, Eric & Hattab, Tarek & Francour, Patrice & Raybaud, Virginie, 2020. "Modelling European small pelagic fish distribution: Methodological insights," Ecological Modelling, Elsevier, vol. 416(C).
    5. Holder, Anna M. & Markarian, Arev & Doyle, Jessie M. & Olson, John R., 2020. "Predicting geographic distributions of fishes in remote stream networks using maximum entropy modeling and landscape characterizations," Ecological Modelling, Elsevier, vol. 433(C).
    6. Iturbide, Maialen & Bedia, Joaquín & Herrera, Sixto & del Hierro, Oscar & Pinto, Miriam & Gutiérrez, Jose Manuel, 2015. "A framework for species distribution modelling with improved pseudo-absence generation," Ecological Modelling, Elsevier, vol. 312(C), pages 166-174.
    7. Daniel, Jeffrey & Horrocks, Julie & Umphrey, Gary J., 2018. "Penalized composite likelihoods for inhomogeneous Gibbs point process models," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 104-116.

    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. Coro, Gianpaolo & Pagano, Pasquale & Ellenbroek, Anton, 2013. "Combining simulated expert knowledge with Neural Networks to produce Ecological Niche Models for Latimeria chalumnae," Ecological Modelling, Elsevier, vol. 268(C), pages 55-63.
    2. Stokland, Jogeir N. & Halvorsen, Rune & Støa, Bente, 2011. "Species distribution modelling—Effect of design and sample size of pseudo-absence observations," Ecological Modelling, Elsevier, vol. 222(11), pages 1800-1809.
    3. Senait D Senay & Susan P Worner & Takayoshi Ikeda, 2013. "Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-16, August.
    4. Liang, Wanwan & Papeş, Monica & Tran, Liem & Grant, Jerome & Washington-Allen, Robert & Stewart, Scott & Wiggins, Gregory, 2018. "The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift," Ecological Modelling, Elsevier, vol. 388(C), pages 1-9.
    5. Saupe, E.E. & Barve, V. & Myers, C.E. & Soberón, J. & Barve, N. & Hensz, C.M. & Peterson, A.T. & Owens, H.L. & Lira-Noriega, A., 2012. "Variation in niche and distribution model performance: The need for a priori assessment of key causal factors," Ecological Modelling, Elsevier, vol. 237, pages 11-22.
    6. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
    7. Giovanelli, João G.R. & de Siqueira, Marinez Ferreira & Haddad, Célio F.B. & Alexandrino, João, 2010. "Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods," Ecological Modelling, Elsevier, vol. 221(2), pages 215-224.
    8. repec:asg:wpaper:1015 is not listed on IDEAS
    9. Krzysztof S. Targiel & Maciej Nowak & Tadeusz Trzaskalik, 2018. "Scheduling non-critical activities using multicriteria approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 585-598, September.
    10. F. Castro-Llanos & G. Hyman & J. Rubiano & J. Ramirez-Villegas & H. Achicanoy, 2019. "Climate change favors rice production at higher elevations in Colombia," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(8), pages 1401-1430, December.
    11. Okitonyumbe Y.F., Joseph & Ulungu, Berthold E.-L., 2013. "Nouvelle caractérisation des solutions efficaces des problèmes d’optimisation combinatoire multi-objectif [New characterization of efficient solution in multi-objective combinatorial optimization]," MPRA Paper 66123, University Library of Munich, Germany.
    12. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    13. Monica Motta & Caterina Sartori, 2020. "Normality and Nondegeneracy of the Maximum Principle in Optimal Impulsive Control Under State Constraints," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 44-71, April.
    14. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    15. Chenchen Wu & Dachuan Xu & Donglei Du & Wenqing Xu, 2016. "An approximation algorithm for the balanced Max-3-Uncut problem using complex semidefinite programming rounding," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1017-1035, November.
    16. Gengping Zhu & Matthew J Petersen & Wenjun Bu, 2012. "Selecting Biological Meaningful Environmental Dimensions of Low Discrepancy among Ranges to Predict Potential Distribution of Bean Plataspid Invasion," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
    17. Uzma Ashraf & Hassan Ali & Muhammad Nawaz Chaudry & Irfan Ashraf & Adila Batool & Zafeer Saqib, 2016. "Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model," Sustainability, MDPI, vol. 8(8), pages 1-11, July.
    18. Ernst Althaus & Felix Rauterberg & Sarah Ziegler, 2020. "Computing Euclidean Steiner trees over segments," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 309-325, October.
    19. World Bank, 2003. "Argentina : Reforming Policies and Institutions for Efficiency and Equity of Public Expenditures," World Bank Publications - Reports 14637, The World Bank Group.
    20. Ceretani, Andrea N. & Salva, Natalia N. & Tarzia, Domingo A., 2018. "Approximation of the modified error function," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 607-617.
    21. Parihar, Amit Kumar Singh & Hammer, Thomas & Sridhar, G., 2015. "Development and testing of tube type wet ESP for the removal of particulate matter and tar from producer gas," Renewable Energy, Elsevier, vol. 74(C), pages 875-883.

    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:220:y:2009:i:24:p:3499-3511. 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.