[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v220y2009i23p3248-3258.html
   My bibliography  Save this article

Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?

Author

Listed:
  • Václavík, Tomáš
  • Meentemeyer, Ross K.
Abstract
Species distribution models (SDMs) based on statistical relationships between occurrence data and underlying environmental conditions are increasingly used to predict spatial patterns of biological invasions and prioritize locations for early detection and control of invasion outbreaks. However, invasive species distribution models (iSDMs) face special challenges because (i) they typically violate SDM's assumption that the organism is in equilibrium with its environment, and (ii) species absence data are often unavailable or believed to be too difficult to interpret. This often leads researchers to generate pseudo-absences for model training or utilize presence-only methods, and to confuse the distinction between predictions of potential vs. actual distribution. We examined the hypothesis that true-absence data, when accompanied by dispersal constraints, improve prediction accuracy and ecological understanding of iSDMs that aim to predict the actual distribution of biological invasions. We evaluated the impact of presence-only, true-absence and pseudo-absence data on model accuracy using an extensive dataset on the distribution of the invasive forest pathogen Phytophthora ramorum in California. Two traditional presence/absence models (generalized linear model and classification trees) and two alternative presence-only models (ecological niche factor analysis and maximum entropy) were developed based on 890 field plots of pathogen occurrence and several climatic, topographic, host vegetation and dispersal variables. The effects of all three possible types of occurrence data on model performance were evaluated with receiver operating characteristic (ROC) and omission/commission error rates. Results show that prediction of actual distribution was less accurate when we ignored true-absences and dispersal constraints. Presence-only models and models without dispersal information tended to over-predict the actual range of invasions. Models based on pseudo-absence data exhibited similar accuracies as presence-only models but produced spatially less feasible predictions. We suggest that true-absence data are a critical ingredient not only for accurate calibration but also for ecologically meaningful assessment of iSDMs that focus on predictions of actual distributions.

Suggested Citation

  • Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:23:p:3248-3258
    DOI: 10.1016/j.ecolmodel.2009.08.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009005742
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.08.013?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. Freeman, Elizabeth A. & Moisen, Gretchen G., 2008. "A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa," Ecological Modelling, Elsevier, vol. 217(1), pages 48-58.
    2. Chris D. Thomas & Alison Cameron & Rhys E. Green & Michel Bakkenes & Linda J. Beaumont & Yvonne C. Collingham & Barend F. N. Erasmus & Marinez Ferreira de Siqueira & Alan Grainger & Lee Hannah & Lesle, 2004. "Extinction risk from climate change," Nature, Nature, vol. 427(6970), pages 145-148, January.
    3. 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.
    4. Lippitt, Christopher D. & Rogan, John & Toledano, James & Sangermano, Florencia & Eastman, J. Ronald & Mastro, Victor & Sawyer, Alan, 2008. "Incorporating anthropogenic variables into a species distribution model to map gypsy moth risk," Ecological Modelling, Elsevier, vol. 210(3), pages 339-350.
    5. John Harte & Annette Ostling & Jessica L. Green & Ann Kinzig, 2004. "Climate change and extinction risk," Nature, Nature, vol. 430(6995), pages 34-34, July.
    6. Peterson, A. Townsend & Papeş, Monica & Soberón, Jorge, 2008. "Rethinking receiver operating characteristic analysis applications in ecological niche modeling," Ecological Modelling, Elsevier, vol. 213(1), pages 63-72.
    7. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    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. Akpoti, Komlavi & Groen, Thomas & Dossou-Yovo, Elliott & Kabo-bah, Amos T. & Zwart, Sander J., 2022. "Climate change-induced reduction in agricultural land suitability of West-Africa's inland valley landscapes," Agricultural Systems, Elsevier, vol. 200(C).
    2. Duque-Lazo, J. & van Gils, H. & Groen, T.A. & Navarro-Cerrillo, R.M., 2016. "Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia," Ecological Modelling, Elsevier, vol. 320(C), pages 62-70.
    3. Ko, Chia-Ying & Root, Terry L. & Lee, Pei-Fen, 2011. "Movement distances enhance validity of predictive models," Ecological Modelling, Elsevier, vol. 222(4), pages 947-954.
    4. Cushman, S.A. & Kilshaw, K. & Campbell, R.D. & Kaszta, Z. & Gaywood, M. & Macdonald, D.W., 2024. "Comparing the performance of global, geographically weighted and ecologically weighted species distribution models for Scottish wildcats using GLM and Random Forest predictive modeling," Ecological Modelling, Elsevier, vol. 492(C).
    5. Robinson, Todd P. & van Klinken, Rieks D. & Metternicht, Graciela, 2010. "Comparison of alternative strategies for invasive species distribution modeling," Ecological Modelling, Elsevier, vol. 221(19), pages 2261-2269.
    6. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    7. Jeong Soo Park & Donghui Choi & Youngha Kim, 2020. "Potential Distribution of Goldenrod ( Solidago altissima L.) during Climate Change in South Korea," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    8. Vuilleumier, S. & Buttler, A. & Perrin, N. & Yearsley, J.M., 2011. "Invasion and eradication of a competitively superior species in heterogeneous landscapes," Ecological Modelling, Elsevier, vol. 222(3), pages 398-406.
    9. Kelly Jane Easterday & Patrick J McIntyre & James H Thorne & Maria J Santos & Maggi Kelly, 2016. "Assessing Threats and Conservation Status of Historical Centers of Oak Richness in California," Urban Planning, Cogitatio Press, vol. 1(4), pages 65-78.
    10. Liu, Fang & McShea, William J. & Li, Diqiang, 2017. "Correlating habitat suitability with landscape connectivity: A case study of Sichuan golden monkey in China," Ecological Modelling, Elsevier, vol. 353(C), pages 37-46.
    11. Omid Ghorbanzadeh & Hashem Rostamzadeh & Thomas Blaschke & Khalil Gholaminia & Jagannath Aryal, 2018. "A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 497-517, November.
    12. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
    13. Buse, Jörn & Griebeler, Eva Maria, 2011. "Incorporating classified dispersal assumptions in predictive distribution models – A case study with grasshoppers and bush-crickets," Ecological Modelling, Elsevier, vol. 222(13), pages 2130-2141.

    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. Di Traglia, Mario & Attorre, Fabio & Francesconi, Fabio & Valenti, Roberto & Vitale, Marcello, 2011. "Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach," Ecological Modelling, Elsevier, vol. 222(4), pages 925-934.
    2. Platts, Philip J. & McClean, Colin J. & Lovett, Jon C. & Marchant, Rob, 2008. "Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty," Ecological Modelling, Elsevier, vol. 218(1), pages 121-134.
    3. Pelayo Acevedo & Alberto Jiménez-Valverde & Jorge M. Lobo & Raimundo Real, 2017. "Predictor weighting and geographical background delimitation: two synergetic sources of uncertainty when assessing species sensitivity to climate change," Climatic Change, Springer, vol. 145(1), pages 131-143, November.
    4. Robinson, Todd P. & van Klinken, Rieks D. & Metternicht, Graciela, 2010. "Comparison of alternative strategies for invasive species distribution modeling," Ecological Modelling, Elsevier, vol. 221(19), pages 2261-2269.
    5. Meineri, Eric & Skarpaas, Olav & Vandvik, Vigdis, 2012. "Modeling alpine plant distributions at the landscape scale: Do biotic interactions matter?," Ecological Modelling, Elsevier, vol. 231(C), pages 1-10.
    6. Keliang Zhang & Yin Zhang & Diwen Jia & Jun Tao, 2020. "Species Distribution Modeling of Sassafras Tzumu and Implications for Forest Management," Sustainability, MDPI, vol. 12(10), pages 1-14, May.
    7. Nenzén, H.K. & Araújo, M.B., 2011. "Choice of threshold alters projections of species range shifts under climate change," Ecological Modelling, Elsevier, vol. 222(18), pages 3346-3354.
    8. Pearce, Joshua M. & Johnson, Sara J. & Grant, Gabriel B., 2007. "3D-mapping optimization of embodied energy of transportation," Resources, Conservation & Recycling, Elsevier, vol. 51(2), pages 435-453.
    9. Henzler, Julia & Weise, Hanna & Enright, Neal J. & Zander, Susanne & Tietjen, Britta, 2018. "A squeeze in the suitable fire interval: Simulating the persistence of fire-killed plants in a Mediterranean-type ecosystem under drier conditions," Ecological Modelling, Elsevier, vol. 389(C), pages 41-49.
    10. Andrew John & Avril Horne & Rory Nathan & Michael Stewardson & J. Angus Webb & Jun Wang & N. LeRoy Poff, 2021. "Climate change and freshwater ecology: Hydrological and ecological methods of comparable complexity are needed to predict risk," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 12(2), March.
    11. John H Matthews & Bart AJ Wickel & Sarah Freeman, 2011. "Converging Currents in Climate-Relevant Conservation: Water, Infrastructure, and Institutions," PLOS Biology, Public Library of Science, vol. 9(9), pages 1-4, September.
    12. Brandt, Laura A. & Benscoter, Allison M. & Harvey, Rebecca & Speroterra, Carolina & Bucklin, David & Romañach, Stephanie S. & Watling, James I. & Mazzotti, Frank J., 2017. "Comparison of climate envelope models developed using expert-selected variables versus statistical selection," Ecological Modelling, Elsevier, vol. 345(C), pages 10-20.
    13. Jorge Velásquez-Tibatá & María H Olaya-Rodríguez & Daniel López-Lozano & César Gutiérrez & Iván González & María C Londoño-Murcia, 2019. "BioModelos: A collaborative online system to map species distributions," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-13, March.
    14. Tasmin L. Rymer & Neville Pillay & Carsten Schradin, 2013. "Extinction or Survival? Behavioral Flexibility in Response to Environmental Change in the African Striped Mouse Rhabdomys," Sustainability, MDPI, vol. 5(1), pages 1-24, January.
    15. Feng, Zhiying & Tang, Wenhu & Niu, Zhewen & Wu, Qinghua, 2018. "Bi-level allocation of carbon emission permits based on clustering analysis and weighted voting: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1122-1135.
    16. Alexander S Anderson & Collin J Storlie & Luke P Shoo & Richard G Pearson & Stephen E Williams, 2013. "Current Analogues of Future Climate Indicate the Likely Response of a Sensitive Montane Tropical Avifauna to a Warming World," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-12, July.
    17. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    18. Liu, Zhu & Feng, Kuishuang & Hubacek, Klaus & Liang, Sai & Anadon, Laura Diaz & Zhang, Chao & Guan, Dabo, 2015. "Four system boundaries for carbon accounts," Ecological Modelling, Elsevier, vol. 318(C), pages 118-125.
    19. Rougier, Thibaud & Drouineau, Hilaire & Dumoulin, Nicolas & Faure, Thierry & Deffuant, Guillaume & Rochard, Eric & Lambert, Patrick, 2014. "The GR3D model, a tool to explore the Global Repositioning Dynamics of Diadromous fish Distribution," Ecological Modelling, Elsevier, vol. 283(C), pages 31-44.
    20. Verboom, Jana & Alkemade, Rob & Klijn, Jan & Metzger, Marc J. & Reijnen, Rien, 2007. "Combining biodiversity modeling with political and economic development scenarios for 25 EU countries," Ecological Economics, Elsevier, vol. 62(2), pages 267-276, April.

    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:23:p:3248-3258. 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.