Does plot size affect the performance of GIS-based species distribution models?

Abstract

Species distribution models are used extensively in predicting the distribution of vegetation across a landscape. Accuracy of the species distribution maps produced by these models deserves attention, since low accuracy maps may lead to erroneous conservation decisions. While plot size is known to influence measures of species richness, its effect on our ability to predict species distribution ranges has not been tested. Our aim is to test whether the accuracy of the distribution maps produced depend on the size of the plot (quadrat) used to collect biological data in the field. In this study, the presences of four plant species were recorded in five sizes of circular plots, with radii ranging from 8 to 100 m. Logistic regression-based models were used to predict the distributions of the four plant species based on empirical evidence of their relationship with eight environmental predictors: distance to river, slope, aspect, altitude, and four principle component axes derived using reflectance values from Aster images. We found that plot size affected the probability of recording the four species, with reductions in plot size generally increasing the frequency of recorded absences. Plot size also significantly affected the likelihood of correctly predicting the distribution of species whenever plot size was below the minimum size required to consistently record species’ presence. Furthermore, the optimal plot size for fitting species distribution models varied among species. Finally, plot size had little impact on overall accuracy, but a strong, positive impact on Kappa accuracy (which provides a stronger measure of model accuracy by accounting for the effects of chance agreements between predictions and observations). Our results suggest that optimal plot size must be considered explicitly in the creation of species distribution models if they are to be successfully adopted into conservation efforts.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43(6):1223–1232

    Article  Google Scholar 

  2. Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157(2–3):101–118

    Article  Google Scholar 

  3. Austin MP (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200(1–2):1–19

    Article  Google Scholar 

  4. Berg Å, Gärdenfors U, von Proschwitz T (2004) Logistic regression models for predicting occurrence of terrestrial molluscs in southern Sweden—importance of environmental data quality and model complexity. Ecography 27(1):83–93

    Article  Google Scholar 

  5. Bio AMF, De Becker P, Bie ED, Huybrechts W, Wassen M (2002) Prediction of plant species distribution in low land river valleys in Belgium: modeling species response to site conditions. Biodivers Conserv 11(12):2189–2216

    Article  Google Scholar 

  6. Bonham-Carter GF (1994) Geographic information systems for geoscientists. Pergamon Press, Oxford

    Google Scholar 

  7. Boone RB, Krohn WB (2002) Modeling tools and accuracy assessment. In: Scott JM et al (eds) Predicting species occurrences: issues of accuracy and scale. Inland Press, Washington, pp 265–270

    Google Scholar 

  8. Buckland ST, Elston DA (1993) Empirical models for the spatial distributions of wildlife. J Appl Ecol 30:478–495

    Article  Google Scholar 

  9. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46

    Article  Google Scholar 

  10. De Leeuw J, Ottichilo WK, Toxopeus AG, Prins HHT (2002) Application of remote sensing and geographic information systems in wildlife mapping and modelling. In: Skidmore A (ed) Environmental modelling with GIS and remote sensing. Taylor and Francis, London, pp 121–145

    Google Scholar 

  11. Dixon B (2004) Prediction of ground water vulnerability using an integrated GIS-based Neuro-Fuzzy techniques. J Spatial Hydro 4(2):1–38

    Google Scholar 

  12. Dungan JL, Perry JN, Dale MRT, Legendre P, Citron-Pousty S, Fortin MJ, Jakomulska A, Miriti M, Rosenberg MS (2002) A balanced view of scale in spatial statistical analysis. Ecography 25(5):626–640

    Article  Google Scholar 

  13. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2):129–151

    Article  Google Scholar 

  14. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–39

    Article  Google Scholar 

  15. Garrison BA, Erickson RA, Patten MA, Timossi IC (2000) Accuracy of wildlife model predictions for bird species occurrences in California counties. Wildl Soc Bull 28(3):667–674

    Google Scholar 

  16. Graham CH, Ferrier S, Huettman F, Moritz C, Peterson AT (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19(3):497–503

    Article  Google Scholar 

  17. Gray A (2003) Monitoring stand structure in mature coastal Douglas-fir forests: effect of plot size. For Ecol Manage 175(1–3):1–16

    Article  Google Scholar 

  18. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135(2):147–186

    Article  Google Scholar 

  19. Guisan A, Graham CH, Elith J, Huettmann F (2007) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13(3):332–340

    Article  Google Scholar 

  20. Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5):773–785

    Article  Google Scholar 

  21. Hurley JF (1986) Summary: development, testing, and application of wildlife–habitat models–the researcher’s viewpoint. In: Verner J, Morrison ML, Ralph CJ (eds) Wildlife 2000: modelling habitat relationships of terrestrial vertebrates. University of Wisconsin Press, USA

    Google Scholar 

  22. Huston MA (1994) Biological diversity: the coexistence of species on changing landscapes. Cambridge University Press, Cambridge

    Google Scholar 

  23. ILWIS 3.0 Academic (2001) The integrated land and water information system (ILWIS) software. International Institute of Geo-information Science and Earth Observation, Enschede

    Google Scholar 

  24. Johnson CJ, Gillingham MP (2005) An evaluation of mapped species distribution models used for conservation planning. Environ Conserv 32(2):117–128

    Article  Google Scholar 

  25. Karl JW, Heglund PJ, Garton EO, Scott JM, Wright NM, Hutto RL (2000) Sensitivity of species habitat-relationship model performance to factors of scale. Ecol Appl 10(6):1690–1705

    Article  Google Scholar 

  26. Kerr JT, Southwood TRE, Cihlar J (2001) Remotely sensed habitat diversity predicts butterfly species richness and community similarity in Canada. PNAS 98(20):11365–11370

    Article  Google Scholar 

  27. Leathwick JR (1998) Are New Zealand’s Nothofagus species in equilibrium with their environment? J Veg Sci 9(5):719–732

    Article  Google Scholar 

  28. Leathwick JR, Austin MP (2001) Competitive interactions between tree species in New Zealand’s old-growth indigenous forests. Ecology 82(9):25–60

    Article  Google Scholar 

  29. Lenton SM, Fa JE, Del Val JP (2000) A simple non-parametric GIS model for predicting species distribution: endemic birds in Bioko Island, West Africa. Biodivers Conserv 9(7):869–885

    Article  Google Scholar 

  30. Leos K, Martin D, Michal H, Ivana J, Tomàš T (2001) Scale-dependent biases in species counts in a grass land. J Veg Sci 12(5):699–704

    Google Scholar 

  31. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28(3):389–393

    Article  Google Scholar 

  32. Lütolf M, Bolliger J, Kienast F, Guisan A (2009) Scenario-based assessment of future land use change on butterfly species distributions. Biodivers Conserv 18(5):1329–1347

    Article  Google Scholar 

  33. Manel S, Dias JM, Buckton ST, Ormerod SJ (1999) Alternative methods for predicting species distribution: an illustration with Himalayan river birds. J Appl Ecol 36(5):734–747

    Article  Google Scholar 

  34. Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence-absence models in ecology: the need to account for prevalence. J Appl Ecol 38(5):921–931

    Article  Google Scholar 

  35. McPherson JM, Jetz W, Rogers DJ (2004) The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J Appl Ecol 41(5):811–823

    Article  Google Scholar 

  36. Miller J, Franklin J (2002) Modeling the distribution of vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol Model 157(3–4):27–47

    Google Scholar 

  37. Miller J, Franklin J, Aspinall R (2007) Incorporating spatial dependence in predictive vegetation models. Ecol Model 202(3–4):225–242

    Article  Google Scholar 

  38. Mushinzimana E, Stephen M, Noboru M, Li L, Chen-chieh F, Ling B, Uriel K, Cindy S, Louisa B, Guofa Z, Andrew KG, Guiyun Y (2006) Landscape determinants and remote sensing of anapheline mosquito larval habitats in the western Kenya highlands. Malar J 5:13

    Article  Google Scholar 

  39. Openshaw S (1996) Developing GIS-relevant zone-based spatial analysis methods. In: Longley P, Batty M (eds) Spatial analysis: modelling in a GIS environment. Wiley, New York, pp 55–74

    Google Scholar 

  40. Openshaw S, Taylor PJ (1981) The modifiable areal unit problem. In: Wrigley N, Bennett RJ (eds) Quantitative geography. Routledge and Keegan Paul Ltd, London, pp 60–69

    Google Scholar 

  41. Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225–245

    Article  Google Scholar 

  42. Reese GC, Wilson KR, Hoeting JA, Flather CH (2005) Factors affecting species distribution predictions: a simulation modeling experiment. Ecol Appl 15(2):554–564

    Article  Google Scholar 

  43. Santika T, Hutchinson MF (2009) The effect of species response form on species distribution model prediction and inference. Ecol Model 220(19):2365–2379

    Article  Google Scholar 

  44. Schlossberg S, King DI (2009) Post logging succession and habitat usage of shrubland birds. J Wildl Manage 73(2):226–231

    Article  Google Scholar 

  45. Segurado P, Araujo MB (2004) An evaluation of methods for modelling species’ distributions. J Biogeogr 3(10):1555–1568

    Article  Google Scholar 

  46. Seoane J, Carrascal LM, Alonso CL, Palomino D (2005) Species-specific traits associated to prediction errors in bird habitat suitability modelling. Ecol Model 185(2–4):299–308

    Article  Google Scholar 

  47. Simon E (1997) Detailed land use plan for the sesfontein constituency. Ministry of Agriculture Water and Rural Development, Windhoek

    Google Scholar 

  48. Skidmore AK (1999) Accuracy assessment of spatial information. In: Stein A, van der Meer F, Gorte BGH (eds) Spatial statistics for remote sensing. Kluwer, Dordrecht

    Google Scholar 

  49. Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 2:1–10

    Google Scholar 

  50. Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148(1):1–13

    Article  Google Scholar 

  51. Stohlgren TJ (2007) Measuring plant diversity: lessons from the field. Oxford University Press, New York

    Google Scholar 

  52. Syartinilia, Tsuyuki S (2008) GIS-based modeling of Javan Hawk-Eagle distribution using logistic and autologistic regression models. Biol Conserv 141(3):756–769

    Article  Google Scholar 

  53. Tsoar A, Allouche O, Steinitz O, Rotem D, Kadmon R (2007) A comparative evaluation of presence-only methods for modelling species distribution. Divers Distrib 13(4):397–405

    Article  Google Scholar 

  54. Venier LA, McKenney DW, Wang Y, McKee J (1999) Models of large-scale breeding-bird distribution as a function of macro-climate in Ontario, Canada. J Biogeogr 26(2):315–328

    Article  Google Scholar 

  55. Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19(2):125–138

    Article  Google Scholar 

  56. Wu JK, Bruce J, Li H, Loucks OL (eds) (2006) Scaling and uncertainty analysis in ecology: methods and applications. Springer, New York, 351 p

  57. Zimmermann NE, Edwards TC, Moisen G, Frescino TS, Blackard JA (2007) Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. J Appl Ecol 44(5):1057–1067

    Article  Google Scholar 

Download references

Acknowledgments

We are indebted to the Polytechnic of Namibia, Windhoek, and the many people of Safoentein who assisted with field work. We are grateful to three anonymous reviewers, whose comments helped to improve this paper. We thank Dr. Iris van Duren for her insightful comments on a previous draft of this paper. This research was supported by a grant from the Netherlands Fellowship Program to SP.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Shubha N. Pandit.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 1862 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Pandit, S.N., Hayward, A., de Leeuw, J. et al. Does plot size affect the performance of GIS-based species distribution models?. J Geogr Syst 12, 389–407 (2010). https://doi.org/10.1007/s10109-010-0106-8

Download citation

Keywords

  • Kappa
  • Map accuracy
  • Species distribution
  • Logistic regression models
  • Species frequency curve
  • Namibia

JEL Classification

  • Q57