Skip to main content

Advertisement

Log in

Effects of positional error on modeling species distributions: a perspective using presettlement land survey records

Plant Ecology Aims and scope Submit manuscript

Abstract

Presettlement land survey records (PLSRs) are the records of early land surveys in North America, and contain data regarding vegetation conditions prior to widespread European-American settlement. Researchers have used the data within PLSRs to develop species distribution models (SDMs), in order to generate predictions of the historical distributions of tree species. Despite their value for SDMs, PLSRs contain positional error, which may hinder their usefulness for modeling species distributions at fine spatial resolution. Using data from the Holland Land Company (HLC) township survey (1797–1799 CE) of Western New York, USA, this study examines the positional error associated with different approaches for georeferencing vegetation data within PLSRs. The study then examines the impact of positional error upon the predictive performance of SDMs that utilize PLSRs. Our study indicates that the magnitude of positional error within PLSRs varies with georeferencing approach, and that more accurate georeferencing approaches produce better-performing SDMs. The study also indicates that the effects of positional error upon SDMs vary with the niche characteristics of species. Overall, this study affirms the importance of accurately georeferencing species data prior to developing SDMs, including applications that involve PLSRs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Abrams MD, Ruffner CM (1995) Physiographic analysis of witness-tree distribution (1765–1798) and present forest cover through north central Pennsylvania. Can J For Res 25:659–668

    Article  Google Scholar 

  • Barrett LR, Liebens J, Brown DG, Schaetzl RJ, Zuwerink P, Cate TW, Nolan DS (1995) Relationships between soils and presettlement forests in Baraga County, Michigan. Am Midl Nat 134:264–285

    Article  Google Scholar 

  • Batek MJ (1994) Presettlement vegetation of the Current River watershed in the Missouri Ozarks. M.A. thesis, Department of Geography, University of Missouri Columbia, Columbia, MO USA

  • Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

    Article  Google Scholar 

  • Bien JR (1895) Atlas of the state of New York. Prepared under the direction of Joseph R. Bien, E.M., civil and topographical engineer from original surveys and various local surveys revised and corrected. Julius Bien & Company, New York

  • Black BA, Abrams MD (2001) Influences of Native Americans and surveyor biases on metes and bounds witness-tree distribution. Ecology 82:2574–2586

    Article  Google Scholar 

  • BLM (2011) Download Public Land Survey System (PLSS) Data. United States Department of the Interior, http://www.geocommunicator.gov/GeoComm/lsis_home/home/index.htm

  • Bouldin J (2003) Location and accuracy of a sample of General Land Office survey corners in a portion of western Yosemite National Park: a summary. Research report to Yosemite National Park and The Yosemite Fund. http://www.plantsciences.ucdavis.edu/bouldin/pdf/GCDB_assessment.pdf

  • Bourdo EA Jr (1956) A review of the general land office survey and of its use in quantitative studies of former forests. Ecology 37:754–768

    Article  Google Scholar 

  • Carlson CL (1995) A comparative analysis of selected tax map conversion methods. M.S. thesis, Department of Geography, Northern Illinois University, DeKalb, IL USA

  • Cartography Associates (2013) David Rumsey Map Collection. http://www.davidrumsey.com/

  • Chazanof W (1970) Joseph Ellicott and the Holland Land Company: the opening of Western New York. Syracuse University Press, Syracuse

    Google Scholar 

  • Cogbill CV, Burk J, Motzkin G (2002) The forests of presettlement New England, USA: spatial and compositional patterns based on town proprietor surveys. J Biogeogr 29:1279–1304

    Article  Google Scholar 

  • Dormann CF, Schymanski SJ, Cabral J, Chuine I, Graham C, Hartig F, Kearney M, Morin X, Römermann C, Schröder B, Singer A (2012) Correlation and process in species distribution models: bridging a dichotomy. J Biogeogr 39:2119–2131

    Article  Google Scholar 

  • Dyer JM (2001) Using witness trees to assess forest change in southeastern Ohio. Can J For Res 31:1708–1718

    Article  Google Scholar 

  • Dyer JM (2009) Assessing topographic patterns in moisture use and stress using a water balance approach. Landsc Ecol 24:391–403

    Article  Google Scholar 

  • Dyer JM (2013) Water Balance Toolbox (v.2) for ArcGIS. Ohio University. http://www.ohio.edu/people/dyer/water_balance.html

  • Elith J, Graham CH (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography 32:66–77

    Article  Google Scholar 

  • 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 29:129–151

    Article  Google Scholar 

  • Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813

    Article  CAS  PubMed  Google Scholar 

  • Ellicott J (1937) Holland Land Company papers: reports of Joseph Ellicott as chief of survey (1797–1800), and as agent (1800–1831) of the Holland Land Company’s purchase in Western New York. Buffalo Historical Society, Buffalo

    Google Scholar 

  • Esri (2012) ArcGIS 10.1. Redlands, CA USA

  • Estopinal SV (2008) A guide to understanding land surveys. Wiley, Hoboken

    Google Scholar 

  • Fagin TD, Hoagland BW (2011) Patterns from the past: modeling public land survey witness tree distributions with weights-of-evidence. Plant Ecol 212:207–217

    Article  Google Scholar 

  • Fahey R, Lorimer C, Mladenoff D (2012) Habitat heterogeneity and life-history traits influence presettlement distributions of early-successional tree species in a late-successional, hemlock-hardwood landscape. Landsc Ecol 27:999–1013

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874

    Article  Google Scholar 

  • Feeley KJ, Silman MR (2010) Modelling the responses of Andean and Amazonian plant species to climate change: the effects of georeferencing errors and the importance of data filtering. J Biogeogr 37:733–740

    Article  Google Scholar 

  • Fenneman NM (1938) Physiography of Eastern United States. McGraw-Hill Book Company, New York

    Google Scholar 

  • Fernandez MA, Blum SD, Reichle S, Guo Q, Holzman B, Hamilton H (2009) Locality uncertainty and the differential performance of four common niche-based modeling techniques. Biodivers Inform 6:36–52

    Google Scholar 

  • Field HL (2004) Landscape surveying. Thomson/Delmar Learning, Clifton

    Google Scholar 

  • Franklin J, Miller JA (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, New York

    Google Scholar 

  • Girden E (1992) ANOVA: repeated measures. Sage, Newbury Park

    Google Scholar 

  • Gottschalk TK, Aue B, Hotes S, Ekschmitt K (2011) Influence of grain size on species–habitat models. Ecol Appl 222:3403–3412

    Google Scholar 

  • Graham CH, Elith J, Hijmans RJ, Guisan A, Peterson AT, Loiselle BA, NCEAS Predicting Species Working Group (2008) The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol 45:239–247

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Guo Q, Liu Y, Wieczorek J (2008) Georeferencing locality descriptions and computing associated uncertainty using a probabilistic approach. Int J Geogr Inf Sci 22:1067–1090

    Article  Google Scholar 

  • Hanberry BB (2013) Finer grain size increases effects of error and changes influence of environmental predictors on species distribution models. Ecol Inform 15:8–13

    Article  Google Scholar 

  • Hanberry BB, He HS, Palik BJ (2012) Comparing predicted historical distributions of tree species using two tree-based ensemble classification methods. Am Midl Nat 168:443–455

    Article  Google Scholar 

  • He HS, Dey DC, Fan XL, Hooten MB, Kabrick JM, Wikle CK, Fan ZF (2007) Mapping pre-European settlement vegetation at fine resolutions using a hierarchical Bayesian model and GIS. Plant Ecol 191:85–94

    Article  Google Scholar 

  • 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 29:773–785

    Article  Google Scholar 

  • Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036

    Article  Google Scholar 

  • Johnson JB (1889) The theory and practice of surveying: designed for the use of surveyors and engineers generally, but especially for the use of students in engineering. Wiley, New York

    Google Scholar 

  • Johnson CJ, Gillingham MP (2008) Sensitivity of species-distribution models to error, bias, and model design: an application to resource selection functions for woodland caribou. Ecol Model 213:143–155

    Article  Google Scholar 

  • Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoust Speech Signal Process 29:1153–1160

    Article  Google Scholar 

  • Kronenfeld BJ, Wang Y-C (2007) Accounting for surveyor inconsistency and bias in estimation of tree density from presettlement land survey records. Can J For Res 37:2365–2379

    Article  Google Scholar 

  • Kronenfeld BJ, Wang Y-C, Larsen CPS (2010) The influence of the “mixed pixel” problem on the detection of analogous forest communities between presettlement and present in Western New York. Prof Geogr 62:182–196

    Article  Google Scholar 

  • Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199:188–196

    Article  Google Scholar 

  • Manies KL, Mladenoff DJ, Nordheim EV (2001) Assessing large-scale surveyor variability in the historic forest data of the original US Public Land Survey. Can J For Res 31:1719–1730

    Article  Google Scholar 

  • Morisette JT, Jarnevich CS, Holcombe TR, Talbert CB, Ignizio D, Talbert MK, Silva C, Koop D, Swanson A, Young NE (2013) VisTrails SAHM: visualization and workflow management for species habitat modeling. Ecography 36:129–135

    Article  Google Scholar 

  • Moudry V, Simova P (2012) Influence of positional accuracy, sample size and scale on modelling species distributions: a review. Int J Geogr Inf Sci 26:2083–2095

    Article  Google Scholar 

  • Naimi B, Skidmore AK, Groen TA, Hamm NAS (2011) Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling. J Biogeogr 38:1497–1509

    Article  Google Scholar 

  • Natural Resources Conservation Service (2013) Geospatial data gateway. United States Department of Agriculture, San Marcos

    Google Scholar 

  • Natural Resources Conservation Service (2014) Description of SSURGO database. United States Department of Agriculture, San Marcos

    Google Scholar 

  • NYS ITS GIS Program Office (2013) NYSGIS Clearinghouse

  • Osborne PE, Leitao PJ (2009) Effects of species and habitat positional errors on the performance and interpretation of species distribution models. Divers Distrib 15:671–681

    Article  Google Scholar 

  • Peterson AT, Soberón J (2012) Integrating fundamental concepts of ecology, biogeography, and sampling into effective ecological niche modeling and species distribution modeling. Plant Biosyst 146:789–796

    Article  Google Scholar 

  • PRISM Climate Group (2013) Gridded climate data for the contiguous USA. Northwest Alliance for Computational Science & Engineering, Oregon State University, http://www.prism.oregonstate.edu/

  • Robillard WG, Wilson DA, Brown CM (2009) Brown’s boundary control and legal principles. Wiley, Hoboken

    Google Scholar 

  • Segurado P, Araujo MB (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31:1555–1568

    Article  Google Scholar 

  • Shi W (2009) Principles of modeling uncertainties in spatial data and spatial analyses. CRC Press/Taylor & Francis, Boca Raton

    Book  Google Scholar 

  • Thrower NJW (1966) Original survey and land subdivision: a comparative study of the form and effect of contrasting cadastral surveys. Published for the association of American Geographers by Rand McNally, Chicago

    Google Scholar 

  • Tulowiecki SJ (2014) Using vegetation data within presettlement land survey records for species distribution modeling: a tale of two datasets. Ecol Model 291:109–120

    Article  Google Scholar 

  • USGS (2013) The National Map Viewer and download platform. United States Department of the Interior, http://nationalmap.gov/viewer.html

  • Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37:360–363

    PubMed  Google Scholar 

  • Wang Y-C (2005) Presettlement land survey records of vegetation: geographic characteristics, quality and modes of analysis. Prog Phys Geogr 29:568–598

    Article  Google Scholar 

  • Wang Y-C (2007) Spatial patterns and vegetation-site relationships of the presettlement forests in western New York USA. J Biogeogr 34:500–513

    Article  Google Scholar 

  • Wang Y-C, Kronenfeld BJ, Larsen CPS (2009) Spatial distribution of forest landscape change in Western New York from presettlement to the present. Can J For Res 39:76–88

    Article  Google Scholar 

  • White CA (1983) A history of the rectangular survey system. U.S. Dept. of the Interior Bureau of Land Management, Washington

    Google Scholar 

  • Whitney GG (1996) From coastal wilderness to fruited plain: a history of environmental change in temperate North America, 1500 to the present. Cambridge University Press, New York

    Google Scholar 

  • Wyckoff W (1988) The developer’s frontier: the making of the western New York landscape. Yale University Press, New Haven

    Google Scholar 

  • Young SM (2010) New York Rare Plant Status Lists. New York Natural Heritage Program, New York State Department of Environmental Conservation, Albany

Download references

Acknowledgments

This study received funding support from the University at Buffalo’s Hugh W. Calkins Applied GIS Award. The authors wish to thank four anonymous reviewers, whose comments helped to improve an earlier version of this research paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephen J. Tulowiecki.

Additional information

Communicated by J. P. Messina.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tulowiecki, S.J., Larsen, C.P.S. & Wang, YC. Effects of positional error on modeling species distributions: a perspective using presettlement land survey records. Plant Ecol 216, 67–85 (2015). https://doi.org/10.1007/s11258-014-0417-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11258-014-0417-9

Keywords

Navigation