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Use of Phytosociological Databases for Species Distribution Models

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Abstract

Species Distribution Models are key in modern ecological studies. They employ information about species locations and environmental factors to generate statistical functions that predict the potential distribution of species on the basis of landscape suitability. Although these models are powerful and useful tools, often the required information about species distribution is lacking, and the only resources are pre-collected museum data. Phytosociological databases contain a myriad of relevés with precious information, but are often considered to be the exclusive ownership of vegetation scientists. Our study tested the efficiency of a phytosociological database in the building of Species Distribution Models, including spatial autocorrelation (SAC) as a predictor to evaluate its effects on model performance. Spatial autocorrelation (SAC) is a natural characteristic of species distribution that depends on exogenous and endogenous processes. The latter’s effects could be overestimated by a subjective sample choice. We chose Festuca riccerii, an Italian endemic species. We split the whole dataset (671 relevés) into a calibration (443 relevés) and testing set (228 relevés) and performed a GLM on these data to identify the main ecological factors that lead distribution in order to build a Species Distribution Model. The dataset’s efficiency was assessed by testing the predicting power of the calibrated model on the testing subset. The phytosociological database proved to be good for building model (AUC = 0.821), providing a useful basis for fast and low cost ecological analysis, and could be used subsequently for more detailed analyses.

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References

  • Anselin L, Florax RJGM, Rey SJ (2004) Advances in spatial econometrics: Methodology, tools and applications. Springer Verlag, Berlin

    Google Scholar 

  • Austin M P (1980) Searching for a model for use in vegetation analysis. Vegetatio 42:11–21

    Article  Google Scholar 

  • Berg A, Gardenfors 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 27:83–93

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Borcard D, Legendre P (2004) Spacemaker2: Generate spatial explanatory variables to be used in multiple regression or canonical ordination. Université de Montréal, Québec

    Google Scholar 

  • Borcard D, Legendre P, Drapeau P (1992) Partialling out the spatial component of ecological variation. Ecology 7:1045–1055

    Article  Google Scholar 

  • Botta-Dukát Z, Kovács-Láng E, Rédei T, Kertész M, Garadnai J (2007) Statistical and biological consequences of preferential sampling in phytosociology: theoretical considerations and case study. Folia Geobot 42:141–152

    Article  Google Scholar 

  • Braun-Blanquet J (1928) Pflanzensoziologie. Grundzüge der Vegetationskunde. Springer Verlag, Berlin

    Google Scholar 

  • Brotons L, Thuiller W, Araùjo MB, Hirzel AH (2004) Presence-Absence versus presence-only modelling methods ofr predicting bird habitat suitability. Ecography 27:437–448

    Article  Google Scholar 

  • Chiarucci A (2007) To sample or not to sample? That is the question … for the vegetation scientist. Folia Geobot 42:209–216

    Article  Google Scholar 

  • Coudun C, Gegout J (2005) Ecological behaviour of herbaceous forest species along a pH gradient: a comparison between oceanic and semicontinental regions in northern France. Global Ecol Biogeogr 14:263–270

    Article  Google Scholar 

  • Crawley MJ (1993) GLIM for ecologist. Blackwell, Oxford

    Google Scholar 

  • Cressie N (1991) Statistics for spatial data. John Whiley & Sons, New York

    Google Scholar 

  • Diekmann M, Kühne A, Isermann M (2007) Random vs non-random sampling: effects on patterns of species abundance, species richness and vegetation-environment relationships. Folia Geobot 42:179–190

    Article  Google Scholar 

  • Dormann CF, McPherson JM, Araújo MB (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628

    Article  Google Scholar 

  • Edwards TC, Cutler R, Zimmermann NE, Geiser L, Alegria J (2005) Model-based stratification for enhancing the detection of rare ecological events. Ecology 86:1081–1090

    Article  Google Scholar 

  • Elith J, Leathwick J (2009) Conservation prioritization using species distribution modeling. In Moilanen A, Wilson KA, Possingham H (eds) Spatial conservation prioritization: quantitative methods and computational tools. Oxford University Press, Oxford, pp 70–93

    Google Scholar 

  • Foggi B, Rossi G (1996) A survey of genus Festuca L. (Poaceae) in Italy. The species of the summit flora of Tuscan – Emilian Appennines and Apuan Alpes. Willdenowia 26:183–215

    Google Scholar 

  • Foggi B, Gennai M, Gervasoni D, Ferretti G, Rosi C, Viciani D, Venturi E (2007) La carta della vegetazione del SIC Alta Valle del Sestaione (Pistoia, Toscana Nord-Occidentale). Parlatorea 9:41–78

    Google Scholar 

  • Fortin MJ, Dale MRT (2005) Spatial analysis: a guide for ecologists. Cambridge University Press, Cambridge

    Google Scholar 

  • Franklin J, Miller J (2010) Mapping species distributions. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Gibson LA, Wilson BA, Cahill DM, Hill J (2004) Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria. Austral Biol Conservation 117:143–150

    Article  Google Scholar 

  • Gimaret-Caprentier C, Péllissier R, Pascal JP, Houllier F (1998) Sampling strategies for the assessment of tree species J Veg Sci 9:161–172

    Google Scholar 

  • Gormley AM, Forsyth DM, Griffioen P, Lindeman M, Ramsey DSL, Scroggie MP, Woodford L (2011) Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species. J Appl Ecol 48:25–34

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  • Haining RP (1990) Spatial data analysis in the social and environmental sciences. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Hastie T, Pregibon D (1992) Generalized linear models. In Chambers TJ, Hastie T (eds) Statistical models in S. Wadsworth & Brooks, Cole, pp 195–248

  • Hédl R (2007) Is sampling subjectivity a distorting factor in surveys of vegetation diversity? Folia Geobot 42:191–198

    Article  Google Scholar 

  • Hirzel AH, Helfer V, Metral F (2001) Assessing habitat suitability models with a virtual species. Ecol Modelling 145:111–121

    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 83:2007–2036

    Article  Google Scholar 

  • Kadmon R, Heller J (1999) Modelling faunal responses to climatic gradients with GIS: land snails as a case study. J Biogeogr 25:527–539

    Article  Google Scholar 

  • Keitt TH, Bjørnstad ON, Dixon PM, Citron-Pousty S (2002) Accounting for spatial pattern when modeling organism – environment interactions. Ecography 25:616–625

    Article  Google Scholar 

  • Knollová I, Chytrý M, Tichý L, Hájek O (2005) Stratified resampling of phytosociological databases: some strategies for obtaining more representative data sets for classification studies. J Veg Sci 16:479–486

    Article  Google Scholar 

  • Lavergne S, Thuiller W, Molina J, Debussche M (2005) Environmental and human factors influencing rare plant local occurrence, extinction and persistence: a 115 year study in the Mediterranean region. J Biogeogr 32:799–811

    Article  Google Scholar 

  • Lájer K (2007) Statistical tests as inappropriate tools for data analysis performed on non-random samples of plants community. Folia Geobot 42:115–122

    Article  Google Scholar 

  • Legendre P (1993) Spatial autocorrelation: problem or new paradigm. Ecology 74:1659–1673

    Article  Google Scholar 

  • Lepš J, Šmilauer P (2007) Subjectively sampled vegetation data: Don’t throw out the baby with the bath water. Folia Geobot 42:169–178

    Article  Google Scholar 

  • Maina G, Howe HE (2000) Inherent rarity in community restoration. Conservation Biol 14:1335–1340

    Article  Google Scholar 

  • Mason RL, Gunst RF, Hess JL (2003) Statistical designs and analysis experiments. With Application for engineering and science. Wiley Interscience, Carthage, Missouri

    Book  Google Scholar 

  • Miller MJ, Franklin MJ, Aspinall R (2007) Incorporating spatial dependence in predictive vegetation models. Ecol Modelling 202:225–242

    Article  Google Scholar 

  • Pereira JMC, Itami RM (1991) Habitat modeling using a logistic multiple regression: a study of Mt.Graham red squirrel. Photogramm Eng Remote Sens 57:1475–1486

    Google Scholar 

  • R Development Core Team (2011) R: A Language and Environment for Statistical Computing. Version 2.13.0. R Foundation for Statistical Computing, Vienna. Available at: http://www.R-project.org.

  • Smith PA (1994) Autocorrelation in logistic regression modeling of species distributions. Global Ecol Biogeogr 4:47–61

    Article  Google Scholar 

  • Store R, Kangas J (2001) Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modeling. Landscape Urban Planning 5:79–93

    Article  Google Scholar 

  • Thuiller W, Araújo MB, Lavorel S (2003) Generalized models vs. classification tree analysis: Predicting spatial distributions of plant species at different scales. J Veg Sci 14:669–680

    Article  Google Scholar 

  • Tomaselli M, Rossi G (1994) Phytosociology and ecology of Caricion curvulae vegetation in the northern Apennines (N Italy). Fitosociologia 26:51–62

    Google Scholar 

  • Wagner HH, Fortin MJ (2005) Spatial analysis of landscapes: concepts and statistics. Ecology 86:1975–1987

    Article  Google Scholar 

  • Wilson JB (2007) Priority in statistics, the sensitive feet of elephants, and don’t transform data. Folia Geobot 42:161–167

    Article  Google Scholar 

  • Wintle BA, Bardos DC (2006) Modeling species – habitat relationships with spatially autocorrelated observed data. Ecol Applic 16:1945–1958

    Article  CAS  Google Scholar 

  • Zhang L, Ma Z, Guo L (2008) Spatially assessing model errors of four regression techniques for three types of forest stands. Forestry 81:209–225

    Article  Google Scholar 

Download references

Acknowledgements

Research was supported by grants from the Administration of Pistoia Province and the University of Florence. Thanks are due to Giovanni Bacaro (Siena) for helping to improve the paper. Thanks also to Sheela Raman (New York) for improving the language.

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Correspondence to Bruno Foggi.

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Guidi, T., Foggi, B. Use of Phytosociological Databases for Species Distribution Models. Folia Geobot 47, 305–316 (2012). https://doi.org/10.1007/s12224-012-9124-2

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  • DOI: https://doi.org/10.1007/s12224-012-9124-2

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