Abstract
Due to the difficulties of field-based species data collection at wide spatial scales, remotely sensed spectral diversity has been advocated as one of the most effective proxies of ecosystem and species diversity. It is widely accepted that the relationship between species and spectral diversity is scale dependent. However, few studies have evaluated the impacts of scale on species diversity estimates from remote sensing data. In this paper we tested the species versus spectral relationship over very large scales (extents) with a varying spatial grain using floristic data of North America. Spectral diversity explained a low amount of variance while spatial extent of the sampling units (floras) explained a high amount of variance based on results from our variance partitioning analyses. This leads to the conclusion that spectral diversity must be carefully related to species diversity, explicitly taking into account potential area effects.
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Abbreviations
- MODIS:
-
Moderate Resolution Imaging Spectrometer
- NDVI:
-
Normalized Difference Vegetation Index
References
Arrhenius, O. 1921. Species and area. J. Ecol. 9: 95–99.
Bino, G., N. Levin, S. Darawshi, N. van der Hal, A. Reich-Solomon and S. Kark. 2008. Landsat derived NDVI and spectral unmixing accurately predict bird species richness patterns in an urban landscape. Internat. J. Remote Sens. 29: 3675–3700.
Borcard, D., P. Legendre, and P. Drapeau. 1992. Partialling out the spatial component of ecological variation. Ecology 73: 1045–1055.
Brown, J.H., M.V. Lomolino. 1998. Biogeography. Sinauer Associates, Sunderland.
Carr, D., N. Lewin-Koh and M. Maechler. 2011. hexbin: hexagonal binning routines. R package version 1.26.0.
Culver, D., M. Christman, B. Sket and P. Trontelj. 2004. Sampling adequacy in an extreme environment: species richness patterns in Slovenian caves. Biodivers. Conserv. 13: 1209–1229.
Currie, D.J., G.G. Mittelbach, H.V. Cornell, R. Field, J.-F. Guégan, B. Hawkins, D.M. Kaufman, J.T. Kerr, T. Oberdorff, E. O’Brien and J.R.G. Turner. 2004. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol. Lett. 7: 1121–1134.
Denslow, M.W., M.W. Palmer, Z.E. Murrell. 2010. Patterns of native and exotic vascular plant richness along an elevational gradient from sea level to the summit of the Appalachian Mountains, USA. J. Torrey Bot. Soc. 137: 67–80.
Feilhauer, H. and S. Schmidtlein. 2009. Mapping continuous fields of forest alpha and beta diversity. Appl. Veg. Sci. 12: 429–439.
Fridley, J.D., H. Qian, P.S. White, and M.W. Palmer. 2006. Plant species invasions along the latitudinal gradient in the United States: comment. Ecology 87: 3209–3213.
Geri, F., D. Rocchini and A. Chiarucci. 2010. Landscape metrics and topographical determinants of large-scale forest dynamics in a Mediterranean landscape. Landscape Urban Plan. 95: 46–53.
Gleason, H.A. 1922. On the relation between species and area. Ecology 3: 158–162.
Gillespie, T.W. 2005. Predicting woody-plant species richness in tropical dry forests: a case study from South Florida, USA. Ecol. Appl. 15: 27–37.
Gillespie, T.W., G.M. Foody, D. Rocchini, A.P. Giorgi and S. Saatchi. 2008. Measuring and modeling biodiversity from space. Progr. Phys. Geogr. 32: 203–221.
Gould, W. 2000. Remote sensing of vegetation, plant species richness, and regional biodiversity hot spots. Ecol. Appl. 10: 1861–1870.
Hardtle, W., G. von Oheimb and C. Westphal. 2003. The effects of light and soil conditions on the species richness of the ground vegetation of deciduous forests in northern Germany (Schleswig-Holstein). Forest Ecol Manag. 182: 327–338.
He, K.S., J. Zhang. 2009. Testing the correlation between beta diversity and differences in productivity among global ecoregions, biomes, and biogeographical realms. Ecol. Inform. 4: 93–98.
He, K.S., J. Zhang and Q. Zhang, 2009. Linking variability in species composition and MODIS NDVI based on beta diversity measurements. Acta Oecol. 35: 14–21.
Hernandez-Stefanoni, J., J. Dupuy and M.A. Castillo-Santiago. 2009. Assessing species density and abundance of tropical trees from remotely sensed data and geostatistics. Appl. Veg. Sci. 12: 398–414.
Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao and L.G. Ferreira. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83: 195–213.
Lechner, A.M., W.T. Langford, S.D. Jones, S.A. Bekessy and A. Gordon. 2012. Investigating species–environment relationships at multiple scales: Differentiating between intrinsic scale and the modifiable areal unit problem. Ecol. Complex. 11: 91–102.
Legendre, P., M.R.T. Dale, M.-J. Fortin, J. Gurevitch, M. Hohn and D. Myers. 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography 25: 601–615.
Legendre, P. and L. Legendre. 1998. Numerical Ecology. Second edition, Elsevier Science, Amsterdam, NL.
Lewin-Koh, N. 2011. Hexagon Binning: an Overview. Free book at: http://project.management6.com/Hexagon-Binning-an-Overview-pdf-e15305.pdf
Lobo, J.M., J.-P. Lumaret and P. Jay-Robert. 2002. Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variables. Global Ecol. Biogeogr. 11: 265–277.
Neteler, M., M.H. Bowman, M. Landa and M. Metz. 2012. GRASS GIS: a multi-purpose Open Source GIS. Environ. Model. Software 31: 124–130.
Neteler, M. and H. Mitasova. 2008. Open Source GIS: A GRASS GIS Approach. The International Series in Engineering and Computer Science, Third Edition. Springer, New York.
Oindo, B. and A. Skidmore. 2002. Interannual variability of NDVI and species richness in Kenya. Int. J. Remote Sens. 23: 285–298.
Oldeland, J., D. Wesuls, D. Rocchini, M. Schmidt and N. Jürgens. 2010. Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity? Ecol. Indicators 10: 390–396.
Oksanen, J., F. Guillaume Blanchet, R. Kindt. P. Legendre, P.R. Minchin, R.B. O’Hara, G.L. Simpson, P. Solymos, M.H.H. Stevens and H. Wagner. 2012. vegan: Community Ecology Package. R package version 2.0-5. http://CRAN.R-project.org/package=vegan.
Orlóci, L., M. Anand, and V.D. Pillar. 2002. Biodiversity analysis: issues, concepts, techniques. Community Ecol. 3: 217–236.
Palmer, M.W. 2005. Temporal trends of exotic species richness in North American floras: an overview. EcoScience 12: 386–390.
Palmer. M.W. 2006. Scale dependence of native and alien species richness in North American floras. Preslia 78: 427–436.
Palmer, M.W. 2007. Species-area curves and the geometry of nature. In: D. Storch, P. A. Marquet and J. H. Brown (eds.), Scaling Biodiversity. Cambridge University Press, Cambridge. pp.15–31.
Palmer, M.W., P. Earls, B.W. Hoagland, P.S. White and T. Wohlgemuth. 2002. Quantitative tools for perfecting species lists. Environmetrics 13: 121–137.
Palmer, M.W., D. McGlinn and J. Fridley. 2008. Artifacts and artifictions in biodiversity research. Folia Geobot. 43: 245–257.
Palmer, M.W. and J.C. Richardson. 2012. Biodiversity data in the Information Age: Do 21st Century floras make the grade? Castanea 77: 46–59.
Palmer, M.W. and P. White. 1994. Scale dependence and species-area relationship. The Amer. Nat. 144: 717–740.
Palmer, M.W., T. Wohlgemuth, P. Earls, J.R. Arévalo and S.D. Thompson. 2000. Opportunities for long-term ecological research at the Tallgrass Prairie Preserve, Oklahoma. In: K. Lajtha and K. Vanderbilt (eds.), Cooperation in Long Term Ecological Research in Central and Eastern Europe. Proceedings of ILTER Regional Workshop, Budapest, Hungary, 22–25 June, 1999, pp. 123–128.
Parviainen, M., M. Luoto and R. Heikkinen. 2009. The role of local and landscape level measures of greenness in modelling boreal plant species richness. Ecol. Model. 220: 2690–2701.
Qian, H., J.D. Fridley and M.W. Palmer. 2007. The latitudinal gradient of species-area relationships for vascular plants of North America. Amer. Nat. 170: 690–701.
R Development Core Team. 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. (http://www.R-project.org,).
Ricotta, C. 2005. Through the jungle of biological diversity. Acta Biotheor. 53: 29–38.
Rocchini, D. 2007. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery. Remote Sens. Environ. 111: 423–434.
Rocchini, D., S. Andreini Butini and A. Chiarucci. 2005. Maximizing plant species inventory efficiency by means of remotely sensed spectral distances. Global Ecol. Biogeogr. 14: 431–437.
Rocchini, D., D. McGlinn, C. Ricotta, M. Neteler and T. Wohlgemuth. 2011. Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey based plant species richness. J. Veg. Sci. 22: 688–698.
Rocchini, D. and M. Neteler. 2012. Let the four freedoms paradigm apply to ecology. Trends Ecol. Evol. 27: 310–311.
Stanton, M.C. and P.J. Diggle. 2013. Geostatistical analysis of binomial data: generalised linear or transformed Gaussian modelling? Environmetrics 24: 158–171.
Waser, L., S. Stofer, M. Schwarz, M. Kchler, E. Ivits and C. Scheidegger. 2004. Prediction of biodiversity: regression of lichen species richness on remote sensing data. Community Ecol. 5: 121–134.
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Rocchini, D., Dadalt, L., Delucchi, L. et al. Disentangling the role of remotely sensed spectral heterogeneity as a proxy for North American plant species richness. COMMUNITY ECOLOGY 15, 37–43 (2014). https://doi.org/10.1556/ComEc.15.2014.1.4
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DOI: https://doi.org/10.1556/ComEc.15.2014.1.4