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Prediction of total and rare plant species richness in agricultural landscapes from satellite images and topographic data

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Abstract

The diversity of future landscapes might depend on our ability to predict their potential species richness. The predictability of patterns of vascular plant species richness in a Finnish agricultural river landscape was studied using generalized linear modeling, floristic records from fifty-three0.25-km grid squares in the “core” study area, and environmental variables derived from Landsat TM images and a digital elevation model. We built multiple regression models for the total number of plant species and the number of rarities, and validated the accuracy of the derived models with a test set of 52 grid squares. We tentatively extrapolated the models from the core study area to the whole study area of 601 km2 and produced species richness probability maps using GIS techniques. The results suggest that the local ‘hotspots’ of total flora (grid squares with > 200species) are mainly found in river valleys, where habitat diversity is high and a semi-open agricultural-forest mosaic occurs. The ‘hotspots’ of rare species (grid squares with > 4 rare species) are also found in river valleys, in sites where extensive semi-natural grasslands and herb-rich deciduous forests occur on steep slopes. We conclude that environmental variables derived from satellite images and topographic data can be used as approximate surrogates of plant species diversity in agricultural landscapes. Modeling of biological diversity based on satellite images and GIS can provide useful information needed in land use planning. However, due to the potential pitfalls in processing satellite imagery and model-building procedures, the results of predictive models should be carefully interpreted.

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References

  • Augustin N.H., Mugglestone M.A. and Buckland S.T. 1996. An autologistic model for spatial distribution of wildlife. Journal of Applied Ecology 33: 339-347.

    Article  Google Scholar 

  • Austin M.P. 1999. The potential contribution of vegetation ecology to biodiversity research. Ecography 22: 465-485.

    Article  Google Scholar 

  • Austin M.P., Cunningham R.B. and Fleming P.M. 1984. New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio 55: 11-27.

    Article  Google Scholar 

  • Austin M.P., Pausas J. and Nicholls A.O. 1996. Patterns of tree species richness in relation to environment in south-eastern New South Wales Australia. Australian Journal of Ecology 21: 154-164.

    Google Scholar 

  • Austin M.P. and Heyligers P.C. 1991. New approach to vegetation survey design: gradsect sampling. In: Margules C.R. and Austin M.P. (eds), Nature Conservation: Cost Effective Biological Surveys and Data Analysis. CSIRO, Australia, pp. 31-36.

    Google Scholar 

  • Birks H.J.B. 1996. Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora. Ecography 19: 332-340.

    Article  Google Scholar 

  • Burrough P.A. and McDonnell R.A. 1998. Principles of Geographical Information Systems. Spatial Information Systems, lOxford University Press, New york, 333 p.

    Google Scholar 

  • Bustamante J. 1997. Predictive models for Lesser Kestrel Falco naumanni distribution, abundance and extinction in southern Spain. Biological Conservation 80: 153-160.

    Article  Google Scholar 

  • Cherril A.J., McClean C., Watson P., Tucker K., Rushton S.P. and Sanderson R. 1995. Predicting the distributions of plant species at the regional scale: hierarchical matrix model. Landscape Ecology 4: 197-207.

    Article  Google Scholar 

  • Finnish Meteorological Institute 1991. Climatological Statistics in Finland 1961-1990. Supplement of Meteorological Yearbook of Finland., Helsinki.

  • Cortijo F.J. and Perez de la Blanca N. 1998. Improving classical contextual classifications. International Journal of Remote Sensing 19: 1591-1613.

    Article  Google Scholar 

  • Cracknell A.P. 1998. Synergy in remote sensing -What's in a pixel? International Journal of Remote Sensing 19: 2025-2047.

    Article  Google Scholar 

  • Crawley M.J. 1993. Glim for Ecologists. Blackwell Scientific Publications, Oxford, 379 pp.

    Google Scholar 

  • Currie D.J. 1991. Energy and large-scale patterns of animal-and plant-species richness. American Naturalist 137: 27-49.

    Article  Google Scholar 

  • Debinski K., Kindscher D.M. and Jakubauskas M.E. 1999. A remote sensing and GIS-based model of habitats and biodiversity in the Greater Yellowstone Ecosystem. International Journal of Remote Sensing 17: 3281-3291.

    Article  Google Scholar 

  • Esseen P.-A., Ehnström B., Ericson L. and Sjöberg K. 1992. Boreal forests-the focal habitats of Fennoscandia. In: Hansson L. (ed.), Ecological Principles of Nature Conservation. Elsevier, London, pp. 254-325.

    Google Scholar 

  • Eurola S., Hicks S. and Kaakinen E. 1984. Key to finnish mire types. In: Moore P.D. (ed.), European Mires. Academic Press, London, pp. 11-117.

    Google Scholar 

  • Eyre M.D. and Rushton S.P. 1989. Quantification of conservation criteria using invertebrates. Journal of Applied Ecology 26: 159-171.

    Article  Google Scholar 

  • Flack V.F. and Chang P.C. 1987. Frequency of selecting noise variables in subset regression analysis: a simulation study. The American Statistician 41: 84-86.

    Article  Google Scholar 

  • Fisher P. 1997. The pixel: a snare and a delusion. International Journal of Remote Sensing 18: 679-685.

    Article  Google Scholar 

  • Gaston K.J. 1994. Rarity. Chapman & Hall, London, 205 pp.

    Google Scholar 

  • Gaston K.J. 1996. Species richness: measure and measurement. In: Gaston K.J. (ed.), Biodiversity. A Biology of Numbers and Difference. Blackwell Science, Oxford, pp. 77-113.

    Google Scholar 

  • Griffiths G.H., Smith J.M., Veitch N. and Aspinall R. 1993. The ecological interpretation of satellite imagery with special reference to bird habitats. In: Young R.H., Green D.R. and Cousins S.H. (eds), Landscape Ecology and Geographic Information Systems. Taylor & Francis Ltd, London, pp. 255-272.

    Google Scholar 

  • Griffiths G.H. and Mather P.M. 2000. Remote sensing and landscape ecology: patterns and landscape change. International Journal of Remote Sensing 21: 2537-2540.

    Article  Google Scholar 

  • Hämet-Ahti L., Suominen J., Ulvinen T., Uotila P. and Vuokko S. 1986. Retkeilykasvio (Field Flora of Finland). Suomen Luonnonsuojelun Tuki Oy., Helsinki, 598 pp.

  • Heikkinen R.K. 1996. Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland. Vegetatio 126: 151-165.

    Article  Google Scholar 

  • Hill N.M. and Keddy P.A. 1992. Prediction of rarities from habitat variables: coastal plain plants on Nova Scotian lakeshores. Ecology 73: 1852-1859.

    Article  Google Scholar 

  • Holmes K.W., Chadwick O.A. and Kyriakidis P.C. 2000. Error in a USGS 30-meter digital elevation model and its impact on terrain modeling. Journal of Hydrology 233: 154-173.

    Article  Google Scholar 

  • Hörnberg G., Zackrisson O., Segerström U., Svensson B.W., Ohlson M. and Bradshaw H.W. 1998. Boreal swamp forests. Biodiversity “hotspots” in an impoverished forest landscape. BioScience 48: 795-802.

    Article  Google Scholar 

  • Iverson L.R., Dale M.E., Scott C.T. and Prasad A. 1997. A GISderived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.). Landscape Ecology 12: 331-348.

    Article  Google Scholar 

  • James F.C. and McCulloch C.E. 1990. Multivariate analysis in ecology and systematics: Panacea or Pandora's box? Annual Review of Ecology and Systematics 21: 129-166.

    Google Scholar 

  • Kohn D.D. and Walsh D.M. 1994. Plant species richness-the effect of island size and habitat diversity. Journal of Ecology 82: 367-377.

    Article  Google Scholar 

  • Kontula T., Lehtomaa L. and Pykälä J. 2000. Land use history vegetation and flora in Rekijoki valley Somero SW Finland. Finnish Environment 306: 1-96.

    Google Scholar 

  • Kotiranta H., Uotila P., Sulkava S. and Peltonen S.-L. 1998. Red Data Book of East Fennoscandia. Ministry of the Environment, Finnish Environment Institute, Finnish Botanical Museum, Finnish Museum of Natural History, Helsinki, 351 pp.

    Google Scholar 

  • Lehtomaa L. 2000. Rural biotopes of SW Finland. Regional environmental series 160. Southwest Finland Regional Environment Centre, Turku.

    Google Scholar 

  • Lillesand T.M. and Kiefer R.W. 1994. Remote Sensing and Image Interpretation. John Wiley & Sons. Inc., USA, 650 pp.

    Google Scholar 

  • Luoto M. 2000. Modelling of rare plant species richness by landscape variables in an agriculture area in Finland. Plant Ecology 149: 157-168.

    Article  Google Scholar 

  • Mac Nally R. 2000. Regression and model-building in conservation biology, biogeography and ecology: The distinction between-and reconciliation of-‘predictive’ and ‘explanatory’ models. Biodiversity and Conservation 9: 655-671.

    Article  Google Scholar 

  • Margules C.R. and Austin M.P. 1991. Nature Conservation: Cost effective biological surveys and data analysis. CSIRO, Australia, 207 pp.

    Google Scholar 

  • Margules C.R., Nicholls A.O. and Austin M.P. 1987. Diversity of Eucalyptus species predicted by a multivariable environmental gradient. Oecologia 71: 229-232.

    Article  Google Scholar 

  • McCullagh P. and Nelder J.A. 1989. Generalized Linear Models. Chapman & Hall, London, 511 pp.

    Google Scholar 

  • McGarical K. and Marks B.J. 1994. Fragstats Spatial Pattern Analysis Program for Quantifying Landscape Structure Version 2.0.Forest Science Department, Oregon State University, Corvallis, Oregon, USA, 67 pp.

    Google Scholar 

  • McIntyre S. and Lavorel S. 1994. Predicting richness of native rare and exotic plants in response to habitat and disturbance variables across a variegated landscape. Conservation Biology 8: 521-531.

    Article  Google Scholar 

  • Miller R.I. 1986. Predicting rare plant distribution patterns in the southern Appalachians of the south-eastern U.S.A. Journal of Biogeography 13: 293-311.

    Article  Google Scholar 

  • Nagendra H. and Gadgil M. 1999. Satellite imagery as a tool for monitoring species diversity: an assessment. Journal of Applied Ecology 36: 388-397.

    Google Scholar 

  • Nicholls A.O. 1991. Examples of the use of Generalized linear models in analysis of survey data for conservation evaluation. In: Margules C.R. and Austin M.P. (eds), Nature Conservation: Cost Effective Biological Surveys and Data Analysis. CSIRO, Australia, pp. 54-63.

    Google Scholar 

  • Nilsson C., Grelsson G., Johansson M. and Sperens U. 1988. Can rarity and diversity be predicted in vegetation along river banks? Biological Conservation 44: 201-212.

    Article  Google Scholar 

  • Payne C.D. 1986. The GLIM System Release 3.77 Manual. Numerical Algorithms Group, Royal Statistical Society.

  • Philippi T.E. 1993. Multiple regression: Herbivory. In: Schneider S.M. and Gurevitch J. (eds), Design and Analysis of Ecological Experiments. Chapman & Hall, New York, pp. 183-210.

    Google Scholar 

  • Prendergast J.R., Quinn R.M., Lawton J.H., Eversham B.C. and Gibbons D.W. 1993. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365: 335-337.

    Article  Google Scholar 

  • Pykälä J. and Lappalainen I. 1998. Nykyaika näkyy maatalousympäristöissä. In: Lappalainen I. (ed.), Suomen luonnon monimuotoisuus (Biodiversity of Finland). Finnish Environment Institute, Helsinki, pp. 184-195.

    Google Scholar 

  • Rassi P., Kaipiainen H., Mannerkoski I. and Ståhls G. 1992. Uhanalaisten eläinten ja kasvien seurantatoimikunnan mietintö (English abstract: report on the monitoring of threatened animals and plants in Finland). Ministry of Environment, Helsinki.

    Google Scholar 

  • Richards J.A. 1996. Classifier performance and map accuracy. Remote Sensing Environment 57: 1616-166.

    Article  Google Scholar 

  • Richerson P.J. and Kwei-Lin L. 1980. Patterns of plant species diversity in California: relation to weather and topography. American Naturalist 116: 504-536.

    Article  Google Scholar 

  • Rossi E. and Kuitunen M. 1996. Ranking of habitats for the assessment of ecological impact in land use planning. Biological Conservation 77: 227-234.

    Article  Google Scholar 

  • Scott J.M., Davis F., Csuti B., Noss R., Butterfield B., Groves C. et al. 1993. Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monographs 57: 1-41.

    Google Scholar 

  • Spellerberg I.F. 1992. Evaluation and Assessment for Conservation. Chapman & Hall, London, 260 pp.

    Google Scholar 

  • Stoms D.M. 1992. Effects of Habitat Map Generalization in Biodiversity Assessment. Photogrammetric Engineering and Remote Sensing 58: 1587-1591.

    Google Scholar 

  • Stoms D.M. and Estes J.E. 1993. A remote sensing research agenda for mapping and monitoring biodiversity. International Journal of Remote Sensing 10: 1839-1860.

    Google Scholar 

  • Stoutjesdijk Ph. and Barkman J.J. 1992. Microclimate Vegetation and Fauna. OPULUS Press AB, Knivsta, Sweden, 216 pp.

    Google Scholar 

  • Tonteri T. 1994. Species richness of boreal understorey forest vegetation in relation to site type and successional factors. Annales Zoologici Fennici 31: 53-50.

    Google Scholar 

  • Turner M.G. 1989. Landscape Ecology: The Effect of Pattern on Process. Annual Review of Ecology and Systematics 20: 171-197.

    Article  Google Scholar 

  • Urban D.L., O'Neill R.V.O. and Shugart H.H. 1987. Landscape ecology: a hierarchical perspective can help scientist understand spatial patterns. BioScience 37: 119-127.

    Article  Google Scholar 

  • Vande Castle J. 1998. Remote sensing applications in Ecosystem Analysis. In: Peterson D.L. and Parker V.T. (eds), Ecological Scale-Theory and Applications. Columbia University Press, New York.

    Google Scholar 

  • Walker P.A. 1990. Modelling wildlife distribution using a geographic information system: kangaroos in relation to climate. Journal of Biogeography 17: 279-289.

    Article  Google Scholar 

  • Whittaker R.H. 1956. Vegetation of the Great Smoky Mountains. Ecological Monographs 26: 1-80.

    Article  Google Scholar 

  • Wiens J.A. 1986. Spatial scale and temporal variation in studies of shrubsteppe birds. In: Diamond J. and Case T.J. (eds), Community Ecology. Harper & Row Publishers, New York, pp. 154-172.

    Google Scholar 

  • Wohlgemuth T. 1996. Modelling floristic species richness on a regional scale: a case study in Switzerland. Biodiversity and Conservation 7: 159-177.

    Article  Google Scholar 

  • Yee T.W. and Mitchell N.D. 1991. Generalized additive models in plant ecology. Journal of Vegetation Science 2: 587-602.

    Article  Google Scholar 

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Authors and Affiliations

  1. GIS and Remote Sensing Unit, Finnish Environment Institute, FIN-00251, Helsinki, Finland

    Miska Luoto

  2. Department of Geography, University of Turku, FIN-20014, Turku, Finland

    Tuuli Toivonen

  3. Nature and Land Use Division, Finnish Environment Institute, FIN-00251, Helsinki, Finland

    Risto K. Heikkinen

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  1. Miska Luoto
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  2. Tuuli Toivonen
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Luoto, M., Toivonen, T. & Heikkinen, R.K. Prediction of total and rare plant species richness in agricultural landscapes from satellite images and topographic data. Landscape Ecology 17, 195–217 (2002). https://doi.org/10.1023/A:1020288509837

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