Empirical realised niche models for British higher and lower plants – development and preliminary testing


  • Smart, S.M. (corresponding author, ssma@ceh.ac.uk), Andrew Scott, W. (was@ceh.ac.uk), Whitaker, J. (jhart@ceh.ac.uk), Crowe, A. (acrowe@ceh.ac.uk): NERC Centre for Ecology and Hydrology, Library Avenue, Bailrigg, Lancaster LA1 4AP, UK.
    Hill, M.O. (moh@ceh.ac.uk) & Roy, D.B. (dbr@ceh.ac.uk): NERC Centre for Ecology and Hydrology, Wallingford, Crowmarsh Gifford, Benson Lane, Cambridgeshire PE28 2LS, UK.
    Nigel Critchley, C. (Nigel.critchley@adas.co.uk): ADAS Woodthorne, Wergs Road, Wolverhampton WV6 8TQ, UK.
    Marini, L. (Lorenzo.marini@unipd.it): Department of Environmental Agronomy and Crop Production, University of Padova, Viale dell'Università 16 - 35020 Legnaro, Padova, Italy.
    Evans, C. (cev@ceh.ac.uk), Emmett, B.A. (bae@ceh.ac.uk), Rowe, E.C. (ecro@ceh.ac.uk): Centre for Ecology and Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK.
    Le Duc, M. (mled@liverpool.ac.uk), Marrs, R.H. (calluna@liverpool.ac.uk): Applied Vegetation Dynamics Laboratory, School of Biological Sciences, Biosciences Building, Crown Street, University of Liverpool, Liverpool L69 7ZB, UK.

  • Co-ordinating Editor: Dr. Philip Dixon.


Question: Can useful realised niche models be constructed for British plant species using climate, canopy height and mean Ellenberg indices as explanatory variables?

Location: Great Britain.

Methods: Generalised linear models were constructed using occurrence data covering all major natural and semi-natural vegetation types (n=40 683 quadrat samples). Paired species and soil records were only available for 4% of the training data (n=1033) so modelling was carried out in two stages. First, multiple regression was used to express mean Ellenberg values for moisture, pH and fertility, in terms of direct soil measurements. Next, species presence/absence was modelled using mean indicator scores, cover-weighted canopy height, three climate variables and interactions between these factors, but correcting for the presence of each target species in training plots to avoid circularity.

Results: Eight hundred and three higher plants and 327 bryophytes were modelled. Thirteen per cent of the niche models for higher plants were tested against an independent survey dataset not used to build the models. Models performed better when predictions were based only on indices derived from the species composition of each plot rather than measured soil variables. This reflects the high variation in vegetation indices that was not explained by the measured soil variables.

Conclusions: The models should be used to estimate expected habitat suitability rather than to predict species presence. Least uncertainty also attaches to their use as risk assessment and monitoring tools on nature reserves because they can be solved using mean environmental indicators calculated from the existing species composition, with or without climate data.