Empirical realised niche models for British coastal plant species

Jarvis, Susan G. ORCID: https://orcid.org/0000-0002-6770-2002; Rowe, Edwin C. ORCID: https://orcid.org/0000-0003-4784-7236; Henrys, Peter A. ORCID: https://orcid.org/0000-0003-4758-1482; Smart, Simon M. ORCID: https://orcid.org/0000-0003-2750-7832; Jones, Laurence ORCID: https://orcid.org/0000-0002-4379-9006; Garbutt, Angus ORCID: https://orcid.org/0000-0002-9145-9786. 2016 Empirical realised niche models for British coastal plant species. Journal of Coastal Conservation, 20 (2). 107-116. 10.1007/s11852-016-0422-3

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Abstract/Summary

Coastal environments host plant taxa adapted to a wide range of salinity conditions. Salinity, along with other abiotic variables, constrains the distribution of coastal plants in predictable ways, with relatively few taxa adapted to the most saline conditions. However, few attempts have been made to quantify these relationships to create niche models for coastal plants. Quantification of the effects of salinity, and other abiotic variables, on coastal plants is essential to predict the responses of coastal ecosystems to external drivers such as sea level rise. We constructed niche models for 132 coastal plant taxa in Great Britain based on eight abiotic variables. Paired measurements of vegetation composition and abiotic variables are rare in coastal habitats so four of the variables were defined using community mean values for Ellenberg indicators, i.e. scores assigned according to the typical alkalinity, fertility, moisture availability and salinity of sites where a species occurs. The remaining variables were the canopy height, annual precipitation, and maximum and minimum temperatures. Salinity and moisture indicator scores were significant terms in over 80 % of models, suggesting the distributions of most coastal species are at least partly determined by these variables. When the models were used to predict species occurrence against an independent dataset 64 % of models gave moderate to good predictions of species occurrence. This indicates that most models had successfully captured the key determinants of the niche. The models could potentially be applied to predict changes to habitats and species-dependent ecosystem services in response to rising sea levels.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1007/s11852-016-0422-3
UKCEH and CEH Sections/Science Areas:	Emmett Parr
ISSN:	1400-0350
Additional Keywords:	climate, Ellenberg, generalised linear model, saltmarsh, sand dune, vegetation
NORA Subject Terms:	Ecology and Environment
Date made live:	15 Feb 2016 10:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/512964

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