The University of Southampton
University of Southampton Institutional Repository

Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data

Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data
Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data
modelling framework for studying the combined effects of climate and land-cover changes on the distribution of species is presented. The model integrates land-cover data into a correlative bioclimatic model in a scale-dependent hierarchical manner, whereby Artificial Neural Networks are used to characterise species' climatic requirements at the European scale and land-cover requirements at the British scale. The model has been tested against an alternative non-hierarchical approach and has been applied to four plant species in Britain: Rhynchospora alba, Erica tetralix, Salix herbacea and Geranium sylvaticum. Predictive performance has been evaluated using Cohen's Kappa statistic and the area under the Receiver Operating Characteristic curve, and a novel approach to identifying thresholds of occurrence which utilises three levels of confidence has been applied. Results demonstrate reasonable to good predictive performance for each species, with the main patterns of distribution simulated at both 10 km and 1 km resolutions. The incorporation of land-cover data was found to significantly improve purely climate-driven predictions for R. alba and E. tetralix, enabling regions with suitable climate but unsuitable land-cover to be identified. The study thus provides an insight into the roles of climate and land-cover as determinants of species' distributions and it is demonstrated that the modelling approach presented can provide a useful framework for making predictions of distributions under scenarios of changing climate and land-cover type. The paper confirms the potential utility of multi-scale approaches for understanding environmental limitations to species' distributions, and demonstrates that the search for environmental correlates with species' distributions must be addressed at an appropriate spatial scale. Our study contributes to the mounting evidence that hierarchical schemes are characteristic of ecological systems.
Geraniaceae, Ericaceae, Dicotyledones, Salicaceae, Spermatophyta, Angiospermae, Monocotyledones, Cyperaceae, mating, Europe, United Kingdom, Geranium, Erica tetralix, Salix alba, Rhynchospora, ecology, spatial scale, Performance, plant, neural network, models, plant cover, climate, Great Britain, spatial distribution
0906-7590
285-298
Pearson, Richard G.
adc32c7b-4c7a-481e-88be-4dba404c5886
Dawson, Terence P.
0c9227ce-1d62-47b5-9571-a8a1864321af
Liu, Canran
3c088869-bc33-4bbc-859c-2684c354eedc
Pearson, Richard G.
adc32c7b-4c7a-481e-88be-4dba404c5886
Dawson, Terence P.
0c9227ce-1d62-47b5-9571-a8a1864321af
Liu, Canran
3c088869-bc33-4bbc-859c-2684c354eedc

Pearson, Richard G., Dawson, Terence P. and Liu, Canran (2004) Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography, 27 (3), 285-298. (doi:10.1111/j.0906-7590.2004.03740.x).

Record type: Article

Abstract

modelling framework for studying the combined effects of climate and land-cover changes on the distribution of species is presented. The model integrates land-cover data into a correlative bioclimatic model in a scale-dependent hierarchical manner, whereby Artificial Neural Networks are used to characterise species' climatic requirements at the European scale and land-cover requirements at the British scale. The model has been tested against an alternative non-hierarchical approach and has been applied to four plant species in Britain: Rhynchospora alba, Erica tetralix, Salix herbacea and Geranium sylvaticum. Predictive performance has been evaluated using Cohen's Kappa statistic and the area under the Receiver Operating Characteristic curve, and a novel approach to identifying thresholds of occurrence which utilises three levels of confidence has been applied. Results demonstrate reasonable to good predictive performance for each species, with the main patterns of distribution simulated at both 10 km and 1 km resolutions. The incorporation of land-cover data was found to significantly improve purely climate-driven predictions for R. alba and E. tetralix, enabling regions with suitable climate but unsuitable land-cover to be identified. The study thus provides an insight into the roles of climate and land-cover as determinants of species' distributions and it is demonstrated that the modelling approach presented can provide a useful framework for making predictions of distributions under scenarios of changing climate and land-cover type. The paper confirms the potential utility of multi-scale approaches for understanding environmental limitations to species' distributions, and demonstrates that the search for environmental correlates with species' distributions must be addressed at an appropriate spatial scale. Our study contributes to the mounting evidence that hierarchical schemes are characteristic of ecological systems.

This record has no associated files available for download.

More information

Published date: 14 May 2004
Keywords: Geraniaceae, Ericaceae, Dicotyledones, Salicaceae, Spermatophyta, Angiospermae, Monocotyledones, Cyperaceae, mating, Europe, United Kingdom, Geranium, Erica tetralix, Salix alba, Rhynchospora, ecology, spatial scale, Performance, plant, neural network, models, plant cover, climate, Great Britain, spatial distribution

Identifiers

Local EPrints ID: 58533
URI: http://eprints.soton.ac.uk/id/eprint/58533
ISSN: 0906-7590
PURE UUID: a0cbfcb4-b01c-4122-bd87-5e6f06575d7b

Catalogue record

Date deposited: 14 Aug 2008
Last modified: 15 Mar 2024 11:11

Export record

Altmetrics

Contributors

Author: Richard G. Pearson
Author: Terence P. Dawson
Author: Canran Liu

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×