The University of Southampton
University of Southampton Institutional Repository

Linking palaeoenvironmental data and models to understand the past and to predict the future

Record type: Article

Complex, process-based dynamic models are used to attempt to mimic the intrinsic variability of the natural environment, ecosystem functioning and, ultimately, to predict future change. Palaeoecological data provide the means for understanding past ecosystem change and are the main source of information for validating long-term model behaviour. As global ecosystems become increasingly stressed by, for example, climate change, human activities and invasive species, there is an even greater need to learn from the past and to strengthen links between models and palaeoecological data. Using examples from terrestrial and aquatic ecosystems, we suggest that better interactions between modellers and palaeoecologists can help understand the complexity of past changes. With increased synergy between the two approaches, there will be a better understanding of past and present environmental change and, hence, an improvement in our ability to predict future changes.

Full text not available from this repository.

Citation

Anderson, N.J., Bugmann, H., Dearing, J.A. and Gaillard-Lemdahl, M-J. (2006) Linking palaeoenvironmental data and models to understand the past and to predict the future Trends in Ecology and Evolution, 21, (12), pp. 696-704. (doi:10.1016/j.tree.2006.09.005).

More information

Published date: 2006

Identifiers

Local EPrints ID: 55690
URI: http://eprints.soton.ac.uk/id/eprint/55690
PURE UUID: 7a5d6181-5922-490e-9e60-09efc27d1b2f
ORCID for J.A. Dearing: ORCID iD orcid.org/0000-0002-1466-9640

Catalogue record

Date deposited: 04 Aug 2008
Last modified: 17 Jul 2017 14:32

Export record

Altmetrics

Contributors

Author: N.J. Anderson
Author: H. Bugmann
Author: J.A. Dearing ORCID iD
Author: M-J. Gaillard-Lemdahl

University divisions


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.

×