Increasing model structural complexity inhibits the growth of initial condition errors

UTSePress Research/Manakin Repository

Search UTSePress Research


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Baird, Mark en_US
dc.contributor.author Suthers, Iain en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T06:28:43Z
dc.date.available 2012-02-02T06:28:43Z
dc.date.issued 2010 en_US
dc.identifier 2010000217 en_US
dc.identifier.citation Baird Mark and Suthers Iain 2010, 'Increasing model structural complexity inhibits the growth of initial condition errors', Elsevier, vol. 7, no. 4, pp. 478-486. en_US
dc.identifier.issn 1476-945X en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/14766
dc.description.abstract One source of inaccuracy in non-linear deterministic ecological models is the growth of initial condition errors. A size-resolved pelagic ecosystem model is used to investigate the effects of changing model structural complexity on the growth rate of initial condition errors. Structural complexity is altered by (1) changing the number of biological size-classes; and (2) changing prey size-ranges which changes the number of linkages for the same number of size-classes. Ensembles of model runs with tiny variations in initial conditions are undertaken and member divergence used to estimate ensemble spread (a measure of the growth of initial condition errors). Increasing prey ranges and therefore the number of linkages greatly reduced the rate of growth of initial condition errors, but ecosystem behaviour is also altered, restricting the generality of the result. At more than 123 size-classes, increasing the number of sizeclasses while not changing either the model equations or parameters does not alter ecosystem behaviour for over 200 days. In this case, increasing structural complexity through increasing the number of size classes did not alter the growth of initial condition errors for the first 30 days of the simulations, but afterwards reduced error growth. There are many advantages of parsimonious ecological models with small numbers of classes and linkages, but they are more likely to suffer from the growth of initial condition errors than structurally complex models. en_US
dc.language en_US
dc.publisher Elsevier en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.ecocom.2009.12.001 en_US
dc.title Increasing model structural complexity inhibits the growth of initial condition errors en_US
dc.parent Ecological Complexity en_US
dc.journal.volume 7 en_US
dc.journal.number 4 en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 478 en_US
dc.identifier.endpage 486 en_US
dc.cauo.name SCI.Faculty of Science en_US
dc.conference Verified OK en_US
dc.for 060200 en_US
dc.personcode 108934 en_US
dc.personcode 0000023644 en_US
dc.percentage 100 en_US
dc.classification.name Ecology en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Initial conditions Structural complexity Pelagic ecosystem Ensemble spread Non-linear dynamics Predictive skill en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record