{"id":{"repo_id":"soton","oai_identifier":"oai:eprints.soton.ac.uk:63762"},"canonical_url":"https://search.dev.ndltd.org/etd/soton/oai:eprints.soton.ac.uk:63762","repository":{"repo_id":"soton","name":"University of Southampton","base_url":"https://eprints.soton.ac.uk/cgi/oai2"},"display":{"title":"Accounting for unpredictable spatial variability in plankton ecosystem models","abstract":"Limitations on our ability to predict fine-scale spatial variability in plankton ecosystems can
seriously compromise our ability to predict coarse-scale behaviour. Spatial variability which
is deterministically unpredictable may distort parameter estimates when the ecosystem model
is fitted to (or assimilates) ocean data, may compromise model validation, and may produce
mean-field ecosystem behaviour discrepant with that predicted by the model. New statistical
methods are investigated to mitigate these effects and thus improve understanding and prediction
of coarse-scale behaviour e.g. in response to climate change. First, the standard model
fitting technique is generalised to allow model-data ‘phase errors’ in the form of time lags,
as has been observed to approximate mesoscale plankton variability in the open ocean. The
resulting ‘variable lag fit’ is shown to enable ‘Lagrangian’ parameter recovery with artificial
ecosystem data. A second approach employs spatiotemporal averaging, fitting a ‘weak prior’
box model to suitably-averaged data from Georges Bank (as an example), allowing liberal
biological parameter adjustments to account for mean effects of unresolved variability. A
novel skill assessment technique is used to show that the extrapolative skill of the box model
fails to improve on a strictly empirical model. Third, plankton models where horizontal variability
is resolved ‘implicitly’ are investigated as an alternative to coarse or higher explicit
resolution. A simple simulation study suggests that the mean effects of fine-scale variability
on coarse-scale plankton dynamics can be serious, and that ‘spatial moment closure’ and
similar statistical modelling techniques may be profitably applied to account for them.","abstract_html":"Limitations on our ability to predict fine-scale spatial variability in plankton ecosystems can<br/>seriously compromise our ability to predict coarse-scale behaviour. Spatial variability which<br/>is deterministically unpredictable may distort parameter estimates when the ecosystem model<br/>is fitted to (or assimilates) ocean data, may compromise model validation, and may produce<br/>mean-field ecosystem behaviour discrepant with that predicted by the model. New statistical<br/>methods are investigated to mitigate these effects and thus improve understanding and prediction<br/>of coarse-scale behaviour e.g. in response to climate change. First, the standard model<br/>fitting technique is generalised to allow model-data ‘phase errors’ in the form of time lags,<br/>as has been observed to approximate mesoscale plankton variability in the open ocean. The<br/>resulting ‘variable lag fit’ is shown to enable ‘Lagrangian’ parameter recovery with artificial<br/>ecosystem data. A second approach employs spatiotemporal averaging, fitting a ‘weak prior’<br/>box model to suitably-averaged data from Georges Bank (as an example), allowing liberal<br/>biological parameter adjustments to account for mean effects of unresolved variability. A<br/>novel skill assessment technique is used to show that the extrapolative skill of the box model<br/>fails to improve on a strictly empirical model. Third, plankton models where horizontal variability<br/>is resolved ‘implicitly’ are investigated as an alternative to coarse or higher explicit<br/>resolution. A simple simulation study suggests that the mean effects of fine-scale variability<br/>on coarse-scale plankton dynamics can be serious, and that ‘spatial moment closure’ and<br/>similar statistical modelling techniques may be profitably applied to account for them.","abstract_has_math":false,"creators":["Wallhead, Philip John"],"institution":"University of Southampton","degree_name":"Ph.D.","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-03","date_published":"2008-03","updated_at":"2026-07-24T04:35:54Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Wallhead, Philip John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2008-03"]},{"key":"dc:date.issued","label":"Date","values":["2008-03"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Ocean and Earth Science (pre 2011 reorg)","School of Ocean and Earth Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Southampton"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://eprints.soton.ac.uk/63762/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Ph.D."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://eprints.soton.ac.uk/63762/1/Wallhead_2008_PhD_28recto_margins_29.pdf","https://eprints.soton.ac.uk/63762/2/Wallhead_2008_PhD_2_28duplex_margins_29.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Limitations on our ability to predict fine-scale spatial variability in plankton ecosystems can
seriously compromise our ability to predict coarse-scale behaviour. Spatial variability which
is deterministically unpredictable may distort parameter estimates when the ecosystem model
is fitted to (or assimilates) ocean data, may compromise model validation, and may produce
mean-field ecosystem behaviour discrepant with that predicted by the model. New statistical
methods are investigated to mitigate these effects and thus improve understanding and prediction
of coarse-scale behaviour e.g. in response to climate change. First, the standard model
fitting technique is generalised to allow model-data ‘phase errors’ in the form of time lags,
as has been observed to approximate mesoscale plankton variability in the open ocean. The
resulting ‘variable lag fit’ is shown to enable ‘Lagrangian’ parameter recovery with artificial
ecosystem data. A second approach employs spatiotemporal averaging, fitting a ‘weak prior’
box model to suitably-averaged data from Georges Bank (as an example), allowing liberal
biological parameter adjustments to account for mean effects of unresolved variability. A
novel skill assessment technique is used to show that the extrapolative skill of the box model
fails to improve on a strictly empirical model. Third, plankton models where horizontal variability
is resolved ‘implicitly’ are investigated as an alternative to coarse or higher explicit
resolution. A simple simulation study suggests that the mean effects of fine-scale variability
on coarse-scale plankton dynamics can be serious, and that ‘spatial moment closure’ and
similar statistical modelling techniques may be profitably applied to account for them."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["Accounting for unpredictable spatial variability in plankton ecosystem models"]}]}],"canonical_facts":{"dc:creator":["Wallhead, Philip John"],"dc:date":["2008-03"],"dc:date.issued":["2008-03"],"dc:description.abstract":["Limitations on our ability to predict fine-scale spatial variability in plankton ecosystems can
seriously compromise our ability to predict coarse-scale behaviour. Spatial variability which
is deterministically unpredictable may distort parameter estimates when the ecosystem model
is fitted to (or assimilates) ocean data, may compromise model validation, and may produce
mean-field ecosystem behaviour discrepant with that predicted by the model. New statistical
methods are investigated to mitigate these effects and thus improve understanding and prediction
of coarse-scale behaviour e.g. in response to climate change. First, the standard model
fitting technique is generalised to allow model-data ‘phase errors’ in the form of time lags,
as has been observed to approximate mesoscale plankton variability in the open ocean. The
resulting ‘variable lag fit’ is shown to enable ‘Lagrangian’ parameter recovery with artificial
ecosystem data. A second approach employs spatiotemporal averaging, fitting a ‘weak prior’
box model to suitably-averaged data from Georges Bank (as an example), allowing liberal
biological parameter adjustments to account for mean effects of unresolved variability. A
novel skill assessment technique is used to show that the extrapolative skill of the box model
fails to improve on a strictly empirical model. Third, plankton models where horizontal variability
is resolved ‘implicitly’ are investigated as an alternative to coarse or higher explicit
resolution. A simple simulation study suggests that the mean effects of fine-scale variability
on coarse-scale plankton dynamics can be serious, and that ‘spatial moment closure’ and
similar statistical modelling techniques may be profitably applied to account for them."],"dc:format":["text"],"dc:identifier.uri":["https://eprints.soton.ac.uk/63762/1/Wallhead_2008_PhD_28recto_margins_29.pdf","https://eprints.soton.ac.uk/63762/2/Wallhead_2008_PhD_2_28duplex_margins_29.pdf"],"dc:publisher.department":["Ocean and Earth Science (pre 2011 reorg)","School of Ocean and Earth Science"],"dc:publisher.institution":["University of Southampton"],"dc:relation.isreferencedby":["https://eprints.soton.ac.uk/63762/"],"dc:title":["Accounting for unpredictable spatial variability in plankton ecosystem models"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["Ph.D."]},"updated_at":"2026-07-24T04:35:54Z"}