Changes between Version 1 and Version 2 of EwEugTimeSeriesFittingInEcosimEvaluatingFisheriesAndEnvironmentalEffects
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 20100128 16:30:48 (9 years ago)
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EwEugTimeSeriesFittingInEcosimEvaluatingFisheriesAndEnvironmentalEffects
v1 v2 3 3 Ecosim can incorporate (and indeed benefits from) time series data. For many of the groups to be incorporated in the model the time series data will be available from single species stock assessments. EwE thus builds on the more traditional stock assessment, using much of the information available from these, while integrating to the ecosystem level. 4 4 5 When an Ecosim model is loaded, you can load time series ‘reference’ data on relative and absolute biomasses of various pools over a particular historical period, along with estimates of changes in fishing impacts over that period. After such data have been loaded and applied (using the [ [Time series.htmTime series]] form), a statistical measure of goodness of fit to these data is generated each time Ecosim is run (using the [[Run ecosim.htmRun Ecosim]] form). This goodness of fit measure is a weighted sum of squared deviations (SS) of log biomasses from log predicted biomasses, scaled in the case of relative abundance data by the maximum likelihood estimate of the relative abundance scaling factor ''q'' in the equation ''y = qB'' (''y'' = relative abundance, ''B'' = absolute abundance).5 When an Ecosim model is loaded, you can load time series ‘reference’ data on relative and absolute biomasses of various pools over a particular historical period, along with estimates of changes in fishing impacts over that period. After such data have been loaded and applied (using the [wiki:TimeSeries Time Series] form), a statistical measure of goodness of fit to these data is generated each time Ecosim is run (using the [wiki:RunEcosim Run Ecosim] form). This goodness of fit measure is a weighted sum of squared deviations (SS) of log biomasses from log predicted biomasses, scaled in the case of relative abundance data by the maximum likelihood estimate of the relative abundance scaling factor ''q'' in the equation ''y = qB'' (''y'' = relative abundance, ''B'' = absolute abundance). 6 6 7 7 Each reference data series can be assigned a relative weight using a simple spreadsheet in the search interface, representing a prior assessment by the user about relatively how variable or reliable that type of data is compared to the other reference time series (low weights imply relatively high variance, unreliable data). 8 8 9 The time series fitting uses either fishing effort or fishing mortality data as driving factors for the Ecosim model runs. A statistical measure of goodness of fit to the time series data outlined above is generated each time Ecosim is run. The model allows four types of analysis with the SS measure (see [ [Fit to time series.htmFit to time series]] for help with implementing these analyses):9 The time series fitting uses either fishing effort or fishing mortality data as driving factors for the Ecosim model runs. A statistical measure of goodness of fit to the time series data outlined above is generated each time Ecosim is run. The model allows four types of analysis with the SS measure (see [wiki:FitToTimeSeries Fit to time series] for help with implementing these analyses): 10 10 11 1. Determine sensitivity of SS to the critical Ecosim [ [Vulnerabilities flow control.htmvulnerability]] parameters by changing each one slightly (1%) then rerunning the model to see how much SS is changed, (i.e., how sensitive the time series predictions ‘supported’ by data are to the vulnerabilities);11 1. Determine sensitivity of SS to the critical Ecosim [wiki:VulnerabilitiesFlowControlForagingArenaParameter Vulnerability]] parameters by changing each one slightly (1%) then rerunning the model to see how much SS is changed, (i.e., how sensitive the time series predictions ‘supported’ by data are to the vulnerabilities); 12 12 13 13 2. Search for vulnerability estimates that give better ‘fits’ of Ecosim to the time series data (lower SS), with vulnerabilities ‘blocked’ by the user into sets that are expected to be similar;