diff --git a/8data.tex b/8data.tex index e34b2077..3dc39ff9 100644 --- a/8data.tex +++ b/8data.tex @@ -76,7 +76,9 @@ \subsubsection{Subseasons and Timing of Events} Time steps can be broken into subseason and the ALK can be calculated multiple times over the course of a year: \vspace*{-\baselineskip} +\vspace*{-\baselineskip} \begin{center} + \begin{tabular}{|p{2.37cm}|p{2.37cm}|p{2.37cm}|p{2.37cm}|p{2.37cm}|p{2.37cm}|} \begin{tabular}{|p{2.37cm}|p{2.37cm}|p{2.37cm}|p{2.37cm}|p{2.37cm}|p{2.37cm}|} \hline ALK & ALK* & ALK* & ALK & ALK* & ALK \Tstrut\Bstrut\\ @@ -84,6 +86,7 @@ \subsubsection{Subseasons and Timing of Events} Subseason 1 & Subseason 2 & Subseason 3 & Subseason 4 & Subseason 5 & Subseason 6 \Tstrut\Bstrut\\ \hline \multicolumn{6}{l}{ALK* only re-calculated when there is a survey that subseason} \Tstrut\Bstrut\\ + \multicolumn{6}{l}{ALK* only re-calculated when there is a survey that subseason} \Tstrut\Bstrut\\ \end{tabular} \end{center} @@ -104,6 +107,7 @@ \subsubsection{Subseasons and Timing of Events} \item Reproductive output now has specified spawn timing (in months fraction) and interpolates growth to that timing. \item Survey numbers calculated at cruise survey timing using $e^{-z}$. \item Continuous Z for entire season. Same as applied in version v.3.24. + \item Continuous Z for entire season. Same as applied in version v.3.24. \end{itemize} \subsection{Terminology} @@ -117,9 +121,12 @@ \subsection{Model Dimensions} \hline \#V3.30.XX.XX & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Model version number. This is written by SS3 in the new files and a good idea to keep updated in the input files.}} \Tstrut\\ & \Bstrut\\ + \#V3.30.XX.XX & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Model version number. This is written by SS3 in the new files and a good idea to keep updated in the input files.}} \Tstrut\\ + & \Bstrut\\ \hline \#C data using new survey & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Data file comment. Must start with \#C to be retained then written to top of various output files. These comments can occur anywhere in the data file, but must have \#C in columns 1-2.}} \Tstrut\\ + \#C data using new survey & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Data file comment. Must start with \#C to be retained then written to top of various output files. These comments can occur anywhere in the data file, but must have \#C in columns 1-2.}} \Tstrut\\ & \Bstrut\\ \hline @@ -133,6 +140,7 @@ \subsection{Model Dimensions} \hline 12 & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Vector with the number of months in each season. These do not need to be integers. Note: If the sum of this vector is close to 12.0, then it is rescaled to sum to 1.0 so that season duration is a fraction of a year. If the sum is not equal to 12.0, then the entered values are summed and rescaled to 1. So, with one season per year and 3 months per season, the calculated season duration will be 0.25, which allows a quarterly model to be run as if quarters are years. All rates in SS3 are calculated by season (growth, mortality, etc.) using annual rates and season duration.}} \Tstrut\\ + 12 & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Vector with the number of months in each season. These do not need to be integers. Note: If the sum of this vector is close to 12.0, then it is rescaled to sum to 1.0 so that season duration is a fraction of a year. If the sum is not equal to 12.0, then the entered values are summed and rescaled to 1. So, with one season per year and 3 months per season, the calculated season duration will be 0.25, which allows a quarterly model to be run as if quarters are years. All rates in SS3 are calculated by season (growth, mortality, etc.) using annual rates and season duration.}} \Tstrut\\ & \\ & \\ & \\ @@ -143,11 +151,13 @@ \subsection{Model Dimensions} \hline 2 & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{The number of subseasons. Entry must be even and the minimum value is 2. This is for the purpose of finer temporal granularity in calculating growth and the associated age-length key.}} \Tstrut\\ + 2 & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{The number of subseasons. Entry must be even and the minimum value is 2. This is for the purpose of finer temporal granularity in calculating growth and the associated age-length key.}} \Tstrut\\ & \\ & \Bstrut\\ \hline \hypertarget{RecrTiminig}{1.5} & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Spawning month; spawning biomass is calculated at this time of year (1.5 means January 15) and used as basis for the total recruitment of all settlement events resulting from this spawning.}} \Tstrut\\ + \hypertarget{RecrTiminig}{1.5} & \multirow{1}{1cm}[-0.1cm]{\parbox{12cm}{Spawning month; spawning biomass is calculated at this time of year (1.5 means January 15) and used as basis for the total recruitment of all settlement events resulting from this spawning.}} \Tstrut\\ & \\ & \Bstrut\\ @@ -159,6 +169,7 @@ \subsection{Model Dimensions} \hline 20 \Tstrut & Number of ages. The value here will be the plus-group age. SS3 starts at age 0. \\ + 20 \Tstrut & Number of ages. The value here will be the plus-group age. SS3 starts at age 0. \\ \hline 1 & Number of areas \Tstrut\Bstrut\\ @@ -168,13 +179,18 @@ \subsection{Model Dimensions} \hline \end{longtable} \vspace*{-1.7\baselineskip} + \vspace*{-1.7\baselineskip} \end{center} +\subsection{Fleet Definitions} +\hypertarget{GenericFleets}{The} catch data input has been modified to improve the user flexibility to add/subtract fishing and survey fleets to a model set-up. The fleet setup input is transposed so each fleet is now a row. Previous versions (v.3.24 and earlier) required that fishing fleets be listed first followed by survey only fleets. In SS3 all fleets have the same status within the model structure and each has a specified fleet type (except for models that use tag recapture data, this will be corrected in future versions). Available types are; catch fleet, bycatch only fleet, or survey. \subsection{Fleet Definitions} \hypertarget{GenericFleets}{The} catch data input has been modified to improve the user flexibility to add/subtract fishing and survey fleets to a model set-up. The fleet setup input is transposed so each fleet is now a row. Previous versions (v.3.24 and earlier) required that fishing fleets be listed first followed by survey only fleets. In SS3 all fleets have the same status within the model structure and each has a specified fleet type (except for models that use tag recapture data, this will be corrected in future versions). Available types are; catch fleet, bycatch only fleet, or survey. \begin{center} + \begin{tabular}{p{2cm} p{2cm} p{2cm} p{2cm} p{2cm} p{4cm}} + \multicolumn{6}{l}{Inputs that define the fishing and survey fleets:} \\ \begin{tabular}{p{2cm} p{2cm} p{2cm} p{2cm} p{2cm} p{4cm}} \multicolumn{6}{l}{Inputs that define the fishing and survey fleets:} \\ \hline @@ -198,6 +214,8 @@ \subsection{Fleet Definitions} \item 2 = bycatch fleet (all catch discarded) and invoke extra input for treatment in equilibrium and forecast; \item 3 = survey: assumes no catch removals even if associated catches are specified below. If you would like to remove survey catch set fleet type to option = 1 with specific month timing for removals (defined below in the ``Timing'' section); and \item 4 = predator (M2) fleet that adds additional mortality without a fleet F (added in v.3.30.18). Ideal for modeling large mortality events such as fish kills or red tide. Requires additional long parameter lines for a second mortality component (M2) in the control file after the natural mortality/growth parameter lines (entered immediately after the fraction female parameter line). + \item 3 = survey: assumes no catch removals even if associated catches are specified below. If you would like to remove survey catch set fleet type to option = 1 with specific month timing for removals (defined below in the ``Timing'' section); and + \item 4 = predator (M2) fleet that adds additional mortality without a fleet F (added in v.3.30.18). Ideal for modeling large mortality events such as fish kills or red tide. Requires additional long parameter lines for a second mortality component (M2) in the control file after the natural mortality/growth parameter lines (entered immediately after the fraction female parameter line). \end{itemize} \hypertarget{ObsTiming}{} @@ -225,6 +243,7 @@ \subsection{Fleet Definitions} \hypertarget{CatchMult}{} \myparagraph{Catch Multiplier} +Invokes use of a catch multiplier, which is then entered as a parameter in the mortality-growth parameter section. The estimated value or fixed value of the catch multiplier is used to adjust the observed catch: Invokes use of a catch multiplier, which is then entered as a parameter in the mortality-growth parameter section. The estimated value or fixed value of the catch multiplier is used to adjust the observed catch: \begin{itemize} \item 0 = No catch multiplier used; and @@ -232,27 +251,38 @@ \subsection{Fleet Definitions} \end{itemize} A catch multiplier can be useful when trying to explore historical unrecorded catches or ongoing illegal and unregulated catches. The catch multiplier is a full parameter line in the control file and has the ability to be time-varying. +A catch multiplier can be useful when trying to explore historical unrecorded catches or ongoing illegal and unregulated catches. The catch multiplier is a full parameter line in the control file and has the ability to be time-varying. \subsection{Bycatch Fleets} The option to include bycatch fleets was introduced in v.3.30.10. This is an optional input and if no bycatch is to be included in to the catches this section can be ignored. +The option to include bycatch fleets was introduced in v.3.30.10. This is an optional input and if no bycatch is to be included in to the catches this section can be ignored. +A fishing fleet is designated as a bycatch fleet by indicating that its fleet type is 2. A bycatch fleet creates a fishing mortality, same as a fleet of type 1, but a bycatch fleet has all catch discarded so the input value for retained catch is ignored. However, an input value for retained catch is still needed to indicate that the bycatch fleet was active in that year and season. A catch multiplier cannot be used with bycatch fleets because catch multiplier works on retained catch. SS3 will expect that the retention function for this fleet will be set in the selectivity section to type 3, indicating that all selected catch is discarded dead. It is necessary to specify a selectivity pattern for the bycatch fleet and, due to generally lack of data, to externally derive values for the parameters of this selectivity. A fishing fleet is designated as a bycatch fleet by indicating that its fleet type is 2. A bycatch fleet creates a fishing mortality, same as a fleet of type 1, but a bycatch fleet has all catch discarded so the input value for retained catch is ignored. However, an input value for retained catch is still needed to indicate that the bycatch fleet was active in that year and season. A catch multiplier cannot be used with bycatch fleets because catch multiplier works on retained catch. SS3 will expect that the retention function for this fleet will be set in the selectivity section to type 3, indicating that all selected catch is discarded dead. It is necessary to specify a selectivity pattern for the bycatch fleet and, due to generally lack of data, to externally derive values for the parameters of this selectivity. +All catch from a bycatch fleet is discarded, so one option to use a discard fleet is to enter annual values for the amount (not proportion) that is discarded in each time step. However, it is uncommon to have such data for all years. An alternative approach that has been used principally in the U.S. Gulf of Mexico is to input a time series of effort data for this fleet in the survey section (e.g., effort is a ``survey'' of F, for example, the shrimp trawl fleet in the Gulf of Mexico catches and discards small finfish and an effort time series is available for this fleet) and to input in the discard data section an observation for the average discard over time using the super year approach. Another use of bycatch fleet is to use it to estimate effect of an external source of mortality, such as a red tide event. In this usage there may be no data on the magnitude of the discards and SS3 will then rely solely on the contrast in other data to attempt to estimate the magnitude of the red tide kill that occurred. The benefit of doing this as a bycatch fleet, and not a block on natural mortality, is that the selectivity of the effect can be specified. All catch from a bycatch fleet is discarded, so one option to use a discard fleet is to enter annual values for the amount (not proportion) that is discarded in each time step. However, it is uncommon to have such data for all years. An alternative approach that has been used principally in the U.S. Gulf of Mexico is to input a time series of effort data for this fleet in the survey section (e.g., effort is a ``survey'' of F, for example, the shrimp trawl fleet in the Gulf of Mexico catches and discards small finfish and an effort time series is available for this fleet) and to input in the discard data section an observation for the average discard over time using the super year approach. Another use of bycatch fleet is to use it to estimate effect of an external source of mortality, such as a red tide event. In this usage there may be no data on the magnitude of the discards and SS3 will then rely solely on the contrast in other data to attempt to estimate the magnitude of the red tide kill that occurred. The benefit of doing this as a bycatch fleet, and not a block on natural mortality, is that the selectivity of the effect can be specified. +Bycatch fleets are not expected to be under the same type of fishery management controls as the retained catch fleets included in the model. This means that when SS3 enters into the reference point equilibrium calculations, it would be incorrect to have SS3 re-scale the magnitude of the F for the bycatch fleet as it searches for the F that produces, for example, F35\%. Related issues apply to the forecast. Consequently, a separate set of controls is provided for bycatch fleets (defined below). Input is required for each fleet designated as fleet type = 2. Bycatch fleets are not expected to be under the same type of fishery management controls as the retained catch fleets included in the model. This means that when SS3 enters into the reference point equilibrium calculations, it would be incorrect to have SS3 re-scale the magnitude of the F for the bycatch fleet as it searches for the F that produces, for example, F35\%. Related issues apply to the forecast. Consequently, a separate set of controls is provided for bycatch fleets (defined below). Input is required for each fleet designated as fleet type = 2. \noindent If a fleet above was set as a bycatch fleet (fleet type = 2), the following line is required: \begin{center} + \vspace*{-\baselineskip} + \begin{tabular}{p{2.25cm} p{2.65cm} p{2.25cm} p{2.5cm} p{2.5cm} p{2cm}} \vspace*{-\baselineskip} \begin{tabular}{p{2.25cm} p{2.65cm} p{2.25cm} p{2.5cm} p{2.5cm} p{2cm}} + \multicolumn{6}{l}{Bycatch fleet input controls:} \\ \multicolumn{6}{l}{Bycatch fleet input controls:} \\ \hline a: & b: & c: & d: & e: & f: \Tstrut\\ Fleet Index & Include in MSY & Fmult & F or First Year & Last Year & Not used \Bstrut\\ + a: & b: & c: & d: & e: & f: \Tstrut\\ + Fleet Index & Include in MSY & Fmult & F or First Year & Last Year & Not used \Bstrut\\ \hline 2 & 2 & 3 & 1982 & 2010 & 0 \Tstrut\Bstrut\\ + 2 & 2 & 3 & 1982 & 2010 & 0 \Tstrut\Bstrut\\ \hline \end{tabular} \end{center} @@ -315,6 +345,7 @@ \subsection{Bycatch Fleets} \end{enumerate} \end{enumerate} +In v.3.30.14 it was identified that there can be an interaction between the use of bycatch fleets and the search for the $F_{0.1}$ reference point which may results in the search failing. Changes to the search feature were implemented to make the search more robust, however, issue may still be encountered. In these instances it is recommended to not select the $F_{0.1}$ reference point calculation in the forecast file. In v.3.30.14 it was identified that there can be an interaction between the use of bycatch fleets and the search for the $F_{0.1}$ reference point which may results in the search failing. Changes to the search feature were implemented to make the search more robust, however, issue may still be encountered. In these instances it is recommended to not select the $F_{0.1}$ reference point calculation in the forecast file. \subsection{Predator Fleets} @@ -341,13 +372,17 @@ \subsection{Catch} \hypertarget{ListBased}{There} is no longer a need to specify the number of records to be read; instead the list is terminated by entering a record with the value of -9999 in the year field. The updated list based approach extends throughout the data file (e.g., catch, length- and age-composition data), the control file (e.g., lambdas), and the forecast file (e.g., total catch by fleet, total catch by area, allocation groups, forecasted catch). +In addition, it is possible to collapse the number of seasons. \ So, if a season value is greater than the number of seasons for a particular model, that catch is added to the catch for the final season. This is one way to easily collapse a seasonal model into an annual model. The alternative option is to the use of season = 0. This will cause SS3 to distribute the input value of catch equally among the number of seasons. SS3 assumes that catch occurs continuously over seasons and hence is not specified as month in the catch data section. However, all other data types will need to be specified by month. In addition, it is possible to collapse the number of seasons. \ So, if a season value is greater than the number of seasons for a particular model, that catch is added to the catch for the final season. This is one way to easily collapse a seasonal model into an annual model. The alternative option is to the use of season = 0. This will cause SS3 to distribute the input value of catch equally among the number of seasons. SS3 assumes that catch occurs continuously over seasons and hence is not specified as month in the catch data section. However, all other data types will need to be specified by month. +The format for a 2 season model with 2 fisheries looks like the table below. Example is sorted by fleet, but the sort order does not matter. In data.ss\_new, the sort order is fleet, year, season. The format for a 2 season model with 2 fisheries looks like the table below. Example is sorted by fleet, but the sort order does not matter. In data.ss\_new, the sort order is fleet, year, season. \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{3cm} p{3cm} p{3cm} p{3cm} p{4cm}} + \multicolumn{5}{l}{Catches by year, season for every fleet:} \\ \multicolumn{5}{l}{Catches by year, season for every fleet:} \\ \hline Year & Season & Fleet & Catch & Catch SE \Tstrut\Bstrut\\ @@ -357,11 +392,13 @@ \subsection{Catch} 1975 & 1 & 1 & 876 & 0.05 \\ 1975 & 2 & 1 & 343 & 0.05 \\ ... & ... & ... & ... & ... \\ + ... & ... & ... & ... & ... \\ -999 & 1 & 2 & 55 & 0.05 \\ -999 & 2 & 2 & 22 & 0.05 \\ 1975 & 1 & 2 & 555 & 0.05 \\ 1975 & 2 & 2 & 873 & 0.05 \\ ... & ... & ... & ... & ... \\ + ... & ... & ... & ... & ... \\ -9999 & 0 & 0 & 0 & 0 \Bstrut\\ \hline \end{tabular} @@ -370,21 +407,28 @@ \subsection{Catch} \begin{itemize} \item Catch can be in terms of biomass or numbers for each fleet, but cannot be mixed within a fleet. \item Catch is retained catch (aka landings). If there is discard also, then it is handled in the discard section below. This is the recommended setup which results in a model estimated retention curve based upon the discard data (specifically discard composition data). However, there may be instances where the data do not support estimation of retention curves. In these instances catches can be specified as all dead (retained + discard estimates). + \item Catch is retained catch (aka landings). If there is discard also, then it is handled in the discard section below. This is the recommended setup which results in a model estimated retention curve based upon the discard data (specifically discard composition data). However, there may be instances where the data do not support estimation of retention curves. In these instances catches can be specified as all dead (retained + discard estimates). \item If there are challenges to estimating discards within the model, catches can be input as total dead without the use of discard data and retention curves. \item If there is reason to believe that the retained catch values underestimate the true catch, then it is possible in the retention parameter set up to create the ability for the model to estimate the degree of unrecorded catch. However, this is better handled with the new catch multiplier option. + \item If there is reason to believe that the retained catch values underestimate the true catch, then it is possible in the retention parameter set up to create the ability for the model to estimate the degree of unrecorded catch. However, this is better handled with the new catch multiplier option. \end{itemize} \subsection{Indices} Indices are data that are compared to aggregate quantities in the model. Typically the index is a measure of selected fish abundance, but this data section also allows for the index to be related to a fishing fleet's F, or to another quantity estimated by the model. The first section of the ``Indices'' setup contains the fleet number, units, error distribution, and whether additional output (SD Report) will be written to the Report file for each fleet that has index data. +Indices are data that are compared to aggregate quantities in the model. Typically the index is a measure of selected fish abundance, but this data section also allows for the index to be related to a fishing fleet's F, or to another quantity estimated by the model. The first section of the ``Indices'' setup contains the fleet number, units, error distribution, and whether additional output (SD Report) will be written to the Report file for each fleet that has index data. \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{3cm} p{3cm} p{3cm} p{7cm}} + \multicolumn{4}{l}{Catch-per-unit-effort (CPUE) and Survey Abundance Observations:} \\ \multicolumn{4}{l}{Catch-per-unit-effort (CPUE) and Survey Abundance Observations:} \\ \hline Fleet/ & & Error & \Tstrut\\ Survey & Units & Distribution & SD Report \Bstrut\\ + Fleet/ & & Error & \Tstrut\\ + Survey & Units & Distribution & SD Report \Bstrut\\ \hline 1 & 1 & 0 & 0 \Tstrut\\ 2 & 1 & 0 & 0 \\ @@ -470,9 +514,12 @@ \subsection{Indices} \item Year values that are before start year or after end year are excluded from model, so the easiest way to include provisional data in a data file is to put a negative sign on its year value. \item Duplicate survey observations for the same year are not allowed. \item Observations that are to be included in the model but not included in the negative log likelihood need to have a negative sign on their fleet ID. Previously the code for not using observations was to enter the observation itself as a negative value. However, that old approach prevented use of a Z-score environmental index as a ``survey''. This approach is best for single or select years from an index rather than an approach to remove a whole index. Removing a whole index from the model should be done through the use of lambdas at the bottom of the control file which will eliminate the index from model fitting. + \item Observations that are to be included in the model but not included in the negative log likelihood need to have a negative sign on their fleet ID. Previously the code for not using observations was to enter the observation itself as a negative value. However, that old approach prevented use of a Z-score environmental index as a ``survey''. This approach is best for single or select years from an index rather than an approach to remove a whole index. Removing a whole index from the model should be done through the use of lambdas at the bottom of the control file which will eliminate the index from model fitting. \item Observations can be entered in any order, except if the super-year feature is used. \item Super-periods are turned on and then turned back off again by putting a negative sign on the season. Previously, super-periods were started and stopped by entering -9999 and the -9998 in the SE field. See the \hyperlink{SuperPeriod}{Data Super-Period} section of this manual for more information. \item If the statistical analysis used to create the CPUE index of a fishery has been conducted in such a way that its inherent size/age selectivity differs from the size/age selectivity estimated from the fishery's size and age composition, then you may want to enter the CPUE as if it was a separate survey and with a selectivity that differs from the fishery's estimated selectivity. The need for this split arises because the fishery size and age composition should be derived through a catch-weighted approach (to appropriately represent the removals by the fishery) and the CPUE should be derived through an area-weighted approach to better serve as a survey of stock abundance. + \item Super-periods are turned on and then turned back off again by putting a negative sign on the season. Previously, super-periods were started and stopped by entering -9999 and the -9998 in the SE field. See the \hyperlink{SuperPeriod}{Data Super-Period} section of this manual for more information. + \item If the statistical analysis used to create the CPUE index of a fishery has been conducted in such a way that its inherent size/age selectivity differs from the size/age selectivity estimated from the fishery's size and age composition, then you may want to enter the CPUE as if it was a separate survey and with a selectivity that differs from the fishery's estimated selectivity. The need for this split arises because the fishery size and age composition should be derived through a catch-weighted approach (to appropriately represent the removals by the fishery) and the CPUE should be derived through an area-weighted approach to better serve as a survey of stock abundance. \end{itemize} \subsection{Discard} @@ -492,12 +539,16 @@ \subsection{Discard} \begin{tabular}{p{2cm} p{3cm} p{3cm} p{3cm} p{3cm}} \hline 1 & \multicolumn{4}{l}{Number of fleets with discard observations} \Tstrut\Bstrut\\ + 1 & \multicolumn{4}{l}{Number of fleets with discard observations} \Tstrut\Bstrut\\ \hline Fleet & Units & \multicolumn{3}{l}{Error Distribution} \Tstrut\Bstrut\\ + Fleet & Units & \multicolumn{3}{l}{Error Distribution} \Tstrut\Bstrut\\ \hline 1 & 2 & \multicolumn{3}{l}{-1} \Tstrut\Bstrut\\ + 1 & 2 & \multicolumn{3}{l}{-1} \Tstrut\Bstrut\\ \hline Year & Month & Fleet & Observation & Standard Error \Tstrut\Bstrut\\ + Year & Month & Fleet & Observation & Standard Error \Tstrut\Bstrut\\ \hline 1980 & 7 & 1 & 0.05 & 0.25 \Tstrut\\ 1991 & 7 & 1 & 0.10 & 0.25 \\ @@ -506,6 +557,7 @@ \subsection{Discard} \end{tabular} \end{center} +Note that although the user must specify a month for the observed discard data, the unit for discard data is in terms of a season rather than a specific month. So, if using a seasonal model, the input month values must corresponding to some time during the correct season. The actual value will not matter because the discard amount is calculated for the entirety of the season. However, discard length or age observations will be treated by entered observation month. Note that although the user must specify a month for the observed discard data, the unit for discard data is in terms of a season rather than a specific month. So, if using a seasonal model, the input month values must corresponding to some time during the correct season. The actual value will not matter because the discard amount is calculated for the entirety of the season. However, discard length or age observations will be treated by entered observation month. \myparagraph{Discard Units} @@ -519,11 +571,13 @@ \subsection{Discard} \myparagraph{Discard Error Distribution} The four options for discard error are: \begin{itemize} + \item >0 = degrees of freedom for Student's t-distribution used to scale mean body weight deviations. Value of error in data file is interpreted as CV of the observation; \item >0 = degrees of freedom for Student's t-distribution used to scale mean body weight deviations. Value of error in data file is interpreted as CV of the observation; \item 0 = normal distribution, value of error in data file is interpreted as CV of the observation; \item -1 = normal distribution, value of error in data file is interpreted as standard error of the observation; \item -2 = lognormal distribution, value of error in data file is interpreted as standard error of the observation in log space; and \item -3 = truncated normal distribution (new with v.3.30, needs further testing), value of error in data file is interpreted as standard error of the observation. This is a good option for low observed discard rates. + \item -3 = truncated normal distribution (new with v.3.30, needs further testing), value of error in data file is interpreted as standard error of the observation. This is a good option for low observed discard rates. \end{itemize} \myparagraph{Discard Notes} @@ -534,26 +588,34 @@ \subsection{Discard} \item Duplicate discard observations from a fleet for the same year are not allowed. \item Observations can be entered in any order, except if the super-period feature is used. \item Note that in the control file you will enter information for retention such that 1-retention is the amount discarded. All discard is assumed dead, unless you enter information for discard mortality. Retention and discard mortality can be either size-based or age-based (new with v.3.30). + \item Note that in the control file you will enter information for retention such that 1-retention is the amount discarded. All discard is assumed dead, unless you enter information for discard mortality. Retention and discard mortality can be either size-based or age-based (new with v.3.30). \end{itemize} \myparagraph{Cautionary Note} The use of CV as the measure of variance can cause a small discard value to appear to be overly precise, even with the minimum standard error of the discard observation set to 0.001. In the control file, there is an option to add an extra amount of variance. This amount is added to the standard error, not to the CV, to help correct this problem of underestimated variance. +The use of CV as the measure of variance can cause a small discard value to appear to be overly precise, even with the minimum standard error of the discard observation set to 0.001. In the control file, there is an option to add an extra amount of variance. This amount is added to the standard error, not to the CV, to help correct this problem of underestimated variance. \subsection{Mean Body Weight or Length} This is the overall mean body weight or length across all selected sizes and ages. This may be useful in situations where individual fish are not measured but mean weight is obtained by counting the number of fish in a specified sample (e.g., a 25 kg basket). +This is the overall mean body weight or length across all selected sizes and ages. This may be useful in situations where individual fish are not measured but mean weight is obtained by counting the number of fish in a specified sample (e.g., a 25 kg basket). \begin{center} \begin{tabular}{p{1.75cm} p{1.75cm} p{1.75cm} p{1.75cm} p{1.75cm} p{2cm} p{2.8cm}} + \multicolumn{7}{l}{Mean Body Weight Data Section:} \\ \multicolumn{7}{l}{Mean Body Weight Data Section:} \\ \hline 1 & \multicolumn{6}{l}{Use mean body size data (0/1)} \Tstrut\Bstrut\\ + 1 & \multicolumn{6}{l}{Use mean body size data (0/1)} \Tstrut\Bstrut\\ \hline \multicolumn{7}{l}{COND > 0:}\Tstrut\\ + 30 & \multicolumn{6}{l}{Degrees of freedom for Student's t-distribution used to evaluate mean body weight} \\ + & \multicolumn{6}{l}{deviation.} \Bstrut\\ 30 & \multicolumn{6}{l}{Degrees of freedom for Student's t-distribution used to evaluate mean body weight} \\ & \multicolumn{6}{l}{deviation.} \Bstrut\\ \hline Year & Month & Fleet & Partition & Type & Observation & CV \Tstrut\Bstrut\\ + Year & Month & Fleet & Partition & Type & Observation & CV \Tstrut\Bstrut\\ \hline 1990 & 7 & 1 & 0 & 1 & 4.0 & 0.95 \Tstrut\\ 1990 & 7 & 1 & 0 & 1 & 1.0 & 0.95 \\ @@ -580,28 +642,35 @@ \subsection{Mean Body Weight or Length} \myparagraph{Observation - Units} Units must correspond to the units of body weight, normally in kilograms, (or mean length in cm). The expected value of mean body weight (or mean length) is calculated in a way that incorporates effect of selectivity and retention. +Units must correspond to the units of body weight, normally in kilograms, (or mean length in cm). The expected value of mean body weight (or mean length) is calculated in a way that incorporates effect of selectivity and retention. \myparagraph{Error} Error is entered as the CV of the observed mean body weight (or mean length) \subsection{Population Length Bins} The first part of the length composition section sets up the bin structure for the population. These bins define the granularity of the age-length key and the coarseness of the length selectivity. Fine bins create smoother distributions, but a larger and slower running model. +The first part of the length composition section sets up the bin structure for the population. These bins define the granularity of the age-length key and the coarseness of the length selectivity. Fine bins create smoother distributions, but a larger and slower running model. First read a single value to select one of three population length bin methods, then any conditional input for options 2 and 3: \begin{center} \begin{tabular}{p{2cm} p{5cm} p{8cm}} \hline 1 & \multicolumn{2}{l}{Use data bins to be read later. No additional input here.} \Tstrut\Bstrut\\ + 1 & \multicolumn{2}{l}{Use data bins to be read later. No additional input here.} \Tstrut\Bstrut\\ \hline 2 & \multicolumn{2}{l}{generate from bin width min max, read next:} \Tstrut\\ \multirow{4}{2cm}[-0.1cm]{} & 2 & Bin width \\ & 10 & Lower size of first bin \\ & 82 & Lower size of largest bin \\ + & 10 & Lower size of first bin \\ + & 82 & Lower size of largest bin \\ \multicolumn{3}{l}{The number of bins is then calculated from: (max Lread - min Lread)/(bin width) + 1}\Bstrut\\ \hline 3 & \multicolumn{2}{l}{Read 1 value for number of bins, and then read vector of bin boundaries} \Tstrut\\ \multirow{2}{2cm}[-0.1cm]{} & 37 & Number of population length bins to be read \\ & 10 12 14 ... 82 & Vector containing lower edge of each population size bin \Bstrut\\ + \multirow{2}{2cm}[-0.1cm]{} & 37 & Number of population length bins to be read \\ + & 10 12 14 ... 82 & Vector containing lower edge of each population size bin \Bstrut\\ \hline \end{tabular} @@ -611,34 +680,45 @@ \subsection{Population Length Bins} There are some items for users to consider when setting up population length bins: \begin{itemize} \item For option 2, bin width should be a factor of min size and max size. For options 2 and 3, the data length bins must not be wider than the population length bins and the boundaries of the bins do not have to align. The transition matrix between population and data length bins is output to echoinput.sso. + \item For option 2, bin width should be a factor of min size and max size. For options 2 and 3, the data length bins must not be wider than the population length bins and the boundaries of the bins do not have to align. The transition matrix between population and data length bins is output to echoinput.sso. \item The mean size at settlement (virtual recruitment age) is set equal to the min size of the first population length bin. \item When using more, finer population length bins, the model will create smoother length selectivity curves and smoother length distributions in the age-length key, but run more slowly (more calculations to do). + \item The mean weight-at-length, maturity-at-length and size-selectivity are based on the mid-length of the population bins. So these quantities will be rougher approximations if broad bins are defined. \item The mean weight-at-length, maturity-at-length and size-selectivity are based on the mid-length of the population bins. So these quantities will be rougher approximations if broad bins are defined. + \item Provide a wide enough range of population size bins so that the mean body weight-at-age will be calculated correctly for the youngest and oldest fish. If the growth curve extends beyond the largest size bin, then these fish will be assigned a length equal to the mid-bin size for the purpose of calculating their body weight. \item Provide a wide enough range of population size bins so that the mean body weight-at-age will be calculated correctly for the youngest and oldest fish. If the growth curve extends beyond the largest size bin, then these fish will be assigned a length equal to the mid-bin size for the purpose of calculating their body weight. + \item While exploring the performance of models with finer bin structure, a potentially pathological situation has been identified. When the bin structure is coarse (note that some applications have used 10 cm bin widths for the largest fish), it is possible for a selectivity slope parameter or a retention parameter to become so steep that all of the action occurs within the range of a single size bin. In this case, the model will see zero gradient of the log likelihood with respect to that parameter and convergence will be hampered. \item While exploring the performance of models with finer bin structure, a potentially pathological situation has been identified. When the bin structure is coarse (note that some applications have used 10 cm bin widths for the largest fish), it is possible for a selectivity slope parameter or a retention parameter to become so steep that all of the action occurs within the range of a single size bin. In this case, the model will see zero gradient of the log likelihood with respect to that parameter and convergence will be hampered. \item A value read near the end of the starter.ss file defines the degree of tail compression used for the age-length key, called ALK tolerance. If this is set to 0.0, then no compression is used and all cells of the age-length key are processed, even though they may contain trivial (e.g., 1 e-13) fraction of the fish at a given age. With tail compression of, say 0.0001, the model, at the beginning of each phase, will calculate the min and max length bin to process for each age of each morphs ALK and compress accordingly. Depending on how many extra bins are outside this range, you may see speed increases near 10-20\%. Large values of ALK tolerance, say 0.1, will create a sharp end to each distribution and likely will impede convergence. It is recommended to start with a value of 0 and if model speed is an issue, explore values greater than 0 and evaluate the trade-off between model estimates and run time. The user is encouraged to explore this feature. + \item A value read near the end of the starter.ss file defines the degree of tail compression used for the age-length key, called ALK tolerance. If this is set to 0.0, then no compression is used and all cells of the age-length key are processed, even though they may contain trivial (e.g., 1 e-13) fraction of the fish at a given age. With tail compression of, say 0.0001, the model, at the beginning of each phase, will calculate the min and max length bin to process for each age of each morphs ALK and compress accordingly. Depending on how many extra bins are outside this range, you may see speed increases near 10-20\%. Large values of ALK tolerance, say 0.1, will create a sharp end to each distribution and likely will impede convergence. It is recommended to start with a value of 0 and if model speed is an issue, explore values greater than 0 and evaluate the trade-off between model estimates and run time. The user is encouraged to explore this feature. \end{itemize} \subsection{Length Composition Data Structure} \begin{tabular}{p{2cm} p{14cm}} + \multicolumn{2}{l}{Enter a code to indicate whether or not length composition data will be used:} \Tstrut\Bstrut\\ \multicolumn{2}{l}{Enter a code to indicate whether or not length composition data will be used:} \Tstrut\Bstrut\\ \hline 1 & Use length composition data (0/1/2) \Tstrut\Bstrut\\ + 1 & Use length composition data (0/1/2) \Tstrut\Bstrut\\ \hline \end{tabular} +If the value 0 is entered, then skip all length related inputs below and skip to the age data setup section. If value 1 is entered, all data weighting options for composition data apply equally to all partitions within a fleet. If value 2 is entered, then the data weighting options are applied by the partition specified. Note that the partitions must be entered in numerical order within each fleet. If the value 0 is entered, then skip all length related inputs below and skip to the age data setup section. If value 1 is entered, all data weighting options for composition data apply equally to all partitions within a fleet. If value 2 is entered, then the data weighting options are applied by the partition specified. Note that the partitions must be entered in numerical order within each fleet. If the value for fleet is negative, then the vector of inputs is copied to all partitions (0 = combined, 1 = discard, and 2 = retained) for that fleet and all higher numbered fleets. This as a good practice so that the user controls the values used for all fleets. \begin{tabular}{p{2cm} p{2cm} p{2cm} p{2cm} p{2cm} p{2cm} p{1.5cm} p{1.7cm}} + \multicolumn{7}{l}{Example table of length composition settings when ``Use length composition data'' = 1 (where here} \\ + \multicolumn{7}{l}{the first fleet has multinomial error structure with no associated parameter, and the second fleet} \\ + \multicolumn{7}{l}{uses Dirichlet-multinomial structure):} \\ \multicolumn{7}{l}{Example table of length composition settings when ``Use length composition data'' = 1 (where here} \\ \multicolumn{7}{l}{the first fleet has multinomial error structure with no associated parameter, and the second fleet} \\ \multicolumn{7}{l}{uses Dirichlet-multinomial structure):} \\ @@ -646,6 +726,9 @@ \subsection{Length Composition Data Structure} Min. & Constant & Combine & & Comp. & & Min. \Tstrut\\ Tail & added & males \& & Compress. & Error & Param. & Sample \\ Compress. & to prop. & females & Bins & Dist. & Select & Size \Bstrut\\ + Min. & Constant & Combine & & Comp. & & Min. \Tstrut\\ + Tail & added & males \& & Compress. & Error & Param. & Sample \\ + Compress. & to prop. & females & Bins & Dist. & Select & Size \Bstrut\\ \hline 0 & 0.0001 & 0 & 0 & 0 & 0 & 0.1 \Tstrut\\ 0 & 0.0001 & 0 & 0 & 1 & 1 & 0.1 \Bstrut\\ @@ -654,12 +737,18 @@ \subsection{Length Composition Data Structure} \begin{tabular}{p{1cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm}} + \multicolumn{9}{l}{Example table of length composition settings when ``Use length composition data'' = 2 (where here} \\ + \multicolumn{9}{l}{the -1 in the fleet column applies the first parameter to all partitions for fleet 1 while fleet 2 has} \\ + \multicolumn{9}{l}{separate parameters for discards and retained fish):} \\ \multicolumn{9}{l}{Example table of length composition settings when ``Use length composition data'' = 2 (where here} \\ \multicolumn{9}{l}{the -1 in the fleet column applies the first parameter to all partitions for fleet 1 while fleet 2 has} \\ \multicolumn{9}{l}{separate parameters for discards and retained fish):} \\ \hline & & Min. & Constant & Combine & & Comp. & & Min. \Tstrut\\ & & Tail & added & males \& & Compress. & Error & Param. & Sample \\ + Fleet & Partition & Compress. & to prop. & females & Bins & Dist. & Select & Size \Bstrut\\ + & & Min. & Constant & Combine & & Comp. & & Min. \Tstrut\\ + & & Tail & added & males \& & Compress. & Error & Param. & Sample \\ Fleet & Partition & Compress. & to prop. & females & Bins & Dist. & Select & Size \Bstrut\\ \hline -1 & 0 & 0 & 0.0001 & 0 & 0 & 1 & 1 & 0.1 \Tstrut\\ @@ -677,12 +766,15 @@ \subsection{Length Composition Data Structure} \myparagraph{Added Constant to Proportions} Constant added to observed and expected proportions at length and age to make logL calculations more robust. Tail compression occurs before adding this constant. Proportions are renormalized to sum to 1.0 after constant is added. +Constant added to observed and expected proportions at length and age to make logL calculations more robust. Tail compression occurs before adding this constant. Proportions are renormalized to sum to 1.0 after constant is added. \myparagraph{Combine Males \& Females} Combine males into females at or below this bin number. This is useful if the sex determination of very small fish is doubtful so allows the small fish to be treated as combined sex. If Combine Males \& Females > 0, then add males into females for bins 1 through this number, zero out the males, set male data to start at the first bin above this bin. Note that Combine Males \& Females > 0 is entered as a bin index, not as the size associated with that bin. Comparable option is available for age composition data. +Combine males into females at or below this bin number. This is useful if the sex determination of very small fish is doubtful so allows the small fish to be treated as combined sex. If Combine Males \& Females > 0, then add males into females for bins 1 through this number, zero out the males, set male data to start at the first bin above this bin. Note that Combine Males \& Females > 0 is entered as a bin index, not as the size associated with that bin. Comparable option is available for age composition data. \myparagraph{Compress Bins} This option allows for the compression of length or age bins beyond a specific length or age by each data source. As an example, a value of 5 in the compress bins column would condense the final five length bins for the specified data source. +This option allows for the compression of length or age bins beyond a specific length or age by each data source. As an example, a value of 5 in the compress bins column would condense the final five length bins for the specified data source. \myparagraph{Composition Error Distribution} The options are: @@ -714,12 +806,16 @@ \subsection{Length Composition Data Structure} \myparagraph{Minimum Sample Size} The minimum value (floor) for all sample sizes. This value must be at least 0.001. Conditional age-at-length data may have observations with sample sizes less than 1. Version 3.24 had an implicit minimum sample size value of 1. +The minimum value (floor) for all sample sizes. This value must be at least 0.001. Conditional age-at-length data may have observations with sample sizes less than 1. Version 3.24 had an implicit minimum sample size value of 1. \myparagraph{Additional information on Dirichlet Parameter Number and Effective Sample Sizes} If the Dirichlet-multinomial error distribution is selected, indicate here which of a list of Dirichlet-multinomial parameters will be used for this fleet. So each fleet could use a unique Dirichlet-multinomial parameter, or all could share the same, or any combination of unique and shared. The requested number of Dirichlet-multinomial parameters are specified as parameter lines in the control file immediately after the selectivity parameter section. Please note that age-compositions Dirichlet-multinomial parameters are continued after length-compositions, so a model with one fleet and both data types would presumably require two new Dirichlet-multinomial parameters. +If the Dirichlet-multinomial error distribution is selected, indicate here which of a list of Dirichlet-multinomial parameters will be used for this fleet. So each fleet could use a unique Dirichlet-multinomial parameter, or all could share the same, or any combination of unique and shared. The requested number of Dirichlet-multinomial parameters are specified as parameter lines in the control file immediately after the selectivity parameter section. Please note that age-compositions Dirichlet-multinomial parameters are continued after length-compositions, so a model with one fleet and both data types would presumably require two new Dirichlet-multinomial parameters. +The Dirichlet estimates the effective sample size as $N_{eff}=\frac{1}{1+\theta}+\frac{N\theta}{1+\theta}$ where $\theta$ is the estimated parameter and $N$ is the input sample size. Stock Synthesis estimates the log of the Dirichlet-multinomial parameter such that $\hat{\theta}_{\text{fishery}} = e^{-0.6072} = 0.54$ where assuming $N=100$ for the fishery would result in an effective sample size equal to 35.7. The Dirichlet estimates the effective sample size as $N_{eff}=\frac{1}{1+\theta}+\frac{N\theta}{1+\theta}$ where $\theta$ is the estimated parameter and $N$ is the input sample size. Stock Synthesis estimates the log of the Dirichlet-multinomial parameter such that $\hat{\theta}_{\text{fishery}} = e^{-0.6072} = 0.54$ where assuming $N=100$ for the fishery would result in an effective sample size equal to 35.7. +This formula for effective sample size implies that, as the Stock Synthesis parameter ln(DM\_theta) goes to large values (i.e., 20), then the adjusted sample size will converge to the input sample size. In this case, small changes in the value of the ln(DM\_theta) parameter has no action, and the derivative of the negative log-likelihood is zero with respect to the parameter, which means the Hessian will be singular and cannot be inverted. To avoid this non-invertible Hessian when the ln(DM\_theta) parameter becomes large, turn it off while fixing it at the high value. This is equivalent to turning off down-weighting of fleets where evidence suggests that the input sample sizes are reasonable. This formula for effective sample size implies that, as the Stock Synthesis parameter ln(DM\_theta) goes to large values (i.e., 20), then the adjusted sample size will converge to the input sample size. In this case, small changes in the value of the ln(DM\_theta) parameter has no action, and the derivative of the negative log-likelihood is zero with respect to the parameter, which means the Hessian will be singular and cannot be inverted. To avoid this non-invertible Hessian when the ln(DM\_theta) parameter becomes large, turn it off while fixing it at the high value. This is equivalent to turning off down-weighting of fleets where evidence suggests that the input sample sizes are reasonable. For additional information about the Dirichlet-multinomial please see \citet{thorson-model-based-2017} and the detailed \hyperlink{DataWeight}{Data Weighting} section. @@ -728,6 +824,7 @@ \subsection{Length Composition Data Structure} \subsection{Length Composition Data} Composition data can be entered as proportions, numbers, or values of observations by length bin based on data expansions. +The data bins do not need to cover all observed lengths. The selection of data bin structure should be based on the observed distribution of lengths and the assumed growth curve. If growth asymptotes at larger lengths, having additional length bins across these sizes may not contribute information to the model and may slow model run time. Additionally, the lower length bin selection should be selected such that, depending on the size selection, to allow for information on smaller fish and possible patterns in recruitment. While set separately users should ensure that the length and age bins align. It is recommended to explore multiple configurations of length and age bins to determine the impact of this choice on model estimation. The data bins do not need to cover all observed lengths. The selection of data bin structure should be based on the observed distribution of lengths and the assumed growth curve. If growth asymptotes at larger lengths, having additional length bins across these sizes may not contribute information to the model and may slow model run time. Additionally, the lower length bin selection should be selected such that, depending on the size selection, to allow for information on smaller fish and possible patterns in recruitment. While set separately users should ensure that the length and age bins align. It is recommended to explore multiple configurations of length and age bins to determine the impact of this choice on model estimation. Specify the length composition data as: @@ -735,8 +832,10 @@ \subsection{Length Composition Data} \begin{tabular}{p{4cm} p{10cm}} \hline 28 & Number of length bins for data \\ + 28 & Number of length bins for data \\ \hline 26 28 30 ... 80 & Vector of length bins associated with the length data \\ + 26 28 30 ... 80 & Vector of length bins associated with the length data \\ \hline \end{tabular} \end{center} @@ -756,14 +855,17 @@ \subsection{Length Composition Data} Example of a single length composition observation: \vspace*{-1cm} % used this because the spacing was off in the pdf \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{1.5cm} p{5cm}} \multicolumn{7}{l}{} \\ \hline Year & Month & Fleet & Sex & Partition & Nsamp & data vector \Tstrut\Bstrut\\ + Year & Month & Fleet & Sex & Partition & Nsamp & data vector \Tstrut\Bstrut\\ \hline 1986 & 1 & 1 & 3 & 0 & 20 & \Tstrut\\ ... & ... & ... & ... & ... & ... & ... \\ + ... & ... & ... & ... & ... & ... & ... \\ -9999 & 0 & 0 & 0 & 0 & 0 & <0 repeated for each element of the data vector above> \Bstrut\\ \hline @@ -798,19 +900,25 @@ \subsection{Length Composition Data} \myparagraph{Note} When processing data to be input into SS3, all observed fish of sizes smaller than the first bin should be added to the first bin and all observed fish larger than the last bin should be condensed into the last bin. +The number of length composition data lines no longer needs to be specified in order to read the length (or age) composition data. Starting in v.3.30, the model will continue to read length composition data until an pre-specified exit line is read. The exit line is specified by entering -9999 at the end of the data matrix. The -9999 indicates to the model the end of length composition lines to be read. The number of length composition data lines no longer needs to be specified in order to read the length (or age) composition data. Starting in v.3.30, the model will continue to read length composition data until an pre-specified exit line is read. The exit line is specified by entering -9999 at the end of the data matrix. The -9999 indicates to the model the end of length composition lines to be read. +Each observation can be stored as one row for ease of data management in a spreadsheet and for sorting of the observations. However, the 6 header values, the female vector and the male vector could each be on a separate line because ADMB reads values consecutively from the input file and will move to the next line as necessary to read additional values. Each observation can be stored as one row for ease of data management in a spreadsheet and for sorting of the observations. However, the 6 header values, the female vector and the male vector could each be on a separate line because ADMB reads values consecutively from the input file and will move to the next line as necessary to read additional values. +The composition observations can be in any order and replicate observations by a year for a fleet are allowed (unlike survey and discard data). However, if the super-period approach is used, then each super-periods' observations must be contiguous in the data file. The composition observations can be in any order and replicate observations by a year for a fleet are allowed (unlike survey and discard data). However, if the super-period approach is used, then each super-periods' observations must be contiguous in the data file. \subsection{Age Composition Option} The age composition section begins by reading the number of age bins. If the value 0 is entered for the number of age bins, then skips reading the bin structure and all reading of other age composition data inputs. +The age composition section begins by reading the number of age bins. If the value 0 is entered for the number of age bins, then skips reading the bin structure and all reading of other age composition data inputs. \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{3cm} p{13cm}} \hline 17 \Tstrut & Number of age bins; can be equal to 0 if age data are not used; do not include a vector of agebins if the number of age bins is set equal to 0. \Bstrut\\ + 17 \Tstrut & Number of age bins; can be equal to 0 if age data are not used; do not include a vector of agebins if the number of age bins is set equal to 0. \Bstrut\\ \hline \end{tabular} \end{center} @@ -819,40 +927,59 @@ \subsection{Age Composition Option} \subsubsection{Age Composition Bins} If a positive number of age bins is read, then reads the bin definition next. \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{3cm} p{13cm}} \hline 1 2 3 ... 20 25 & Vector of ages \Tstrut\Bstrut\\ + 1 2 3 ... 20 25 & Vector of ages \Tstrut\Bstrut\\ \hline \end{tabular} \end{center} The bins are in terms of observed age (here age) and entered as the lower edge of each bin. Each ageing imprecision definition is used to create a matrix that translates true age structure into age structure. The first and last age' bins work as accumulators. So in the example any age 0 fish that are caught would be assigned to the age = 1 bin. +The bins are in terms of observed age (here age) and entered as the lower edge of each bin. Each ageing imprecision definition is used to create a matrix that translates true age structure into age structure. The first and last age' bins work as accumulators. So in the example any age 0 fish that are caught would be assigned to the age = 1 bin. \subsubsection{Ageing Error} Here, the capability to create a distribution of age (e.g., age with possible bias and imprecision) from true age is created. One or many ageing error definitions can be created. For each, the model will expect an input vector of mean age and a vector of standard deviations associated with the mean age. \begin{center} + \vspace*{-\baselineskip} + \begin{tabular}{p{2cm} p{2cm} p{2cm} p{2cm} p{3.5cm} p{2.5cm}} \vspace*{-\baselineskip} \begin{tabular}{p{2cm} p{2cm} p{2cm} p{2cm} p{3.5cm} p{2.5cm}} \hline \multicolumn{1}{l}{2} & \multicolumn{5}{l}{Number of ageing error matrices to generate} \Tstrut\Bstrut\\ \hline \\ \multicolumn{6}{l}{Example with no bias and very little uncertainty at age Tstrut} \Bstrut\\ + \multicolumn{1}{l}{2} & \multicolumn{5}{l}{Number of ageing error matrices to generate} \Tstrut\Bstrut\\ + \hline \\ + \multicolumn{6}{l}{Example with no bias and very little uncertainty at age Tstrut} \Bstrut\\ \hline Age-0 & Age-1 & Age-2 & ... & Max Age & \Tstrut\Bstrut\\ + Age-0 & Age-1 & Age-2 & ... & Max Age & \Tstrut\Bstrut\\ \hline -1 & -1 & -1 & ... & -1 & \#Mean Age \Tstrut\\ 0.001 & 0.001 & 0.001 & ... & 0.001 & \#SD \Bstrut\\ \hline \\ \multicolumn{6}{l}{Example with no bias and some uncertainty at age:} \Tstrut\Bstrut\\ + -1 & -1 & -1 & ... & -1 & \#Mean Age \Tstrut\\ + 0.001 & 0.001 & 0.001 & ... & 0.001 & \#SD \Bstrut\\ + \hline \\ + \multicolumn{6}{l}{Example with no bias and some uncertainty at age:} \Tstrut\Bstrut\\ \hline 0.5 & 1.5 & 2.5 & ... & Max Age + 0.5 & \#Mean Age \Tstrut\\ 0.5 & 0.65 & 0.67 & ... & 4.3 & \#SD Age \Bstrut\\ \hline \\ \multicolumn{6}{l}{Example with bias and uncertainty at age:} \Tstrut\Bstrut\\ + 0.5 & 1.5 & 2.5 & ... & Max Age + 0.5 & \#Mean Age \Tstrut\\ + 0.5 & 0.65 & 0.67 & ... & 4.3 & \#SD Age \Bstrut\\ + \hline \\ + \multicolumn{6}{l}{Example with bias and uncertainty at age:} \Tstrut\Bstrut\\ \hline 0.5 & 1.4 & 2.3 & ... & Max Age + Age Bias & \#Mean Age \Tstrut\\ 0.5 & 0.65 & 0.67 & ... & 4.3 & \#SD Age \Bstrut\\ + 0.5 & 1.4 & 2.3 & ... & Max Age + Age Bias & \#Mean Age \Tstrut\\ + 0.5 & 0.65 & 0.67 & ... & 4.3 & \#SD Age \Bstrut\\ \hline 0.5 & 1.4 & 2.3 & ... & Max Age + Age Bias & \#Mean Age \Tstrut\\ 0.5 & 0.65 & 0.67 & ... & 4.3 & \#SD Age \Bstrut\\ @@ -861,8 +988,10 @@ \subsubsection{Ageing Error} \end{center} \vspace*{-1.2cm} +In principle, one could have year or laboratory specific matrices for ageing error. For each matrix, enter a vector with mean age for each true age; if there is no ageing bias, then set age equal to true age + 0.5. Alternatively, -1 value for mean age means to set it equal to true age plus 0.5. The addition of +0.5 is needed so that fish will get assigned to the intended integer age. The length of the input vector is equal to the population maximum age plus one (0-max age), with the first entry being for age 0 fish and the last for fish of population maximum age even if the maximum age bin for the data is lower than the population maximum age. The following line is a a vector with the standard deviation of age for each true age with a normal distribution assumption. In principle, one could have year or laboratory specific matrices for ageing error. For each matrix, enter a vector with mean age for each true age; if there is no ageing bias, then set age equal to true age + 0.5. Alternatively, -1 value for mean age means to set it equal to true age plus 0.5. The addition of +0.5 is needed so that fish will get assigned to the intended integer age. The length of the input vector is equal to the population maximum age plus one (0-max age), with the first entry being for age 0 fish and the last for fish of population maximum age even if the maximum age bin for the data is lower than the population maximum age. The following line is a a vector with the standard deviation of age for each true age with a normal distribution assumption. +The model is able to create one ageing error matrix from parameters, rather than from an input vector. The range of conditions in which this new feature will perform well has not been evaluated, so it should be considered as a preliminary implementation and subject to modification. To invoke this option, for the selected ageing error vector, set the standard deviation of ageing error to a negative value for age 0. This will cause creation of an ageing error matrix from parameters and any age or size-at-age data that specify use of this age error pattern will use this matrix. Then in the control file, add a full parameter line below the cohort growth deviation parameter (or the movement parameter lines if used) in the mortality growth parameter section. These parameters are described in the control file section of this manual. The model is able to create one ageing error matrix from parameters, rather than from an input vector. The range of conditions in which this new feature will perform well has not been evaluated, so it should be considered as a preliminary implementation and subject to modification. To invoke this option, for the selected ageing error vector, set the standard deviation of ageing error to a negative value for age 0. This will cause creation of an ageing error matrix from parameters and any age or size-at-age data that specify use of this age error pattern will use this matrix. Then in the control file, add a full parameter line below the cohort growth deviation parameter (or the movement parameter lines if used) in the mortality growth parameter section. These parameters are described in the control file section of this manual. Code for ageing error calculation can be found in \href{https://github.com/nmfs-stock-synthesis/stock-synthesis/blob/main/SS_miscfxn.tpl}{SS\_miscfxn.tpl}, search for function ``get\_age\_age'' or ``SS\_Label\_Function 45''. @@ -871,11 +1000,15 @@ \subsubsection{Age Composition Specification} If age data are included in the model, the following set-up is required, similar to the length data section. \begin{tabular}{p{2cm} p{2cm} p{2cm} p{1.5cm} p{1.5cm} p{2cm} p{2cm}} + \multicolumn{7}{l}{Specify bin compression and error structure for age composition data for each fleet:} \\ \multicolumn{7}{l}{Specify bin compression and error structure for age composition data for each fleet:} \\ \hline Min. & Constant & Combine & & Comp. & & Min. \Tstrut\\ Tail & added & males \& & Compress. & Error & Param. & Sample \\ Compress. & to prop. & females & Bins & Dist. & Select & Size \Bstrut\\ + Min. & Constant & Combine & & Comp. & & Min. \Tstrut\\ + Tail & added & males \& & Compress. & Error & Param. & Sample \\ + Compress. & to prop. & females & Bins & Dist. & Select & Size \Bstrut\\ \hline 0 & 0.0001 & 1 & 0 & 0 & 0 & 1 \Tstrut\\ 0 & 0.0001 & 1 & 0 & 0 & 0 & 1 \Bstrut\\ @@ -886,17 +1019,24 @@ \subsubsection{Age Composition Specification} \begin{tabular}{p{1cm} p{14cm}} & \\ \multicolumn{2}{l}{Specify method by which length bin range for age obs will be interpreted:} \\ + \multicolumn{2}{l}{Specify method by which length bin range for age obs will be interpreted:} \\ \hline 1 & Bin method for age data \Tstrut\\ & 1 = value refers to population bin index \\ & 2 = value refers to data bin index \\ & 3 = value is actual length (which must correspond to population length bin \\ & boundary) \Bstrut\\ + & 1 = value refers to population bin index \\ + & 2 = value refers to data bin index \\ + & 3 = value is actual length (which must correspond to population length bin \\ + & boundary) \Bstrut\\ \hline \end{tabular} \begin{tabular}{p{1cm} p{1cm} p{1cm} p{1cm} p{1.5cm} p{1cm} p{1cm} p{1cm} p{1cm} p{2.1cm}} + \multicolumn{10}{l}{} \\ + \multicolumn{10}{l}{An example age composition observation:} \\ \multicolumn{10}{l}{} \\ \multicolumn{10}{l}{An example age composition observation:} \\ \hline @@ -918,11 +1058,13 @@ \subsubsection{Age Composition Specification} \myparagraph{Lbin Low and Lbin High} Lbin lo and Lbin hi are the range of length bins that this age composition observation refers to. Normally these are entered with a value of -1 and -1 to select the full size range. Whether these are entered as population bin number, length data bin number, or actual length is controlled by the value of the length bin range method above. +Lbin lo and Lbin hi are the range of length bins that this age composition observation refers to. Normally these are entered with a value of -1 and -1 to select the full size range. Whether these are entered as population bin number, length data bin number, or actual length is controlled by the value of the length bin range method above. \begin{itemize} \item Entering value of 0 or -1 for Lbin lo converts Lbin lo to 1; \item Entering value of 0 or -1 for Lbin hi converts Lbin hi to Maxbin; \item It is strongly advised to use the -1 codes to select the full size range. If you use explicit values, then the model could unintentionally exclude information from some size range if the population bin structure is changed. + \item It is strongly advised to use the -1 codes to select the full size range. If you use explicit values, then the model could unintentionally exclude information from some size range if the population bin structure is changed. \item In reporting to the comp\_report.sso, the reported Lbin\_lo and Lbin\_hi values are always converted to actual length. \end{itemize} @@ -932,15 +1074,22 @@ \subsubsection{Age Composition Specification} \subsection{Conditional Age-at-Length} Use of conditional age-at-length will greatly increase the total number of age composition observations and associated model run time but there can be several advantages to inputting ages in this fashion. First, it avoids double use of fish for both age and size information because the age information is considered conditional on the length information. Second, it contains more detailed information about the relationship between size and age so provides stronger ability to estimate growth parameters, especially the variance of size-at-age. Lastly, where age data are collected in a length-stratified program, the conditional age-at-length approach can directly match the protocols of the sampling program. +Use of conditional age-at-length will greatly increase the total number of age composition observations and associated model run time but there can be several advantages to inputting ages in this fashion. First, it avoids double use of fish for both age and size information because the age information is considered conditional on the length information. Second, it contains more detailed information about the relationship between size and age so provides stronger ability to estimate growth parameters, especially the variance of size-at-age. Lastly, where age data are collected in a length-stratified program, the conditional age-at-length approach can directly match the protocols of the sampling program. +However, simulation research has shown that the use of conditional age-at-length data can result in biased growth estimates in the presence of unaccounted for age-based movement when length-based selectivity is assumed \citep{lee-effects-2017}, when other age-based processes (e.g., mortality) are not accounted for \citep{lee-use-2019}, or based on the age sampling protocol \citep{piner-evaluation-2016}. Understanding how data are collected (e.g., random, length-conditioned samples) and the biology of the stock is important when using conditional age-at-length data for a fleet. However, simulation research has shown that the use of conditional age-at-length data can result in biased growth estimates in the presence of unaccounted for age-based movement when length-based selectivity is assumed \citep{lee-effects-2017}, when other age-based processes (e.g., mortality) are not accounted for \citep{lee-use-2019}, or based on the age sampling protocol \citep{piner-evaluation-2016}. Understanding how data are collected (e.g., random, length-conditioned samples) and the biology of the stock is important when using conditional age-at-length data for a fleet. +In a two sex model, it is best to enter these conditional age-at-length data as single sex observations (sex = 1 for females and = 2 for males), rather than as joint sex observations (sex = 3). Inputting joint sex observations comes with a more rigid assumption about sex ratios within each length bin. Using separate vectors for each sex allows 100\% of the expected composition to be fit to 100\% observations within each sex, whereas with the sex = 3 option, you would have a bad fit if the sex ratio were out of balance with the model expectation, even if the observed proportion at age within each sex exactly matched the model expectation for that age. Additionally, inputting the conditional age-at-length data as single sex observations isolates the age composition data from any sex selectivity as well. In a two sex model, it is best to enter these conditional age-at-length data as single sex observations (sex = 1 for females and = 2 for males), rather than as joint sex observations (sex = 3). Inputting joint sex observations comes with a more rigid assumption about sex ratios within each length bin. Using separate vectors for each sex allows 100\% of the expected composition to be fit to 100\% observations within each sex, whereas with the sex = 3 option, you would have a bad fit if the sex ratio were out of balance with the model expectation, even if the observed proportion at age within each sex exactly matched the model expectation for that age. Additionally, inputting the conditional age-at-length data as single sex observations isolates the age composition data from any sex selectivity as well. +Conditional age-at-length data are entered within the age composition data section and can be mixed with marginal age observations for other fleets of other years within a fleet. To treat age data as conditional on length, Lbin\_lo and Lbin\_hi are used to select a subset of the total size range. This is different than setting Lbin\_lo and Lbin\_hi both to -1 to select the entire size range, which treats the data entered on this line within the age composition data section as marginal age composition data. Conditional age-at-length data are entered within the age composition data section and can be mixed with marginal age observations for other fleets of other years within a fleet. To treat age data as conditional on length, Lbin\_lo and Lbin\_hi are used to select a subset of the total size range. This is different than setting Lbin\_lo and Lbin\_hi both to -1 to select the entire size range, which treats the data entered on this line within the age composition data section as marginal age composition data. +\vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{0.9cm} p{1cm} p{0.9cm} p{0.9cm} p{1.5cm} p{0.9cm} p{0.9cm} p{0.9cm} p{1cm} p{2.4cm}} + \multicolumn{10}{l}{} \\ + \multicolumn{10}{l}{An example conditional age-at-length composition observations:} \\ \multicolumn{10}{l}{} \\ \multicolumn{10}{l}{An example conditional age-at-length composition observations:} \\ \hline @@ -951,6 +1100,11 @@ \subsection{Conditional Age-at-Length} 1987 & 1 & 1 & 1 & 0 & 2 & 14 & 14 & 16 & \Tstrut\\ 1987 & 1 & 1 & 1 & 0 & 2 & 16 & 16 & 30 & \Tstrut\\ -9999 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \Bstrut\\ + 1987 & 1 & 1 & 1 & 0 & 2 & 10 & 10 & 18 & \Tstrut\\ + 1987 & 1 & 1 & 1 & 0 & 2 & 12 & 12 & 24 & \Tstrut\\ + 1987 & 1 & 1 & 1 & 0 & 2 & 14 & 14 & 16 & \Tstrut\\ + 1987 & 1 & 1 & 1 & 0 & 2 & 16 & 16 & 30 & \Tstrut\\ + -9999 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \Bstrut\\ \hline \end{tabular} @@ -958,12 +1112,15 @@ \subsection{Conditional Age-at-Length} \subsection{Mean Length or Body Weight-at-Age} The model also accepts input of mean length-at-age or mean body weight-at-age. This is done in terms of observed age, not true age, to take into account the effects of ageing imprecision on expected mean size-at-age. If the value of the Age Error column is positive, then the observation is interpreted as mean length-at-age. If the value of the Age Error column is negative, then the observation is interpreted as mean body weight-at-age and the abs(Age Error) is used as Age Error. +The model also accepts input of mean length-at-age or mean body weight-at-age. This is done in terms of observed age, not true age, to take into account the effects of ageing imprecision on expected mean size-at-age. If the value of the Age Error column is positive, then the observation is interpreted as mean length-at-age. If the value of the Age Error column is negative, then the observation is interpreted as mean body weight-at-age and the abs(Age Error) is used as Age Error. \begin{center} + \begin{tabular}{p{0.75cm} p{1cm} p{0.75cm} p{1cm} p{0.75cm} p{1cm} p{1cm} p{3.2cm} p{3.2cm}} \begin{tabular}{p{0.75cm} p{1cm} p{0.75cm} p{1cm} p{0.75cm} p{1cm} p{1cm} p{3.2cm} p{3.2cm}} \hline 1 & \multicolumn{8}{l}{Use mean size-at-age observation (0 = none, 1 = read data matrix)} \Tstrut\\ \multicolumn{9}{l}{An example observation:} \Bstrut\\ + \multicolumn{9}{l}{An example observation:} \Bstrut\\ \hline & & & & & Age & & Data Vector & Sample Size \Tstrut\\ Yr & Month & Fleet & Sex & Part. & Err. & Ignore & (Female - Male) & (Female - Male) \Bstrut\\ @@ -971,6 +1128,7 @@ \subsection{Mean Length or Body Weight-at-Age} 1989 & 7 & 1 & 3 & 0 & 1 & 999 & & \Tstrut\\ ... & & & & & & & & \\ -9999 & 0 & 0 & 0 & 0 & 0 & 0 & 0 0 0 0 0 0 0 & 0 0 0 0 0 0 0 \Bstrut\\ + -9999 & 0 & 0 & 0 & 0 & 0 & 0 & 0 0 0 0 0 0 0 & 0 0 0 0 0 0 0 \Bstrut\\ \hline \end{tabular} \end{center} @@ -978,6 +1136,9 @@ \subsection{Mean Length or Body Weight-at-Age} \myparagraph{Note} \begin{itemize} + \item Negatively valued mean size entries with be ignored in fitting. This feature allows the user to see the fit to a provisional observation without having that observation affect the model. + \item A number of fish value of 0 will cause mean size value to be ignored in fitting. If the number of fish is zero, a non-zero mean size or body weight-at-age value, such as 0.01 or -999, still needs to be added. This feature allows the user to see the fit to a provisional observation without having that observation affect the model. + \item Negative value for year causes observation to not be included in the working matrix. This feature is the easiest way to include observations in a data file but not to use them in a particular model scenario. \item Negatively valued mean size entries with be ignored in fitting. This feature allows the user to see the fit to a provisional observation without having that observation affect the model. \item A number of fish value of 0 will cause mean size value to be ignored in fitting. If the number of fish is zero, a non-zero mean size or body weight-at-age value, such as 0.01 or -999, still needs to be added. This feature allows the user to see the fit to a provisional observation without having that observation affect the model. \item Negative value for year causes observation to not be included in the working matrix. This feature is the easiest way to include observations in a data file but not to use them in a particular model scenario. @@ -991,15 +1152,21 @@ \subsection{Mean Length or Body Weight-at-Age} \hypertarget{env-dat}{} \subsection{Environmental Data} The model accepts input of time series of environmental data. Parameters can be made to be time-varying by making them a function of one of these environmental time series. In v.3.30.16 the option to specify the centering of environmental data by either using the mean of the by mean and the z-score. +The model accepts input of time series of environmental data. Parameters can be made to be time-varying by making them a function of one of these environmental time series. In v.3.30.16 the option to specify the centering of environmental data by either using the mean of the by mean and the z-score. \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{1cm} p{3cm} p{3cm} p{7.5cm}} + \multicolumn{4}{l}{Parameter values can be a function of an environmental data series:} \\ \multicolumn{4}{l}{Parameter values can be a function of an environmental data series:} \\ \hline 1 & \multicolumn{3}{l}{Number of environmental variables} \Tstrut\Bstrut\\ \multicolumn{4}{l}{The environmental data can be centered by subtracting the mean and dividing by stdev (z-score, -1) or} \\ \multicolumn{4}{l}{by subtracting the mean of the environmental variable (-2) based on the year column value.} \\ + 1 & \multicolumn{3}{l}{Number of environmental variables} \Tstrut\Bstrut\\ + \multicolumn{4}{l}{The environmental data can be centered by subtracting the mean and dividing by stdev (z-score, -1) or} \\ + \multicolumn{4}{l}{by subtracting the mean of the environmental variable (-2) based on the year column value.} \\ \hline \multicolumn{4}{l}{COND > 0 Example of 2 environmental observations:} \Tstrut\\ & Year & Variable & Value \Bstrut\\ @@ -1014,10 +1181,12 @@ \subsection{Environmental Data} \end{center} The final two lines in the example above indicate in that variable series 1 will be centered by subtracting the mean and dividing by the standard deviation (indicated by the -1 value in the year column). The environmental variable series 2 will be centered by subtracting the mean of the time series (indicated by the -2 value in the year column). The input in the ``value'' column for both of the final two lines specifying the centering of the time series is ignored by the model. The control file also will need to be modified to in the long parameter line column ``env-var'' for the selected parameter. This feature was added in v.3.30.16. +The final two lines in the example above indicate in that variable series 1 will be centered by subtracting the mean and dividing by the standard deviation (indicated by the -1 value in the year column). The environmental variable series 2 will be centered by subtracting the mean of the time series (indicated by the -2 value in the year column). The input in the ``value'' column for both of the final two lines specifying the centering of the time series is ignored by the model. The control file also will need to be modified to in the long parameter line column ``env-var'' for the selected parameter. This feature was added in v.3.30.16. \myparagraph{Note} \begin{itemize} + \item Any years for which environmental data are not read are assigned a value of 0.0. None of the current link functions contain a link parameter that acts as an offset. Therefore, you should subtract the mean from your data. This lessens the problem with missing observations, but does not eliminate it. A better approach for dealing with missing observations is to use a different approach for the environmental effect on the parameter. Set up the parameter to have random deviations for all years, then enter the zero-centered environmental information as a \hyperlink{SpecialSurvey}{special survey of type 35} and set up the catchability of that survey to be a link to the deviation vector. This is a more complex approach, but it is superior in treatment of missing values and superior in allowing for error in the environmental relationship. \item Any years for which environmental data are not read are assigned a value of 0.0. None of the current link functions contain a link parameter that acts as an offset. Therefore, you should subtract the mean from your data. This lessens the problem with missing observations, but does not eliminate it. A better approach for dealing with missing observations is to use a different approach for the environmental effect on the parameter. Set up the parameter to have random deviations for all years, then enter the zero-centered environmental information as a \hyperlink{SpecialSurvey}{special survey of type 35} and set up the catchability of that survey to be a link to the deviation vector. This is a more complex approach, but it is superior in treatment of missing values and superior in allowing for error in the environmental relationship. \item Users can assign environmental conditions for the initial equilibrium year by including environmental data for one year before the start year. However, this works only for recruitment parameters, not biology or selectivity parameters. \item Environmental data can be read for up to 100 years after the end year of the model. Then, if the recruitment-environment link has been activated, the future recruitments will be influenced by any future environmental data. This could be used to create a future ``regime shift'' by setting historical values of the relevant environmental variable equal to zero and future values equal to 1, in which case the magnitude of the regime shift would be dictated by the value of the environmental linkage parameter. Note that only future recruitment and growth can be modified by the environmental inputs; there are no options to allow environmentally-linked selectivity in the forecast years. @@ -1034,6 +1203,7 @@ \subsection{Generalized Size Composition Data} \item The generalized size composition data can be from the combined discard and retained, discard only, or retained only. \item There are two options for treating fish that in population size bins are smaller than the smallest size frequency bin. \begin{itemize} + \item Option 1: By default, these fish are excluded (unlike length composition data where the small fish are automatically accumulated up into the first bin). \item Option 1: By default, these fish are excluded (unlike length composition data where the small fish are automatically accumulated up into the first bin). \item Option 2: If the first size bin is given as a negative value, then accumulation is turned on and the absolute value of the entered value is used as the lower edge of the first size bin. \end{itemize} @@ -1042,7 +1212,7 @@ \subsection{Generalized Size Composition Data} \begin{center} \begin{tabular}{p{1.4cm} p{0.7cm} p{12.8 cm}} \multicolumn{3}{l}{Example entry:} \\ - \multicolumn{3}{l}{Example entry:} \\ + \multicolumn{3}{l}{Example entry:} \\ \hline 2 & & Number (N) of size frequency methods to be read. If this value is 0, then omit all entries below. A value of -1 (or any negative value) triggers expanded optional inputs below that allow for Dirichlet % or two parameter Multivariate (MV) Tweedie likelihood (add when MV Tweedie is implemented) @@ -1058,10 +1228,6 @@ \subsection{Generalized Size Composition Data} \multicolumn{2}{r}{2 2} & Units per each method (1 = biomass, 2 = numbers) \\ \multicolumn{2}{r}{3 3} & Scale per each method (1 = kg, 2 = lbs, 3 = cm, 4 = inches) \\ \multicolumn{2}{r}{1e-9 1e-9} & Min compression to add to each observation (entry for each method) \\ - \multicolumn{2}{r}{25 15} & Number of bins per method \Tstrut\\ - \multicolumn{2}{r}{2 2} & Units per each method (1 = biomass, 2 = numbers) \\ - \multicolumn{2}{r}{3 3} & Scale per each method (1 = kg, 2 = lbs, 3 = cm, 4 = inches) \\ - \multicolumn{2}{r}{1e-9 1e-9} & Min compression to add to each observation (entry for each method) \\ \multicolumn{2}{r}{2 2} & Number of observations per weight frequency method \Bstrut\\ \hline \multicolumn{3}{l}{COND < 0 - Number of size frequency} \Tstrut\\ @@ -1077,11 +1243,15 @@ \subsection{Generalized Size Composition Data} \begin{center} \begin{tabular}{p{0.4cm} p{0.4cm} p{0.4cm} p{0.4cm} p{0.4cm} p{0.4cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.5cm} p{0.25cm}} + \multicolumn{18}{l}{Then enter the lower edge of the bins for each method. The two row vectors shown} \\ + \multicolumn{18}{l}{below contain the bin definitions for methods 1 and 2 respectively:} \\ \multicolumn{18}{l}{Then enter the lower edge of the bins for each method. The two row vectors shown} \\ \multicolumn{18}{l}{below contain the bin definitions for methods 1 and 2 respectively:} \\ \hline -26 & 28 & 30 & 32 & 34 & 36 & 38 & 40 & 42 & ... & 60 & 62 & 64 & 68 & 72 & 76 & 80 & 90 \Tstrut\\ -26 & 28 & 30 & 32 & 34 & 36 & 38 & 40 & 42 & 44 & 46 & 48 & 50 & 52 & \multicolumn{4}{l}{54} \Bstrut\\ + -26 & 28 & 30 & 32 & 34 & 36 & 38 & 40 & 42 & ... & 60 & 62 & 64 & 68 & 72 & 76 & 80 & 90 \Tstrut\\ + -26 & 28 & 30 & 32 & 34 & 36 & 38 & 40 & 42 & 44 & 46 & 48 & 50 & 52 & \multicolumn{4}{l}{54} \Bstrut\\ \hline \end{tabular} \end{center} @@ -1093,6 +1263,7 @@ \subsection{Generalized Size Composition Data} \hline & & & & & & Sample & \Bstrut\\ + Method & Year & Month & Fleet & Sex & Part & Size & females then males> \Bstrut\\ \hline 1 & 1975 & 1 & 1 & 3 & 0 & 43 & \Tstrut\\ 1 & 1977 & 1 & 1 & 3 & 0 & 43 & \\ @@ -1121,31 +1292,42 @@ \subsection{Tag-Recapture Data} \begin{center} \begin{tabular}{p{1.1cm} p{1.1cm} p{1.1cm} p{1.1cm} p{1.1cm} p{1.1cm} p{1.1cm} p{1.1cm} p{3cm}} + \multicolumn{9}{l}{Example set-up for tagging data:} \\ \multicolumn{9}{l}{Example set-up for tagging data:} \\ \hline 1 & & \multicolumn{7}{l}{Do tags - 0/1/2. If this value is 0, then omit all entries below.} \\ & & \multicolumn{7}{l}{If value is 2, read 1 additional input.} \Tstrut\Bstrut\\ + 1 & & \multicolumn{7}{l}{Do tags - 0/1/2. If this value is 0, then omit all entries below.} \\ + & & \multicolumn{7}{l}{If value is 2, read 1 additional input.} \Tstrut\Bstrut\\ \hline \multicolumn{9}{l}{COND > 0 All subsequent tag-recapture entries must be omitted if ``Do Tags'' = 0} \Tstrut\\ + & 3 & \multicolumn{7}{l}{Number of tag groups} \Bstrut\\ & 3 & \multicolumn{7}{l}{Number of tag groups} \Bstrut\\ \hline & 7 & \multicolumn{7}{l}{Number of recapture events} \Tstrut\Bstrut\\ + & 7 & \multicolumn{7}{l}{Number of recapture events} \Tstrut\Bstrut\\ \hline & 2 & \multicolumn{7}{l}{Mixing latency period: N periods to delay before comparing observed} \Tstrut\\ & & \multicolumn{7}{l}{to expected recoveries (0 = release period).} \Bstrut\\ + & 2 & \multicolumn{7}{l}{Mixing latency period: N periods to delay before comparing observed} \Tstrut\\ + & & \multicolumn{7}{l}{to expected recoveries (0 = release period).} \Bstrut\\ \hline & 10 & \multicolumn{7}{l}{Max periods (seasons) to track recoveries, after which tags enter} \Tstrut\\ & & \multicolumn{7}{l}{accumulator} \Bstrut\\ + & 10 & \multicolumn{7}{l}{Max periods (seasons) to track recoveries, after which tags enter} \Tstrut\\ + & & \multicolumn{7}{l}{accumulator} \Bstrut\\ \hline \multicolumn{9}{l}{COND = 2} \Tstrut\\ & 2 & \multicolumn{7}{l}{Minimum recaptures. The number of recaptures >= mixperiod must be} \\ + & 2 & \multicolumn{7}{l}{Minimum recaptures. The number of recaptures >= mixperiod must be} \\ & & \multicolumn{7}{l}{>= min tags recaptured specified to include tag group in log likelihood}\Bstrut\\ \hline & \multicolumn{8}{l}{Release Data} \Tstrut\\ & TG & Area & Year & Season & & Sex & Age & N Release \Bstrut\\ + & TG & Area & Year & Season & & Sex & Age & N Release \Bstrut\\ \hline & 1 & 1 & 1980 & 1 & 999 & 0 & 24 & 2000 \Tstrut\\ & 2 & 1 & 1995 & 1 & 999 & 1 & 24 & 1000 \\ @@ -1153,6 +1335,8 @@ \subsection{Tag-Recapture Data} \hline & \multicolumn{8}{l}{Recapture Data} \Tstrut\\ & TG & & Year & & Season & & Fleet & Number \Bstrut\\ + & \multicolumn{8}{l}{Recapture Data} \Tstrut\\ + & TG & & Year & & Season & & Fleet & Number \Bstrut\\ \hline & 1 & & 1982 & & 1 & & 1 & 7 \Tstrut\\ & 1 & & 1982 & & 1 & & 2 & 5 \\ @@ -1172,6 +1356,7 @@ \subsection{Tag-Recapture Data} \item Analysis of the tag-recapture data has one negative log likelihood component for the distribution of recaptures across areas and another negative log likelihood component for the decay of tag recaptures from a group over time. Note the decay of tag recaptures from a group over time suggests information about mortality is available in the tag-recapture data. More on this is in the \hyperlink{tagrecapture}{control file documentation}. \item Do tags option 2 adds an additional input compared to do tags option 1, minimum recaptures. Minimum recaptures allows the user to exclude tag groups that have few recaptures after the mixing period from the likelihood. This may be useful when few tags from a group have been recaptured as an alternative to manually removing the groups with these low numbers of recaptured tags from the tagging data. \item Warning for earlier versions of SS3: A shortcoming in the recapture calculations when also using Pope's F approach was identified and corrected in v.3.30.14. + \item Warning for earlier versions of SS3: A shortcoming in the recapture calculations when also using Pope's F approach was identified and corrected in v.3.30.14. \end{itemize} \subsection{Stock (Morph) Composition Data} @@ -1252,19 +1437,25 @@ \subsection{Data Super-Periods} Not all time steps within the extent of a super-period need be included. For example, in a three season model, a super-period could be set up to combine information from season 2 across 3 years, e.g., skip over the season 1 and season 3 for the purposes of calculating the expected value for the super-period. The key is to create a dummy observation (negative fleet value) for all time steps, except 1, that will be included in the super-period and to include one real observation (positive fleet value; which contains the real combined data from all the specified time steps). \begin{center} + \vspace*{-\baselineskip} \vspace*{-\baselineskip} \begin{tabular}{p{1cm} p{1cm} p{1cm} p{1cm} p{1cm} p{9cm}} + \multicolumn{6}{l}{Super-period example:} \\ \multicolumn{6}{l}{Super-period example:} \\ \hline Year & Month & Fleet & Obs & SE & Comment \Tstrut\Bstrut\\ \hline 1982 \Tstrut & \textbf{-2} & 3 & 34.2 & 0.3 & Start super-period. This observation has positive fleet value, so is expected to contain combined data from all identified periods of the super-period. The standard error (SE) entered here is use as the SE of the combined observation. The expected value for the survey in 1982 will have a relative weight of 1.0 (default) in calculating the combined expected value.\Bstrut\\ + 1982 \Tstrut & \textbf{-2} & 3 & 34.2 & 0.3 & Start super-period. This observation has positive fleet value, so is expected to contain combined data from all identified periods of the super-period. The standard error (SE) entered here is use as the SE of the combined observation. The expected value for the survey in 1982 will have a relative weight of 1.0 (default) in calculating the combined expected value.\Bstrut\\ \hline 1983 \Tstrut & 2 & \textbf{-3} & 55 & 0.3 & In super-period; entered observation is ignored. The expected value for the survey in 1983 will have a relative weight equal to the value in the standard error field (0.3) in calculating the combined expected value. \Bstrut\\ + 1983 \Tstrut & 2 & \textbf{-3} & 55 & 0.3 & In super-period; entered observation is ignored. The expected value for the survey in 1983 will have a relative weight equal to the value in the standard error field (0.3) in calculating the combined expected value. \Bstrut\\ \hline 1985 \Tstrut & 2 & \textbf{-3}& 88 & 0.40 & Note that 1984 is not included in the super-period. Relative weight for 1985 is 0.4 \Bstrut\\ + 1985 \Tstrut & 2 & \textbf{-3}& 88 & 0.40 & Note that 1984 is not included in the super-period. Relative weight for 1985 is 0.4 \Bstrut\\ \hline 1986 & \textbf{-2} & \textbf{-3} & 88 & 0.40 & End super-period \Tstrut\Bstrut\\ + 1986 & \textbf{-2} & \textbf{-3} & 88 & 0.40 & End super-period \Tstrut\Bstrut\\ \hline \end{tabular} \end{center}