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The current version of the DLMtool package is available for download from CRAN.
LBSPR when calendar years are provided in
Data@YearCV_Ind in
IratioLtargetE MP where it was incorrectly
averaging across simulations (thanks to Ernesto Carrella
for spotting this)LBSPR MP updated to handle missing CAL dataLBSPR is now recursiveV6.0.0 is a major update to the DLMtool package. It is
not backwards compatible with previous versions of DLMtool
or MSEtool.
DLMtool package is now limited to the
collection of the data-limited management procedures.MSEtool (v3+).MSEtool v3+ is a dependency for DLMtool
v6+, and all operating model simulation and projection functions are
available after installing and loading DLMtool.cpars$DR_y - matrix nsim rows and nyears+proyears
columnsRec@DR)OM@cpars$qs). Doesn’t optimize for depletion and updates
OM@D.Data@AddInd) can now
be specified using Data@AddIunits (numeric vector length
n.ind 1=biomass (default) 0=numbers).Data@AddIndType (numeric vector length n.ind
1=total stock (default), 2=spawning stock, 3=vulnerable stock).Misc$Removals wasn’t being returned in
parallel mode.XL2OM for latest version of R (behavior
of base::data.matrix changed)OM@cpars$control$Sample_Area; e.g.,
OM@cpars$control$Sample_Area$Catch The following names are
accepted:
Catch (includes average catch);BInd total biomass indexSBInd spawning biomass indexVInd vulnerable biomass indexRecInd recruitment indexCAA - catch-at-ageCAL - catch-at-length (also applies to mean length,
length-at-first capture, etc) Each must be an array with dimensions
c(nsim, nyears+proyears, nareas).DT package broke
PMObj and PMLimit tablesOM@cparsLBSPR_MLL MPvalidcpars now returns a HTML data table with
information on parameters that can be passed in
OM@cparsData@VInd and
Data@SpInd for relative vulnerable and spawning abundance
respectively. Also add CV slots for each. Note that these use the same
observation error and betas as Data@Ind (total abundance)
and all built-in MPs use Data@Ind.Sub function now subsets Data in MSE object by MP.Converge now returns a summary table of convergence
diagnostics (thanks to Sarah Valencia)MSE@Misc$RemovalsData@AddInd can now be specified with
cpars$AddIUnits: numeric vector of length
n.ind with 1 specifying biomass (default) and 0 abundance
by numbers.SuggestsForceCor, use
LH2OM insteadcpars$Mat_age) are greater than 0.5. Assume A50 is 1 in
this case.cpars$DataCAL_bins or binWidth in
cparsData@AddIndData object has been updated, main new features are
the addition of an Effort slot (Data@Effort and
Data@CV_Effort) and all CV by year for all time-series
information.Data@RInd slot has been removed and replaced with
Data@AddIndCplot (thanks Sarah Valencia for picking
this up)runMSE(Hist=TRUE) where under some
conditions the Hist object could not be stitched back
together.AAVE)
performance objective methodPMObj functionCurCDom function (and removed old DOM
function)SS2OM). To use a plus-group, use
OM@cpars$plusgroup = 1OM@cpars$binWidthTradePlotcpars$mov when nareas>2runMSE function is now more robust. MPs that fail
(i.e. crash R) are now skipped without stopping the entire MSE. A
warning message alerts users which MPs have been dropped from the
analysis.runMSE(Hist=TRUE) returns a new S4 object of class
‘Hist’. This change means that information from historical simulations
is now accessed using @ instead of $. For
example: Hist <- runMSE(Hist=TRUE)
TS <- Hist@TSdata # time-series data
Data <- Hist@Data # Data object from end of historical periodrunMSE(Hist=TRUE) now works with parallel
processingcheatsheetsOM@cpars$mov.Uses function has been added to return MPs that use a
particular data slot. For example: Uses('AvC)OM@cpars$initD.PMLimit and PMObj have been
added to generate interacive HTML tables showing the performance of MPs
(using objects of class PM)Labels has been added to the
TradePlot function and related functions
(Tplot etc). This allows users to replace the default MP
name with something more user-friendly. For example:
TradePlot(MSE, Labels=list(AvC="Average Catch")). An
argument cols has also been added so users have full
control on the colors of the points and labels. The MP labels can be
removed completely by setting the lab.size argument to
NULLStock@Mgrad, Stock@Linfgrad) have been
removed. Time-varying parameters should now be specified with
OM@cpars.AnnualMSY argument is now deprecated. MSY reference
points are now always calculated in every time-step.runMSErobust function has been now removed from
package (it was broken in the previous version).Data objects with larger
number of length binscurE75 which was ratcheting down
effortTuring functionTuring functionsummary(Data)cheatsheets()Growth2OM)LW2OM).plotMPA to include relative size of areasMSE@OM has been updated to only include historical
conditions.Converge in 5.2.1XL2Data to read files from other than
working directorysummary(Data)PM functions when
MSE@nMPs == 1runMSE. OM@interval can now be a vector of
length nMPs, to specify different management intervals for
different MPs. For example, with OM@interval=c(1,5) and
runMSE(OM, MPs=c('AvC', 'BK')),AvCis applied every year andBKis applied every 5 years.OM@interval`
is recycled if not the same length as number of MPs.makeMeanMP has been added to generate a MP that
averages output from a list of other MPs.Turing has been added to compare simulated data from an
OM to the real data in the fishery Data object.LH2OM and predictLH have been added to
predict correlated life-history parameters from FishBase using FishLife
model.Converge
functionReplace function has been updated to replace directly
from individual Stock, Fleet, Obs, and Imp objects instead of from only
OM objectssummary(Data) function have been
improvedmakePerf has been replaced with tinyErr
function with options to remove process error, observation error, and
gradientsPM (Performance Metric) methods have been updated and
described in the user guideTrade_Plot,
Tplot, Tplot2, and Tplot3Fease has been simplified and updated. Fease object has
now been removedplot and examples in the
online documentationOM@cpars) have been updated and now
have better documentationuserguide() has been updated and
additional chapters added on new featuresData object:
Common_Name, Species, Region, and
for data-rich MPs in MSEtool: sigmaR (recruitment
error)setup() if
runsMSE(parallel=TRUE) and cluster hasn’t been
initializedrunsMSE(parallel=TRUE)SPmodMixed MPs: it is now possible to design MPs that combine Output and Input controls. Previously, MPs in DLMtool where either class ‘Input’ or class ‘Output’. This has changed, and all MPs are now class ‘MP’ and return a standard recommendation object (‘Rec’) that allows specification of any combination of catch limit, effort limit, spatial closure, and size-at-selection and/or size-at-retention regulations.
MP types: For convenience, MPs can be categorised into 4 types:
‘Output’ (only catch limits), ‘Input’ (only spatial, effort, and/or size
regulations), ‘Mixed’ (some combination of Output and Input
regulations), and ‘Reference’ (MPs such as FMSYref or NFref which are
designed to be used a references and not for actual applications). The
function MPtype can be used to determine an MP’s type, e.g
MPtype('AvC').
Increased flexibility of selectivity, retention, and discard mortality controls: MPs can now modify both selectivity and retention curves, and also modify the level of discard mortality.
Historical MPAs: A new slot MPA has been added to
the Fleet object. This allows users to specify the
historical year and the fraction of an area that was closed. See
?plotMPA for an example.
Improvements to MSY reference point calculations: MSY reference points are now calculate for every year. This accounts for changes in productivity (e.g., increases in growth) that affect reference points such as BMSY and FMSY.
Increased number of areas: By default DLMtool is a 2-area model. The code has now been updated to include more areas if required.
Movement by age: It is now possible to specify movement by age, such as ontogenetic migration offshore
Parallel processing: runMSE has been optimized for
parallel processing. By default, parallel processing is not used if
nsim<=48. We suggest testing your OM with a low number
of simulations (e.g nsim=48) and then increasing the number
of simulations and using parallel processing:
runMSE(OM, MPs, parallel=TRUE). Update messages are not
displayed in parallel mode.
Taxonomic information: New slots have been added to the
Stock object to record common name and taxonomic
details.
The Converge function has been updated with a
improved of convergence diagnostics.
A new performance metric (PM) class has been added.
This allows users to develop a custom set of performance metrics for
their management system. A series of example PM objects
have been created (see avail('PM')). Over time the summary
and plotting functions will be updated to take a set of PM
objects. See Tplot3 for an example.
Many improvements to functions (e.g MPs) to improve robustness and avoid model crashes due to stochastic sampling of parameters.
All code in DLMtool is now tested using the testthat
package to ensure that the functions work as expected. Please continue
to file bug reports at:
https://github.com/DLMtool/DLMtool/issues
The User Manual has been completely revised and updated. Use
userguide() to access. Please submit any typo corrections
or suggestions for improvement at
https://github.com/DLMtool/DLMtool_UserGuide/issues. Pull requests are
welcome.
We are developing an online library of DLMtool OMs (http://www.datalimitedtoolkit.org/fishery_library) which has the OM Report, OM Excel file, and OM R data object for each DLMtool OM. A standardized MSE report is in development. The OM library will be expanded as new OMs are developed. Please contact us if you have developed OMs and are able to make them publicly available.
A DLMtool user forum is in development and will be launched soon. The idea of the forum is to provide a platform for DLMtool users to ask questions and share experience with each other and the DLMtool team.
The following slots have been added to the OM object:
OM@intervalOM@pstarOM@maxFOM@repsThis was done so that an OM object is completely self-contained and includes all information used in the MSE.
Objects names have been shortened and modified for consistency:
Implementation error and a dedicated implementation error object called ‘Imp’, (like Stock, Fleet and Obs) that can account for variability and overages/underages in both effort, catch and size limit advice (discarding rate and post release mortality rate are coming soon)
At least 10 new example operating models from real DLMtool applications in the US and Canada
Stochastic SRA operating model functions (i.e. build a full operating model accounting for correlations in parameters using historical catch trends and some composition data)
Full plots of operating model conditions
Stock-synthesis assessment to DLM function for specifying operating models (currently MLE only but adding MCMC and hessian options)
iSCAM assessment to DLM support for specifying operating models (MPD only but adding MCMC and hessian options soon)
Tracking of historical versus current simulated age composition in catches and population over simulations
New function ‘Replace’ for copying parts of an operating model to another operating model (e.g. Robin Hood approach,where you may want to borrow say, the fleet characteristics from another operating model)
Estimation of a new reference point ‘Blow’ for calculation of conservation-related performance metrics (Blow is the spawning biomass where it takes HZN number of mean generation times to reach Bfrac fraction of SSBMSY given zero catches, where the user can specify HZN and Bfrac).
Canadian DFO performance plots (DFO_hist, DFO_proj, DFO_plot2)
Biomass recovery relative to Blow plots (COSEWIC_plot)
A dedicated custom parameters slot in the operating model object. This is a list where the user can place custom parameters samples (from any distribution / correlation structure you wish) that are named as they appear in the operating model.
DLMdat have now been added as
separate data objects. This means that it is no longer necessary to
unpack data objects at the beginning of an R session.setup()runMSE and runMSErobust functions have a
new logical argument Hist. When Hist=TRUE the
model returns the historical simulations only.CheckConverg has been deprecated - use
Converge now to check MSE for convergencecustompars section in runMSE.r and
renamed parameters to match OM objectR0 can be a vector
nsim longcustompars has been moved in the MSE code so that more
parameters can be passed in to the MSE. A message alerts users which
valid parameters have been found in custompars and any
invalid parameters are displayed in a warning messageBMSY_B0 to
SSBMSY_SSB0 to avoid confusionSSB and
vulnerable biomass VB to MSE objectSpAbun slot to data object for abundance of
spawning stock. The Abun slot relates to vulnerable
biomassntrials and fracD control arguments
to runMSE. Allows user to modify the maximum number of
re-samples in the optimization to achieve the sampled depletion. The
model will stop if a proportion greater than fracD of the
simulated depletion values are not the same as the sampled values after
more than ntrials re-samplesgetr functionChooseEffort function where nyears was
being set to 0 in some casesPplot2 for F/FMSYhcv is 0 in
the runMSE codeFMSYref
results in exactly F/FSMY = 1maxF now also applies to the catch that is taken from
the populationA number of additional plotting functions, and a few new MPs have been added in this version. Also a few minor changes to improve performance and reliability of the model.
For improved stability, especially with large files, the
runMSErobust function has been changed so that it now uses
the saveRDS function to write the MSE objects to disk. MSE
objects saved with this version of the function need to be loaded with
readRDS.
The plotFun function can be used to print out all
available plotting functions for objects of class MSE or
DLM_data
MSE Object
Pplot and Kplot functions have been modified for extra control of various features of the plots.
Two functions, barplot.MSE and boxplot.MSE,
have been added to plot the MSE object. Call them using the generic
barplot or boxplot functions, and see
?barplot.MSE and ?boxplot.MSE for information
on the arguments for the function.
New functions: Jplot, Splot,
Cplot, and VOIplot have been added.
DLM_data Object
boxplot.DLM_data has been added and can be used to plot
boxplots of the TACs recommendations from different methods. Call with
boxplot(DLM_data)
New MPs
Three new input control methods, developed by Helena Geremont, have
been added to the package: EtargetLopt,
L95target, and minlenLopt1.
A new slot (Effort) has been added to the MSE object. This stores the fishing effort for each year, simulation, and MP in the projection years. The addition of the new slot may cause a warning message to be thrown up if an MSE object from a previous version of the DLMtool is loaded.
You can update the old MSE object by adding an empty
Effort slot:
MSEobj <- updateMSE(MSEobj).
There were some issues with a couple of the input control MPs, which have now been addressed (thanks, Helena, for identifying these). There was also a problem with effort and selectivity being calculated for the input controls, which has also been fixed.
A number of small but important bugs have been fixed, with special thanks to Liz Brooks, Helena Geromont, and Bill Harford for alerting us to some of these issues.
Quang Huynh has recoded the mean length methods in C++, and they now run much faster, so should pass the time-limit constraint.
A new function (runMSErobust) has been added which is a
wrapper for the runMSE function. In time, this may replace
runMSE as the primary function to use when running a MSE.
runMSErobust splits large simulations into a series of
smaller packets and stitches them together to return an MSE object. This
has the benefit of increasing speed and efficiency, particularly for
runs with large number of simulations. The function also checks for
errors and re-starts the MSE if the model crashes.
A set of functions OM_xl and Fease_xl have
been added. These are used to read in operating model and feasibility
parameters from an Excel spreadsheet rather than a CSV file. These are
essentially wrappers for the new function, but allow you to
store all operating model tables in a single spreadsheet rather than a
number of CSV files. This is mainly useful if you are working on
multiple species/stocks.
The size limit feature has been updated to include an upper slot
limit. See slotlim for an example MP. The slot limit is
specified as the last element in the input control vector. Similar to
the lower size limit, all individuals above the slot limit experience no
fishing mortality.
A number of new MPs have been added. There are now 63 output and 22 input control MPs in the DLMtool.
A new function makePerf has been added. This function
takes an OM object, and returns the same OM object with no process or
observation error. This is useful for testing the performance of methods
under perfect conditions, to see if they work as expected. And for
debugging!
Two new plotting functions have been added: wormplot
which creates worm plots of the likelihood of meeting biomass targets in
future years, and VOIplot which is another value of
information plot, similar to the VOI function, and shows
how observation and operating model parameter values affect trends in
long-term yield and biomass.
Coming soon: bag limit MPs for recreational fisheries
In order to simulate fisheries that have experienced important shifts
in historical length selectivity, this can now be user specified using a
graphical user interface (the ‘ChooseSelect’ function) or by manually
editing a series of new slots in the Fleet object
(SelYears, AbsSelYears, L5Upper,
L5Lower, LFSUpper, LFSLower,
VmaxUpper, VmaxLower).
Persistent shifts in stock productivity are a particular concern for
fishery management. These can now be generated in the toolkit using a
new function SetRecruitCycle that generates cyclical
pattern in recruitment strength.
Length-based spawning potential ratio (SPR) MPs have been added. Currently these methods are slow and often don’t pass the time constraint.
Two features have been added to allow MPs to return additional
information for future reference. (1) The DLM_data object that MPs
operate on now has a miscellaneous slot Misc. (2) MPs can
now return a list. The first position is the management recommendation
(e.g. TAC) the second is information that is stored in the
Misc slot that can be used by the MP in the next iteration.
This can be useful for storing information that is time-consuming to
calculate each year.
A new generic trade-off performance plot TradePlot has
also been added.
Operating model effort is now simulated by a time-series of year
vertices and relative magnitude of effort at each vertex. It follows
that the slot Fleet@Fgrad, and has been replaced by three
slots with vectors of equal length: Fleet@EffYear,
Fleet@EffUpper and Fleet@EffLower. These
effort trajectories can now be specified by a new graphical interface
(function ChooseEffort()) which uses points to determine
the three slots described above.
Operating model fleet selectivity has been robustified to prevent
users from specifying length at first capture (Fleet@L5)
and length at full selection (Fleet@LFS) that are
unrealistically high. According to our view of reality these now have
upper limits of L50 and maximum length, respectively.
A function DOM() has been added that evaluates how often
one MP outperforms another across simulations. It is possible that an MP
could have higher average performance but perform worse on higher
fraction of simulations. The DOM() function provides a
diagnostic to analyze this.
An additional function Sub() has been added which allows
users to subset an MSE object according to either (or both) a vector of
MPs and simulations. This means you no longer have to rerun everything
to provide results for a smaller number of MPs or particular
simulations.
A bug was found in which length at first capture was being sampled
from a uniform distribution U(LB,UB*2) rather than
U(LB,UB). When depletion could not be simulated by even
very high fishery catchabilities an error could occur after more than 10
attempts to find a suitable value of depletion. Length composition
simulation in 2.1.1 was not correctly implemented leading to minor
biases.
In response to popular demand, simulation and data are entirely length-based now. It follows that many objects that worked with 2.0 will no longer be compatible. In most cases it is very quick to make files/objects compatible with version 2.1, but nonetheless we apologize if this is frustrating!
The DLMtool package is stochastic, so if you run into problems with the code, please report them (along with a random seed). In the meantime, simply try running it again; the problem may be attributable to a rare combination of sampled parameters.
Be warned that if you abort a parallel process
(e.g. runMSE()) half-way through, you are in the lap of the
gods! It will often be necessary to restart the cluster
sfInit() or even restart R.
The package is not designed for very short lived stocks (that live for less than 5 years) due to the problems with approximating fine-scale temporal dynamics with an annual model. Technically, you could just divide all your parameters by a sub-year resolution, but the TAC would be set by sub-year and the data would also be available at this fine-scale, which is highly unlikely in a data-limited setting.
The DLMtool has moved to a length-based simulator (maturity, fisheries selectivity by length)
The spatial targeting function has been removed for the moment as its implementation was flawed , so could not distribute fishing correctly with respect to both density and the amount of the resource among the two areas.
Tplot2 adds a different set of trade-offs including
long-term and short-term probability of achieving 50% of FMSY yield and
average annual variability in yields.
Version 2.0 did not include observation error in estimates of current stock abundance and depletion (only biases were simulated). Many thanks to Helena Geromont for spotting this. This has now been corrected.
DLM_data objects now have a slot LHYear
which is a numeric value corresponding with the last historical year.
This is needed for some MPs that want to run off only the past data
rather than the updated (projected, closed-loop simulation)
data.
Post-MSE, you can now run a Convergence function
CheckConverg() to see if performance metrics are
stable.
The package now contains CSRA, a tool for
calculating very rough estimates of current depletion and fishing
mortality rate from mean catch data.
getAFC now can be used for converting length
estimates to age estimates through a stochastic growth model.
The value of information function (VOI) contained
bugs in version 2.0. This now has been fixed.
Users can now send their own parameter values to the
runMSE function allowing outputs from stock assessments or
correlated parameters (e.g. K and age at maturity)
values.
After deliberation, Pope’s approximation has been used to account for intra-year mortality (i.e., TACs are taken from biomass at the start of the year subject to half of natural mortality rate). This is probably a reasonable approximation in a data-limited setting: alternative structural assumptions for M are eclipsed by uncertainty in M itself and other operating model parameters such as selectivity and bias in observation of data such as annual catches.
The simulation of length composition data was bugged in version 2.0. The variability in length at age was taken from the observation model. Using the perfect information observation model therefore led to no variability in length at age and hence very odd length composition data. This has been solved; now a fixed 10% CV in length-at-age is assumed (normally distributed).
A bug with Delay-Difference MPs has been fixed (DD
and DD4010) in which stochastic TACs were sampled when reps
=1. This should just be the mean estimate. The result is that DD is much
less variable between years but comes with less contrast in the data. In
addition to the much less variable catch recommendations, long-term mean
performance of the MP is reduced while medium-term performance has been
improved.
In the move to length-based inputs it is possible to prescribe wild biases for maximum length and length at maturity. In this version these sampled biases are not correlated so it is possible to create simulated data sets where maximum length is lower than length-at-95% maturity and length-at-50% maturity. We put a hard ceiling on this such that length at 95 percent maturity must be below 90 percent of maximum length and length-at-50% maturity must be below 90% of length-at-95% maturity. This isn’t great and this will be improved for v2.11.
The package now works without initiating a cluster
sfInit().
A simple modification to DCAC has been added
EDCAC (Harford and Carruthers, 2015) that better accounts
for absolute stock depletion.
Three new slots are available to run MPs on that related to mean
length of catches (ML), modal length of captures
(Lc), and the mean length of catches of fish over
Lc (Lbar)
Much has changed in the DLMtool terminology to make it more generally applicable. For example, OFL (overfishing limits, FMSY x current biomass), now belongs to a larger class of TACs (Total Allowable Catches).
There are now just two classes of DLM MPs, DLM_output (MPs linked to output controls e.g. TACs) and DLM_input (MPs linked to input controls such as time-area closures, age selectivity and effort). The new DLM_input function classes have four components, fractional reallocation of spatial effort, fraction of effort in final historical year prescribed in the current year, spatial limits on fishing mortality and a user-defined age-selectivity curve. For example, given an hypothetical stock with 8 age classes a DLM_input method might return a vector c(0.5, 0.8, 0,1, 0,0,0,0,1,1,1,1). This is interpreted as a 50% reallocation (Allocation = 0.5) of spatial effort, with a total effort that is 80% of historical levels (Effort = 0.8) with a closure in area 1 and full fishing in area 2 (Spatial = c(0,1)) and knife-edge selectivity at age class 5 (Selectivity = c(0,0,0,0,1,1,1,1)) [note that Selectivity has changed in newer versions of the package]. To demonstrate this new feature there are four new input controls, current effort (curE), 75% of current effort (curE75), age selectivity that matches the maturity ogive (matagelim) and a marine reserve in area 1 (area1MR) [note that matagelim has changed to matlenlim in recent versions].
A ‘dumb’ MP has been added: Mean Catch Depletion (MCD) that simply calculates a TAC based on mean catches and depletion. This is to demonstrate the (theoretically) very high information content of a reliable estimate of current stock depletion.
A better length composition simulator has been added. Note that this still renews the normal length structure between ages and does not properly simulate the higher mortality rate of larger, faster growing fish (a growth type group simulator is on its way).
Help documentation has been much improved including complete
guides for Fleet, Stock,
Observation and MSE objects. Eg
class?MSE.
Minor bugs have been found with the help of Helena Geromont including a problem with update intervals of 1 and low simulated steepness values.
Reliability is much improved following a full combinatorial test of all Fleet, Stock, Observation objects against all MPs.
A dedicated Value of information function is now available for
MSE objects: VOI(MSEobject) which is smarter than the
former version which was included in plot(MSE object class).
Plotting functions have been improved, particularly
Tplot, Kplot, Pplot and
plot(DLM_data object class).
SPmod has been robustified to stop strongly negative
surplus production estimates from leading to erratic behavior.
The butterfish stock type now has less variable recruitment and slightly lower natural mortality rate as previous values were rather extreme and led to data generation errors (with natural mortality rate as high as 0.9, butterfish is right at the limit of what can be simulated reasonably with an annual age-structured operating model).
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.