Package List and Search - Stock Assessment Toolbox

Background

This web site arose after a review of Australia’s stock assessment practices. From this study, many Australian assessments were defined as bespoke. Although there was often good reason for this, several assessments could have been undertaken using peer-reviewed, well tested and documented, freely available packages, with a large support base of users and developers. When assessment scientists were asked why bespoke packages were used extensively in Australia, a key issue was finding packages that would suit their use and whether training was available. This web site, we hope, will be a tool for all assessment scientists wanting to find a package that is appropriate for their needs. However, we do not intend for this web site to lead you to a single “best” assessment. This might be the role of other tools. We provide our recommendations regarding packages, but this remains our own opinions.

The project conducted an in-depth search of freely available packages. Initially, about 135 packages were found. Of these, about 63 were removed from the list. The main reasons for the removals were that the packages were either:

No longer supported or freely available,
Had since been replaced by a more modern approach by the developer (or others),
Not ultimately an assessment (as defined on the Purpose of this site page) or
Not a package – rather bespoke code and as such not supported by the developer for general use.

Most of the removed package fell into the fourth group. It is unfortunate that so many very good and generalizable methods were not developed further into packages for the stock assessment community.

Another observation during this project is that more data-rich packages are being (or have been) modified to be able to undertake data-limited and -moderate assessments. This is a very exciting development.

About 70 assessment packages appear in the table below, which is being regularly updated.

The full methods are described in Dichmont, et al. 2021. Collating stock assessment packages to improve stock assessments. Fisheries Research. Volume 236, April 2021, 105844

Package types

A first step was to classify the packages into different types. There are:

Catch curve: catch curve analysis is a method for estimating the total mortality of a stock (Z) from the slope of the relative numbers present in each age or length class.
Catch only: there is a large group of packages that use mainly or only catch data to assess the status of a stock.
Delay difference: these assessment approaches are mid-way in complexity between surplus production models and full age-structured assessments. Delay difference models represent the population using two stages – recruits and spawners.
Depletion model: few packages fell in this category. This assessment methods model in-season catch dynamics and are different enough to stand on their own.
Integrated assessment: these are assessments that are based on age- or length- structured (or both) population dynamics models and integrates data from various disparate sources into a single framework for parameter estimation.
Length-based: these are data-limited or -moderate approaches that use length data to estimate mortality and length-based reference points.
Mean length: these data-limited assessments use a time series of mean lengths to estimate mortality and length-based reference points.
Size-structured: are data-rich integrated assessments that are similar to age-based integrated assessments, but are aimed at hard to age species such as crustaceans and abalone that use at their basis size-frequency data. Only one package falls into this category despite these assessments being very common.
Surplus production: surplus production models are one of the simplest approaches that nevertheless represent stock dynamics. Here the population is placed in a dynamic pool. It does not keep track of age or length structure.
VPA: Virtual Population Analyses are a class of age-structured models that are based on the assumption that the catch-at-age data are known with negligible error. Many assessments previously conducted using VPA are now being conducted using integrated methods.

The following flow diagram illustrates the ten classes, with a few package examples:

Package specifications

In order to facilitate the use of this web site, we thought it would be useful to have reasonably detailed specifications for each package. These can be found by clicking the Details button for a specific package in the table below. We thank the developers for their input (either as part of a CAPAM census to which we added further information) or by the project team. This dataset has been checked by several developers and other stock assessment experts, but may still need input from anyone who notices discrepancies. The Contact us page is available for this purpose. Another option is to use this Survey where you can add the requested details in a more structured manner (our preferred way).

Package status

The final step was to Comment on the package and provide a Status remark. The categories are:

Supported: A package that is maintained and is based on a statistically and mathematically appropriate analysis method (about 20 packages).
Supported and Recommended: Packages classified within each class of assessment methods as ‘Supported and Recommended’ using the the qualitative criteria described in Table 3 of the above-mentioned paper (noting that not all criteria are applicable to all types of assessments) (~27 packages):
- their support of multiple functional forms for biological processes (e.g., types of selectivity patterns for methods based on age- and size-structured models;
- whether allowance can be made for process and observation error for surplus production models), and the breadth of the types of data that can be used for parameter estimation;
- whether the packages / methods have been subjected to testing using simulation by analysts other than the package developers and are being applied in more than a single case;
- whether documentation is available in the form of peer-reviewed publications;
- whether a technical user manual and documentation are available;
- the size of the current user base that identified potential bugs and provides suggestions for updates, and the responsiveness of the development team to these comments; and
- whether the method applies state-of-the-art statistical estimation methods and can adequately quantify the uncertainty of model outputs. When multiple implementations of the same basic approach are available, we have recommended the version we have found easiest to use. Note that the methods are recommended within assessment types and are all hence not equally preferred. For example, we would also advocate the use of assessment methods that utilize all of the available data over methods that rely only on catch data.
Inactive: The package has not been modified for several years (~3 packages).
Not evaluated: Packages that are new and are not yet actively used or peer-reviewed (~2 packages). This status may change over time.
Still under development: A new package that will replace an existing package, but the process is not yet completed (~1 package).
Superseded: The method is no longer being updated by the developer, but is still being used in some cases (~15 packages).

The below table is a summary of a much larger table containing extensive specification for each package we have included. They range from data-limited to data-rich. If you see any missing packages or any of the data is incorrect please Contact Us.

The complete list with specifications can be downloaded here: Packages spreadsheet

Table filters

Some basic instructions about the table:

You can sort any of the column headings.
You can filter various aspects of the table. The filter options allow you to click more than one option.
You can search for words in the table. This search is quite broad.
Package status may be useful to narrow the list of packages down, noting that the category “Supported and Recommended” are packages we recommend. This filter can be combined with the filer “Type” which will help to further reduce the final list of packages.
The full table of package information is downloadable at the bottom of the page.

There are 2 columns with live links:

The last column (Details) links to a detailed description and allied specifications of the chosen package.
The second column (Web link) links to the package.

31 March 2021

Package name:

Model type:

Package status:

Expertise required:

Programming language:

GUI:

Sex structure:

Tagging:

Spatial resolution:

Stock structure:

Catch:

Abundance index:

Short-lived species:

Package name	Web link	Model type	Single.multiple.spp	Package status	Comment	FullName	Description	Developers	Organization	Training	Expertise	Citation	Peer.reviewed	Programming language	GUI	R.package	Temporal.resolution	Sex structure	Multi.species.technical.interactions	Growth.platoons	Tagging	Area.structure	Spatial resolution	Stock structure	Logistic	Double.normal	Spline.1	Parameter.for.each.age.nonparametric	Other.21	Seasonal	Maximum.likelihood	Bayesian	Random.effects.state.space	Catch	Retained	Discarded	Length.composition	Age.composition	Weight.composition	Conditional.age.at.length	Mean.weight.at.age	Mean.length.at.age	Sex	Morph.composition	Aging.error	Custom.composition.bin.size	Stock.origin	Other.3	Abundance index	Index.of.effort	Fishing.mortality	Absolute.estimate.of.abundance	Environmental.index..of.a.parameter.random.effect.	Other.4	Mark.recapture..to.estimate.movement.and.or.F.or.B.	Growth.increment	Close.kin	Gene.tagging..Maybe.some.special.requirements.	Other.5	MSY	FMSY	SPRx	Spawning.biomass.per.recruit	Biomass.target	Dynamic.B0	Random.deviates	Normal.approximation	Parameter.uncertainty	Fishery.impact.plots	Other.29	Short-lived species
2BOX	https://nmfs-fish-tools.github.io/2BOX/	VPA	Single species	Supported	Not ideal for Australian fisheries, because the package needs catch-at-age data for every year. Statistical Catch-At-Age models such as SS an CASAL can utilise datasets with missing years of catch-at-age data	Dual Zone Virtual Population Analysis	"The Dual Zone VPA Model (VPA-2BOX) is a flexible software tool for analyzing the abundance and mortality of exploited animal populations that is based on the ADAPT framework developed by Parrack (1986) and Gavaris (1988). The primary difference between this package and other versions of ADAPT is the capability of analyzing two different stocks simultaneously, making possible routine quantitative analyses of the effect of sex-specific growth or stock intermixing. In addition, a wide variety of options are provided with respect to the types of data that may be used and the way the parameters are estimated. "	Clay Porch	NOAA/NMFS/SEFSC	No	Medium	Porch, C.E, S.C. Turner and J.E. Powers. 2001. Virtual population analyses of Atlantic bluefin tuna with alternative models of transatlantic migration: 1970-1997. ICCAT Collective Volume of Scientific Papers 52 (3): 1022-45. https://www.iccat.int/Documents/CVSP/CV052_2001/n_3/CV052031022.pdf	Yes	FORTRAN	Yes	No	Annual	Yes	No	No	Yes	Yes	Two zones	Yes	No	No	No	No	Yes - see reference citation	No	Yes	Yes	Yes	Yes	No	No	No	Yes	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes; annual time step
AMAK	https://github.com/nmfs-toolbox/amak	Integrated assessment	Single species	Supported	Supported, although SAM, SS or CASAL may be alternatives as they have more features. Designed for Alaskan fisheries.	Assessment Method For Alaska	"The Assessment Method for Alaska (AMAK) was developed in the NMFS Alaska Fisheries Science Center by Dr. James Ianelli using AD Model Builder. This is an age based estimation model that supports multiple fisheries and sparse data availability. The AMAK model is an explicit age-structured model that uses a forward projection approach and maximum likelihood estimation to solve for model parameters."	Jim Ianelli	NMFS Alaska Fisheries Science Center	No	High	Anon, 2015 https://github.com/NMFS-toolbox/AMAK/blob/master/docs/AMAK%20Documentation.pdf	Yes	ADMB	Yes	No	Annual	No	No	No	No	No	Stock	No	Yes	Yes	No	No	No	No	Yes	No	No	Yes	No	No	No	Yes	Yes	No	Yes	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Yes	Yes	Yes	No	Yes; annual time step
ASAP	https://nmfs-fish-tools.github.io/ASAP/	Integrated assessment	Single species	Supported	Supported, although SAM, SS or CASAL may be alternatives as they have more features. Used mainly within the USA.	ASAP	"The Age Structured Assessment Program (ASAP) is an age-structured model that uses forward computations assuming separability of fishing mortality into year and age components to estimate population sizes given observed catches, catch-at-age, and indices of abundance. Discards can be treated explicitly. The separability assumption is relaxed by allowing for fleet-specific computations and by allowing the selectivity at age to change smoothly over time or in blocks of years. The software can also allow the catchability associated with each abundance index to vary smoothly with time. The problem’s dimensions (number of ages, years, fleets and abundance indices) are defined at input and limited by hardware only. The input is arranged assuming data is available for most years, but missing years are allowed. The model currently does not allow use of length data nor indices of survival rates. Diagnostics include index fits, residuals in catch and catch-at-age, and effective sample size calculations. Weights are input for different components of the objective function and allow for relatively simple age-structured production model type models up to fully parameterized models. The calculation engine was built using AD Model Builder by Drs. Christopher M. Legault (currently at the NMFS Northeast Fisheries Science Center) and Victor R. Restrepo (currently at the NMFS Southeast Fisheries Science Center). ASAP has been used as an assessment tool for red grouper (SEFSC), yellowtail flounder (NEFSC), Pacific sardine (SWFSC), Pacific mackerel (SWFSC), Greenland halibut (ICES), Norther Gulf of St. Lawrence cod (DFO), Gulf of Maine cod (NEFSC), Florida lobster (FFWCC), and fluke (NEFSC)."	Chris Legault and Victor Restrepo	NMFS	Yes	High	Legault CM, Restrepo VR. 1998. A flexible forward age-structured assessment program. ICCAT Working DocumentSCRS/98/58. 15p. https://www.researchgate.net/publication/266334996_A_Flexible_Forward_Age-Structured_Assessment_Program	Yes	ADMB	Yes	Yes	Annual	No	No	No	No	No	Stock	No	Yes	No	No	Yes	Double logistic	No	Yes	Yes	No	Yes	Yes	Yes	No	Yes	Yes	No	No	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	Yes	No	No	No	Yes	No	No	No	Yes; annual time step
ASPIC	https://nmfs-fish-tools.github.io/ASPIC/	Surplus production	Single species	Superseded	Superseded, we recommend JABBA and spm	A Stock Production Model Incorporating Covariates (ASPIC)	"A non-equilibrium implementation of the well-known surplus production model of Schaefer (1954, 1957). ASPIC also fits the generalized stock production model of Pella and Tomlinson (1969) using the alternative parameterization of Fletcher (1978).The analytic engine for the ASPIC model, written by Dr. Michael Prager of the Southeast Fisheries Science Center, NMFS, incorporates several extensions to the classical stock-production models. ASPIC can fit data from up to 10 data series of fishery-dependent or fishery-independent indices, and uses bootstrapping to construct approximate nonparametric confidence intervals and to correct for bias."	Michael Prager	NOAA/NMFS	Unknown	Medium	Prager, M. H. 1992. ASPIC: A Surplus-Production Model Incorporating Covariates. Coll. Vol. Sci. Pap., Int. Comm. Conserv. Atl. Tunas (ICCAT) 28: 218–229.	Yes	FORTRAN	Yes	No	Annual	No	No	No	No	No	Stock	No	No	No	No	No	No	No	Yes	No	No	Yes	Yes	Ideally added in	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	No	No	No	Yes	Yes	No	Yes	No	No	No	No	No	No	No	No
aspm	https://github.com/haddonm/datalowSA	Surplus production	Single species	Supported	Unlike most surplus production models, this version based on an age-structured surplus production. However, SS or CASAL may be alternatives as they have more features.	Age-structured production model	"The age-structured production model (ASPM or aspm) is literally a surplus production model which is based up and age-structured model of production rather than an accumulated biomass model (e.g. spm)"	Malcolm Haddon	Malcolm Haddon Consulting	No	Medium	https://github.com/haddonm/datalowSADocs/blob/master/Vignette_for_aspm.pdf	No	R	No	Yes	Annual	No	No	No	No	No	Stock	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	Yes	No	No	No	Yes	Yes	No	No
BAM	Not available online	Integrated assessment	Single species	Supported	Supported, although SAM, SS or CASAL may be alternatives as they have more features. Focused on fisheries in the USA south-east	Beaufort Assessment Model	"The Beaufort Assessment Model (BAM) applies a statistical catch-age formulation implemented with the AD Model Builder software (Fournier et al. 2012) and fitted to multiple data sources simultaneously in a single integrated analysis (Maunder and Punt 2013). In essence, the model simulates a population forward in time while including fishing and biological processes (Quinn and Deriso 1999; Shertzer et al. 2014). Quantities to be estimated are systematically varied until characteristics of the simulated population match available data on the real population. Its basic structure is similar to that of other packages such as Stock Synthesis (Methot 2012; Methot and Wetzel 2013) and Age Structured Assessment Program (Legault 2008)"	Erik Williams and Kyle Shertzer	NOAA/NMFS/SEFSC/Beaufort	No	Medium	Williams, E.H., and K.W. Shertzer. 2015. Technical documentation of the Beaufort Assessment Model (BAM). U.S. Department of Commerce, NOAA Technical Memorandum NMFS-SEFSC-671. 43 p. http://dx.doi.org/10.7289/V57M05W6	Yes	ADMB	No	No	Annual	Yes	No	No	No	No	Stock	No	No	No	No	No	No	No	Yes	No	No	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	No	No	Yes	No	Yes	Yes	No	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	No	Yes	Yes	No	Yes; annual time step
BSM	https://github.com/cfree14/datalimited2	Surplus production	Single species	Superseded	Superseded, we recommend JABBA and spm	Bayesian state-space surplus production model	"Estimates biomass, fishing mortality, and stock status (i.e., B/BMSY, F/FMSY) time series and biological/management quantities (i.e., r, K, MSY, BMSY, FMSY) from a time series of catch and a resilience estimate using the Bayesian surplus production model from Froese et al. 2017."	Christopher M. Free	Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA	No	Medium	Free CM (2018) datalimited2: More stock assessment methods for data-limited fisheries. R package version 0.1.0. https://github.com/cfree14/datalimited2; Froese R, Demirel N, Coro G, Kleisner KM, Winker H (2017) Estimating fisheries reference points from catch and resilience. Fish & Fisheries 18(3): 506-526.	Yes	JAG	No	Yes	Annual	No	No	No	No	No	Stock	No	No	No	No	No	No	No	No	Yes	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	Yes	Yes	No	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	Yes	No	No	Yes	No	No	No
CASAL	https://www.niwa.co.nz/fisheries/tools-resources/casal	Integrated assessment	Single species	Supported and Recommended	CASAL2 is still under development. SS is an alternative that also has many features. Used globally.	CASAL V2.3-2012/03/21	"CASAL (C++ Algorithmic Stock Assessment Laboratory) is an advanced software package developed by NIWA for fish stock assessment. The software implements a generalised age- or length-structured fish stock assessment model that allows a great deal of choice in specifying the population dynamics, parameter estimation, and model outputs. CASAL is designed for flexibility. It can implement either an age- or size-structured model, optionally also structuring the population by sex, maturity, and/or growth-path. It can be used for a single stock for a single fishery, or for multiple stocks, areas, and/or fishing methods. The user can choose the sequence of events in a model year. The data used can be from many different sources of information, for example catch-at-age or catch-at-size data from commercial fishing, survey and other biomass indices, survey catch-at-age or catch-at-size data, and tag-release and tag-recapture data. Estimation can be by either maximum likelihood or Bayes."	B. Bull, C. Francis, A. Dunn, A. McKenzie, D. Gilbert, M. Smith, R. Bian, D. Fu	NIWA	No	High	Bull, B.; Francis, R.I.C.C.; Dunn, A.; McKenzie, A.; Gilbert, D.J. ; Smith, M.H.; Bian, R.; Fu, D. (2012) CASAL (C++ algorithmic stock assessment laboratory): CASAL User Manual v2.30-2012/03/21. NIWA Technical Report 135. 280 p. https://niwa.co.nz/sites/niwa.co.nz/files/sites/default/files/casalv230-2012-03-21.pdf	No	C++	No	Yes	Annual cycle of time steps defined within a calendar year or as an abstract sequence of events.	Yes	No	Yes	Yes	Yes	Stock	Yes	Yes	Yes	No	Yes	Many other	Yes	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes	Maturation and migration data	Yes	No	Yes	Yes	No	Index to shift A50 in fishing selectivity	Yes	Yes	No	No	No	Yes	Yes	No	No	No	No	Yes	Yes	No	No	No	Yes; seasonal time step
Catch curve (datalowSA)	https://github.com/haddonm/datalowSA	Catch curve	Single species	Supported	We recommend "Catch curve with selectivity" or "Catch curve (TropFishR)" because they better account for selectivity	Classical catch curves (F)	Catch curve analysis is a method for estimating the total mortality of a stock (Z): the rate at which individuals die can be estimated from the slope of the relative numbers present in each age class. It can be used whenever there is one or more years of catch-at-age data (or at-length data, if it can be converted to age). The data can be fishery dependent or independent so long as data are representative of the population's relative age/length structure. Given an estimate of natural mortality (M) from another source (e.g., from literature, from a marine protected area, or from tagging studies), fishing mortality (F) can be estimated as Z-M.	Malcolm Haddon	Formerly CSIRO, now private	Yes	Low	Haddon, M. Burch, P., Dowling, N., and Little, R. 2019. Reducing the Number of Undefined Species in Future Status of Australian Fish Stocks Reports: Phase Two - training in the assessment of data-poor stocks. FRDC Final Report 2017/102. CSIRO Oceans and Atmosphere and Fisheries Research Development Corporation. Hobart 125 p. https://www.frdc.com.au/Archived-Reports/FRDC%20Projects/2017-102-DLD.pdf	No	R	No	Yes	User specified	No; but user should undertake sex-specific analysis if sex-specific life history available	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	No	No	No	No	No	Yes - in lieu of age composition data	Yes	No	Yes	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes; but not recommended with annual time steps
Catch curve (fishe)	http://fishe.edf.org/	Catch curve	Single species	Supported	We recommend "Catch curve with selectivity" or "Catch curve (TropFishR)" because they better account for selectivity	Catch curve	Catch curve analysis is a method for estimating the total mortality of a stock (Z): the rate at which individuals die can be estimated from the slope of the relative numbers present in each age class. It can be used whenever there is one or more years of catch-at-age data (or at-length data, if it can be converted to age). The data can be fishery dependent or independent so long as data are representative of the population's relative age/length structure. Given an estimate of natural mortality (M) from another source (e.g., from literature, from a marine protected area, or from tagging studies), fishing mortality (F) can be estimated as Z-M.	Rod Fujita	Environmental Defense Fund	Yes	Low	Fujita, R., Karr, K., Battista, W., and Rader, D.N. 2013. A framework for developing scientific management guidance for data-limited fisheries. Proceedings of the 66th Gulf and Caribbean Fisheries Institute, 83-9	Yes	Visual Basic (Excel)	No	No	User specified	No; but user should undertake sex-specific analysis if sex-specific life history available	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	No	No	No	No	No	Yes - in lieu of age composition data	Yes	No	Yes	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes; but not recommended with annual time steps
Catch curve (TropFish)	https://cran.r-project.org/web/packages/TropFishR/index.html	Catch curve	Single species	Supported and Recommended	Recommended, with guidelines based on our test data set. The package is similar to "Catch curve with selectivity".	Catch curve, or cumulative catch curve	From the package: "A compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. Includes methods and examples included in the FAO Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish stock assessment". The function 'catchCurve' This function applies the (length-converted) linearised catch curve to age composition and length frequency data, respectively. It allows to estimate the instantaneous total mortality rate (Z). Optionally, the gear selectivity can be estimated and the cumulative catch curve can be applied." Other: Catch curve analysis is a method for estimating the total mortality of a stock (Z): the rate at which individuals die can be estimated from the slope of the relative numbers present in each age class. It can be used whenever there is one or more years of catch-at-age data (or at-length data, if it can be converted to age). The data can be fishery dependent or independent so long as data are representative of the population's relative age/length structure. Given an estimate of natural mortality (M) from another source (e.g., from literature, from a marine protected area, or from tagging studies), fishing mortality (F) can be estimated as Z-M.	Tobias K. Mildenberger, Marc H. Taylor, Matthias Wolff	Technical University of Denmark; DTU National Institute of Aquatic Resources	Yes	Low	Sparre, P. and Venema, S.C. (1998). "Introduction to tropical fish stock assessment" FAO Manual. FAO, Rome, Italy. http://www.fao.org/documents/card/en/c/9bb12a06-2f05-5dcb-a6ca-2d6dd3080f65/	Yes	R	No	Yes	User specified	No; but user should undertake sex-specific analysis if sex-specific life history available	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	No	No	Yes, if cumulative catch curve applied	Yes, if cumulative catch curve applied	Yes, if cumulative catch curve applied	Yes - in lieu of age composition data	Yes	No	Yes	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	Yes; but not recommended with annual time steps
Catch curve with selectivity	https://github.com/haddonm/datalowSA	Catch curve	Single species	Supported and Recommended	Recommended, with guidelines based on our test data set. The package is similar to "Catch curve (Tropfish)".	Catch curves with selectivity (F)	This catch curve uses a simple age-structured model to include the estimation of selectivity from the age data. It provides an estimate of fully selected fishing mortality rather than an average fishing mortality applied to all included age classes. Catch curve analysis is a method for estimating the total mortality of a stock (Z): the rate at which individuals die can be estimated from the slope of the relative numbers present in each age class. It can be used whenever there is one or more years of catch-at-age data (or at-length data, if it can be converted to age). The data can be fishery dependent or independent so long as data are representative of the population’s relative age/length structure. Given an estimate of natural mortality (M) from another source (e.g., from literature, from a marine protected area, or from tagging studies), fishing mortality (F) can be estimated as Z-M.	Malcolm Haddon	Formerly CSIRO, now private	Yes	Low	Haddon, M. Burch, P., Dowling, N., and Little, R. 2019. Reducing the Number of Undefined Species in Future Status of Australian Fish Stocks Reports: Phase Two - training in the assessment of data-poor stocks. FRDC Final Report 2017/102. CSIRO Oceans and Atmosphere and Fisheries Research Development Corporation. Hobart 125 p. https://www.frdc.com.au/Archived-Reports/FRDC%20Projects/2017-102-DLD.pdf	No	R	No	Yes	User specified	No; but user should undertake sex-specific analysis if sex-specific life history available	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	No	No	No	No	No	Yes - in lieu of age composition data	Yes	No	Yes	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes; but not recommended with annual time steps
CatchMSY (datalimited)	https://github.com/datalimited/datalimited	Catch only	Single species	Superseded	Superseded by CMSY approaches	Catch MSY	From the package: "The package implements the methods used in Rosenberg et al. (2014) including Catch-MSY based on Martell and Froese (2013)". Other: The Catch-MSY and the updated version CMSY (Froese et al. 2017) are Monte-Carlo based methods that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices.	Sean Anderson	Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia	Unknown	Low	Rosenberg, A. A., M. J. Fogarty, A. B. Cooper, M. Dickey-Collas, E. A. Fulton, N. L. Gutiérrez, K. J. W. Hyde, K. M. Kleisner, C. Longo, C. V. Minte-Vera, C. Minto, I. Mosqueira, G. C. Osio, D. Ovando, E. R. Selig, J. T. Thorson, and Y. Ye. 2014. Developing new approaches to global stock status assessment and fishery production potential of the seas. FAO Fisheries and Aquaculture Circular, Rome, Italy. http://www.fao.org/3/i3491e/i3491e.pdf	Yes	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
CatchMSY (fishmethods)	https://CRAN.R-project.org/package=fishmethods	Catch only	Single species	Superseded	Superseded by CMSY approaches	Catch MSY	From the package: "The method of Martell and Froese (2012) is used to produce estimates of MSY where only catch and information on resilience is known." Other: The Catch-MSY and the updated version CMSY (Froese et al. 2017) are Monte-Carlo based methods that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices. (ref: Froese, R., Demirel, N., Coro, G., Kleisner, K. and Winkler, H. 2017. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506-526).	Gary A. Nelson	Commonwealth of Massachusetts Division of Marine Fisheries	Unknown	Low	Martell, S. and Froese, R. 2012. A simple method for estimating MSY from catch and resilience. Fish and Fisheries 14(4): 504-514. https://doi.org/10.1111/j.1467-2979.2012.00485.x	Unknown	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
CatchMSY (Martell)	https://github.com/smartell/CatchMSY	Catch only	Single species	Superseded	Superseded by CMSY approaches	Catch MSY	From the package: "The catchMSY package is intended to be used to determine MSY-based reference points. The catchMSY package is based on the initial work for Martell and Froese (2012), "A simple method for estimating MSY from catch and resilience"." Other: The Catch-MSY and the updated version CMSY (Froese et al. 2017) are Monte-Carlo based methods that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices. (ref: Froese, R., Demirel, N., Coro, G., Kleisner, K. and Winkler, H. 2017. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506-526).	Steve Martell and Merrill Rudd	Sea State Inc., University of Washington	Unknown	Low	Martell, S. and Froese, R. 2012. A simple method for estimating MSY from catch and resilience. Fish and Fisheries 14(4): 504-514. https://doi.org/10.1111/j.1467-2979.2012.00485.x	Yes	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
CatchMSY (MSEtool)	https://cran.r-project.org/web/packages/MSEtool/vignettes/MSEtool.html	Catch only	Single species	Superseded	Superseded by CMSY approaches	Catch MSY	From the package: "MSEtool is an extension of the DLMtool package for implementing closed-loop simulation testing of management procedures, a process often referred to as management strategy evaluation (MSE). The components of the operating model for conducting MSEs for data-rich stocks is wholly contained within DLMtool, but MSEtool provides the tools for implementing additional features to facilitate such MSEs. Whereas DLMtool is primarily designed for testing data-limited management procedures, MSEtool provides an object-oriented framework for creating data-rich management procedures (MPs), which typically involve combining an assessment model with a harvest control rule to provide a fishery management recommendation. Management procedures created by MSEtool are designed to be used with the operating model of DLMtool through the runMSE function." Other: The Catch-MSY and the updated version CMSY (Froese et al. 2017) are Monte-Carlo based methods that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices. (ref: Froese, R., Demirel, N., Coro, G., Kleisner, K. and Winkler, H. 2017. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506-526).	Quang Huynh, Tom Carruthers, Adrian Hordyk	University of British Columbia	Yes	Low	Carruthers, T.R. and Hordyk, A.R. (2018). The Data- Limited Methods Toolkit (DLMtool): An R package for informing management of data- limited populations. Methods in Ecology and Evolution 9: 2388–2395. https://doi.org/10.1111/2041-210X.13081	Yes	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
CatDyn	https://rdrr.io/cran/CatDyn/man/CatDyn-package.html	Depletion model	Single species	Supported and Recommended	Designed for short-lived species with within-year recruitment. Not yet widely used. Developed for Falkland Island fisheries.	Fishery Stock Assessment by Catch Dynamics Models	"Based on fishery Catch Dynamics instead of fish Population Dynamics (hence CatDyn) and using high-frequency or medium-frequency catch in biomass or numbers, fishing nominal effort, and mean fish body weight by time step, from one or two fishing fleets, estimate stock abundance, natural mortality rate, and fishing operational parameters."	Ruben H. Roa-Ureta	AZTI Tecnalia, Marine Research Unit	??	Medium	Ruben H. Roa-Ureta (2012). Modeling In-Season Pulses of Recruitment and Hyperstability-Hyperdepletion in the Loligo gahi Fishery of the Falkland Islands with Generalized Depletion Models ICES Journal of Marine Science, 68(8), 1403-1415. URL https://doi.org/10.1093/icesjms/fss110.	Yes	R	No	Yes	Day/week/month	No	No	No	No	No	Stock	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes; user specified time step
CC-SRA	https://github.com/James-Thorson/CCSRA	Length-based	Single species	Superseded	SS-CL can be set up similar to CC-SRA. We recommend LBSPR (barefootecologist), LIME or SS-CL. SS-CL is the most flexible.	Catch Curve - Stock Reduction Analysis	CC-SRA combines a catch curve analysis (see this assessment method for more details) and a stock-reduction analysis (see this assessment method for more details) to estimate fishing mortality and sustainable catch. The use of age frequency data theoretically allows one to bypass the requirement for a stock status estimate as an input or prior, which is a typical requirement of many catch-based assessment methods.	James Thorson	NOAA	Yes	Low	Thorson, J.T., and Cope, J.M. 2015. Catch curve stock-reduction analysis: an alternative solution to the catch equation. Fish. Res. 171: 33–41. http://www.sciencedirect.com/science/article/pii/S0165783614001507	Yes	R	No	Yes	User specified	Method intends that the user is following the female spawning biomass	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	Yes - optional	No	Yes	Yes	Optional - but ideally should be considering total removals	Yes - in lieu of age composition data	Yes	No	No	Yes	Yes	Yes	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	Fishing mortality; SB/SBo	Yes; user specified time step
CEDA	https://mrag.co.uk/resources/ceda-version-30	Surplus production	Single species	Not evaluated	We recommend use of JABBA and spm.	The Catch Effort Data Analysis package	"A PC-based software package for analysing catch, effort and abundance index data. Version 3.0 allows calculation of estimates of current and unexploited stock sizes, catchability and associated population dynamics parameters. Both depletion and several types of stock production (biomass dynamic) models can be fitted, using one of three different assumptions about the distribution of residuals. Both point estimates and bootstrap confidence intervals for the estimated parameters can be calculated. CEDA also includes the facility to do projections of stock size into the future under various scenarios of catch or effort levels, so that different management strategies can be investigated. Output is presented both graphically and textually, and can be printed or saved to disk for further use. As with previous versions of CEDA, the package includes a comprehensive context-sensitive Help system and a detailed example analysis. The download file also includes the graphics server programme required to plot the data."	MRAG	MRAG	No	Medium	Kirkwood,G.P., Auklandm R. and Zara, S. J. (2001) Catch Effort Data Aanalysis(CEDA), Version 3.0. MRAG Ltd, London, UK.	No	Unknown	Yes	No	Annual	No	No	No	No	No	Flexible	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No	Yes	Yes	No	No	Yes	No	No	No
cmsy (Froese)	https://github.com/SISTA16/cmsy	Catch only	Single species	Supported	Look at our "Supported and Recommended" Catch only versions (cMSY, COM-SIR, DB-SRA, OCOM, SS-COM, SSS, zBRT), as they are updated more regularly, are used more extensively within Australia or are more recent.	Catch MSY	From the package: "The official CMSY method for data-limited stock assessment, per Froese et al. (2016)". Other: This is a Monte-Carlo based method that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices. (ref: Froese, R., Demirel, N., Coro, G., Kleisner, K. and Winkler, H. 2016. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506-526).	Developed by Rainer Froese, Gianpaolo Coro and Henning Winker in 2016, version of November 2019 PDF creation added by Gordon Tsui and Gianpaolo Coro. Correction for effort creep added by RF. Multivariate normal r-k priors added to CMSY by HW, RF and GP in October 2019. Multivariate normal plus observation error on catch added to BSM by HW in November 2019. Retrospective analysis added by GP in November 2019	GEOMAR Helmholtz Centre for Ocean Research Kiel, Duesternbrooker Weg 20, 24105 Kiel, Germany; Istituto di Scienza e Tecnologie dell’ Informazione “A. Faedo”, Consiglio Nazionale delle Ricerche (CNR), via Moruzzi 1, 56124 Pisa, Italy	Unknown	Low	Froese, R., Demirel, N., Coro, G., Kleisner, K. M., & Winker, H. (2016). Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18(3): 506-526. http://onlinelibrary.wiley.com/doi/10.1111/faf.12190/full	Yes	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	Can handle multiple stocks sequentially	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
cMSY (datalowSA)	https://github.com/haddonm/datalowSA	Catch only	Single species	Supported and Recommended	Recommended, with guidelines based on our test data set. This package is an alternative to SSS.	Catch MSY	From the package: "The Catch-MSY method described here can be regarded as a model-assisted data-poor method. It uses a form of stock reduction analysis where the productivity of a given stock (its unfished biomass and its reproductive rate) is characterised within the parameters of s simple mathematical model, and how that modelled stock responds to the history of known catch (a stock reduction analysis) forms the basis of the alternative methods used to characterise productivity in management useable terms". Other: The Catch-MSY and the updated version CMSY (Froese et al. 2017) are Monte-Carlo based methods that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices. (ref: Froese, R., Demirel, N., Coro, G., Kleisner, K. and Winkler, H. 2017. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506-526).	Malcolm Haddon	Formerly CSIRO, now private	Yes	Low	Haddon, M. Burch, P., Dowling, N., and Little, R. 2019. Reducing the Number of Un-defined Species in Future Status of Australian Fish Stocks Reports: Phase Two - training in the assessment of data-poor stocks. FRDC Final Report 2017/102. CSIRO Oceans and Atmosphere and Fisheries Research Development Corpora-tion. Hobart 125 p.	No	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
cmsy2 (datalimited2)	https://github.com/cfree14/datalimited2	Catch only	Single species	Supported	Look at our "Supported and Recommended" Catch only versions (cMSY, COM-SIR, DB-SRA, OCOM, SS-COM, SSS, zBRT), as they are updated more regularly, are used more extensively within Australia or are more recent.	Catch MSY	From the package: "This package implements (among many methods) cMSY from Froese et al. (2017)". Other: The Catch-MSY and the updated version CMSY (Froese et al. 2017) are Monte-Carlo based methods that estimates maximum sustainable yield (MSY) from a time series of catch data, resilience of the species being assessed, and expert judgment regarding stock size during the first and terminal year of the time series. Upon completion of the assessment, estimates are provided for MSY, Fmsy, Bmsy, relative stock size (B/Bmsy), and exploitation (F/Fmsy). The method partially relies on an equilibrium-based Schaefer production model and requires priors on depletion and resilience, so the lower margin of error in terms of MSY estimates should be used in accordance with precautionary management practices. (ref: Froese, R., Demirel, N., Coro, G., Kleisner, K. and Winkler, H. 2016. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18: 506-526).	Christopher M. Free	Sustainable Fisheries Group, Bren School, UC Santa Barbara, CA, USA	Unknown	Low	Free, C.M., Rudd, M.B., Kleisner, K.M., Thorson, J.T., Longo, C., Minto, C., and Jensen, O.P. (in prep). Multispecies catch-only models for assessing data-limited fisheries.	No	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No - because you provide this via r	No	No	No	No	No	No	Yes	Yes	No	No
Coleraine_Awatea	https://github.com/pbs-software/pbs-awatea	Integrated assessment	Single species	Superseded	SAM, SS or CASAL may be alternatives as they have more features.	Coleraine and Awatea	"A general model is developed for salmon run reconstruction based on catch, escapement, and age composition data. The model is based on “groups” of salmon, each of which share the same characteristics but can differ from other groups in run timing, abundance, gear selectivity, and migration routes."	Hilborn, Maunder, Parma, Ernst, Payne, Starr, Hicks, and many others	UW mostly	No	High	Coleraine: a generalized age-structured stock assessment model. User's Manual Version 2.0. SAFS-UW-0116. Revised May 2003. https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/4524/0116.pdf?sequence=1&isAllowed=y	No	ADMB	No	No	Annual	Yes	No	No	No	No	Single area	No	Yes	Yes	No	No	No	No	Yes	Yes	No	Yes	No	No	Yes	Yes	No	No	No	No	Yes	No	Yes	Yes	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	Yes	No	No	No	Yes	No	Yes	Yes	No	No	CSP	Yes; annual time step
COM-SIR	https://github.com/datalimited/datalimited	Catch only	Single species	Supported and Recommended	Bayesian alternative method to, for example, cMSY (datalowSA). SSS has similar features	Catch Only Model - Sampling Importance Resampling Model	From the package: "Catch-only model with sampling-importance-resampling based on the method described in Vasconcellos and Cochrane (2005)." Other: COM-SIR is a catch estimation method (COM) that combines a harvest dynamics model and a biomass dynamics (Schaffer) model in order to estimate maximum sustainable yield (MSY). Catch estimation is made using the Bayesian method of sampling importance resampling (SIR). The model used can be sensitive to changes in harvest dynamics over time, necessitating a different harvest dynamic formulation if this occurs (e.g., due to management). Another approach is to fit the model only to the time series without implemented management measures.	Sean Anderson	Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia	Unknown	Low	Vasconcellos, M., and K. Cochrane. 2005. Overview of World Status of Data-Limited Fisheries: Inferences from Landings Statistics. Pages 1-20 in G. H. Kruse, V. F. Gallucci, D. E. Hay, R. I. Perry, R. M. Peterman, T. C. Shirley, P. D. Spencer, B. Wilson, and D. Woodby, editors. Fisheries Assessment and Management in Data-Limited Situations. Alaska Sea Grant, University of Alaska Fairbanks.	Yes	R	No	Yes	User specified	Method intends that the user is following the female spawning biomass	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes (in sampling-importance resampling)	Yes	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No
CSA	https://nmfs-fish-tools.github.io/CSA/	Delay difference	Single species	Superseded	No longer updated. Consider using CASAL instead	Collie-Sissenwine Analysis	"The Collie-Sissenwine Analysis (CSA) model (sometimes called catch-survey analysis or the DeLury model) is a relatively simple two-stage stock assessment model that estimates abundance, fishing mortality and recruitment using total catch numbers and survey data (Collie and Sissenwine 1983; Conser 1995). The “recruit” stage group consists of animals that recruit at, just before, or during the current time step. The rest of the population comprises the “post-recruit” stage group. The two stages may correspond to age groups, length groups or any other natural division (e.g. genders in hermaphroditic species). Typically, both groups are assumed fully available to the fishery but this assumption can be relaxed in practice if fishing mortality rates are viewed as rates for fully recruited animals."	Seaver, A	NOAA/NMFS	No	Medium	Collie, J. S. and M. P. Sissenwine, 1983. Estimating population size from relative abundance data measured with error. Can. J. Fish. Aquat. Sci., 40: 1871-1879. https://doi.org/10.1139/f83-217	Yes	ADMB	Yes	No	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	No	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	Yes; annual time step
DB-SRA (DLMTool)	https://CRAN.R-project.org/package=DLMtool	Catch only	Single species	Supported and Recommended	Requires a full catch series. SSS could be an alternative given it has greater flexibility by being based on an age-structured model. However, DB-SRA has more flexibility in the production function. Several resources are available on https://www.datalimitedtoolkit.org/, including a Shiny demo case.	Depletion-Based Stock Reduction Analysis	From the package: "Depletion-Based Stock Reduction Analysis (DB-SRA) is a method designed for determining a catch limit and management reference points for data-limited fisheries where catches are known from the beginning of exploitation. User prescribed BMSY/B0, M, FMSY/M are used to find B0 and therefore the a catch limit by back-constructing the stock to match a user specified level of stock depletion. The DB-SRA method of this package isn't exactly the same as the original method of Dick and MacCall (2011) because it has to work for simulated depletions above BMSY/B0 and even on occasion over B0. It also doesn't have the modification for flatfish life histories that has previously been applied by Dick and MacCall (2011)." Other: A combination of stochastic Stock Reduction Analysis and Depletion-Corrected Average Catch, Depletion-Based Stock Reduction Analysis (DB-SRA) uses an extended time series of catch, the species’ approximate natural mortality rate, and the age at maturity to help estimate sustainable yields and management reference points. Probability distributions are provided for key management reference points dealing with yield and biomass; uncertainty is accounted for through Monte Carlo simulation. (ref: Dick, E. J., & MacCall, A. D. (2011). Depletion-based stock reduction analysis: a catch-based method for determining sustainable yields for data-poor fish stocks. Fisheries Research, 110, 331-341. http://dx.doi.org/10.1016/j. fishres.2011.05.007)	Tom Carruthers	University of British Columbia	Yes	Medium	Carruthers, T.R. and Hordyk, A.R. 2018. The Data- Limited Methods Toolkit (DLMtool): An R package for informing management of data- limited populations. Methods in Ecology and Evolution 9: 2388–2395. https://doi.org/10.1111/2041-210X.13081	Yes	R	No	Yes	User specified	Method intends that the user is following the female spawning biomass	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No (but requires FMSY/M)	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	B/Bo, BMSY/Bo, FMSY/M	No
DB-SRA (fishmethods)	https://cran.r-project.org/web/packages/fishmethods/index.html	Catch only	Single species	Supported and Recommended	Requires a full catch series. SSS could be an alternative given it has greater flexibility by being based on an age-structured model. However, DB-SRA has more flexibility in the production function	Depletion-Based Stock Reduction Analysis	From the package: "This function estimates MSY from catch following Dick and MacCall (2011)." Other: A combination of stochastic Stock Reduction Analysis and Depletion-Corrected Average Catch, Depletion-Based Stock Reduction Analysis (DB-SRA) uses an extended time series of catch, the species’ approximate natural mortality rate, and the age at maturity to help estimate sustainable yields and management reference points. Probability distributions are provided for key management reference points dealing with yield and biomass; uncertainty is accounted for through Monte Carlo simulation.	Gary A. Nelson	Commonwealth of Massachusetts Division of Marine Fisheries	Unknown	Medium	Dick, E. J., and MacCall, A. D. 2011. Depletion-based stock reduction analysis: a catch-based method for determining sustainable yields for data-poor fish stocks. Fisheries Research 110: 331-341. http://dx.doi.org/10.1016/j. fishres.2011.05.007	Yes	R	No	Yes	User specified	Method intends that the user is following the female spawning biomass	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No (but requires FMSY/M)	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No
DD_TMB	https://rdrr.io/cran/MSEtool/man/DD_TMB.html	Delay difference	Single species	Supported and Recommended	Not yet widely used. Implemented in TMB.	Delay - Difference Stock Assessment in TMB	"A simple delay-difference assessment model using a time-series of catches and a relative abundance index and coded in TMB. The model is conditioned on effort and estimates predicted catch. In the state-space version, recruitment deviations from the stock-recruit relationship are estimated."	T. Carruthers & Z. Siders. Zach Siders coded the TMB function.	University of British Columbia	No	Medium	Carruthers, T, Walters, C.J,, and McAllister, M.K. 2012. Evaluating methods that classify fisheries stock status using only fisheries catch data. Fisheries Research 119-120:66-79. https://doi.org/10.1016/j.fishres.2011.12.011	Yes	TMB	No	Yes	Annual	No	No	No	No	Yes	Single area	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	No	Yes	No	No	Yes; annual time step
Depletion analysis	https://cran.r-project.org/web/packages/fishmethods/index.html	Depletion model	Single species	Supported	We recommend CatDyn as it has more features. This package use a Leslie or DeLury approach assuming a closed population, whereas CatDyn allows for an open population. SSS is an alternative approach	Depletion analysis	Depletion analyses are typically applied to species with rapid growth rates, short life-spans, little generation overlap and weak or no stock-recruitment relationships (they have been commonly applied to cephalopods). The approach assumes high steepness, a closed population (i.e. negligible recruitment, and immigration/emigration), and, critically, no within-season natural mortality. Depletion analyses are conducted by plotting catch-per-unit-effort (CPUE) versus cumulative catch (i.e. total catch of the season thus far). Assuming linearity, the method extrapolates via linear regression to determine the projected 1) total catch and 2) length of the season. The slope of the regression approximates the catchability. The response would be to limit the season or limit the total catch.	Gary A. Nelson	Commonwealth of Massachusetts Division of Marine Fisheries	Unknown	Low	Delury, D. B. 1947. On the estimation of biological populations. Biometrics 3: 145-167	Yes	R	No	Yes	User specified	No	No	No	No	No	Single area	Single stock; closed population assumption	Implied	No	No	No	No	User specified	For some of the options	No	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Gives estimates of total catch that may be removed, season length, catchability and abundance	Yes; user specified time step
FLa4a (FLR)	https://github.com/flr/FLa4a	Integrated assessment	Single species	Supported	SAM, SS or CASAL may be alternatives as they have more features. Designed for ICES and data limited fisheries.	Assessment For All (Fisheries Library in R)	"The stock assessment model framework is a non-linear catch-at-age model implemented in R/FLR/ADMB that can be applied rapidly to a wide range of situations with low parametrization requirements. In the a4a assessment model, the model structure is defined by submodels, which are the different parts of a statistical catch at age model that require structural assumptions. There are 5 submodels in operation: - a model for F-at-age, - a (list) of model(s) for abundance indices catchability-at-age, - a model for recruitment, - a list of models for the observation variance of catch-at-age and abundance indices, - a model for the initial age structure. In practice, we fix the variance models and the initial age structure models, but in theory these can be changed."	Colin Millar, Ernesto Jardim	European Commission Joint Research Centre	Yes	High	Jardim, et.al, 2017: http://www.flr-project.org/FLa4a/articles/sca.pdf	No	ADMB	No	Yes	Annual	No	No	No	No	No	Single area	No	Yes	Yes	No	Yes	All selectivities which can be described by a R formulae can, in theory, be used.	No	Yes	Yes	No	Yes	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	Yes	No	No	No	Yes	Index of biomass, any index that can be related to recruitment, biomass, fishing mortality or observation variance.	No	No	No	No	No	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	Yes	No	Done via other FLR packages.	Yes; annual time step
FLAssess (FLR)	https://github.com/flr/FLAssess; http://www.flr-project.org/	Integrated assessment	Single species	Superseded	SAM, SS or CASAL may be alternatives as they have more features. Designed for ICES fisheries	The Fishery Library in R - Stock Assessment	"FLAssess is the basic structure for age-based stock assessment. It provides a standard class for data input, diagnostic inspection and stock status estimation; either for use within a working group setting or as part of a formal Management Strategy Evaluation (MSE). The FLAssess class can be extended to create specific implementations of assessment methods e.g. FLICA, FLSURBA, FLXSA, providing a common interface for all assessment methods. For example, within ICES there are two main stock assessment methods, ICA for pelagic and XSA for demersal stocks. However, differences between the methods are mainly artefacts of how they were independently developed rather than methodological. By incorporating such methods in a common class this problem will hopefully be avoided in the future. FLAssess also incorporates methods for performing virtual population analysis (VPA) and stock projection."	Laurence T. Kell	FLR	No	Medium	Kell, L. T., Mosqueira, I., Grosjean, P., Fromentin, J-M., Garcia, D., Hillary, R., Jardim, E., Mardle, S., Pastoors, M. A., Poos, J. J., Scott, F., and Scott, R. D. 2007. FLR: an open-source framework for the evaluation and development of management strategies. – ICES Journal of Marine Science, 64: 640–646: https://doi.org/10.1093/icesjms/fsm012	Yes	R	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	No	Yes	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes; annual time step
frasyr	https://github.com/ichimomo/frasyr	VPA	Single species	Not evaluated	Not yet peer reviewed. We recommend SAM if there is a need to apply VPA-like approach	frasyr	"Estimating stock status with stock assessment models and calculating RPs and ABC for Japanese fisheries management"	Hiroshi Okamura, Shota Nishijima, Momoko Ichinokawa	Japan Fisheries Research and Education Agency	No	Medium	Not yet published; Guideline - https://ichimomo.github.io/frasyr/doc/SRR-guidline.html	Some related journal papers	TMB	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	Yes	No	No	Yes	Yes	No	Yes	No	No	No	No	Yes; annual time step
GADGET	https://github.com/Hafro/gadget2	Integrated assessment	Multiple species	Supported	Supported, although SS or CASAL may be alternatives as they have more features. Designed for ICES fisheries.	Globally applicable Area Disaggregated General Ecosystem Toolbox	"Gadget is a software tool that can run complicated statistical ecosystem models, which take many features of the ecosystem into account. Gadget works by running an internal model based on many parameters, and then comparing the data from the output of this model to ''real'' data to get a goodness-of-fit likelihood score. These parameters can then be adjusted, and the model re-run, until an optimum is found, which corresponds to the model with the lowest likelihood score. Gadget allows you to include a number of features into your model: One or more species, each of which may be split into multiple stocks; multiple areas with migration between areas; predation between and within species; maturation; reproduction and recruitment; multiple commercial and survey fleets taking catches from the populations."	Current: Bjarki Þór Elvarsson, Umar Ibrahim, Pamela Woods and Jamie Lentin. In the past: James Begley, Daniel Howell, Höskuldur Björnsson, Narfi Stefánsson and others	Marine and Freshwater Research Institute, Iceland	Yes	High	Gadget Userguide (James Begley 2006) ; https://hafro.github.io/gadget2/docs/userguide/	No	other auto Diff	No	Yes	User defined (e.g. monthly, quarterly or irregular intra year time steps)	Yes	Yes	Yes	Yes	Yes	User defined, no limit	Yes	Yes	Yes	No	Yes	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Mean weight at length	Yes	Yes	No	Yes	No	No	Yes	Yes	No	No	No	Yes	Yes	No	Yes	Yes	Yes	Yes	No	Yes	No	No	Yes; seasonal time step
GMACS	https://seacode.github.io/gmacs/	Size structured	Single species	Supported	Few size structured packages available. Presently focused on Alaskan crab.	General Model for Assessing Crustacean Stocks	"Generalized model originally designed for Alaska crab stocks. GMACs is a generalized modelling framework for developing size-structured stock assessment models. It is an open source program developed using AD Model Builder (ADMB)."	Andre Punt, Jim Ianelli, Postdocs	NOAA	No	High	Martell, S., Fournier, D., Whitten, A., Ianelli, J. 2014. GMACS documentation. https://github.com/seacode/gmacs/blob/develop/docs/developer/ModelDescription.pdf	Yes	ADMB	No	Yes	Flexible (with annual)	Yes	No	No	Yes	No	Single area	No	Yes	Yes	No	No	No	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	Yes	No	No	No	Yes	No	No	No	No	Yes	No	Yes	No	No	Yes	No	Yes	No	No	Yes; seasonal time step
JABBA	https://github.com/jabbamodel/JABBA	Surplus production	Single species	Supported and Recommended	State space model. Recommended, with guidelines based on our test data set.	Just Another Bayesian Biomass Assessment	"Just Another Bayesian Biomass Assessment" (JABBA) can be used for biomass dynamic stock assessment applications, and has emerged from the development of a Bayesian State-Space Surplus Production Model framework, already applied in stock assessments of sharks, tuna, and billfishes around the world. JABBA presents a unifying, flexible framework for biomass dynamic modelling, runs quickly, and generates reproducible stock status estimates and diagnostic tools. Specific emphasis has been placed on flexibility for specifying alternative scenarios, achieving high stability and improved convergence rates. Default JABBA features include: 1) an integrated state-space tool for averaging and automatically fitting multiple catch per unit effort (CPUE) time series; 2) data-weighting through estimation of additional observation variance for individual or grouped CPUE; 3) selection of Fox, Schaefer, or Pella-Tomlinson production functions; 4) options to fix or estimate process and observation variance components; 5) model diagnostic tools; 6) future projections for alternative catch regimes; and 7) a suite of inbuilt graphics illustrating model fit diagnostics and stock status results. "	Henning Winker, Felipe Carvalho, Maia Kapur	Forestry and Fisheries South Africa, NOAA-PIFSC	No	Medium	doi.org/10.1016/j.fishres.2018.03.010	Yes	JAG	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	Production model so none explicit	No	No	Yes	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	Yes; annual time step
LBRP	https://github.com/shcaba/LBRP	Length-based	Single species	Supported	Can deal with aggregated size data. Otherwise LBSPR (barefootecologist), LIME or SS-CL are recommended. SS-CL is the most flexible.	Analysis of sustainability indicators based on length-based reference points (LBRP)	This is an assessment option based on length-based reference points (LBRP). It was developed by Cope and Punt in 2009 and offers a more robust extension of the Froese (2004) assessment using size-based indicators. When establishing length-based reference points for sustainable management, Cope and Punt highlight the importance of distinguishing the selectivity pattern. They show that Froese's (2004) size indicators (Pmat, Popt, and Pmega), when used in isolation, can take on a wide range of values for an overfished stock. As such, they may not adequately reflect sustainable fishing practices. Instead, Cope and Punt suggest the use of Pobj, defined as the sum of the 3 size indicators used in the Froese (2004) assessment model. In their assessment model based on length-based reference points, Cope and Punt present a decision tree (Figure 10) that allows users to determine whether a stock's biomass is below a target or limit reference point using Pobj, the 3 catch proportions used in the 2004 model, and the ratio of Lmat/Lopt. The decision tree does not require fishing mortality rate (F), recruitment compensation (h), or spawning biomass data, and it should be used in concert with the size indicators used in the 2004 model when possible. (ref: Froese, R. 2004. Keep it simple: Three indicators to deal with overfishing. Fish and Fisheries 5:86– 91.)	Jason Cope and Andre Punt	NOAA, University of Washington	Yes	Low	Cope, J. M., and Punt, A.E. 2009. Length-based reference points for data-limited situations: Applications and restrictions. Marine and Coastal Fisheries 1(1):169-186. http://dx.doi.org/10.1577/C08-025.1	Yes	R	No	Yes	User specified	Method intends that the user is following the female spawning biomass	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	No	No	No	No	No	No	Yes (in lieu of age composition data)	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	Estimates relative stock status	No
LBSPR	https://cran.r-project.org/web/packages/LBSPR/index.html	Length-based	Single species	Supported and Recommended	Recommended, with guidelines based on our test data set. LBSPR is an equilibrium method, whereas LIME and SS-CL are non-equilibrium approaches.	Length-Based Spawning Potential Ratio	From the package: "Simulates expected equilibrium length composition, yield-per-recruit, and the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fits the LBSPR model to length data to estimate selectivity, relative apical fishing mortality, and the spawning potential ratio for data-limited fisheries." Other: The length-based spawning potential ratio (LB-SPR) assessment method estimates spawning potential ratio (SPR), the ratio of reproductive potential of a fished relative to an unfished population. In total, the method requires at least on year of length composition information, an estimate for the ratio M/k, maximum size (Linf), the coefficient of variation (CV) of Linf, and knowledge of size-at-maturity, from which SPR is calculated. The ratio of M/k is used because this value is less variable across stocks and species than either the individual parameters for natural mortality rate (M) or the von Bertalanffy growth coefficient (k).	Adrian Hordyk	University of British Columbia	No	Low	Hordyk, A., Ono, K., Prince, J.D., and Walters, C.J. 2016. A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks. Can. J. Fish. Aquat. Sci. 73(12): 1787-1799. https://doi.org/10.1139/cjfas-2015-0422	Yes	R	No	Yes	User specified	Method is intended to be used on female data and using female life history parameters	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	Flagged for future (current Monte Carlo stochastic version)	No	No	No	No	Yes	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	Yes	Yes	Estimates F/M	No
LBSPR (barefootecologist)	http://barefootecologist.com.au/lbspr	Length-based	Single species	Supported and Recommended	LBSPR is an equilibrium method, whereas LIME and SS-CL are non-equilibrium approaches.	Length-based spawner potential ratio	From the package: "The LBSPR method has been developed for data-limited fisheries, where few data are available other than a representative sample of the size structure of the vulnerable portion of the population (i.e., the catch) and an understanding of the life history of the species. The LBSPR method does not require knowledge of the natural mortality rate (M), but instead uses the ratio of natural mortality and the von Bertalanffy growth coefficient (K) (M/K), which is believed to vary less across stocks and species than M (Prince et al. 2015). Like any assessment method, the LBSPR model relies on a number of simplifying assumptions. In particular, the LBSPR models are equilibrium based, and assume that the length composition data is representative of the exploited population at steady state." (ref: Prince, J.D., Hordyk, A.R., Valencia, S.R., Loneragan, N.R. and Sainsbury, K.J. 2015. Revisiting the concept of Beverton–Holt life-history invariants with the aim of informing data-poor fisheries assessment. ICES J. Mar. Sci. 72: 194-203 .)	Adrian Hordyk and Jeremy Prince	Murdoch University, Biospherics	Yes	Low	Hordyk, A., Ono, K., Prince, J.D., and Walters, C.J. 2016. A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks. Can. J. Fish. Aquat. Sci. 13: 1-13. https://doi.org/10.1139/cjfas-2015-0422	Yes	R-Shiny	No	R-Shiny package	User specified	Method is intended to be used on female data and using female life history parameters	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	Flagged for future (current Monte Carlo stochastic version)	No	No	No	No	Yes	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	Yes	Yes	Estimates F/M	No
LBSPR (MSEtool)	https://cran.r-project.org/web/packages/MSEtool/vignettes/MSEtool.html	Length-based	Single species	Supported	Recommend LBSPR (barefootecologist), LIME, SS-CL. SS-CL is the most flexible. LBSPR is an equilibrium method, whereas LIME and SS-CL are non-equilibrium approaches.	Length-based spawner potential ratio	The length-based spawning potential ratio (LB-SPR) assessment method estimates spawning potential ratio (SPR), the ratio of reproductive potential of a fished relative to an unfished population. In total, the method requires at least on year of length composition information, an estimate for the ratio M/k, maximum size (Linf), the coefficient of variation (CV) of Linf, and knowledge of size-at-maturity, from which SPR is calculated. The ratio of M/k is used because this value is less variable across stocks and species than either the individual parameters for natural mortality rate (M) or the von Bertalanffy growth coefficient (k).	Quang Huynh, Tom Carruthers, Adrian Hordyk	University of British Columbia	Yes	Low	Hordyk, A., Ono, K., Prince, J.D., and Walters, C.J. 2016. A simple length-structured model based on life history ratios and incorporating size-dependent selectivity: application to spawning potential ratios for data-poor stocks. Can. J. Fish. Aquat. Sci. 13: 1–13. https://doi.org/10.1139/cjfas-2015-0422	Yes	R	No	Yes	User specified	Method is intended to be used on female data and using female life history parameters	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	Flagged for future (current Monte Carlo stochastic version)	No	No	No	No	Yes	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	Yes	Yes	Estimates F/M	No
LIME	https://github.com/merrillrudd/LIME	Length-based	Single species	Supported and Recommended	Recommended, with guidelines based on our test data set. Can be used with catches. Non-equilibrium approach. The SS-CL and SS-LO is alternative worth considering as a Rshiny web driven version	Length-based Integrated Mixed Effects (LIME)	From the package: "Developed by Rudd and Thorson (2017), Length-based Integrated Mixed Effects (LIME) is a flexible stock assessment method for fisheries with developing data collection programs and/or limited capacity for monitoring. For these fisheries, it is often easier to collect length measurements from fishery catch than to quantify total catch. Conventional stock assessment tools that rely on length measurements without total catch do not directly account for variable fishing mortality and recruitment over time. However, this equilibrium assumption is likely violated in almost every fishery, degrading estimation performance. LIME is an extension of length-only approaches that accounts for time-varying recruitment and fishing mortality."	Merrill Rudd and James Thorson	University of Washington	No	Low	Rudd, MB and Thorson, JT. 2017. Accounting for variable recruitment and fishing mortality in length-based stock assessments for data-limited fisheries. Canadian Journal of Fisheries and Aquatic Sciences https://doi.org/10.1139/cjfas-2017-0143.	Yes	R	No	Yes	User specified	Method is intended to be used on female data and using female life history parameters	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	Yes	Yes	Optional	Optional	Optional - but ideally should be considering total removals	Yes	No	No	No	No	No	No	No	No	Yes	No	No	Optional	No	No	No	No	No	No	No	No	No	No	Yes	No	Yes	No	No	No	No	No	Yes	Yes	No	No
MLZ	https://cran.r-project.org/web/packages/MLZ/index.html	Mean length	Multiple species	Supported	This can be useful in situations where only mean length data are available. Otherwise we recommend LBSPR (barefootecologist), LIME or SS-CL. SS-CL is the most flexible.	Mean Length-Based Estimators of Mortality using TMB	"MLZ is a package that facilitates data preparation and estimation of mortality with statistical diagnostics using the mean length-based mortality estimator and several extensions."	Quang Huynh, Todd Gedamke, Amy Then	UBC	No	Medium	Gedamke, T. and Hoenig, J.M. (2006), Estimating Mortality from Mean Length Data in Nonequilibrium Situations, with Application to the Assessment of Goosefish. Transactions of the American Fisheries Society, 135: 476-487. .	Yes	TMB	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No
MS-cMSY	https://github.com/cfree14/datalimited2	Catch only	Multiple species	Supported	Look at our "Supported and Recommended" Catch only versions (cMSY, COM-SIR, DB-SRA, OCOM, SS-COM, SSS, zBRT), as they are updated more regularly, are used more extensively within Australia or are more recent. Note: This model is still in beta version.	Multispecies Catch MSY	Estimates stock status (B/BMSY) time series for multispecies fisheries from time series of catch and estimates of resilience using the multispecies cMSY (MS-cMSY) method of Free et al. (in prep). Note: this model is still in beta version.	Christopher M. Free	Sustainable Fisheries Group, Bren School, UC Santa Barbara, CA, USA	No	Low	Free CM, Rudd MB, Kleisner KM, Thorson JT, Longo C, Minto C, Jensen OP (in prep) Multispecies catch-only models for assessing data-limited fisheries.	Yes	R	No	Yes	User specified	No: there is no differentiation between sexes in r and K, and no other biological input	No, but multi-species catches	No	No	No	Single area	No	Implied	No	No	No	No	User specified	No	Yes - Monte Carlo; posteriors modified due to filtering process	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No
Multifan-CL	http://www.multifan-cl.org/index.php?option=com_content&view=category&id=2&Itemid=101	Integrated assessment	Single species	Supported and Recommended	Designed for fisheries where primary data sources are tagging and length data. Can account for spatial structure	MULTIFAN-CL	"MULTIFAN-CL MULTIFAN-CL is a computer program that implements a statistical, length-based, age-structured model for use in fisheries stock assessment. The model is a convergence of two previous approaches. The original MULTIFAN model (Fournier et al. 1990) provided a method of analysing time series of length-frequency data using statistical theory to provide estimates of von Bertalanffy growth parameters and the proportions-at-age in the length-frequency data. The model and associated software were developed as an analytical tool for fisheries in which large-scale age sampling of catches was infeasible or not cost effective, but where length-frequency sampling data were available. MULTIFAN provided a statistically-based, robust method of length-frequency analysis that was an alternative to several ad hoc methods being promoted in the 1980s."	David Fournier, Nick Davies	Otter Research Ltd and Pacific Community (SPC)	No	High	Fournier, D.A., Hampton, J., and Sibert, J.R. (1998). MULTIFAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga.(pdf - 287k) Canadian Journal of Fisheries and Aquatic Sciences 55, 2105-2116. https://www.researchgate.net/publication/240671683_MULTIFAN-CL_A_length-based_age-structured_model_for_fisheries_stock_assessment_with_application_to_South_Pacific_albacore_Thunnus_alalunga	Hampton, J., and D.A. Fournier. (2001). A spatially disaggregated, length-based, age-structured population model of yellowfin tuna (Thunnus albacares) in the western and central Pacific Ocean.(pdf - 4502k) Marine and Freshwater Research 52, 937-963.	other auto Diff	No	Yes	Flexible both for fisheries and recruitment/age structure	Yes	No	No	Yes	Yes	Can be flexibly specified	Yes	Yes	Yes	No	Yes	No	No	Yes	No	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No	Yes	No	No	Yes	No	No	Yes	Yes	No	No	Yes	No	Yes	No	No	No	No	Yes	Yes	No	No	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes; seasonal time step
OCOM	https://github.com/cfree14/datalimited2	Catch only	Single species	Supported and Recommended	Similar to DB-SRA but OCOM depletion values comes from analysis of catch time trends as opposed to being an input value. Possible to also consider SSS.	Optimised Catch-only Method	From the package: "The "optimized catch-only model" (OCOM) developed by Zhou et al. (2017) employs a stock reduction analysis (SRA) using priors for r and stock depletion derived from natural mortality and saturation estimated using the Zhou-BRT method, respectively." Other: The Optimized Catch-Only Method (OCOM) uses time series of catches and two priors - one for the intrinsic population growth rate derived from life history parameters, and another for stock depletion based on catch trends. The estimated parameters include carrying capacity, intrinsic population growth rate, maximum sustainable yield, and depletion. OCOM also estimates biomass, fishing mortality, and stock status (B/BMSY, F/MSY), and biological/management quantities (i.e., r,k, MSY, BMSY,FMSY) from a time series of catch and a natural mortality (M).	Christopher M. Free	Sustainable Fisheries Group, Bren School, UC Santa Barbara, CA, USA	Unknown	Low	Zhou S, Punt AE, Smith ADM, Ye Y, Haddon M, Dichmont CM, Smith DC (2017b) An optimised catch-only assessment method for data poor fisheries. ICES Journal of Marine Science: doi:10.1093/icesjms/fsx226.	Yes	R	No	Yes	User specified	No	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	Uses R function "optimize": The method used is a combination of golden section search and successive parabolic interpolation, and was designed for use with continuous functions.	No	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	Estimates biomass, fishing mortality, and stock status (B/BMSY, F/MSY) time series and biological/management quantities (i.e., r,k, MSY, BMSY,FMSY)	No
SAM	https://github.com/fishfollower/SAM	Integrated assessment	Single species	Supported and Recommended	Preferred method because it is based on a state-space estimation method. Designed for ICES fisheries, but is being applied globally. User expertise needs to be quite high, especially if being applied outside of the ICES assessment process. This package can be used in R or within a web interface (https://www.stockassessment.org/login.php).	State-space Assessment Model	SAM is a State-Space stock assessment model that uses the mixed effects strength of Template Model Builder (TMB) to formulate a flexible stock assessment. It is most widely used in ICES assessments, but use in tunas and elsewhere are becoming more common. It can be used standalone in a web application, in R and as part of the FisheriesLibrary in R (FLR)	Anders Nielsen, Casper Berg, Christoffer Albertsen, Kasper Kristensen, Venessa Trijoulet, Olav Brevik, Mollie Brooks	DTU Aqua and others	Yes	High	Nielsen, A., Berg, C.W. 2014. Estimation of time-varying selectivity in stock assessments using state-space models. Fisheries Research. 158:96-101. https://doi.org/10.1016/j.fishres.2014.01.014	Yes	TMB	Yes	Yes	Annual (default)	No	No	No	Yes	No	Single area	No	No	No	No	No	Random process for F implies time varying selectivity	No	Yes	No	Yes	Yes	Yes	Yes	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	Yes	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	Yes; annual time step
SCA	https://cran.r-project.org/web/packages/MSEtool/vignettes/SCA.html	Integrated assessment	Single species	Superseded	SS or CASAL may be alternatives as they have more features.	Statistical Catch at Age	"A generic statistical catch-at-age model (single fleet, single season) that uses catch, index, and catch-at-age composition data. SCA parameterizes R0 and steepness as leading productivity parameters in the assessment model. Recruitment is estimated as deviations from the resulting stock-recruit relationship. In SCA2, the mean recruitment in the time series is estimated and recruitment deviations around this mean are estimated as penalized parameters (similar to Cadigan 2016). The standard deviation is set high so that the recruitment is almost like free parameters. Unfished and MSY reference points are inferred afterwards from the assessment output (SSB and recruitment estimates). SCA_Pope is a variant of SCA that fixes the expected catch to the observed catch, and Pope's approximation is used to calculate the annual harvest rate (U). In SCA2, no stock-recruit relationship is assumed in the assessment model, i.e., annual recruitment is estimated as deviations from the mean recruitment over the observed time series, similar to Cadigan (2016). After the assessment, a stock-recruit function can be fitted post-hoc to the recruitment and spawning stock biomass estimates from the assessment model to obtain MSY reference points."	MSEtool: Thomas Carruthers and Adrian Hordyk	University of British Columbia	No	High	Cadigan, N.G. 2016. A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates. Canadian Journal of Fisheries and Aquatic Science 72:296-308. https://doi.org/10.1139/cjfas-2015-0047	No	R	No	Yes	Annual	No	No	No	No	No	Single area	No	Yes	Yes	No	No	No	No	Yes	No	No	Yes	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	No	No	No	Yes; annual time step
SCA2	https://cran.r-project.org/web/packages/MSEtool/vignettes/MSEtool.html	Integrated assessment	Single species	Superseded	SAM, SS or CASAL may be alternatives as they have more features. Designed for used in management startegy evaluation and simulation testing of estimation methods	Statistical Catch at Age 2	"A generic statistical catch-at-age model (single fleet, single season) that uses catch, index, and catch-at-age composition data. SCA parameterizes R0 and steepness as leading productivity parameters in the assessment model. Recruitment is estimated as deviations from the resulting stock-recruit relationship. In SCA2, the mean recruitment in the time series is estimated and recruitment deviations around this mean are estimated as penalized parameters (similar to Cadigan 2016). The standard deviation is set high so that the recruitment is almost like free parameters. Unfished and MSY reference points are inferred afterwards from the assessment output (SSB and recruitment estimates). SCA_Pope is a variant of SCA that fixes the expected catch to the observed catch, and Pope's approximation is used to calculate the annual harvest rate (U). In SCA2, no stock-recruit relationship is assumed in the assessment model, i.e., annual recruitment is estimated as deviations from the mean recruitment over the observed time series, similar to Cadigan (2016). After the assessment, a stock-recruit function can be fitted post-hoc to the recruitment and spawning stock biomass estimates from the assessment model to obtain MSY reference points."	MSEtool: Thomas Carruthers and Adrian Hordyk	University of British Columbia	No	High	Cadigan, N.G. 2016. A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates. Canadian Journal of Fisheries and Aquatic Science 72:296-308. https://doi.org/10.1139/cjfas-2015-0047	No	R	No	Yes	Annual	No	No	No	No	No	Single area	No	Yes	Yes	No	No	No	No	Yes	No	No	Yes	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	No	No	No	Yes; annual time step
SCALE	https://nmfs-fish-tools.github.io/SCALE/	Integrated assessment	Single species	Inactive	SAM, SS or CASAL may be alternatives as they have more features. Designed for used in management startegy evaluation and simulation testing of estimation methods	Statistical Catch at Length Model	The Statistical Catch At LEngth (SCALE) model, previously known as the Length Tuned Model (LTM), is a forward projecting age-structured model tuned with total catch (mt), catch at length or proportional catch at length, recruitment at a specified age (usually estimated from first length mode in the survey), survey indices of abundance of the larger/older fish (usually adult fish) and the survey length frequency distributions. The SCALE model was developed in the AD model builder framework. The model parameter estimates are fishing mortality and recruitment in each year, fishing mortality to produce the initial population (Fstart), logistic selectivity parameters for each year or blocks of years and Qs for each survey index. The SCALE model was developed as an age-structured model that does not rely on age-specific information on a yearly basis. The model is designed to fit length information, abundance indices, and recruitment at age which can be estimated by using survey length slicing. However the model does require an accurate representation of the average overall growth of the population which is input to the model as mean lengths at age. Growth can be modeled as sex-specific growth and natural mortality or growth and natural mortality can be model with the sexes combined. The SCALE model will allow for missing data. The SCALE model can be used in two different ways. First, the model can be used as a stock assessment tool for a species which has overall growth information, abundance indices at length, total removals, and size information of the catch but may not possess production ageing on a yearly basis. Second, the model can be used as a diagnostic tool for comparison to other fully age-structured models that are tuned with age-specific data. Failure of the SCALE model in fitting observed length information over time can provide insight on inaccuracies in the basic life history understanding of a population.	Nitscke, P. and C. Legault	NOAA	Yes	High	43rd Northeast Regional Stock Assessment Workshop (43rd SAW) 43rd SAW Assessment Summary Report: https://www.nefsc.noaa.gov/nefsc/publications/crd/crd0614/	Yes	Windows exe file, admb	Yes	No	Annual	No	No	No	No	No	Single area	No	Yes	No	No	No	No	No	Yes	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	Index of recruitment	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	No	No	Yes	No	No	Yes; annual time step
Schaefer Biomass dynamics (FISHE)	http://fishe.edf.org/	Surplus production	Single species	Superseded	We recommend use of JABBA and spm.	Primer for Biomass Dynamics Method – Schaefer Depletion Estimators (Framework for Integrated Stock and Habitat Evaluation)	In the simplest sense, all populations change from year to year based on how many how many are born and how many die. The same general principles apply when looking at fish stock biomass, where the biomass of fish available in any given year can be determined by the biomass that existed last year, which is then increased by the biomass of "recruits and the growth in overall biomass over the year and decreased by the amount of biomass that dies from both natural mortality and catch (Hilborn and Walters, 1992)."	FISHE team	Environmental Defense Fund	No	Medium	Hilborn R., and C.J. Walters. 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics, & Uncertainty. Chapman and Hall, New York.	Yes	Excel	No	No	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	Yes	No	No	No	No	No	No	No	No	Yes	Yes	No	No	Yes	Yes	No	No	Yes	No	No	No
SP	https://cran.r-project.org/web/packages/MSEtool/vignettes/Surplus_production.html	Surplus production	Single species	Superseded	We recommend use of JABBA and spm.	Surplus-production (SP) model (MSEtool)	"In MSEtool, assessment models are of class Assess. This appendix provides a brief description and references for the Assess objects. Further details regarding parameterization, e.g., fixing parameters, and tuning, e.g., adjusting start parameters, are provided in the function documentation."	Quang Huynh	UBC	No	Medium	http://www.datalimitedtoolkit.org	Unknown	R	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	Yes	No	No	No	Yes	No	No	No
SP_SS	https://cran.r-project.org/web/packages/MSEtool/vignettes/Surplus_production.html	Surplus production	Single species	Superseded	We recommend use of JABBA and spm.	Surplus-production State-space version (SP_SS) model (MSEtool)	"In MSEtool, assessment models are of class Assess. This appendix provides a brief description and references for the Assess objects. Further details regarding parameterization, e.g., fixing parameters, and tuning, e.g., adjusting start parameters, are provided in the function documentation."	Quang Huynh	UBC	No	Medium	http://www.datalimitedtoolkit.org	Unknown	R	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	No	Yes	No	No	No	Yes	No	No	No
spm	https://github.com/haddonm/datalowSA	Surplus production	Single species	Supported and Recommended	Deterministic model with observation error. Recommended, with guidelines based on our test data set. We recommend users try JABBA first.	Surplus Production Modelling	From the package: "Surplus production models are one of the simplest analytical methods available that provides for a full fish stock assessment of the population dynamics of the stock being examined."	Malcolm Haddon	Formerly CSIRO, now private	Yes	Medium	Haddon, M. Burch, P., Dowling, N., and Little, R. 2019. Reducing the Number of Undefined Species in Future Status of Australian Fish Stocks Reports: Phase Two - training in the assessment of data-poor stocks. FRDC Final Report 2017/102. CSIRO Oceans and Atmosphere and Fisheries Research Development Corporation. Hobart 125 p. https://www.frdc.com.au/Archived-Reports/FRDC%20Projects/2017-102-DLD.pdf	No	R	No	Yes	Annual	No	No	No	No	No	Single area	No	No	No	No	No	No	No	Yes	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	Yes	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No	Yes	Yes	No	No	No	No	No	No
SRA	http://www.datalimitedtoolkit.org/	Catch only	Single species	Supported	Stock Reduction Analysis is alternative method to CMSY based on an age-structured model. SSS is an alternative for this method	Stochastic Stock Reduction Analysis	Stochastic Stock Reduction Analysis (SRA) is a stochastic age-structured population model that uses the Beverton-Holt stock-recruitment function to estimate stock status forward in time (Walters et al., 2006). This assessment method uses maximum sustainable yield (MSY) and Umsy (the annual exploitation rate producing MSY at equilibrium) as leading parameters. Given these parameters, the model simulates changes in biomass by subtracting estimates of mortality and adding recruits. A single trajectory of biomass over time is produced, as well as estimates of MSY, Umsy, exploitation in the terminal year, and stock status.	Tom Carruthers and Adrian Hordyk	University of British Columbia	Yes	Low	Walters, C.J., Martell, S. J. D., and Korman, J. 2006. A stochastic approach to stock reduction analysis. Canadian Journal of Fisheries and Aquatic Sciences 63:212-223. https://doi.org/10.1139/f05-213	Yes	R	No	Yes	User specified	Can be sex-specific if inputs are such	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Monte Carlo simulations	Yes	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	Model produces estimates of MSY, Umsy, exploitation in the terminal year, and stock status	No
SS	https://vlab.ncep.noaa.gov/web/stock-synthesis	Integrated assessment	Single species	Supported and Recommended	Preferred model because it is the most general model with extensive documentation, diagnostics, flexibility, testing and scalability. Applied globally.	Stock Synthesis	"Stock Synthesis provides a statistical framework for calibration of a population dynamics model using a diversity of fishery and survey data. It is designed to accommodate both age and size structure in the population and with multiple stock sub-areas. Selectivity can be cast as age specific only, size-specific in the observations only, or size-specific with the ability to capture the major effect of size-specific survivorship. The overall model contains subcomponents which simulate the population dynamics of the stock and fisheries, derive the expected values for the various observed data, and quantify the magnitude of difference between observed and expected data. Some SS features include ageing error, growth estimation, spawner-recruitment relationship, movement between areas. SS is most flexible in its ability to utilize a wide diversity of age, size, and aggregate data from fisheries and surveys. The ADMB C++ software in which SS is written searches for the set of parameter values that maximize the goodness-of-fit, then calculates the variance of these parameters using inverse Hessian and MCMC methods. A management layer is also included in the model allowing uncertainty in estimated parameters to be propagated to the management quantities, thus facilitating a description of the risk of various possible management scenarios, including forecasts of possible annual catch limits. The structure of Stock Synthesis allows for building of simple to complex models depending upon the data available."	Richard Methot and others	NOAA	Yes	High	Methot, R.D. (Jr) and Wetzel, C.R. 2013. Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management. Fisheries Research. 142: 86-99: https://doi.org/10.1016/j.fishres.2012.10.012	Yes	C++ with ADMB	Yes	Yes	Customizable	Yes	No	Yes	Yes	Yes	Flexible	No	Yes	Yes	No	Yes	Knife-edge; logistic-exponential	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	No	Yes	No	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes; seasonal time step
SS-COM	https://github.com/datalimited/datalimited	Catch only	Single species	Supported and Recommended	Special case of the catch only approach as it depends on a effort model that assumes effort changes as a linear function of biomass. Includes process and observation error. SSS is an alternative for this method	State-space Catch Only Model	Combines effort dynamics and population dynamics models to estimate maximum sustainable yield (MSY) and stock status based on a series of catches and assumed linear effort dynamics relative to the logarithm of biomass.	Sean Anderson	Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia	Unknown	Low	Thorson, J. T., Minto, C., Minte-Vera, C.V., Kleisner, K.M. and Longo, C. 2013. A New Role for Effort Dynamics in the Theory of Harvested Populations and Data-Poor Stock Assessment. Canadian Journal of Fisheries and Aquatic Sciences 70(12): 1829–1844. https://doi.org/10.1139/cjfas-2013-0280	Yes	R	No	Yes	User specified	Can be sex-specific if inputs are such	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	Yes, with MCMC sampling to approximate posterior	Yes	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	Yes	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	Estimates MSY, B/BMSY, F/MSY	No
SSS	https://github.com/shcaba/SSS	Catch only	Single species	Supported and Recommended	There are several forms of SS	Simple Stock Synthesis	Simple Stock Synthesis (SSS) is a version of Stock Synthesis (SS) that has been modified to behave as a catch-only approach (similar to DB-SRA), while also becoming a first step towards building a fully implemented quantitative stock assessment. Developed to mimic the Depletion-Based Stock Reduction Analysis (DB-SRA) estimation of overfishing limits (OFLs) currently applied to data-limited U.S. west coast groundfish species, SSS-MC uses Monte Carlo draws of natural mortality, steepness, and stock depletion, and estimates initial recruitment, while SSS-MCMC estimates natural mortality, steepness, and initial recruitment while fitting to an artificial abundance survey representing stock depletion with an error distribution equivalent to the stock depletion prior used in DB-SRA.	Jason Cope	NOAA	Yes	Medium	Cope, J. M. (2013). Implementing a statistical catch-at-age model (Stock Synthesis) as a tool for deriving overfishing limits in data-limited situations Fisheries Research 142: 3-14. http://dx.doi.org/10.1016/j.fishres.2012.03.006	Yes	R	No	Yes	User specified	Yes	No	Yes	No	No	Single area	No	Yes	Yes	Yes	Yes	No	User specified	No	Monte Carlo	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Yes	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	Yes	No	No	Yes	Yes	No	No
STATCAM	https://nmfs-fish-tools.github.io/STATCAM/	Integrated assessment	Single species	Inactive	SAM, SS or CASAL may be alternatives as they have more features.	Statistical Catch at Age Model	"STATCAM is a statistical catch-at-age model. It is a likelihood-based assessment model for joint analyses of age-specific fishery and survey data. Age-structured population stock dynamics are modeled using standard forward-projection methods for statistical catch-at-age analyses. The population dynamics model is fit to observed fishery and survey data using an iterative maximum likelihood estimation approach. The calculation engine was developed by Dr. Jon Brodziak, NMFS Pacific Islands Fishery Science Center, using AD Model Builder." STATCAM is placed as Inactive on the NOAA Toolbox where Stock Synthesis is recommended instead.	Jon Brodziak	NMFS Pacific Islands Fishery Science Center	No	High	STATCAM. 2005. Statistical catch at age model, version 1.3. NOAA Fisheries Toolbox. NEFSC, Woods Hole, MA.	Yes	ADMB	Yes	No	Annual	No	No	No	No	No	Single area	No	Yes	Yes	No	No	No	No	Yes	Yes	No	Yes	Yes	Yes	Yes	Yes	No	No	No	No	Yes	No	Yes	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	No	Yes	Yes	No	Yes; annual time step
WHAM	https://github.com/timjmiller/wham	Integrated assessment	Single species	Still under development	Will be a state-space replacement of ASAP.	Tim Miller/WHAM (Woods Hole Assessment Model)	"The Woods Hole Assessment Model (WHAM) is a state-space age-structured stock assessment model that can include environmental effects on population processes. WHAM can be configured to estimate a range of assessment models from a traditional statistical catch-at-age (SCAA) model with recruitments as (possibly penalized) fixed effects, SCAA with recruitments as random effects, or abundance at all ages treated as random effects. WHAM is a generalization of the R and TMB code from Miller et al. (2016), Miller and Hyun 2018, and Miller et al. 2018. WHAM has many similarities to ASAP (code, Legault and Restrepo 1999), including the input data file structure. Many of the plotting functions for input data, results, and diagnostics are modified from ASAP code written by Chris Legault and Liz Brooks (ASAPplots)."	Tim Miller, Brian Stock	NOAA/NEFSC	No	High	Timothy J. Miller, Jonathan A. Hare, Larry A. Alade. 2016. A state-space approach to incorporating environmental effects on recruitment in an age-structured assessment model with an application to southern New England yellowtail flounder. Canadian Journal of Fisheries and Aquatic Sciences, 2016, 73:1261-1270, https://doi.org/10.1139/cjfas-2015-0339	Yes	TMB	No	Yes	Annual	No	No	No	No	No	Single area	No	Yes	No	No	Yes	Double logistic	No	Yes	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	No	Yes	No	Internal estimation of reference points and projections to propagate error in inputs	Yes; annual time step
XDB-SRA	Not available online	Surplus production	Single species	Supported	Extension of DB-SRA with index data. As yet not available online.	Extended Depletion-Based Stock Reduction Analysis	This builds on the Depletion-Based Stock Reduction Analysis (DB-SRA) method (see the DB-SRA assessment option for its description) by incorporating an index of abundance. The index is used to help refine the user specified prior on stock status, providing a posterior estimate of stock status informed by the index. The main application is to estimate a sustainable catch.	E.J. Dick and Alec MacCall	NOAA SWFSC	No	Medium	Wetzel, Chantel & Punt, André. (2015). Evaluating the performance of data-moderate and catch-only assessment methods for U.S. west coast groundfish. Fisheries Research. 171. 10.1016/j.fishres.2015.06.005.	Yes	Unknown	Unknown	Unknown	Annual	No	No	No	No	No	Single area	No	No	No	No	No	Assumes selectivity is consistent over time	No	Yes	No	No	Yes	Yes	Optional	No	No	No	No	No	No	No	No	No	No	No	No	Estimate of abundance relative to B0 (can be direct or via CPUE data)	Yes (if CPUE is being used as proxy abundance index)	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No	No	No	No	No	Yes	Yes	No	No
XSSA	https://github.com/johnrsibert/XSSA	Integrated assessment	Single species	Inactive	We recommend Multifan-CL, SS or CASAL	State-space Stock Assessment with "off-line coupling"	"Development of state-space stock assessment models for application to open systems where the stock under analysis is connected to a wider population. The preliminary applications is the yellowfin tuna population which supports fisheries in the Main Hawaiian Islands. The MHI population is connected to a larger tuna population for which stock assessments are available. The estimated population size in areas surrounding the MHI used to "force" the population dynamics."	John Silbert	Joint Institute of Marine and Atmospheric Research, University of Hawai'i at Manoa, Honolulu, HI 96822 U.S.A.	No	Medium	John Silbert. 2015. Stock assessment models for the Main Hawaiian Islands yellowfin tuna fishery. https://github.com/johnrsibert/XSSA/blob/master/Reports/xssa.pdf	No	ADMB,TMB	No	Yes	quarter, annual	No	No	No	No	Yes	Single area	No	No	No	No	No	No	No	Yes	No	Yes	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes; seasonal time step
XSSS	https://github.com/chantelwetzel-noaa/XSSS	Integrated assessment	Single species	Supported and Recommended	Recommended for catch plus index cases. There are several forms of SSS - SS-CL is appropriate for cases with catch and length data; XSSS is appropriate for catch and index data	Extended Simple Stock Synthesis	This approach builds on Simple Stock Synthesis (SSS) (see description of the SSS assessment method for more details) by incorporating an index of abundance. The index is used to help refine the user specified stock status priors by using adaptive importance sampling to update the posterior stock status with the information provided by the abundance index. By using the Stock Synthesis modelling framework, XSSS, like SSS, allows for flexibility in exploring uncertainty in any life history parameter, as the number of fleets and their associated selectivity values. The main application is to estimate a sustainable catch.	Jason Cope and Chantel Wetzel	NOAA	Yes	Medium	Wetzel, C.R., and Punt, A.E. 2015. Evaluating the performance of data-moderate and catch-only assessment methods for U.S. west coast groundfish. Fisheries Research 171: 170-187. https://www.sciencedirect.com/science/article/pii/S016578361500185X	Yes	R	No	Yes	User specified	Yes	No	Yes	No	No	Single area	No	Yes	Yes	Yes	Yes	No	User specified	Yes	Yes	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Yes (if CPUE is being used as proxy abundance index)	No	Yes	No	No	No	No	No	No	No	Yes	No	No	No	No	Yes	No	No	Yes	Yes	No	No
Z estimate	http://www.datalimitedtoolkit.org/	Mean length	Single species	Supported	This can be useful in situations when only mean length data are available. Otherwise we recommend LBSPR (barefootecologist), LIME or SS-CL. SS-CL is the most flexible.	Non-equilibrium mean length mortality estimator	To estimate total mortality (Z) for a fished stock, the original Beverton-Holt mortality estimator used the von Bertalanffy growth parameters (K and L∞), the length at first capture (Lc), and the mean length of the catch. However, this method was rightly criticized for its reliance on equilibrium conditions. This is because Z can change for a variety of reasons, such as in response to increased fishing pressure or environmental changes. In response to such criticism, Gedamke and Hoenig (2006) developed a new procedure for estimating Z reliably (and also, therefore, fishing mortality rate [F]) in non-equilibrium conditions (i.e., when the stock has experienced different Z values throughout its history). Users must specify how many times mortality is thought to have changed, initial guesses of the years during which mortality is thought to have changed, and the original von Bertalanffy parameters K, L∞, Lc, and mean length to estimates Z and F. From there, maximum likelihood estimation (MLE) is used to calculate variable values with an associated confidence interval, so uncertainty is partially accounted for.	Todd Gedamke and John Hoenig	Mer Consultants, Virginia Institute of Marine Science	No	Low	Gedamke, T., and Hoenig, J. M. 2006. Estimating mortality from mean length data in nonequilibrium situations, with application to the assessment of goosefish. Transactions of the American Fisheries Society 135: 476-487. http://dx.doi.org/10.1577/T05-153.1	Yes	Unknown	Unknown	Unknown	User specified	No; but user should undertake sex-specific analysis if sex-specific life history available	No	No	No	No	Single area	No	Yes	No	No	No	No	User specified	Yes	No	No	No	No	No	No	No	No	No	No	Yes	No	No	No	Yes	No	No	No	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	Estimates total mortality, Z, from which fishing mortality, F, can be derived.	No
zBRT	https://github.com/cfree14/datalimited2	Catch only	Single species	Supported and Recommended	Can be used for methods that require a depletion prior.	Zhou Boosted Regression Tree	From the package: "Estimates saturation (B/K) and stock status (B/BMSY) time series from a time series of catch using the boosted regression tree (BRT) model from Zhou et al. 2017." Other: This assessment option uses a Boosted Regression Tree (BRT) approach to infer stock depletion status based only on a time series of catch data. The method uses 8 predictors of depletion status based on linear regressions of scaled catch (i.e., the catch in a given year divided by the maximum historical catch). Although this assessment method only performs well for heavily fished stocks (a problem faced in any catch-only method), results can be used to provide depletion priors for other data-limited assessments or to directly estimate the probability that depletion is below a threshold.	Shijie Zhou (method); Christopher Free (package)	CSIRO (Zhou), Sustainable Fisheries Group, Bren School, UC Santa Barbara, CA, USA (Free)	No	Low	Zhou S., Punt, A. E., Ye, Y., Ellis, N., Dichmont, C.M., Haddon, M., Smith, D.C. and Smith, A.D.M. 2017. Estimating stock depletion level from patterns of catch history. Fish and Fisheries 18(4): 742-751. https://doi.org/10.1111/faf.12201	Yes	R	No	Yes	User specified	No	No	No	No	No	Single area	No	Implied	No	No	No	No	User specified	Regression tree	No	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	Estimates saturation (B/K) and stock status (B/BMSY) time series	No
SS-CL (SS-DL)	https://github.com/shcaba/SS-DL-tool	Length-based	Single species	Supported and Recommended	Recommended for cases with catch, length and index data. Note there are several forms of SS. SS-CL can be set up to look like LBSPR. Can be used with catch data.	Stock Synthesis - Catch or Length only (Stock Synthesis Data-limited Tool)	Stock Synthesis fitted only to length data. Catch data can be included in the analysis if estimates of biomass as well as SPR are required. Computes biomass (if catches are provided), depletion, and catch limits from standard harvest control rules	Merrill Rudd, Chantel Wetzel, and Jason Cope	NOAA	No	Medium	Rudd, M., Wetzel, C. and J. Cope. Evaluating the performance of length and catch models in the Stock Synthesis framework (mimeo)	Yes	ADMB, R, Rshiny	Yes	Yes	User specified	Yes	No	Yes	No	No	Single area	No	Yes	Yes	Yes	Yes	No	User specified	Yes	No	No	Optional - but ideally should be considering total removals	Optional - but ideally should be considering total removals	Optional - but ideally should be considering total removals	Yes	No	No	No	No	No	Yes	Yes	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No
SSS (SS-DL)	https://github.com/shcaba/SS-DL-tool	Catch only	Single species	Supported and Recommended	There are several forms of SS	Simple Stock Synthesis (Stock Synthesis Data-limited Tool)	This is a RShiny version of the stand alone SSS. Simple Stock Synthesis (SSS) is a version of Stock Synthesis (SS) that has been modified to behave as a catch-only approach (similar to DB-SRA), while also becoming a first step towards building a fully implemented quantitative stock assessment. Developed to mimic the Depletion-Based Stock Reduction Analysis (DB-SRA) estimation of overfishing limits (OFLs) currently applied to data-limited U.S. west coast groundfish species, SSS-MC uses Monte Carlo draws of natural mortality, steepness, and stock depletion, and estimates initial recruitment, while SSS-MCMC estimates natural mortality, steepness, and initial recruitment while fitting to an artificial abundance survey representing stock depletion with an error distribution equivalent to the stock depletion prior used in DB-SRA.	Jason Cope	NOAA	Yes	Medium	Cope, J. M. (2013). Implementing a statistical catch-at-age model (Stock Synthesis) as a tool for deriving overfishing limits in data-limited situations Fisheries Research 142: 3-14. http://dx.doi.org/10.1016/j.fishres.2012.03.006	Yes	ADMB, R, Rshiny	Yes	Yes	User specified	Yes	No	Yes	No	No	Single area	No	Yes	Yes	Yes	Yes	No	User specified	No	Monte Carlo	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Yes	No	No	No	No	No	No	No	No	No	Yes	No	No	No	No	Yes	No	No	Yes	Yes	No	No
SS-LO (SS-DL)	https://github.com/shcaba/SS-DL-tool	Length-based	Single species	Supported and Recommended	Recommended for cases with length only data. Note there are several forms of SS. SS-LO can be set up to look like LBSPR and LIME and is therefore versatile.	Stock Synthesis - Length only (Stock Synthesis Data-limited Tool)	Stock Synthesis fitted only to length data. Catch data can be included in the analysis if estimates of biomass as well as SPR are required. Computes biomass (if catches are provided), depletion, and catch limits from standard harvest control rules	Merrill Rudd, Chantel Wetzel, and Jason Cope	NOAA	No	Medium	Rudd, M., Wetzel, C. and J. Cope. Evaluating the performance of length and catch models in the Stock Synthesis framework (mimeo)	Yes	ADMB, R, Rshiny	Yes	Yes	User specified	Yes	No	Yes	No	No	Single area	No	Yes	Yes	Yes	Yes	No	User specified	Yes	No	No	Optional - but ideally should be considering total removals	Optional - but ideally should be considering total removals	Optional - but ideally should be considering total removals	Yes	No	No	No	No	No	Yes	Yes	No	Yes	No	No	No	No	No	No	No	No	No	No	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	No	No
XSSS (SS-DL)	https://github.com/shcaba/SS-DL-tool	Integrated assessment	Single species	Supported and Recommended	Recommended for catch plus index cases. There are several forms of SSS - SS-CL is appropriate for cases with catch and length data; XSSS is appropriate for catch and index data	Extended Simple Stock Synthesis	This approach builds on Simple Stock Synthesis (SSS) (see description of the SSS assessment method for more details) by incorporating an index of abundance. The index is used to help refine the user specified stock status priors by using adaptive importance sampling to update the posterior stock status with the information provided by the abundance index. By using the Stock Synthesis modelling framework, XSSS, like SSS, allows for flexibility in exploring uncertainty in any life history parameter, as the number of fleets and their associated selectivity values. The main application is to estimate a sustainable catch.	Jason Cope and Chantel Wetzel	NOAA	Yes	Medium	Wetzel, C.R., and Punt, A.E. 2015. Evaluating the performance of data-moderate and catch-only assessment methods for U.S. west coast groundfish. Fisheries Research 171: 170-187. https://www.sciencedirect.com/science/article/pii/S016578361500185X	Yes	ADMB, R, Rshiny	Yes	Yes	User specified	Yes	No	Yes	No	No	Single area	No	Yes	Yes	Yes	Yes	No	User specified	Yes	Yes	No	Yes	Yes	Optional - but ideally should be considering total removals	No	No	No	No	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Yes (if CPUE is being used as proxy abundance index)	No	Yes	No	No	No	No	No	No	No	Yes	No	No	No	No	Yes	No	No	Yes	Yes	No	No
Package name		Model type		Package status							Expertise			Programming language	GUI			Sex structure			Tagging		Spatial resolution	Stock structure										Catch															Abundance index																						Short-lived species