MLMA Master Plan Appendix I. Stock Assessment and Data-limited Techniques

This appendix provides an overview of stock assessments and data-limited techniques. As with the other appendices, it is anticipated this overview will continue to be expanded and refined as part of Master Plan implementation so it can serve as an effective resource to managers and stakeholders.


Existing data, and the quality of those data, will generally dictate what types of assessment options are available to aid managers in making management decisions. The term assessment is generally interpreted to mean a quantitative analysis, but there are several data-limited assessment techniques to assist managers in analyzing the available information and making management recommendations. For fisheries with little data, qualitative assessments that rely on stakeholder information, expert judgment, and borrowed information from related fish stocks can be used to fill information gaps and understand relative vulnerability.

This appendix groups different data types into tiers and discusses the data required and possible data-limited assessment techniques available for use at each tier. The tiers are in ascending order with higher levels having more data available. The types of reference points that these assessments produce are also provided. This information is intended to assist managers in understanding the assessment techniques available now and the data that should be collected in the future to employ a particular assessment technique. Table I1 provides a summary of the data-limited assessment techniques available at each tier, dependent on the level of information available.

Tier 1: Qualitative information

In the lowest informational tier, there is little to no quantitative data available with which to conduct an assessment. However, there is generally qualitative information that can be used to make management decisions. Some of the methods available for this tier (Table I1) are frameworks that have been developed to address vulnerabilities and threats at a wide variety of scales, including for target species, bycatch species, and entire ecosystems. Using these tools, the current level of knowledge of the fishery is assessed using information gathered from managers, stakeholders, and expert judgment. Extrapolation, or borrowing information from related fish stocks, can be used to fill information gaps to better understand the biology of the species (Punt et al. 2011). Outputs from this tier might include a determination as to whether the fishery is likely to be vulnerable to exploitation, and recommendations on what data are most valuable to collect to improve the current level of understanding of the fishery (e.g., size of maturity and mean length of the catch). In highly data-limited California fisheries, the Department may be able to use data collected through landing receipts to monitor for major changes in species landed, participation, price, gear used, spatial extent, etc. A significant change in these indicators over a short period of time could alert managers to changes in abundance or fishing effort that might need to be addressed through increased management or data collection.

Measuring a spiny lobster. (CDFW photo)
A female spiny lobster with eggs. (CDFW photo)
Measuring a halibut. (CDFW photo)
Measuring a California halibut. (CDFW photo)

Tier 2: Size data

A number of methods have been developed to infer fishing mortality and reproductive capacity of the stock from size information. One of the simplest indicators of stock status is the average length of fish in the catch. If an understanding of the approximate mean size of the catch is available, this can be compared to the size at first maturity to understand how much of the catch is composed of mature vs. immature individuals (size relative to size-at-maturity; see Table I1). Management recommendations from this tier might include altering size limits, seasons, or gear selectivity to target mature fish, and suggested data collection protocols may involve collection of an unbiased size structure that is representative of the population. For some species, MPAs might provide protection for a portion of the adult biomass in unfished areas, which could increase spawning stock biomass and potentially allow for less stringent fishery controls. This is described in more detail in the ‘Marine Protected Area data – Fishery-independent surveys within MPAs’ section below.

With some additional knowledge of growth parameters, average length can be used to estimate the total mortality (both fishing and natural) of the stock. Natural mortality can be empirically derived, estimated from the maximum age of the stock, or borrowed from a related stock. With an estimate of the natural mortality, the fishing mortality can be calculated by subtracting the natural mortality from the total mortality (mean length; see Table I1). While this method only requires a single year of data, multiple years of size data could be used to track exploitation trends over time and compared against targets.

Length composition data can be used to calculate the proportion of mature fish, optimally sized fish, and large, highly fecund females in a population to determine if stock spawning biomass is at or above a specified target reference point (length-based reference point; see Table I1). Length composition data can also be used to infer the Spawning Potential Ratio (SPR), which is the ratio of the total egg production in fished and unfished states, of the stock (fractional change in lifetime egg production and length-based SPR; see Table I1).

Length-based methods are relatively straight forward to use, but it is important to understand the implications of each method. Typically, these methods assume that the current population is in equilibrium, which allows them to be applied with only a single year of data. Length-based methods are not appropriate for very short-lived stocks, which tend to be dominated by a single year class, or stocks whose abundance fluctuates a great deal from year-to-year. Additionally, length-based methods assume a constant growth rate, and thus are not appropriate for species that have highly variable growth between cohorts or from year-to-year.

Yellowtail, blacksmith, and a lone halfmoon in waters off Southern California. (CDFW photo by Miranda Haggerty)
Pacific hagfish. (CDFW photo)

Tier 3: Catch data

If time series of catch data are available, data-moderate assessment methods may be used. A number of methods have been developed to estimate a sustainable catch level based on the logic that historic catches during times of stock stability reflect a level of exploitation the stock can sustain (Zhou 2013). Thus, a simple average catch taken from a period of stability is assumed to be sustainable. The Depletion-Corrected Average Catch (DCAC) (Table I1) method is based on this principle, but it uses historical catch data and an estimated natural mortality rate to correct for the initial depletion in fish abundance typical during the “fish-down” phase in many fisheries (MacCall 2009). The Depletion-Based Stock Reduction Analysis (DB-SRA) (Table I1) combines DCAC with a probability analysis to account for uncertainties in historical biomass estimates (Dick and MacCall 2011). The Cumulative Sum (CUSUM) (Table I1) technique uses catch data as an indicator of trends in abundance. It looks for deviations beyond the standard deviation from the mean to determine trends in catch and, by extension, biomass. With historical catch information, biological parameters, and approximate estimates of the biomass in the first and last years of data, it is possible to use a Schaefer production model to calculate annual biomass. The Schaefer production model is most widely known as the model that is used to estimate the biomass that will produce MSY. This model can be used to set catch limits despite uncertainty about the carrying capacity and growth rate of the population. With in-season CPUE data, it is possible to use the in-season depletion estimator ( Table I1) to set sustainable catch limits. This method assumes that effort efficiency is constant throughout the season, and thus any declines in CPUE are due to a reduction in abundance. By graphing the cumulative catch and effort over the season it is possible to see the point at which an additional unit of effort no longer yields additional catch. Catch-based methods tend to be thought of as data-moderate assessment techniques because many data-poor fisheries have very little historical data or no way to accurately monitor catch. However, with California’s logbook system, catch-based methods may be appropriate for many fisheries that lack the other types of data necessary for a stock assessment. Catch-based methods are primarily used to set catch limits and are most appropriate for fisheries with systems in place to monitor catch in real time and enforce closures once catch limits have been reached.

A spiny lobster with a tag. (CDFW photo)
A tagged halibut. (CDFW photo)

Tier 4: Age or size structure, time series of catch, and indices of abundance

At this information level, there are many quantitative stock assessment methods available to managers. Nearly all these models are based on a population dynamics model. They use mathematical equations to model the recruitment, growth from one age or size class to the next, and mortality (from fishing and natural causes) of a fish population from year-to-year. Modelers fit these population models to the available data to estimate parameters of interest, which are typically the number of fish in the stock and current fishing mortality rate. Having time series of a number of different types of data makes the ability to estimate these parameters more robust. While Table I1 does not provide information on the various types of quantitative stock assessment models available for use, there are several resources online and in the literature that describe the types of analytical techniques available for this tier. See A Guide to Fisheries Stock Assessment: From Data to Recommendations (PDF)(opens in new tab) for a simple description of the different stock assessment models available.

Marine Protected Area data – fishery-independent surveys within Marine Protected Areas

MPAs present new opportunities for fisheries management by acting as reference areas and sources of biological information. Several data-poor assessment methods have been developed to use data from MPAs to assess stock status. One such method, called the density ratio control rule, compares a survey-based estimate of the density of fish outside an MPA to an estimate of density inside the MPA and provides a representation of the stock under unfished conditions. Another MPA-based method, a decision tree that compares size and CPUE data inside and outside of MPAs (Wilson et al. 2010), uses no-take areas as a proxy for historical conditions to determine targets. One potential benefit of this method over those that compare current stock status against historical unfished conditions is that the MPA incorporates contemporary environmental conditions. MPAs may also provide a way to estimate biological parameters that are usually biased by the effects of fishing. In particular, natural mortality is very difficult to estimate in any fished system, however it is one of the most informative biological parameters for fish stocks because it provides information about their natural productivity level. Length-based mortality estimators have been applied to size data sampled from inside of MPAs in the Channel Islands to estimate natural mortality of Spiny Lobster (Kay and Wilson 2012).

While MPA-based assessment methods are promising, they have some caveats. Because fishing is not allowed in MPAs, these methods rely on fishery-independent sampling protocols, which are typically costlier. Additionally, the MPA must be well enforced. The size of the MPA relative to the size of the species’ home range must also be considered since MPAs can provide effective protection for species that spend a significant portion of time in fished areas. Thus, MPAs generally provide more appropriate information for relatively sedentary species with local reproductive input. Finally, MPAs take time to return to equilibrium unfished conditions, and so may not be useful in assessing fish stocks for 15+ years, depending on the life history of the species.

Stock assessments traditionally assume that the stock in question is homogeneously distributed over the management area and targeted with uniform fishing intensity. MPAs violate this assumption (Bohnsack 1999) by creating patches of high biomass inside their borders and potentially fueling stock depletion outside (Hilborn et al. 2006). As such, MPAs and their effects on the spatial distribution of both fish and fishermen may introduce biases in stock assessments (McGilliard et al. 2015). This can lead to mis-specification of catch or effort limits. There is also the question of whether populations within MPAs should be considered when assessing depletion levels and setting harvest limits (Field et al. 2006). Given the mandates to rebuild populations, there is an incentive for managers to count protected biomass in stock assessments to demonstrate increased stock health (Field et al. 2006). There may be pressure from the fishing industry to count the fraction of population in MPAs as part of the total stock when setting catches. Including protected fish when calculating catch limits based on the total vulnerable biomass can lead to unsustainable fishing mortality rates because in reality only a portion of the stock is targeted. Conversely, not taking protected populations into account when determining stock status is likely to lead to a reduction in catch limits in the short-term as well as extend the time period until recovery targets are achieved, both of which may have severe economic impacts.

Empirical vs. model-based indicators to assess stock status

The output of a stock assessment model is usually some form of indicator (e.g., an estimate of fishing mortality or stock abundance) that can be compared to a pre-determined reference point to assess whether the stock is overfished or if overfishing is occurring. However, empirical indicators, which are based on directly measurable indicators such as CPUE or average length, are being used in several data-poor fisheries (Dowling et al. 2016). In some cases, these empirical indicators lead directly to HCRs, effectively replacing the assessment with the monitoring aspect of the harvest strategy. In other cases, the data feed into an HCR, which includes calculations that effectively function as a type of stock assessment, such as decision tree-type HCRs (Prince et al. 2011; Dowling et al. 2016). The Department’s Spiny Lobster FMP uses catch and CPUE as empirical indicators and SPR as a modeled indicator. Empirical indicators can serve as a type of stock assessment tool if managers are able to make inferences about stock status and decisions to adjust fishing behavior. Empirical harvest indicators are not constrained by the need for quantitative population models and can provide some measure of exploitation status. Empirical harvest strategies are often more applicable to data-poor fisheries management as quantitative models are often difficult to apply to data-poor fisheries. It is possible to design indicators that reflect the status of the stock (e.g., acceptable, unacceptable, or somewhere in between) for data-poor fisheries.

Determining the appropriate level of complexity for assessments

Management strategies based on integrated stock assessments are considered the gold standard for fisheries management because they have been shown to outperform those based on data-poor assessments and empirical indicators (Punt et al. 2002). However, these assessments require many different types of data collected over many years. It is very costly to initiate and maintain these types of sampling programs. This type of investment may be practical only for specific situations, such as high-value fisheries or high-risk stocks. Alternative assessment methods that have been shown to adequately achieve management targets and prevent stock collapse may be more appropriate for other stocks. In addition, harvest strategies based on simple assessment methods can be designed to scale in complexity as needed by requiring further data collection or a more defensible assessment when a reference point is passed.

Tradeoffs between ecological and economic risks and the costs associated with management must be considered when making decisions about the required complexity of the management system for a fishery. In scenarios with lower data quality and quantity, management responses can be adjusted in proportion to data limitations to buffer against scientific uncertainty. This may result in a smaller catch than might be obtained under a management system with higher levels of monitoring to offset uncertainty, but the increase in potential management costs to implement such a system might outweigh the potential benefits of increased yield. MSE (discussed in Appendix L) can provide objective methods for deciding what level of assessment is appropriate for a given fishery.

Table I1. A summary of the data-limited assessment techniques available at various levels of information.
Tier Method Description and reference Necessary data Assumptions/caveats Reference point
1 Ecological risk assessment Information from the literature, surveys, and stakeholder interviews are used to generate a risk assessment that identifies the most vulnerable parts of the system. This is used to detect high-risk activities that require immediate management attention and to screen out low-risk activities from further analysis (Smith et al. 2007). • Knowledge of the fishery.
• Knowledge of other activities that could potentially impact the system.
Assumes fishing to be the most important threat facing any given system. Predicts potential future risk based on current (static) conditions. None
1 Comprehensive assessment of risk to ecosystems Quantitatively considers the interaction of all system threats and assesses the risk to the entire ecosystem through inclusion of a comprehensive suite of attributes to characterize system productivity and functioning. Comprehensive assessment of risk to ecosystems generates risk values for each threat-target pair, for ecosystem service production, and for the ecosystem as a whole. • Knowledge of the fishery and external threats.
• Knowledge of ecosystem characteristics and processes.
• Life history parameters (may be borrowed).
Relies on expert knowledge (where data are missing). Precautionary approach may result in overestimation of risk. Predicts potential future risk based on current (static) conditions. None
1 Productivity-susceptibility analysis Productivity is ranked from low to high and based on life history parameters. Susceptibility of the stock to fishing pressure is scaled from low to high based on the fishing mortality rate (including discards) and species behavior, such as schooling and seasonal migrations, which may alter catchability (Patrick et al. 2009). • Knowledge of the fishery.
• Life history parameters, including fecundity.
Assumes that risk depends on the extent of the impact due to fishing, and the productivity of the stock. Where information is missing the scores are set "high", so final risk scores may overestimate actual risk. None
1 Monitoring for major changes Examining logbook/landing receipt data for major changes in a fishery over a five-year period. Could be changes in participation, price, spatial extent of fishery, gear type, etc., that would signal a change in either fishery demand or population status (Dowling et al. 2016). • Knowledge of one or more of the following: species ratios, dominant species landed, spatial extent of fishing, price, number of participants, or gear type. Assumes that sudden changes in peripheral fishery information may be indicative of changes in fishing mortality or abundance. None
2 Length-based reference point Catch-length data are used to calculate the proportion of mature fish, optimally sized fish, and large, highly fecund females in a population to determine if stock spawning biomass is at or above a specified target reference point (Cope and Punt 2009). • Length data for at least one year (catch data are not needed).
• Life history parameters.
Does not estimate optimal harvest levels. Assumes length data are representative of the stock. Proxy for depletion
2 Size relative to size at maturity Compares the size of the catch to the average size at maturity to understand whether the fishery is catching mature fish. If a large proportion of the catch is immature a size limit should be recommended (Punt et al. 2001). • Mean size or approximate proportions at size.
• Size at maturity data.
Assumes length data are representative of the stock. Proxy for fishing mortality (F)
2 Mean length Uses average length and biological parameters from a single year of data to estimate exploitation status (Ault et al. 2005). • Length data from the catch and independent monitoring.
• Life history parameters.
Assumes length data are representative of the stock and equilibrium dynamics. F
2 Fractional change in lifetime egg production Length-frequency data from an unfished (or early exploited) population and the current population, along with information on growth and maturity, are used to determine a limit reference point that represents the persistence of a population. The fractional change is calculated as the ratio of lifetime egg production between the unfished and current populations (O’Farrell and Botsford 2006). • Length data from the fishery and an unfished population.
• Length-egg production relationship.
• Life history parameters.
Does not estimate optimal harvest levels. Can use historical size data or data from an MPA. SPR and F
2 Length-based spawning potential ration) Uses length composition, life history, and selectivity information to estimate SPR and fishing mortality. SPR has been shown to track depletion for some life history types (e.g., long lived, slow growing; Hordyk and Prince 2013). • Length data from the fishery.
• Selectivity at length.
• Life history parameters.
Assumes length data are representative of the stock. Assumes an equilibrium population. SPR, F, and depletion
2 Visual survey spatial assessment Uses visual survey of fish length frequencies and habitat quality/extent to extrapolate stock depletion estimates (Prince 2010). • Fishery-independent length frequency and habitat data. Assumes species-habitat associations are a good indicator of species presence. Depletion
2 Spawning potential ratio-based decision tree The SPR-based decision tree uses length data from the catch and CPUE to improve an initial allowable catch limit by adjusting it based on changes in the size composition of the catch using a SPR as a reference point. Size composition of the catch is broken down into three length classes: small (recruits), medium (prime), and large (old). The decision tree then uses CPUE of each length class (Prince 2010). • Length data from catch.
• Life history parameters, including fecundity.
Assumes linear relationship between CPUE and abundance. Overfishing limit (OFL)
3 In-season depletion estimator Calculates the current stock biomass of target species. Abundance data from completed seasons is compared to current season information, allowing managers to apply harvest rates to biomass estimates to determine appropriate catch limits. • Life history characteristics.
• CPUE over the course of the season.
• Cumulative catch.
Trend indicator only. CPUE is not always accurate due to effort creep, fishermen behavior, and/or stock dynamics. Assumes ecosystem and fishery dynamics in equilibrium. OFL
3 Cumulative sum Uses catch data as an indicator to detect trends in abundance and discern significant changes away from the mean (Scandol 2003). •Time series of landed catch. Assumes that the underlying dynamic of the system have remained constant over time. Assumes that catch is proportional to abundance. Depletion
3 Static average catch Average catches are used to estimate an OFL. Catches can be adjusted downward to reflect uncertainty about stock status (Carruthers et al. 2014). •Historical average catch for a period when there was no evidence of decline.
•Adequate catch data stream to objectively identify such a time period.
Assumes a period of no depletion existed, assumes average catch during this period is representative of MSY. OFL
3 Depletion-corrected average catch Uses historical catch data (10+ yrs) and an estimated natural mortality rate (preferably 0.2 or smaller) to determine potential sustainable yield. An extension of potential-yield models, DCAC is based on the theory that average catch is sustainable if stock abundance has not changed substantially. DCAC divides the target stock into two categories: a sustainable yield component and an unsustainable “windfall” component, which is based upon a one-time drop in stock abundance for a newly-established fishery. DCAC calculates a sustainable fishery yield, provided the stock is kept at historical abundance levels (MacCall 2009). • Catch records >10 years.
• Estimated initial catch.
• Life history parameters.
Requires reliable catch data (landings plus bycatch); does not work well for highly depleted stocks. OFL
3 Depletion-based stock reduction analysis Combines DCAC with a probability analysis to more closely link stock production with biomass and evaluate potential changes in abundance over time. Using Monte Carlo simulations, DB-SRA provides probability distributions for stock size over a given time period, under varying recruitment rates (Dick and MacCall 2011). • Catch records >10 years.
• Estimated initial catch.
• Life history parameters.
Requires reliable catch data (landings plus bycatch); does not work well for highly depleted stocks. OFL
3 Catch maximum sustainable yield Estimates MSY from catch data, resilience of the respective species, and simple assumptions about relative stock sizes at the first and final year of the catch data time series. Uses the Schaefer production model to calculate annual biomasses for a given set of growth and carrying capacity parameters (Martell and Froese 2013). • Catch records.
• Estimated ranges of stock size in the first and final years of the catch data.
• Life history parameters.
Assumes population growth rate and carrying capacity do not change over time. OFL
MPA Marine Protected Area density ratio Fish densities (measured in kg/ha) inside and outside of the MPA can be estimated from the results of fishing or visual surveys. The MPA density ratio (fished/unfished fish density) can then be calculated to serve as an indicator of stock status (McGilliard et al. 2015). • Fish density inside and outside of effectively-managed MPAs.
• Life history parameters.
Assumes reserves are well-enforced and conditions inside represent an unfished population. Depletion
MPA Reserve-based spawning potential ratio Combines age or length data from inside and outside of no-take marine reserves with life history characteristics to estimate sustainable yield from SPRs (Kay and Wilson 2012). • Length or age data inside and outside of MPAs.
• Life history parameters, including fecundity.
Assumes reserves are well-enforced and conditions inside represent an unfished population. SPR and F
MPA Marine Protected Area-based decision tree Similar to the length-based reference point method, the MPA-based decision tree uses spatially explicit, easy to gather catch and age-length data to set and further refine TAC. Additionally, data gathered from inside of no-take MPAs are used as a baseline for an unfished population. TAC is calculated using the current CPUE and target CPUE levels, and then further adjusted with each successive step of the decision tree (Wilson et al. 2010). • CPUE, fish density surveys, or visual census data.
• Age-length data inside and outside of MPAs.
• Life history parameters.
Assumes reserves are well-enforced, conditions inside represent an unfished population and CPUE surveys are unbiased by targeting or aggregation behavior. Assumes linear relationship between CPUE and abundance. OFL


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Photo at top of page: Dungeness crab in the hold of a fishing vessel. (CDFW photo by Christy Juhasz)