Introduction

Step 4: Initial Stock Assessment

Fishery assessment is complex. FISHE makes things easier and less complex. To increase certainty in assessment results, take your time and work with stakeholders to review and interpret results.

After conducting a PSA on target species, the next step is to determine whether target stocks are overfished and if so, by how much. Several data-limited methods can be used (in both single and multi-species fisheries) to estimate the degree of stock depletion relative to unfished levels. Examining catch trends or Catch per Unit Effort (CPUE) trends is the simplest way to learn something about stock abundance, however there are important caveats with this method. If catch records are not available, if records do not include catches early in the history of the fishery, or if important changes in management and fishing effort have occurred (thus confounding the relationship between catch and stock size), catch and CPUE trends are difficult to interpret and are not usually good indicators of stock depletion status.

If catch or CPUE trend data do not exist or are difficult to interpret, depletion status can be estimated in at least three other different ways: (1) by comparing fish biomass data from inside and outside well-enforced references areas where fishing is banned, (2) by using catch-based length information, and/or (3) by using visual survey data or some other source of fishery independent fish abundance data. Use the Method Matrix to determine which method is most appropriate for your stocks. It is good to use several methods, if you have the right kind of data, to see if they yield consistent results.

When working in a multispecies fishery, it may again be tempting at this step to group species together and conduct these simple assessments on just one representative from each group. As with the PSA, we strongly recommend against creating these groupings at this step. Instead, choose a simple assessment method that can be applied to each and every species caught in the fishery. Methods available range in terms of data and time requirements as well as complexity (see the Method Matrix), and managers will have to weigh the trade-offs of applying each method to all impacted stocks.

If it is absolutely not possible to apply one of the simple assessment methods to every species in the fishery, species can be grouped at this step according to their PSA-generated vulnerability scores. Making the decision to group stocks before understanding their differential levels of depletion is risky as some stocks may be much closer to collapse than others; remember that the PSA vulnerability score is an estimate of risk, not actual depletion. But if grouping is the only option for a severely capacity-limited fishery, managers should do so based on PSA vulnerability scores, groups should be as small as possible, and groups should consist of species that are as similar to each other as possible. When choosing a species from each group to act as the representative species on which the assessments will be conducted, managers should consider: which species has the most data available; which species has the highest vulnerability score of the group (choose this species as the representative to be precautionary); which species has a vulnerability score that falls right in the middle of the others in the group; and which species has characteristics/ life cycle patterns and behaviors that might make it a good representative for the others in the group (e.g., perhaps one species uses multiple key habitats throughout its life). Finally, note that the criteria applied to choose the representative species may be different for each group.

Methods

Catch Trends

Data on catch and catch-per-unit effort (CPUE) over time (i.e. time series, or trends) can be used directly as indicators of fishery performance without any further analysis. For example, a catch time series that starts off high, decreases, and then levels off may indicate that current catches are sustainable. A catch time series that continuously declines, especially if effort is increasing, may indicate stock depletion and/or overcapitalization of the fleet. These conclusions are based on the assumption that the amount of fish being caught is a reliable proxy for the amount of fish left in the water. 

However, catch trends are difficult to interpret and very easy to misinterpret because so many factors affect catch and catch per unit effort. For example, an increase in catch over time could mean that fish abundance or productivity has been steadily increasing, or it could mean that there are a few strong year classes that are being fished down, or it could mean that the entire population is being fished down from initial high abundance levels, or that prices have increased and therefore fishermen are catching more irrespective of fish abundance. For certain kinds of fisheries (e.g., those targeting spawning aggregations), catch trends provide little useful information regarding stock status. This is because catches will remain high until stock abundance declines to quite low levels that can affect catches on the aggregation. Always use length composition data and/or fishery independent surveys to help interpret catch trends.

Inputs:

  • Total catch for more than one year
  • Catch-Per-Unit-Effort (CPUE) for more than one year
  • Abundance of the catch for more than one year
  • Length-frequency of the catch for more than one year

Outputs:

  • Total catch and trends in total catch
  • CPUE and trends in CPUE
  • Abundance and trends in abundance
  • Average length and trends in average length

Input Sensitivities:

It can be difficult to attribute a change in catch to a corresponding increase or decrease in biomass. Therefore, seeing an increase in catch could provide a false sense of security. Inferring stock status from catch statistics.

Caveats:

  • This method depends on reliably tracking the total catch
  • For example, raw CPUE is seldom proportional to abundance over a whole exploitation history and an entire geographic range, because numerous factors affect catch rates.

Recommendations: 

  • Catch trends can support the interpretation of other analyses, for example of fishing morality or spawning potential ratio (SPR).
  • Understanding how the trends in catch fluctuate from one year to next or in comparison to the historic trends is essential to use catch trends for management.

MPA Density Ratio

No-take marine protected areas (MPAs) and other well-enforced reference areas provide excellent baselines against which to compare fished stocks - better in many respects than even the longest of catch histories. This is because they provide empirical information on the unfished density and length structure of the stock, rather than estimates. Fish densities (measured in kg/ha) inside and outside 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. Effort-based harvest control rules based on measured fish densities can be generated directly, or the results of the analysis can be used in conjunction with PSA results to prioritize stocks for further assessment in order to set catch limits and other management measures. 

When using marine protected areas as a reference baseline, it is important that both fished and unfished areas contain similar habitats and the area has been effectively managed for a long enough period of time for fish stocks to exhibit the characteristics of an unfished population.

Use the calculator below to determine the density ratio of your fishery. Enter the fish density (number or biomass of fish surveyed by area surveyed) of a fished area in the left box, and the fish density of an unfished area in the right box.

Inputs:

  • Fish density inside and outside of a well-managed no-take area

Outputs:

  • Ratio of fished to unfished density as proxy measure of depletion

Assumptions:

  • Habitat quality and productivity are similar inside and outside of the no-take area and the sampled fishing area(s)
  • Fish density in the no-take area is representative of unfished density (i.e., no-take area is well-designed, with representative habitats inside and outside of the managed area; well-enforced; and old enough for fish populations to have equilibrated to no-take conditions)
  • Consistent monitoring program

Reference points:

  • Proxy reference points for stock status, since MSY reference points cannot be calculated using this method
  • Target Reference Point: above 60% (fished/ reserve); no restrictions
  • Limit Reference Point: between 20 % and 60 % (fished/ reserve), reduce length of open season
  • Below 20% (fished/ reserve) - close all year

Recommendations:

  • Effort controls (e.g., season length) can be adjusted in response to changes in MPA density ratio as an indirect way to adjust fishing mortality aimed at moving the MPA density ratio toward targets and away from limits
  • For data on fish density inside and outside of MPAs that are not paired:
    • Calculate average density across fished sites (density.fished)
    • Calculate average density across reserve sites (density.reserve)
    • DR=density.fished/density.reserve
  • For paired sites inside and outside of MPAs
    • Calculate density.fished/density.reserve for each pair and take the mean
  • May need to divide by the density ratio observed during the year the MPA  was established to account for differences in habitat between fished and reserve sites.
  • Combined with methods that estimate F or a reference value for F, effort can be adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far the MPA density ratio is from the proxy reference points
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Fish Density Ratio

The Froese Sustainability Indicator

Using length-frequency of the catch, three simple and easily understood indicators are estimated to assess the status and trends of the fishery.  In this method, the percentage of mature fish, optimal sized fish and "mega-spawners" are calculated using life history parameters, maximum length (L∞), and length-frequency catch data.

Note on length-based methods and gear: Because length-based methods like this one depend on the assumption that the reduction in the numbers of fish in the size classes above Lc (the average size) is due only to mortality, it’s important to ensure that the gear or the way it is being used is not causing peaks in certain size classes, or otherwise affecting the length-frequency composition. Another way to think about this is to regard the catch as a sample of the population of fish and ask, is this sample likely to be representative of the true length composition of the population? If there are good reasons to believe that it is not (if, for example, fishers are discarding small individuals before they are counted in the catch), then length-based methods may not be appropriate for your data.

Inputs:

  • Length-frequency of the catch
  • Theoretical maximum length (L∞)
  • Theoretical length at age zero (T0)
  • Length at maturity (Lm)
  • Expert knowledge of any biases in the data that would make the length composition of the catch less representative of a random sample (e.g., catchability of different sexes or length classes by the gear; catch coming from nursery grounds; etc.)

Input Sensitivities:

  • Accuracy of individual fish length measurements and format of measurements (i.e., total length, standard length, or fork length)
  • Accuracy of length at maturity
  • Selectivity of sampling method
  • Representativeness of sample

Outputs:  

Three metrics of fisheries sustainability (the length-frequency indicators):

  • the percentage of mature fish in catch, with 100% as target;
  • the percentage of individuals in the catch with optimum length (Lopt), with 100% as target; and
  • the percentage of "mega-spawners" in the catch. If catch can be assumed to reflect the age structure of the population (i.e., if no selectivity mechanisms are in place) than 30-40% of mega-spawners in the catch would likely represent a healthy population, with 20% being a lower limit. If, however, the catch cannot be assumed to reflect the age structure of the population, than 0% would be the target for mega-spawners in the catch. 

Assumptions:

  • Life history parameters (e.g., maximum length and size frequency) are known
  • Length composition of the catch reflects that of the population OR selectivity of catch is accounted for by selecting a portion of the length composition data that is relatively unaffected
  • Lm = length at sexual maturity
  • Length-based proxy for MSY is Lmsy = 0.75*Lc + 0.25*Linf (where Lc is the length at first capture) 
  • Length of optimal yield is Lopt = 2/3 of Linf
  • Lmega = Lopt + 10%

Reference points:

  • proxy reference points for proportions of catch in different length categories for precautionary management, monitoring, and assessment.  Also useful for creating harvest control methods (e.g., size or slots limits to manage both recruitment and growth overfishing) to help with rebuilding a target fishery.

Recommendations:

Can be combined with methods that estimate F or a reference value for F, effort can be adjusted through harvest control methods (e.g. catch size limits, seasons, or spatial closures) based on how far apart these values are from sized based recommendations.

Cope and Punt Length-Based Reference Point

Length-based methods can be used to estimate how heavily fished a stock may be. Sustainable fishing practices generally require fishermen to leave large proportions of juveniles in the water so they can spawn at least once to avoid growth overfishing. Large, highly fecund adults should also remain in the water to reduce the risk of recruitment overfishing. Because of this, the length composition of fish in the catch (the number of individual fish in each size category) can be used to calculate indicators of whether or not fishing is sustainable. Because fishing tends to reduce average fish size, declines in fish size can also be a useful indicator of overfishing and overfished status. The Length-Based Reference Point analysis is a recent improvement in length-based analyses that accounts for differences in the selectivity of the fishery.

This method begins by computing the same three percentages calculated for the Froese Sustainability Indicators: the percent of mature fish in the catch (Pmat); the percent of optimally-sized fish in the catch (Popt), and the percent of mega-spawners in the catch (Pmega). Then, these three values are summed to create a new metric called “Pobj.” The Pobj value indicates where the stock stands in relation to recommendations for sustainability: if Pobj is < 1, the fishery is likely not sustainable (fish being caught are either too small); if Pobj is > 1 but < 2, the fishery is catching only a small number of immature and sub-optimally sized fish, and is likely sustainable; if Pobj =2, the fishery is likely sustainable (fish being caught are all optimally sized or larger).

Length-based assessment methods may be difficult to use with some fish species, including those whose growth patterns do not allow easy categorization of length classes into juvenile, adult and highly fecund megaspawners. This is fairly typical in coral reef fishes such as butterflyfish. Length-based assessment is also difficult for species that show little difference in size between length classes. For some species that suffer low rates of natural mortality (e.g., some sharks), it may be more appropriate to protect older juveniles that are close to maturity than young juveniles.

Note on length-based methods and gear: Because length-based methods like this one depend on the assumption that the reduction in the numbers of fish in the size classes above Lc (the average size) is due only to mortality, it’s important to ensure that the gear or the way it is being used is not causing peaks in certain size classes, or otherwise affecting the length-frequency composition. Another way to think about this is to regard the catch as a sample of the population of fish and ask, is this sample likely to be representative of the true length composition of the population? If there are good reasons to believe that it is not (if, for example, fishers are discarding small individuals before they are counted in the catch), then length-based methods may not be appropriate for your data.

Inputs:

  • Length-frequency of the catch
  • Expert knowledge of any biases in the data that would make the length composition of the catch less representative of a random sample (e.g., catchability of different sexes or length classes by the gear; catch coming from nursery grounds; etc.)
  • Theoretical maximum length (L∞)
  • Theoretical length at age zero (T0)
  • Length at maturity (Lm)
  • All length data needs to be measured as Total Length (TL) in order to calculate natural mortality

Input Sensitivities:

  • Accuracy of individual fish length measurements and format of measurements (TL required for this method)
  • Accuracy of length at maturity
  • Selectivity

Outputs:

Three metrics that describe the catch:

  1. Percentage of the catch made up of mature adults (Pmat), with 100% as the target;
  2. Percentage of the catch made up of optimum length individuals (Popt), with 100% ast the target;
  3. Percentage of "mega-spawners" in the catch (Pmega).

These metrics are taken together (Px) to determine if growth and/ or recruitment overfishing is occurring, and can be summed to create "Pobj" which describes the selectivity of the fishery. The value of Pobj can be compared with the Cope and Punt Management Decision Tree to inform Harvest Control Rule(s).

Assumptions/ Caveats:

  • Life history parameters (e.g., maximum length and size frequency) are known
  • Length composition of the catch reflects that of the population OR selectivity of catch is accounted for by selecting a portion of the length composition data that is relatively unaffected
  • Lm = length at sexual maturity
  • Length-based proxy for MSY is Lmsy = 0.75*Lc + 0.25*Linf (where Lc is the length at first capture) 
  • Length of optimal yield is Lopt = 2/3 of Linf
  • Lmega = Lopt + 10%
  • Froese's (2004) sustainability recommendations are effective
  • Due to the reliance on specific size classes, this method may not be appropriate for stocks that exhibit little difference between mature (small) and optimum-sized (medium) individuals.

Reference points:

  • percentage optimum sized individual in catch (Popt) = 1 or  100% as target;
  • Pobj (Pmat + Popt+Pmega) = 2

Recommendations:

  • Pobj < 1; fishing small sized fish, consider size restrictions
  • 1< Pobj < 2;

Can be combined with methods that estimate F or a reference value for F, effort can be adjusted through harvest control methods (e.g. catch size limits, seasons, or spatial closures) based on how far apart these values are from sized based recommendations.

Catch Curve

This method utilizes length-frequency data (fish lengths) to estimate the fishing mortality affecting the fished population. Total mortality (Z) is estimated using the slope of the log transformed age-frequency histogram. Fishing mortality can then be calculated based on the difference between total mortality and natural mortality (F = Z – M). Estimates of M can come from the literature. 

Note on length-based methods and gear: Because length-based methods like this one depend on the assumption that the reduction in the numbers of fish in the size classes above Lc (the average size) is due only to mortality, it’s important to ensure that the gear or the way it is being used is not causing peaks in certain size classes, or otherwise affecting the length-frequency composition. Another way to think about this is to regard the catch as a sample of the population of fish and ask, is this sample likely to be representative of the true length composition of the population? If there are good reasons to believe that it is not (if, for example, fishers are discarding small individuals before they are counted in the catch), then length-based methods may not be appropriate for your data.

Inputs:

  • Length-Frequency data
  • Life history/ growth parameters (k, M, etc.)

Input Sensitivities:   

  • accuracy of individual fish length measurements
  • accuracy of length-at-age relationships (von Bertalanffy growth parameters)
  • Correct fitting of the curve (i.e., preferred fish size)

Outputs:        

  • Estimate of fishing mortality (F)

Assumptions:

  • Life history parameters are known
  • Length is related to age throughout life (i.e., growth is indeterminate – the species just keeps growing longer and longer as it ages until it dies)
  • Depends on reliably tracking population size structure changes, thus may be less accurate for small, fast-growing species
  • Recruitment is constant (i.e., juveniles are becoming adults at about the same rate each year) – this is a simplifying assumption that probably does not hold for any species
  • Mortality is constant – another simplifying assumption that probably does not hold for any species

Reference points:

  • stock status based reference points for F
  • Target Reference Point: M=F
  • Limit Reference Point: F=2M

Recommendations:

  • Stakeholders set management target F/M based on community objectives and thresholds of risk
  • Target F/M is compared with F/M from assessment
  • Effort is adjusted through harvest control rules based on how far apart these values are

When combined with methods that estimate F or a reference value for F, fishing mortality can be adjusted through harvest control methods (e.g. catch size limits, seasons, or spatial closures) based on how far apart these values are from Target and Limit Reference Points.

MPA Catch Curve

This method utilizes length-frequency data (fish lengths) from inside and outside a Marine Protected Area (MPA, here assumed to be a no-take zone) to compare the slope of the right-hand side of the log transformed age-frequency histogram from inside the no-take zone (an estimate of natural mortality (M)) to the slope of the log transformed age-frequency histogram outside the no-take zone (an estimate of total mortality (Z)).   Fishing mortality (F) can then be calculated based on the difference between these two (F = Z – M).

Note on length-based methods and gear: Because length-based methods like this one depend on the assumption that the reduction in the numbers of fish in the size classes above Lc (the average size) is due only to mortality, it’s important to ensure that the gear or the way it is being used is not causing peaks in certain size classes, or otherwise affecting the length-frequency composition. Another way to think about this is to regard the catch as a sample of the population of fish and ask, is this sample likely to be representative of the true length composition of the population? If there are good reasons to believe that it is not (if, for example, fishers are discarding small individuals before they are counted in the catch), then length-based methods may not be appropriate for your data.

Inputs:

  • Length-frequency data inside and outside MPA (preferably collected in the same manner)
  • Life history parameters (growth parameters)
  • Number of years that the MPA has been well-enforced
  • Information on the sizes of fish preferred by the fishery

Input Sensitivities:

  • Accuracy of individual fish length measurements
  • Accuracy of length-at-age relationships (von Bertalanffy growth parameters
  • Correcting fitting of the curve (sensitive to estimates of MPA age, preferred fish size)

Outputs:

  • An estimate of fishing mortality (F)

Assumptions:

  • Assumes that a MPA has been sited appropriately, well-enforced, and been in place long enough for the population living inside the MPA to be a proxy for an un-fished population
  • Implication: May be less accurate for highly-mobile species that do not remain exclusively inside the MPA, such as snapper, tuna and mackerel
  • This method depends on reliably tracking population size structure changes, thus may be less accurate with small, fast-growing species

Reference points:

  • Stock status based reference points for F
  • Target Reference Point: M=F
  • Limit Reference Point: F=2M

Recommendations:

  • Target F/M is compared with F/M from assessment
  • Combined with methods that estimate F or a reference value for F, effort can be adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far apart these values are from target F.

The Mean Length Assessment (LBAR)

Mean length (LBAR) uses fishery dependent or fishery independent length frequency data to estimate fishing pressure using the method described in Ault et al. (2005). In this method, an estimate of fishing mortality is calculated using the minimum (Lc), maximum (L), and average length reported in the catch, together with estimates of the von Bertalanffy growth parameter k and natural mortality (which can be estimated from k). In addition, the first length at full selectivity is used to estimate fishing mortality. Together, assuming that Fmsy equals natural mortality, this method allows the user to calculate F/Fmsy. The control rule of LBAR then adjusts fishing pressure according to the distance of current F/Fmsy from the target F/Fmsy.

Note on length-based methods and gear: Because length-based methods like this one depend on the assumption that the reduction in the numbers of fish in the size classes above Lc (the average size) is due only to mortality, it’s important to ensure that the gear or the way it is being used is not causing peaks in certain size classes, or otherwise affecting the length-frequency composition. Another way to think about this is to regard the catch as a sample of the population of fish and ask, is this sample likely to be representative of the true length composition of the population? If there are good reasons to believe that it is not (if, for example, fishers are discarding small individuals before they are counted in the catch), then length-based methods may not be appropriate for your data.

Inputs:

  • Fishery-dependent or fishery-independent length-frequency data of fished population
  • Theoretical maximum length (L∞)
  • Theoretical length at age zero (T0)
  • Length at maturity (Lm)
  • Natural morality rate (M)
  • von Bertalanffy growth parameter (k)
  • Average length reported in the catch
  • First length at full selectivity to the fishery

Input Sensitivities:

  • Estimate of M and growth parameters
  • Accuracy of individual fish length measurements
  • Representativeness of sampling

Outputs:

  • An estimate of fishing mortality (F)

Assumptions/ Caveats:

  • Life history parameters are known
  • Length is related to age throughout life (i.e., growth is indeterminate – the species just keeps growing longer and longer as it ages until it dies)
  • Recruitment is constant (i.e., juveniles are becoming adults at about the same rate each year) – this is a simplifying assumption that probably does not hold for any species
  • Mortality is constant – another simplifying assumption that probably does not hold for any species
  • Natural mortality (M) is known (this is often not the case)
  • Fmsy = natural mortality (M)
  • System is at equilibrium
  • This method depends on reliably tracking population size structure changes, thus may be less accurate for small, fast-growing species
  • This method is less reliable when mean fish length is very low

Reference points:

  • Stock status based F reference points
  • Target Reference Point: M=F
  • Limit Reference Point: F=2M

Recommendations:

Fishing mortality is adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far apart these values are from Target and Limit Reference Points.

Length-Based Spawning Potential Ratio (SPR)

The spawning potential ratio incorporates the principle that enough fish have to survive to spawn and replenish the stock at a sustainable level. It is a measure of current egg production relative to maximum possible production at un-fished levels. SPR is used to assess a fishery for overfishing. Recruitment overfishing is defined as when the fishing mortality rate is so high that fish are captured before they reach maturity and have the ability to spawn. In general, it is assumed that spawning is proportional to the weight of a species. For this reason, information about the size and weight structure of a stock is particularly helpful in determining the amount of spawning potential retained. Spawning Potential Ratio (SPR) is defined as the percent of unfished spawning potential retained under a given harvest policy. As the fishing mortality increases, the number of fish surviving to a time when they can produce eggs decreases, and so the total egg production decreases. This fished egg production is always expressed as a percentage of the total expected eggs produced under unfished conditions.

This method estimates the fraction of unfished reproductive potential that a fished stock may be theoretically capable of producing by calculating egg production from each length class sampled in the catch.

Note on length-based methods and gear: Because length-based methods like this one depend on the assumption that the reduction in the numbers of fish in the size classes above Lc (the average size) is due only to mortality, it’s important to ensure that the gear or the way it is being used is not causing peaks in certain size classes, or otherwise affecting the length-frequency composition. Another way to think about this is to regard the catch as a sample of the population of fish and ask, is this sample likely to be representative of the true length composition of the population? If there are good reasons to believe that it is not (if, for example, fishers are discarding small individuals before they are counted in the catch), then length-based methods may not be appropriate for your data.

Inputs:

  • Length-frequency data from a fished population
  • Gear selectivity
  • Life history parameters (fecundity, von Bertalanffy (k), natural mortality (M), age-at-maturity, length at age relationships)
  • Weight to length parameters (Wa, Wb)
  • Fecundity at age parameters (fa, fb)

Input Sensitivities:   

  • Accuracy of individual fish length measurements
  • Representativeness of the length data
  • Accuracy of life history information, particularly growth and maturity parameter
  • Natural mortality rate (M)

Outputs:

  • Spawning Potential Ratio (the ratio of current reproductive capacity to maximum potential reproductive capacity of an unfished population)

Assumptions:

  • Dependent on reliably tracking changes in population size structure – may be less accurate for small, fast-growing species
  • Fishery is in equilibrium and conditions are relatively stable (environmental conditions, fishing pressure, stock status, etc).
  • Less accurate if fishing pressures has been changing dramatically year to year

Reference points:

  • Uses stock status based reference points to estimate sustainable yield and maintain F.
  • Target Reference Point: for fast growing species, target is SPR20, or 0.20; for slow growing species, target is SPR40, or 0.40

Recommendations:

  • Uses estimates of F and Target Reference Point for SPR to see if current F supports sustainable yield. Estimate F prior to using this method.
  • Fishing mortality is adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far reference value for F and RPs for SPR.