The FISHE Process

Raw catch data should generally not be used as an indicator of stock depletion. This is because many factors besides in-the-water abundance can affect catch (e.g., weather, gear changes, fish behavior, etc.), and because without an understanding of effort (e.g., How many people are fishing at a time? Where and when are they fishing? Are they targeting the stock when it aggregates? What methods are they using to locate and track the fish? Etc.) there may be significant decoupling of catch trends from abundance trends. For example, an increase in catch over time could mean that fish abundance has been steadily increasing, or it could mean that there were a few strong year classes in the recent past that are being fished down, or it could mean that the entire population is being fished down from initial high abundance levels as fishery effort increases, 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 until stock abundance declines to quite low levels that can affect catches on the aggregation. 

In addition, the use of catch data alone may inaccurately represent abundance trends and status if the catch trend is not reflective of the entire history of the fishery. For example, catch records from the earliest history of the fishery tend to be low because the capacity of the fishery has not fully developed. Therefore, low historical catch records do not typically reflect low historical abundance levels and are thus a poor proxy for current abundance levels. Similarly, if catch records are not available for a portion of the time series (i.e., a set of years where fishers were not reporting their catch) this can lead to uncertainty in trends. Catches can be high when abundance is high, of course, but catch can also be high when abundance is low if effort (e.g., the number of fishers) has increased dramatically. This further reduces the utility of catch trends as a proxy for abundance. Moreover, using catch and other historical data to evaluate current stock status or estimate reference points is risky because ocean conditions change and are changing even more rapidly and dramatically as a result of climate change. Abundance levels of the past simply may not be possible today.

Catch per Unit Effort (CPUE) is generally an improvement on raw catch data because it accounts for changes in effort. Trends in CPUE, either among season or within season, can be used directly as an indicator of fishery performance without any further analysis. However, it’s preferable to use statistical methods to correct or standardize ‘raw’ CPUE data for external effects such as seasonality, use of gears that target fish (e.g., light sticks on longlines), or other factors that change the catchability: the relationship between catch rates and abundance (see Campbell 2004 in Resources).  Length composition data and/or fishery independent biomass surveys can also help you interpret CPUE trends.

CPUE can be a useful indicator of profitability, because when catch is high and effort is low (resulting in high CPUE), revenues from the sale of the catch can be high while fishing costs can be low because low levels of effort (which entails fuel, labor, and other costs) are expended to catch the fish.  Hence profits can be high.  CPUE trend is often also used as an indicator of stock abundance, since it seems logical that CPUE would be high when stock abundance is high, and that CPUE would be low when stock abundance is low – less fish in the water means that they are harder to find and catch, resulting in lower catch and higher effort (lower CPUE).  However, it is important to critically examine other factors that could explain CPUE trends before concluding that stock abundance is low or high based on CPUE.  For example, if fishermen have improved their ability to target patches of fish that remain in a declining population, CPUE could be high or stable even if stock abundance is low or decreasing.  For this reason, it’s best to use other indicators of fish abundance that are more direct measures, such as fish density estimated from scientific (random) fishing surveys or visual surveys.

Inputs:

• Catch-Per-Unit-Effort (CPUE) for more than three years (ideally more than 5 years)
• Length-frequency of the catch for more than three years (ideally more than 5 years)

Outputs:

• CPUE and trends in CPUE
• Proxy for abundance and trends in abundance
• Average length and trends in average length

Input Sensitivities, Assumptions and Caveats:

• This method depends on reliably tracking the total catch and effort
• Assumes that change in CPUE corresponds to change in biomass/ abundance, which can be very difficult to confirm as a result of targeting behavior or the formation of patches of fish.
• CPUE is seldom proportional to abundance over a whole exploitation history and an entire geographic range, because numerous factors affect catch and effort.
• CPUE can remain relatively stable or even increase while stock abundance is declining.  Thus, stable or increasing CPUE levels could provide a false sense of security

Reference points:

Reference points for trends in CPUE will depend on the characteristics of the stock and fishery, as well as the values, goals, and risk tolerance levels of stakeholders.

Recommendations:

• CPUE trends can support the interpretation of other analyses, for example of fishing morality of spawning potential ratio (SPR).
• CPUE can also serve as an indicator of economic status, because high CPUE is often related to higher profit levels (fishing costs are lower when CPUE is high)
• Understanding how the trends in CPUE fluctuate from one year to next or in comparison to the historic trends is essential to use catch trends for management.

Note: For the purposes of the below discussion, and throughout the FISHE website, the term “Marine Protected Area (MPA)” refers explicitly to an area that has been fully closed off to fishing and other marine uses. Sometimes these areas are called “No-Take Zones” or have other, more specific names, while “MPA” in some places may refer to a less strict form of area-based management (i.e., a “mixed-use area,” or an area where just some forms of fishing are prohibited). However, the following method, and all FISHE methods that rely on “MPAs,” will only work if the area in question is fully closed to all fishing activities. Furthermore, for this method to work, the MPA must also be designed based on scientific guidance, well-enforced, and old enough to have allowed species within its bounds to recover to “unfished” levels.

No-take marine protected areas (MPAs) and other well-enforced, fishing-restricted areas provide excellent reference baselines against which to evaluate the status of stocks that are fished. 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 in-the-water visual surveys. The MPA Density Ratio (fished/unfished fish density) can then be calculated to serve as an indicator of stock status. 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 reference area has been effectively managed (i.e., truly excluding fishing pressure completely) for a long enough period of time for fish stocks to exhibit the characteristics of an unfished population (the necessary length of time for this to happen will depend on the growth rate of the species and other local characteristics). Make sure the same survey methods are used in the fished and unfished areas.

Use the calculator in the Workbook to determine the density ratio of your fishery. 

Inputs:

• Common Life History Parameters
• Fish density inside and outside of a well-managed no-take area

Outputs:

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

Input Sensitivities, Assumptions and Caveats:

• Assumes that a fully-functioning and well-enforced MPA has been sited appropriately with representative habitat inside and outside of the MPA, and has 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.
• Assumes life history data are accurate
• Time trends in this data can be difficult to interpret if densities inside the MPA are changing rapidly.
• Habitat quality and productivity are similar inside and outside of the no-take area and the sampled fishing area(s)
• Survey data in fished and unfished data are taken with the same methods and are unbiased
• 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
• For most finfish species: TRP, above 60% (fished/ reserve); no restrictions
• For most finfish species: LRP, 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) or catch can be adjusted in response to changes in MPA density ratio 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

Using length-frequency of the catch (the numbers of fish in each size class), three simple and easily understood indicators are estimated to assess the status and trends of the fishery using this method. The percentage of mature fish, optimal sized fish and "mega-spawners" (large fish that contribute disproportionately to egg production) are calculated using life history parameters, maximum length (L∞), and length-frequencies from catch data.

Inputs:

• Common Life History Parameters
• Length-frequency of the catch
• Theoretical maximum length (L_∞)
• Theoretical length at age zero (t₀)
• Length at maturity (L_m)
• 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., variable catchability of different sexes or length classes by the gear; catch coming from nursery grounds; etc.)

Outputs:  

Three metrics of fisheries sustainability:

• Percentage of mature fish in catch, with 100% as target;
• Percentage of individuals in the catch with optimum length (L_OPT), with 100% as target; and
• 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, then 0% would be the target for mega-spawners in the catch. 

Input Sensitivities, Assumptions and Caveats:

• Assumes individual fish length measurements are accurate and format of measurements (i.e., total length, standard length, or fork length) are consistent
• Assumes life history parameters (e.g., maximum length, length at maturity and size frequency) are accurate
• Assumes length composition of the sample is representative of total catch (i.e., not overly selective of some size classes for any reason)
• Assumes length composition of the catch is representative of the population OR selectivity of catch is accounted for by selecting a portion of the length composition data that is relatively unaffected
• L_M = length at sexual maturity
• Length-based proxy for MSY is L_MSY = 0.75*L_C + 0.25*L_INF (where L_C is the length at first capture) 
• Length of optimal yield is L_OPT = 2/3 of L_INF
• L_MEGA = L_OPT + 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.

By design, the goal of the Froese method (the previous method) is to avoid growth and recruitment overfishing, but there was no connection to stock status and calculation of future sustainable catches.  To make this connection, Cope and Punt (2009) extended the used of Length-Based Reference Points to include sensitivity to fishery selectivity (i.e., how efficiently the gear captures different size classes of fish), as well as life history traits, and recruitment compensation into the development of length-based reference points. The inclusion of fishery selectivity is an important consideration when interpreting length-frequency data from the catch as it can strongly influence length composition.  Instead of estimating only the proportions of the catch that are in each size category (juveniles, optimally sized adults, and megaspawners) and comparing them to the relatively subjective reference points of the Froese method,  a slightly more complex decision tree is used to estimate a separate somewhat more objective reference point. The extension establishes a critical link between the identification of trends and the design of harvest control rules with a decision tree.  Ultimately, using easy-to-gather catch-length data, the Length-Based Reference Point model (LBRP; Cope and Punt 2009) provides managers with an assessment tool that evaluates whether a stock’s spawning biomass is at or above a specified reference point.

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 (L_M)
• All length data needs to be measured as Total Length (TL) in order to calculate natural mortality

Outputs:

Three metrics that describe the catch:

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

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

Input Sensitivities, Assumptions and Caveats:

• Assumes individual fish length measurements are accurate and format of measurements (TL required for this method) are consistent
• Assumes life history parameters (e.g., maximum length, length at maturity and size frequency) are accurate
• Assumes length composition of the sample is representative of total catch (i.e., not overly selective of some size classes for any reason)
• Assumes length composition of the catch is representative of the population OR selectivity of catch is accounted for by selecting a portion of the length composition data that is relatively unaffected
• L_M = length at sexual maturity
• Length-based proxy for MSY is L_MSY = 0.75*L_c + 0.25*L_INF (where L_c is the length at first capture) 
• Length of optimal yield is L_OPT = 2/3 of L_INF
• L_MEGA = L_OPT + 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 (P_OPT) = 1 or  100% as target;
• P_OBJ (P_MAT + P_OPT+P_MEGA) = 2

Recommendations:

• P_OBJ < 1; fishing small sized fish, consider size restrictions
• 1< P_OBJ < 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.

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 fishing mortality (F) and M, the natural mortality (F = Z – M). Estimates of M can come from the literature, from MPA data (see MPA catch curve, Step 5.6), or can be calculated using the Barefoot Ecologist Tool at this link.

Inputs:

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

Outputs:          

• Estimate of fishing mortality (F)

Input Sensitivities, Assumptions and Caveats:

• Assumes individual fish length measurements are accurate and format of measurements (i.e., total length, standard length, or fork length) are consistent
• Assumes life history parameters (e.g., maximum length, length at maturity and size frequency) are accurate
• Sensitive to accuracy of length-at-age relationships (Von Bertalanffy growth parameters)
• Correct fitting of the curve (i.e., preferred fish size)
• 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:

• TRP: M=F
• LRP: F=2M

Recommendations:

• Stakeholders set target and/or limit F/M ratio (i.e., management Reference Points) based on community objectives and thresholds of risk
• F/M Reference Point is compared with F/M found through assessment
• Effort/ fishing mortality is adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far apart these values are

Note: For the purposes of the below discussion, and throughout the FISHE website, the term “Marine Protected Area (MPA)” refers explicitly to an area that has been fully closed off to fishing and other marine uses. Sometimes these areas are called “No-Take Zones” or have other, more specific names, while “MPA” in some places may refer to a less strict form of area-based management (i.e., a “mixed-use area,” or an area where just some forms of fishing are prohibited). However, the following method, and all FISHE methods that rely on “MPAs,” will only work if the area in question is fully closed to all fishing activities. Furthermore, for this method to work, the MPA must also be designed based on scientific guidance, well-enforced, and old enough to have allowed species within its bounds to recover to “unfished” levels.

This method utilizes length-frequency data (fish lengths) from inside and outside a Marine Protected Area (MPA) 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).

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

Outputs:

• an estimate of fishing mortality (F)

Input Sensitivities, Assumptions and Caveats:

• Assumes life history data are accurate
• Assumes accuracy of individual fish length measurements
• Assumes accuracy of length-at-age relationships (Von Bertalanffy growth parameters
• Assumes correcting fitting of the curve (sensitive to estimates of MPA age, preferred fish size)
• Assumes that a MPA has been sited appropriately (including all critical habitats and large enough to protect species whole range), is well-enforced, and has been in place long enough for the population living inside the MPA to be a proxy for an un-fished population (appropriate length of time will depend on species reproductive and growth characteristics, but at least 1 generation (i.e. the lifespan of one age class of the target species) is recommended)
  • Implication: May be less accurate for slower-growing species that may have had fewer recruitment events within the MPA
  • 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
• Appropriate F/M ratio reference points (targets and limits) are dependent on species life history characteristics, fishery selectivity, and community risk tolerances

Reference points:

• stock status based reference points for F
• TRP:  M=F
• LRP:  F=2M

Recommendations:

• Stakeholders decide on the appropriate target and/or limit F/M ratio (i.e., management Reference Points) based on community objectives and thresholds of risk. Higher F/M ratios (where fishing mortality is closer to, or exceeds, natural mortality) create greater risk of overfishing.
• F/M Reference Point is compared with F/M found through assessment
• Effort/ fishing mortality is adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far apart these values are.
• Can provide a local estimate of M (natural mortality) to be used in other assessment methods that estimate fishing mortality from observation within the MPA.

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). Fishery-dependent data primarily samples the fish that have recruited to a fishery or length and therefore life stages that are select by the gear used in the fishery. Whereas, fishery-independent data can also sample the same length distribution as fishery dependent data, if using the same fishing gear or it can be a more complete representation of the fish that have recruit to the area, including most if not all length classes. It will depend on how the data is collected. Either way, when using length data, the user needs to consider the season the data is collect, if fishing gear is used, and how selective the gear is to various length-classes, and if a species is cryptic or not observable, when using and interpreting length-based methods.

In this method, an estimate of fishing mortality is calculated using the minimum (L_C), 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 uses to estimate fishing mortality. Together, assuming that F_MSY equals natural mortality, this method allows the user to calculate F/F_MSY. The control rule of LBAR then adjusts fishing pressure according to the distance of current F/F_MSY from the target F/F_MSY.

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 (L_M)
• Natural morality rate (M)
• von Bertalanffy growth parameter (k)
• Average length reported in the catch
• First length at full selectivity to the fishery

Outputs:

• An estimate of fishing mortality (F)

Input Sensitivities, Assumptions and Caveats:

• Assumes accuracy of individual fish length measurements
• Sensitive to representativeness of sampling
• Assumes life history parameters, including natural mortality and growth parameters, are known and accurate
• 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)
• F_MSY = 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:

• TRP: M=F
• LRP: F=2M

Recommendations:

• Stakeholders set target and/or limit F/M ratio (i.e., management Reference Points) based on community objectives and thresholds of risk
• F/M Reference Point is compared with F/M found through assessment
• Effort/ fishing mortality is adjusted through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on how far apart these values are

The spawning potential ratio is based on the concept that enough fish have to survive to spawn and replenish the stock at a sustainable level in order for the population to remain viable. 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 recruitment overfishing. Recruitment overfishing occurs when a fishing mortality is so high that fish are captured before they reach maturity and have the ability to spawn. In general, it is assumed that egg production 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, at a given level of fishing mortality.

Inputs:

• Length-frequency data from a fished population
• Gear selectivity
• Common Life history parameters (fecundity, von Bertalanffy (k), natural mortality (M), age-at-maturity, length at age relationships)
• Weight to length parameters (W_a, W_b)
• Fecundity at age parameters (f_a, f_b)

Outputs:

• Spawning Potential Ratio (the ratio of current reproductive capacity to maximum potential reproductive capacity of an unfished population) at different levels of fishing mortality.

Input Sensitivities, Assumptions and Caveats:

• Assumes accuracy of individual fish length measurements
• Sensitive to representativeness of the length data
• Assumes accuracy of life history information, particularly growth and maturity parameter
• Natural mortality rate (M)
• 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.
• TRP, for fast growing targets: SPR₂₀; slow growing targets: SPR₄₀

Recommendations:

• Estimate F using another method such as catch curve or LBAR, and then identify SPR at that F level from the SPR-F output curve.
• Adjust fishing mortality through harvest control methods (e.g. catch limits, seasons, or spatial closures) based on the difference between estimated SPR and the SPR reference point (often 40% for finfish species).

In the term “yield-per-recruit (YPR),” yield refers to the expected weight of fish caught by the fishery in a given season, and recruits are the youngest fish to be caught by the fishery each season. The concept behind this method is that it is possible to maximize the fishery’s yield by catching fish of an “optimal” age.  YPR examines the potential for “growth overfishing” (fishing too hard on the juvenile members of a stock, resulting in low recruitment to the fishery) under a given fishing strategy, and it can help to identify what the optimal fishing rate is that would lead to the maximum yield per recruit, providing an understanding of how fishery selectivity and fishing mortality will affect yield. YPR models can be age or length based depending on the information available (i.e., whether the user has the ability to accurately age the fish or determine the growth rate of a fish).

This method estimates the harvest rate that maximizes cohort biomass under equilibrium conditions by estimating the number of fish surviving to each subsequent age class (given both natural and fishing mortality), as well the length (or weight) of an individual fish at each age. The number of fish surviving to each age class will decrease continually (as some fish die at each age), while the size of each individual surviving fish will increase.

Inputs:

• Length-frequency data from a fished population
• Gear selectivity
• Common Life history parameters (fecundity, von Bertalanffy (k), natural mortality (M), age-at-maturity, length at age relationships)
• Weight to length parameters (W_a, W_b)
• Fecundity at age parameters (f_a, f_b)

Outputs:

• Yield-per-recruit analyses, reflecting schedules of mortality and weight at age in the catch, in the context of growth overfishing

Input Sensitivities, Assumptions and Caveats:

• Assumes constant natural mortality (M) over time
• Assumes constant recruitment to the population.
• Assumes accuracy of individual fish length measurements
• Sensitive to representativeness of the length/weight data
• Assumes accuracy of life history information, particularly growth and maturity parameter
• 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:

• F_max, the (fully-recruited) fishing mortality rate which produces the maximum yield per recruit.
• F0.1, the fishing mortality rate corresponding to 10% of the slope of the yield-per-recruit curve at the origin. The F0.1 reference point was conceptualized as a biologically precautionary target relative to F_max: at F0.1, catch-per-unit-effort is not reduced substantially, but the fishing mortality rate is lower than F_max.

Recommendations:

• Estimate F using another method such as Catch Curve or LBAR, and then identify YPR at that F level from the YPR-F output curve.
• Adjust fishing mortality through harvest control measures (e.g. catch limits, seasons, or spatial closures) based on the difference between estimated YPR and the YPR Reference Point (often 40% for finfish species).