In tcarruth/MERA: Method Evaluation and Risk Assessment

Fishery Questions

The Fishery panel is a set of questions about the characteristics of the fish population and its fishery. The operating model parameter mappings of each answer are provided in Appendix Table A.1

Fishery description

Describe the fishery you are modelling and identify yourself and the relevant management agency.

The 'Fishery start' and 'End' dates are important and will be used to simulate your historical fishery. These are the years where there has been substantive fishing (that could impact the stock). The 'End' year after which an MP has been in use. When loading fishery data they must include this range of year. If indicator data are to be uploaded for the Management Performance mode, these data can be in years after the End year.

The text input box provides a place for you to document important qualitative aspects of the fishery that provide necessary context for the questionnaire including

1. The history and current status of the fishery, including fleets, sectors, vessel types and practices/gear by vessel type, landing ports, economics/markets, whether targeted/bycatch, other stocks caught in the fishery.

2. The stock's ecosystem functions, dependencies, and habitat types.

3. Any relevant reference materials and supporting documents, such as stocks assessments, research, and other analyses.

FigInd<-FigInd+1

Figure r paste(SecInd,FigInd,sep="."). The fields of the fishery description question.

Longevity

How long lived is the fish species? This is a critical input determining stock productivity. The parameter M is the instantaneous natural mortality rate. For a review of data-limited methods of estimating M see Kenchington (2014).

FigInd<-FigInd+1
plotM()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified longevity expressed as 'maximum age' and the instantaneous natural mortality rate M.

Stock depletion

Depletion D, refers to current spawning stock biomass relative to unfished.

Since depletion is a data-rich quantity it may not be readily quantified and it may be necessary to specify a wide range of uncertainty for this input to identify MPs that are suitably robust.

In a data-limited situation, coarse information regarding depletion may be obtained from examining length compositions, historical versus current catch rates, or by use of so-called Robin-Hood approaches.

For further information see Carruthers et al. (2014) and Punt et al (2011)

FigInd<-FigInd+1
plotD()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified stock depletion - current spawnign stock biomass relative to 'unfished' levels.

Resilience

How resilient to exploitation is the stock? This question controls recruitment compensation - the extent to which recruitment is reduced from unfished levels (R0) as the spawning stock becomes increasingly depleted below unfishe levels (SSB0). Here resilence is expressed in terms of steepness (h): the fraction of unfished recruitment at 1/5 unfished spawning biomass.

For a useful review of compensatory density dependence in fish populations see Rose et al. (2001).

FigInd<-FigInd+1
ploth()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified recruitment compensation (resilience) expressed as steepness: the fraction of unfished recruitment expected to occur at 1/5 unfishned spawning stock levels.

Historical effort pattern

What temporal pattern best describes the trend in historical annual fishing effort (e.g. boat-days per year, number of trips per year)?

Here the user clicks on the Figure to shape a historical pattern in effort.

If more than one times series of historical effort is given, historical fishing will be simulated subject to all trends in equal frequency.

This question specifies the possible range of mean trends, you will have an opportunity to adjust the extent of inter-annual variability and changes in fishing efficiency (catchability) in the following questions.

Here is an introduction to fishing effort courtesy of the UN FAO.

FigInd<-FigInd+1
plotFP()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified historical trend in fishing effort.

Inter-annual variability in historical effort

The extent of inter-annual variability in historical exploitation rates around the mean trend(s) specified in Fishery question #5. Again, here is the introduction to effort and exploitation rate by the UN FAO..

FigInd<-FigInd+1
plotF()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified historical trend in fishing effort rate subject to additional inter-annual variability.

Historical fishing efficiency changes

The annual percentage increase or decrease in historical fishing efficiency. In targeted fisheries gear efficiency may improve over time given techological improvements in the gear, changes in fishing behavior, fish distribution and information sharing among fishers, among other things. Conversely, non-target or bycatch species may be subject to declining fishing efficiency due to regulations or avoidance behaviors. The catchability (q) is the fraction of available fish caught per unit of effort. For example, a 2% per annum increase in fishing efficiency means that after 35 years twice as many fish will be caught for the same effort as today.

The introduction to fishing efficiency by the FAO provides a basic summary and Arrenguin-Sanchez provide a more comprehensive review of catchability.

FigInd<-FigInd+1
plotqh()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified historical fishing efficiency changes.

Future fishing efficiency changes

This is similar to the previous question but determines the future relationship between fishing mortality rate and effort. This is a principal driver determining the performance differential of management procedures that set catch (TAC) versus effort advice (TAE).

FigInd<-FigInd+1
plotq()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified future fishing efficiency changes.

Length at maturity

Size a maturity relative to asymptotic length (LM).

Note 1: 'maturity' as used by this model (and most fish population dynamics models) is not really whether a fish has fully developed gonads, but rather the fraction of maximum spawning potential per weight. For example, some fishes mature early, but at small sizes they spawn infrequently and their recruits have poor survival (low spawning fraction).

Note 2: asymptotic length is not the maximum length observed but rather the mean expected size of fish at their maximum age under unfished conditions

An ICES workshop report provides an overview of maturity estimation

FigInd<-FigInd+1
plotLM()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified maturity at length, relative to asymptotic length.

Selectivity of small fish

Fishing gear selectivity relative to asymptotic length (S) (ascending limb selectivity). For example, if 50% of 40cm fish are caught and maximum length is 100cm, S = 0.4.

The UN FAO provides an introduction to gear selectivity and how it may be quantified. For a more involved discussion on selectivity see the IATTC CAPAM workshop report

FigInd<-FigInd+1
plotsel()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified selectivity at length (relative to asymptotic length) for the ascending (smaller fish) limb of the curve.

Selectivity of large fish

Fishing gear selectivity of the largest individuals (SL). For example, if only 20% of the longest fish are caught by the gear SL = 0.2. Again here is the FAO introductory document and the IATTC CAPAM workshop report.

FigInd<-FigInd+1
plotdome()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified selectivity at length (relative to asymptotic length) for the descending (larger fish) limb of the curve.

Discard rate

Discard rate: what fraction of fish that are caught are discarded (includes fish that are dead and alive)?

The US National Marine Fisheries Service have a general guide to Understanding Fish Bycatch Discard and Escapee Mortality and one of the authors of that guide, Michael Davis also has a useful article: Key principles for understanding fish bycatch discard mortality.

FigInd<-FigInd+1
plotDR()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified discarding rate (fraction of fish caught that are released).

Post-release mortality rate

Post-release mortality rate (PRM). What fraction of discarded fish die after release?

Again here is NOAA's general guide to Understanding Fish Bycatch Discard and Escapee Mortality and one of the authors and the article fo Michael Davis: Key principles for understanding fish bycatch discard mortality.

FigInd<-FigInd+1
plotPRM()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified post-release mortality rate (fraction of fish that are discarded that subsequently die due to capture).

Recruitment variability

Interannual variability in recruitment (the coefficient of variation in log-normal recruitment deviations, sigma R). Recruitment is expected to change among years in response to changing spawning biomass levels. On top of this is additional variability that may be driven by many factors including varying ocean conditions, amount of spawning habitat, food availability and predation. Sigma R controls the extent of this additional variability in annual recruitments. For example, a sigma R of 10% means that 95% of recruitments will fall in approximately 80-120% of the mean recruitment predicted from spawning biomass.

Edward Houde authored a Comprehensive Review of Recruitment and Sources of Variability and if that isn't sufficient there is Chambers and Trippel (1997).

FigInd<-FigInd+1
plotsigR()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified annual recruitment variability expressed as a log-normal standard deviation.

Size of existing spatial closures

The size of a existing spatial closure (e.g. Marine Protected Area, MPA). The size A, is the % of habitat that is protected (the same fraction closed is applied to the habitats of all life stages, for example spawning and rearing grounds.

The FAO provides a comprehensive review of Marine Protected Areas.

FigInd<-FigInd+1
plotAh()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified historical spatial closure.

Spatial mixing in/out of existing spatial closures

The degree of stock mixing in/out of existing spatial closure. The degree of the spatial mixing of the fish stock is represented as the probability (P) of a fish leaving the spatial closure (i.e. the marine protected area, MPA) between years

FigInd<-FigInd+1
plotVh()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified mixing among existing open/closed areas.

Size of existing spatial closures

The size of a hypothetical future spatial closure (e.g. Marine Protected Area, MPA). The size A, is the % of habitat that is protected (the same fraction closed is applied to the habitats of all life stages, for example spawning and rearing grounds. This question addresses a possible future MPA allowing for the testing of MPs that use this closed area.

FigInd<-FigInd+1
plotA()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified hypothetical future spatial closure.

Spatial mixing in/out of existing spatial closures

The degree of stock mixing in/out of the future hypothetical spatial closure. The degree of the spatial mixing of the fish stock is represented as the probability (P) of a fish leaving the spatial closure (i.e. the marine protected area, MPA) between years

FigInd<-FigInd+1
plotV()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified mixing among future hypothetical open/closed areas.

Initial stock depletion

Initial depletion of the stock relative to asymptotic unfished levels (D1: spawning stock biomass in year 1 relative to equilibrium unfished conditions).

Many fisheries undertake large fluctuations in productivity. In some of these cases, a fishery may have began at a time when the stock was naturally low. This question provides an opportunity to specify this initial depletion. The default however is that the stock was at asymptotic unfished levels in the first year of the fishery

FigInd<-FigInd+1
plotDh()

Figure r paste(SecInd,FigInd,sep="."). An example of MERA specified initial stock depletion (spawning stock biomass relative to unfished levels at the start of the historical simulated time period)

tcarruth/MERA documentation built on Jan. 29, 2021, 3:15 a.m.