About this document
This is a prototype of an automatic report that documents how the user specified the operating model and their various justifications.
Introduction
The history of the fishery is uncertain. Catch data are only available since 2000. The duration of the fishery (No years) is unclear and requires further consultation with industry experts. For now, we are assuming that fishing began in 1960 (hence 58 years). A potentially important source of uncertainty is the fact that Bocinegro is a protogynous hermaphrodite (female -> male) and DLMtool does not currently model such life histories. This feature is a high priority for DLMtool development and is expected to become available in early 2019. The group noted that there is a recreational fishery for Bocinegro but the magnitude and duration of the fishery is currently uncertain.
Fishery Characteristics
Longevity
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Answered
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Very short-lived (5 < maximum age < 7)
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Short-lived (7 < maximum age < 10)
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Moderate life span (10 < maximum age < 20)
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Moderately long-lived (20 < maximum age < 40)
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Long-lived (40 < maximum age < 80)
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Very long-lived (80 < maximum age < 160)
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Stock depletion
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Answered
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Crashed (D < 0.05)
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Very depleted (0.05 < D < 0.1)
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Depleted (0.1 < D < 0.15)
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Moderately depleted (0.15 < D < 0.3)
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Healthy (0.3 < D < 0.5)
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Underexploited (0.5 < D)
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Justification
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The current depletion level for Bocinegro is highly uncertain, as is the case for most data-limited fisheries. Ideally, we would use standardized CPUE data to derive an estimate of depletion. The annual CPUE indicate a 20-30% decline, but are only available since 2000. This would put maximum depletion at 80-70% of unfished, but this still does not account for the historical fishing prior to 2000. As such, we concluded that the stock is unlikely to be ‘crashed’ but also unlikely to be ‘underexploited’. Formal stock reduction analysis (SRA) should be used to determine a more credible range of depletion (Advanced mode). This could make use of length composition data, mean length data or patchy indices.
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Resilence
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Answered
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Not resilient (steepness < 0.3)
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Low resilience (0.3 < steepness < 0.5)
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Moderate resilence (0.5 < steepness < 0.7)
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Resilient (0.7 < steepness < 0.9)
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Very Resilient (0.9 < steepness)
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Justification
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The current depletion level for Bocinegro is highly uncertain, as is the case for most data-limited fisheries. Ideally, we would use standardized CPUE data to derive an estimate of depletion. The annual CPUE indicate a 20-30% decline, but are only available since 2000. This would put maximum depletion at 80-70% of unfished, but this still does not account for the historical fishing prior to 2000. As such, we concluded that the stock is unlikely to be ‘crashed’ but also unlikely to be ‘underexploited’. Formal stock reduction analysis (SRA) should be used to determine a more credible range of depletion (Advanced mode). This could make use of length composition data, mean length data or patchy indices.
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Historical effort pattern
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Answered
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Stable
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Two-phase
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Boom-bust
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Gradual increases
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Stable, recent increases
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Stable, recent declines
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Inter-annual variability in historical effort
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Answered
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Not variable (less than 20% inter-annual change (IAC))
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Variable (maximum IAC between 20% to 50%)
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Highly variable (maximum IAC between 50% and 100%)
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Justification
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Fishing effort has been declining since 2002. On average this appears to be around a 50% reduction over the last 15 years. However, there is likely a poor relationship between nominal effort (days) and fishing mortality rate (and hence CPUE with abundance) due to the variability in species targeting among years.
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Historical fishing efficiency changes
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Answered
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Declining by 2-3% pa (halves every 25-35 years)
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Declining by 1-2% pa (halves every 35-70 years)
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Stable -1% to 1% pa (may halve/double every 70 years)
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Increasing by 1-2% pa (doubles every 35-70 years)
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Increasing by 2-3% pa (doubles every 25-35 years)
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Justification
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Fishing effort has been declining since 2002. On average this appears to be around a 50% reduction over the last 15 years. However, there is likely a poor relationship between nominal effort (days) and fishing mortality rate (and hence CPUE with abundance) due to the variability in species targeting among years.
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Future fishing efficiency changes
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Answered
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Declining by 2-3% pa (halves every 25-35 years)
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Declining by 1-2% pa (halves every 35-70 years)
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Stable -1% to 1% pa (may halve/double every 70 years)
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Increasing by 1-2% pa (doubles every 35-70 years)
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Increasing by 2-3% pa (doubles every 25-35 years)
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Justification
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If fishing gear is composed of pole and line and spatial distribution is assumed to have a well determined spatial fishing distribution, then, unless there are key technological innovations (e.g. live monitoring of spatial catch rates) fishing efficiency should be expected to remain relatively stable.
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Length at maturity
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Answered
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Very small (0.4 < LM < 0.5)
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Small (0.5 < LM < 0.6)
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Moderate (0.6 < LM < 0.7)
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Moderate to large (0.7 < LM < 0.8)
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Large (0.8 < LM < 0.9)
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Justification
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This question was not included in the version of the questionnaire used for our meeting.
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Selectivity of small fish
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Answered
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Very small (0.1 < S < 0.2)
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Small (0.2 < S < 0.4)
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Half asymptotic length (0.4 < S < 0.6)
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Large (0.6 < S < 0.8)
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Very large (0.8 < S < 0.9)
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Justification
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This question was not included in the version of the questionnaire used for our meeting.
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Selectivity of large fish
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Answered
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Asymptotic selectivity (SL = 1)
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Declining selectivity with length (0.75 < SL < 1)
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Dome-shaped selectivity (0.25 < SL < 0.75)
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Strong dome-shaped selectivity (SL < 0.25)
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Justification
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Highly uncertain. Unlikely to be asymptotic due to the fact that fishing is up to 80m in depth, but distribution can be as deep as 250m (and we suspect offshore ontogeny). In addition hook size experiments may indicate that hook size is limiting.
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Discard rate
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Answered
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Low (DR < 1%)
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Low - moderate (1% < DR < 10%)
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Moderate (10% < DR < 30%)
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Moderate - high (30% < DR < 50%)
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High (50% < DR < 70%)
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Justification
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Very few fish are discarded.
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Post-release mortality rate
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Answered
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Low (PRM < 5%)
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Low - moderate (5% < PRM < 25%)
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Moderate (25% < PRM < 50%)
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Moderate - high (50% < PRM < 75%)
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High (75% < PRM < 95%)
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Almost all die (95% < PRM < 100%)
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Justification
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Likely to be low, but rendered largely inconsequential according to previous question F12.
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Recruitment variability
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Answered
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Very low (less than 20% inter-annual changes (IAC))
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Low (max IAC of between 20% and 60%)
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Moderate (max IAC of between 60% and 120%)
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High (max IAC of between 120% and 180%)
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Very high (max IAC greater than 180%)
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Justification
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Likely to be low, but rendered largely inconsequential according to previous question F12.
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Size of an existing MPA
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Answered
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None
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Small (A < 5%)
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Small-moderate (5% < A < 10%)
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Moderate (10% < A < 20%)
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Large (20% < A < 30%)
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Very large (30% < A < 40%)
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Huge (40% < A < 50%)
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Justification
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Assuming that the SEDAR 2012 assessment is relevant to this fishery (on the other side of the Atlantic). The assessment (http://sedarweb.org/docs/wsupp/S60_RD01_2012_S1_UpdateAssessment.pdf, page 76) estimates relatively low recruitment variability (CV ~0.3) for the same species in a different location. Here we bracket that with an answer both below and above this level. However, the Gulf of Cadiz is unique and annual impacts on juvenile survival could include temperature changes and other habitat suitability covariates that are region specific. Note: the answers in the version of the questionnaire were provided in terms of sigma R (Low (0.1 < sigma R < 0.3) and Moderate (0.3 < sigma R < 0.6)).
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Spatial mixing (movement) in/out of existing MPA
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Answered
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Very low (P < 1%)
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Low (1% < P < 5%)
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Moderate (5% < P < 10%)
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High (10% < P < 20%)
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Fully mixed
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Size of a future potential MPA
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Answered
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None
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Small (A < 5%)
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Small-moderate (5% < A < 10%)
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Moderate (10% < A < 20%)
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Large (20% < A < 30%)
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Very large (30% < A < 40%)
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Huge (40% < A < 50%)
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Justification
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Proposal is for a very small MPA with respect to this stock.
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Spatial mixing (movement) in/out of future potential MPA
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Answered
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Very low (P < 1%)
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Low (1% < P < 5%)
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Moderate (5% < P < 10%)
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High (10% < P < 20%)
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Fully mixed
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Justification
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No idea, but diagnostics results should be analyzed to determine whether this factor is of consequence to the MPA management procedure.
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Initial stock depletion
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Answered
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Very low (0.1 < D1 < 0.15)
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Low (0.15 < D1 < 0.3)
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Moderate (0.3 < D < 0.5)
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High (0.5 < D1)
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Asymptotic unfished levels (D1 = 1)
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Management Characteristics
Types of fishery management that are possible
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Answered
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TAC (Total Allowable Catch): a catch limit
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TAE (Total Allowable Effort): an effort limit
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Size limit
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Time-area closures (a marine reserve)
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Justification
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For demonstration purposes, we kept all management types to visualize results. More information is required for the following topics.
1. Describe what, if any, current management measures are used to constrain catch/effort.
2. Describe historical management measures, if any.
3. Describe main strengths and weaknesses of current monitoring and enforcement capacity.
4. Describe and reference any legal/policy requirements for management, monitoring and enforcement.
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TAC offset: consistent overages/underages
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Answered
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Large underages (40% - 70% of recommended)
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Underages (70% - 90% of recommended)
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Slight underages (90% - 100% of recommended)
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Taken exactly (95% - 105% of recommended)
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Slight overages (100% - 110% of recommended)
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Overages (110% - 150% of recommended)
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Large overages (150% - 200% of recommended)
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Justification
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Various aspects of the fishery suggest that TACs and TAEs would be followed fairly closely. For example, there is a single port for landings. However, the recreational catch is unknown, but could comprise 20-40% of the commercial catch. We assume the recreational catch is proportional to the commercial catch and reflected in catch underreporting.
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TAC implementation variability
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Answered
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Constant (V < 1%)
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Not variable (1% < V < 5%)
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Low variability (5% < V < 10%)
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Variable (10% < V < 20%)
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Highly variable (20% < V < 40%)
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Justification
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Uncertain, but unlikely to be either closely followed or not adhered to strongly.
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TAE offset: consistent overages/underages
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Answered
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Large underages (40% - 70% of recommended)
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Underages (70% - 90% of recommended)
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Slight underages (90% - 100% of recommended)
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Taken exactly (95% - 105% of recommended)
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Slight overages (100% - 110% of recommended)
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Overages (110% - 150% of recommended)
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Large overages (150% - 200% of recommended)
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Justification
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This question was not included in the version of the questionnaire used for our meeting.
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TAE implementation variability
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Answered
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Constant (V < 1%)
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Not variable (1% < V < 5%)
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Low variability (5% < V < 10%)
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Variable (10% < V < 20%)
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Highly variable (20% < V < 40%)
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Justification
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This question was not included in the version of the questionnaire used for our meeting.
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Size limit offset: consistent overages/underages
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Answered
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Much smaller (40% - 70% of recommended)
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Smaller (70% - 90% of recommended)
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Slightly smaller (90% - 100% of recommended)
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Taken exactly (95% - 105% of recommended)
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Slightly larger (100% - 110% of recommended)
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Larger (110% - 150% of recommended)
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Much larger (150% - 200% of recommended)
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Justification
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This question was not included in the version of the questionnaire used for our meeting.
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Size limit implementation variability
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Answered
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Constant (V < 1%)
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Not variable (1% < V < 5%)
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Low variability (5% < V < 10%)
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Variable (10% < V < 20%)
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Highly variable (20% < V < 40%)
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Justification
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This question was not included in the version of the questionnaire used for our meeting.
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Data Characteristics
Available data types
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Answered
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Historical annual catches (from unfished)
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Recent annual catches (at least 5 recent years)
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Historical relative abundance index (from unfished)
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Recent relative abundance index (at least 5 recent years)
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Fishing effort
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Size composition (length samples)
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Age composition (age samples)
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Growth (growth parameters)
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Absolute biomass survey
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Justification
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Self explanatory. However, historical catches may be reconstructed. More information should be gathered to address the following topics.
1. Provide the time series (specify years, if possible) that exist for catch, effort, and CPUE/abundance indices.
2. Describe how these data collected (e.g., log books, dealer reporting, observers).
3. Describe what types of sampling programs and methodologies exist for data collection, including the time-series of available sampling data and quality.
4. Describe all sources of uncertainty in the status, biology, life history and data sources of the fishery. Include links to documentation, reports.
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Catch reporting bias
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Answered
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Strong under-reporting (30% - 50%)
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Under-reporting (10% - 30%)
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Slight under-reporting (0% - 10%)
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Reported accurately (+/- 5%)
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Slight over-reporting (less than 10%)
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Justification
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No justification was provided
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Hyperstability in indices
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Answered
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Strong hyperdepletion (2 < Beta < 3)
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Hyperdepletion (1.25 < Beta < 2)
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Proportional (0.8 < Beta < 1.25)
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Hyperstability (0.5 < Beta < 0.8)
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Strong hyperstability (0.33 < Beta < 0.5)
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Justification
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Gear configuration is likely to be constant. However, spatial targeting dynamics (range contraction on high CPUE areas) cannot be ruled out and spatial CPUE standardization is not available to counteract spatial targeting. Thus, a wider range was included here.
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Available data types
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Answered
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Perfect
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Good (accurate and precise)
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Data moderate (some what inaccurate and imprecise)
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Data poor (inaccurate and imprecise)
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Justification
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Assumed to be data-moderate due to precision in observations of annual catches. A sensitivity test is recommended for ‘data poor’ conditions to determine if dataquality would affect the performance of different management strategies.
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Version Notes
The package is subject to ongoing testing. If you find a bug or a problem please send a report to t.carruthers@oceans.ubc.ca so that it can be fixed!
tcar_-2019-11-26-10:25:40
Open Source, GPL-2 2019