Performance review of simple management procedures

Carruthers, Tom; Kell, Laurence T.; Butterworth, Doug; Maunder, Mark N.; Geromont, Helena F; Walters, Carl J.; McAllister, Murdoch K.; Hillary, Richard; Levontin, Polina; Kitakado, Toshihide; Davies, Campbell R.

doi:10.1093/icesjms/fsv212

Cited by 53 publications

(55 citation statements)

References 23 publications

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“…Similar results were reported in other studies (Carruthers et al. , ). Catch‐only approaches that provide fixed catch are frequently implemented by managers; however, such approaches are not robust to a variety of conditions, such as environmental variability, initial depletion level, and unstable (nonequilibrium) stock status when constant catch was initially established.…”

Section: Discussionsupporting

confidence: 93%

“…Within the DLMtool, the operating model is defined as an age‐structured, spatial model and has been detailed thoroughly in Carruthers et al. (, ), SEDAR (), and Harford and Carruthers ().…”

Section: Methodsmentioning

confidence: 99%

“…This metric is the mean difference in the yield of adjacent simulation years (starting from the last historical year) divided by the mean yield over the same time period:

AAVY = \frac{(n_{p} + 1) \sum_{y = n_{h}}^{n_{h} + n_{p - 1}} | {Cat}_{y + 1} - {Cat}_{y} false|}{n_{p} \sum_{y = n_{h}}^{n_{h} + n_{p}} {Cat}_{y}}

where n p is the number of simulation years, n h is the number of historical years, and Cat is the true simulated total removals in year y or y + 1 (Carruthers et al. ). A cutoff of 15% allowable variation in interannual yield was specified by the SEDAR 46 Data and Assessment Workshop Panel (SEDAR ) as follows:

AAVY15 false(% false) = \frac{\sum_{y = t_{1}}^{t_{2}} simulations where AAVY < 0.15}{total simulations} \times 100

where t 1 is the start year of the simulation period and t 2 is the end year of the simulation period.…”

Section: Methodsmentioning

confidence: 99%

“…Catch‐only approaches limiting catch landings to either the mean catch or the third highest catch, as determined over some select period, have performed poorly in simulation by exhibiting greater probabilities of overfishing and lower long‐term yields across a wide range of stock types (Carruthers et al. , ). A key limitation of these approaches is the lack of feedback between stock abundance and prescribed catch advice (i.e., total allowable catch; Geromont and Butterworth , ).…”

mentioning

confidence: 99%

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Working Towards a Framework for Stock Evaluations in Data‐Limited Fisheries

Sagarese

Rios

Cass-Calay

et al. 2018

N American J Fish Manag

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Data‐limited approaches to managing fisheries are widespread in regions where insufficient data prevent traditional stock assessments from determining stock status with sufficient certainty to be useful for management. Where severe data limitations persist, a catch‐only approach is commonly employed, such as in the U.S. Caribbean region. This approach, however, has not received the level of scrutiny required to determine the potential long‐term risks (e.g., probability of overfishing) to fish stocks. In this study, we present a framework for comparison and implementation of data‐limited methods, including the static Status Quo approach, which uses average catch landings. Candidate species for stock evaluation were identified through a data triage and included Yellowtail Snapper Ocyurus chrysurus (Puerto Rico), Queen Triggerfish Balistes vetula (St. Thomas and St. John), and Stoplight Parrotfish Sparisoma viride (St. Croix). Feasible data‐limited methods, based on data availability and quality, included empirical indicator approaches using relative abundance (i.e., catch per unit effort) or mean length. Results from the management strategy evaluation support the use of adaptive data‐limited methods, which incorporate feedback in contrast to the static Status Quo approach. The proposed framework can help guide the development of catch advice for dynamic fisheries management in data‐limited regions.

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Section: Discussionsupporting

confidence: 93%

Section: Methodsmentioning

confidence: 99%

“…This metric is the mean difference in the yield of adjacent simulation years (starting from the last historical year) divided by the mean yield over the same time period:

AAVY = \frac{(n_{p} + 1) \sum_{y = n_{h}}^{n_{h} + n_{p - 1}} | {Cat}_{y + 1} - {Cat}_{y} false|}{n_{p} \sum_{y = n_{h}}^{n_{h} + n_{p}} {Cat}_{y}}

AAVY15 false(% false) = \frac{\sum_{y = t_{1}}^{t_{2}} simulations where AAVY < 0.15}{total simulations} \times 100

where t 1 is the start year of the simulation period and t 2 is the end year of the simulation period.…”

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Working Towards a Framework for Stock Evaluations in Data‐Limited Fisheries

Sagarese

Rios

Cass-Calay

et al. 2018

N American J Fish Manag

View full text Add to dashboard Cite

show abstract

“…A hindcast can help to identify how data and assumptions carry through into future projections, i.e., does the overall character of a projection change when different models and datasets are considered. It can also be used to help identify potential time series to use in empirical or model based harvest control rules (e.g., Hillary et al, 2016;Carruthers et al, 2016).…”

Section: Discussionmentioning

confidence: 99%