Evaluation of alternative modelling approaches to account for spatial effects due to age-based movement

2017 ◽  
Vol 74 (11) ◽  
pp. 1832-1844 ◽  
Author(s):  
Hui-Hua Lee ◽  
Kevin R. Piner ◽  
Mark N. Maunder ◽  
Ian G. Taylor ◽  
Richard D. Methot
Keyword(s):  
Population Dynamics ◽  
Stock Assessment ◽  
Simulated Data ◽  
Assessment Method ◽  
Error Modeling ◽  
Assessment Model ◽  
Future Research ◽  
Observation Error ◽  
Time Varying ◽  
Spatial Effects

Spatial patterns due to age-specific movement have been a source of unmodelled process error. Modeling movement in spatially explicit stock assessments is feasible, but hampered by a paucity of data from appropriate tagging studies. This study uses simulation analyses to evaluate alternative model structures that either explicitly or implicitly account for the process of time-varying age-based movement in a population dynamics model. We simulated synthetic populations using a two-area stochastic population dynamics operating model. Simulated data were fit in seven different estimation models. Only the model that includes the correct spatial dynamic results in unbiased and precise estimates of derived and management quantities. In a single-area assessment model, using the fleets-as-area (FAA) approach may be the second best option to estimate both length-based and time-varying age-based selectivity to implicitly account for the contact selectivity and annual availability. An FAA approach adds additional observation error performed nearly as well. Future research could evaluate which stock assessment method is robust to uncertainty in movement and is more appropriate for achieving intended management objectives.

scholarly journals Time-varying natural mortality in fisheries stock assessment models: identifying a default approach

2014 ◽  
Vol 72 (1) ◽  
pp. 137-150 ◽  
Author(s):  
Kelli F. Johnson ◽  
Cole C. Monnahan ◽  
Carey R. McGilliard ◽  
Katyana A. Vert-pre ◽  
Sean C. Anderson ◽  
...  
Keyword(s):  
Stock Assessment ◽  
Assessment Method ◽  
Reference Points ◽  
Natural Mortality ◽  
Time Varying ◽  
Fishing Mortality ◽  
Total Allowable Catch ◽  
Robust Approach ◽  
Structured Population Models ◽  
Trade Offs

Abstract A typical assumption used in most fishery stock assessments is that natural mortality (M) is constant across time and age. However, M is rarely constant in reality as a result of the combined impacts of exploitation history, predation, environmental factors, and physiological trade-offs. Misspecification or poor estimation of M can lead to bias in quantities estimated using stock assessment methods, potentially resulting in biased estimates of fishery reference points and catch limits, with the magnitude of bias being influenced by life history and trends in fishing mortality. Monte Carlo simulations were used to evaluate the ability of statistical age-structured population models to estimate spawning-stock biomass, fishing mortality, and total allowable catch when the true M was age-invariant, but time-varying. Configurations of the stock assessment method, implemented in Stock Synthesis, included a single age- and time-invariant M parameter, specified at one of the three levels (high, medium, and low) or an estimated M. The min–max (i.e. most robust) approach to specifying M when it is thought to vary across time was to estimate M. The least robust approach for most scenarios examined was to fix M at a high value, suggesting that the consequences of misspecifying M are asymmetric.

scholarly journals Looking in the rear-view mirror: bias and retrospective patterns in integrated, age-structured stock assessment models

2014 ◽  
Vol 72 (1) ◽  
pp. 99-110 ◽  
Author(s):  
Felipe Hurtado-Ferro ◽  
Cody S. Szuwalski ◽  
Juan L. Valero ◽  
Sean C. Anderson ◽  
Curry J. Cunningham ◽  
...  
Keyword(s):  
Life Histories ◽  
Temporal Change ◽  
Stock Assessment ◽  
Model Misspecification ◽  
Assessment Model ◽  
Time Varying ◽  
Simulation Framework ◽  
Zero Values ◽  
Age Structured ◽  
Management Advice

Abstract Retrospective patterns are systematic changes in estimates of population size, or other assessment model-derived quantities, that occur as additional years of data are added to, or removed from, a stock assessment. These patterns are an insidious problem, and can lead to severe errors when providing management advice. Here, we use a simulation framework to show that temporal changes in selectivity, natural mortality, and growth can induce retrospective patterns in integrated, age-structured models. We explore the potential effects on retrospective patterns of catch history patterns, as well as model misspecification due to not accounting for time-varying biological parameters and selectivity. We show that non-zero values for Mohn’s ρ (a common measure for retrospective patterns) can be generated even where there is no model misspecification, but the magnitude of Mohn’s ρ tends to be lower when the model is not misspecified. The magnitude and sign of Mohn’s ρ differed among life histories, with different life histories reacting differently from each type of temporal change. The value of Mohn’s ρ is not related to either the sign or magnitude of bias in the estimate of terminal year biomass. We propose a rule of thumb for values of Mohn’s ρ which can be used to determine whether a stock assessment shows a retrospective pattern.

Performance of a fisheries catch-at-age model (Stock Synthesis) in data-limited situations

2011 ◽  
Vol 62 (8) ◽  
pp. 927 ◽  
Author(s):  
Chantell R. Wetzel ◽  
André E. Punt
Keyword(s):  
Stock Assessment ◽  
Estimation Method ◽  
Assessment Method ◽  
Assessment Model ◽  
Limited Data ◽  
Composition Data ◽  
Spawning Stock ◽  
Age Model ◽  
Age Structured ◽  
The Impact

Limited data are a common challenge posed to fisheries stock assessment. A simulation framework was applied to examine the impact of limited data and data type on the performance of a widely used catch-at-age stock-assessment method (Stock Synthesis). The estimation method provided negatively biased estimates of current spawning-stock biomass (SSB) relative to the unfished level (final depletion) when only recent survey indices were available. Estimation of quantities of management interest (unfished SSB, virgin recruitment, target fishing mortality and final depletion) improved substantially even when only minimal-length-composition data from the survey were available. However, the estimates of some quantities (final depletion and unfished SSB) remained biased (either positively or negatively) even in the scenarios with the most data (length compositions, age compositions and survey indices). The probability of overestimating yield at the target SSB relative to the true such yield was ~50%, a risk-neutral result, for all the scenarios that included length-composition data. Our results highlight the importance of length-composition data for the performance of an age-structured assessment model, and are encouraging for the assessment of data-limited stocks.

scholarly journals Evaluating the role of data quality when sharing information in hierarchical multistock assessment models, with an application to Dover sole

2019 ◽  
Vol 76 (10) ◽  
pp. 1819-1835
Author(s):  
Samuel D.N. Johnson ◽  
Sean P. Cox
Keyword(s):  
Statistical Power ◽  
Fishery Management ◽  
Stock Assessment ◽  
Simulated Data ◽  
Assessment Model ◽  
Production Model ◽  
Estimation Errors ◽  
Lower Estimation ◽  
Relative Errors ◽  
Dover Sole

An emerging approach to data-limited fisheries stock assessment uses hierarchical multistock assessment models to group stocks together, sharing information from data-rich to data-poor stocks. In this paper, we simulate data-rich and data-poor fishery and survey data scenarios for a complex of Dover sole (Microstomus pacificus) stocks. Simulated data for individual stocks were used to compare estimation performance for single-stock and hierarchical multistock versions of a Schaefer production model. The single-stock and best-performing multistock models were then used in stock assessments for the real Dover sole data. Multistock models often had lower estimation errors than single-stock models when assessment data had low statistical power. Relative errors for productivity and relative biomass parameters were lower for multistock assessment model configurations. In addition, multistock models that estimated hierarchical priors for survey catchability performed the best under data-poor scenarios. We conclude that hierarchical multistock assessment models are useful for data-limited stocks and could provide a more flexible alternative to data pooling and catch-only methods; however, these models are subject to nonlinear side effects of parameter shrinkage. Therefore, we recommend testing hierarchical multistock models in closed-loop simulations before application to real fishery management systems.

scholarly journals Model averaging to streamline the stock assessment process

2014 ◽  
Vol 72 (1) ◽  
pp. 93-98 ◽  
Author(s):  
Colin P. Millar ◽  
Ernesto Jardim ◽  
Finlay Scott ◽  
Giacomo Chato Osio ◽  
Iago Mosqueira ◽  
...  
Keyword(s):  
Large Scale ◽  
Stock Assessment ◽  
Model Averaging ◽  
Assessment Model ◽  
Assessment Process ◽  
Fish Stock ◽  
Assessment Procedure ◽  
Observation Error ◽  
Fishery Policy ◽  
Fishery Science

Abstract The current fish stock assessment process in Europe can be very resource- and time-intensive. The scientists involved require a very particular set of skills, acquired over their career, drawing from biology, ecology, statistics, mathematical modelling, oceanography, fishery policy, and computing. There is a particular focus on producing a single “best” stock assessment model, but as fishery science advances, there are clear needs to address a range of hypotheses and uncertainties, from large-scale issues such as climate change to specific ones, such as high observation error on young hake. Key to our discussion is the use of the assessment for all frameworks to translate hypotheses into models. We propose a change to the current stock assessment procedure, driven by the use of model averaging to address a range of plausible hypotheses, where increased collaboration between the varied disciplines within fishery science will result in more robust advice.

Separating recruitment and mortality time lags for a delay-difference production model

2015 ◽  
Vol 72 (2) ◽  
pp. 161-165 ◽  
Author(s):  
Emilius A. Aalto ◽  
E.J. Dick ◽  
Alec D. MacCall
Keyword(s):  
Stock Assessment ◽  
Recovery Period ◽  
Time Lag ◽  
Simulated Data ◽  
Assessment Method ◽  
Production Model ◽  
Time Lags ◽  
Age At Maturity ◽  
Production Models ◽  
Pacific Rockfish

Many fishery production models implicitly incorporate a single time lag for both recruitment and mortality despite the fact that in populations of breeding adults, deaths occur yearly while the entry of new adults comes from juveniles born potentially many years prior to adulthood. Models that do not account for this difference in timing will overestimate abundance for a decreasing stock and underestimate increases during a recovery period. We investigated the effect of incorporating unequal recruitment and mortality time lags into depletion-based stock reduction analysis (DB-SRA), a stock assessment method for data-poor species. Using both simulated data and catch series of Pacific rockfish (Sebastes spp.), we found that for declining stocks with no mortality delay and a recruitment time lag equal to age-at-maturity, estimated overfishing limits were up to 40% lower than those from the model with both time lags equal to age-at-maturity. Deviation between the two models’ predictions increases with age-at-maturity and natural mortality rate, suggesting that time lag separation is most important for long-lived species. We propose a correction factor for net production models that eliminates stock overestimation due to implicitly equal time lags.

scholarly journals Standardizing compositional data for stock assessment

2014 ◽  
Vol 71 (5) ◽  
pp. 1117-1128 ◽  
Author(s):  
James T. Thorson
Keyword(s):  
Sample Size ◽  
Approximation Method ◽  
Compositional Data ◽  
Normal Approximation ◽  
Stock Assessment ◽  
Simulated Data ◽  
Future Research ◽  
Effective Sample Size ◽  
Sampling Intensity ◽  
Index Standardization

Abstract Stock assessment models frequently integrate abundance index and compositional (e.g. age, length, sex) data. Abundance indices are generally estimated using index standardization models, which provide estimates of index standard errors while accounting for: (i) differences in sampling intensity spatially or over time; (ii) non-independence of available data; and (iii) the effect of covariates. However, compositional data are not generally processed using a standardization model, so effective sample size is not routinely estimated and these three issues are unresolved. I therefore propose a computationally simple “normal approximation” method for standardizing compositional data and compare this with design-based and Dirichlet-multinomial (D-M) methods for analysing compositional data. Using simulated data from a population with multiple spatial strata, heterogeneity within strata, differences in sampling intensity, and additional overdispersion, I show that the normal-approximation method provided unbiased estimates of abundance-at-age and estimates of effective sample size that are consistent with the imprecision of these estimates. A conventional design-based method also produced unbiased age compositions estimates but no estimate of effective sample size. The D-M failed to account for known differences in sampling intensity (the proportion of catch for each fishing trip that is sampled for age) and hence provides biased estimates when sampling intensity is correlated with variation in abundance-at-age data. I end by discussing uses for “composition-standardization models” and propose that future research develop methods to impute compositional data in strata with missing data.

scholarly journals Consequences of variations in carrying capacity or migration for the perception of Atlantic bluefin tuna (Thunnus thynnus) population dynamics

2007 ◽  
Vol 64 (5) ◽  
pp. 827-836 ◽  
Author(s):  
Jean-Marc Fromentin ◽  
Laurence T Kell
Keyword(s):  
Population Dynamics ◽  
Carrying Capacity ◽  
Stock Assessment ◽  
Population Analysis ◽  
Bluefin Tuna ◽  
Assessment Model ◽  
Thunnus Thynnus ◽  
Commercial Catch ◽  
Atlantic Bluefin Tuna

Spectacular long-term cycles (around 110 years), independent of human exploitation, have been seen in historical catches of Atlantic bluefin tuna (Thunnus thynnus). Previous studies indicated that such variations could have been generated by contrasting but equally plausible dynamic processes, i.e., changes in carrying capacity or migration. A simulation framework was therefore used to test whether the International Commission for the Conservation of Atlantic Tuna stock assessment model, i.e., a virtual population analysis (VPA), can capture such dynamics. The main outcome is that knowledge of the underlying process is crucial, because distinct hypotheses lead to different population dynamics and contrasting performances of the stock assessment model. The VPA is indeed able to reconstruct accurately the historical stock parameters under the carrying-capacity hypothesis, but not under the migratory hypothesis, for which there is often strong bias (up to 500%) in absolute values and in trends of spawning stock biomass and F. Furthermore, it was shown that (i) different phases between exploitation and long-term cycle can induce contrasting terminal F for a same effort and (ii) that there was considerable confounding between the dynamics and increasing effort (as currently seen). We conclude that it is difficult to infer the actual dynamics on the basis of commercial catch data and that novel fishery-independent observation is needed.

Decrease in size at maturity of female American lobsters Homarus americanus (H. Milne Edwards, 1837) (Decapoda: Astacidea: Nephropidae) over a 50-year period in Maine, USA

2019 ◽  
Vol 39 (4) ◽  
pp. 509-515 ◽  
Author(s):  
Jesica D Waller ◽  
Kathleen M Reardon ◽  
Sarah E Caron ◽  
Henry M Masters ◽  
Erin L Summers ◽  
...  
Keyword(s):  
Egg Production ◽  
Gulf Of Maine ◽  
Stock Assessment ◽  
Homarus Americanus ◽  
Assessment Method ◽  
Cement Gland ◽  
Assessment Model ◽  
Size At Maturity ◽  
Significant Difference ◽  
Bank Stock

ABSTRACTThe size at which female American lobsters Homarus americanus (H. Milne Edwards, 1837) reach maturity was determined for females collected from inshore (< 3 nautical miles, or 5.5 km) Boothbay Harbor (BBH), Maine, USA in 2018. A total of 272 females were collected during a three-week period in the spring and each female was assigned a maturity status (immature or mature) based on ovarian staging. These determinations were then compared to two similar female-maturity studies undertaken in BBH by the Maine Department of Marine Resources over the last 50 years. The comparison revealed that the length at which 50% of females reach maturity has decreased by 5 mm over the last 25 years and a significant difference between maturity ogives generated over time in BBH. Cement-gland stage was also recorded for all females and further analysis revealed no significant difference between maturity ogives generated using ovarian and cement-gland staging. Results indicate that cement-gland staging is an effective maturity assessment method in female lobsters from this region. Because there is a well-documented relationship between temperature and size at maturity in crustaceans, we also examined changes in sea-surface temperature during this period in BBH. We found that the region has warmed significantly over this period of time with extreme high temperatures occurring more frequently during the last 25 years. Our results can be used to update key parameters in the stock-assessment model related to growth and egg production in the Gulf of Maine/George’s Bank stock. These findings can also be used to inform future management decisions related to the carapace length of harvested lobsters and the preservation of mature females.

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