General state-space population dynamics model for Bayesian stock assessment

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Detecting Regime Shifts in Fish Stock Dynamics

Environmental factors such as the water temperature, salinity and the abundance of zooplankton can have significant effects on certain fish stocks’ ability to produce juveniles and, thus, stock renewal ability. This variability in stock productivity manifests itself as different productivity regimes. Here, we detect productivity regime shifts by analyzing recruit-per-spawner time series with Bayesian online change point detection algorithm. The algorithm infers the time since the last regime shift (change in mean or variance or both) as well as the parameters of the data generating process for the current regime sequentially. We demonstrate the algorithm’s performance using simulated recruitment data from an individual-based model, and further apply the algorithm to stock assessment estimates for four Atlantic cod stocks obtained from RAM legacy data base. Our analysis shows that the algorithm performs well when the variability between the regimes is high enough compared to the variability within the regimes. The algorithm found several productivity regimes for all four cod stocks, and the findings suggest that the stocks are currently in low productivity regimes, which have started during the 1990s and 2000s.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Disentangling conditional effects of multiple regime shifts on Atlantic cod productivity

Regime shifts are increasingly prevalent in the ecological literature. However, definitions vary and detection methods are still developing. Here, we employ a novel statistical algorithm based on the Bayesian online change-point detection framework to simultaneously identify shifts in the mean and (or) variance of time series data. We detected multiple regime shifts in long-term (59–154 years) patterns of coastal Norwegian Atlantic cod (>70% decline) and putative drivers of cod productivity: North Atlantic Oscillation (NAO); sea-surface temperature; zooplankton abundance; fishing mortality (F). The consequences of an environmental or climate-related regime shift on cod productivity are accentuated when regime shifts coincide, fishing mortality is high, and populations are small. The analyses suggest that increasing F increasingly sensitized cod in the mid 1970s and late 1990s to regime shifts in NAO, zooplankton abundance, and water temperature. Our work underscores the necessity of accounting for human-induced mortality in regime shift analyses of marine ecosystems.peerReviewe

Examining non-stationarity in the recruitment dynamics of fishes using Bayesian change point analysis

Marine ecosystems can undergo regime shifts, which result in non-stationarity in the dynamics of the fish populations inhabiting them. The assumption of time-invariant parameters in stock-recruitment models can lead to severe errors when forecasting renewal ability of stocks that experience shifts in their recruitment dynamics. We present a novel method for fitting stock-recruitment models using the Bayesian online change point detection algorithm, which is able to cope with sudden changes in the model parameters. We validate our method using simulations, and apply it to empirical data of four demersal fishes in the southern Gulf of St. Lawrence. We show that all of the stocks have experienced shifts in their recruitment dynamics that cannot be captured by a model which assumes time-invariant parameters. The detected shifts in the recruitment dynamics result in clearly different parameter distributions and recruitment predictions between the regimes. The present study illustrates how stock-recruitment relationships can experience shifts which, if not accounted for, can lead to false predictions about a stockâ s recovery ability and resilience to fishing.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Comparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton

Consumer diet estimation with biotracer-based mixing models provides valuable information about trophic interactions and the dynamics of complex ecosystems. Here, we assessed the performance of four Bayesian and three numerical optimization-based diet estimation methods for estimating the diet composition of herbivorous zooplankton using consumer fatty acid (FA) profiles and resource library consisting of the results of homogeneous diet feeding experiments. The method performance was evaluated in terms of absolute errors, central probability interval checks, the success in identifying the primary resource in the diet, and the ability to detect the absence of resources in the diet. Despite occasional large inconsistencies, all the methods were able to identify the primary resource most of the time. The numerical optimization method QFASA using χ2(QFASA-CS) or Kullback­–Leibler (QFASA-KL) distance measures had the smallest absolute errors, most frequently found the primary resource, and adequately detected the absence of resources. While the Bayesian methods usually performed well, some of the methods produced ambiguous results and some had much longer computing times than QFASA. Therefore, we recommend using QFASA-CS or QFASA-KL. Our systematic tests showed that FA models can be used to accurately estimate complex dietary mixtures in herbivorous zooplankton.peerReviewe

Workshop on ICES reference points (WKREF2)

The ICES Workshop on ICES reference points (WKREF2) was tasked review the WKREF1 report and based on the outcome develop updated guidelines for the ICES reference points system and recommendations for ACOM consideration. The WKREF1 report has suggested 5 key recommendations to simplify and harmonise the ICES reference points framework representing a major change to the current guidelines. At WKREF2, we detailed discussions and four key concerns were raised about the proposed approach. The first related to the simplification of rules to define Blim. Around two thirds of category 1 stocks would end up as WKREF1 “Blim Type 2” where Blim would be set as a fraction of B0. The Allee effect or “depensation” maybe more important than previously thought and should be furthered explored for ICES stocks since it has important consequences for Blim. A number of challenges and issues around defining Blim using the current guidelines were documented. Some suggestions on improvement criteria were discussed including using classifiers to define spasmodic stocks and using change point algorithms to address non-stationary productivity regimes. However, further work is need to make these approaches operational and there was no consensus that the WKREF1 Blim types should replace the current guidelines. WKREF1 recommended that the FMSY proxy should be based on a biological proxies and should be less than the deterministic FMSY. It was pointed out that the stochastic FMSY estimated in EqSim for example, is lower than the deterministic FMSY and that the current guidelines ensure that the FMSY should not pose a more than 5% risk to Blim. A large amount of work described in WD 1 was carried out to develop an MSE framework to consistency and robustness test a candidate reference point system for North East Atlantic stocks. However, WKREF2 recommended that further work needs to be carried out to condition and test the simulation framework before the conclusions could be adopted by ICES and incorporated into the guidelines. A number of considerations for defining MSY related reference points were discussed including using model validation and prediction skill to ensure that ICES provide robust and credible advice. There is evidence that density dependence (DD) is important in the majority of ICES stocks (68% in recruitment and 54% in growth). The correct prediction of the shape and strength of density-dependence in productivity is key to predicting future stock development and providing the best possible long-term fisheries management advice. A suggested approach to use surplus production models (SPMs) to account for DD in FMSY was suggested and discussed but there was no consensus on whether that approach was appropriate. There was consensus that the FECO approach as a means of adapting target fishing mortality to medium-term changes in productivity should be included in the guidelines subject to a benchmark and ACOM approval. While WKREF1 and 2 focused mainly on Category 1 stocks ToR c) called for a “simplified and harmonised set of guidelines for estimating MSY and precautionary reference points applicable in the advice framework across various ICES stock categories.” Ideally the ICES assessment categories should provide equivalent risk across all stocks. This issue was discussed but no recommendations emerged. There was no consensus a revised reference point framework was proposed at WKREF2. However, it was agreed that it should be presented here for further discussion at ACOM and other fora. The key feature of the suggested approach is that the stock status evaluation is treated independent of the Advice Rule (AR). The main feature of the system is that the biomass trigger is not linked to a stock status evaluation, it is linked to the expected biomass when fishing at the target fishing mortality, in contrast to the current ICES approach. It also entailed that FMSY would also become an upper limit of fishing mortality and that the advised fishing mortality would be set at or lower than that level. WKREF2 did not discuss what to do in situations where SSB< Blim or alternative forms of HCR for the advice rule. Building community understanding and consensus around simplified and harmonised guidelines has yet to be achieved. A further workshop WKREF3 will be required to achieve that aim. The report includes 6 recommendations for ACOM consideration.nonPeerReviewe