Evaluating evidence for alternative natural mortality and process error assumptions using a state-space, age-structured assessment model

State-space models explicitly separate uncertainty associated with unobserved, time-varying parameters from that which arises from sampling the population. The statistical aspects of formal state-space models are appealing and these models are becoming more widely used for assessments. However, treating natural mortality as known and constant across ages continues to be common practice. We developed a state-space, age-structured assessment model that allowed different assumptions for natural mortality and the degree of temporal stochasticity in abundance. We fit a suite of models where natural mortality was either age-invariant or an allometric function of mass and interannual transitions of abundance were deterministic or stochastic to observations on Gulf of Maine – Georges Bank Acadian redfish (Sebastes fasciatus). We found that allowing stochasticity in the interannual transition in abundance was important and estimating age-invariant natural mortality was sufficient. A simulation study showed low bias in annual biomass estimation when the estimation and simulation model matched and the Akaike imformation criterion accurately measured relative model performance, but it was important to allow simulated data sets to include the stochasticity in interannual transitions of abundance-at-age.

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Estimating the maximum sustainable yield of snow crab ( Chionoecetes opilio ) off Tohoku, Japan via a state‐space stock assessment model with time‐varying natural mortality

Population Ecology ◽

10.1002/1438-390x.12068 ◽

2020 ◽

Author(s):

Yasutoki Shibata ◽

Jiro Nagao ◽

Yoji Narimatsu ◽

Eisuke Morikawa ◽

Yuto Suzuki ◽

...

Keyword(s):

State Space ◽

Stock Assessment ◽

Maximum Sustainable Yield ◽

Assessment Model ◽

Snow Crab ◽

Sustainable Yield ◽

Natural Mortality ◽

Time Varying ◽

Chionoecetes Opilio

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Contrasting decadal trends in mortality between large and small individuals in skate populations in Atlantic Canada

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2012-0179 ◽

2013 ◽

Vol 70 (1) ◽

pp. 74-89 ◽

Cited By ~ 12

Author(s):

Douglas P. Swain ◽

Ian D. Jonsen ◽

James E. Simon ◽

Trevor D. Davies

Keyword(s):

State Space ◽

Early Life ◽

Mortality Rates ◽

State Space Models ◽

Natural Mortality ◽

Juvenile Growth ◽

Life Stages ◽

Grey Seal ◽

Early Life Stages ◽

Scotian Shelf

Mature thorny (Amblyraja radiata), winter (Leucoraja ocellata), and smooth (Malacoraja senta) skates have declined to very low abundance in the southern Gulf of St. Lawrence (SGSL) and on the eastern Scotian Shelf (ESS). We used stage-structured state-space models to examine decadal patterns in mortality rates in these skates. Mortality at early life stages (embryos in egg cases, hatchlings, and (or) small juveniles) appeared to decrease between the 1970s and the 2000s. In contrast, estimated mortality rates increased for larger individuals over this period. Although potentially confounded in models with effects of any changes in juvenile growth, the estimated increases in mortality could not instead be attributed solely to changes in growth. Increases in the mortality of large individuals appeared to reflect increases in natural mortality, possibly due to predation by grey seals. Increases in natural mortality were not evident for skates on the neighbouring western Scotian Shelf, where grey seal abundance has remained lower. Even in the absence of fishing, recovery of skates is unlikely under current ecosystem conditions in the SGSL and on the ESS.

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A state-space stock assessment model for northern cod, including under-reported catches and variable natural mortality rates

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2015-0047 ◽

2016 ◽

Vol 73 (2) ◽

pp. 296-308 ◽

Cited By ~ 44

Author(s):

Noel G. Cadigan

Keyword(s):

State Space ◽

Gadus Morhua ◽

Stock Assessment ◽

Mortality Rates ◽

Assessment Model ◽

Data Sources ◽

Natural Mortality ◽

Similar Data ◽

Subjective Data ◽

Northern Cod

A state-space assessment model for the northern cod (Gadus morhua) stock off southern Labrador and eastern Newfoundland is developed here. The model utilizes information from offshore trawl surveys, inshore acoustic surveys, fishery catch age compositions, partial fishery landings, and tagging. This is done using an approach that avoids the use of subjective data-weighting. Estimates of fishing mortality rates (F) are usually conditional on assumptions about natural mortality rates (M) in stock assessment models. However, by integrating much of the information on northern cod, it is possible to estimate F and M separately. It is also possible to estimate a change in the offshore survey catchability by including inshore acoustic biomass estimates. The proposed model also accounts for biased total catch statistics, which is a common problem in stock assessments. The main goal of the model is to provide realistic projections of the impacts of various levels of future fishery catches on the recovery of this stock. The projections incorporate uncertainty about M and catch. This is vital information for successful future fisheries. The model has been developed for the specific data sources available for northern cod, but it could be adapted to other stocks with similar data sources.

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Fitting deterministic population models to stochastic data: from trajectory matching to state-space models

10.1101/2021.09.13.460028 ◽

2021 ◽

Author(s):

Benjamin Rosenbaum ◽

Emanuel A. Fronhofer

Keyword(s):

State Space ◽

Empirical Data ◽

Community Dynamics ◽

Simulated Data ◽

Single Species ◽

State Space Models ◽

Model Parameters ◽

Observation Error ◽

Ode Models ◽

Stochastic Data

Population and community ecology traditionally has a very strong theoretical foundation with well-known models, such as the logistic and its many variations, and many modification of the classical Lotka-Volterra predator-prey and interspecific competition models. More and more, these classical models are confronted to data via fitting to empirical time-series, from the field or from the laboratory, for purposes of projections or for estimating model parameters of interest. However, the interface between mathematical population or community models and data, provided by a statistical model, is far from trivial. In order to help empiricists make informed decisions, we here ask which error structure one should use when fitting classical deterministic ODE models to empirical data, from single species to community dynamics and trophic interactions. We use both realistically simulated data and empirical data from microcosms to answer this question in a Bayesian framework. We find that pure observation error models mostly perform adequately overall. However, state-space models clearly outperform simpler approaches when observation errors are sufficiently large or biological models sufficiently complex. Finally, we provide a comprehensive tutorial for fitting these models in R.

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Spatiotemporal blocking of the bouncy particle sampler for efficient inference in state-space models

Statistics and Computing ◽

10.1007/s11222-021-10034-6 ◽

2021 ◽

Vol 31 (5) ◽

Author(s):

Jacob Vorstrup Goldman ◽

Sumeetpal S. Singh

Keyword(s):

State Space ◽

Continuous Time ◽

Simulated Data ◽

State Space Models ◽

High Dimensional ◽

Effective Sample Size ◽

Stat Assoc ◽

Dimensional State Space ◽

State Inference ◽

New Algorithms

AbstractWe propose a novel blocked version of the continuous-time bouncy particle sampler of Bouchard-Côté et al. (J Am Stat Assoc 113(522):855–867, 2018) which is applicable to any differentiable probability density. This alternative implementation is motivated by blocked Gibbs sampling for state-space models (Singh et al. in Biometrika 104(4):953–969, 2017) and leads to significant improvement in terms of effective sample size per second, and furthermore, allows for significant parallelization of the resulting algorithm. The new algorithms are particularly efficient for latent state inference in high-dimensional state-space models, where blocking in both space and time is necessary to avoid degeneracy of MCMC. The efficiency of our blocked bouncy particle sampler, in comparison with both the standard implementation of the bouncy particle sampler and the particle Gibbs algorithm of Andrieu et al. (J R Stat Soc Ser B Stat Methodol 72(3):269–342, 2010), is illustrated numerically for both simulated data and a challenging real-world financial dataset.

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Estimating maximum sustainable yield of snow crab (Chionoecetes opilio) off Tohoku Japan via a state-space assessment model with time-varying natural mortality

10.1101/2020.02.02.931428 ◽

2020 ◽

Author(s):

Yasutoki Shibata ◽

Jiro Nagao ◽

Yoji Narimatsu ◽

Eisuke Morikawa ◽

Yuto Suzuki ◽

...

Keyword(s):

State Space ◽

Maximum Sustainable Yield ◽

Biological Characteristics ◽

Assessment Model ◽

Fish Stock ◽

Snow Crab ◽

Sustainable Yield ◽

Natural Mortality ◽

Time Varying ◽

Chionoecetes Opilio

AbstractYield from fisheries is a tangible benefit of ecosystem services and sustaining or restoring a fish stock level to achieve a maximum sustainable yield (MSY). Snow crab (Chionoecetes opilio) off Tohoku has been managed by a total allowable catch since 1996, although their abundance has not increased even after 2011, when fishing pressure rapidly decreased because of the Great East Japan Earthquake. This implies that their biological characteristics, such as recruits, natural mortality coefficient (M), and terminal molting probabilities (p), might have changed. We developed “just another state-space stock assessment model (JASAM)” to estimate the MSY of the snow crab off Tohoku, Japan, considering interannual variations in M and p. The multi-model inference revealed that M increased from 0.2 in 1997 to 0.59 in 2018, although it was not different among the instars, sex, nor terminal molt of crabs. The parameter p also increased by 1.34–2.46 times depending on the instar growth stages from 1997 to 2018. We estimated the MSYs in three scenarios, which drastically changed if M and p were set as they were in the past or at the current values estimated from this study. This result indicated that the MSY of snow crab would also be time-varying based on their time-varying biological characteristics.

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Performance of multispecies assessment models: insights on the influence of diet data

ICES Journal of Marine Science ◽

10.1093/icesjms/fsz053 ◽

2019 ◽

Vol 76 (6) ◽

pp. 1464-1476 ◽

Cited By ~ 3

Author(s):

Vanessa Trijoulet ◽

Gavin Fay ◽

Kiersten L Curti ◽

Brian Smith ◽

Timothy J Miller

Keyword(s):

Stock Assessment ◽

Small Sample Size ◽

Model Fitting ◽

Model Performance ◽

Simulated Data ◽

Small Sample ◽

Data Availability ◽

Data Sets ◽

Age Structured

Abstract Multispecies stock assessment models require predator diet data, e.g. stomach samples. Diet data can be unavailable, sparse, of small sample size, or very noisy. It is unclear if multispecies interactions can be estimated without bias when interactions are weak. Research is needed about how model performance is affected by the availability or quality of diet data and by the method for fitting it. We developed seven age-structured operating models that simulate trophic interactions for two fish species and different scenarios of diet data availability or quality. The simulated data sets were fitted using four statistical catch-at-age models that estimated fishing, predation and residual natural mortality and differed in the way the diet data was fitted. Fitting the models to diet data averaged over time should be avoided since it resulted in estimation bias. Fitting annual diet composition per stomach produced bias estimates due to the occurrence of zeros in the observed proportions and the statistical assumptions for the diet model. Fitting to annual stomach proportions averaged across stomachs led to unbiased results even if the number of stomachs was small, the interactions were weak or some sampled years and ages were missing. These methods should be preferred when fitting multispecies models.

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