An Individual-based Probabilistic Model for Fish Stock Simulation

We define an individual-based probabilistic model of a sole (Solea solea) behaviour. The individual model is given in terms of an Extended Probabilistic Discrete Timed Automaton (EPDTA), a new formalism that is introduced in the paper and that is shown to be interpretable as a Markov decision process. A given EPDTA model can be probabilistically model-checked by giving a suitable translation into syntax accepted by existing model-checkers. In order to simulate the dynamics of a given population of soles in different environmental scenarios, an agent-based simulation environment is defined in which each agent implements the behaviour of the given EPDTA model. By varying the probabilities and the characteristic functions embedded in the EPDTA model it is possible to represent different scenarios and to tune the model itself by comparing the results of the simulations with real data about the sole stock in the North Adriatic sea, available from the recent project SoleMon. The simulator is presented and made available for its adaptation to other species.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

Changes in catch rates and length and age at maturity, but not growth, of an estuarine plotosid (Cnidoglanis macrocephalus) after heavy fishing

The hypothesis that heavy fishing pressure has led to changes in the biological characteristics of the estuary cobbler (Cnidoglanis macrocephalus) was tested in a large seasonally open estuary in southwestern Australia, where this species completes its life cycle and is the most valuable commercial fish species. Comparisons were made between seasonal data collected for this plotosid (eeltail catfish) in Wilson Inlet during 2005–08 and those recorded with the same fishery-independent sampling regime during 1987–89. These comparisons show that the proportions of larger and older individuals and the catch rates in the more recent period were far lower, i.e., they constituted reductions of 40% for fish ≥430 mm total length, 62% for fish ≥4 years of age, and 80% for catch rate. In addition, total mortality and fishing-induced mortality estimates increased by factors of ~2 and 2.5, respectively. The indications that the abundance and proportion of older C. macrocephalus declined between the two periods are consistent with the perception of long-term commercial fishermen and their shift toward using a smaller maximum gill net mesh to target this species. The sustained heavy fishing pressure on C. macrocephalus between 1987–89 and 2005–08 was accompanied by a marked reduction in length and age at maturity of this species. The shift in probabilistic maturation reaction norms toward smaller fish in 2005–08 and the lack of a conspicuous change in growth between the two periods indicate that the maturity changes were related to fishery-induced evolution rather than to compensatory responses to reduced fish densities

General state-space population dynamics model for Bayesian stock assessment

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Integration of biological, economic and sociological knowledge by Bayesian belief networks: the interdisciplinary evaluation of potential Baltic salmon management plan

There is a growing need to evaluate fisheries management plans in a comprehensive interdisciplinary context involving stakeholders. In this paper we demonstrate a probabilistic management model to evaluate potential management plans for Baltic salmon fisheries. The analysis is based on several studies carried out by scientists from respective disciplines. The main part consisted of biological and ecological stock assessment with integrated economic analysis of the commercial fisheries. Recreational fisheries were evaluated separately. Finally, a sociological study was conducted aimed at understanding stakeholder perspectives and potential commitment to alternative management plans. In order to synthesize the findings from these disparate studies a Bayesian Belief Network (BBN) methodology is used. The ranking of management options can depend on the stakeholder perspective. The trade-offs can be analysed quantitatively with the BBN model by combining, according to the decision maker’s set of priorities, utility functions that represent stakeholders’ views. We show how BBN can be used to evaluate robustness of management decisions to different priorities and various sources of uncertainty. In particular, the importance of sociological studies in quantifying uncertainty about the commitment of fishermen to management plans is highlighted by modelling the link between commitment and implementation success.Baltic salmon, bio-economic modelling, Bayesian Belief Network, expert knowledge, fisheries management, commitment and implementation uncertainty, management plan, recreational fisheries, stakeholders., Resource /Energy Economics and Policy,

Measuring reproducibility of high-throughput experiments

Reproducibility is essential to reliable scientific discovery in high-throughput experiments. In this work we propose a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using reproducibility. Unlike the usual scalar measures of reproducibility, our approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. Our curve is fitted by a copula mixture model, from which we derive a quantitative reproducibility score, which we call the "irreproducible discovery rate" (IDR) analogous to the FDR. This score can be computed at each set of paired replicate ranks and permits the principled setting of thresholds both for assessing reproducibility and combining replicates. Since our approach permits an arbitrary scale for each replicate, it provides useful descriptive measures in a wide variety of situations to be explored. We study the performance of the algorithm using simulations and give a heuristic analysis of its theoretical properties. We demonstrate the effectiveness of our method in a ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bayesian fitting of probabilistic maturation reaction norms to population-level data

Probabilistic maturation reaction norms (PMRNs) are an important tool for studying fisheries-induced evolution and environmental effects on life history. To date there has been no way to fit a PMRN to population-level fisheries data; instead individual-level data must be used. This limits the stocks and time periods that can be studied.We introduce a Bayesian method for fitting PMRNs to population-level data. The method is verified against both an existing result and simulated data, and applied to historical Barents Sea cod data which combines observations of population-level variation in age, size and maturity status from Russia and Norway.The method shows a clear and rapid trend towards greater probability of maturation at smaller lengths in the Barents Sea cod.The new model fitting algorithm allows us to study historic changes in life history despite the lack of individual-level data seen in much long term data. Access to more data will aid the study of evolutionary hypotheses in a wide range of organisms