Samu Mäntyniemi | University of Helsinki (original) (raw)

Papers by Samu Mäntyniemi

List of abbreviations used; table presenting all the model variables with their sources of input;... more List of abbreviations used; table presenting all the model variables with their sources of input; information on the Bayesian networks; descriptions of the submodels; results and discussion concerning the background uncertainty; link to the model file.

Environmental science & technology, Jan 17, 2015

The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea,... more The growth of maritime oil transportation in the Gulf of Finland (GoF), North-Eastern Baltic Sea, increases environmental risks by increasing the probability of oil accidents. By integrating the work of a multi-disciplinary research team and information from several sources, we have developed a probabilistic risk assessment application that considers the likely future development of maritime traffic and oil transportation in the area and the resulting risk of environmental pollution. This metamodel is used to compare the effects of two preventative management actions on the tanker collision probabilities and the consequent risk. The resulting risk is evaluated from four different perspectives. Bayesian networks enable large amounts of information about causalities to be integrated and utilized in probabilistic inference. Compared with the baseline period of 2007-2008, the worst-case scenario is that the risk level increases four-fold by the year 2015. The management measures are eva...

In this paper, we demonstrate how information from broodstocks can be combined with lab informati... more In this paper, we demonstrate how information from broodstocks can be combined with lab information on alevins to obtain annual stock-specific mortality estimates from early mortality syndromes (EMS) using a probabilistic approach, how a hierarchical model structure can be used to predict these mortality rates for related, partly sampled, or unsampled stocks, and why these estimates should be used to

Environmental Science & Technology, 2013

Oil transport has greatly increased in the Gulf of Finland over the years, and risks of an oil ac... more Oil transport has greatly increased in the Gulf of Finland over the years, and risks of an oil accident occurring have risen. Thus, an effective oil combating strategy is needed. We developed a Bayesian Network (BN) to examine the recovery efficiency and optimal disposition of the Finnish oil combating vessels in the Gulf of Finland (GoF), Eastern Baltic Sea. Four alternative home harbors, five accident points, and ten oil combating vessels were included in the model to find the optimal disposition policy that would maximize the recovery efficiency. With this composition, the placement of the oil combating vessels seems not to have a significant effect on the recovery efficiency. The process seems to be strongly controlled by certain random factors independent of human action, e.g. wave height and stranding time of the oil. Therefore, the success of oil combating is rather uncertain, so it is also important to develop activities that aim for preventing accidents. We found that the model developed is suitable for this type of multidecision optimization. The methodology, results, and practices are further discussed.

Environmental Reviews, 2012

Excessively high rates of fishing mortality have led to rapid declines of several commercially im... more Excessively high rates of fishing mortality have led to rapid declines of several commercially important fish stocks. To harvest fish stocks sustainably, fisheries management requires accurate information about population dynamics, but the generation of this information, known as fisheries stock assessment, traditionally relies on conservative and rather narrowly data-driven modelling approaches. To improve the information available for fisheries management, there is a demand to increase the biological realism of stock-assessment practices and to better incorporate the available biological knowledge and theory. Here, we explore the development of fisheries stock-assessment models with an aim to increasing their biological realism, and focus particular attention on the possibilities provided by the hierarchical Bayesian modelling framework and ways to develop this approach as a means of efficiently incorporating different sources of information to construct more biologically realistic stock-assessment models. The main message emerging from our review is that to be able to efficiently improve the biological realism of stock-assessment models, fisheries scientists must go beyond the traditional stock-assessment data and explore the resources available in other fields of biological research, such as ecology, lifehistory theory and evolutionary biology, in addition to utilizing data available from other stocks of the same or comparable species. The hierarchical Bayesian framework provides a way of formally integrating these sources of knowledge into the stock-assessment protocol and to accumulate information from multiple sources and over time.

Environmental Management 51(6): 1247-1261., 2013

A participatory Bayesian approach was used to investigate how the views of stakeholders could be ... more A participatory Bayesian approach was used to investigate how the views of stakeholders could be utilized to develop models to help understand the Central Baltic herring fishery. In task one, we applied the Bayesian belief network methodology to elicit the causal assumptions of six stakeholders on factors that influence natural mortality, growth, and egg survival of the herring stock in probabilistic terms. We also integrated the expressed views into a meta-model using the Bayesian model averaging (BMA) method. In task two, we used influence diagrams to study qualitatively how the stakeholders frame the management problem of the herring fishery and elucidate what kind of causalities the different views involve. The paper combines these two tasks to assess the suitability of the methodological choices to participatory modeling in terms of both a modeling tool and participation mode. The paper also assesses the potential of the study to contribute to the development of participatory modeling practices. It is concluded that the subjective perspective to knowledge, that is fundamental in Bayesian theory, suits participatory modeling better than a positivist paradigm that seeks the objective truth. The methodology provides a flexible tool that can be adapted to different kinds of needs and challenges of participatory modeling. The ability of the approach to deal with small data sets makes it cost-effective in participatory contexts. However, the BMA methodology used in modeling the biological uncertainties is so complex that it needs further development before it can be introduced to wider use in participatory contexts.

Comprehensive problem framing that includes different perspectives is essential for holistic unde... more Comprehensive problem framing that includes different perspectives is essential for holistic understanding of complex problems and as the first step in building models. We involved five stakeholders to frame the management problem of the Central Baltic herring fishery. By using the Bayesian belief networks (BBNs) approach, the views of the stakeholders were built into graphical influence diagrams representing variables and their dependencies. The views of the scientists involved concentrated on biological concerns, whereas the fisher, the manager, and the representative of an environmental nongovernmental organization included markets and fishing industry influences. Management measures were considered to have a relatively small impact on the development of the herring stock; their impact on socioeconomic objectives was greater. Overall, the framings by these stakeholders propose a focus on socioeconomic issues in research and management and explicitly define management objectives, not only in biological but also in social and economic terms. We find the approach an illustrative tool to structure complex issues systematically. Such a tool can be used as a forum for discussion and for decision support that explicitly includes the views of different stakeholder groups. It enables the examination of social and biological factors in one framework and facilitates bridging the gap between social and natural sciences. A benefit of the BBN approach is that the graphical model structures can be transformed into a quantitative form by inserting probabilistic information.

Ecology, 2011

A Poisson process is a commonly used starting point for modeling stochastic variation of ecologic... more A Poisson process is a commonly used starting point for modeling stochastic variation of ecological count data around a theoretical expectation. However, data typically show more variation than implied by the Poisson distribution. Such overdispersion is often accounted for by using models with different assumptions about how the variance changes with the expectation. The choice of these assumptions can naturally have apparent consequences for statistical inference. We propose a parameterization of the negative binomial distribution, where two overdispersion parameters are introduced to allow for various quadratic mean-variance relationships, including the ones assumed in the most commonly used approaches. Using bird migration as an example, we present hypothetical scenarios on how overdispersion can arise due to sampling, flocking behavior or aggregation, environmental variability, or combinations of these factors. For all considered scenarios, mean-variance relationships can be appropriately described by the negative binomial distribution with two overdispersion parameters. To illustrate, we apply the model to empirical migration data with a high level of overdispersion, gaining clearly different model fits with different assumptions about mean-variance relationships. The proposed framework can be a useful approximation for modeling marginal distributions of independent count data in likelihood-based analyses.

Ecological Modelling, 2012

Meta-analytic and multi-level stock-recruit analyses have traditionally focussed on the similar s... more Meta-analytic and multi-level stock-recruit analyses have traditionally focussed on the similar stock approach, but for a specific stock-recruit model. For six European herring stocks we embed both the stock and model levels within a fully Bayesian hierarchical framework, thus permitting the consideration of a wider class of models, specifically those that do not admit parameterisation via the steepness and unfished spawning potential. Model and parametric uncertainty is jointly characterised and the challenge of addressing model selection when the model itself is part of the hierarchy is addressed using the deviance information criterion (DIC) and posterior predictive analysis. For the six herring stocks the across-stock posterior evidence in favour of over-compensatory dynamics is fairly strong, with the Ricker and Shepherd models performing the best across the model-selection criteria. For a specific model form we perform a 20 year retrospective analysis (hierarchical and non-hierarchical) to see how temporal information flow occurs in a hierarchical framework, how this can improve our estimates of key parameters, and how this might influence management paradigms (such as Maximum Sustainable Yield) that are based on such estimates. (R.M. Hillary).

Canadian Journal of Fisheries and Aquatic Sciences, 2002

We developed a Bayesian probability model for mark-recapture data. Three alternative versions of ... more We developed a Bayesian probability model for mark-recapture data. Three alternative versions of the model were applied to two sets of data on the abundance of migrating Atlantic salmon (Salmo salar) smolt populations, and the results were then compared with those of two widely used maximum likelihood models (Petersen method and a model using stratified data). Our model follows the basic principles of stochastic models presented for stratified data. In contrast to the earlier models, our model can deal with sparse data. Moreover, even weak dependencies between the studied parameters and the possible factors affecting them can be used to improve the plausibility of the estimates. The assumptions behind our approach are more realistic than those of earlier models, taking into account such factors as overdispersion, which is expected to be present in the mark-recapture data of salmon smolts because of their schooling behavior. Our examples also show that assumptions about the model structure can have a substantial impact on the resulting inferences on the size of the smolt run, especially in terms of the precision of the estimate.

Canadian Journal of Fisheries and Aquatic Sciences, 2006

... 1). To relate the proportion of females with M74-affected off-spring with the actual mortalit... more ... 1). To relate the proportion of females with M74-affected off-spring with the actual mortality among offspring, detailed information has been obtained for two wild salmon stocks, ie, the Tornionjoki and Simojoki rivers (Table 2). By incu-bating in the lab batches of eggs from ...

... q;1 g(Sq \ Lq )g(Lq \ Lq 1 ) (4) In a DAG (Fig. ... The basic idea of the initial values have... more ... q;1 g(Sq \ Lq )g(Lq \ Lq 1 ) (4) In a DAG (Fig. ... The basic idea of the initial values have reached the same stationary distribution. ... WinBUGS calculates certain convergence di, agnostics, but it remains responsibility of the user to decide how many of ...

Aquaculture, 2003

The migration of Atlantic salmon smolts in a river phase was studied in 1996–1999 in the Simojoki... more The migration of Atlantic salmon smolts in a river phase was studied in 1996–1999 in the Simojoki, a river flowing into the northernmost part of the Gulf of Bothnia, northern Finland. The influence of migration distance on the recapture rate of stocked smolts was evaluated and the timing of their migration compared with that of wild smolts. We assumed that

ICES CM, Jan 1, 2009

An important factor influencing fishers' compliance with fisheries regulation is a feeling of leg... more An important factor influencing fishers' compliance with fisheries regulation is a feeling of legitimacy of the policy. Legitimacy includes belief that the management is based on reliable advice. This requires a two-way process rather than a one-way flow of information. We involve stakeholders in the development of biological models for Baltic Sea herring fishery and the evaluation of policy advice using these models. The study concentrates in factors behind the negative biomass trend and poor growth rates of the herring stocks. Alternative model structures are built based on current knowledge of the stakeholders. A "meta-model" is built as a synthesis of the different stakeholder models. This is compared to a model provided by biological research and discussed with the stakeholders. Differences between the views are analysed: how well the scientific model covers the views of the stakeholders, and how the inclusion and linking of additional variables help the stakeholders link the different risk components to their own argumentation? Using a model as an interactive forum can enhance common understanding about the fishery system and consensus about management actions. Participatory approach to modelling may reduce criticism about the use of models in natural resource policy advice where the assumptions and uncertainties are difficult to communicate. It may be a way to involve stakeholders in decision-making processes including a demand for increased transparency and understanding.

The mainstream ICES stock assessment methods were evaluated in their conceptual ability to provid... more The mainstream ICES stock assessment methods were evaluated in their conceptual ability to provide quantitative measures of uncertainty about variables of management interest. Probability statements generated about future of the stock were found to be conceptually inconsistent with the statistical methods used. The Bayesian approach to statistical inference is recommended to substitute the current uncertainty methods in order to achieve conceptually consistent probability statements about the consequences of alternative management actions.