| 11. |
- Sahlin, Ullrika, et al.
(author)
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An evaluation of analyses and data collection of winter loss in honey bees in Sweden
- 2018
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Reports (other academic/artistic)abstract
- This report evaluates current collection and use of data on winter loss in honey bees in Sweden and is a commission from the Swedish Professional BeekepersAssociation. It includes an overview of factors which may influence winter loss in honey bees in Sweden. In Sweden, data on winter loss is collected by twoinstances, the COLOSS survey and the Swedish Beekeepers Association (SBR). We identify several ways to improve his data collection and make it more cost efficient. Several recommendations are provided such as creating a Swedish partnership for bee health which can specify shared goals for winter loss management and identify needs for data and analyses.
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| 12. |
- Sahlin, Ullrika
(author)
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Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?
- 2015
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In: Journal of Computer-Aided Molecular Design. - : Springer Science and Business Media LLC. - 1573-4951 .- 0920-654X. ; 29:7, s. 583-594
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Journal article (peer-reviewed)abstract
- A prediction of a chemical property or activity is subject to uncertainty. Which type of uncertainties to consider, whether to account for them in a differentiated manner and with which methods, depends on the practical context. In chemical modelling, general guidance of the assessment of uncertainty is hindered by the high variety in underlying modelling algorithms, high-dimensionality problems, the acknowledgement of both qualitative and quantitative dimensions of uncertainty, and the fact that statistics offers alternative principles for uncertainty quantification. Here, a view of the assessment of uncertainty in predictions is presented with the aim to overcome these issues. The assessment sets out to quantify uncertainty representing error in predictions and is based on probability modelling of errors where uncertainty is measured by Bayesian probabilities. Even though well motivated, the choice to use Bayesian probabilities is a challenge to statistics and chemical modelling. Fully Bayesian modelling, Bayesian meta-modelling and bootstrapping are discussed as possible approaches. Deciding how to assess uncertainty is an active choice, and should not be constrained by traditions or lack of validated and reliable ways of doing it.
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| 13. |
- Sahlin, Ullrika, et al.
(author)
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Bayesian Evidence Synthesis and the quantification of uncertainty in a Monte Carlo simulation
- 2016
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In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. - 1748-006X. ; 230:5, s. 445-456
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Journal article (peer-reviewed)abstract
- Monte Carlo simulation is a useful technique to propagate uncertainty through a quantitative model, but that is all. When the quantitative modelling is used to support decision-making, a Monte Carlo simulation must be complemented by a conceptual framework that assigns a meaningful interpretation of uncertainty in output. Depending on how the assessor or decision maker choose to perceive risk, the interpretation of uncertainty and the way uncertainty ought to be treated and assigned to input variables in a Monte Carlo simulation will differ. Bayesian Evidence Synthesis is a framework for model calibration and quantitative modelling which has originated from complex meta-analysis in medical decision-making that conceptually can frame a Monte Carlo simulation. We ask under what perspectives on risk that Bayesian Evidence Synthesis is a suitable framework. The discussion is illustrated by Bayesian Evidence Synthesis applied on a population viability analysis used in ecological risk assessment and a reliability analysis of a repairable system informed by multiple sources of evidence. We conclude that Bayesian Evidence Synthesis can conceptually frame a Monte Carlo simulation under a Bayesian perspective on risk. It can also frame an assessment under a general perspective of risk since Bayesian Evidence Synthesis provide principles of predictive inference that constitute an unbroken link between evidence and assessment output that open up for uncertainty quantified taking qualitative aspects of knowledge into account.
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| 14. |
- Sahlin, Ullrika, et al.
(author)
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Differences in the strengths of evidence matters in risk–risk trade-offs
- 2017
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In: Journal of Risk Research. - : Informa UK Limited. - 1366-9877 .- 1466-4461. ; 20:8, s. 988-994
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Journal article (peer-reviewed)abstract
- Making decisions between alternatives are challenging when there is weak or unreliable knowledge about the risks and benefits of the alternatives. This requires a trade-off between risks (and benefits). Here, we comment on a recent paper on risk–risk trade-offs and highlight the difficulties of making such trade-offs when the available evidence is of different strength. One current example of a risk–risk trade-off under weak evidence is the restriction and reevaluation of the risks of neonicotinoid insecticides to bees conducted by the European Food Safety Authority (EFSA). We argue that a risk–risk trade-off is essential in this context. Although considerable research efforts have been focused at determining the risks of neonicotinoids to bees, the evidence base is still limited. However, focus on strengthening evidence on impacts of one substance may lead policy-makers and public to believe that its substitutes are less harmful, when in fact evidence is weak on the impacts of these substitutes as well. We argue that a broader management of uncertainty is needed and that the difference in uncertainty underlying evidence of risk for different alternatives needs to be communicated to policy-makers. We suggest that this can be done, for example, using measures of uncertainty, which take into account strength in evidence, and combine these with principles to guide decision-making.
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| 15. |
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| 16. |
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| 17. |
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| 18. |
- Stjernman, Martin, et al.
(author)
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Estimating effects of arable land use intensity on farmland birds using joint species modeling
- 2019
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In: Ecological Applications. - : Wiley. - 1051-0761 .- 1939-5582. ; 29:4
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Journal article (peer-reviewed)abstract
- Declines in European farmland birds over past decades have been attributed to the combined effects of agricultural intensification and abandonment. Consequently, aspirations to stop declines should focus attention on reversing these changes through voluntary or policy-driven interventions. The design of such interventions should ideally be informed by scientific knowledge of which aspects of the transformation of agricultural landscapes have contributed to the farmland bird declines. Declines may be associated with loss of natural habitats or the intensification and homogenization of land use management on production land, and furthermore, these changes may interact. Here, we applied an orthogonal design exploiting spatial variation in land use in a major agricultural region of Sweden to seek evidence for benefits to farmland birds of reversing some of the intensifications on and among arable fields and whether effects are modified by the availability of seminatural habitats (pastures and field borders) in the landscape. We accounted for the potentially confounding effect of interactions between species by using a joint species distribution model explicitly controlling for additional variation and covariation among species. We found that interventions aimed specifically at land in production could provide benefits to farmland birds. Landscapes with a higher proportion leys or fallows and/or with a more diverse set of crops held higher abundances of most farmland birds. However, effects were only apparent in landscapes with low availability of seminatural habitats and were sometimes even negative in landscapes with high amounts of such habitats, demonstrating context dependence. Even if we found little evidence of interactions between species, the joint modeling approach provided several benefits. It allowed information to be shared between species making analyses robust to uncertainty due to low abundances and provided direct information about the mean and variability in effects of studied predictors among species. We also found that care needs to be taken regarding prior and distributional assumptions as the importance of species interactions might otherwise be overstated. We conclude that this approach is well suited for evaluating agricultural policies by providing evidence for or against certain interventions or to be linked to policy scenarios of land use change.
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| 19. |
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