| 11. |
- Knapp, Jessica L., et al.
(author)
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Pollinators, pests and yield—Multiple trade-offs from insecticide use in a mass-flowering crop
- 2022
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In: Journal of Applied Ecology. - : Wiley. - 0021-8901 .- 1365-2664. ; 59:9, s. 2419-2429
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Journal article (peer-reviewed)abstract
- Multiple trade-offs likely occur between pesticide use, pollinators and yield (via crop flowers) in pollinator-dependent, mass-flowering crops (MFCs), causing potential conflict between conservation and agronomic goals. To date, no studies have looked at both outcomes within the same system, meaning win-win solutions for pollinators and yield can only be inferred. Here, we outline a new framework to explore these trade-offs, using red clover (Trifolium pratense) grown for seed production as an example. Specifically, we address how the insecticide thiacloprid affects densities of seed-eating weevils (Protapion spp.), pollination rates, yield, floral resources and colony dynamics of the key pollinator, Bombus terrestris. Thiacloprid did not affect the amount of nectar provided by, or pollinator visitation to, red clover flowers but did reduce weevil density, correlating to increased yield and gross profit. In addition, colonies of B. terrestris significantly increased their weight and reproductive output in landscapes with (compared with without) red clover, regardless of insecticide use. Synthesis and applications. We propose a holistic conceptual framework to explore trade-offs between pollinators, pesticides and yield that we believe to be essential for achieving conservation and agronomic goals. This framework applies to all insecticide-treated mass-flowering crops (MFCs) and can be adapted to include other ecological processes. Trialling the framework in our study system, we found that our focal insecticide, thiacloprid, improved red clover seed yield with no detected effects on its key pollinator, B. terrestris, and that the presence of red clover in the landscape can benefit pollinator populations.
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| 12. |
- Perepolkin, Dmytro, et al.
(author)
-
Hybrid elicitation and quantile-parametrized likelihood
- 2023
-
Other publication (other academic/artistic)abstract
- This paper extends the application of quantile-based Bayesian inference to probability distributions defined in terms of quantiles of observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and parameter interpretability, making them useful for eliciting information about observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a variant of the Dirichlet prior. We discuss the resulting hybrid expert elicitation protocol, which aims to characterize uncertainty in parameters by asking questions about observable quantities. We also compare and contrast this approach with parametric and predictive elicitation methods.
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| 13. |
- Perepolkin, Dmytro, et al.
(author)
-
Hybrid elicitation and quantile-parametrized likelihood
- 2024
-
In: Statistics and Computing. - 0960-3174. ; 34
-
Journal article (peer-reviewed)abstract
- This paper extends the application of quantile-based Bayesian inference to probability distributions defined in terms of quantiles of observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and parameter interpretability, making them useful for eliciting information about observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a variant of the Dirichlet prior. We discuss the resulting hybrid expert elicitation protocol, which aims to characterize uncertainty in parameters by asking questions about observable quantities. We also compare and contrast this approach with parametric and predictive elicitation methods.
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| 14. |
- Perepolkin, Dmytro, et al.
(author)
-
The tenets of indirect inference in Bayesian models
- 2024
-
Other publication (other academic/artistic)abstract
- This paper extends the application of Bayesian inference to probability distributions defined in terms of its quantile function. We describe the method of *indirect likelihood* to be used in the Bayesian models with sampling distributions which lack an explicit cumulative distribution function. We provide examples and demonstrate the equivalence of the "quantile-based" (indirect) likelihood to the conventional "density-defined" (direct) likelihood. We consider practical aspects of the numerical inversion of quantile function by root-finding required by the indirect likelihood method. In particular, we consider a problem of ensuring the validity of an arbitrary quantile function with the help of Chebyshev polynomials and provide useful tips and implementation of these algorithms in Stan and R. We also extend the same method to propose the definition of an *indirect prior* and discuss the situations where it can be useful.
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| 15. |
- Perepolkin, Dmytro, et al.
(author)
-
The tenets of quantile-based inference in Bayesian models
- 2023
-
In: Computational Statistics and Data Analysis. - 0167-9473. ; 187
-
Journal article (peer-reviewed)abstract
- Bayesian inference can be extended to probability distributions defined in terms of their inverse distribution function, i.e. their quantile function. This applies to both prior and likelihood. Quantile-based likelihood is useful in models with sampling distributions which lack an explicit probability density function. Quantile-based prior allows for flexible distributions to express expert knowledge. The principle of quantile-based Bayesian inference is demonstrated in the univariate setting with a Govindarajulu likelihood, as well as in a parametric quantile regression, where the error term is described by a quantile function of a Flattened Skew-Logistic distribution.
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| 16. |
- Raices Cruz, Ivette, et al.
(author)
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A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
- 2022
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In: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 41:17, s. 3365-3379
-
Journal article (peer-reviewed)abstract
- Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.
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| 17. |
- Raices Cruz, Ivette, et al.
(author)
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A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments
- 2022
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In: Risk Analysis. - : Wiley. - 0272-4332 .- 1539-6924. ; 42:2, s. 239-253
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Journal article (peer-reviewed)abstract
- An honest communication of uncertainty about quantities of interest enhances transparency in scientific assessments. To support this communication, risk assessors should choose appropriate ways to evaluate and characterize epistemic uncertainty. A full treatment of uncertainty requires methods that distinguish aleatory from epistemic uncertainty. Quantitative expressions for epistemic uncertainty are advantageous in scientific assessments because they are nonambiguous and enable individual uncertainties to be characterized and combined in a systematic way. Since 2019, the European Food Safety Authority (EFSA) recommends assessors to express epistemic uncertainty in conclusions of scientific assessments quantitatively by subjective probability. A subjective probability can be used to represent an expert judgment, which may or may not be updated using Bayes's rule to integrate evidence available for the assessment and could be either precise or approximate. Approximate (or bounded) probabilities may be enough for decision making and allow experts to reach agreement on certainty when they struggle to specify precise subjective probabilities. The difference between the lower and upper bound on a subjective probability can also be used to reflect someone's strength of knowledge. In this article, we demonstrate how to quantify uncertainty by bounded probability, and explicitly distinguish between epistemic and aleatory uncertainty, by means of robust Bayesian analysis, including standard Bayesian analysis through precise probability as a special case. For illustration, the two analyses are applied to an intake assessment.
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| 18. |
- Raices Cruz, Ivette, et al.
(author)
-
Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis
- 2022
-
In: Computational Statistics and Data Analysis. - : Elsevier BV. - 0167-9473. ; 176
-
Journal article (peer-reviewed)abstract
- Bayesian inference under a set of priors, called robust Bayesian analysis, allows for estimation of parameters within a model and quantification of epistemic uncertainty in quantities of interest by bounded (or imprecise) probability. Iterative importance sampling can be used to estimate bounds on the quantity of interest by optimizing over the set of priors. A method for iterative importance sampling when the robust Bayesian inference relies on Markov chain Monte Carlo (MCMC) sampling is proposed. To accommodate the MCMC sampling in iterative importance sampling, a new expression for the effective sample size of the importance sampling is derived, which accounts for the correlation in the MCMC samples. To illustrate the proposed method for robust Bayesian analysis, iterative importance sampling with MCMC sampling is applied to estimate the lower bound of the overall effect in a previously published meta-analysis with a random effects model. The performance of the method compared to a grid search method and under different degrees of prior-data conflict is also explored.
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| 19. |
- Rodríguez Leal, Inés
(author)
-
Prioritization of phytotoxins according to their threat to water quality
- 2022
-
Licentiate thesis (other academic/artistic)abstract
- Natural toxins are pollutants of emerging concern. Despite being ubiquitous in the European environment, thus far little action has been taken to conduct screening-level assessment of their presence in drinking water. The need to assess and prioritize natural toxins is more acute in the context of climate change, since distributions and environmental behaviour of plants are expected to change. To address the need for screening assessment, estimated properties representing the persistence and mobility of natural toxins are needed, but experimentally obtained values to build predictive models are still scarce. Existing QSPR models can be applied to estimate Kow and half-lives, with an important caveat; compounds have to lie within the applicability domain of the QSPR model to assure a reliable prediction. This thesis reports the assessment of the applicability domain of two popular QSPR models from the US EPA’s EPI Suite™ software package that estimate Kow and biodegradability, and evaluates how many toxins in a database for Switzerland lie within domain of the models. Results demonstrate that nearly half of the plant toxins in the database lie outside the applicability domain of one or both models, and thus that screening predictions of the toxins’ persistence and mobility are subject to unquantifiable uncertainties. This work points to a need to measure property data for more natural toxins to improve the empirical basis for predictive QSPR modeling.
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| 20. |
- Sahlin, Ullrika, et al.
(author)
-
Robust Decision Analysis under Severe Uncertainty and Ambiguous Tradeoffs : An Invasive Species Case Study
- 2021
-
In: Risk Analysis. - : Wiley. - 0272-4332 .- 1539-6924. ; 41:11, s. 2140-2153
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Journal article (peer-reviewed)abstract
- Bayesian decision analysis is a useful method for risk management decisions, but is limited in its ability to consider severe uncertainty in knowledge, and value ambiguity in management objectives. We study the use of robust Bayesian decision analysis to handle problems where one or both of these issues arise. The robust Bayesian approach models severe uncertainty through bounds on probability distributions, and value ambiguity through bounds on utility functions. To incorporate data, standard Bayesian updating is applied on the entire set of distributions. To elicit our expert's utility representing the value of different management objectives, we use a modified version of the swing weighting procedure that can cope with severe value ambiguity. We demonstrate these methods on an environmental management problem to eradicate an alien invasive marmorkrebs recently discovered in Sweden, which needed a rapid response despite substantial knowledge gaps if the species was still present (i.e., severe uncertainty) and the need for difficult tradeoffs and competing interests (i.e., value ambiguity). We identify that the decision alternatives to drain the system and remove individuals in combination with dredging and sieving with or without a degradable biocide, or increasing pH, are consistently bad under the entire range of probability and utility bounds. This case study shows how robust Bayesian decision analysis provides a transparent methodology for integrating information in risk management problems where little data are available and/or where the tradeoffs are ambiguous.
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