| 1. |
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| 2. |
- Amano, Tatsuya, et al.
(author)
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Transforming Practice : Checklists for Delivering Change
- 2022
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In: Transforming Conservation : A Practical Guide to Evidence and Decision Making - A Practical Guide to Evidence and Decision Making. - 9781800648562 - 9781800648586 ; , s. 367-386
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Book chapter (peer-reviewed)abstract
- Delivering a revolution in evidence use requires a cultural change across society. For a wide range of groups (practitioners, knowledge brokers, organisations, organisational leaders, policy makers, funders, researchers, journal publishers, the wider conservation community, educators, writers, and journalists), options are described to facilitate a change in practice, and a series of downloadable checklists is provided.
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| 3. |
- Baey, Charlotte, et al.
(author)
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A model to account for data dependency when estimating floral cover in different land use types over a season
- 2017
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In: Environmental and Ecological Statistics. - : Springer Science and Business Media LLC. - 1352-8505 .- 1573-3009. ; 24:4, s. 505-527
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Journal article (peer-reviewed)abstract
- We propose a model to consider data dependencies and assess spatial and temporal variability in land use specific floral coverage across landscapes. Data dependence arising from repeated measurements across the flowering season is taken into account using hierarchical Archimedean copulas, where the correlation is assumed to be stronger within seasonal periods than between periods. For each seasonal period, a bounded probability distribution is assigned to capture spatial variability in floral cover. The model uses a Bayesian approach and can assess land-use-specific floral covers by integrating experts judgments and field data. The model is applied to assess floral covers in four land use types in southern Sweden, where seasonal variability is captured by dividing the season into two periods according to winter oilseed rape flowering. Floral cover is updated using Markov Chain Monte Carlo sampling based on data from 16 landscapes and 2 years, with repeated measures available from each of the two seasonal periods. Our results indicate that considering data dependence improved the estimation of floral cover based on data observed during a season. Different copula families specifying multivariate probability distributions were tested, and no family had a consistently higher performance in the four tested land use types. Uncertainty in both mode and variability of floral cover was higher when data dependence were accounted for. Posterior modes of floral covers in semi-natural grassland were higher than in field edges, but both expert’s best guesses were higher than these estimates. This confirms previous findings in expert elicitation processes that experts may fail to discriminate extreme values on a bounded range. Floral cover in flower strips were estimated to be smaller/higher than semi-natural grasslands early/late in the season. The mode of floral cover in oil seed rape was estimated to be close to 100%, and higher than estimates provided by expert judgment. Floral covers for different land use classes are key parameters when quantifying floral resources at a landscape level whose assessments rely on both expert judgment and field measurements.
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| 4. |
- Baey, Charlotte, et al.
(author)
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Calibration of a bumble bee foraging model using Approximate Bayesian Computation
- 2023
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In: Ecological Modelling. - : Elsevier BV. - 0304-3800. ; 477
-
Journal article (peer-reviewed)abstract
- 1. Challenging calibration of complex models can be approached by using prior knowledge on the parameters. However, the natural choice of Bayesian inference can be computationally heavy when relying on Markov Chain Monte Carlo (MCMC) sampling. When the likelihood of the data is intractable, alternative Bayesian methods have been proposed. Approximate Bayesian Computation (ABC) only requires sampling from the data generative model, but may be problematic when the dimension of the data is high. 2. We studied alternative strategies to handle high dimensional data in ABC applied to the calibration of a spatially explicit foraging model for Bombus terrestris. The first step consisted in building a set of summary statistics carrying enough biological meaning, i.e. as much as the original data, and then applying ABC on this set. Two ABC strategies, the use of regression adjustment leading to the production of ABC posterior samples, and the use of machine learning approaches to approximate ABC posterior quantiles, were compared with respect to coverage of model estimates and true parameter values. The comparison was made on simulated data as well as on data from two field studies. 3. Results from simulated data showed that some model parameters were easier to calibrate than others. Approaches based on random forests in general performed better on simulated data. They also performed well on field data, even though the posterior predictive distribution exhibited a higher variance. Nonlinear regression adjustment performed better than linear ones, and the classical ABC rejection algorithm performed badly. 4. ABC is an interesting and appealing approach for the calibration of complex models in biology, such as spatially explicit foraging models. However, while ABC methods are easy to implement, they often require considerable tuning.
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| 5. |
- Blanke, Jan Hendrik, et al.
(author)
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Assessing the impact of changes in land-use intensity and climate on simulated trade-offs between crop yield and nitrogen leaching
- 2017
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In: Agriculture, Ecosystems and Environment. - : Elsevier BV. - 0167-8809. ; 239, s. 385-398
-
Journal article (peer-reviewed)abstract
- In this study, a global vegetation model (LPJ-GUESS) is forced with spatial information (Nomenclature of Units for Territorial Statistics (NUTS) 2 level) of land-use intensity change in the form of nitrogen (N) fertilization derived from a model chain which informed the Common Agricultural Policy Regionalized Impact (CAPRI) model. We analysed the combined role of climate change and land-use intensity change for trade-offs between agricultural yield and N leaching in the European Union under two plausible scenarios up until 2040. Furthermore, we assessed both driver importance and uncertainty in future trends based on an alternative land-use intensity dataset derived from an integrated assessment model. LPJ-GUESS simulated an increase in wheat and maize yield but also N leaching for most regions when driven by changes in land-use intensity and climate under RCP 8.5. Under RCP 4.5, N leaching is reduced in 53% of the regions while there is a trade-off in crop productivity. The most important factors influencing yield were CO2 (wheat) and climate (maize), but N application almost equaled these in importance. For N leaching, N application was the most important factor, followed by climate. Therefore, using a constant N application dataset in the absence of future projections has a substantial effect on simulated ecosystem responses, especially for maize yield and N leaching. This study is a first assessment of future N leaching and yield responses based on projections of climate and land-use intensity. It further highlights the importance of accounting for changes in future N applications and land-use intensity in general when evaluating environmental impacts over long time periods.
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| 6. |
- Blanke, Jan, et al.
(author)
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Implications of accounting for management intensity on carbon and nitrogen balances of European grasslands
- 2018
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 13:8
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Journal article (peer-reviewed)abstract
- European managed grasslands are amongst the most productive in the world. Besides temperature and the amount and timing of precipitation, grass production is also highly controlled by applications of nitrogen fertilizers and land management to sustain a high productivity. Since management characteristics of pastures vary greatly across Europe, land-use intensity and their projections are critical input variables in earth system modeling when examining and predicting the effects of increasingly intensified agricultural and livestock systems on the environment. In this study, we aim to improve the representation of pastures in the dynamic global vegetation model LPJ-GUESS. This is done by incorporating daily carbon allocation for grasses as a foundation to further implement daily land management routines and land-use intensity data into the model to discriminate between intensively and extensively used regions. We further compare our new simulations with leaf area index observations, reported regional grassland productivity, and simulations conducted with the vegetation model ORCHIDEE-GM. Additionally, we analyze the implications of including pasture fertilization and daily management compared to the standard version of LPJ-GUESS. Our results demonstrate that grassland productivity cannot be adequately captured without including land-use intensity data in form of nitrogen applications. Using this type of information improved spatial patterns of grassland productivity significantly compared to standard LPJ-GUESS. In general, simulations for net primary productivity, net ecosystem carbon balance and nitrogen leaching were considerably increased in the extended version. Finally, the adapted version of LPJ-GUESS, driven with projections of climate and land-use intensity, simulated an increase in potential grassland productivity until 2050 for several agro-climatic regions, most notably for the Mediterranean North, the Mediterranean South, the Atlantic Central and the Atlantic South.
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| 7. |
- Blasi, Maria, et al.
(author)
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A model of wild bee populations accounting for spatial heterogeneity and climate-induced temporal variability of food resources at the landscape level
- 2022
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In: Ecology and Evolution. - : Wiley. - 2045-7758. ; 12:6
-
Journal article (peer-reviewed)abstract
- The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modeling. The model is developed for Bombus sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered by a theoretical generalized seasonal progression, which can be informed by growing degree days. The purpose of the LandscapePhenoBee model is to evaluate the impact of system changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.
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| 8. |
- Brandmaier, Stefan, et al.
(author)
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PLS-Optimal: A stepwise D-Optimal design based on latent variables
- 2012
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In: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 52:4, s. 975-983
-
Journal article (peer-reviewed)abstract
- Several applications, such as risk assessment within REACH or drug discovery, require reliable methods for the design of experiments and efficient testing strategies. Keeping the number of experiments as low as possible is important from both a financial and an ethical point of view, as exhaustive testing of compounds requires significant financial resources and animal lives. With a large initial set of compounds, experimental design techniques can be used to select a representative subset for testing. Once measured, these compounds can be used to develop quantitative structure–activity relationship models to predict properties of the remaining compounds. This reduces the required resources and time. D-Optimal design is frequently used to select an optimal set of compounds by analyzing data variance. We developed a new sequential approach to apply a D-Optimal design to latent variables derived from a partial least squares (PLS) model instead of principal components. The stepwise procedure selects a new set of molecules to be measured after each previous measurement cycle. We show that application of the D-Optimal selection generates models with a significantly improved performance on four different data sets with end points relevant for REACH. Compared to those derived from principal components, PLS models derived from the selection on latent variables had a lower root-mean-square error and a higher Q2 and R2. This improvement is statistically significant, especially for the small number of compounds selected.
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| 9. |
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| 10. |
- Cassani, Stefano, et al.
(author)
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Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction
- 2013
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In: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 41:1, s. 49-64
-
Journal article (peer-reviewed)abstract
- QSAR regression models of the toxicity of triazoles and benzotriazoles ([B] TAZs) to an alga (Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by five partners in the FP7-EU Project, CADASTER. The models were developed by different methods - Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative Neural Network (ASNN) - by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPR-THESAURUS web). In addition, different procedures were used for variable selection, validation and applicability domain inspection. The predictions of the models developed, as well as those obtained in a consensus approach by averaging the data predicted from each model, were compared with the results of experimental tests that were performed by two CADASTER partners. The individual and consensus models were able to correctly predict the toxicity classes of the chemicals tested in the CADASTER project, confirming the utility of the QSAR approach. The models were also used for the prediction of aquatic toxicity of over 300 (B)TAZs, many of which are included in the REACH pre-registration list, and were without experimental data. This highlights the importance of QSAR models for the screening and prioritisation of untested chemicals, in order to reduce and focus experimental testing.
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| 11. |
- Durjava, Mojca Kos, et al.
(author)
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Experimental Assessment of the Environmental Fate and Effects of Triazoles and Benzotriazole
- 2013
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In: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 41:1, s. 65-75
-
Journal article (peer-reviewed)abstract
- The environmental fate and effects of triazoles and benzotriazoles are of concern within the context of chemical regulation. As part of an intelligent testing strategy, experimental tests were performed on endpoints that are relevant for risk assessment. The experimental tests included the assessment of eco-toxicity to an alga, a daphnid and zebrafish embryos, and the assessment of ready biodegradability. Triazole and benzotriazole compounds were selected for testing, based on existing toxicity data for vertebrate and invertebrate species, as well as on the principal component analysis of molecular descriptors aimed at selecting the minimum number of test compounds in order to maximise the chemical domain spanned for both compound classes. The experimental results show that variation in the toxicities of triazoles and benzotriazole across species was relatively minor; in general, the largest factor was approximately 20. The study conducted indicated that triazoles are not readily biodegradable.
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| 12. |
- Dänhardt, Juliana, et al.
(author)
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Mot en evidensbaserad CAP
- 2016
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Other publication (other academic/artistic)abstract
- EU:s gemensamma jordbrukspolitik (CAP) är ett av de viktigaste styrmedlen för svenskt jordbruk, både när det gäller ekonomiska resurser och med avseende på hur stora arealer mark som påverkas. I den nuvarande CAP finns medel avsatta för miljöåtgärder såväl inom pelare 1 genom de så kallade förgröningsåtgärderna, som genom Landsbygdsprogrammet (pelare 2) via miljöstöden. Därmed skulle det kunna finnas stora möjligheter att genom CAP:s olika kanaler påverka biologisk mångfald och ekosystemtjänster i jordbrukslandskapet. För att utveckla välfungerande och kostnadseffektiva styrmedel krävs att dessa byggs på en vetenskaplig kunskapsbas, att man utvärderar om stöden uppnår sina syften och att man undersöker hur ersättningarnas effekt skulle kunna förbättras med en alternativ utformning. En generell slutsats från vår forskning är att den vetenskapliga underbyggnaden av CAP uppvisar brister och att en utvärdering och utveckling av stöden borde byggas in som en organisk del av CAP. Vi föreslår att ett sådant utvärderingssystem, inklusive insamling av ett tillräckligt dataunderlag med lämpliga metoder, integreras i CAP och täcker hela programperioden.
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| 13. |
- Ekelund Ugge, Gustaf Magnus Oskar, et al.
(author)
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Transcriptional Responses as Biomarkers of General Toxicity : A Systematic Review and Meta-Analysis on Metal-Exposed Bivalves
- 2023
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In: Environmental Toxicology and Chemistry. - : John Wiley & Sons. - 0730-7268 .- 1552-8618. ; 42:3, s. 628-641
-
Research review (peer-reviewed)abstract
- Through a systematic review and a series of meta-analyses, we evaluated the general responsiveness of putative transcriptional biomarkers of general toxicity and chemical stress. We targeted metal exposures performed on bivalves under controlled laboratory conditions, and selected six transcripts associated with general toxicity for evaluation: catalase (cat), glutathione-S-transferase (gst), heat shock proteins 70 and 90 (hsp70, hsp90), metallothionein (mt) and superoxide dismutase (sod). Transcriptional responses (n = 396) were extracted from published scientific articles (k = 22) and converted to log response ratios (lnRRs). By estimating toxic units (TUs), we normalized different metal exposures to a common scale, as a proxy of concentration. Using Bayesian hierarchical random effect models, we then tested the effects of metal exposure on lnRR, both for metal exposure in general and in meta-regressions using TU and exposure time as independent variables. Corresponding analyses were also repeated with transcript and tissue as additional moderators. Observed patterns were similar for general as for transcript- and tissue-specific responses. The expected overall response to arbitrary metal exposure was a lnRR of 0.50, corresponding to a 65 % increase relative a non-exposed control. However, when accounting for publication bias, the estimated ‘true’ response showed no such effect. Furthermore, expected response magnitude increased slightly with exposure time, but there was little support for general monotonic concentration-dependence with regards to TU. Altogether, this work reveals potential limitations that need consideration prior to applying the selected transcripts as biomarkers in environmental risk assessment. This article is protected by copyright. All rights reserved. Environ Toxicol Chem 2022;00:0–0.
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| 14. |
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| 15. |
- Golsteijn, Laura, et al.
(author)
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Assessing predictive uncertainty in comparative toxicity potentials of triazoles
- 2014
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In: Environmental Toxicology and Chemistry. - : Wiley. - 0730-7268 .- 1552-8618. ; 33:2, s. 293-301
-
Journal article (peer-reviewed)abstract
- Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species. (c) 2013 SETAC
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| 16. |
- Govender, Indrani Hazel, et al.
(author)
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Bayesian Network Applications for Sustainable Holistic Water Resources Management : Modeling Opportunities for South Africa
- 2022
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In: Risk Analysis. - : Wiley. - 0272-4332 .- 1539-6924. ; 42:6, s. 1346-1364
-
Journal article (peer-reviewed)abstract
- Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.
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| 17. |
- Grönholdt Palm, Julia, et al.
(author)
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Strategies to deal with information of different reliability exemplified by the use of QSARs to fill the algae data gaps in LCIAs of plastic additives
- 2015
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In: SETAC Europe 25th Annual Meeting.
-
Conference paper (other academic/artistic)abstract
- Data gaps are problematic when screening fordangerous substances or in impact assessments where several chemicals are considered for evaluation. Lacking testing information can be replaced by non-testing information such as Quantitative Structure Activity Relationships (QSARs), but even though this latter information comes with lower reliability, this is seldom taken into account in theforthcoming assessments. The difficulty to meet standards for best information calls for strategies to handle data gaps which take varying reliability in information into account. Using safety factors when reliability is low can be problematic since this result in more conservative evaluations of substances for which information is of lowreliability and an unknown level of risk aversion in the assessment. An alternative is to reflect lower reliability using probability distributions representing the expected error in the information and propagate this uncertainty in the forthcoming assessments using Monte Carlo analysis.It is even possible to let the error to expect from QSARs depend to what extent a substance falls inside the models domain of applicability.QSARs cannot fill all gaps in data. Default values can be used instead of leaving substances out of assessments, but if so, these should reflect lowreliability as well. We demonstrate the practical implications of four strategies to handle varying reliability in information on algal toxicity in a Life Cycle Impact Assessment on 159 plastic additives of concernusing emissions from societal plastic materials in Sweden. A review concluded that a small amount of these substances had toxicity data for algae Pseudokirchneriella subcapitata. A QSAR was constructed which provided non-testing algal information of substances inside and on theborder of the models domain of applicability evaluated by PmodXPS.Substances with neither testing nor non-testing information were assigned default values. Screening based on characterization factors resulted in different rankings of substances when changing the level of cautiousness. The different strategies to handle varying reliability ininformation do more or less open up for quantifying uncertainty in Life Cycle Impact Assessments.
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| 18. |
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| 19. |
- Hedlund, Katarina, et al.
(author)
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Utmaningar och möjligheter
- 2022
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In: Markanvändning för en klimatpositiv framtid : En rapport om utmaningar och möjligheter i Skåne - En rapport om utmaningar och möjligheter i Skåne. - 9789198434996 - 9789198434989 ; , s. 48-60
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Book chapter (other academic/artistic)
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| 20. |
- Holmes, Mark, et al.
(author)
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Marine protected areas modulate habitat suitability of the invasive round goby (Neogobius melanostomus) in the Baltic Sea
- 2019
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In: Estuarine, Coastal and Shelf Science. - : Elsevier BV. - 0272-7714. ; 229
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Journal article (peer-reviewed)abstract
- Biological invasions are one of the leading causes of biodiversity loss worldwide. Given that eradication of invasive species is not usually a practical option, conservationists may attempt to limit their impacts through the designation and management of protected areas. Here, we investigate the effect of marine protected areas on the habitat suitability of an invasive species, the round goby (Neogobius melanostomus). By modelling its environmental niche space in the Baltic Sea, we demonstrated that gobies prefer shallow, warmer waters, sheltered from significant wave action. They are more likely to be found near areas of intense shipping, this being their primary method of long-distance dispersal. Comparison of the goby's occurrences inside/outside protected areas indicated that suitable habitats within protected areas are more resistant to the round goby's invasion compared to adjacent unprotected areas, however the opposite is true for suboptimal habitats. This has important ecosystem management implications with marine conservation areas providing mitigation measures to control the spread of round goby in its optimal habitats in the Baltic Sea environment. Being subjected to reduced human impacts, native species within protected areas may be more numerous and diverse, helping to resist invasive species incursion.
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| 21. |
- Holmquist, Hanna, 1982, et al.
(author)
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The potential to use QSAR to populate ecotoxicity characterisation factors for simplified LCIA and chemical prioritisation
- 2018
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In: International Journal of Life Cycle Assessment. - : Springer Science and Business Media LLC. - 1614-7502 .- 0948-3349. ; 23:11, s. 2208-2216
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Journal article (peer-reviewed)abstract
- Purpose: Today’s chemical society use and emit an enormous number of different, potentially ecotoxic, chemicals to the environment. The vast majority of substances do not have characterisation factors describing their ecotoxicity potential. A first stage, high throughput, screening tool is needed for prioritisation of which substances need further measures. Methods: USEtox characterisation factors were calculated in this work based on data generated by quantitative structure-activity relationship (QSAR) models to expand substance coverage where characterisation factors were missing. Existing QSAR models for physico-chemical data and ecotoxicity were used, and to further fill data gaps, an algae QSAR model was developed. The existing USEtox characterisation factors were used as reference to evaluate the impact from the use of QSARs to generate input data to USEtox, with focus on ecotoxicity data. An inventory of chemicals that make up the Swedish societal stock of plastic additives, and their associated predicted emissions, was used as a case study to rank chemicals according to their ecotoxicity potential. Results and discussion: For the 210 chemicals in the inventory, only 41 had characterisation factors in the USEtox database. With the use of QSAR generated substance data, an additional 89 characterisation factors could be calculated, substantially improving substance coverage in the ranking. The choice of QSAR model was shown to be important for the reliability of the results, but also with the best correlated model results, the discrepancies between characterisation factors based on estimated data and experimental data were very large. Conclusions: The use of QSAR estimated data as basis for calculation of characterisation factors, and the further use of those factors for ranking based on ecotoxicity potential, was assessed as a feasible way to gather substance data for large datasets. However, further research and development of the guidance on how to make use of estimated data is needed to achieve improvement of the accuracy of the results.
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| 22. |
- Hristov, Jordan, et al.
(author)
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Impacts of the EU's Common Agricultural Policy “Greening” Reform on Agricultural Development, Biodiversity, and Ecosystem Services
- 2020
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In: Applied Economic Perspectives and Policy. - : Wiley. - 2040-5790 .- 2040-5804. ; 42:4, s. 716-738
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Journal article (peer-reviewed)abstract
- The EU's Common Agricultural Policy (CAP) has had limited success in mitigating agriculture's environmental degradation. In this paper we simulate the impacts of the 2013 “greening” reform on biodiversity and ecosystem services in environmentally contrasting landscapes. We do this by integrating an agent-based model of structural change with spatial ecological production functions, and show that the reform will likely fail to deliver substantial environmental benefits. Our study implies that greening measures need to be tailored to local conditions and priorities, to generate environmental improvements. Such spatial targeting of measures is though incompatible with the design of a common direct payments scheme.
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| 23. |
- Häussler, Johanna, et al.
(author)
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Pollinator population size and pollination ecosystem service responses to enhancing floral and nesting resources
- 2017
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In: Ecology and Evolution. - : Wiley. - 2045-7758. ; 7:6, s. 1898-1908
-
Journal article (peer-reviewed)abstract
- Modeling pollination ecosystem services requires a spatially explicit, process-based approach because they depend on both the behavioral responses of pollinators to the amount and spatial arrangement of habitat and on the within- and between-season dynamics of pollinator populations in response to land use. We describe a novel pollinator model predicting flower visitation rates by wild central-place foragers (e.g., nesting bees) in spatially explicit landscapes. The model goes beyond existing approaches by: (1) integrating preferential use of more rewarding floral and nesting resources; (2) considering population growth over time; (3) allowing different dispersal distances for workers and reproductives; (4) providing visitation rates for use in crop pollination models. We use the model to estimate the effect of establishing grassy field margins offering nesting resources and a low quantity of flower resources, and/or late-flowering flower strips offering no nesting resources but abundant flowers, on bumble bee populations and visitation rates to flowers in landscapes that differ in amounts of linear seminatural habitats and early mass-flowering crops. Flower strips were three times more effective in increasing pollinator populations and visitation rates than field margins, and this effect increased over time. Late-blooming flower strips increased early-season visitation rates, but decreased visitation rates in other late-season flowers. Increases in population size over time in response to flower strips and amounts of linear seminatural habitats reduced this apparent competition for pollinators. Our spatially explicit, process-based model generates emergent patterns reflecting empirical observations, such that adding flower resources may have contrasting short- and long-term effects due to apparent competition for pollinators and pollinator population size increase. It allows exploring these effects and comparing effect sizes in ways not possible with other existing models. Future applications include species comparisons, analysis of the sensitivity of predictions to life-history traits, as well as large-scale management intervention and policy assessment.
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| 24. |
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| 25. |
- Iqbal, Muhammad Sarfraz, et al.
(author)
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Treatment of Epistemic Uncertainty in Environmental Fate Models –Consequences on Chemical Safety Regulatory Strategies
- 2012
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Conference paper (other academic/artistic)abstract
- The practical impact of treatment of epistemic uncertainty on decision making wasillustrated on two kinds of decisions from chemical regulation. First, regulatory strategies derivedfrom a simplified decision model based on toxicity and persistence showed that regulated level ofexposure is more conservative (safe) when uncertainty has been given a non-probabilistictreatment. Persistence and its uncertainty had been assessed by a Level II fugacity model forwhich input parameters had been quantified either by Bayesian probabilities, fuzzy numbers(non-probabilistic), or combinations of these (probability boxes). These findings are restricted tohow we let decision makers respond to uncertainty in model predictions by the chosen set ofdecision rules. Further, the use of either treatment depends on the quality and quantity ofbackground knowledge and the required level of detail on the assessment. In the absence ofexperimentally tested physicochemical endpoints, European chemical regulation REACH allowsthe use of non-testing strategies such as Quantitative Structure-Property Relationships (QSPR) topredict the required information. The second decision problem was to select which chemicalsubstances to prioritize for experimental testing in order to strengthen the background knowledgefor chemical regulation with respect to the uncertainty in QSPR predictions. We found that thevalue of reducing uncertainty, given by the expected gain in net benefit for society, was affectedby its treatment and there were no consistent order of testing of the three compounds. However,value of information is a Bayesian probabilistic approach that, unless developed further, loose itsinterpretability under other treatments of uncertainty. The framework of a predictive model, riskmodel, decision model and value of information analysis provides a computational template forfurther evaluation of the effect of treatment of uncertainty on decision making.
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| 26. |
- Iqbal, Muhammad Sarfraz, et al.
(author)
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Understanding quantitative structure-property relationships uncertainty in environmental fate modeling
- 2013
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In: Environmental Toxicology and Chemistry. - : Wiley. - 0730-7268 .- 1552-8618. ; 32:5, s. 1069-1076
-
Journal article (peer-reviewed)abstract
- In cases in which experimental data on chemical-specific input parameters are lacking, chemical regulations allow the use of alternatives to testing, such as in silico predictions based on quantitative structure–property relationships (QSPRs). Such predictions are often given as point estimates; however, little is known about the extent to which uncertainties associated with QSPR predictions contribute to uncertainty in fate assessments. In the present study, QSPR-induced uncertainty in overall persistence (POV) and long-range transport potential (LRTP) was studied by integrating QSPRs into probabilistic assessments of five polybrominated diphenyl ethers (PBDEs), using the multimedia fate model Simplebox. The uncertainty analysis considered QSPR predictions of the fate input parameters' melting point, water solubility, vapor pressure, organic carbon–water partition coefficient, hydroxyl radical degradation, biodegradation, and photolytic degradation. Uncertainty in POV and LRTP was dominated by the uncertainty in direct photolysis and the biodegradation half-life in water. However, the QSPRs developed specifically for PBDEs had a relatively low contribution to uncertainty. These findings suggest that the reliability of the ranking of PBDEs on the basis of POV and LRTP can be substantially improved by developing better QSPRs to estimate degradation properties. The present study demonstrates the use of uncertainty and sensitivity analyses in nontesting strategies and highlights the need for guidance when compounds fall outside the applicability domain of a QSPR.
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| 27. |
- Kerr, John R., et al.
(author)
-
Correlates of intended COVID-19 vaccine acceptance across time and countries : Results from a series of cross-sectional surveys
- 2021
-
In: BMJ Open. - : BMJ. - 2044-6055. ; 11:8
-
Journal article (peer-reviewed)abstract
- Objective Describe demographical, social and psychological correlates of willingness to receive a COVID-19 vaccine. Setting Series of online surveys undertaken between March and October 2020. Participants A total of 25 separate national samples (matched to country population by age and sex) in 12 different countries were recruited through online panel providers (n=25 334). Primary outcome measures Reported willingness to receive a COVID-19 vaccination. Results Reported willingness to receive a vaccine varied widely across samples, ranging from 63% to 88%. Multivariate logistic regression analyses reveal sex (female OR=0.59, 95% CI 0.55 to 0.64), trust in medical and scientific experts (OR=1.28, 95% CI 1.22 to 1.34) and worry about the COVID-19 virus (OR=1.47, 95% CI 1.41 to 1.53) as the strongest correlates of stated vaccine acceptance considering pooled data and the most consistent correlates across countries. In a subset of UK samples, we show that these effects are robust after controlling for attitudes towards vaccination in general. Conclusions Our results indicate that the burden of trust largely rests on the shoulders of the scientific and medical community, with implications for how future COVID-19 vaccination information should be communicated to maximise uptake.
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| 28. |
- Knaggård, Åsa, et al.
(author)
-
Researchers’ approaches to stakeholders: Interaction or transfer of knowledge?
- 2019
-
In: Environmental Science and Policy. - : Elsevier BV. - 1462-9011 .- 1873-6416. ; 97, s. 25-35
-
Journal article (peer-reviewed)abstract
- Stakeholder interaction is important for enabling environmental research to support the societal transition to sustainability. We argue that it is crucial to take researchers’ approaches to and perceptions of stakeholder interaction into account, to enable more clarity in discussions about interaction, as well as more systematic interaction approaches. Through a survey and focus group interviews with environmental researchers at three Swedish universities, we investigate the effects of two models of stakeholder interaction, as well as high and low levels within each. The ‘transfer model’ implies that interaction is understood as communication and should be separated from research. The ‘interaction model’ implies that interaction happens throughout the research process. Our study shows some significant differences between researchers in the two models, but also between high and low levels of stakeholder interaction regardless of model. The result indicates that the transfer model needs to be considered in studies and practice of stakeholder interaction, but also that the low levels of the interaction model consists of a number of different types of approaches. The major difference between the two models was about how large researchers understood the benefits and risks with stakeholder interaction to be. Transfer researchers saw interaction as a threat to the integrity of research, whereas interaction researchers saw it as enabling research. © 2019 The Authors
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| 29. |
- Knapp, Jessica L., et al.
(author)
-
Pollinators, pests and yield—Multiple trade-offs from insecticide use in a mass-flowering crop
- 2022
-
In: Journal of Applied Ecology. - : Wiley. - 0021-8901 .- 1365-2664. ; 59:9, s. 2419-2429
-
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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| 30. |
- 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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| 31. |
- 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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| 32. |
- 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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| 33. |
- 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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| 34. |
- Raices Cruz, Ivette, et al.
(author)
-
A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
- 2022
-
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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| 35. |
- Raices Cruz, Ivette, et al.
(author)
-
A suggestion for the quantification of precise and bounded probability to quantify epistemic uncertainty in scientific assessments
- 2022
-
In: Risk Analysis. - : Wiley. - 0272-4332 .- 1539-6924. ; 42:2, s. 239-253
-
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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| 36. |
- 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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| 37. |
- 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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| 38. |
- Sahlin, Ullrika, et al.
(author)
-
A benefit analysis of screening for invasive species - base-rate uncertainty and the value of information
- 2011
-
In: Methods in Ecology and Evolution. - 2041-210X. ; 2:5, s. 500-508
-
Journal article (peer-reviewed)abstract
- 1.. Implementation of the full spectra of screening tools to prevent the introduction of invasive species results in a need to evaluate the cost-efficiency of gathering the information needed to screen for these species. 2. We show how the Bayesian value of information approach can be used to derive the benefit of a screening model based on species traits, which in combination with the base rate of invasiveness, i.e. the proportion of invasive species among those introduced and established, predicts species-specific invasiveness. 3. A pre-posterior Bayesian analysis demonstrated that the benefit of the screening model of invasiveness depends on both the accuracy in predictions and the uncertainty in the base rate of invasiveness. However, even though increasing model accuracy always generates higher model benefit, acknowledging or neglecting the uncertainty in the base rate of invasiveness does not. This means that uncertainty in the base rate is important to consider in the cost-benefit analysis of the screening model. 4. As an example, we derived the benefit of basing decisions on a screening model trained for a data set on species traits of invasive and non-invasive marine macroalgae introduced into Europe. The benefit ranged from 0.6% to 19% of the loss of introducing an invasive species, where the actual value can be estimated if we know the monetary values of impacts from introducing invasive and not introducing non-invasive species. 5. Cost-benefit analyses of screening models for invasive species is one means to reach efficient management of the risks of non-indigenous species. Value of information is a useful tool for benefit analysis of predictive models with respect to decision-making, which goes beyond the investigations of model accuracy. Here, we use value of information analysis to evaluate which sources of uncertainty that is most worth while to reduce and how to set the cost of gathering further species-specific information which will improve the accuracy of a screening.
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| 39. |
- Sahlin, Ullrika, et al.
(author)
-
A note on EFSA’s ongoing efforts to increase transparency of uncertainty in scientific opinions
- 2017
-
In: Journal of Risk Research. - : Informa UK Limited. - 1366-9877 .- 1466-4461. ; , s. 1-8
-
Journal article (peer-reviewed)abstract
- This is a comment on Lofstedt and Bouder’s paper, which explores the prospects of evidence based uncertainty analysis in Europe, focusing on the ongoing development on uncertainty analysis at the European Food Safety Authority (EFSA). We very much welcome a discussion on the need to develop better treatment and communication of uncertainty in risk analysis, as we believe that such discussion is long overdue. Lofstedt and Bouder raise many relevant points, in particular the call for evidence based uncertainty analysis. However, there is need to distinguish different types of communication in the discussion and facilitate – not diminish – the description and communication of uncertainty between risk assessors and decision-makers. We find that EFSA has taken steps toward a novel approach to guide their scientific experts and risk assessors in uncertainty analysis based on a modern and scientific view on uncertainty.
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| 40. |
- Sahlin, Ullrika, et al.
(author)
-
A risk assessment perspective of current practice in characterizing uncertainties in QSAR regression predictions
- 2011
-
In: Molecular Informatics. - : Wiley. - 1868-1751 .- 1868-1743. ; 30:6-7, s. 551-564
-
Journal article (peer-reviewed)abstract
- The European REACH legislation accepts the use of non-testing methods, such as QSARs, to inform chemical risk assessment. In this paper, we aim to initiate a discussion on the characterization of predictive uncertainty from QSAR regressions. For the purpose of decision making, we discuss applications from the perspective of applying QSARs to support probabilistic risk assessment. Predictive uncertainty is characterized by a wide variety of methods, ranging from pure expert judgement based on variability in experimental data, through data-driven statistical inference, to the use of probabilistic QSAR models. Model uncertainty is dealt with by assessing confidence in predictions and by building consensus models. The characterization of predictive uncertainty would benefit from a probabilistic formulation of QSAR models (e.g. generalized linear models, conditional density estimators or Bayesian models). This would allow predictive uncertainty to be quantified as probability distributions, such as Bayesian predictive posteriors, and likelihood-based methods to address model uncertainty. QSAR regression models with point estimates as output may be turned into a probabilistic framework without any loss of validity from a chemical point of view. A QSAR model for use in probabilistic risk assessment needs to be validated for its ability to make reliable predictions and to quantify associated uncertainty.
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| 41. |
- Sahlin, Ullrika, et al.
(author)
-
An evaluation of analyses and data collection of winter loss in honey bees in Sweden
- 2018
-
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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| 42. |
- Sahlin, Ullrika, et al.
(author)
-
Applicability Domain Dependent Predictive Uncertainty in QSAR Regressions
- 2014
-
In: Molecular Informatics. - : Wiley. - 1868-1751 .- 1868-1743. ; 33:1, s. 26-35
-
Journal article (peer-reviewed)abstract
- Predictive models used in decision making, such as QSARs in chemical regulation or drug discovery, call for evaluated approaches to quantitatively assess associated uncertainty in predictions. Uncertainty in less reliable predictions may be captured by locally varying predictive errors. In the current study, model-based bootstrapping was combined with analogy reasoning to generate predictive distributions varying in magnitude over a model's domain of applicability. A resampling experiment based on PLS regressions on four QSAR data sets demonstrated that predictive errors assessed by k nearest neighbour or weighted PRedicted Error Sum of Squares (PRESS) on samples of external test data or by internal cross-validation improved the performance of the uncertainty assessment. Analogy using similarity defined by Euclidean distances, or differences in standard deviation in perturbed predictions, resulted in better performances than similarity defined by distance to, or density of, the training data. Locally assessed predictive distributions had on average at least as good coverage as Gaussian distribution with variance assessed from the PRESS. An R-code is provided that evaluates performances of the suggested algorithms to assess predictive error based on log likelihood scores and empirical coverage graphs, and which applies these to derive confidence intervals or samples from the predictive distributions of query compounds.
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| 43. |
- Sahlin, Ullrika, et al.
(author)
-
Arguments for considering uncertainty in QSAR predictions in hazard and risk assessments
- 2013
-
In: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 41:1, s. 91-110
-
Journal article (peer-reviewed)abstract
- Chemical regulation allows non-in vivo testing (i.e. in silico-derived and in vitro-derived) information to replace experimental values from in vivo studies in hazard and risk assessments. Although non-in vitro testing information on chemical activities or properties is subject to added uncertainty as compared to in vivo testing information, this uncertainty is commonly not (fully) taken into account. Considering uncertainty in predictions from quantitative structure-activity relationships (QSARs), which are a form of non-in vivo testing information, may improve the way that QSARs support chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) system. We argue that it is useful to consider uncertainty in QSAR predictions, as it: a) supports rational decision-making; b) facilitates cautious risk management; c) informs uncertainty analysis in probabilistic risk assessment; d) may aid the evaluation of QSAR predictions in weight-of-evidence approaches; and e) provides a probabilistic model to verify the experimental data used in risk assessment. The discussion is illustrated by using case studies of QSAR integrated hazard and risk assessment from the EU-financed CADASTER project.
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| 44. |
- Sahlin, Ullrika
(author)
-
Assessment of uncertainty in chemical models by Bayesian probabilities: Why, when, how?
- 2015
-
In: Journal of Computer-Aided Molecular Design. - : Springer Science and Business Media LLC. - 1573-4951 .- 0920-654X. ; 29:7, s. 583-594
-
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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| 45. |
- Sahlin, Ullrika, et al.
(author)
-
Bayesian Evidence Synthesis and the quantification of uncertainty in a Monte Carlo simulation
- 2016
-
In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. - 1748-006X. ; 230:5, s. 445-456
-
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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| 46. |
- Sahlin, Ullrika, et al.
(author)
-
Benefits of biofuels in Sweden: A probabilistic re-assessment of the indexof new cars’ climate impact
- 2012
-
In: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 92, s. 473-479
-
Journal article (peer-reviewed)abstract
- The climate impact of new cars in Sweden 2009 has been evaluated by the Swedish Transport Administration. Their report takes into account reduction factors to attribute the positive impact of renewable fuels on CO2 emissions. The Swedish Transport Administration recommends the public to buy cars that can run on biofuels. Besides acknowledging prevailing uncertainties for many of the input parameters to the index of new cars’ climate impact, reduction factors are based on calculations from point estimates of input parameters. A probabilistic re-assessment of the index is presented to find out the importance of these uncertainties and to assess whether the point estimated recommendation might be misguiding. Probabilistic reduction factors for CO2 emissions were derived with the same deterministic model proposed by the Swedish Transport Administration, were Bayesian probability distributions or intervals assigned by expert judgements were used to describe uncertainty in the model input parameters. The use of biofuels most likely reduces CO2 emissions. Probabilistic modelling indicated a CO2 reduction for E85 as a fuel of 30% (95% credibility interval = 10–52%) in the same order as the 20% given by the Swedish Transport Administration. The best estimate of 28% decrease for gas cars (95% credibility interval = 3–44%) and is lower than the originally proposed reduction of 42%, but still within a similar range. The difference is due to the large extent of optimistic values used in the assessment by the Swedish Transport Administration. The CO2 emissions from the production of the biofuel had most influence on the model results. We conclude that the recommendation of the Swedish Transport Administration to consumers is still valid after probabilistic recalculation.
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| 47. |
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| 48. |
- Sahlin, Ullrika, et al.
(author)
-
Differences in the strengths of evidence matters in risk–risk trade-offs
- 2017
-
In: Journal of Risk Research. - : Informa UK Limited. - 1366-9877 .- 1466-4461. ; 20:8, s. 988-994
-
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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| 49. |
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| 50. |
- Sahlin, Ullrika
(author)
-
From data to decision - learning by probabilistic risk analysis of biological invasions
- 2010
-
Doctoral thesis (other academic/artistic)abstract
- Predicting an uncertain future with uncertain knowledge is a challenge. The success of efforts to preserve biodiversity, to maintain biosecurity and to reduce a negative impact from climate change, depend on scientifically based predictions of future events. The ongoing introduction of non-indigenous species threatens ecological systems for which empirical data is sparse and scientific knowledge is uncertain. Since biological invasions constitute a type of risk characterized by small probability events with possible large consequences, the use of subjective judgements and how knowledge based uncertainty are dealt with is a critical issue. In this thesis I do case studies of probabilistic analysis of biological invasions with the purpose to get more insight into what it means to predict future events under uncertainty and go into the methodology of probabilistic analysis, with special focus on risk analysis of biological invasions. In the first study I produced an overview to probabilistic models of establishment success. I found that probabilistic models for a common endpoint can be different, depending on how the endpoint event is measured and the type of available data. In study two to five I quantified uncertainty in some relevant biological invasion endpoints, using empirical and artificial data and probabilistic analysis. From these studies I learned that a probabilistic model estimated with empirical data is information on the goodness of the model to describe the world, whereas the same probabilistic model is information on the uncertainty in the future event. I find information theoretic approaches as suitable to derive good models, and Bayesian approach as suitable for combing various sources of knowledge into predictions. At the end, I discuss what it means to predict uncertainty under uncertainty using probabilistic analysis for various strengths of background knowledge.
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