From data to decision - learning by probabilistic risk analysis of biological invasions

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

Robust decision analysis under severe uncertainty and ambiguous tradeoffs: an invasive species case study

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

Researchers\u27 approaches to stakeholders: Interaction or transfer of knowledge?

Stakeholder interaction is important for enabling environmental research to support the societal transition to sustainability. We argue that it is crucial to take researchers\u27 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 \u27transfer model\u27 implies that interaction is understood as communication and should be separated from research. The \u27interaction model\u27 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

Imprecise swing weighting for multi-attribute utility elicitation based on partial preferences.

We describe a novel approach to multi-attribute utility elicitation which is both general enough to cover a wide range of problems, whilst at the same time simple enough to admit reasonably straightforward calculations. We allow both utilities and probabilities to be only partially specified, through bounding. We still assume marginal utilities to be precise. We derive necessary and sufficient conditions under which our elicitation procedure is consistent. As a special case, we obtain an imprecise generalization of the well known swing weighting method for eliciting multi-attribute utility functions. An example from ecological risk assessment demonstrates our method

Imprecise swing weighting for multi-attribute utility elicitation based on partial preferences

We describe a novel approach to multi-attribute utility elicitation which is both general enough to cover a wide range of problems, whilst at the same time simple enough to admit reasonably straightforward calculations. We allow both utilities and probabilities to be only partially specified, through bounding. We still assume marginal utilities to be precise. We derive necessary and sufficient conditions under which our elicitation procedure is consistent. As a special case, we obtain an imprecise generalization of the well known swing weighting method for eliciting multi-attribute utility functions. An example from ecological risk assessment demonstrates our method

Pollinators, pests and yield-Multiple trade-offs from insecticide use in a mass-flowering crop

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

Correlates of intended COVID-19 vaccine acceptance across time and countries: results from a series of cross-sectional surveys.

Funder: David and Claudia Harding FoundationOBJECTIVE: 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