Global change will alter the supply of ecosystem services that are vital for human well-being. To investigate ecosystem service supply during the 21st century, we used a range of ecosystem models and scenarios of climate and land-use change to conduct a Europe-wide assessment. Large changes in climate and land use typically resulted in large changes in ecosystem service supply. Some of these trends may be positive (for example, increases in forest area and productivity) or offer opportunities (for example, "surplus land" for agricultural extensification and bioenergy production). However, many changes increase vulnerability as a result of a decreasing supply of ecosystem services (for example, declining soil fertility, declining water availability, increasing risk of forest fires), especially in the Mediterranean and mountain regions.

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Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of ‘omics’ techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.

Safety factors are used in ecological risk assessments to extrapolate from the toxic responses of laboratory test species to all species representing that group in the environment. More accurate extrapolation of species responses is important. Advances in understanding the mechanistic basis for toxicological responses and identifying molecular response pathways can provide a basis for extrapolation across species and, in part, an explanation for the variability in whole organism responses to toxicants. We highlight potential short- and medium-term development goals to meet our long-term aspiration of truly predictive in silico extrapolation across wildlife species' response to toxicants. A conceptual approach for considering cross-species extrapolation is presented. Critical information is required to establish evidence-based species extrapolation, including identification of critical molecular pathways and regulatory networks that are linked to the biological mode of action and species' homologies. A case study is presented that examines steroidogenesis inhibition in fish after exposure to fadrozole or prochloraz. Similar effects for each compound among fathead minnow, medaka, and zebrafish were attributed to similar inhibitor pharmacokinetic/pharmacodynamic distributions and sequences of cytochrome P45019A1/2 (CYP19A1/2). Rapid advances in homology modeling allow the prediction of interactions of chemicals with enzymes, for example, CYP19 aromatase, which would eventually allow a prediction of potential aromatase toxicity of new compounds across a range of species. Eventually, predictive models will be developed to extrapolate across species, although substantial research is still required. Knowledge gaps requiring research include defining differences in life histories (e.g., reproductive strategies), understanding tissue-specific gene expression, and defining the role of metabolism on toxic responses and how these collectively affect the power of interspecies extrapolation methods.

Antimicrobial resistance is a complex issue with a large volume of published literature, and there is a need for synthesis of primary studies for an integrated understanding of this topic. Our research team aimed to have a more complete understanding of antimicrobial resistance in Canada (IAM.AMR Project) using multiple methods including the literature reviews and quantitative modelling. To accomplish this goal, qualitative features of publications (e.g., geographical location, study population) describing potential relationships between the occurrence of antimicrobial resistance and factors (e.g., antimicrobial use; management system) were of particular interest. The objectives of this review were to (a) describe the available peer-reviewed literature reporting potential relationships between factors and antimicrobial resistance; and (b) to highlight data gaps. A comprehensive literature search and screening were performed to identify studies investigating factors potentially linked with antimicrobial resistance in Campylobacter species, Escherichia coli and Salmonella enterica along the farm-to-fork pathway (farm, abattoir (slaughter houses) and retail meats) for the major Canadian livestock species (beef cattle, broiler chicken and pigs). The literature search returned 14,966 potentially relevant titles and abstracts. Following screening of titles, abstracts and full-text articles, the qualitative features of retained studies (n = 28) were extracted. The most common factors identified were antimicrobial use (n = 13 studies) and type of farm management system (e.g., antibiotic-free, organic; n = 8). Most studies were conducted outside of Canada and involved investigations at the farm level. Identified data gaps included the effect of vaccination, industry-specific factors (e.g., livestock density) and factors at sites other than farm along the agri-food chain. Further investigation of these factors and other relevant industry activities are needed for the development of quantitative models that aim to identify effective interventions to mitigate the occurrence of antimicrobial resistance along the agri-food chain.

Global climate change is expected to impact drinking water quality through multiple weather-related phenomena. We conducted a systematic review and meta-analysis of the relationship between various weather-related variables and the occurrence and concentration of Cryptosporidium and Giardia in fresh surface waters. We implemented a comprehensive search in four databases, screened 1,228 unique citations for relevance, extracted data from 107 relevant articles, and conducted random-effects meta-analysis on 16 key relationships. The average odds of identifying Cryptosporidium oocysts and Giardia cysts in fresh surface waters was 2.61 (95% CI ¼ 1.63-4.21; I 2 ¼ 16%) and 2.87 (95% CI ¼ 1.76-4.67; I 2 ¼ 0%) times higher, respectively, during and after extreme weather events compared to baseline conditions. Similarly, the average concentration ofCryptosporidium and Giardia identified under these conditions was also higher, by approximately 4.38 oocysts/100 L (95% CI ¼ 2.01-9.54; I 2 ¼ 0%) and 2.68 cysts/100 L (95% CI ¼ 1.08-6.55; I 2 ¼ 48%).Correlation relationships between other weather-related parameters and the density of these pathogens were frequently heterogeneous and indicated low to moderate effects. Meta-regression analyses identified different study-level factors that influenced the variability in these relationships.The results can be used as direct inputs for quantitative microbial risk assessment. Future research is warranted to investigate these effects and potential mitigation strategies in different settings and contexts.

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