Optimizing an integrated biovigilance toolbox to study the spatial distribution and dynamic changes of airborne mycobiota, with a focus on cereal rust fungi in western Canada.

In the face of evolving agricultural practices and climate change, tools towards an integrated biovigilance platform to combat crop diseases, spore sampling, DNA diagnostics and predictive trajectory modelling were optimized. These tools revealed microbial dynamics and were validated by monitoring cereal rust fungal pathogens affecting wheat, oats, barley and rye across four growing seasons (2015-2018) in British Columbia and during the 2018 season in southern Alberta. ITS2 metabarcoding revealed disparity in aeromycobiota diversity and compositional structure across the Canadian Rocky Mountains, suggesting a barrier effect on air flow and pathogen dispersal. A novel bioinformatics classifier and curated cereal rust fungal ITS2 database, corroborated by real-time PCR, enhanced the precision of cereal rust fungal species identification. Random Forest modelling identified crop and land-use diversification as well as atmospheric pressure and moisture as key factors in rust distribution. As a valuable addition to explain observed differences and patterns in rust fungus distribution, trajectory HYSPLIT modelling tracked rust fungal urediniospores' northeastward dispersal from the Pacific Northwest towards southern British Columbia and Alberta, indicating multiple potential origins. Our Canadian case study exemplifies the power of an advanced biovigilance toolbox towards developing an early-warning system for farmers to detect and mitigate impending disease outbreaks.

Research priorities for global food security under extreme events.

Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.

A coupled stochastic/deterministic model to estimate the evolution of the risk of water contamination by pesticides across Canada.

Periodic assessments of the risk of water contamination by pesticides help decision makers improve the sustainability of agricultural management practices. In Canada, when evaluating the risk of water contamination by pesticides, 2 main constraints arise. First, because the area of interest is large, a pesticide transport model with low computational running time is mandatory. Second, some relevant input data for simulations are not known, and most are known only at coarse scale. This study aims to develop a robust methodology to estimate the evolution of the risk of water contamination by pesticides across Canada. To circumvent the 2 aforementioned issues, we constructed a stochastic model and coupled it to the 1-dimensional pesticide fate model Pesticide Root Zone Model (PRZM). To account for input data uncertainty, the stochastic model uses a Monte Carlo approach to generate several pesticide application scenarios and to randomly select PRZM parameter values. One hundred different scenarios were simulated for each of over 2000 regions (Soil Landscapes of Canada [SLC] polygons) for the years 1981 and 2006. Overall, the results indicated that in those regions in which the risk increased from 1981 to 2006, the increase in risk was mainly attributable to the increased area treated by pesticides or an increase in the number of days with runoff. More specifically, this work identifies the areas at higher risk, where further analyses with finer-scale input data should be performed. The model is specific for Canadian data, but the framework could be adapted for other large countries.

Inferring resource distributions from Atlantic bluefin tuna movements: an analysis based on net displacement and length of track.

We use observed movement tracks of Atlantic bluefin tuna in the Gulf of Maine and mathematical modeling of this movement to identify possible resource patches. We infer bounds on the overall sizes and distribution of such patches, even though they are difficult to quantify by direct observation in situ. To do so, we segment individual fish tracks into intervals of distinct motion types based on the ratio of net displacement to length of track (DeltaD/DeltaL) over a time window Deltat. To find the best segmentation, we optimize the fit of a random-walk movement model to each motion type. We compare results from two distinct movement models: biased turning and biased speed, to check the model-dependence of our inferences, and find that uncertainty in choice of movement model dominates the uncertainties of our conclusions. We find that our data are best described using two motion types: "localized" (DeltaD/DeltaL small) and "long-ranged" (DeltaD/DeltaL large). The biased turning model leads to significantly better resolution of localized movement intervals than the biased speed model. We hypothesize that localized movement corresponds to exploitation of resource patches. Comparison with visual behavior observations made during tracking suggests that many inferred intervals of localized motion do indeed correspond to feeding activity. From our analysis, we estimate that, on average, bluefin tuna in the Gulf of Maine encounter a resource patch every 2h, that those patches have an average radius of 0.7-1.2 km, and that, overall, there are at most 5-9 such patches per 100 km(2) in the region studied.