Comparing ecologically relevant communities of insects in heterogeneous environments requires methods capable of sampling a sufficient number of individuals and diversity of species to measure β diversity. A battery‐operated computer fan powers a 1.5 m high Voegtlin suction trap. These traps are efficient at capturing small, weakly flying insects, and can be used to sample the α and β diversity of Microhymenoptera in discrete habitats within a temperate forest ecosystem. During a preliminary study comparing Voegtlin‐style suction and Townes‐style Malaise traps, we found that the suction traps caught a greater number and a greater diversity of Hymenoptera than the Malaise traps, especially of those OTUs smaller than 1.5 mm. Placed along a transect at 50 m intervals, the suction traps also yielded more heterogeneous samples than the Malaise traps, suggesting they may be particularly useful for quantifying β diversity at small spatial scales. The same analyses with brachyceran Diptera were more nuanced. Malaise traps outperformed suction traps in terms of measuring α diversity, but suction traps resolved a higher degree of brachyceran community heterogeneity using β diversity. Insofar as Hymenoptera are amongst the most diverse of insect orders and the vast majority of species are specialist parasitoids of other insects, suction trapped Hymenoptera diversity may be a useful proxy for measuring α and β insect diversity in general.

Agricultural activities can result in the contamination of surface runoff with pathogens, pesticides, and nutrients. These pollutants can enter surface water bodies in two ways: by direct discharge into surface waters or by infiltration and recharge into groundwater, followed by release to surface waters. Lack of financial resources makes risk assessment through analysis of drinking water pollutants challenging for drinking water suppliers. Inability to identify agricultural lands with a high-risk level and implement action measures might lead to public health issues. As a result, it is essential to identify hazards and conduct risk assessments even with limited data. This study proposes a risk assessment model for agricultural activities based on available data and integrating various types of knowledge, including expert and literature knowledge, to estimate the levels of hazard and risk that different agricultural activities could pose to the quality of withdrawal waters. To accomplish this, we built a Bayesian network with continuous and discrete inputs capturing raw water quality and land use upstream of drinking water intakes (DWIs). This probabilistic model integrates the DWI vulnerability, threat exposure, and threats from agricultural activities, including animal and crop production inventoried in drainage basins. The probabilistic dependencies between model nodes are established through a novel adaptation of a mixed aggregation method. The mixed aggregation method, a traditional approach used in ecological assessments following a deterministic framework, involves using fixed assumptions and parameters to estimate ecological outcomes in a specific case without considering inherent randomness and uncertainty within the system. After validation, this probabilistic model was used for four water intakes in a heavily urbanized watershed with agricultural activities in the south of Quebec, Canada. The findings imply that this methodology can assist stakeholders direct their efforts and investments on at-risk locations by identifying agricultural areas that can potentially pose a risk to DWIs.

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