Alterations to temporal patterns of river flow regimes resulting from damming and flow regulation practices may have negative consequences for freshwater communities. However, little has been performed to develop a holistic approach to assess the effects of hydrologic alterations on fish communities across a wide range of rivers and between different regulation strategies. To address this, we used daily and hourly hydrologic data from gauges in 10 regulated and 14 unregulated Canadian rivers. Building on the Ecological Limits of Hydrologic Alteration concept, hydrologic alterations for many ecologically relevant flow indices were combined to obtain river‐specific hydrologic alteration scores. Extensive community surveys to estimate fish abundance, biomass, diversity indices and habitat guild representation provided data for the derivation of similar river‐specific biotic alteration scores relative to unregulated river conditions. Our results indicate that biological impairment consisting of significant biotic alteration relative to the means from unregulated rivers was directly related to increasing flow alteration scores, with the smallest fish and flow alteration scores observed in run‐of‐river systems and the greatest alteration scores under hydro‐peaking regimes. Our approach not only examined the relationship between river‐specific hydrologic alteration scores and the associated biotic responses, but also provided a more comprehensive assessment of the flow‐response alteration relationship between regulation practices, which may better inform future environmental flow management guidelines. Copyright © 2015 John Wiley & Sons, Ltd.

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Bicycle flow data is crucial for transportation agencies to evaluate and improve cycling infrastructure. Average annual daily bicyclists (AADB) is commonly used in research and practice as a metric for cycling studies such as ridership analysis, infrastructure planning, and injury risk. AADB is estimated by averaging the daily cyclist totals measured throughout the year using a long-term automated bicycle counter, or by using long-term bicycle counting data to extrapolate data from a short-term counting site. Extrapolation of a short-term bicycle counting site requires an accurate and complete set of daily factors from a group of references: long-term bicycle counters. In practice, validation of reference data is done manually, an exercise that is time-consuming but crucial as significant error can be introduced into AADB extrapolation if reference data are not validated. This paper proposes an automated method to validate long-term bicycle count data and interpolate anomalous portions of data. As part of this work, the methods are validated using a relatively large dataset of automated bicycle counts. For validation of our approach, data anomalies are created artificially in a way that removes data (first trial), or reduces counts to 25% or 40% of the measured bicycle counts (second and third trials), for 6 hours, 12 hours, and full days. Of the more than 100 generated anomalies, the validation process flagged approximately 90% in the first and second trials and 80% in the third trial. The average absolute relative error of the interpolated daily values was approximately 10% for all three trials.