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Abstract Plastic litter is a globally pervasive pollutant. Storms are likely key drivers of plastic transport to oceans, but plastic transport during rising and falling limbs of storm hydrographs is rarely measured. Measurements of plastic movement throughout individual storms will improve watershed models of plastic dynamics. We used cameras to quantify macroplastic movement (i.e., particles > 5 mm) in rivers before, during, and after individual storms (N = 18) at 10 sites within three North American watersheds. Most storms showed no difference in macroplastic transport between rising and falling hydrograph limbs or evidence of hysteresis (transport rate range = 0–236 items/30 min). Total macroplastic exported during storm events was positively related to storm magnitude and was greatest at more urban sites. Thus, macroplastic transport during storms was driven by storm size and land use. The quantitative relationships between macroplastic movement and hydrology will improve discharge‐weighted calculations of macroplastic transport which can benefit modeling, monitoring, and mitigation efforts. Practitioner PointsMacroplastic particles (i.e, > 5 mm) are both retained in urban streams (e.g., in debris dams), and move downstream during baseflow and stormflow conditionsStorm flows are key periods of macroplastic transport: transport rates are higher on both rising and falling limbs of storm hydrographs relative to baseflow.The amount of macroplastics moving during storm flows is positively related to storm intensity.The predictive relationships generated between storm flow and macroplastic transport will improve estimates of annual export, and policies for macroplastic pollution reduction. 

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.