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Abstract Horticultural peat extraction can mobilize dissolved organic matter (DOM) and inorganic nutrients (nitrogen and phosphorous) to surface waters, harming aquatic ecosystems and water quality. However, it is uncertain how peat extraction affects solute concentration across hydrological and seasonal conditions and how biogeochemical processing in downstream drainage networks responds. Over two years, we used repeated, spatially extensive sampling in stream networks of two mixed land‐use catchments (<200 km²) on the subhumid interior plains of western Canada. We used random forest models to disentangle the effects of land cover, hydrology, and temperature on water chemistry. Peatlands were the dominant source of DOM to streams, but we detected no substantial effect of peat extraction on DOM concentration or composition. Stream discharge was the most important predictor of DOM composition, with generally humic‐like DOM becoming fresher during snowmelt and summer base flow. We detected no effect from peat extraction on soluble reactive phosphorous (SRP) or nitrate (NO₃⁻). However, total ammonia nitrogen (TAN) was an order of magnitude higher in subcatchments with >40% extracted peatland cover (median: 1.5 mg TAN L⁻¹) compared to catchments with similar intact peatland cover. Mass balance analysis suggested that DOM and inorganic nutrients synchronously attenuated during low flows. During high flows, DOM and inorganic nitrogen were conservatively transported, while SRP was attenuated, likely sorbing to suspended particles. Our study suggests that excess TAN mobilized by peat extraction is utilized in headwaters during low flow but propagates downstream during high flow, with implications for eutrophication that land managers should consider. 

Social network data are complex and dependent data. At the macro-level, social networks often exhibit clustering in the sense that social networks consist of communities; and at the micro-level, social networks often exhibit complex network features such as transitivity within communities. Modeling real-world social networks requires modeling both the macro- and micro-level, but many existing models focus on one of them while neglecting the other. In recent work, [28] introduced a class of Exponential Random Graph Models (ERGMs) capturing community structure as well as microlevel features within communities. While attractive, existing approaches to estimating ERGMs with community structure are not scalable. We propose here a scalable two-stage strategy to estimate an important class of ERGMs with community structure, which induces transitivity within communities. At the first stage, we use an approximate model, called working model, to estimate the community structure. At the second stage, we use ERGMs with geometrically weighted dyadwise and edgewise shared partner terms to capture refined forms of transitivity within communities. We use simulations to demonstrate the performance of the two-stage strategy in terms of the estimated community structure. In addition, we show that the estimated ERGMs with geometrically weighted dyadwise and edgewise shared partner terms within communities outperform the working model in terms of goodness-of-fit. Last, but not least, we present an application to high-resolution human contact network data.