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Urban lakes are heavily impacted by human activities and climate variability, and they provide many ecosystem services to residents. The MSP LTER program is studying long term changes in urban lake water quality, ecology and management as part of our long term studies of urban environments. The goal of this dataset is to understand how land-use change, management, and climate have impacted urban lake biogeochemistry over time. This dataset includes parameters characterizing the long term (> 5 years) surface water quality and chemistry of 294 lakes and ponds in the Minneapolis-Saint Paul Seven County Metropolitan Area, Minnesota, USA. The dataset draws from data publicly available through the Minnesota Pollution Control Agency and data provided by individual agencies, park districts and cities. The dataset is distinct from other lake datasets because it is curated to only report a single value per lake x date x parameter, minimizing the amount of data manipulation needed before use in statistical analyses. All data come from the top two meters of the water column. In the case of multiple spatial measurements on a single lake or multiple agencies sampling the same lake on the same day, chemistry data were averaged to generate a single value. For Secchi data, the deepest reported observation on a given lake x date was used. Parameters: total phosphorus, total nitrogen, total Kjeldahl nitrogen, nitrate, nitrite, nitrate + nitrite (NOx), ammonium, chlorophyll a (corrected and not corrected for pheophytin), specific conductivity, chloride, and Secchi depth. These waterbodies are identified by their DNR Division of Water (DOW) number with minor alterations for subbasin identification. This dataset does not comprehensively represent all lentic waterbodies that have substantial water quality data in the metro area, and some included waterbodies may be considered wetlands according to state classifications. The data brought together in this database has undergone QAQC by the organizations that originally collected it, as well as a screening process during data harmonization. While we believe that the resulting dataset is robust, we cannot guarantee that it is free of errors or inaccuracies. 

Abstract Understanding the drivers of food chain length in natural communities has intrigued ecologists since Elton publicized “food cycles” in the early 20th century. Proposed drivers of food chain length have included productivity, disturbance regime, ecosystem size, and trophic omnivory. However, current theories have largely assumed simple, two‐dimensional habitat architectures and may not be adequate to predict food chain length in ecosystems with a complex, branching structure. Here, we develop a spatially explicit theoretical model that provides an integrated framework for understanding variation in food chain length in branching networks. We show independent, positive influences of ecosystem size and complexity (as indicated by branching properties) on food chain length. However, the effects of ecosystem size and complexity were contingent upon other factors, appearing more clearly in high‐disturbance and high‐productivity regimes. Our results suggest that ecosystem complexity is an important yet overlooked driver of food chain length that may increase the resilience to anthropogenic environmental changes. 

Abstract River networks regulate carbon and nutrient exchange between continents, atmosphere, and oceans. However, contributions of riverine processing are poorly constrained at continental scales. Scaling relationships of cumulative biogeochemical function with watershed size (allometric scaling) provide an approach for quantifying the contributions of fluvial networks in the Earth system. Here we show that allometric scaling of cumulative riverine function with watershed area ranges from linear to superlinear, with scaling exponents constrained by network shape, hydrological conditions, and biogeochemical process rates. Allometric scaling is superlinear for processes that are largely independent of substrate concentration (e.g., gross primary production) due to superlinear scaling of river network surface area with watershed area. Allometric scaling for typically substrate-limited processes (e.g., denitrification) is linear in river networks with high biogeochemical activity or low river discharge but becomes increasingly superlinear under lower biogeochemical activity or high discharge, conditions that are widely prevalent in river networks. The frequent occurrence of superlinear scaling indicates that biogeochemical activity in large rivers contributes disproportionately to the function of river networks in the Earth system. 

Agriculture’s global environmental impacts are widely expected to continue expanding, driven by population and economic growth and dietary changes. This Review highlights climate change as an additional amplifier of agriculture’s environmental impacts, by reducing agricultural productivity, reducing the efficacy of agrochemicals, increasing soil erosion, accelerating the growth and expanding the range of crop diseases and pests, and increasing land clearing. We identify multiple pathways through which climate change intensifies agricultural greenhouse gas emissions, creating a potentially powerful climate change–reinforcing feedback loop. The challenges raised by climate change underscore the urgent need to transition to sustainable, climate-resilient agricultural systems. This requires investments that both accelerate adoption of proven solutions that provide multiple benefits, and that discover and scale new beneficial processes and food products.