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Maintaining regional‐scale freshwater connectivity is challenging due to the dendritic, easily fragmented structure of freshwater networks, but is essential for promoting ecological resilience under climate change. Although the importance of stream network connectivity has been recognized, lake‐stream network connectivity has largely been ignored. Furthermore, protected areas are generally not designed to maintain or encompass entire freshwater networks. We applied a coarse‐filter approach to identify potential freshwater corridors for diverse taxa by calculating connectivity scores for 385 lake‐stream networks across the conterminous United States based on network size, structure, resistance to fragmentation, and dam prevalence. We also identified 2080 disproportionately important lakes for maintaining intact networks (i.e., hubs; 2% of all network lakes) and analyzed the protection status of hubs and potential freshwater corridors. Just 3% of networks received high connectivity scores based on their large size and structure (medians of 1303 lakes, 498.6 km north–south stream distance), but these also contained a median of 454 dams. In contrast, undammed networks (17% of networks) were considerably smaller (medians of six lakes, 7.2 km north–south stream distance), indicating that the functional connectivity of the largest potential freshwater corridors in the conterminous United States currently may be diminished compared with smaller, undammed networks. Network lakes and hubs were protected at similar rates nationally across different levels of protection (8%–18% and 6%–20%, respectively), but were generally more protected in the western United States. Our results indicate that conterminous United States protection of major freshwater corridors and the hubs that maintain them generally fell short of the international conservation goal of protecting an ecologically representative, well‐connected set of fresh waters (≥17%) by 2020 (Aichi Target 11). Conservation planning efforts might consider focusing on restoring natural hydrologic connectivity at or near hubs, particularly in larger networks, less protected, or biodiverse regions, to support freshwater biodiversity conservation under climate change.

Multiple studies have reported widespread browning of Northern Hemisphere lakes. Most examples are from boreal lakes that have experienced limited human influence, and browning has alternatively been attributed to changes in atmospheric deposition, climate, and land use. To determine the extent and possible causes of browning across a more geographically diverse region, we examined watercolor and dissolved organic carbon (DOC) time series in hundreds of northeastern U.S. lakes. The majority of lakes have increased in both DOC and color, but there were neither coherent spatial patterns in trends nor relationships with previously reported drivers. Color trends were more variable than DOC trends, and DOC and color trends were not strongly correlated, indicating a cause other than or in addition to increased loading of terrestrial carbon. Browning may be pronounced in regions where climate and atmospheric deposition are dominant drivers but muted in more human‐dominated landscapes with a limited extent of organic soils where other disturbances predominate.

Given how important lakes are to people, it might seem safe to assume that careful thought has been put into the naming of lakes, and that lake names reflect the high societal value people place on lakes. We examined these assumptions by analyzing the official names in the U.S. Geographic Names Information System for the 479,950 lakes ≥ 1 ha in the conterminous U.S. We found that 83% of lakes were unnamed and most of these were small lakes with 80% of unnamed lakes being smaller than 4 ha. Based on the 83,115 named lakes, we found that lake names reflect peoples' everyday lives, that lakes can inspire creativity (although the most common lake name is “Mud”), that Native American and indigenous languages have played a role in lake naming, and that there are regional differences in lake names. Unfortunately, we also found that derogatory terms were part of some lake names. We advocate for thoughtful and inclusive official naming of the 400,000 unnamed lakes in the U.S., as well as renaming of the lakes with derogatory terms to help focus attention on the importance of lakes to local communities and nations.

Biodiversity–ecosystem functioning (BEF) theory has largely focused on species richness, although studies have demonstrated that evenness may have stronger effects. While theory and numerous small‐scale studies support positive BEF relationships, regional studies have documented negative effects of evenness on ecosystem functioning. We analysed a lake dataset spanning the continental US to evaluate whether strong evenness effects are common at broad spatial scales and if BEF relationships are similar across diverse regions and trophic levels. At the continental scale, phytoplankton evenness explained more variance in phytoplankton and zooplankton resource use efficiency (RUE; ratio of biomass to resources) than richness. For individual regions, slopes of phytoplankton evenness–RUE relationships were consistently negative and positive for phytoplankton and zooplankton RUE, respectively, and most slopes did not significantly differ among regions. Findings suggest that negative evenness effects may be more common than previously documented and are not exceptions restricted to highly disturbed systems.

Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake’s watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near‐stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near‐stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake‐specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.

Although spatial and temporal variation in ecological properties has been well‐studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation – spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.

Lake water residence time and depth are known to be strong predictors of phosphorus (P) retention. However, there is substantial variation in P retention among lakes with the same depth and residence time. One potential explanatory factor for this variation is differences in freshwater connectivity of lakes (i.e., the type and amount of freshwater connections to a lake), which can influence watershed P trapping or the particulate load fraction of P delivered to lakes via stream connections. To examine the relationship between P retention and connectivity, we quantified several different measures of connectivity including those that reflect downstream transport of material (sediment, water, and nutrients) within lake‐stream networks (lake‐stream‐based metrics) as well as those that reflect transport of material from hillslope and riparian areas adjacent to watershed stream networks (stream‐based metrics). Because it is not always clear what spatial extent is appropriate for determining functional differences in connectivity among lakes, we compared connectivity metrics at two important spatial extents: the lake subwatershed extent and the lake watershed extent. We found that variation in P retention among lakes was more strongly associated with connectivity metrics measured at the broader lake watershed extent rather than metrics measured at the finer lake subwatershed extent. Our results suggest that both connectivity between lakes and streams as well as connectivity of lakes and their terrestrial watersheds influence P retention.

Wildfires are becoming larger and more frequent across much of the United States due to anthropogenic climate change. No studies, however, have assessed fire prevalence in lake watersheds at broad spatial and temporal scales, and thus it is unknown whether wildfires threaten lakes and reservoirs (hereafter, lakes) of the United States. We show that fire activity has increased in lake watersheds across the continental United States from 1984 to 2015, particularly since 2005. Lakes have experienced the greatest fire activity in the western United States, Southern Great Plains, and Florida. Despite over 30 years of increasing fire exposure, fire effects on fresh waters have not been well studied; previous research has generally focused on streams, and most of the limited lake‐fire research has been conducted in boreal landscapes. We therefore propose a conceptual model of how fire may influence the physical, chemical, and biological properties of lake ecosystems by synthesizing the best available science from terrestrial, aquatic, fire, and landscape ecology. This model also highlights emerging research priorities and provides a starting point to help land and lake managers anticipate potential effects of fire on ecosystem services provided by fresh waters and their watersheds.

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll aon observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.