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Abstract Quantifying and predicting precipitation and water flow and their influences is challenged by the dynamic relationships between and timing of precipitation and water fluxes. To help with these challenges, scientists use “water year” to examine and predict the impacts of precipitation and relevant extreme climatic and hydrological events on ecosystems. However, traditional water year definitions used in the US lack a consideration of areal variation in climate and hydrology, which is needed when studying ecosystems at regional or national scales. We developed local water year (LWY) values that consider spatial variation using existing definitions whereby the water year begins in the month with the lowest or highest average monthly streamflow. We employed spatial interpolation to assign LWY start and end months to 202 subregions across the conterminous United States that range from 4,384 to 134,755 km². This dataset can be linked with diverse climate, terrestrial, and aquatic data for broad‐scale studies. 

Abstract Local and regional‐scaled studies point to the important role of lake type (natural lakes vs. reservoirs), surface water connectivity, and ecological context (multi‐scaled natural settings and human factors) in mediating lake responses to disturbances like drought. However, we lack an understanding at the macroscale that incorporates multiple scales (lake, watershed, region) and a variety of ecological contexts. Therefore, we used data from the LAGOS‐US research platform and applied a local water year timeframe to 62,927 US natural lakes and reservoirs across 17 ecoregions to examine how chlorophyllaresponds to drought across various ecological contexts. We evaluated chlorophyllachanges relative to each lake's baseline and drought year. Drought led to lower and higher chlorophyllain 18% and 20%, respectively, of lakes (both natural lakes and reservoirs included). Natural lakes had higher magnitudes of change and probabilities of increasing chlorophylladuring droughts than reservoirs, and these differences were particularly pronounced in isolated and highly‐connected lakes. Drought responses were also related to long‐term average lake chlorophyllain complex ways, with a positive correlation in less productive lakes and a negative correlation in more productive lakes, and more pronounced drought responses in higher‐productivity lakes than lower‐productivity lakes. Thus, lake chlorophyll responses to drought are related to interactions between lake type and surface connectivity, long‐term average chlorophylla, and many other multi‐scaled ecological factors (e.g., soil erodibility, minimum air temperature). These results reinforce the importance of integrating multi‐scaled ecological context to determine and predict the impacts of global changes on lakes. 

Abstract Although understanding nutrient limitation of primary productivity in lakes is among the oldest research priorities in limnology, there have been few broad‐scale studies of the characteristics of phosphorus (P)‐, nitrogen (N)‐, and co‐limited lakes and their environmental context. By analyzing 3342 US lakes with concurrent P, N, and chlorophylla(Chla) samples, we showed that US lakes are predominantly co‐limited (43%) or P‐limited (41%). Majorities of lakes were P‐limited in the Northeast, Upper Midwest, and Southeast, and co‐limitation was most prevalent in the interior and western United States. N‐limitation (16%) was more prevalent than P‐limitation in the Great Basin and Central Plains. Nutrient limitation was related to lake, watershed, and regional variables, including Chlaconcentration, watershed soil, and wet nitrate deposition. N and P concentrations interactively affected nutrient–chlorophyll relationships, which differed by nutrient limitation. Our study demonstrates the value of considering P, N, and environmental context in nutrient limitation and nutrient–chlorophyll relationships. 

Abstract A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad‐scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophylla(Chla), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chla) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change. 

Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases in algal biomass do not consistently correlate with air temperature or precipitation, and evidence is lacking for a causal effect of climate or the nonlinear dynamics needed to demonstrate regime shifts. We modeled the causal effects of climate on annual lake chlorophyll (a measure of algal biomass) over 34 y for 24,452 lakes across broad ecoclimatic zones of the United States and evaluated the potential for regime shifts. We found that algal biomass was causally related to climate in 34% of lakes. In these cases, 71% exhibited abrupt but mostly temporary shifts as opposed to persistent changes, 13% had the potential for regime shifts. Climate was causally related to algal biomass in lakes experiencing all levels of human disturbance, but with different likelihood. Climate causality was most likely to be observed in lakes with minimal human disturbance and cooler summer temperatures that have increased over the 34 y studied. Climate causality was variable in lakes with low to moderate human disturbance, and least likely in lakes with high human disturbance, which may mask climate causality. Our results explain some of the previously observed heterogeneous climate responses of lake algal biomass globally and they can be used to predict future climate effects on lakes. 

Local‐scale environmental justice studies of freshwaters have found that marginalized populations are more likely than others to be burdened with poor‐quality waters. However, studies have yet to examine whether monitoring data are sufficient to determine the generality of such results at the national scale. We analyzed racial and ethnic community composition surrounding lakes and the presence of one‐time and long‐term (≥15 years) water‐quality data across the conterminous US. Relative to lakes in White and non‐Hispanic communities, lakes in communities of color and Hispanic communities were three times less likely to be monitored at least once. Moreover, as compared to lakes in White communities, lakes in communities of color were seven times less likely to have long‐term monitoring data; similarly, as compared to lakes in non‐Hispanic communities, lakes in Hispanic communities were nineteen times less likely to have long‐term monitoring data. Given this evidence, assessing the current water quality of and temporal changes in lakes in communities of color and Hispanic communities is extremely difficult. To achieve equitable management outcomes for people of all racial and ethnic backgrounds, freshwater monitoring programs must expand their sampling and revise their designs. 

Key Points We observed post‐wildfire increases in nutrients, dissolved organic carbon, sediments, and acidity and reduced water clarity in lakes Water quality responses were often persistent or cumulative throughout the summer, especially for lakes with tributaries from burned areas High‐severity and shoreline burns resulted in a nearly two‐fold increase in total phosphorus concentration compared to control lakes 

Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species. 

Abstract The LAGOS‐US RESERVOIR data module classifies all 137,465 lakes ≥ 4 ha in the conterminous U.S. into three categories using a machine learning predictive model based on visual interpretation of lake outlines and a lake shape classification rule. Natural Lakes (NLs) are defined as naturally formed, lacking large, flow‐altering structures; Reservoir Class A's (RSVR_A) are defined as lakes likely human‐made or human‐altered by a large water control structure; and Reservoir Class B's (RSVR_Bs) are lakes likely human‐made but are not connected to streams and have a shape rare in NLs. We trained machine learning models on 12,162 manually classified lakes to predict assignment as an NL or RSVR, then further classified RSVRs based on NHD Fcodes, isolation, and angularity. Our classification indicates that > 46% of lakes ≥ 4 ha in the conterminous U.S. are reservoir lakes. These data can be easily combined with other LAGOS‐US modules and U.S. national databases for the broad‐scale study of reservoir lakes and NLs.