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Abstract Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor's law of fluctuation scaling, which states that the variance of a population's size or density is proportional to a positive power of the mean size or density, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. Here, we use water clarity observations from 10,531 days of high‐frequency measurements in 35 globally distributed lakes, and lower‐frequency measurements over multiple decades from 6342 lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide the first documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor's law, and that model coefficients increase in a predictable yet non‐linear manner with decreasing observation frequency. This discovery effectively links high‐frequency sensor network observations with long‐term historical monitoring records, thereby affording new opportunities to understand and predict ecological dynamics on time scales from days to decades. 

Sediment traps were deployed to assess the mass and composition (iron, manganese, total organic carbon, and total nitrogen) of settling particulates in the water column of two drinking water reservoirs—Beaverdam Reservoir and Falling Creek Reservoir, both located in Vinton, Virginia, USA. Sediment traps were deployed at two depths in each reservoir to capture both epilimnetic and hypolimnetic (total) sediment flux. The particulates were collected from the traps approximately fortnightly from April to December from 2018 to 2022, then filtered, dried, and analyzed for either iron and manganese or total organic carbon and total nitrogen. Beaverdam and Falling Creek are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia. The sediment trap dataset consists of logs detailing the sample filtering process, the mass of dried particulates from each filter, and the raw concentration data for iron (Fe) and manganese (Mn), total organic carbon (TOC) and total nitrogen (TN). The final products are the calculated downward fluxes of solid Fe, Mn, TOC and TN during the deployment periods. 

Abstract Lake ecosystems, as integrators of watershed and climate stressors, are sentinels of change. However, there is an inherent time-lag between stressors and whole-lake response. Aquatic metabolism, including gross primary production (GPP) and respiration (R), of stream–lake transitional zones may bridge the time-lag of lake response to allochthonous inputs. In this study, we used high-frequency dissolved oxygen data and inverse modeling to estimate daily rates of summer epilimnetic GPP and R in a nutrient-limited oligotrophic lake at two littoral sites located near different major inflows and at a pelagic site. We examined the relative importance of stream variables in comparison to meteorological and in-lake predictors of GPP and R. One of the inflow streams was substantially warmer than the other and primarily entered the lake’s epilimnion, whereas the colder stream primarily mixed into the metalimnion or hypolimnion. Maximum GPP and R rates were 0.2–2.5 mg O 2 L −1  day −1 (9–670%) higher at littoral sites than the pelagic site. Ensemble machine learning analyses revealed that > 30% of variability in daily littoral zone GPP and R was attributable to stream depth and stream–lake transitional zone mixing metrics. The warm-stream inflow likely stimulated littoral GPP and R, while the cold-stream inflow only stimulated littoral zone GPP and R when mixing with the epilimnion. The higher GPP and R observed near inflows in our study may provide a sentinel-of-the-sentinel signal, bridging the time-lag between stream inputs and in-lake processing, enabling an earlier indication of whole-lake response to upstream stressors. 

Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming acrossthe globe. Consequently, there is an increased need to project futurechanges in lake thermal structure and resulting changes in lakebiogeochemistry in order to plan for the likely impacts. Previous studies ofthe impacts of climate change on lakes have often relied on a single modelforced with limited scenario-driven projections of future climate for arelatively small number of lakes. As a result, our understanding of theeffects of climate change on lakes is fragmentary, based on scatteredstudies using different data sources and modelling protocols, and mainlyfocused on individual lakes or lake regions. This has precludedidentification of the main impacts of climate change on lakes at global andregional scales and has likely contributed to the lack of lake water qualityconsiderations in policy-relevant documents, such as the Assessment Reportsof the Intergovernmental Panel on Climate Change (IPCC). Here, we describe asimulation protocol developed by the Lake Sector of the Inter-SectoralImpact Model Intercomparison Project (ISIMIP) for simulating climate changeimpacts on lakes using an ensemble of lake models and climate changescenarios for ISIMIP phases 2 and 3. The protocol prescribes lakesimulations driven by climate forcing from gridded observations anddifferent Earth system models under various representative greenhouse gasconcentration pathways (RCPs), all consistently bias-corrected on a0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lakemodels were forced with these data to project the thermal structure of 62well-studied lakes where data were available for calibration underhistorical conditions, and using uncalibrated models for 17 500 lakesdefined for all global grid cells containing lakes. In ISIMIP phase 3, thisapproach was expanded to consider more lakes, more models, and moreprocesses. The ISIMIP Lake Sector is the largest international effort toproject future water temperature, thermal structure, and ice phenology oflakes at local and global scales and paves the way for future simulations ofthe impacts of climate change on water quality and biogeochemistry in lakes.