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Climate change is leading to shifts in not only the average timing of phenological events, but also their variance and predictability. Increasing phenological variability creates a stochastic environment that is critically understudied, particularly in aquatic ecosystems. We provide a perspective on the possible implications for increasingly unpredictable aquatic habitats, including more frequent trophic asynchronies and altered hydrologic regimes, focusing on ice-off phenology in lakes. Increasingly frequent phenological extremes may limit the ability of organisms to optimize traits required to adapt to a warming environment. Using a unique, long-term ecological dataset on Escanaba Lake, WI, USA, as a case study, we show that the average date of ice-off is shifting earlier and becoming more variable, thus altering limnological conditions and yielding uncoupled food web responses with ramifications for fish spawn timing and recruitment success. A genes-to-ecosystems understanding of the responses of aquatic communities to increasingly variable phenology is needed. Our perspective suggests that management for diversity, at the intra- and interspecific levels, will become paramount for conserving resilient aquatic ecosystems.

Lakes and reservoirs, as most humans experience and use them, are dynamic bodies of water, with surface extents that increase and decrease with seasonal precipitation patterns, long-term changes in climate, and human management decisions. This paper presents a new global dataset that contains the location and surface area variations of 681,137 lakes and reservoirs larger than 0.1 square kilometers (and south of 50 degree N) from 1984 to 2015, to enable the study of the impact of human actions and climate change on freshwater availability. Within its scope for size and region covered, this dataset is far more comprehensive than existing datasets such as HydroLakes. While HydroLAKES only provides a static shape, the proposed dataset also has a timeseries of surface area and a shapefile containing monthly shapes for each lake. The paper presents the development and evaluation of this dataset and highlights the utility of novel machine learning techniques in addressing the inherent challenges in transforming satellite imagery to dynamic global surface water maps.

Abstract Forest and freshwater ecosystems are tightly linked and together provide important ecosystem services, but climate change is affecting their species composition, structure, and function. Research at nine US Long Term Ecological Research sites reveals complex interactions and cascading effects of climate change, some of which feed back into the climate system. Air temperature has increased at all sites, and those in the Northeast have become wetter, whereas sites in the Northwest and Alaska have become slightly drier. These changes have altered streamflow and affected ecosystem processes, including primary production, carbon storage, water and nutrient cycling, and community dynamics. At some sites, the direct effects of climate change are the dominant driver altering ecosystems, whereas at other sites indirect effects or disturbances and stressors unrelated to climate change are more important. Long-term studies are critical for understanding the impacts of climate change on forest and freshwater ecosystems.

Anthropogenic freshwater salinization affects thousands of lakes worldwide, and yet little is known about how salt loading may shift timing of lake stratification and spring mixing in dimictic lakes. Here, we investigate the impact of salinization on mixing in Lakes Mendota and Monona, Wisconsin, by deploying under-ice buoys to record salinity gradients, using an analytical approach to quantify salinity thresholds that prevent spring mixing, and running an ensemble of vertical one-dimensional hydrodynamic lake models (GLM, GOTM, and Simstrat) to investigate the long-term impact of winter salt loading on mixing and stratification. We found that spring salinity gradients between surface and bottom waters persist up to a month after ice-off, and that theory predicts a salinity gradient of 1.3–1.4 g kg-1 would prevent spring mixing. Numerical models project that salt loading delays spring mixing and increases water column stability, with ramifications for oxygenation of bottom waters, biogeochemistry, and lake habitability.

Abstract

This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.

Abstract. Since the 1960s, a deep groundwater system in Wright Valley, Antarctica, has been the hypothesized source of brines to hypersaline Don Juan Pond and Lake Vanda, both of which are rich in calcium and chloride. Modeling studies do not support other possible mechanisms, such as evaporative processes, that could have led to the current suite of ions present in both waterbodies. In 2011 and 2018, an airborne electromagnetic survey was flown over Wright Valley to map subsurface resistivity (down to 600 m) in exploration of liquid water. The surveys revealed widespread unfrozen brine in the subsurface near Lake Vanda, Don Juan Pond, and the North Fork of Wright Valley. While our geophysical survey can neither confirm nor deny deep groundwater connectivity between Lake Vanda and Don Juan Pond, it does point to the potential for deep valley-wide brine, likely within the Ferrar Dolerite formation.