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Climate change is generating complex responses in both natural and human ecosystems that vary in their geographic distribution, magnitude, and timing across the global landscape. One of the major issues that scientists and policy makers now confront is how to assess such massive changes over multiple scales of space and time. Lakes and reservoirs comprise a geographically distributed network of the lowest points in the surrounding landscape that make them important sentinels of climate change. Their physical, chemical, and biological responses to climate provide a variety of information-rich signals. Their sediments archive and integrate these signals, enabling paleolimnologists to document changes over years to millennia. Lakes are also hot spots of carbon cycling in the landscape and as such are important regulators of climate change, processing terrestrial and atmospheric as well as aquatic carbon. We provide an overview of this concept of lakes and reservoirs as sentinels, integrators, and regulators of climate change, as well as of the need for scaling and modeling these responses in the context of global climate change. We conclude by providing a brief look to the future and the creation of globally networked sensors in lakes and reservoirs around the world.

Recent climate-change research largely confirms the impacts on US ecosystems identified in the 2009 National Climate Assessment and provides greater mechanistic understanding and geographic specificity for those impacts. Pervasive climate-change impacts on ecosystems are those that affect productivity of ecosystems or their ability to process chemical elements. Loss of sea ice, rapid warming, and higher organic inputs affect marine and lake productivity, while combined impacts of wildfire and insect outbreaks decrease forest productivity, mostly in the arid and semi-arid West. Forests in wetter regions are more productive owing to warming. Shifts in species ranges are so extensive that by 2100 they may alter biome composition across 5-20% of US land area. Accelerated losses of nutrients from terrestrial ecosystems to receiving waters are caused by both winter warming and intensification of the hydrologic cycle. Ecosystem feedbacks, especially those associated with release of carbon dioxide and methane release from wetlands and thawing permafrost soils, magnify the rate of climate change.

Increases in terrestrially-derived dissolved organic matter (DOM) have led to the browning of inland waters across regions of northeastern North America and Europe. Short-term experimental and comparative studies highlight the important ecological consequences of browning. These range from transparency-induced increases in thermal stratification and oxygen (O 2 ) depletion to changes in pelagic food web structure and alteration of the important role of inland waters in the global carbon cycle. However, multi-decadal studies that document the net ecological consequences of long-term browning are lacking. Here we show that browning over a 27 year period in two lakes of differing transparency resulted in fundamental changes in vertical habitat gradients and food web structure and that these responses were stronger in the more transparent lake. Surface water temperatures increased by 2–3 °C in both lakes in the absence of any changes in air temperature. Water transparency to ultraviolet (UV) radiation showed a fivefold decrease in the more transparent lake. The primary zooplankton grazers decreased and in the more transparent lake were largely replaced by a two trophic level zooplankton community. These findings provide new insights into the net effects of the complex and contrasting mechanisms that underlie the ecosystem consequences of browning.

Mosquitoes have increased in their abundance and geographic distribution in northeastern North America, coinciding with an increase in extreme precipitation events and up to a doubling of dissolved organic matter (DOM) concentrations in some inland waters. Increases in DOM can reduce exposure of mosquito larvae to solar ultraviolet (UV) radiation. Although mosquito larvae are most common in shaded habitats, almost nothing is known about their susceptibility to damage by solar UV radiation, or the ability of DOM to create a refuge from damaging UV in their shallow-water habitats. We hypothesize that 1) exposure to solar UV radiation is lethal to mosquito larvae, 2) larvae lack photo-enzymatic repair to fix UV-damaged DNA, and 3) DOM shades larvae from lethal solar UV radiation. We tested these hypotheses with experiments that manipulated UV radiation, the photo-repair radiation necessary for photo-enzymatic DNA repair, and DOM. Exposure to solar UV radiation significantly decreased larval survivorship, while DOM significantly increased it. There was no evidence of photo-enzymatic DNA repair. Our findings confirm that solar UV radiation decreases habitat suitability for mosquito larvae, but DOM provides a refuge from UV. This highlights the need for vector control managers to prioritize high DOM and shaded habitats in their efforts to reduce mosquito populations.

Abstract Freshwater ecosystems, including lakes, streams, and wetlands, are responsive to climate change and other natural and anthropogenic stresses. These ecosystems are frequently hydrologically and ecologically connected with one another and their surrounding landscapes, thereby integrating changes throughout their watersheds. The responses of any given freshwater ecosystem to climate change depend on the magnitude of climate forcing, interactions with other anthropogenic and natural changes, and the characteristics of the ecosystem itself. Therefore, the magnitude and manner in which freshwater ecosystems respond to climate change are difficult to predict a priori. We present a conceptual model to elucidate how freshwater ecosystems are altered by climate change. We identify eleven indicators that describe the response of freshwater ecosystems to climate change, discuss their potential value and limitations, and describe supporting measurements. Indicators are organized in three interrelated categories: hydrologic, water quality, and ecosystem structure and function. The indicators are supported by data sets with a wide range of temporal and spatial coverage, and they inform important scientific and management needs. Together, these indicators improve the understanding and management of the effects of climate change on freshwater ecosystems.

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Prognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge “checks and balances”. Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.

Recent advances in our understanding of the importance of continental- to global-scale connectivity among terrestrial and aquatic ecosystems make consideration of aquatic-terrestrial linkages an urgent ecological and environmental issue. Here, we describe the role of inland waters as sentinels and integrators of the impact of humans on terrestrial and aquatic ecosystems. The metabolic responses of lakes and streams (ie the rates at which these systems process carbon) are proposed as a common metric to integrate the impacts of environmental change across a broad range of landscapes. Lakes and streams transport and alter nutrients, contaminants, and energy, and store signals of environmental change from local to continental scales over periods ranging from weeks to millennia. A carefully conceived and well-integrated network that includes monitoring and experimental approaches to terrestrial-aquatic connectivity is critical to an understanding of basic ecosystem-level processes and to forecasting and mitigating future environmental impacts at the continental scale.