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Wildfire activity is increasing globally. The resulting smoke plumes can travel hundreds to thousands of kilometers, reflecting or scattering sunlight and depositing particles within ecosystems. Several key physical, chemical, and biological processes in lakes are controlled by factors affected by smoke. The spatial and temporal scales of lake exposure to smoke are extensive and under-recognized. We introduce the concept of the lake smoke-day, or the number of days any given lake is exposed to smoke in any given fire season, and quantify the total lake smoke-day exposure in North America from 2019 to 2021. Because smoke can be transported at continental to intercontinental scales, even regions that may not typically experience direct burning of landscapes by wildfire are at risk of smoke exposure. We found that 99.3% of North America was covered by smoke, affecting a total of 1,333,687 lakes ≥10 ha. An incredible 98.9% of lakes experienced at least 10 smoke-days a year, with 89.6% of lakes receiving over 30 lake smoke-days, and lakes in some regions experiencing up to 4 months of cumulative smoke-days. Herein we review the mechanisms through which smoke and ash can affect lakes by altering the amount and spectral composition of incoming solar radiation and depositing carbon, nutrients, or toxic compounds that could alter chemical conditions and impact biota. We develop a conceptual framework that synthesizes known and theoretical impacts of smoke on lakes to guide future research. Finally, we identify emerging research priorities that can help us better understand how lakes will be affected by smoke as wildfire activity increases due to climate change and other anthropogenic activities., (© 2024 Oak Ridge National Laboratory and The Author(s). Global Change Biology published by John Wiley & Sons Ltd.)

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the "black-box" nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having "no change" (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

Ponds are often identified by their small size and shallow depths, but the lack of a universal evidence-based definition hampers science and weakens legal protection. Here, we compile existing pond definitions, compare ecosystem metrics (e.g., metabolism, nutrient concentrations, and gas fluxes) among ponds, wetlands, and lakes, and propose an evidence-based pond definition. Compiled definitions often mentioned surface area and depth, but were largely qualitative and variable. Government legislation rarely defined ponds, despite commonly using the term. Ponds, as defined in published studies, varied in origin and hydroperiod and were often distinct from lakes and wetlands in water chemistry. We also compared how ecosystem metrics related to three variables often seen in waterbody definitions: waterbody size, maximum depth, and emergent vegetation cover. Most ecosystem metrics (e.g., water chemistry, gas fluxes, and metabolism) exhibited nonlinear relationships with these variables, with average threshold changes at 3.7 ± 1.8 ha (median: 1.5 ha) in surface area, 5.8 ± 2.5 m (median: 5.2 m) in depth, and 13.4 ± 6.3% (median: 8.2%) emergent vegetation cover. We use this evidence and prior definitions to define ponds as waterbodies that are small (< 5 ha), shallow (< 5 m), with < 30% emergent vegetation and we highlight areas for further study near these boundaries. This definition will inform the science, policy, and management of globally abundant and ecologically significant pond ecosystems., (© 2022. The Author(s).)

Many estuarine and freshwater ecosystems worldwide have undergone substantial changes due to multiple anthropogenic stressors. Over the past two decades, the Sacramento-San Joaquin Delta (Delta) in California, USA, saw a severe decline in pelagic fishes, a shift in zooplankton community composition, and a rapid expansion of invasive aquatic vegetation. To evaluate whether major changes have also occurred in the littoral fish community, we analyzed a beach seine survey dataset collected from 1995 to 2015 from 26 sites within the Delta. We examined changes in the Delta fish community at three different ecological scales (species, community, and biomass), using clustering analyses, trend tests, and change-point analyses. We found that the annual catch per effort for many introduced species and some native species have increased since 1995, while few experienced a decline. We also observed a steady pattern of change over time in annual fish community composition, driven primarily by a steady increase in non-native Centrarchid species. Lastly, we found that littoral fish biomass has essentially doubled over the 21-year study period, with Mississippi Silverside Menidia audens and fishes in the Centrarchidae family driving most of this increase. The changes in the catch per effort, fish community composition, and biomass per volume indicate that a shift has occurred in the Delta littoral fish community and that the same factors affecting the Delta's pelagic food web may have been a key driver of change., Competing Interests: The authors have declared that no competing interests exist.

A Kenyan woman, a retired nurse, and a nurse executive in America are miraculously led together to start a library in Kima, Kenya. Small beginnings grow into the Heather May-MashoodAbiola Children's Resource Centre (HEMAMA). Named after two infant children lost by the Kenyan woman and the nurse executive, HEMAMA is making a difference in the lives of children in the Kima, Kenya community.

Situated learning theory is gaining increased attention in the fields of human cognition and learning. The authors discuss the key researchers and basic assumptions of situated learning, and outline implications for the design and development of instruction. The authors describe how they applied the situated model to teaching and learning nursing research, an area identified as problematic for staff, administrators, and educators. They describe their personal observations and discuss feedback from participants at the workshop. The authors conclude that the situated model is useful for exposing nurses with little prior research experience to this domain of knowledge.