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1. Variability in Plant–Herbivore Interactions

   https://doi.org/10.1146/annurev-ecolsys-102221-045015  

   Wetzel, William C.; Inouye, Brian D.; Hahn, Philip G.; Whitehead, Susan R.; Underwood, Nora (November 2023, Annual Review of Ecology, Evolution, and Systematics)

   Plants and herbivores are remarkably variable in space and time, and variability has been considered a defining feature of their interactions. Empirical research, however, has traditionally focused on understanding differences in means and overlooked the theoretically significant ecological and evolutionary roles of variability itself. We review the literature with the goal of showing how variability-explicit research expands our perspective on plant–herbivore ecology and evolution. We first clarify terminology for describing variation and then review patterns, causes, and consequences of variation in herbivory across scales of space, time, and biological organization. We consider how incorporating variability improves existing hypotheses and leads to new ones. We conclude by suggesting future work that reports full distributions, integrates effects of variation across scales, describes nonlinearities, and considers how stochastic and deterministic variation combine to determine herbivory distributions. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 54 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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2. Seasonal Variability in Baffin Bay

   https://doi.org/10.1029/2024JC021038  

   Shan, Xuan; Spall, Michael A; Pennelly, Clark; Myers, Paul G (October 2024, Journal of Geophysical Research: Oceans)

   Abstract Three dominant characteristics and underlying dynamics of the seasonal cycle in Baffin Bay are discussed. The study is based on a regional, high‐resolution coupled sea ice‐ocean numerical model that complements our understanding drawn from observations. Subject to forcing from the atmosphere, sea ice, Greenland, and other ocean basins, the ocean circulation exhibits complex seasonal variations that influence Arctic freshwater storage and export. The basin‐scale barotropic circulation is generally stronger (weaker) in summer (winter). The interior recirculation (∼2 Sv) is primarily driven by oscillating along‐topography surface stress. The volume transport along the Baffin Island coast is also influenced by Arctic inflows (∼0.6 Sv) via Smith Sound and Lancaster Sound with maximum (minimum) in June‐August (October‐December). In addition to the barotropic variation, the Baffin Island Current also has changing vertical structure with the upper‐ocean baroclinicity weakened in winter‐spring. It is due to a cross‐shelf circulation associated with spatially variable ice‐ocean stresses that flattens isopycnals. Greenland runoff and sea ice processes dominate buoyancy forcing to Baffin Bay. Opposite to the runoff that freshens the west Greenland shelf, stronger salinification by ice formation compared to freshening by ice melt enables a net densification in the interior of Baffin Bay. Net sea ice formation in the past 30 years contributes to ∼25% of sea ice export via Davis Strait. The seasonal variability in baroclinicity and water mass transformation changes in recent decades based on the simulation. 

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3. Capturing variability in flipped mathematics instruction

   Otten, S.; de Araujo, Z.; Sherman, M. (January 2018, Proceedings of the 40th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Flipped instruction is being implemented in an increasing number of mathematics classes but the research base is not yet well developed. Many studies of flipped instruction involve a small number of flipped classes being compared to non-flipped classes, but this methodology fails to account for variations in implementations. To aid in the systematic attention to variation, this article presents a framework for flipped mathematics instruction that identifies key features of the videos assigned as homework as well as features of the in-class activities. The components of the framework are accompanied by proposed quality indicators to further distinguish between flipped implementations that are structurally similar but different in enactment 

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4. Temporal Variability in Implicit Online Learning

   Campolongo, N.; Orabona, F. (January 2020, Advances in neural information processing systems)

   null (Ed.)

   In the setting of online learning, Implicit algorithms turn out to be highly successful from a practical standpoint. However, the tightest regret analyses only show marginal improvements over Online Mirror Descent. In this work, we shed light on this behavior carrying out a careful regret analysis. We prove a novel static regret bound that depends on the temporal variability of the sequence of loss functions, a quantity which is often encountered when considering dynamic competitors. We show, for example, that the regret can be constant if the temporal variability is constant and the learning rate is tuned appropriately, without the need of smooth losses. Moreover, we present an adaptive algorithm that achieves this regret bound without prior knowledge of the temporal variability and prove a matching lower bound. Finally, we validate our theoretical findings on classification and regression datasets. 

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5. Underestimated MJO Variability in CMIP6 Models

   https://doi.org/10.1029/2020GL092244  

   Le, Phong V. V.; Guilloteau, Clément; Mamalakis, Antonios; Foufoula‐Georgiou, Efi (June 2021, Geophysical Research Letters)

   Abstract The Madden‐Julian Oscillation (MJO) is the leading mode of intraseasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet‐based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state‐of‐the‐art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intraseasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase‐longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent. 

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