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Individuals with depression tend to focus on negativity and interpret neutral situations in a negative light. Cognitive theories commonly posit that depressed persons’ focus on negative stimuli leaves them incapable of prioritizing positive emotional information. Reward Devaluation Theory (RDT; Winer and Salem, 2016 ), which stipulates that depressed persons not only focus on negativity, but also exhibit a systematic and motivated avoidance of positive and potentially rewarding stimuli, offers an alternative theoretical framework to understanding cognitive/affective biases in depression. We here unpack the theoretical underpinnings of RDT, reviewing the empirical evidence surrounding positivity avoidance and depression. Studies using cognitive/behavioral tasks, as well as those examining anticipatory and responsive devaluation strategies, are summarized. Future directions in the measurement of RDT, including expanded investigation of self-referential processing, are introduced for consideration. The clinical implications of RDT, which are potentially profound, are also discussed.

Reliable boundary‐layer turbulence parametrizations for polar conditions are needed to reduce uncertainty in projections of Arctic sea ice melting rate and its potential global repercussions. Surface turbulent fluxes of sensible and latent heat are typically represented in weather/climate models using bulk formulae based on the Monin‐Obukhov Similarity Theory, sometimes finely tuned to high stability conditions and the potential presence of sea ice. In this study, we test the performance of new, machine‐learning (ML) flux parametrizations, using an advanced polar‐specific bulk algorithm as a baseline. Neural networks, trained on observations from previous Arctic campaigns, are used to predict surface turbulent fluxes measured over sea ice as part of the recent MOSAiC expedition. The ML parametrizations outperform the bulk at the MOSAiC sites, with RMSE reductions of up to 70 percent. We provide a plug‐in Fortran implementation of the neural networks for use in models. Plain Language Summary Heat can make its way into or out of sea ice via unpredictable air movements, known as turbulence, near the sea surface. In order to predict how quickly Arctic sea ice will melt in the future, we need to know how much heat the turbulence can transport in different weather conditions. Traditionally, turbulence calculations have been performed using sophisticated mathematical formulae from physics. In this study, we test an alternative method for predicting turbulent heat exchange: a computer algorithm known as an artificial neural network. By showing turbulence data, measured in the Arctic during previous scientific expeditions, to the network, it can be “trained” to make predictions in a process known as machine learning. We compare turbulence measurements, taken above sea ice in the recent MOSAiC expedition, with predictions from trained neural networks. We find that the neural networks are better than the traditional physics at predicting what the scientists at MOSAiC observed. The trained neural networks have been made publicly available so that they can be used by scientists for predicting climate change. Key Points Neural networks trained on previous Arctic campaigns predict surface turbulent fluxes from MOSAiC more accurately than bulk methods Updated parametrizations using the MOSAiC data have been developed and implemented in Fortran for deployment in weather/climate models Modest performance gains (up to +7% R2) from recalibration on MOSAiC indicate good generalizability to the pan‐Arctic sea ice domain

Reward motivation, a construct tied to depression, has been studied using the Effort-Expenditure for Rewards Task (EEfRT). Prior work indicates that anhedonia can reduce reward motivation on the EEfRT, as those with higher levels of anhedonia tend to engage in low reward tasks that require less effort as opposed to expending higher levels of effort to obtain a larger reward. Action orientation has shown to act as a buffer at low levels of anhedonia, but this effect has not been seen at high levels of anhedonia. The current study examined if these findings replicated without a stress manipulation and explored the interaction between anxiety and anhedonia in predicting persistence on the EEfRT using two moderation models. Participants ( N = 101) with varying levels of depressive symptoms took part in the study. The first model examined the effects of anhedonia and action orientation on reward motivation. The second model investigated the influence of anhedonia and anxiety on reward motivation. Findings revealed that higher levels of anhedonia were significantly associated with lower reward motivation in both models. Additionally, the interaction between anhedonia and action orientation on reward motivation was significant. Trend analyses revealed that, at low levels of anhedonia, participants generally made more high-effort/high-reward choices or were willing to exude more effort for the possibility of obtaining a greater reward. However, as anhedonia increased, individuals with higher levels of action orientation exhibited greater effort as opposed to those with lower action orientation. The findings indicate that anhedonia has a strong impact on limiting reward motivation. However, high levels of action orientation can mitigate the negative influence of anhedonia on reward motivation.

Damage from infestations of Lymantria dispar L. in oak-dominated stands and southern pine beetle ( Dendroctonus frontalis Zimmermann) in pine-dominated stands have far exceeded impacts of other disturbances in forests of the mid-Atlantic Coastal Plain over the last two decades. We used forest census data collected in undisturbed and insect-impacted stands combined with eddy covariance measurements made pre- and post-disturbance in oak-, mixed and pine-dominated stands to quantify how these infestations altered forest composition, structure and carbon dynamics in the Pinelands National Reserve of southern New Jersey. In oak-dominated stands, multi-year defoliation during L . dispar infestations resulted in > 40% mortality of oak trees and the release of pine saplings and understory vegetation, while tree mortality was minimal in mixed and pine-dominated stands. In pine-dominated stands, southern pine beetle infestations resulted in > 85% mortality of pine trees but had minimal effect on oaks in upland stands or other hardwoods in lowland stands, and only rarely infested pines in hardwood-dominated stands. Because insect-driven disturbances are both delaying and accelerating succession in stands dominated by a single genus but having less effect in mixed-composition stands, long-term disturbance dynamics are favoring the formation and persistence of uneven age oak-pine mixedwood stands. Changes in forest composition may have little impact on forest productivity and evapotranspiration; although seasonal patterns differ, with highest daily rates of net ecosystem production (NEP) during the growing season occurring in an oak-dominated stand and lowest in a pine-dominated stand, integrated annual rates of NEP are similar among oak-, mixed and pine-dominated stands. Our research documents the formation of mixedwood stands as a consequence of insect infestations in the mid-Atlantic region and suggests that managing for mixedwood stands could reduce damage to forest products and provide greater continuity in ecosystem functioning.

Over the past century, fire suppression has facilitated broad ecological changes in the composition, structure, and function of fire-dependent landscapes throughout the eastern US, which are in decline. These changes have likely contributed mechanistically to the enhancement of habitat conditions that favor pathogen-carrying tick species, key wildlife hosts of ticks, and interactions that have fostered pathogen transmission among them and to humans. While the long-running paradigm for limiting human exposure to tick-borne diseases focuses responsibility on individual prevention, the continued expansion of medically important tick populations, increased incidence of tick-borne disease in humans, and emergence of novel tick-borne diseases highlights the need for additional approaches to stem this public health challenge. Another approach that has the potential to be a cost-effective and widely applied but that remains largely overlooked is the use of prescribed fire to ecologically restore degraded landscapes that favor ticks and pathogen transmission. We examine the ecological role of fire and its effects on ticks within the eastern United States, especially examining the life cycles of forest-dwelling ticks, shifts in regional-scale fire use over the past century, and the concept that frequent fire may have helped moderate tick populations and pathogen transmission prior to the so-called fire-suppression era that has characterized the past century. We explore mechanisms of how fire and ecological restoration can reduce ticks, the potential for incorporating the mechanisms into the broader strategy for managing ticks, and the challenges, limitations, and research needs of prescribed burning for tick reduction.

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern , following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products.

Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data but must be calibrated to field data to derive meaning. The composite burn index (CBI) is the most widely used field-based method used to calibrate satellite-based burn severity data but important limitations of this approach have yet to be resolved. The objective of this study was focused on predicting CBI from point cloud and visible-spectrum camera (RGB) metrics derived from single-scan terrestrial laser scanning (TLS) datasets to determine the viability of TLS data as an alternative approach to estimating burn severity in the field. In our approach, we considered the predictive potential of post-scan-only metrics, differenced pre- and post-scan metrics, RGB metrics, and all three together to predict CBI and evaluated these with candidate algorithms (i.e., linear model, random forest (RF), and support vector machines (SVM) and two evaluation criteria (R-squared and root mean square error (RMSE)). In congruence with the strata-based observations used to calculate CBI, we evaluated the potential approaches at the strata level and at the plot level using 70 TLS and 10 RGB independent variables that we generated from the field data. Machine learning algorithms successfully predicted total plot CBI and strata-specific CBI; however, the accuracy of predictions varied among strata by algorithm. RGB variables improved predictions when used in conjunction with TLS variables, but alone proved a poor predictor of burn severity below the canopy. Although our study was to predict CBI, our results highlight that TLS-based methods for quantifying burn severity can be an improvement over CBI in many ways because TLS is repeatable, quantitative, faster, requires less field-expertise, and is more flexible to phenological variation and biomass change in the understory where prescribed fire effects are most pronounced. We also point out that TLS data can also be leveraged to inform other monitoring needs beyond those specific to wildland fire, representing additional efficiency in using this approach.

The challenges inherent in field validation data, and real-world light detection and ranging (lidar) collections make it difficult to assess the best algorithms for using lidar to characterize forest stand volume. Here, we demonstrate the use of synthetic forest stands and simulated terrestrial laser scanning (TLS) for the purpose of evaluating which machine learning algorithms, scanning configurations, and feature spaces can best characterize forest stand volume. The random forest (RF) and support vector machine (SVM) algorithms generally outperformed k-nearest neighbor (kNN) for estimating plot-level vegetation volume regardless of the input feature space or number of scans. Also, the measures designed to characterize occlusion using spherical voxels generally provided higher predictive performance than measures that characterized the vertical distribution of returns using summary statistics by height bins. Given the difficulty of collecting a large number of scans to train models, and of collecting accurate and consistent field validation data, we argue that synthetic data offer an important means to parameterize models and determine appropriate sampling strategies.

As of 2023, the use of prescribed fire to manage ecosystems accounts for more than 50% of area burned annually across the United States. Prescribed fire is carried out when meteorological conditions, including temperature, humidity, and wind speed are appropriate for its safe and effective application. However, changes in these meteorological variables associated with future climate change may impact future opportunities to conduct prescribed fire. In this study, we combine climate projections with information on prescribed burning windows for ecoregions across the contiguous United States (CONUS) to compute the number of days when meteorological conditions allow for the safe and effective application of prescribed fire under present-day (2006–2015) and future climate (2051–2060) conditions. The resulting projections, which cover 57% of all vegetated area across the CONUS, indicate fewer days with conditions suitable for prescribed burning across ecoregions of the eastern United States due to rising maximum daily temperatures, but opportunities increase in the northern and northwestern United States, driven primarily by rising minimum temperatures and declining wind speeds.

Prescribed fire is a management tool that is frequently used to foster biodiversity. Simultaneously, insects that provide essential ecosystem services are globally declining. Within the pyroentomology literature, there are mixed reports of positive and negative effects that prescribed fires have on insect communities. This is likely due to not accounting for fire heterogeneity created by fire severity. To better understand prescribed fire severity effects on insect communities, we used multispectral reflectance data collected by Sentinel-2 to methodically quantify prescribed fire severity and compared ground beetle (Coleoptera: Carabidae) taxonomic and functional community composition responses between an unburned site and two burned sites with contrasting fire impacts. We found 23 ground beetle species and used 30 morphological, physiological, phenological, and ecological functional traits for each species. We found that our moderate fire severity site had different taxonomic and functional community compositions from both our unburned and high-severity sites. Surprisingly, we did not find a strong difference in taxonomic or functional ground beetle composition between our unburned and high-severity sites. Our results encourage future pyroentomology studies to account for fire severity, which will help guide conservation managers to make more accurate decisions and predictions about prescribed fire effects on insect biodiversity.