Extreme Shifts Research Articles

Dramatic effect of extreme rainfall event and storm on microbial community dynamics in a subtropical coastal region.

Extreme weather events, such as heavy rainfall and typhoons, are becoming more frequent due to climate change and can significantly impact coastal microbial communities. This study examines the short-term alterations in microbial food webs-viruses, bacteria, picophytoplankton, nanoflagellates, ciliates, and diatom-following Typhoon Krathon in Taiwan's coastal waters in October 2024. Daily in situ sampling revealed a significant post-typhoon increased in viral, nanoflagellate, and Synechococcus spp. abundance. Furthermore, viral, Synechococcus spp., and nanoflagellate abundance increased by approximately 4.2 to 12.8-fold, 33.3 to 160 fold, and 0.5 to 9.4 fold in response to these weather events, compared to periods pre-typhoon, respectively. Modified dilution experiments showed that, before the typhoon, nanoflagellate grazing was the major cause of bacterial mortality, while viral lysis was the main cause of Synechococcus spp. mortality. Post-typhoon, there was a notable shift, with nanoflagellate grazing increasing mortality rates of bacteria and Synechococcus spp., suggesting that grazing became the dominant top-down control mechanism after the disturbance. Our findings suggest that extreme weather events shift microbial mortality dynamics, increasing the role of grazing in controlling microbial populations, with potential implications for carbon cycling in coastal ecosystems.

Discovering Climate Change during the Early 21st Century via Wasserstein Stability Analysis

Climate change is an essential topic in climate science, and the accessibility of accurate, high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources. Nonetheless, current research predominantly focuses on mean-value changes and largely overlooks changes in the probability distribution. In this study, a novel method called Wasserstein Stability Analysis (WSA) is developed to identify probability density function (PDF) changes, especially the extreme event shift and nonlinear physical value constraint variation in climate change. WSA is applied to the early 21st century and compared with traditional mean-value trend analysis. The results indicate that despite no significant trend, the equatorial eastern Pacific experienced a decline in hot extremes and an increase in cold extremes, indicating a La Niña-like temperature shift. Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature. This impact is diminishing as the sea ice melts. By revealing PDF shifts, WSA emerges as a powerful tool to re-examine climate change dynamics, providing enhanced data-driven insights for understanding climate evolution.

Affordability of healthy and water-saving dietary patterns in The Gambia

Dietary modification has the potential to improve nutritional status and reduce environmental impacts of the food system. However, for many countries, the optimal composition of locally contextualized healthy and sustainable diets is unknown. The Gambia is vulnerable to climate-change-induced future water scarcity which may affect crop yields and the ability to supply healthy diets. This study identifies potential shifts in Gambian diets that could make diets healthier and reduce the associated agricultural water footprint (WF), and assesses the cost and affordability implications of such dietary changes. Gambian Integrated Household Survey (IHS) food consumption data was combined with market prices, food expenditure and agricultural WF data. Current dietary patterns were compared with World Health Organization (WHO) dietary guidelines and optimized using linear programming to identify least-cost diets that met nutrition recommendations and reduced agricultural water use. Optimization scenarios explored the maximum reduction in green water use that could be achieved with 'culturally-acceptable' dietary shifts, and the magnitude of shifts required to maintain green water use at current levels. On average, current diets provide adequate energy and have appropriate macronutrient composition. However, only 14% of households consume enough fruit and vegetables (F&Vs), and consumption of added sugars exceeds recommendations. With 'culturally-acceptable' changes in consumption, agricultural water use could decrease by 10%-13% or increase by 9%, depending on the baseline dietary pattern. Extreme dietary shifts will be required to maintain water use at 2015 levels with projected population growth. To meet WHO recommendations, dietary costs would increase by 43% compared to the current baseline. Healthy and green water-saving diets would require 48%-63% of average household expenditure to purchase, which is unaffordable for almost half of the population. F&Vs alone account for 31%-40% of the cost of optimized diets compared to 12% of current diets. Dietary modification has the potential to improve the nutritional quality of Gambian diets while reducing agricultural water use, but the required changes are likely to be unaffordable for a large proportion of the population. Improving availability and affordability of nutritious foods-particularly F&Vs-will be crucial for the accessibility of healthy and sustainable diets in the Gambian population.

Comparative Evolutionary Genomics Reveals Genetic Diversity and Differentiation in Bacteroides fragilis.

Bacteroides fragilis is the pathogenic anaerobe most commonly isolated from intra-abdominal infections, abscesses, and blood. Despite its clinical importance, research on its pan-genome-scale evolution is still limited. Herein, we analyzed the pan-genome architecture of 374 B. fragilis strains to explore their intra-species genomic diversity and evolutionary patterns. Our analysis revealed an open pan-genome with a high proportion of accessory genomes, indicating high genetic variability. Accessory genome genes were substantially enriched in the functions of "Replication, Recombination, and Repair" suggesting their roles in gene transfer and divergence. Phylogenomic analysis divided B. fragilis into two distinct clades: divisions I and II, differing in gene content, antimicrobial resistance genes, and mobile genetic elements. Division II revealed higher Tajima's D values, suggesting that it separated after B. fragilis's recent species diversification. The extreme shift in the distribution of gene-wise Hudson's fixation index (Fst) values for each division suggested that several genes are highly differentiated or evolved between the two clades. Average nucleotide identity and 16S rRNA analyses showed that B. fragilis division II represents a distinct species, Bacteroides hominis. Additionally, a considerable depletion of recombination in genes with Fst values > 0.99 was noted, suggesting that the highest Fst genes with little recombination are the basis for differentiation between divisions. Overall, this study enhances the understanding of B. fragilis's genomic diversity, evolutionary dynamics, and potential role in pathogenesis, shedding light on its adaptation and diversification.

Structural Transition Analysis of Bilayer Black Phosphorus under High Pressure via Ultralow-Frequency Raman Spectroscopy.

The unique anisotropic electron-photon and electron-phonon interactions of black phosphorus (BP) set it apart from other isotropic 2D materials. These anisotropic properties can be adjusted by varying the stacking thickness and sequence as well as by applying external pressure and strain. In contrast to multilayer or bulk BP, the effects of pressure on bilayer BP are still not fully elucidated. This study presents experimental evidence that explores the high-pressure response (0-20 GPa) of bilayer BP within the extreme low Raman shift region (5-150 cm-1), aiming at verifying structural phase transitions and stacking sequence variations. Notably, phase transitions were observed at ∼7 GPa for the orthorhombic to rhombohedral transition and at ∼14-16 GPa for a further transition to a simple cubic structure. The effects of pressure on bilayer BP with three distinct stacking sequences (AA, AB, and AC) were analyzed using angular-resolved polarized Raman spectroscopy (ARPR). The recovery of all characteristic Raman modes after the application of pressure substantiates the reversibility of the structural phase transitions in bilayer BP across all three stacking sequences. However, significant alterations in the stacking sequences were observed before and after the application of hydrostatic pressure. Specifically, AA and AC stacking sequences showed a tendency to relax into an optimized interlayer AB stacking structure upon the release of pressure from 20 to 0 GPa. This observed change in the stacking sequences of bilayer BP under pressure is detected for the first time in this work. The pressure-induced modifications in the stacking sequences, and consequently in the anisotropic properties of bilayer BP, highlight its potential as a promising material for pressure- (or strain-) sensitive optoelectronic nanodevices. Additionally, this study offers novel insights into the analysis of anisotropic interlayer interactions in other van der Waals-stacked 2D materials.

An overview of the psychological complexities in sports performance

Our special issue comprises a diverse collection of ten research and review articles, each offering unique insights into the psychological dimensions of athletic performance. From studies examining the role of resilience in overcoming extreme score shifts to explorations of the mental toll caused by external crises like the COVID-19 pandemic and geopolitical conflict, these papers provide a broad spectrum of perspectives. The issue digs into critical topics such as the influence of crowd dynamics, athlete-coach relationships, and the psychological challenges of breaking long-standing records, all while highlighting innovative methodologies like ecological approaches and creative non-fiction. Together, these ten papers underscore the complexity of the psychological factors that impact athletes’ success and well-being, offering both theoretical advances and practical applications for the sporting world.

In silico analysis predicts mutational consequences of CITED2, NUDT4, and Ar18B in patients with bipolar disorder

Bipolar disorder is a mood-related disorder, which can be portrayed as extreme shifts in energy, mood, and activity levels which can also be characterized by manic highs and depressive lows that can be often misdiagnosed as unipolar disorder due to primitive diagnostics techniques based on clinical assessments as well as diagnostic complexities arising due to its heterogeneous nature and overlapping symptoms with conditions like schizophrenia. leading to delays in treatment Strong evidence in support of genetic and epigenetic aspects of bipolar disorder, including mechanisms such as compromised hypothalamic-pituitary-adrenal axis, immune-inflammatory imbalances, oxidative stress, and mitochondrial dysfunction are found. Moreover, some previous research has already stated the role of genes like CITED2, NUDT4, and Arl8B in these processes. The primary goal of this study is to investigate the involvement of the genes in exploring and validating their potential as biomarkers for bipolar disorder. In silico tools like MutationTaster, PolyPhen2, SIFT, GTEx, PhenoScanner, and RegulomeDB were used to perform mutational and gene expression analyses. Results revealed potentially dangerous mutations caused in CITED2, NUDT4, and Arl8B, those which can have diverse outcomes. RegulomeDB, GTEx, and PhenoScanner reveal the involvement of these genes in various brain regions highlighting their relevance to bipolar disorder. This analysis suggests the potential utility of CITED2, NUDT4, and Arl8B as diagnostic markers hence shedding light on their roles to elaborate the molecular range of bipolar disorder. The study also contributes to providing valuable insights into the genetic and molecular basis of bipolar disorders.

Spatiotemporal characterization of heatwave exposure across historically vulnerable communities

Heatwaves pose a serious threat and are projected to amplify with changing climate and social demographics. A comprehensive understanding of heatwave exposure to the communities is imperative for the development of effective strategies and mitigation plans. This study explores spatiotemporal characterization of heatwaves across the historically vulnerable communities in Mississippi, United States. We derive multiple heatwave metrics including frequency, duration, and magnitude based on temperature data for urban-specific daytime, nighttime, and day–night combined conditions. Our analysis depicts a rising heatwave trend across all counties, with the most extreme shifts observed in prolonged day–night events lacking overnight relief. We integrate physical heatwave hazards with a socioeconomic vulnerability index to develop an integrated urban heatwave risk index. Integrated metric identifies the counties in northwest Mississippi as heat-prone areas, exhibiting an urgent need to prioritize heat resilience and adaptive strategies in these regions. The compounding urban heatwave and vulnerability risks in these communities highlights an environmental justice imperative to implement equitable policies that protect disadvantaged populations. Although this study is focused on Mississippi, our framework is scalable and can be employed to urban regions globally. This study provides a solid foundation for developing timely heatwave preparedness and mitigation to avert preventable heat-related tragedies as extremes intensify with climate change.

Ex-situ restoration of the Mediterranean forest-forming macroalga Ericaria amentacea: Optimizing growth in culture may not be the key to growth in the field

Evidence of local and regional declines in the canopy-forming alga Ericaria amentacea, a foundation species of diverse marine forest communities on exposed Mediterranean coasts, have spurred restoration efforts focused on sustainable ex-situ techniques. The need to balance the costs of culture maintenance and the susceptibility of early life stages to stressors in the native habitat, including rapid, often extreme shifts in temperature, hydrodynamics and nutrient availability, have driven current efforts to create a culture environment that primes seedlings for outplant, increasing their resilience rather than maximizing growth. We tested the effects of 1) higher culture temperature (25 °C) combined with wave simulation and 2) reduced nutrient loads (10% of standard protocol) with wave simulation on post-culture and post-outplant outcomes relative to optimal growth conditions in established protocols (20 °C, no waves, high-nutrient culture medium). While increased temperature and water motion negatively affected seedling growth in culture, and higher nutrients caused oxidative stress likely associated with enhanced epiphyte overgrowth, these effects were not clearly translated into patterns of long-term growth in the field. Instead, survival in the initial days post-outplant appeared to be the bottleneck for restoration potential, where substrates with persisting seedlings at one month were generally found with flourishing juveniles at four months. Larger clumps of seedlings, in turn, were strongly associated with both initial survival and future growth. These results underscore the importance of the zygote settlement phase to establish high seedling densities, which may be optimized by phenological monitoring of the donor population. They also suggest that less-controlled, more environmentally-realistic culture conditions involving the introduction of mild stress may enhance the survival of early life stages of E. amentacea during the transition to the native environment, providing a means to simultaneously reduce human resource costs in culture and move toward scaling up.

Canadians' experiences of alternative protein foods and their intentions to alter current dietary patterns

Despite established evidence about the environmental and health benefits of alternative protein foods (APF), considerable knowledge gaps and misconceptions remain toward APF. Drawing on a national survey (1800+ responses), the study explores Canadians' experiences of APF and intentions to alter current dietary patterns focusing on four APF – plant-based alternative proteins (PBAP) – plant-based (PB) milk, PB meat, lab-grown (LG) meat, and other alternative proteins (OAP) – and two animal-based foods (ANBF)– milk and meat. Data were collected based on a proportional stratified sampling method from all regions of Canada and analyzed using econometric models. While PB milk was the most consumed APF, followed by PB meat, LG meat was the least tried among participants. Perceived complexity of APF discouraged individuals from cutting back on ANBF, while increasing the consumption of APF. Perceived relative advantage was positively related to the adoption of APF. Perceived compatibility strongly influenced individuals’ adoption of PB meat and OAP. Likewise, perceived trialability significantly influenced the adoption of PB milk and PB meat. Despite the perception that APF may have high health related risks associated with processing, additives, calorie and sugar content, this did not prevent individuals from consuming PB milk and OAP. However, perceived risks did affect intentions to alter dietary patterns in the next 12 months. Additionally, meat attachment and sustainability orientation significantly predicted current consumption decisions of APF and intentions to alter dietary patterns. Although sustainability orientation motivates dietary changes, it did not always lead to extreme shifts. Finally, several demographic variables (age, gender, and education), dietary preferences (being omnivore), and residential area and region had influence on current consumption decisions and future intentions. In conclusion, by controlling several factors and through a comparative analysis of various protein sources, the study offers insights into the interplay of innovation-adoption characteristics, perceived risks, meat attachment and sustainability orientation in understanding dietary choices and provides some implications for industry stakeholders and policies promoting APF.

Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach

Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

SF6 is a useful expander for post-pneumonectomy syndrome in the long-term course: a case report

BackgroundPost-pneumonectomy syndrome (PPS) is a rare but serious condition that can occur after pneumonectomy. It is characterized by a mediastinal shift towards the vacated hemithorax, which can potentially lead to respiratory failure. The management of PPS poses a clinical challenge, especially in the context of the limited availability of certain therapeutic devices due to regulatory restrictions in Japan.Case presentationA 36-year-old female with stage IB non-small cell lung cancer underwent left pneumonectomy. Approximately 2 years later, she developed dyspnea. After consulting with our hospital, subsequent imaging revealed an extreme mediastinal shift causing bronchial obstruction. Emergency thoracotomy and subsequent sulfur hexafluoride (SF6) injections were successfully used to manage her condition. Over the course of follow-up, the interval between SF6 injections was extended from 3 to 11 months, indicating an improvement in the intrathoracic condition.ConclusionsThis case illustrates the efficacy of SF6 gas in treating PPS and in reducing the frequency of medical interventions. SF6 gas administration is safe and effective for the treatment of patients with PPS.

Human-Environment Interactions Shape Mosquito Seasonal Population Dynamics.

Mosquito species, including the Asian tiger mosquito, can transmit disease-causing pathogens such as dengue, Zika, and chikungunya, with their population dynamics influenced by a variety of factors including climate shifts, human activity, and local environmental conditions. Understanding these dynamics is vital for effective control measures. Our study, conducted in Jardí Botanic Marimurtra from May to November 2021, monitored Ae. albopictus activity using BG-Traps and investigated larval control effects. We employed Generalized Linear Mixed Models to analyze variables like weather, human presence, and larvicidal control on adult mosquito abundance. Adults of Ae. albopictus exhibited a seasonal pattern influenced by temperature but with bimodal peaks linked to cumulative rainfall. Proximity to stagnant water and visitor influx directly affected mosquito captures. Additionally, the effectiveness of larvicide treatments depended on interactions between preceding rainfall levels and treatment timing. Our research emphasizes the significance of studying vector ecology at local scales to enhance the efficacy of control programs and address the escalating burden of vector-borne diseases. Considering the impacts of extreme weather events and climate shifts is essential for the development of robust vector control strategies. Furthermore, our distinct findings serve as a prime illustration of utilizing statistical modeling to gain mechanistic insights into ecological patterns and processes.

Application of locally regularized extremal shift to the problem of realization of a prescribed motion

Abstract A controlled system of differential equations under the action of an unknown disturbance is considered. The problem discussed in the paper consists in constructing algorithms for forming a control that provides the realization of a prescribed motion for any admissible disturbance. Namely these algorithms should provide the closeness in the metric of the space of differentiable functions of a phase trajectory of a given controlled system and some etalon trajectory of an analogous system functioning when any outer actions are absent. As the space of admissible disturbances, we take the space of measurable square integrable (with respect to the Euclidean norm) functions. The cases of inaccurate measurements of phase trajectories of both systems at all times and at discrete times are under study. Two computer oriented algorithms for solving the problem are designed. The algorithms are based on the (well-known in the theory of guaranteed control) method of extremal shift. In the process, its local (at each time of control correction) regularization is performed by the method of smoothing functional (the Tikhonov method). In addition, estimates for algorithm’s convergence rate are presented.

Intensification and Poleward Shift of Compound Wind and Precipitation Extremes in a Warmer Climate

AbstractCompound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, by analyzing the event both on oceans and landmasses via state‐of‐the‐art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, and is most pronounced under a high‐emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development.

Extreme shifts in pyrite sulfur isotope compositions reveal the path to bonanza gold

Pyrite is the most common sulfide mineral in hydrothermal ore-forming systems. The ubiquity and abundance of pyrite, combined with its ability to record and preserve a history of fluid evolution in crustal environments, make it an ideal mineral for studying the genesis of hydrothermal ore deposits, including those that host critical metals. However, with the exception of boiling, few studies have been able to directly link changes in pyrite chemistry to the processes responsible for bonanza-style gold mineralization. Here, we report the results of high-resolution secondary-ion mass spectrometry and electron microprobe analyses conducted on pyrite from the Brucejack epithermal gold deposit, British Columbia. Our δ34S and trace element results reveal that the Brucejack hydrothermal system experienced abrupt fluctuations in fluid chemistry, which preceded and ultimately coincided with the onset of ultra-high-grade mineralization. We argue that these fluctuations, which include the occurrence of extraordinarily negative δ34S values (e.g., -36.1‰) in zones of auriferous, arsenian pyrite, followed by sharp increases of δ34S values in syn-electrum zones of nonarsenian pyrite, were caused by vigorous, fault valve-induced episodic boiling (flashing) and subsequent inundation of the hydrothermal system by seawater. We conclude that the influx of seawater was the essential step to forming bonanza-grade electrum mineralization by triggering, through the addition of cationic flocculants and cooling, the aggregation of colloidal gold suspensions. Moreover, our study demonstrates the efficacy of employing high-resolution, in situ analytical techniques to map out individual ore-forming events in a hydrothermal system.

Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change

Understanding microclimate spatial variability is crucial for sustainable and optimised grape production within vineyard plots. By employing a combination of a microclimate model (NicheMapR) and multiple climate data sources, this study aimed to achieve microclimatic analysis in two vineyard plots, Quinta do Bomfim (northern Portugal) and Herdade do Esporão (southern Portugal). This approach provides an innovative 10 m spatial resolution for climate variables. This study incorporated local station hourly data with quantile mapping bias correction on the ERA5-land data. The microclimate model output was employed to perform bias correction on a EURO-CORDEX model ensemble. Climate extreme and bioclimatic indices specifically targeted to viticulture were calculated for each vineyard plot. The 10 m scale was analysed to identify potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables for grape cultivation within each specific plot. The significance of microclimate analyses was higher in areas with intricate topography, while in areas with smooth slopes, the variation of climatic variables was determined to be negligible. There was a projected increase in the median temperature of approximately 3.5 °C and 3.6 °C and a decrease in precipitation of approximately 98 mm and 105 mm in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing a future scenario for the period 2071–2100 against the historical period (1981–2010). Hence, this study offers a comprehensive and future-oriented method for analysing microclimates in vineyard plots. By incorporating geospatial data, ERA5-land data, and the microclimate NicheMapR model, this research aimed to enhance the understanding of current microclimates and future climate scenarios for viticulturists.

Influence of seasonal hydrological regimes on benthic macroinvertebrates in two the Brazilian biodiversity hotspots

Global warming has changed climate patterns worldwide causing an increase of extreme weather conditions that have altered annual seasonal hydrological regimes. These extreme climate-driven shifts modify habitat availability and can influence freshwater communities in disruptive ways. Our study investigates how changes in annual seasonal hydrological regimes affect the community structure and the Functional Feeding Groups (FFG) of benthic macroinvertebrates in two Brazilian biodiversity hotspots, the Brazilian Tropical Savannas (a.k.a., Cerrado) and Atlantic Forest biomes. We investigate whether annual variation in precipitation between biomes influence composition, richness, and abundance of benthic macroinvertebrates and their proportions of FFG. We demonstrate that differences in annual precipitation rates affect the composition and abundance, but not richness of benthic macroinvertebrates. Changes in community structure are related to changes in annual precipitation, which modify stream variables. Our findings suggest that annual seasonal changes in hydrological precipitation modify benthic macroinvertebrate communities, especially in Cerrado, where dry seasons are more pronounced. Therefore, annual changes in precipitation rates may disrupt the benthic macroinvertebrate communities in tropical savannas, potentially leading to biodiversity loss.

Bibimbap : Pre-trained models ensemble for Domain Generalization

This paper addresses a machine learning problem often challenged by differences in the distributions of training and real-world data. We propose a framework that addresses the problem of underfitting in the ensembling method using pre-trained models and improves the performance and robustness of deep learning models through ensemble diversity. For the naive weight ensembling framework, we discovered that the ensembled models could not lie in the same loss basin under extreme domain shift conditions, suggesting that a loss barrier may exist. We used a fine-tuning step after the weighted ensemble to address the underfitting problem caused by the loss barrier and stabilize the batch normalization running parameters. We also inferred through qualitative analysis that the diversity of ensemble models affects domain generalization. We validate our method on a large-scale image dataset (ImageNet-1K) and chemical molecule data, which is suitable for testing with domain shift problems due to its data-splitting method.