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Climatic extremes, especially extreme droughts, are occurring more frequently and profoundly impacting biogeochemical processes. However, the relative importance of microbial communities on soil nutrient cycling and community maintenance under natural extreme drought events remains elusive. During a record-breaking drought in the Yangtze River Basin (YRB) in the summer of 2022, we collected ambient soils and drought-affected bare and vegetated soils in ecological buffer zones from two sites with similar soil and vegetation characteristics along the YRB, and examined the relative contribution of soil bacterial communities in supporting multi-nutrient cycling index (MNCI) involving carbon-, nitrate- and phosphorus-cycling and their associations with microbial network. Extreme drought decreased (p < 0.05) bacterial a-diversity but increased MNCI in vegetated soils at both sites, while both remained unchanged (p > 0.05) in bare soils, possibly as a result of vegetation releasing rhizodeposits under drought which selectively recruited bacterial communities. Bacterial community compositions were shifted (p < 0.05) only in vegetated soils, and they exerted more influence than a-diversity on soil MNCI. Notably, the Anaerolineae, identified as a biomarker enriched in vegetated soils, had close associations with enzyme activities and soil MNCI at both sites, suggesting their potential recruitment by vegetation to withstand drought. Furthermore, key ecological clusters (Module 1) in bacterial co-occurrence networks at both sites supported (p < 0.05) higher MNCI, despite no substantial variation in network structure due to drought. Specifically, the most important taxa within Module 1 for predicting soil MNCI revealed by random forest modeling analysis (R² = 0.44 - 0.63, p < 0.001), such as B1-7BS, SBR1031 and Nocardioides, could be deeply involved in soil nitrogen-cycling, suggesting an essential role of specialized interactions of bacterial communities in maintaining soil multifunctionality. Overall, this study demonstrates that changes in biomarkers and functional taxa under extreme drought may better reflect the biological mechanisms involved in microbial communities impacting ecosystem function, which may aid in forecasting the ecological consequences of ongoing climate change in the ecological buffer zones along the YRB. [ABSTRACT FROM AUTHOR]

Predicting carbon dioxide emissions is crucial for addressing climate change and achieving environmental sustainability. Accurate emission forecasts provide policymakers with a basis for evaluating the effectiveness of policies, facilitating the design and implementation of emission reduction strategies, and helping businesses adjust their operations to adapt to market changes. Various methods, such as statistical models, machine learning, and grey prediction models, have been widely used in carbon dioxide emission prediction. However, existing research often lacks comparative analysis with other forecasting techniques. This paper constructs a new Discrete Fractional Accumulation Grey Gompertz Model (DFAGGM(1,1) based on grey system theory and provides a detailed solution process. The Whale Optimization Algorithm (WOA) is used to find the hyperparameters in the model. By comparing it with five benchmark models, the effectiveness of DFAGGM(1,1) in predicting carbon dioxide emissions data for China and the United States is validated. [ABSTRACT FROM AUTHOR]

The importance of masting for ecosystem processes is well established; now we need to understand its evolutionary and physiological drivers. Synchronous interannual variation in reproduction is driven by a combination of environmental variation, weather cues, and resource dynamics. These three major masting drivers, which span both proximate and ultimate factors, are not mutually exclusive and likely apply in all species, with varying importance. Masting improves plant fitness via well-documented density-dependent processes, but the costs of masting remain stubbornly understudied, preventing the integration required to fully understand masting variation across species. Improved understanding of masting drivers and links between weather variation and seed production will improve conservation outcomes, ecological forecasts, and guide management under climate change. Many perennial plants show mast seeding, characterized by synchronous and highly variable reproduction across years. We propose a general model of masting, integrating proximate factors (environmental variation, weather cues, and resource budgets) with ultimate drivers (predator satiation and pollination efficiency). This general model shows how the relationships between masting and weather shape the diverse responses of species to climate warming, ranging from no change to lower interannual variation or reproductive failure. The role of environmental prediction as a masting driver is being reassessed; future studies need to estimate prediction accuracy and the benefits acquired. Since reproduction is central to plant adaptation to climate change, understanding how masting adapts to shifting environmental conditions is now a central question. [ABSTRACT FROM AUTHOR]

The Labrador Shelf is integral to the North Atlantic Ocean's climate system, exerting a significant influence on both regional and global scales. This study examines continuous, high‐temporal resolution hydrographic profiles collected by two Argo floats located on the Labrador Shelf. Our focus centers on the subsurface changes. Among observed seasonal variations, the most pronounced and consistent change is a marked temperature increase during autumn in the Cold Intermediate Layer, which is also confirmed in an eddy resolving global reanalysis data set. Contrary to previous studies that attributed this warming to the autumnal deepening of the mixed layer, our analysis indicates that the warming extends to underneath the deepest mixed layer. Thus, mixed layer development cannot account for the observed warming in the deeper layer. On the other hand, analysis of velocity fields from reanalysis data set reveals active onshore intrusions at several locations, with a section at 58°N emerging as the northernmost hotspot. Budget analysis further indicates that the dominant factor driving autumn warming at Section 58°N is cross‐isobath advection that is associated with the intrusion of slope waters onto the shelf. Subsequently, the positive temperature anomaly at Section 58°N and other enhanced intrusion locations are transported downstream through along‐isobath currents, resulting in lagged yet intensified warming at lower latitudes. Our findings underscore the essential role of cross‐isobath intrusion, in combination with along‐isobath movements in governing seasonal temperature variability in the deep layer of the Labrador Shelf. Incorporating this mechanism is crucial for accurately hindcasting and forecasting bottom environmental conditions in the region. Plain Language Summary: The Cold Intermediate Layer (CIL) is a cold and relatively fresh water overlying the Labrador Shelf, capped by a thin, seasonal, warmer layer in summer. Observations and reanalysis data set reveal that the water mass becomes significantly warmer in autumn. Previous studies attributed the warming to the deepening of the heated summer upper layers in fall, or the downstream transport of upstream waters. Here, we demonstrate that warming in the deep CIL layer is initially driven by enhanced onshore velocity carrying warmer slope water to the shelf. Subsequently, the positive temperature anomaly at higher latitude is transported downstream through along‐isobath currents, resulting in lagged yet intensified warming at lower latitudes. This study reveals the essential role of cross‐shelf exchange in modulating seasonal temperature variability under the surface on the Labrador Shelf. Key Points: The subsurface water along the Labrador Shelf undergoes pronounced autumn warmingThe autumn warming of the subsurface water is triggered by enhanced onshore intrusionLagged yet intensified warming at the southern shelf result from the along‐shelf spread and accumulation of onshore‐intruded warm water [ABSTRACT FROM AUTHOR]

Understanding mechanisms stabilizing ecosystem functions, such as primary production, is crucial for forecasting global environmental responses. While biological diversity is expected to enhance stability through compensatory reactions to environmental changes, empirical evidence is lacking, especially in old‐growth forests vital for biodiversity conservation and climate change mitigation. Moreover, whether increased niche complementarity and stronger intraspecific than interspecific competition are key mechanisms promoting compensatory dynamics and stabilizing ecosystem functions in diverse forests remains unexplored. This study investigates productivity and stability in temperate old‐growth forests over 20 years at community and individual levels. Analysing 4380 trees in a 4‐ha plot in northern Japan with over 35 tree species, structural equation models evaluated the effects of biodiversity and average asynchrony in species fluctuations (compensatory dynamics) on productivity stability across 100 m2 grid quadrats. Functional traits and taxonomic diversity represented species complementarity and the insurance effect. Temporal growth correlations between conspecific and heterospecific neighbours and neighbourhood effects on growth performance indicated intra‐ and interspecific interactions at the individual level. Communities with greater stability exhibited higher diversity and asynchronous species fluctuations, suggesting that compensatory dynamics buffer community productivity against environmental variability. The inverse relationship between tree size variation and stability indicates that communities with less pronounced size and abundance hierarchies have more efficient compensatory mechanisms, ensuring stable forest functioning. The absence of negative temporal correlations in biomass production among heterospecific neighbours suggests the limited significance of interspecific competition in compensatory dynamics. Conversely, positive correlations among conspecific neighbours and their suppressed growth in dense conspecific patches highlight the importance of conspecific negative density‐dependent mechanisms in sustaining tree species diversity and ensuring stable productivity. Synthesis: The study underscores the critical role of tree species richness in stabilizing ecosystem functioning via asynchronous growth in one of the world's most diverse temperate forests. Stronger intraspecific than interspecific competition helps prevent single‐species dominance, maintaining diversity and productivity stability. Despite occasional destabilization from size‐asymmetric interspecific competition, species‐trait complementarity enhances stability by promoting overall biomass production. This study highlights the importance of overall diversity for the stability of forest productivity, with implications for nature conservation and ecosystem functionality. [ABSTRACT FROM AUTHOR]

Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large‐scale meta‐analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well‐studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long‐term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non‐parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species‐specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single‐species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management. [ABSTRACT FROM AUTHOR]

Dynamic multimodal networks are networks with node attributes from different modalities where the attributes and network relationships evolve across time, i.e., both networks and multimodal attributes are dynamic; for example, dynamic relationship networks between companies that evolve across time due to changes in business strategies and alliances, which are associated with dynamic company attributes from multiple modalities such as textual online news, categorical events, and numerical financial-related data. Such information can be useful in predictive tasks involving companies. Environmental, social, and governance (ESG) ratings of companies are important for assessing the sustainability risks of companies. The process of generating ESG ratings by expert analysts is, however, laborious and time-intensive. We thus explore the use of dynamic multimodal networks extracted from the web for forecasting ESG ratings. Learning such dynamic multimodal networks from the web for forecasting ESG ratings is, however, challenging due to its heterogeneity and the low signal-to-noise ratios and non-stationary distributions of web information. Human analysts cope with such issues by learning concepts from past experience through relational thinking and scanning for such concepts when analyzing new information about a company. In this article, we propose the Dynamic Multimodal Slot Concept Attention-based Network (DynScan) model. DynScan utilizes slot attention mechanisms together with slot concept alignment and disentanglement loss functions to learn latent slot concepts from dynamic multimodal networks to improve performance on ESG rating forecasting tasks. DynScan is evaluated on forecasting tasks on six datasets, comprising three ESG ratings across two sets of companies. Our experiments show that DynScan outperforms other state-of-the-art models on these forecasting tasks. We also visualize the slot concepts learned by DynScan on five synthetic datasets and three real-world datasets and observe distinct and meaningful slot concepts being learned by DynScan across both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]

The concept of the circular economy represents the most relevant mainstream approach to reducing the negative environmental impact of waste. Anaerobic digestion has proved to be one of the leading and widely adopted techniques for sewage sludge treatment under the principles of the circular economy. The purpose of this study is to forecast environmental and economic indicators through modeling the extensive utilization of biogas technologies with a case study of an administrative territorial unit. The proposed methodological framework involves the use of averaged specific indicators and is based on the relationship between inhabitants, waste generation rates, biogas yield, greenhouse gas emission mitigation and biogas energy potential. The widespread use of anaerobic digestion techniques according to the proposed methodology in the instant scenario will ensure the biogas yield of 10 million Nm3 within the considered administrative territory unit with a population of 4.2 million P.E., which ultimately can be expressed in electricity and thermal generation potential of 20.8 and 24.8 million kWh*y, respectively, annual greenhouse gas elimination of 119.6 thousand tons of CO2 equivalent and capital investment attraction of EUR 65.18 million. Furthermore, all sewage sludge will be subjected to disinfection and stabilization procedures to ensure its safe utilization. The findings of this study offer an opportunity for a wide range of stakeholders to assess the environmental and economic benefits of the widespread adoption of biogas technologies. The developed methodology can be utilized to inform management decisions through the use of the instant and scenario forecasts. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery dependent (conventional mark-recapture tags, fisheries observer records) and two fishery independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage the strengths of individual data types while statistically accounting for limitations, such as sampling biases.

Observing System Simulation Experiments (OSSEs) provide an effective way to evaluate the impact of assimilating data from a specific observing system on hindcasting, nowcasting, and forecasting of environmental systems. The NSF NCAR's Data Assimilation (DA) Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) tool, to be hosted at the NASA Community Coordinated Modeling Center, serves as a valuable and accessible community resource for quantitatively evaluating the impact of observations from both current and future ionosphere‐thermosphere (IT) observing systems. This study demonstrates the utility of DART/TIEGCM as an IT OSSE tool, using synthetic observations simulated using a currently planned NASA Geospace Dynamics Constellation (GDC) observing system design. Five sets of OSSEs are carried out to compare the effects of assimilating various combinations of prospective GDC observations (e.g., neutral temperature, neutral wind, neutral composition, atomic oxygen ion density, and ion and electron temperature) during a major geomagnetic storm period of the St Patrick's Day Storm on 17 March 2013. The maximum error reduction in neutral temperature and atomic ion oxygen density is 24.6% and 43.3% compared to the control experiment. These OSSEs indicate the benefits of coupled IT DA approaches implemented in DART/TIEGCM to maximize the impact of multi‐parameter IT observations, such as those expected from the GDC mission. Although more work is required to draw any definitive conclusion on the GDC data impact, the study provides an illustrative example of how the DART/TIEGCM community tool can be used to evaluate observational impacts of planned or existing missions for geospace research and applications. Plain Language Summary: Data assimilation (DA) is a method that optimally combines observation information with a model to improve hindcasting, nowcasting, and forecasting of environmental conditions. An Observing System Simulation Experiment (OSSE) is a type of DA experiment that can help evaluate the impact of assimilating data from a specific observation system. The NSF NCAR's DA Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) tool serves as a community resource for DA of the ionosphere‐thermosphere (IT) observing system. This study demonstrates the utility of DART/TIEGCM using synthetic observations from a notional observing system provided by the planned NASA Geospace Dynamics Constellation mission under geophysical conditions that mimic a major geomagnetic storm period of the St Patrick's Day Storm on 17 March 2013. This preliminary study provides an illustrative example of how DART/TIEGCM can be used to evaluate the impact of an observing system on geospace research and applications. Key Points: Data Assimilation (DA) Research Testbed/Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (DART/TIEGCM) is a community DA tool that facilitates ionosphere‐thermosphere Observing System Simulation ExperimentsDART/TIEGCM's utility as a viable Observing System Simulation Experiment tool is demonstrated with the currently planned Geospace Dynamics Constellation (GDC) observing systemCoupled ionosphere‐thermosphere DA approaches can help maximize the impact of multi‐parameter observations, such as from GDC [ABSTRACT FROM AUTHOR]

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities. [ABSTRACT FROM AUTHOR]

Conducting assessments to understand the effects of changing environmental conditions on polar bear (Ursus maritimus) demography has become increasingly important to inform management and conservation. Here, we combined physical (2005–2007) and genetic (2017–2018) mark‐recapture with harvest recovery data (2005–2018) to estimate demographic rates of the Davis Strait polar bear subpopulation and examine the possible effects of climate, dynamic ice habitat, and prey resources on survival. Large sample sizes (e.g., 2,513 marked animals) allowed us to estimate temporal variation in annual survival rates using multistate mark‐recapture‐recovery models. We did not detect statistically significant effects of climate, ice habitat, and prey during the 13‐year study. Estimated total abundance in 2006 was 2,190, credible interval (CRI) [1,954, 2,454] and 1,944, CRI [1,593, 2,366] in 2018. Geometric mean population growth rate (0.99, 95% CRI [0.97, 1.01]) indicated the subpopulation may have declined slightly between 2006 and 2018. However, we did not detect a declining trend in survival or substantial change in reproductive metrics over this period. Given forecasts of major environmental change we emphasize the need to review monitoring programs for this subpopulation. [ABSTRACT FROM AUTHOR]

Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and precise cloud segmentation. By integrating Positional Visual Transformers (PVT) with this architecture, our method advances high-resolution feature delineation and segmentation accuracy. Ultimately, our research offers a novel perspective for surmounting traditional challenges in cloud detection and contributes to the advancement of precise and dependable image analysis across various domains. [ABSTRACT FROM AUTHOR]

Low-frequency communication constitutes a vital component of essential communication systems, serving a pivotal role in remote radio communication, navigation, timing, and seismic analysis. To enhance the predictive precision of low-frequency skywave propagation and address the demands of engineering applications, we propose a high-precision prediction method based on the ITU-R P.684 wave-hop theory and real-time environmental parameter forecasts. This method features several distinctive attributes. Firstly, it employs real-time ionospheric prediction data instead of relying on long-term ionospheric model predictions. Secondly, it utilizes a detailed map of land–sea surface electrical characteristics, surpassing the simplistic land–sea dichotomy previously employed. Compared with measured data, the findings demonstrate that we attained a reasonable propagation pattern and achieved high-precision field strength predictions. Comparatively, the improved method exhibits an improvement in the time and spatial domains over the ITU-R P.684 standard. Finally, the improved method balances computational efficiency with enhanced prediction accuracy, supporting the advancement of low-frequency communication system design and performance evaluation. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme ‘pulses’ against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time‐limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology and do not consider biological effects as a basis of MHW severity. The re‐emergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across (1) three thermal tolerance adaptive strategies, (2) interannual temperature variation and (3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration‐high magnitude) and chronic (long duration‐low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and greenhouse gas emissions to combat global climate change has become an international necessity. A large portion of energy consumption is attributed to old buildings, with increased operational costs and decreased occupational comfort. These buildings require renovation measures that can be viewed as an opportunity to implement sustainable construction and create traditionally designed buildings based on sustainable construction. This study proposes a combination of building information modeling (BIM) with Design Builder and Meteonorm software to evaluate the environmental impact of renovating buildings during the operational phase. The process involved is demonstrated in a case study application to a typical two-story single-family residential building in Tehran city, in which the building's materials, weight, and volume are determined using Revit software, and Design Builder is used to simulate the renovation effect on various building components – showing that, by combining the most effective insulation, glass, heating system, and doors, a 15% reduction in energy consumption and 44% reduction in CO2 emissions could be achieved. The study significantly advances knowledge in sustainable building practices by introducing a practical framework for decision-making in environmentally conscious building renovations, focusing on such crucial elements as insulation materials, window glass types, heating systems, and door materials. It also provides valuable insights into the practical implications of weather data for accurate environmental forecasting, emphasizing the methodology's applicability and adaptability to similar contexts, as evidenced by its successful application in a real-world case in Tehran. [ABSTRACT FROM AUTHOR]

Ecological forecasts are becoming increasingly valuable tools for conservation and management. However, there are few examples of near‐real‐time forecasting systems that account for the wide range of ecological complexities. We developed a new coral disease ecological forecasting system that explores a suite of ecological relationships and their uncertainty and investigates how forecast skill changes with shorter lead times. The Multi‐Factor Coral Disease Risk product introduced here uses a combination of ecological and marine environmental conditions to predict the risk of white syndromes and growth anomalies across reefs in the central and western Pacific and along the east coast of Australia and is available through the US National Oceanic and Atmospheric Administration Coral Reef Watch program. This product produces weekly forecasts for a moving window of 6 months at a resolution of ~5 km based on quantile regression forests. The forecasts show superior skill at predicting disease risk on withheld survey data from 2012 to 2020 compared with predecessor forecast systems, with the biggest improvements shown for predicting disease risk at mid‐ to high‐disease levels. Most of the prediction uncertainty arises from model uncertainty, so prediction accuracy and precision do not improve substantially with shorter lead times. This result arises because many predictor variables cannot be accurately forecasted, which is a common challenge across ecosystems. Weekly forecasts and scenarios can be explored through an online decision support tool and data explorer, co‐developed with end‐user groups to improve use and understanding of ecological forecasts. The models provide near‐real‐time disease risk assessments and allow users to refine predictions and assess intervention scenarios. This work advances the field of ecological forecasting with real‐world complexities and, in doing so, better supports near‐term decision making for coral reef ecosystem managers and stakeholders. Secondarily, we identify clear needs and provide recommendations to further enhance our ability to forecast coral disease risk. [ABSTRACT FROM AUTHOR]

Dissolved oxygen is fundamental for chemical and biochemical processes occurring in natural waters and critical for the life of aquatic organisms. Many organisms are responsible for altering organic matter and oxygen transfers across ecosystem or habitat boundaries and, thus, engineering the oxygen balance of the system. Due to such Lemna features as small size, simple structure, vegetative reproduction and rapid growth, as well as frequent mass occurrence in the form of thick mats, they make them very effective in oxygenating water. The research was undertaken to assess the impact of various species of duckweed (L. minor and L. trisulca) on dissolved oxygen content and detritus production in water and the role of ecological factors (light, atmospheric pressure, conductivity, and temperature) in this process. For this purpose, experiments were carried out with combinations of L. minor and L. trisulca. On this basis, the content of oxygen dissolved in water was determined depending on the growth of duckweed. Linear regression models were developed to assess the dynamics of changes in oxygen content and, consequently, organic matter produced by the Lemna. The research showed that the presence of L. trisulca causes an increase in dissolved oxygen content in water. It was also shown that an increase in atmospheric pressure had a positive effect on the ability of duckweed to produce oxygen, regardless of its type. The negative correlation between conductivity and water oxygenation, obtained in conditions of limited light access, allows us to assume that higher water conductivity limits oxygen production by all combinations of duckweeds when the light supply is low. Based on the developed models, it was shown that the highest increase in organic matter would be observed in the case of mixed duckweed and the lowest in the presence of the L. minor species, regardless of light conditions. Moreover, it was shown that pleustophytes have different heat capacities, and L. trisulca has the highest ability to accumulate heat in water for the tested duckweed combinations. The provided knowledge may help determine the good habitat conditions of duckweed, indicating its role in purifying water reservoirs as an effect of producing organic matter and shaping oxygen conditions with the participation of various Lemna species. [ABSTRACT FROM AUTHOR]

Background: Seagrass, a vital primary producer habitat, is crucial for maintaining high biodiversity and offers numerous ecosystem services globally. The increasing severity and frequency of marine heatwaves, exacerbated by climate change, pose significant risks to seagrass meadows. Aims: This study acknowledges the uncertainty and variability of marine heatwave scenarios and aims to aid managers and policymakers in understanding simulated responses of seagrass to different durations, frequencies and recurrence gaps of marine heatwaves. Materials and Methods: Using expert knowledge and observed data, we refined a global Dynamic Bayesian Network (DBN) model for a specific case study on Halophila ovalis in Leschenault Estuary, Australia. The model evaluates the potential impact of marine heatwaves on seagrass resilience, examining stress resistance, recovery and extinction risk. Results: Simulations of different marine heatwave scenarios reveal significant impacts on seagrass ecosystems. Scenarios ranged from 30‐ to 90‐day heatwaves, with longer durations causing more significant biomass decline, reduced resistance, higher extinction risk and prolonged recovery. For instance, recovery time may increase from 18 to 26 months with four 60‐day and from 24 to 47 months with four 90‐day marine heatwave events. Increasing the frequency of marine heatwaves from one to four annual events, with no gaps between occurrences, could raise extinction risk from 11% to 55% for 60‐day events and from 17% to 83% for 90‐day events. However, introducing gaps between heatwaves enhanced resilience, with spaced events showing lower extinction risks and quicker recovery than consecutive yearly events. Discussion: The study demonstrates the DBN model's utility in simulating the impact of marine heatwaves on seagrass, providing tools for risk‐informed assessment of management and restoration efforts. While these simulations align with existing research on temperature impacts on seagrass, they are not empirical. Conclusion: Further research is necessary to expand our understanding of climate change effects on seagrass ecosystems, guide policy and develop strategies to strengthen marine ecosystem resilience. [ABSTRACT FROM AUTHOR]

The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future. Plain Language Summary: This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future. Key Points: Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challengesThe scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effortFor assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs [ABSTRACT FROM AUTHOR]

Accurately predicting species' range shifts in response to environmental change is paramount for understanding ecological processes and global change. In synthetic analyses, traits emerge as significant but weak predictors of species' range shifts across recent climate change. These studies assume linear responses to traits, while detailed empirical work often reveals trait responses that are unimodal and contain thresholds or other nonlinearities. We hypothesize that the use of linear modeling approaches fails to capture these nonlinearities and, therefore, may be under‐powering traits to predict range shifts. We evaluate the predictive performance of approaches that can capture nonlinear relationships (ridge‐regularized linear regression, support vector regression with linear and nonlinear kernels, and random forests). We apply our models using six multidecadal range shift datasets for plants, moths, marine fish, birds, and small mammals. We show that nonlinear approaches can perform better than least‐squares linear modeling in reproducing historical range shifts. Consistent with expectations, we identify dispersal and climatic niche traits as primary determinants of distribution shifts. Traits identified as important predictors and the direction of trait effects are generally consistent across models, but there are notable exceptions. Among important predictors, there are more consistent responses to climatic niches than dispersal ability. Modest improvements in predictability when accounting for nonlinearities and interactions, and the overall low amount of variance accounted for by trait predictors suggest limits to trait‐based statistical predictive frameworks. [ABSTRACT FROM AUTHOR]

Efficient methods for predicting weather-related hazards are crucial for stakeholders managing environmental risks. Many environmental hazards depend on the evolution of meteorological conditions over protracted periods, requiring assessments that account for evolving conditions. The TAMSAT-ALERT approach addresses this challenge by combining observational monitoring with a weighted climatological ensemble. As such, it enhances the utility of existing systems by enabling users to combine multiple streams of monitoring and forecasting data into holistic hazard assessments. TAMSAT-ALERT forecasts are now used in a number of regions in the Global South for soil moisture forecasting, drought early warning and agricultural decision support. The model presented here, General TAMSAT-ALERT, represents a significant scientific and functional advance on previous implementations. Notably, General TAMSAT-ALERT is applicable to any variable for which time series data are available. In addition, functionality has been introduced to account for climatological non-stationarity (for example due to climate change); large-scale modes of variability (for example El Nino), and persistence (for example of land-surface condition). In this paper, we present a full description of the model, along with case studies of its application to prediction of Central England Temperature, Pakistan vegetation condition and African precipitation. [ABSTRACT FROM AUTHOR]

Legacy National Weather Service verification techniques, when applied to current static severe convective warnings, exhibit limitations, particularly in accounting for the precise spatial and temporal aspects of warnings and severe convective events. Consequently, they are not particularly well suited for application to some proposed future National Weather Service warning delivery methods considered under the Forecasting a Continuum of Environmental Threats (FACETs) initiative. These methods include threats-in-motion (TIM), wherein warning polygons move nearly continuously with convective hazards, and probabilistic hazard information (PHI), a concept that involves augmenting warnings with rapidly updating probabilistic plumes. A new geospatial verification method was developed and evaluated, by which warnings and observations are placed on equivalent grids within a common reference frame, with each grid cell being represented as a hit, miss, false alarm, or correct null for each minute. New measures are computed, including false alarm area and location-specific lead time, departure time, and false alarm time. Using the 27 April 2011 tornado event, we applied the TIM and PHI warning techniques to demonstrate the benefits of rapidly updating warning areas, showcase the application of the geospatial verification method within this novel warning framework, and highlight the impact of varying probabilistic warning thresholds on warning performance. Additionally, the geospatial verification method was tested on a storm-based warning dataset (2008–22) to derive annual, monthly, and hourly statistics. [ABSTRACT FROM AUTHOR]

Biological invasions pose significant threats to biodiversity, with invasive species spread often facilitated by human activities. Consequently, this research utilized ecological niche modeling (ENM) to overcome this limitation and map the potential suitability of Microtheca ochroloma Stål, 1860 and Microtheca semilaevis Stål, 1860, which have been evaluated as potential insect pests in the Americas, zones for four genera of Brassicaceae, which include globally cultivated species such as Sinapis L., Raphanus L., Eruca Mill., and Brassica L. We utilized multiple methods to forecast the ecological habitat of Microtheca Stål, 1860 species based on distribution data and various environmental indicators. Our models, exhibiting high-performance metrics (TSS ranging from 0.84 to 0.96), revealed extensive environmental suitability for these species across the Americas, including previously unreported regions. The predicted zones overlapped significantly with areas where Brassicaceae crops were grown. Contrary to some previous assertions, our findings suggest that while these Microtheca species are recognized pests on these crops, their consistent widespread damage may be overstated. Nevertheless, their invasive potential could have broad ecological impacts, including biodiversity loss. Our research emphasizes the need for focused sampling in potential distribution zones and underlines the value of integrating ENM in predicting and managing invasive species spread. [ABSTRACT FROM AUTHOR]

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This article, titled "Editorial: Bird movements and migration under global environmental change: current and future implications," explores the impact of global environmental changes on bird movements and migration patterns. The authors discuss how climate change, urbanization, pollution, and habitat degradation are affecting the natural pathways and stopovers that birds rely on during migration. They also highlight the importance of technological advancements in studying avian migration and emphasize the need for tailored conservation strategies that consider seasonal dynamics and regional variation. The article concludes by emphasizing the role of data-driven science in understanding and mitigating the effects of global change on bird populations. [Extracted from the article]

Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model >80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.

The Ecological Footprint evaluates the difference between the availability of renewable resources and the extent of human consumption of these resources. Over the past few decades, historical records have shown an accelerated decline in the availability of resources. Based on national footprint and biocapacity accounts, this analysis aims to advance the forecasting of the G20 countries' ecological footprints over a 30-year time frame. We employed a time series forecasting approach implemented in Python, which included-modular regression (Prophet) and Autoregressive Integrated Moving Average (ARIMA & Auto-ARIMA) methods. We evaluated and combined the performance of these three methods. The results indicated that among the largest economies of the G20, only four countries are projected to have a positive ecological footprint balance by 2050. These countries share the common denominator of large land areas and a moderate population growth projection. However, the overall trend of the indicator suggests that it will continue to decline. [ABSTRACT FROM AUTHOR]

Climate change has accelerated the frequency of catastrophic wildfires; however, the drivers that control the time‐to‐recover of forests are poorly understood. We integrated remotely sensed data, climate records, and landscape features to identify the causes of variability in the time‐to‐recover of canopy leaf area in southeast Australian eucalypt forests. Approximately 97% of all observed burns between 2001 and 2014 recovered to a pre‐fire leaf area index (±0.25 sd) within six years. Time‐to‐recover was highly variable within individual wildfires (ranging between ≤1 and ≥5 years), across burn seasons (90% longer January to September), and year of fire (median time‐to‐recover varying four‐fold across fire years). We used the logistic growth function to estimate the leaf area recovery rate, burn severity, and the long‐term carrying capacity of leaf area. Time‐to‐recover was most correlated with the leaf area recovery rate. The leaf area recovery rate was largest in areas that experienced high burn severity, and smallest in areas of intermediate to low burn severity. The leaf area recovery rate was also strongly accelerated by anomalously high post‐fire precipitation, and delayed by post‐fire drought. Finally we developed a predictive machine‐learning model of time‐to‐recover (R2: 0.68). Despite the exceptionally high burn severity of the 2019–2020 Australian megafires, we forecast the time‐to‐recover to be only 15% longer than the average of previous fire years. Plain Language Summary: Australian eucalypt forests have evolved different strategies to recover from fire. While the meteorological drivers of bushfire are reasonably well understood, the various processes explaining how long a forest takes to recover from fire are not. We investigated a range of static (landscape) and dynamic (vegetation condition or meteorological) factors that could influence how long a forest's canopy leaf area would take to recover from fire. "Time‐to‐recover" after fire is highly variable, ranging from less than 1 year to more than 5 years even within an individual burn location. More intense fires cause greater forest canopy damage and generally (but not always) lead to longer recovery times, whereas wetter conditions after the fire can accelerate recovery. Using these factors and others, we developed a model capable of predicting the time‐to‐recover and applied it to the 2019–2020 Australian megafires. Our analysis suggests the canopy damage caused by these fires was far more severe than fires in years prior. This would normally lead to a prolonged time‐to‐recover, however we predict that anomalously high rainfall in the year following the fires will shorten recovery time, compensating for the high burn severity. Ultimately we predict the time‐to‐recover will be only slightly longer than average. Key Points: Pre‐fire leaf area, burn severity, and post‐fire meteorological conditions combine to determine time‐to‐recover after fireLarge geographic variation in time‐to‐recover can be explained by mean climate and landscape differencesTime‐to‐recover can be predicted with high accuracy using information limited to the first year following fire [ABSTRACT FROM AUTHOR]

The climatic warming-induced shrinking of permafrost currently encompasses 65% of alpine areas in North China, where a large population relies on its water and land resources. With increasing recognition of the economic and ecological impacts of permafrost basins, forecasts of environmental vulnerability have gained prominence. However, the links between permafrost and winter water resources remain inadequately explored, with most studies focusing on in-situ measurements related to snow cover and frozen layer thickness. Evaluating more complex phenomena, such as the magnitude and persistence of air temperature or low streamflow, depends on numerous climate-driven factors interacting through various subsurface flow mechanisms, basin drainage mechanics, and hydro-climatic correlations at a macroscale. The present study focuses on winter warming, flow increases, and their teleconnections in Xinjiang, China. The research analyzes their links to the atmospheric cycle of the Siberian High (SH) using long-term data spanning 55 years from two large alpine permafrost basins. Changes in variability and correlation persistence were explored for the past decades, and significant variability and connections were constructed using statistical correlation. The years 1980 and 1990 were a turning point when both winter temperatures and winter river flow began to exhibit a notable and consistent upward trend. Subsequently, the period from the mid-1990s to 2013 was characterized by high variability and persistence in these trends. The influence of the SH plays a dominant role in regard to both winter temperatures and river flow, and these variabilities and correlations can be utilized to estimate and predict winter flow in ungauged permafrost rivers in Xinjiang China. [ABSTRACT FROM AUTHOR]

The reliable detection of environmental contaminants can correctly forecast the degree of environmental pollution that has occurred, which contributes to improving the environmental purification rate and maintaining the ecological balance. Herein, a novel hierarchical biomimetic catalysis MnO2@CuAl-CLDHs was designed and synthesized using a facile method, which exhibited significantly enhanced peroxidase-like activity due to the unique composition and hierarchical mesoporous structure. Under optimized operational conditions, a visible colorimetric array based on the superior nanozyme activity of MnO2@CuAl-CLDHs was developed for the quantitative determination of hydroquinone with a wide linear detection range (1–100 μM) and a low detection limit (0.183 μM). Simultaneously, our presented strategy could analyze hydroquinone in real water samples with high accuracy. Therefore, the bimetallic co-catalyzed nanozymes are expected to be the perfect replacement for natural enzymes to develop convenient and efficient sensors. [ABSTRACT FROM AUTHOR]

As the major form of coral reef regime shift, stony coral to macroalgal transitions have received considerable attention. In the Caribbean, however, regime shifts in which scleractinian corals are replaced by octocoral assemblages hold potential for maintaining reef associated communities. Accordingly, forecasting the resilience of octocoral assemblages to future disturbance regimes is necessary to understand these assemblages' capacity to maintain reef biodiversity. We parameterised integral projection models quantifying the survival, growth, and recruitment of the octocorals, Antillogorgia americana, Gorgonia ventalina, and Eunicea flexuosa, in St John, US Virgin Islands, before, during, and after severe hurricane disturbance. Using these models, we forecast the density of populations of each species under varying future hurricane regimes. We demonstrate that although hurricanes reduce population growth, A. americana, G. ventalina, and E. flexuosa each display a capacity for quick recovery following storm disturbance. Despite this recovery potential, we illustrate how the population dynamics of each species correspond with a longer-term decline in their population densities. Despite their resilience to periodic physical disturbance events, ongoing global change jeopardises the future viability of octocoral assemblages. [ABSTRACT FROM AUTHOR]

Simple Summary: This study addresses the critical need for accurate prediction of key environmental factors—temperature, humidity, ammonia, and hydrogen sulfide—in pig houses, essential for pigs' growth and health. Traditional methods face challenges in predictive accuracy and stability. We introduce an innovative OTDBO–TCN–GRU model, a hybrid framework combining the dung beetle algorithm, temporal convolutional network, and gated recurrent unit, enhanced by the Osprey optimization algorithm (OOA). This model synergistically merges DBO's optimization power, TCN's long-term dependency handling, and GRU's proficiency in nonlinear sequence management, offering improved global detection capabilities. The OTDBO–TCN–GRU model showcases superior accuracy in environmental prediction, evident from its mean absolute error (MAE) of 0.0474, mean squared error (MSE) of 0.0039, and correlation coefficient of 0.9871. It significantly surpasses the traditional DBO–TCN–GRU and OOA models, reducing MAE and MSE by 37.2% and 66.7%, and 48.7% and 74.2%, respectively. Moreover, it outperforms mainstream models like GRU, LSTM, and XGBoost in terms of accuracy. This model significantly improves the forecasting of environmental conditions within pig houses, which is vital for maintaining optimal living conditions and ensuring the well-being of pigs. Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses. [ABSTRACT FROM AUTHOR]

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method. [ABSTRACT FROM AUTHOR]

The powerful community-structuring roles of foundation species (FS) are well recognized; however, ecological interactions involving their dead structures have yet to be broadly integrated within ecological theory or ecosystem management. As interacting components of ecosystems, dead foundation species (DFS) modify environments and mediate feedbacks underpinning ecosystem resilience. DFS are vulnerable to global change and often respond differently than living FS. Changes in DFS quantity, composition, and frequency of occurrence influence habitat heterogeneity within systems and subsidy dynamics across systems, provoking far-reaching effects on biodiversity and ecosystem functions. Enhanced recognition of DFS traits and ecological roles will improve restoration outcomes, inform ecological forecasts, and guide conservation of ecosystems challenged by global change. Foundation species facilitate communities, modulate energy flow, and define ecosystems, but their ecological roles after death are frequently overlooked. Here, we reveal the widespread importance of their dead structures as unique, interacting components of ecosystems that are vulnerable to global change. Key metabolic activity, mobility, and morphology traits of foundation species either change or persist after death with important consequences for ecosystem functions, biodiversity, and subsidy dynamics. Dead foundation species frequently mediate ecosystem stability, resilience, and transitions, often through feedbacks, and harnessing their structural and trophic roles can improve restoration outcomes. Enhanced recognition of dead foundation species and their incorporation into habitat monitoring, ecological theory, and ecosystem forecasting can help solve the escalating conservation challenges of the Anthropocene. Foundation species facilitate communities, modulate energy flow, and define ecosystems, but their ecological roles after death are frequently overlooked. Here, we reveal the widespread importance of their dead structures as unique, interacting components of ecosystems that are vulnerable to global change. Key metabolic activity, mobility, and morphology traits of foundation species can either change or persist after death with important consequences for ecosystem function, biodiversity, and subsidy dynamics. Dead foundation species frequently mediate ecosystem stability, resilience, and transitions, often through feedbacks, and harnessing their structural and trophic roles can improve restoration outcomes. Enhanced recognition of dead foundation species and their incorporation into habitat monitoring, ecological theory, and ecosystem forecasting can help solve the escalating conservation problems of the Anthropocene. [ABSTRACT FROM AUTHOR]

Aim: Ecological forecasting is critical in understanding of ecological responses to climate change and is increasingly used in climate mitigation plans. The forecasts from correlative models can be challenged by model complexity, training collinearity, collinearity shift and novel conditions of predictors that are common during model extrapolation. The individual effect of these four factors has been investigated, but it is still unclear how these four factors interactively affect forecasting. To fill this gap, we conducted a comprehensive simulation experiment to quantify how the four factors interactively influence model forecasting. Location: Simulated regions. Time Period: Simulated scenarios. Methods: We modelled three response variables commonly used in ecological forecasting following normal, Poisson and binomial distributions as a function of three functional relationships that represented model complexity under three levels of training collinearity using generalized linear models. By calculating prediction error under 3,780,000 testing scenarios, we partitioned its variance to model complexity, training collinearity, collinearity shift, predictor novelty and their interactions. Results: We found that increased predictor novelty and collinearity shift degraded model performance, leading up to double prediction errors when a predictor's range increased by ~22% or when the correlation r between two predictors changed >~0.8 for the combination of high training collinearity and interaction functional relationship. Predictor novelty reduced the influence of collinearity shift on model performance, suggesting a negative interaction between them. This pattern was more pronounced under high model complexity and high training collinearity. Main Conclusions: The accuracy of ecological forecasting using correlative models depends on model complexity, training collinearity, collinearity shift, predictor novelty and their interactions. Besides the consideration of parsimonious models and r of 0.7 in model training, our study further recommends a threshold of <22%–50% increased predictor range depending on training collinearity and/or <0.8 correlation change for making reliable forecasting. [ABSTRACT FROM AUTHOR]

In a globalized world, factors such as increasing population, rising production rates, changing consumption habits, and continuous economic growth contribute significantly to climate change. Therefore, successfully forecasting the Ecological Footprint (EF) effectively indicates global sustainable development. Despite the significant role of the EF as one of the indicators of sustainable development, there is a gap in the literature regarding time series methods and forward-looking predictions. To address this gap, Ecological Footprint (EF) forecasting was performed using deep learning methods such as LSTMs, classical time series methods like ARIMA and Holt–Winters, and the developed hybrid ARIMA-SVR model. In the scope of the study, first, a spreadsheet was created using the total Ecological Footprint (EF) worldwide between 1961 and 2022, obtained from the Global Footprint Network database. Second, the forecasting performances of the ARIMA, Holt–Winters, LSTM, and the hybrid ARIMA-SVR models were compared using MAPE and RMSE metrics. Finally, the forecasting performances of the time series models were statistically validated through Wilcoxon Signed-Rank and Friedman tests. The study findings indicate that the proposed ARIMA (1,1,0) model demonstrated better performance with an average MAPE of 2.12%, compared to Holt–Winters (MAPE of 2.27%), LSTM (MAPE of 3.19%), and ARIMA-SVR (MAPE of 2.68%) methods in the test dataset. Additionally, it was observed that the ARIMA model forecasted the EF, which experienced a sudden decrease due to the COVID-19 lockdown, with a lower error compared to other models. These findings highlight the adaptability of the ARIMA model to variable and uncertain conditions. [ABSTRACT FROM AUTHOR]

Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts. [ABSTRACT FROM AUTHOR]

Summary: Understanding and predicting recruitment in species‐rich plant communities requires identifying functional determinants of both density‐independent performance and interactions.In a common‐garden field experiment with 25 species of the woody plant genus Protea, we varied the initial spatial and taxonomic arrangement of seedlings and followed their survival and growth during recruitment. Neighbourhood models quantified how six key functional traits affect density‐independent performance, interaction effects and responses.Trait‐based neighbourhood models accurately predicted individual survival and growth from the initial spatial and functional composition of species‐rich experimental communities. Functional variation among species caused substantial variation in density‐independent survival and growth that was not correlated with interaction effects and responses. Interactions were spatially restricted but had important, predominantly competitive, effects on recruitment. Traits increasing the acquisition of limiting resources (water for survival and soil P for growth) mediated trade‐offs between interaction effects and responses. Moreover, resprouting species had higher survival but reduced growth, likely reinforcing the survival–growth trade‐off in adult plants.Resource acquisition of juvenile plants shapes Protea community dynamics with acquisitive species with strong competitive effects suffering more from competition. Together with functional determinants of density‐independent performance, this makes recruitment remarkably predictable, which is critical for efficient restoration and near‐term ecological forecasts of species‐rich communities. [ABSTRACT FROM AUTHOR]

Preserving lacustrine ecosystems is vital for sustainable watershed development, and forecasting the environmental water availability of lakes would support policymakers in developing sound management strategies. This study proposed a methodology that merges the lake water level prediction and environmental water availability evaluation. The temporal fusion transformer (TFT) model forecasted the lake water levels for the next 7 days by inputting the streamflow and lake water level data for the past 30 days. The environmental water availability was assessed by comparing the forecasted lake water levels with the environmental water requirements, resulting in adequate, regular, scarce, and severely scarce environmental water availability. The methodology was tested in two case studies: Poyang Lake and Dongting Lake, the two largest freshwater lakes in the Yangtze River Basin, China. The TFT model performed well in forecasting the lake water levels, as shown by the high coefficient of determination and finite root mean square error. The coefficients of determination exceeded 0.98 during the model training, validation, and test for both Poyang Lake and Dongting Lake, and the root mean square errors ranged from 0.06 to 0.46 m. The accurate prediction of lake water level promoted the precise forecasting of the environmental water availability with the high Kappa coefficient exceeding 0.90. Results indicated the rationality and effectiveness of integrating the lake water level prediction and environmental water availability evaluation. Future research includes the applicability of the TFT model to other lakes worldwide to test the proposed approach and investigate strategies to cope with environmental water scarcity. [ABSTRACT FROM AUTHOR]

Context: Nomadic waterbird species move erratically, which makes it difficult to predict site use and connectivity over time. This is particularly pertinent for long-distance movements, during which birds may move between sites hundreds to thousands of kilometres apart. Objectives: This study aimed to understand how landscape and weather influence long-distance waterbird movements, to predict the probability of connectivity between locations and forecast short-term movements for a nomadic species, the straw-necked ibis (Threskiornis spinicollis) in Australia’s Murray–Darling basin. Methods: We used 3.5 years of satellite tracking data together with high-resolution landscape and weather variables to model the expected distance travelled under environmental scenarios for long-distance movements. We generated least-cost paths between locations of interest and simulated the probability that birds could exceed the least cost-distance as a measure of connectivity. We also generated short-term forecasts (1–3 days; conditional on departure) of the probability of bird occurrence at a location given the expected environmental conditions. Results: Our results suggested that wind is the dominant predictor of distance travelled during long-distance movements, with significant but smaller effects from month. Birds travelled further when wind benefit was higher and during summer. Further work is required to validate our forecasts of bird positions over short time periods. Conclusions: Our method infers the predictors of poorly understood movements of nomadic birds during flight. Understanding how partial migrants use landscapes at large scales will help to protect birds and the landscapes where they live. [ABSTRACT FROM AUTHOR]

Facing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high‐frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over‐ and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges. [ABSTRACT FROM AUTHOR]

Ecosystems around the globe are experiencing changes in both the magnitude and fluctuations of environmental conditions due to land use and climate change. In response, ecologists are increasingly using near‐term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near‐term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost‐prohibitive or impossible for forecasting ecological variables that lack high‐frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using Forecasting Lake and Reservoir Ecosystems (FLARE), an open source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1‐ to 35‐day‐ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1‐day‐ahead forecast root mean square error (RMSE) of 0.81°C, mean 7‐day RMSE of 1.15°C, and mean 35‐day RMSE of 1.94°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1‐ to 7‐day‐ahead horizons, but weekly data assimilation resulted in the most skillful forecasts at 8‐ to 35‐day‐ahead horizons. Within a year, forecasts with weekly data assimilation consistently outperformed forecasts with daily data assimilation after the 8‐day forecast horizon during mixed spring/autumn periods and 5‐ to 14‐day‐ahead horizons during the summer‐stratified period, depending on depth. Our results suggest that lower frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high‐frequency sensor data. [ABSTRACT FROM AUTHOR]

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator–prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator–prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

Climate change presents a major threat to biodiversity globally. Northern ecosystems, such as Canada's boreal forest, are predicted to experience particularly severe climate-induced changes. These changes may reduce the carrying capacity and habitat suitability of the boreal forest for many wildlife species. Boreal birds are susceptible to both direct and indirect effects of climate change, and several studies have predicted northward shifts in species distributions as temperatures become warmer. We forecasted spatially-explicit changes in the densities of 72 boreal landbird species using integrated climate change projections and a forest dynamics model in the Taiga Plains ecozone of the Northwest Territories (NT), Canada, over the 2011–2091 horizon. We 1) identified ''winner,'' ''loser,'' and ''bellringer'' species over short (2031) and long-term (2091) forecasts, 2) mapped landbird range and density changes under three contrasting Global Circulation Models (GCMs), and 3) quantify differences in landbird density predictions across a latitudinal gradient. Species that showed a moderate increase or decrease in their predicted abundance were considered ''winners'' and ''losers,'' respectively. Species that showed a marked increase or decrease – a doubling or halving – of their predicted abundance in all three GCMs, were termed ''bellringers''. From 2011–2031, only 2/72 (2.8%) were considered winners, and 3/72 (4.2%) were losers. From 2011–2091, the abundance of more species was predicted to change: 26/72 (36.1%) were winners, and 10/72 species (13.9%) were losers. Four species were considered bellringers: Gray-cheeked Thrush, White-crowned Sparrow, Fox Sparrow, and American Tree Sparrow. Overall, projected range shifts were strongly oriented along a southeast-to-northwest axis. Shifts to the north and south were evenly distributed among all three GCMs. Our results suggest that future climate-mitigated distribution shifts and population declines of boreal landbirds will require targeted conservation actions. They also highlight the importance of the NT as a potential refugium for many boreal-breeding landbird species in Canada.

Abstract Accurately predicting species' range shifts in response to environmental change is paramount for understanding ecological processes and global change. In synthetic analyses, traits emerge as significant but weak predictors of species' range shifts across recent climate change. These studies assume linear responses to traits, while detailed empirical work often reveals trait responses that are unimodal and contain thresholds or other nonlinearities. We hypothesize that the use of linear modeling approaches fails to capture these nonlinearities and, therefore, may be under‐powering traits to predict range shifts. We evaluate the predictive performance of approaches that can capture nonlinear relationships (ridge‐regularized linear regression, support vector regression with linear and nonlinear kernels, and random forests). We apply our models using six multidecadal range shift datasets for plants, moths, marine fish, birds, and small mammals. We show that nonlinear approaches can perform better than least‐squares linear modeling in reproducing historical range shifts. Consistent with expectations, we identify dispersal and climatic niche traits as primary determinants of distribution shifts. Traits identified as important predictors and the direction of trait effects are generally consistent across models, but there are notable exceptions. Among important predictors, there are more consistent responses to climatic niches than dispersal ability. Modest improvements in predictability when accounting for nonlinearities and interactions, and the overall low amount of variance accounted for by trait predictors suggest limits to trait‐based statistical predictive frameworks.

The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5-month-long laboratory experiment. We tested how forecasting of species abundances depends on the number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more interactions a species had, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.

Abstract Wildlife movement is an important process affecting species population biology and community interactions in myriad ways. Studies of wildlife movement have focused on retrospectively estimating movements of small numbers of individuals by outfitting them with GPS and telemetry tags. Recent developments in spatial capture–recapture modelling permit the integration of movement models that can estimate the movement of untagged and undetected individuals. Additionally, hidden Markov movement models provide a framework for forecasting individuals' movements, which may be valuable in the conservation of threatened species facing risks that vary across space and time. We describe maximum likelihood estimators for spatial capture–recapture models integrated with simple, biased and correlated random walk movement models formulated as hidden Markov models. Additionally, we demonstrate how to forecast wildlife movement based on these models and hidden Markov model algorithms. We conducted a simulation study to test the performance of the models' abundance estimators and movement forecasts when fit to data simulated under different movement models. We also fit the models to spatial capture–recapture data collected on North Atlantic right whales off the Atlantic Coast of the southeastern United States. Random walk movement models improved abundance estimation and movement forecasts in our simulation study and received greater support from the data in the right whale case study than did activity centre movement models. Forecasts of wildlife movement made under integrated spatial capture–recapture movement models will be most valuable when individuals have been observed recently, when sampling for individuals is extensive and efficient, and when the scale of individuals' movements is small relative to the scale of the study area and sampling process.

Presently, India is in the stage of installation of solar photovoltaic panels and no focus is being given towards the impending problem of handling solar waste. The absence of adequate regulations, guidelines and operational infrastructure for photovoltaic waste in the country may lead to waste being inappropriately landfilled or incinerated in a manner that may be detrimental to human health and the environment. Business as usual projection estimates 6.64 million tonnes and 5.48 million tonnes of waste generation due to the early and regular losses using the Weibull distribution function, respectively by 2040 in India. The current study also systematically investigates various policies and legislation developments on the end-of-life of photovoltaic modules in various regions of the world, to identify gaps for further assessment. Using life cycle assessment methodology, this paper compares the environmental impacts of landfilling end-of-life crystalline silicon panels with avoided burden approach due to the recycling of materials. It has been demonstrated that solar photovoltaic recycling and reusing the recovered materials will result in impact reduction in the forthcoming production phase by as high as 70%. Further, the outcomes of carbon footprint, single score indicator with the application of Intergovernmental Panel on Climate Change also predicts lower values for avoided burden approach due to recycling (15,393.96 kgCO2 eq) in comparison to landfill approach (19,844.054 kg CO2 eq). The outcomes of this study aim to illuminate the importance of the sustainable management of photovoltaic panels at end-of-life. [ABSTRACT FROM AUTHOR]