Environmental Variability Research Articles

Longitudinal analysis of production process variability: an organizational information processing theory perspective

PurposeThis study applies organizational information processing theory (OIPT) to investigate managing production process variability in uncertain environments using information technology (IT) capabilities.Design/methodology/approachWe conduct an empirical analysis using the three-stage least squares (3SLS) technique on 1,612 manufacturing firms over ten years.FindingsThe findings show that IT capability plays a dual role: it reduces the positive impact of environmental uncertainty on production process variability and mitigates the negative relationship between production process variability and operational performance.Practical implicationsOur findings suggest that managers should focus on reducing production process variability by strengthening their firms' IT capabilities. This is particularly crucial in volatile environments where external uncertainties can significantly impact operational processes.Originality/valueVariability in the production process is a significant source of inefficiency and disruption within business processes. Using OIPT, our study contributes to the field by empirically analyzing the role of IT capabilities in reducing production process variability under environmental uncertainty.

Trade-off and chaotic dynamics in a two-prey one-predator model with refuge, environmental noise and seasonal effects

In nature, poisonous prey species have developed toxins as an attack against predators. However, predator’s decisions to consume aposematic toxic prey are not solely determined by their toxicity. Predators often weigh the costs of ingesting toxins against the benefits of obtaining nutrients, with body size acting as a significant cue. In terms of avoiding predation, prey commonly respond by seeking refuge, a behavior that fluctuates considerably due to seasonality and environmental variability. In this study, we integrate these factors into the dynamics of two prey and one predator model. We explore the cost of fear to illustrate the trade-off dynamics of prey, and we adjust the predator’s death rate by integrating a function that serves predator loss resulting from encounters with toxic prey. Our model demonstrates bistability as a consequence of hysteresis effect and undergoes various bifurcations, including saddle–node, Hopf, Bautin, and cusp bifurcations. We observe that increasing prey refuge and reducing the level of fear contribute to system stabilization, yet chaotic behavior persists with respect to both parameters. While seasonality can introduce stability and periodicity, it may also give rise to complex and unpredictable dynamics. We comprehend that the system might undergo transitions through multiple periodic cycles as a result of changes in the degree of seasonality, yet it eventually settles into a periodic solution. Furthermore, our analysis using the stochastic model reveals that low noise levels maintain species densities close to deterministic equilibrium, whereas high noise levels result in broader dispersion or even extinction of species. The information presented in this article may help readers understand the biological knowledge gained by investigating interactions between predators and prey.

Weather patterns determine success rates of two biocontrol agents on Cytisus scoparius in the USA

AbstractTo be effective, a biocontrol agent must survive and persist in the same habitat as the target species and reduce target population growth. When multiple biocontrol agents are used against a single invasive species, they may each perform better under a subset of the habitat in which the target lives. This complementarity allows for a more consistent level of control and a higher resilience to environmental variability. Two species that feed on the same plant tissues would compete, but niche partitioning in the native range may be replicated when both species are introduced to the invasive range. When biocontrol agents are released, they may self‐sort to perform best in their respective niches. We hypothesized that two biocontrol agents—Scotch broom seed beetle, Bruchidius villosus (Fabricius) (Coleoptera: Chrysomelidae) and Scotch broom seed weevil, Exapion fuscirostre (Fabricius) (Coleoptera: Brentidae)—that both feed on seeds of the invasive legume Scotch broom, Cytisus scoparius L. Link (Fabaceae), would be impacted differently by different weather conditions and plant traits, and would show different success rates at field sites with different conditions. We used a 5‐year study to reveal that the two biocontrol agents are not uniformly distributed in the field and that weather and plant factors influence their success. We confirmed that weather conditions of the previous year influenced biocontrol agent attack rate and found that weather patterns at a field site may predict the impact of each biocontrol agent.

Hand Gesture Recognition System using Deep Learning

This paper presents a comprehensive exploration of a hand gesture recognition system centered on the YOLOv5 object detection algorithm. The system is meticulously designed to fulfill specific objectives, including real-time gesture detection, high accuracy, scalability, robustness to environmental variability, and seamless integration with other system components. Drawing upon the strengths of YOLOv5, the proposed system aims to provide a reliable and efficient solution for hand gesture recognition, thereby enhancing human- computer interaction and enabling various applications across different domains. The methodology encompasses several stages, including data acquisition, preprocessing, object detection using YOLOv5, integration with other system modules, evaluation, and validation. Through a series of experiments and extensive testing in real-world scenarios, the effectiveness and usability of the system are thoroughly assessed, demonstrating its potential for practical deployment and widespread adoption. Additionally, this paper discusses the implications of the findings, identifies areas for further research and development, and offers insights into the future directions of hand gesture recognition systems utilizing YOLOv5. Overall, this study contributes to advancing the field of human-computer interaction and gesture recognition technology, paving the way for innovative applications and enhanced user experiences in various domains. Keywords— Hand Gesture Recognition, YOLOv5, Real-time Detection, Human-Computer Interaction, Object Detection, Machine Learning

Two decades of riparian woodland water vapor and carbon dioxide flux responses to environmental variability

Riparian woodlands occupy a small area of global drylands but are hotspots for carbon and water cycling because groundwater supplements a small moisture supply from precipitation (P). Despite their regional importance, it is unclear if and how climate variability alters water vapor and carbon dioxide (CO2) fluxes in these ecosystems, and how ecosystem drivers vary across annual and seasonal scales. Here we use 21 years of eddy covariance measurements to understand land-atmosphere controls on water vapor and CO2 fluxes of a semiarid riparian mesquite (Prosopis velutina) woodland with year-round access to deep groundwater and highly variable summer and winter rainfall. Access to groundwater supplemented evapotranspiration (ET) that exceeded precipitation (ET:P range 1.9–5.4), making this riparian forest a substantial CO2 sink (367 ± 83 g C m-2yr-1). Contrary to general expectations of regional climatic drying, there was a shift to more favorable water conditions as P and soil moisture increased over time. Annual gross ecosystem production (GEP) and respiration (Reco) increased at the same rate (∼9 g C m-2yr-2) due to GEP and Reco increases during the wettest periods of the year. Growth year separation based on GEP phenology and regression models show that water availability and antecedent phenophase fluxes control seasonal ET and GEP, with CO2 fertilization detected only during winter dormancy, the least-active phenophase. The major Reco driver during the spring and summer was GEP, and this coupling intensified following the onset of summer rainfall. Groundwater subsidies support ET during the dry growing season and decouple water vapor and CO2 fluxes. These results highlight the dynamic nature of water vapor and CO2 cycling in semiarid riparian woodlands, the value of groundwater subsidies to buffering ecosystem responses from the interannual effects of climate variability, and the importance of two water sources in driving seasonal ecosystem responses.

Evaluation of the bilateral symmetry assumption in manual wheelchair propulsion: a systematic review of literature in daily-life and sports contexts.

This systematic review aimed to 1) verify bilateral symmetry assumption in manual wheelchair (MWC) propulsion in daily-life and sports, and its relationship with injury risk and sports performance; 2) evaluate methods for assessing bilateral symmetry. Scopus, Web-Of-Science, PubMed, and EBSCO databases were searched for articles published before January 2024 investigating bilateral symmetry in MWC users and/or healthy participants during MWC propulsion. Two independent reviewers screened, extracted data, and assessed methodological quality of retrieved papers. Twenty-five studies were included. In daily ground-level propulsion, minimal asymmetries were observed in kinematic, kinetic, and temporal parameters when averaging ≥3 push cycles. In the sports context, diverse findings emerged, ranging from up to 27% side-to-side differences in propulsion kinetics and kinematics during sprinting, to descriptions of both symmetrical and asymmetrical upper extremity motions. Limited evidence exists regarding the role of asymmetry in MWC propulsion as a risk factor for injury and pain, as well as the association between sprinting performance and symmetry. In conclusion, bilateral symmetry assumption in MWC propulsion is valid only under specific conditions (i.e., slow/moderate speed, averaging ≥3 push cycles, smooth level ground). The wheeling environment and inter-individual variability impact symmetry research outcome and require consideration in future studies.

The open field assay is influenced by room temperature and by drugs that affect core body temperature

Background The open field assay is used to study anxiety-related traits and anxiolytic drugs in rodents. This assay entails measuring locomotor activity and time spent in the center of a chamber that is maintained at ambient room temperature. However, the ambient temperature in most laboratories varies daily and seasonally and can differ between buildings. We sought to evaluate how varying ambient temperature and core body temperature (CBT) affected open field locomotor activity and center time of male wild-type (WT, C57BL/6) and Transient Receptor Potential Subfamily M Member 8 (Trpm8) knock-out (Trpm8-/- ) mice. TRPM8 is an ion channel that detects cool temperatures and is activated by icilin. Methods Mice were placed in the open field at 4°C and 23°C for 1 hour. Distance traveled and time spent in the center were measured. Mice were injected with icilin, M8-B, diazepam, or saline, and changes in activity level were recorded. Results The cooling agent icilin increased CBT and profoundly reduced distance traveled and center time of WT mice relative to controls. Likewise, cooling the ambient temperature to 4°C reduced distance traveled and center time of WT mice relative to Trpm8-/- mice. Conversely, the TRPM8 antagonist (M8-B) reduced CBT and increased distance traveled and center time of WT mice when tested at 4°C. The TRPM8 antagonist (M8-B) had no effect on CBT or open field behavior of Trpm8-/- mice. The anxiolytic diazepam reduced CBT in WT and Trpm8-/- mice. When tested at 4°C, diazepam increased distance traveled and center time in WT mice but did not alter open field behavior of Trpm8-/- mice. Conclusions Environmental temperature and drugs that affect CBT can influence locomotor behavior and center time in the open field assay, highlighting temperature (ambient and core) as sources of environmental and physiologic variability in this commonly used behavioral assay.

Pursuit and escape drive fine-scale movement variation during migration in a temperate alpine ungulate

Climate change reduces snowpack, advances snowmelt phenology, drives summer warming, alters growing season precipitation regimes, and consequently modifies vegetation phenology in mountain systems. Elevational migrants track spatial variation in seasonal plant growth by moving between ranges at different elevations during spring, so climate-driven vegetation change may disrupt historic benefits of migration. Elevational migrants can furthermore cope with short-term environmental variability by undertaking brief vertical movements to refugia when sudden adverse conditions arise. We uncover drivers of fine-scale vertical movement variation during upland migration in an endangered alpine specialist, Sierra Nevada bighorn sheep (Ovis canadensis sierrae) using a 20-year study of GPS collar data collected from 311 unique individuals. We used integrated step-selection analysis to determine factors that promote vertical movements and drive selection of destinations following vertical movements. Our results reveal that relatively high temperatures consistently drive uphill movements, while precipitation likely drives downhill movements. Furthermore, bighorn select destinations at their peak annual biomass and maximal time since snowmelt. These results indicate that although Sierra Nevada bighorn sheep seek out foraging opportunities related to landscape phenology, they compensate for short-term environmental stressors by undertaking brief up- and downslope vertical movements. Migrants may therefore be impacted by future warming and increased storm frequency or intensity, with shifts in annual migration timing, and fine-scale vertical movement responses to environmental variability.

An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

AbstractTraditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real‐time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real‐time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi‐objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing.

A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

Mitigation of unmeasured environmental and operational variability in long-term bridge health monitoring by unsupervised statistical learning

Environmental and operational variations present a significant challenge in the long-term health monitoring of bridge structures due to their potential to cause serious confounding influences and wrong decisions. These influences may lead to false alarms of damage during normal operation of a civil structure or, conversely, mask actual damage, resulting in a failure to correctly signal a warning. Consequently, a critical task in structural health monitoring, particularly for bridges, is to mitigate the negative impact of environmental variability. An initial approach is to measure the most influential environmental and operational factors and then apply statistical tools to eliminate their effects. However, this approach may not be effective under all conditions. Therefore, the primary objective of this paper is to propose an innovative unsupervised statistical learning method that addresses the challenges posed by environmental and operational variability by considering unmeasured factors. The proposed method involves three steps: data clustering, unsupervised feature selection, and novelty detection. The initial step utilizes k-means clustering to categorize features (modal frequencies) into five types of clusters, reflecting the influence of five environmental and operational factors on bridge structures; that is, temperature, humidity/rain/snow, wind speed/direction, traffic, and damage. These clustered features are then processed through an unsupervised feature selection algorithm based on reconstruction independent component analysis to extract new features. These reduced features are subsequently employed in a novelty detection model, developed using the Mahalanobis-squared distance, to analyze the environmental and operational effects. Real dynamic data of a steel arch bridge is utilized to verify the proposed method. Results demonstrate that this method can effectively handle the demanding issue stemming from unmeasured environmental and operational variability conditions.

Factors impacting the timing of reproductive development in female Antarctic krill at the northwestern Antarctic Peninsula

The northwestern Antarctic Peninsula is an important spawning, recruitment, and overwintering ground for Antarctic krill. The region is warming rapidly, and the current impacts of climate and environmental variability on the reproductive cycle of krill remain unclear. Here, we examine the reproductive stage of female krill in the austral winter from 2012 to 2016 in relation to climate and environmental data to assess what factors influence the timing of reproductive development. We observed significant interannual variability in the degree of maturation in female krill, ranging from 48% of female krill measured at a station in 2016 to a maximum of 94% of female krill measured at a station in 2014. On average, across all five years, three-quarters of the female krill sampled were in the stage known as previtellogenesis, the point at which the onset of sexual maturity begins. The preceding spring, summer, and autumn Southern Annular Mode and the Multivariate El Niño Index explained most of the variance in the data and indicated a strong, preconditioning storm-related effect on environmental conditions leading up to winter, affecting krill maturation status at the end of the winter season. Results from our study can be used to improve krill population models that are necessary for the management of the krill fishery and for conservation at the northwestern Antarctic Peninsula.

Competition between mixo‐ and heterotrophic ciliates under dynamic resource supply

AbstractThe outcome of species competition strongly depends on the traits of the competitors and associated trade‐offs, as well as on environmental variability. Here, we investigate the relevance of consumer trait variation for species coexistence in a ciliate consumer–microalgal prey system under fluctuating regimes of resource supply. We focus on consumer competition and feeding traits, and specifically on the consumer's ability to overcome periods of resource limitation by mixotrophy, that is, the ability of photosynthetic carbon fixation via algal symbionts in addition to phagotrophy. In a 48‐day chemostat experiment, we investigated competitive interactions of different heterotrophic and mixotrophic ciliates of the genera Euplotes and Coleps under different resource regimes, providing prey either continuously or in pulses under constant or fluctuating light, entailing periods of resource depletion in fluctuating environments, but overall providing the same amount of prey and light. Although ultimate competition results remained unaffected, population dynamics of mixotrophic and heterotrophic ciliates were significantly altered by resource supply mode. However, the effects differed among species combinations and changed over time. Whether mixotrophs or heterotrophs dominated in competition strongly depended on the genera of the competing species and thus, species‐specific differences in the minimum resource requirements that are associated with feeding on shared prey, nutrient uptake, light harvesting, and access to additional resources such as bacteria. Potential differences in the curvature of the species' resource‐dependent growth functions may have further mediated the species‐specific responses to the different resource supply modes. Overall, our study demonstrates that genus‐ or species‐specific traits other than that related to nutritional mode may override the relevance of acquired phototrophy by heterotrophs in competitive interactions, and that the potential advantage of photosynthetic carbon fixation of symbiont‐bearing mixotrophs in competition with pure heterotrophs may differ greatly among different mixotrophs, playing out under different environmental conditions and depending on the specific requirements of the species. Complex trophic interactions determine the outcome of competition, which can only be understood by taking on a multidimensional trait perspective.

The use of piezoelectric coupling in structural monitoring as a feature robust to EOV

The use of piezoelectric materials is becoming more and more attractive in many different fields such as mechanical, civil, aerospace, etc. Indeed, their special features and light weight allow introducing them in structures and systems without causing significant load effects and allowing their use to the purpose of many different tasks, such as vibration control, structural monitoring, energy harvesting, etc. The present paper analyses a new approach for exploiting piezoelectric elements for structural monitoring. The main idea is to use an estimate of the coupling between the transducer and the structure. This coupling represents the efficiency of the energy conversion between the hosting structure/system and the piezoelectric element (either bender or stack). Its estimate can be obtained by differential measurements of modal parameters. Due to its estimation procedure, the coupling is shown being a feature robust to possible environmental and operating condition variations, without any need of training a detection algorithm with all the possible environmental and operational conditions. The paper explains at first why the piezoelectric coupling is able to evidence the presence of a structural damage or alteration. Then, it is shown that the proposed damage feature is robust to environmental and operational variability, which constitutes one of its main merits. Finally, the results of an experimental campaign aimed at confirming the main theoretical points presented in the paper are discussed.

IMPROVEMENT OF THE CHANGE MANAGEMENT MODEL AT THE HOTEL INDUSTRY ENTERPRISES

The article is devoted to the development of methodological approaches and practical recommendations for improving the change management model in the enterprises of the hotel industry. Based on the definition of the functioning peculiarities of modern enterprises of the hotel industry, it is proved that traditional and classical models of change management are ineffective in modern changing conditions, and it is substantiated that the model of change management at enterprises of the hotel industry should be based on a situational approach, which transforms it into an "improvisational" one. The change management model, in which managers make decisions based on intuition and change management is carried out through constant improvisation, represents a flexible and adaptive approach to managing an organization in conditions of constant uncertainty and variability of the external environment. Key aspects of this model include the acceptance of intuition's importance in the decision-making process, where managers value intuitive propositions and apply them in their activities. Instead of pre-designed and tough change strategies, change management is based on the organization's ability to quickly adapt to changes and respond to situations that arise in real time. The effectiveness of such a model is supported by an open and active exchange of information and ideas between management and employees, which helps stimulate improvisation and making non-standard decisions. The article proposes an algorithm for managing changes in hotel industry enterprises, developed taking into account the survey results of employees of 25 Ukrainian hotels of various categories. This algorithm works in the form of a continuous cycle and consists of six stages. The results of the survey also emphasize the relevance of the problem of effective change management in the modern hotel sector.

Hydroclimate variability in the Tropical Andes recorded by δ18O isotopes from a new network of Polylepis tarapacana tree-rings

Stable oxygen isotopes records (δ18O) in tree-rings are commonly used to assess the response of trees to environmental variability being a valuable tool for studying past climate at different temporal and spatial scales. This is particularly relevant in semi-arid regions like the southern Tropical Andes, where ongoing environmental changes coincide with a rapidly increasing demand for hydrological resources, presenting a challenge for ecosystem dynamics and water resource management. In this study, we aim to determine the main spatio-temporal variability of a new network of δ18O Polylepis tarapacana chronologies during the last century, and their relationships with hydroclimate and tropical circulation at local to subcontinental scales throughout the Tropical Andes. For this purpose, we develop six δ18O P. tarapacana tree-ring chronologies across a 450 km latitudinal moisture gradient in the southern Tropical Andes adjacent to the Atacama Desert, covering the period 1900–2007. Results show a clear latitudinal gradient in the δ18O values across the network and significant relationships are observed with other δ18O tree-ring chronologies in Tropical South America, demonstrating clear regional climate influences at a subcontinental scale. A principal component analysis of the δ18O tree-ring chronologies demonstrate a strong regional environmental signal contained in the network, exhibiting a main temporal pattern (PC1 δ18O) that explains 63% of the total variance during the period 1900–2007. Comparisons between PC1 δ18O and environmental variables showed significant negative relationships with precipitation and soil moisture, and positive relationships with temperature and vapor pressure deficit (VPD) during summer when the South American monsoon occurs. The main δ18O tree-ring network signal clearly records tropical atmospheric and circulation patterns across South America. The easterly wind flux conditions from the Amazon basin favor lower δ18O values, and the PC1 δ18O exhibit significant positive correlations with VPD across the entire Tropical Andes and the northern portion of the Amazon basin, and as well as outgoing longwave radiation across the southern Tropical Andes and part of the Amazon basin. The close relationships between the regional signals from our δ18O tree-ring network with the previously mentioned parameters, highlight the potential to develop future hydroclimatic-related reconstructions with these δ18O records to assess climate variability and change across the Tropical Andes.

Macrozoobenthos in the estuary of the small salmon river of Sakhalin Island on example of the Gornaya River

Estuaries as a connecting link between freshwater and marine ecosystems play a special role in salmon rivers. The success of adaptation of juvenile salmonids during the transition to the marine period of life depends on the conditions in estuaries. The work continues the previously started series of publications on the rivers of Sakhalin Island. Using the example of one of the small watercourses in the southeastern part of the island, the hydrological parameters and the structural organization of benthos communities in the atypical estuary have been considered. In the absence of storm surges, penetration of sea water into the water body does not occur; at high tide, water with a salinity of more than 12 psu is observed only at the mouth at the bottom; at low tide, the estuary is completely filled with water with a salinity of 0.1 psu and lower. Studies of the hydrological regime and composition of the bottom fauna were carried out in July 2022. Zoobenthos samples were taken along the longitudinal profile of the estuary at five sections (75 samples in total). Most of the estuarine reach is inhabited by species characteristic of the lower parts of the island watercourses. High environmental variability determines the formation in the estuary of young and unstable benthos communities represented by rapidly reproducing small-sized species.

Marine heatwaves alter the nursery function of coastal habitats for juvenile Gulf of Alaska Pacific cod

Marine Heatwaves (MHWs) can directly influence survival of marine fishes, particularly for early life stages, including age-0 juveniles during their residence in coastal nursery habitats. However, the ability of nurseries to support high fish densities, optimize foraging and growth, and protect against predators may be altered during MHWs. Gulf of Alaska Pacific cod (Gadus macrocephalus) larval, juvenile, and adult abundances declined dramatically following MHW events in 2014–2016 and 2019. To evaluate coastal nursery function during MHWs, we compared diet composition, recent growth, size, condition, and abundance of age-0 juveniles throughout their first summer before, during, and between MHWs. Diet shifted to larger prey during MHWs, particularly mysids, but diet did not appear to influence growth. We observed faster growth rates during MHWs, yet even when accounting for growth, we could not explain the higher body sizes observed in August during MHWs. Together with lower abundance and the near absence of small fish in the nursery by August during MHWs, these patterns highlight potential for size-selection and a reduced ability of nursery habitats to buffer against environmental variability during MHWs, with only a small number of large “super survivors” persisting through the summer.

Remote sensing of depth-induced variations in soil organic carbon stocks distribution within different vegetated landscapes

The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model’s prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change.

Phytoplankton assemblages in response to environmental variability in tropical coastal waters of the Malacca Straits, Malaysia

AbstractSurface phytoplankton community, abundance, and species spatial distributions were investigated in the Malacca Straits (MS) of Peninsular Malaysia during the late northeast monsoon (March) and southwest monsoon (August) in 2019 to understand factors controlling their community dynamics. This study reveals that the monsoonal transitions lead to changes in sea surface temperature (SST), sea surface salinity (SSS), total nitrogen (TN), total chlorophyll‐a (Tchl‐a), and phytoplankton density in the MS. A total of 204 and 163 phytoplankton species were identified during March and August, respectively, with diatoms representing the most (80.6%), followed by dinoflagellates (9.9%), cyanobacteria (8.3%), and others (1.2%). Meanwhile, the average total phytoplankton density was lower in March (10.78 × 103 ± 14.70 cell L−1) and greater in August (26.98 × 103 ± 45.63 cell L−1), with Chaetoceros compressus and Thalassiosira sp. Seven found the highest in each season, respectively. There was no significant correlation between phytoplankton density and environmental parameters in the MS, except for the cyanobacteria and dinoflagellate communities. However, the Canonical Correspondence Analysis results showed that environmental parameters such as SST, SSS, and nutrients in the MS could affect the dominant phytoplankton species, particularly diatoms. The findings suggest that environmental changes between the seasons may act as ecological drivers in the formation of phytoplankton communities in marine habitats of the MS.