Spatial Influences Research Articles

Spatial, Geographical, Climatic, and Edaphic Influences on Moss Community Structure: A Case Study from Qinhuangdao, China

In the realms of ecology and biogeography, the interaction between biodiversity and environmental factors is a critical area of research. This intersection highlights how biological communities, especially among groups like bryophytes, are influenced and shaped by their surrounding environmental conditions. This study presents a pioneering investigation into the diversity and community structure of mosses in Qinhuangdao, Hubei Province, China, a region marked by its diverse topography and climate. Employing extensive field surveys across 30 plots, we gathered and analyzed the relationship between moss species distribution and environmental variables, including topographical, climatic, and soil factors. Utilizing a range of analytical techniques, such as cluster analysis, canonical correspondence analysis (CCA), and partial least squares path modeling (PLS-PM), we characterized the intricate relationships between moss biodiversity and environmental gradients. The research has documented 84 species distributed among 36 genera and 13 families. Solar radiation has a great impact on moss diversity. There were significant differences between Form. Entodon compressus and Form. Plagiobryum demissum. Climate has a great impact on the community structure of mosses. Geographical factors were also identified as key secondary influences, affecting moss community structures both directly and indirectly by creating suitable microenvironments and influencing climate and soil properties. Additionally, the study highlights the indirect impact of spatial factors on these environmental variables, which in turn shape the structure of biological communities. The findings indicate that the annual temperature range is a key factor influencing the distribution and formation of moss community structures. The findings provide new insights into the ecological adaptation of mosses in diverse environmental settings and lay a crucial foundation for biodiversity conservation and ecosystem management in the Qinhuangdao area.

Ecological and economic influencing factors on the spatial and temporal evolution of carbon balance zoning in the Taihu Basin

The escalation in carbon dioxide concentration has precipitated global climate warming, accentuating ecological and environmental concerns. Notably, China stands as the world’s largest carbon emitter, with the Taihu Lake basin emerging as a carbon-intensive region within the country. This paper undertakes a comprehensive analysis spanning 2005 to 2020, calculating the economic contribution coefficient of carbon emissions and the ecological carrying coefficient of carbon absorption in the Taihu Lake basin. The study includes a delineation of carbon balance zones and an exploration of the geographical and spatial influences of both ecosystem and economic factors. The overarching trend in carbon emissions within the Taihu Lake Basin initially exhibited rapid growth, followed by a fluctuating decline, with the pivotal year being 2012, recording the apex of emissions at 575.8293 million tons. Concurrently, total carbon absorption demonstrated a fluctuating growth trajectory, ascending from 82.3503 million tons in 2005 to 85.6488 million tons in 2020. The carbon emission intensity in the basin manifested a pattern of high concentration in the northeast and low concentration in the southwest, while the carbon absorption intensity displayed the inverse pattern. The carbon balance across the Taihu Lake Basin revealed a spatial incongruity, characterized by a suboptimal pattern in the northeast and a favorable pattern in the southwest. Zhejiang Province emerged as an ecological stronghold within the basin, acting as the primary carbon sink functional area. Urban built-up areas and forested regions emerged as principal influencers of carbon balance in the Taihu Lake basin. Urban construction land, population density, and arable land area were identified as primary contributors to carbon emissions, whereas per capita GDP, forests, grasslands, and water bodies were identified as main contributors to carbon absorption in the watershed.

Agricultural price forecasting based on the spatial and temporal influences factors under spillover effects

PurposeIntegrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.Design/methodology/approachTo explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.FindingsUsing the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.Originality/valueThis paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

Temporal and spatial comparisons of ocean quahog (Arctica islandica) growth and lifespan on the mid-Atlantic continental shelf during inshore transgressions of their range from the Neoglacial through the twentieth century

Arctica islandica provide long-term records of climate change on the U.S. northeast continental shelf transgressing and regressing across the shelf numerous times synchronously with cold and warm climatic periods. The availability of A. islandica in the death assemblage over a wide geographic and temporal range makes this species well suited for documenting both spatial and temporal influences of climate change in the Mid-Atlantic through the correlation of growth rates in response to changing water conditions. This study focuses on comparing regional growth of subfossil ocean quahogs obtained offshore of the Delmarva Peninsula (Delmarva), and living during the cold periods since the Holocene Climate Optimum, with living A. islandica from offshore New Jersey, offshore Long Island, and Georges Bank. These populations exhibited different growth rates, with subfossil individuals from Delmarva death assemblages, representing previous Holocene cold periods, having growth rates as greater than or equal the growth rates of living individuals. Moreover, the growth rates for subfossil A. islandica from Delmarva that were alive from 1740 to 1940 were more rapid than contemporaneous individuals of the same age alive today. Higher growth rates for A. islandica from off Delmarva suggest that conditions supported near maximum growth during the cold periods after the Holocene Climate Optimum, possibly due to increased food supply in water shallower than that inhabited today. Unlike many bivalves, evidence for range recession of A. islandica as bottom water temperatures warm is found first in juvenile abundance, suggesting that recruitment ceases long before the population's demise: range recession in this species is a 100+ year process determined by the survivorship of the oldest and largest individuals. This study is the largest spatial and temporal growth comparison of A. islandica ever recorded and the first record of the process by which this species' inshore range regresses as temperatures rise.

Toward a more comprehensive shift‐share analysis: An illustration using regional data

AbstractShift‐share analysis has been extensively used to investigate the different drivers of changes in socioeconomic variables in both spatial and non‐spatial contexts. This paper presents a comprehensive shift‐share formulation that, by considering all the possible interactions between the geographical and sectoral variables that interplay in a non‐spatial context, accounts for the intrinsic conditions of the regions that could affect the regional changes when spatial influences are not detected. The proposed technique is illustrated by analyzing the growth of industrial gross value added in the Spanish provinces between 2015 and 2019. Our results show the relevance of the intrinsic effects: the advantages (disadvantages) of the industrial sector within each province and the regional dynamism are important factors to explain the industrial changes in the Spanish regions. These findings provide evidence for the use of the comprehensive shift‐share as a tool that contribute to the regional analysis by identifying the characteristics of vulnerable regions that require further attention to avoid or lessen the effects of low economic growth.

Spatially resolved indoor overheating evaluation using microscale meteorological simulation as input for building simulation – opportunities and limitations

To assess the spatial heat resilience of buildings in urban development we test the suitability of a toolchain approach from microscale meteorological simulations, resolving the spatial influences on local urban climate, to building performance simulations, evaluating the indoor overheating risk in buildings. This approach makes it possible to investigate how much microscale effects (e.g. buildings, trees e.g. roads) in open space influence the overheating intensity in a building depending on its location within a district. In this context, the question arises how realistic the microscale meteorological simulation is to be used as input for indoor overheating evaluation. In this context, we applied a 3D urban climate model (ENVI-met) and a 1D boundary layer model (HIRVAC) for two urban districts in Germany as meteorological input for an indoor thermal comfort evaluation of two representative buildings. The results demonstrate that ENVI-met simulations without using measured temperature data create unrealistically low diurnal variations in outdoor air temperature despite an overestimated solar irradiance. The implementation to building simulation leads to a significant underestimation of the heat resilience for both buildings and to wrong conclusions about the efficacy of passive heat adaptation measures. In contrast, the HIRVACsimulations show a more realistic representation of the meteorological variables (when measured data is used for calibration) but are not able to resolve urban 3D structures. Our findings point out that an adjusted boundary layer representation in microscale meteorological simulations is crucial to provide meteorological input suitable for realistic spatially resolved indoor overheating analysis.

Looking at GDP from a Statistical Perspective: Spatio-Temporal GSTAR(1;1) Model

The gross domestic product (GDP) is a significant indicator for evaluating the performance of an economy. The GDP of a nation can be used to get a sense of the size and health of that nation's economy. Indonesia is the only nation from Southeast Asia to be represented in the G20. All G20’s countries play vital roles in creating the economic landscape of the region, the world, and everything in between. This research is focused on the increase of the GDP in Indonesia, Malaysia, Singapore, Thailand, and Brunei Darussalam. The spatial influence of GDP can be seen in the growth of each nation's infrastructure and industrial sector, for example. at the regional level, the increase of a country's GDP can also have an effect on the countries that are its neighbors. Using the GSTAR model, the aim of this study is to investigate the spatial and temporal influences on the GDP statistics of five different countries. The GSTAR model is distinguished by the presence of a weight matrix, which is one of its distinguishing features. In addition, the aim of this research is to select the most appropriate weight matrix for the purpose of representing the spatial effect on GDP statistics. Uniform, queen contiguity, and inverse distance weight matrices are the types of weight matrices that are utilized. Calculating each weight matrix, estimating relevant parameters, and performing diagnostic tests are the primary activities involved in this investigation. As a consequence of this, a weight matrix that is uniform in its distribution is the one that performs the best. The spatial and temporal correlations of GDP data may be accurately represented by the GSTAR model when it is equipped with a uniform weight matrix. This model is applied to five different countries.

Seasonal and flow regime differences should be considered in explaining zooplankton metacommunity assembly

Environmental and spatial filtering collectively regulate zooplankton communities, while their relative contributions to community structure may change with seasonal variations and flow regime differences. However, there is a lack of research on the combined effect of these two factors. To explore these differences, we studied zooplankton populations as well as environmental and geographical data from different seasons in rivers with variable flows in the Shaying River basin. According to our results, the assembly mechanisms for intermittent river (IR) communities fluctuated more and were more responsive to geographical factors than were the assembly mechanisms of perennial river (PR) communities during the wet and dry seasons. We discovered that the rotifer/protozoan assemblage was more vulnerable to spatial influences, whereas the composition of the crustacean group was heavily impacted by the environment. Our findings contribute to a better understanding of metacommunity assembly mechanisms in highly dynamic ecosystems.

Post-earthquake highway reconstruction: Impacts and mitigation opportunities for New Zealand pinniped population

Knowledge of how roads impact wildlife populations is limited but required to inform management and mitigation. Prioritising sites for mitigation involves identifying the most at-risk areas and populations, particularly after substantial changes to roading infrastructure.We identify hotspots for New Zealand fur seal (NZFS: Arctocephalus forsteri) incidents (live or dead NZFS) on State Highway 1 (SH1) around Kaikōura, on New Zealand's South Island, and analyse whether hotspot locations have persisted following earthquake-induced road reconstruction. We also assess spatial, environmental, and temporal influences of NZFS incidents.Spatial records of incidents along SH1 were analysed to identify contemporary and former hotspots using Kernel Density Estimation Plus and a Poisson-based method. Spatial, temporal and environmental data were collected to assess these factors’ effects on incident location and timing.Between 2012 and 2022, an average of 59 incidents were recorded annually along 90 km of SH1. Ten significant hotspots accounted for 89% of incidents, along 2.75 km of road. Hotspot concentration shifted following road reconstruction. Incident numbers were significantly positively associated with traffic volumes and windspeed, and significantly negatively associated with temperature and rainfall. Autumn experienced significantly more incidents than any other season. Road-abutting NZFS breeding areas explained most of the spatial variation in incidents.SH1 is a threat to Kaikōura's NZFS, with its effects changing following an earthquake impacting NZFS distribution, and associated highway reconstruction. Hotspot analysis and current road protections suggest the risks could be substantially reduced by barrier construction along short stretches of road. This type of assessment should continue as climate change raises sea levels and increases storm events globally. This analysis and mitigation approach could be used for any wildlife across numerous landscapes.

Mapping suitable habitats for globally endangered raptors in Kenya: Integrating climate factors and conservation planning.

Raptors face global threats like electrocution, collisions, and habitat fragmentation. Many species remain understudied, and their distribution patterns are unknown. Understanding their current and future distribution is crucial for conservation. Protecting these top predators requires knowledge of their spatial distribution and environmental influences. This study addresses knowledge gaps in raptor habitats and distributions in Kenya, considering current and future climate changes. Using species distribution models and occurrence data from the Global Biodiversity Information Facility, we evaluated suitable habitats for four endangered Kenyan raptor species: Martial eagle, Secretarybird, Bateleur, and Steppe Eagle. We assessed the impact of climatic predictors on their distribution, considering two climate change scenarios for 2020-2040. Our findings reveal that raptor distribution in Kenya is predominantly concentrated in the southwestern region, extending into the central region of the country. The most significant predictors of raptor species distribution varied for each species, with Steppe eagle and Secretarybird being highly influenced by precipitation during the warmest quarter, Martial eagle being influenced by mean temperature during the driest quarter, and Bateleur being primarily influenced by precipitation during the coldest quarter. When projecting our model into the climate change scenarios for 2020-2040, all species except the Bateleur exhibited a negative range shift. The results of our study suggest that climate change may have adverse impacts on the raptor species examined. In light of these findings, we recommend implementing targeted monitoring and conducting surveys in accordance with our current model predictions. Specifically, our focus should be on monitoring areas that exhibit the highest climate suitability, as these areas are likely to undergo significant shifts in the near future. By conducting regular monitoring and engaging in further research, we can enhance our understanding of these raptor species and gather valuable data to improve the accuracy and reliability of our model predictions.

The Hidden Facets: Uncovering the Influence of Region on Social Housing Unit Distribution in Brazil

The Brazilian housing program, Minha Casa, Minha Vida (MCMV) (My House, My Life), was launched in 2009 to address the housing deficit issue, with the goal of distributing contracted housing units throughout the Brazilian territory. However, the program faces criticisms regarding the distribution of these units. Thus, this paper aims to analyze the distribution heterogeneity of these contracted housing units (CHUs). Two analytical approaches were employed: temporal and spatial (states). To achieve this objective, inferential methods such as Ordinary Least Squares (OLS), Spatial Autoregressive Model (SAR), and panel data regressions were employed. The findings indicate that, from a temporal perspective, there is a positive relationship between the urban housing deficit (UHD) and CHUs. However, the relationship is negative from a spatial perspective, characterizing such heterogeneity among the states. In addition, bordering regions are subject to mutual spatial influences in terms of contracted units, thereby reinforcing this heterogeneity over time.

A regional assessment of ecological environment quality in Thailand special economic zone: Spatial heterogeneous influences and future prediction

Abstract“Eastern Economic Corridor” is a development plan under the Thailand 4.0 scheme to revitalize the economy. However, it has influenced the environment through numerous infrastructural and industrial projects. The ecological environment is the most fragile component since it is susceptible to land use, land cover changes (LULCC) and rapid development. Meanwhile, there is currently a lack of comprehensive studies on spatial heterogeneity and future simulation of ecological environment quality (EEQ). Therefore, this study aims to analyze changes in EEQ and controlling factors and to predict near‐future EEQ by adopting an integrated approach of remote sensing and spatial analyses. It revealed that the regional EEQ has considerably fluctuated in the period 2013–2021. The dynamic areas, accounting for nearly half of the area, include ecologically degraded areas in coastal urban and surrounding areas and ecologically improved areas in the eastern forest and perennial plantations. Chonburi is the most vulnerable province due to its long coast with extensive urban infrastructures and industrial estates. These changes are an integrated consequence of natural and socioeconomic impacts. Particularly, LULCC, infrastructural projects, and climate are the most crucial contributors since they directly regulate the functions of the ecological environment, which will amplify the future changes of EEQ reflected via the near‐future prediction. This research identified key controlling factors and spatial heterogeneities that are useful for policymakers and local authorities to have appropriate local‐oriented plans for each area relevant to their uniqueness. Besides, regional plans should be also considered along with regional concerns such as LULCC, crop structures, water management, and green infrastructure, as well as legal interventions to promote sustainable development in this special economic zone.

Chemical element distribution in Arctic soils: Assessing vertical, spatial, animal and anthropogenic influences in Elsa and Ebba Valleys, Spitsbergen, Svalbard

The Arctic region is threatened by climate change and pollution caused by human activities which potentially influence the elemental concentrations available to and from the biota. To better understand this delicate balance, it is crucial to investigate the role of several factors. Therefore, we quantified the level of 43 chemical elements in soils from Elsa and Ebba Valleys, Petunia Bay, Spitsbergen, a region that has experienced lasting environmental impacts from historical mining activities. We evaluated the a) vertical sampling influence by examining the variation in element distribution between the soil upper and deeper layers, b) animal influence by verifying the role of native animals, particularly vertebrates, in introducing thought faeces elements to the soil and c) anthropogenic influence by studying the spatial geographical differences in element distribution based on the degree of human pressure between the valleys. Our analysis also includes data on soil organic matter (SOM) and mineral composition. Both valleys exhibited similar mineralogical composition, but Elsa Valley had higher concentrations of most analyzed elements compared to Ebba Valley. Despite the contribution of vertebrate feaces, no increase in element concentrations was observed in the animal-influenced soils. The sampled soil layers had similar chemical element profiles for most elements. SOM content tended to be higher in superficial soils and showed a strong positive correlation with most quantified elements. The higher concentrations in Elsa Valley reflect its past mining and mineral exploration, making this area more significantly impacted than Ebba Valley. Surprisingly, vertebrate animals do not appear to influence the concentrations of chemical elements or organic matter in soils. Our findings provide valuable insights into the legacy of past mining activities and mechanisms driving environmental change in the Arctic.

Exploring the spatial belonging of students in higher education

ABSTRACT This paper presents a conceptual framework of spatial belonging. The aim is to enrich our conceptual understanding of space and belonging in higher education, with a provisional spatial belonging framework for researchers and practitioners to critically reflect on the different ways in which higher education spaces can facilitate and shape belonging and inclusion for students. Understanding spatial influences on student belonging is key to appreciating the nuances and multidimensionality of their experiences, especially for underrepresented students whose belonging at university is often more precarious. Drawing on theories from education, sociology and geography – especially Foucault’s (1986. “Of Other Spaces.” Diacritics 16 (1): 22–27) heterotopia, Lefebvre’s (1991. The Production of Space. Oxford: Blackwell) spatial production, Massey’s (2005. For Space. London: Sage) spatial relations and literature on spatial justice – four dimensions of spatial belonging are proposed as a lens to better recognise the influence of multiple spaces on student belonging. The physical dimension includes the material and visible aspects of space and how that can shape student experiences and belonging, while the digital dimension considers the role of technology, especially virtual learning and hybrid spaces. The relational dimension highlights the importance of social relationships and connections, which transcends physicality and digitality, and likewise, the structural dimension focuses on the wider societal system and how dominant discourses shape the way space is experienced by different students. By examining the physical, digital, relational and structural spaces, the spatial belonging framework provides an innovative conceptual insight into student belonging in higher education. This paper lays the groundwork for future research to examine how multiple spaces intersect and contribute to student belonging.

Eukaryotic algal community composition in tropical environments from solar salterns to the open sea

Tropical environments with unique abiotic and biotic factors—such as salt ponds, mangroves, and coral reefs—are often in close proximity. The heterogeneity of these environments is reflected in community shifts over short distances, resulting in high biodiversity. While phytoplankton assemblages physically associated with corals, particularly their symbionts, are well studied, less is known about phytoplankton diversity across tropical aquatic environments. We assess shifts in phytoplankton community composition along inshore to offshore gradients by sequencing and analyzing 16S rRNA gene amplicons using primers targeting the V1-V2 region that capture plastids from eukaryotic phytoplankton and cyanobacteria, as well as heterotrophic bacteria. Microbial alpha diversity computed from 16S V1-V2 amplicon sequence variant (ASV) data from 282 samples collected in and around Curaçao, in the Southern Caribbean Sea, varied more within the dynamic salt ponds, salterns, and mangroves, compared to the seemingly stable above-reef, off-reef, and open sea environments. Among eukaryotic phytoplankton, stramenopiles often exhibited the highest relative abundances in mangrove, above-reef, off-reef, and open sea environments, where cyanobacteria also showed high relative abundances. Within stramenopiles, diatom amplicons dominated in salt ponds and mangroves, while dictyochophytes and pelagophytes prevailed above reefs and offshore. Green algae and cryptophytes were also present, and the former exhibited transitions following the gradient from inland to offshore. Chlorophytes and prasinophyte Class IV dominated in salt ponds, while prasinophyte Class II, including Micromonas commoda and Ostreococcus Clade OII, had the highest relative abundances of green algae in mangroves, above-reef, off-reef, and the open sea. To improve Class II prasinophyte classification, we sequenced 18S rRNA gene amplicons from the V4 region in 41 samples which were used to interrelate plastid-based results with information on uncultured prasinophyte species from prior 18S rRNA gene-based studies. This highlighted the presence of newly described Ostreococcus bengalensis and two Micromonas candidate species. Network analyses identified co-occurrence patterns between individual phytoplankton groups, including cyanobacteria, and heterotrophic bacteria. Our study reveals multiple uncultured and novel lineages within green algae and dictyochophytes in tropical marine habitats. Collectively, the algal diversity patterns and potential co-occurrence relationships observed in connection to physicochemical and spatial influences help provide a baseline against which future change can be assessed.

Estimation of spatial autoregressive models for origin–destination flows: A partial likelihood approach

We extend LeSage and Pace (2008)’s spatial autoregressive model for origin–destination flows by accommodating two-way fixed effects. A partial likelihood approach is used for estimation by applying an orthogonal transformation to remove fixed effects in the model. The quasi-maximum likelihood (QML) estimator of the partial log-likelihood function is consistent and asymptotically centered normal. Monte Carlo experiments verify this advantage in finite samples. From the U.S. migration flows, significant spatial influences are captured with smaller magnitudes than those from the model without fixed effects.

Comparison of Queen Contiguity and Customized Weighting Matrices on Spatial Regression to Identify Factors Impacting Poverty in East Java

Poverty is crucial problem that negative impact on all sectors, including economic, social, and cultural development in East Java Province. Poverty can also increase unemployment, crime, trigger social disasters and hinder progress East Java province. One efforts overcome problem of poverty in East Java province is detect factors that influence. This effort can be done through statistical modeling to determine factors that influence poverty in East Java province. statistical model that can identify factors that influence poverty and explain relationship between region and surrounding area is spatial regression analysis. In spatial regression analysis, spatial weighting matrix is needed to determine spatial influences between regions where one region influences neighboring regions. spatial weighting matrices that is often used is queen contiguity, and according to Anselin (1988:20), this spatial weighting also considers initial information, purpose of case studied, and theory underlying the research. This weighting uses social and economic variables case under study, namely customized weighting matrix. Based on results of this study, shows that best spatial regression and spatial weighting models are General Spatial Model (GSM) with customized weighting because customized weighting produces better estimation results than SAR, SEM and GSM models with queen contiguity weighting in district and city poverty modeling in East Java province with Akaike Infomation Criterion (AIC) value of 188.77 and detemination coefficient (R2) of 84.95%. School's Expected Time, Life Expectancy Score, and Employment Participation Rate are factors that will have substantial impact on percentage of population living in poverty East Java's districts and cities in 2021.

Spatial Influences in Upward Mobility

Spatial Influences in Upward Mobility

A scoping review of luxury yachting and wellness: study trends and research prospects

PurposeThis study aims to advance the scholarship of yachting in the context of wellness by exploring the following research question: in what ways is wellness created and experienced by luxury yachting?Design/methodology/approachSince the extant journal literature from 2012–2023 in peer-reviewed journals is limited on the topic that links luxury yachting and wellness, the authors drew on other sources such as books and chapters in edited volumes. A third layer of material was drawn from the grey literature such as mass media and business websites. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) method, the authors examined 18 publications relating to the intersectionality of yachting and wellness.FindingsManufacturing and charter companies in the yachting industry are focused on creating and emphasizing features onboard that create a self-caring and wellness experience. The yacht design, spatial influences and the leisure time spent at destinations on anchor can also add value to wellness. This study enables us to understand the kinds of signals received by manufacturers and the media from yachting clients. These signals relate to the types of wellness needs and activities, as well as how crews and chefs create the best hospitality experiences for their clients.Originality/valueGiven the scarce and niche nature of research on yachting and wellness, this paper explores future research areas in wellness through luxury yachting which include hospitality aspects of creating and co-creating wellness experience on board the yacht and viewing yachting as a lifestyle necessity product for all levels of wealth and well-being.

Spatial Discrepancy of Hydrodynamics‐Driven Impacts on Organic Biomarkers Deposition and ′ and TEX86 Temperature Proxies Applications

AbstractThere is evidence that sedimentary organic matter is prone to lateral transport under hydrodynamic processes before its final deposition on the seafloor, restricting the applicability of molecular proxies. In this study, we examine the abundances of marine and terrestrial biomarkers in bulk and the grain‐size fractionated samples (<20, 20–63, and >63 μm fractions) from surface sediments in the South Yellow Sea to decipher the spatial influences of hydrodynamic processes on the biomarker distributions and molecule‐proxies' applications. Our results show that spatial deviations between proxies‐derived sea surface temperature (SST; the ′ and TEX86 indexes) and satellite‐derived annual mean SST may result from the lateral transportation of the alkenones and isoprenoid Glycerol Dialkyl Glycerol Tetraethers driven by dominated nearshore coastal currents. We propose a spatial‐SST correction approach to obtain more accurate SST information by removing the hydrodynamically introduced SST bias. Our investigations imply that hydrodynamic processes could be an important factor for controlling the spatial distribution of biomarkers in the ocean, further influencing the applications of biomarker‐based proxies for paleo‐environmental reconstruction. We suggest that this investigation would shed new light on the biogeochemical dynamics of sedimentary organic carbon pump in the shallow ocean, particularly in the passive continental marginal seas with strong hydrodynamic conditions.