Fine Scale Research Articles

Temporal evolution of plankton and particles distribution across a mesoscale front during the spring bloom

AbstractThe effect of mesoscale features on the distribution of planktonic organisms are well documented. Yet, the interaction between these spatial features and the temporal scale, which can result in sudden increases of the planktonic biomass, is less known and not described at high resolution. A permanent mesoscale front in the Ligurian Sea (north‐western Mediterranean) was repeatedly sampled between January and June 2021 using a SeaExplorer glider equipped with an Underwater Vision Profiler 6 (UVP6), a versatile in situ imager. Both plankton and particle distributions were resolved throughout the spring bloom to assess whether the front was a location of increased zooplankton concentration and whether it constrained particle distribution. Over the 5 months, the glider performed more than 5000 dives and the UVP6 collected 1.1 million images. We focused our analysis on shallow (300 m) transects, which gave a horizontal resolution of 900 m. About 13,000 images of planktonic organisms were retained. Ordination methods applied to particles and plankton concentrations revealed strong temporal variations during the bloom, with a succession of various zooplankton communities. Changes in particle abundance and size could be explained by changes in the plankton community. The front had a strong influence on particle distribution, while the signal was not as clear for plankton, probably because of the relatively small number of imaged organisms. This work confirms the need to sample both plankton and particles at fine scale to understand their interactions, a task for which automated in situ imaging is particularly adapted.

Total CO2 budget estimate and degassing dynamics for an active stratovolcano: Turrialba Volcano, Costa Rica

Distributions and concentrations of carbon dioxide being emitted from active volcanoes elucidate the subsurface controls on gas ascent from the source and provide important information regarding the extent and state of the magmatic system. The main goal of this study was to determine if degassing followed open- or closed-system dynamics, and to define a baseline for eruption monitoring of degassing across the volcanic edifice through the CO2 budget estimate from the combined results of two CO2 gas emission surveys from 2021 and 2022 on Turrialba volcano, Costa Rica. This was accomplished by utilizing a new method for estimating total carbon flux on and around this persistently degassing and intermittently erupting volcano by integrating fine and coarse spatial scales of measurements; including an analysis of carbon isotopes to determine the source contributions to the gas emissions. Approximately 99% (2287 ± 1719 t CO2 day−1) of magma-derived degassing activity is advective and concentrated at the summit crater, with a smaller, continuous component of ∼1% (23.73 ± 6.65 tonnes CO2 day−1) flank diffuse soil degassing. As the majority of the gas emissions from Turrialba are concentrated in the summit plume, the system is likely experiencing open-system degassing dynamics through one dominant degassing pathway. Though at relatively low levels, the locations and distributions of diffuse degassing on the volcanic flanks allow us to delineate subsurface features that likely reveal the extent of the magmatic system of the volcano. Volcanic CO2 outputs at Turrialba primarily concentrate along faults and fractures near the summit and across the flanks where permeable zones allow gas ascent, and with limited emissions elsewhere. The results of this study provide a baseline for monitoring future changes in the Turrialba magmatic system and demonstrate the potential for applying this method to other volcanic complexes, particularly those that are poorly monitored or where there is a greater prevalence of diffuse and distal degassing.

Fine-Scale Spatial Variability of Greenhouse Gas Emissions From a Subantarctic Peatland Bog.

Peatlands are recognized as crucial greenhouse gas sources and sinks and have been extensively studied. Their emissions exhibit high spatial heterogeneity when measured on site using flux chambers. However, the mechanism by which this spatial variability behaves on a very fine scale remains unclear. This study investigates the fine-scale spatial variability of greenhouse gas emissions from a subantarctic Sphagnum peatland bog. Using a recently developed skirt chamber, methane emissions and ecosystem respiration (as carbon dioxide) were measured at a submeter scale resolution, at five specific 3 × 3 m plots, which were examined across the site throughout a single campaign during the Austral summer season. The results indicated that methane fluxes were significantly less homogeneously distributed compared with ecosystem respiration. Furthermore, we established that the spatial variation scale, i.e., the minimum spatial domain over which notable changes in methane emissions and ecosystem respiration occur, was <0.56 m2. Factors such as ground height relative to the water table and vegetation coverage were analyzed. It was observed that Tetroncium magellanicum exhibited a notable correlation with higher methane fluxes, likely because of the aerenchymatous nature of this species, facilitating gas transport. This study advances understanding of gas exchange patterns in peatlands but also emphasizes the need for further efforts for characterizing spatial dynamics at a very fine scale for precise greenhouse gas budget assessment.

Fine scale diversity in the lava: genetic and phenotypic diversity in small populations of Arctic charr Salvelinus alpinus.

A major goal in evolutionary biology is to understand the processes underlying phenotypic variation in nature. Commonly, studies have focused on large interconnected populations or populations found along strong environmental gradients. However, studies on small fragmented populations can give strong insight into evolutionary processes in relation to discrete ecological factors. Evolution in small populations is believed to be dominated by stochastic processes, but recent work shows that small populations can also display adaptive phenotypic variation, through for example plasticity and rapid adaptive evolution. Such evolution takes place even though there are strong signs of historical bottlenecks and genetic drift. Here we studied 24 small populations of the freshwater fish Arctic charr (Salvelinus alpinus) found in groundwater filled lava caves. Those populations were found within a few km2-area with no apparent water connections between them. We studied the relative contribution of neutral versus non-neutral evolutionary processes in shaping phenotypic divergence, by contrasting patterns of phenotypic and neutral genetic divergence across populations in relation to environmental measurements. This allowed us to model the proportion of phenotypic variance explained by the environment, taking in to account the observed neutral genetic structure. These populations originated from the nearby Lake Mývatn, and showed small population sizes with low genetic diversity. Phenotypic variation was mostly correlated with neutral genetic diversity with only a small environmental effect. Phenotypic diversity in these cave populations appears to be largely the product of neutral processes, fitting the classical evolutionary expectations. However, the fact that neutral processes did not explain fully the phenotypic patterns suggests that further studies can increase our understanding on how neutral evolutionary processes can interact with other forces of selection at early stages of divergence. The accessibility of these populations has provided the opportunity for long-term monitoring of individual fish, allowing tracking how the environment can influence phenotypic and genetic divergence for shaping and maintaining diversity in small populations. Such studies are important, especially in freshwater, as habitat alteration is commonly breaking populations into smaller units, which may or may not be viable.

Detecting heterogeneity of spatial accessibility to sports facilities for adolescents at fine scale: A case study in Changsha, China

Abstract It is crucial for social sustainability that adolescents have access to social sports services fairly. However, there are few research studies on requirements for sports facilities and spatial accessibility of adolescents at a fine scale. Further, identifying the heterogeneity of the relationship between spatial accessibility and other factors and their scales simultaneously would be conducive to reveal the variations of spatial accessibility effectively under the potential scale effect. This research aims to explore the heterogeneity of spatial accessibility to sports facilities at a fine scale in Changsha, China. The Gaussian-based two-step floating catchment area model is first employed to evaluate spatial accessibility. Then, multiscale geographically weighted regression (MGWR) is applied to examine the relationship between spatial accessibility and its associated factors. The mean accessibility in Yuelu District (0.158) is the highest as well, and the standard deviation (0.236) is also the highest one. Both the accessibility (0.019) and its standard derivation (0.029) in Furong District are the lowest. The results show that there is a lack of balance of spatial accessibility for the clusters with different accessibility levels distributed in the study area. Some socio-economic factors, such as housing price and nighttime light intensity, have significant impacts on spatial accessibility for adolescents with spatial and scale heterogeneity by using MGWR. Based on heterogeneous distribution and association, suggestions for promoting spatial accessibility to sports facilities for adolescents are proposed.

Spatial-temporal modeling of the relationship between surface temperature and air temperature in metropolitan urban systems

Research about urban local climate and urban heat island often relies on land surface temperature (LST) data to characterize the distribution of temperature near the surface. Although using remotely sensed data for such work has the advantage of continuous spatial coverage at regular temporal intervals, it is recognized that surface temperature is not an ideal proxy for air temperature (AT). This study's goal is to develop a spatiotemporal model revealing the relationship between LST and AT within the complexities of the urban environment. A mobile weather monitoring unit was used to collect spatially-explicit fine-scale AT data while Landsat 8 and 9 passed overhead collecting LST data. A spatiotemporal model of the relationship between LST and AT in Philadelphia was constructed with this data utilizing basis functions to account for spatial and temporal autocorrelation. The spatiotemporal model results show a strong relationship between LST and AT and indicate that it is possible to predict fine scale AT (120 m) using remotely sensed LST in an urban context (r-squared = 0.99, RMSE = 0.89 °C). The spatiotemporal model outperforms models that do not account for spatial and temporal autocorrelation, highlighting the importance of considering these dependencies in temperature modeling. City-wide AT predictions were generated for Philadelphia demonstrating the ability of the model to improve understanding of local urban climate.

Polycyclic aromatic compounds in a northern freshwater ecosystem: Patterns, sources, and the influences of environmental factors

Polycyclic aromatic compounds (PACs) - a large group of organic chemicals naturally present in petroleum deposits (i.e., petrogenic) or released into the environment by incomplete combustion of organic materials (i.e., pyrogenic) - represent a potential risk to the health of aquatic ecosystems. In high latitude freshwater ecosystems, concentrations of PACs may be increasing, yet there are limited studies in such systems to assess change and to understand threats. Using 10 years of contemporary data from passive samplers deployed across five regions (n = 43 sites) in the Mackenzie River Basin, we (i) describe baseline levels of PACs, (ii) assess spatiotemporal patterns, and (iii) evaluate the extent to which environmental factors (fire, snowmelt, and proximity to oil infrastructure) influence concentrations in this system. Measured concentrations were low, relative to those in more southern systems, with mixtures primarily being dominated by non-alkylated, low molecular weight compounds. Concentrations were spatially consistent, except for two sites near Norman Wells (an area of active oil extraction) with increased levels. Similarly, observed annual variation was minimal, with 2014 having generally higher levels of PACs. We did not detect effects of fire, snowmelt, or oil infrastructure on concentrations. Taken together, our findings suggest that PACs in the Mackenzie River are currently at low levels and are primarily petrogenic in origin. They further indicate that ongoing monitoring and testing of environmental drivers (especially at finer spatial scales) are needed to better predict how ecosystem change will influence PAC levels in the basin and in other northern systems.

Spatial phylogenetics of Fagales: Investigating drivers of temperate forest distributions

AbstractAimQuantifying the phylogenetic diversity of temperate trees is essential for understanding the processes that have shaped the modern distribution of temperate broadleaf forest and other major forest biomes. Here, we focus on Fagales, an iconic member of forests worldwide, to uncover global diversity and endemism patterns and investigate the distribution of root nodule symbiosis (RNS), an important morphological specialisation in this clade, as a key factor behind these patterns.LocationGlobal.TaxonFagales.MethodsWe combined phylogenetic data covering 60.2% of living species, fine‐scale distribution models covering 90% of species, and nodulation data covering all species to investigate the distribution of species richness and phylogenetic diversity at fine spatial scales compared to the distribution of RNS. We identify abiotic environmental factors associated with RNS and with Fagales diversity in general.ResultsWe find the highest species richness in temperate east Asia, eastern North America, and equatorial montane regions of Asia and Central America. By contrast, relative phylogenetic diversity (RPD) is highest at higher latitudes, where RNS also predominates. We found a strong spatial structuring of regionalisations of Fagales floras, reflecting distinct Northern and Southern Hemisphere floras (except a unique Afro‐Boreal region), each with distinct RNS‐environment relationships.Main ConclusionsAlthough species richness and phylogenetic regionalisation for Fagales accord well with traditional biogeographic concepts for temperate forests, this is not the case for RPD. RNS is almost universal in the highest RPD regions, which may reflect ecological filtering promoting RNS in these regions. Our results highlight the utility of global‐scale, clade‐specific spatial phylogenetics and its utility for understanding drivers of diversity in species‐rich clades.

Unraveling microbial community structure–function relationships in the horizontal and vertical spatial dimensions in extreme environments

A fundamental challenge in soil macroecology is to understand how microbial community structure shapes ecosystem function along environmental gradients of the land surface at broad spatial scales (i.e. the horizontal dimension). However, little is known about microbial community structure–function relationships in extreme environments along environmental gradients of soil depth at finer spatial scales (i.e. the vertical dimension). Here, we propose a general spatial dimension partitioning approach for assessing the patterns and drivers of soil microbial community structure–function relationships across horizontal and vertical spatial gradients simultaneously. We leveraged a 200‐km desert soil salinity gradient created by a 12‐year saline‐water irrigation in the Tarim basin of Taklamakan Desert. Specifically, using a general linear model, hierarchical variance partitioning, and a path model, we assessed the patterns and key ecological processes controlling spatial turnover in microbial community structure (i.e. β‐diversity) and enzymatic activity relevant to carbon, nitrogen, and phosphorus cycling along soil salinity gradients across study sites (horizontal dimension) and soil depths (vertical dimension). We found a decoupled relationship between soil microbial β‐diversity and enzymatic activity. Differences in soil depth (on the scale of meters) were as important as geographic distance (on the scale of kilometers) in shaping bacterial and fungal β‐diversity. However, the vertical and horizontal turnover in enzymatic activity was largely attributed to an increase in the heterogeneity of soil properties, such as soil texture, water content, and pH. Our findings suggest that dispersal limitation controls microbial community β‐diversity and that environmental heterogeneity, rather than soil salinization, controls enzymatic activity. Taken together, this work highlights that in the face of ongoing environmental alterations, soil depth is an under‐explored spatial dimension that must be considered in soil conservation efforts as a critical factor in determining microbial community structure and function in extreme environments.

Exposure to ambient air pollution and cognitive function: an analysis of the English Longitudinal Study of Ageing cohort

BackgroundAn increasing number of studies suggest adverse effects of exposure to ambient air pollution on cognitive function, but the evidence is still limited. We investigated the associations between long-term exposure to air pollutants and cognitive function in the English Longitudinal Study of Ageing (ELSA) cohort of older adults.MethodsOur sample included 8,883 individuals from ELSA, based on a nationally representative study of people aged ≥ 50 years, followed-up from 2002 until 2017. Exposure to air pollutants was modelled by the CMAQ-urban dispersion model and assigned to the participants’ residential postcodes. Cognitive test scores of memory and executive function were collected biennially. The associations between these cognitive measures and exposure to ambient concentrations of NO2, PM10, PM2.5 and ozone were investigated using mixed-effects models adjusted for time-varying age, physical activity and smoking status, as well as baseline gender and level of education.ResultsIncreasing long-term exposure per interquartile range (IQR) of NO2 (IQR: 13.05 μg/m3), PM10 (IQR: 3.35 μg/m3) and PM2.5 (IQR: 2.7 μg/m3) were associated with decreases in test scores of composite memory by -0.10 (95% confidence interval [CI]: -0.14, -0.07), -0.02 [-0.04, -0.01] and -0.08 [-0.11, -0.05], respectively. The same increases in NO2, PM10 and PM2.5 were associated with decreases in executive function score of -0.31 [-0.38, -0.23], -0.05 [-0.08, -0.02] and -0.16 [-0.22, -0.10], respectively. The association with ozone was inverse across both tests. Similar results were reported for the London-dwelling sub-sample of participants.ConclusionsThe present study was based on a long follow-up with several repeated measurements per cohort participant and long-term air pollution exposure assessment at a fine spatial scale. Increasing long-term exposure to NO2, PM10 and PM2.5 was associated with a decrease in cognitive function in older adults in England. This evidence can inform policies related to modifiable environmental exposures linked to cognitive decline.

Do recolonising wolves trigger non‐consumptive effects in European ecosystems? A review of evidence

Predators can affect ecosystems through non‐consumptive effects (NCE) on their prey, which can lead to cascading effects on the vegetation. In mammalian communities, such cascading effects on whole ecosystems have mainly been demonstrated in protected areas, but the extent to which such effects may occur in more human‐dominated landscapes remains disputable. With the recolonisation of wolves Canis lupus in Europe, understanding the potential for such cascading processes becomes crucial for understanding the ecological consequences of wolf recovery and making appropriate management recommendations. Here, we investigate the evidence for non‐consumptive effects of wolves on their wild ungulate prey and cascading effects on the vegetation in European landscapes. We reviewed empirical studies reporting wild ungulate responses to wolves involving spatio‐temporal behaviour at large and fine spatial scales, activity patterns, vigilance, grouping, physiological effects, and effects on the vegetation. We reveal that non‐consumptive effects of wolves in Europe have been studied in few regions and with focus on regions with low human impact, are highly context‐dependent, and might often be overruled by human‐related factors. Hence, we highlight the need for a description of human influence in NCE studies. We discuss challenges in NCE research and the potential for advances in future research on NCE of wolves in a human‐dominated landscape. We emphasise the need for wildlife management to restore ecosystem complexity and processes, to allow non‐consumptive predator effects to occur.

Mule deer (Odocoileus hemionus) resource selection: trade-offs between forage and predation risk

Ungulates commonly select habitat with higher forage biomass and or nutritional quality to improve body condition and fitness. However, predation risk can alter ungulate habitat selection and foraging behavior and may affect their nutritional condition. Ungulates often choose areas with lower predation risk, sometimes sacrificing higher quality forage. This forage–predation risk trade-off can be important for life history strategies and influences individual nutritional condition and population vital rates. We used GPS collar data from adult female mule deer (Odocoileus hemionus) and mountain lions (Puma concolor) to model mule deer habitat selection in relation to forage conditions, stalking cover and predation risk from mountain lions to determine if a forage-predation risk trade-off existed for mule deer in central New Mexico. We also examined mountain lion kill sites and mule deer foraging locations to assess trade-offs at a finer scale. Forage biomass and protein content were inversely correlated with horizontal visibility, hence associated with higher stalking cover for mountain lions, suggesting a forage-predation risk trade-off for mule deer. Mule deer habitat selection was influenced by forage biomass and protein content at the landscape and within home range spatial scales, with forage protein being related to habitat selection during spring and summer and forage biomass during winter. However, mule deer selection for areas with better foraging conditions was constrained by landscape-scale encounter risk for mountain lions, such that increasing encounter risk was associated with diminished selection for areas with better foraging conditions. Mule deer also selected for areas with higher visibility when mountain lion predation risk was higher. Mountain lion kill sites were best explained by decreasing horizontal visibility and available forage protein, suggesting that deer may be selecting for forage quality at the cost of predation risk. A site was 1.5 times more likely to be a kill site with each 1-meter decrease in visibility (i.e., increased stalking cover). Mule deer selection of foraging sites was related to increased forage biomass, further supporting the potential for a trade-off scenario. Mule deer utilized spatio-temporal strategies and risk-conditional behavior to reduce predation risk, and at times selected suboptimal foraging areas with lower predation risk.

Projection of Mortality Burden Attributable to Nonoptimum Temperature with High Spatial Resolution in China.

The updated climate models provide projections at a fine scale, allowing us to estimate health risks due to future warming after accounting for spatial heterogeneity. Here, we utilized an ensemble of high-resolution (25 km) climate simulations and nationwide mortality data from 306 Chinese cities to estimate death anomalies attributable to future warming. Historical estimation (1986-2014) reveals that about 15.5% [95% empirical confidence interval (eCI):13.1%, 17.6%] of deaths are attributable to nonoptimal temperature, of which heat and cold corresponded to attributable fractions of 4.1% (eCI:2.4%, 5.5%) and 11.4% (eCI:10.7%, 12.1%), respectively. Under three climate scenarios (SSP126, SSP245, and SSP585), the national average temperature was projected to increase by 1.45, 2.57, and 4.98 °C by the 2090s, respectively. The corresponding mortality fractions attributable to heat would be 6.5% (eCI:5.2%, 7.7%), 7.9% (eCI:6.3%, 9.4%), and 11.4% (eCI:9.2%, 13.3%). More than half of the attributable deaths due to future warming would occur in north China and cardiovascular mortality would increase more drastically than respiratory mortality. Our study shows that the increased heat-attributable mortality burden would outweigh the decreased cold-attributable burden even under a moderate climate change scenario across China. The results are helpful for national or local policymakers to better address the challenges of future warming.

Unmasking the sky: high-resolution PM2.5 prediction in Texas using machine learning techniques.

Although PM2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies. This study aimed to predict PM2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables. We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations. Our predictive models demonstrate excellent in-sample model performance, as indicated by high R2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM2.5 concentrations over time is observed in Eastern Texas. We utilized machine learning approaches to predict PM2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R2 > 0.9).

Local Productivity Spillovers

Using Canadian administrative data, this paper presents evidence of revenue and productivity spillovers across firms at fine spatial scales. Accounting for the endogenous sorting of firms across space, we estimate an average elasticity of firm revenue and productivity with respect to the average quality of other firms within 75 meters of 0.024. We find scant evidence that the average firm benefits from being surrounded by a greater amount of economic activity at this spatial scale. Sorting of higher-quality firms into more productive locations and higher average and aggregate quality peer groups is salient in the data. (JEL D22, D24, G32, L25, R11, R32)

AFM-IR nanospectroscopy of nanoglobule-like particles in Ryugu samples returned by the Hayabusa2 mission

Context. The JAXA Hayabusa2 mission returned well-preserved samples collected from the carbonaceous asteroid Ryugu, providing unique non-terrestrially weathered samples from a known parent body. Aims. This work aims to provide a better understanding of the formation and evolution of primitive asteroidal matter by studying the fine scale association of organic matter and minerals in Ryugu samples. We characterized the samples by IR nanospectroscopy using infrared photothermal nanospectroscopy (AFM-IR) technique. This technique overcomes the diffraction limit (of several microns) of conventional infrared microspectroscopy (µ-FTIR). The samples were mapped in the mid-IR range at a lateral spatial resolution about a hundred times better than with µ-FTIR. This provided us with unique in situ access to the distribution of the different infrared signatures of organic components at the sub-micron scale present in the Ryugu whole-rock samples as well as to the characterization of the compositional variability of Ryugu in the insoluble organic matter (IOM) chemically extracted from the Ryugu samples. Methods. The AFM-IR maps of whole-rock particles and IOM residues from Ryugu samples were recorded with a lateral resolution of tens of nanometers. Spectra were recorded in the 1900–900 cm−1 spectral range by AFM-IR (Icon-IR) for all samples, and additional spectra were recorded from 2700 to 4000 cm−1 for one IOM sample by an optical photothermal IR (O-PTIR) technique using a mIRage® IR microscope. Results. Organic matter is present in two forms in the whole-rock samples: as a diffuse phase intermixed with the phyllosilicate matrix and as individual organic nanoparticles. We identify the Ryugu organic nanoparticles as nanoglobule-like inclusions texturally resembling nanoglobules present in primitive meteorites. Using AFM-IR, we record for the first time the infrared spectra of Ryugu organic nanoparticles that clearly show enhanced carbonyl (C=O) and CH contributions with respect to the diffuse organic matter in Ryugu whole-rock and IOM residue.

THE FEASIBILITY OF FUSING SATELLITE IMAGERIES FOR HIGH-FREQUENCY SEA LEVEL MONITORING

Enhancing the capacity to monitor swift environmental shifts at finer scales requires satellite image that offers high spatial and temporal resolution. However, no individual satellite can offer images meeting both criteria simultaneously. To tackle this challenge, spatial temporal fusion algorithms have been developed to derive fine-scale and time-series images. Conversely, effective monitoring of water levels is crucial for preventing natural disaster, such as flood and tsunami mitigation. Yet, monitoring these natural changes regularly poses challenges for remote sensing satellites, given their limitations in either spatial or temporal resolution. For instance, the spatial resolution of 30 meters of Landsat 8 provides imagery with a but lacks the temporal resolution needed to capture dynamic events. On the contrary, the Himawari 8 has the capability to monitor the entire hemisphere every 10 minutes. However, its inadequate resolution affects the precision of sea water change mapping. This research seeks to utilize Landsat OLI and Himawari-8 images jointly for tracking sea level variation patterns. Our approach involves calculating a water index from both Landsat and Himawari images, then using an image fusion algorithm to merge these indices. Next, we identify water coverage by applying a specific threshold on the water index. The comparison of water percentages with reference water height observations has delivered encouraging outcomes.

Spatial Distribution and Ecological Determinants of Coexisting Hybrid Oak Species: A Study in Yushan's Mixed Forest.

Ecological niche partitioning is crucial in reducing interspecific competition, fostering species coexistence, and preserving biodiversity. Our research, conducted in a hybrid mixed oak forest in Yushan, Jiangsu, China, focuses on Quercus acutissima, Q. variabilis, Q. fabri, and Q. serrata var. brevipetiolata. Using Point Pattern Analysis, we investigated the spatial relationships and ecological trait autocorrelation, including total carbon (TC), nitrogen (TN), phosphorus (TP), potassium (TK), and breast height diameter (DBH). Our findings show aggregated distribution patterns within the oak populations. The Inhomogeneous Poisson Point model highlights the impact of environmental heterogeneity on Q. variabilis, leading to distinct distribution patterns, while other species showed wider dispersion. This study reveals aggregated interspecific interactions, with a notable dispersal pattern between Q. acutissima and Q. variabilis. We observed significant variability in nutrient elements, indicating distinct nutrient dynamics and uptake processes. The variations in total carbon (TC), nitrogen (TN), phosphorus (TP), and potassium (TK) suggest distinct nutrient dynamics, with TK showing the highest variability. Despite variations in TC, TK, and TP, the species did not form distinct classes, suggesting overlapping nutritional strategies and environmental adaptations. Furthermore, spatial autocorrelation analysis indicates strong positive correlations for DBH, TC, and TP, whereas TK and TN correlations are non-significant. The results suggest habitat filtering as a key driver in intraspecific relationships, with a finer spatial scale of ecological niche division through TC and TP, which is crucial for maintaining coexistence among these oak species.

Trechus (Coleoptera: Carabidae) of Appalachia: A Phylogenetic Insight into the History of High Elevation Leaf Litter Communities

Elevation gradients provide a wealth of habitats for a wide variety of organisms. The southern Appalachian Mountains in eastern United States are known for their high biodiversity and rates of endemism in arthropods, including in high-elevation leaf-litter taxa that are often found nowhere else on earth. Trechus Clairville (Coleoptera: Carabidae) is a genus of litter inhabitants with a near-global distribution and over 50 Appalachian species. These span two subgenera, Trechus s. str. and Microtrechus Jeannel, largely restricted to north and south of the Asheville basin, respectively. Understanding the diversification of these 3–5 mm flightless beetles through geological time can provide insights into how the litter-arthropod community has responded to historical environments, and how they may react to current and future climate change. We identified beetles morphologically and sequenced six genes to reconstruct a phylogeny of the Appalachian Trechus. We confirmed the Asheville Basin as a biogeographical barrier with a split between the north and south occurring towards the end of the Pliocene. Finer scale biogeography, including mountain-range occupancy, was not a reliable indication of relatedness, with group ranges overlapping and many instances of species-, species group-, and subgeneric sympatry. This may be because of the recent divergence between modern species and species groups. Extensive taxonomic revision of the group is required for Trechus to be useful as a bioindicator, but their high population density and speciose nature make them worth additional time and resources.

Variable importance and scale of influence across individual scottish wildcat hybrid habitat models

Understanding the scale dependence of species-habitat relationships is an important area of research in species distribution modeling. There has been little research focused on how habitat selection may depend on individual variation among organisms, geographical location and ecological context of that location. Furthermore, little is known about the extent and drivers of heterogeneity of scale dependence among individuals of a species inhabiting different ecological contexts, and few studies have compared scale dependence and variable importance in a spatially replicated framework. Two of the most important factors for interpreting habitat relationships models include: (1) the relative importance of variables in the model and (2) the spatial scale at which each variable has the largest influence. Based on the existing evidence we hypothesize a priori that landcover variables will generally be the most important predictors, followed by topography, then soil type (which influence both vegetation and prey), Normalized Difference Vegetation Index (NDVI) as an indicator of total vegetation density and perhaps a proxy for prey density, vegetation cover and rabbit abundance. We also expected a priori that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. We expected topographical features to be selected at broad scales, as they influence broad-scale climatic and ecological conditions. We also expected that land cover classes and vegetation cover density to be selected at relatively broad scales given past research showing land cover generally influences habitat selection at relatively broad scales. We expected NDVI and soil type to be selected at finer scales, as their variation influences the distribution of resources and limiting conditions within landscapes. Finally, we expected that rabbit abundance and linear features would affect wildcat hybrid occurrence at the finest scales, given these are resources and conditions that vary over short distances and strongly influence wildcat and wildcat hybrid behavior and habitat use. Our results were consistent with the hypothesis that there may be consistency regarding which variables or variable groups are most important as predictors of wildcat hybrid occurrence in Scotland. Based on previous research we expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. Finally, our results identify a clear and consistent trend of increasing frequency of inclusion of variables at increasingly broad scales. This is a linear trend in frequency of variables retained increasing as the scale increased. This suggests a consistent and monotonic pattern of more frequent retention of variables at increasingly broad scales.