Heterogeneity Of Environmental Factors Research Articles

Cycling Greenway Planning towards Sustainable Leisure and Recreation: Assessing Network Potential in the Built Environment of Chengdu

In the quest to enhance urban green mobility and promote sustainable leisure activities, this study presents a comprehensive analysis of the potential for cycling greenways within the urban fabric of Chengdu, China. Leveraging the built environment and cycling routes, simulated by dockless bike-sharing (DBS) big data on weekend afternoons, the cycling flow on existing networks reflects the preference for leisure cycling in surroundings, thus indicating the potential for future enhancements to cycling greenway infrastructure. Employing Multi-Scale Geographically Weighted Regression (MGWR), this research captures the spatial heterogeneity in environmental factors influencing leisure cycling behaviors. The findings highlight the significant roles of mixed land use, network diversity, public transit accessibility, human-scale urban design, road network thresholds, and the spatially variable impacts of architectural form in determining cycling greenway potential. This study culminates with the development of an evaluation model, offering a scientific approach for cities to identify and prioritize the expansion of cycling infrastructure. Contributing to urban planning efforts for more livable and sustainable environments, this research underscores the importance of data-driven decision-making in urban green mobility enhancement by accurately identifying and efficiently upgrading infrastructure guided by public preferences.

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM.

Effect of Light Heterogeneity Caused by Photovoltaic Panels on the Plant–Soil–Microbial System in Solar Park

The large-scale construction of photovoltaic (PV) panels causes heterogeneity in environmental factors, such as light, precipitation, and wind speed, which may lead to microhabitat climate changes that may affect ecosystems. In this study, plant–soil–microbial systems in shady and non-shady gaps of PV panels in a solar park in Northern China were investigated. The shading caused by the PV panels significantly affected the alpha diversity of plant and fungal communities (p < 0.05). The compositions of plant and soil microbial (bacteria, fungi, and protists) communities were significantly different between shady and non-shady areas (p < 0.05), and the beta diversity of the plant community was significantly correlated with that of the soil microbial community (p < 0.05). Shading enhanced the complexity of microbial communities by strengthening the associations among soil microbes. Photosynthetically active radiation was the main driving factor in the assembly of aboveground and belowground communities on a small scale, and it indirectly shaped the microbial community through its effects on the plant community. This study highlights the important effects of light on microbial community formation and on the relationships among communities in plant–soil–microbial systems. Thus, the effects of solar park establishment on degraded ecosystems should be considered.

Comparison between Machine-Learning-Based Turbidity Models Developed for Different Lake Zones in a Large Shallow Lake

Machine learning has been used to mine the massive data collected by automatic environmental monitoring systems and predict the changes in the environmental factors in lakes. However, further study is needed to assess the feasibility of the development of a universal machine-learning-based turbidity model for a large shallow lake with considerable spatial heterogeneity in environmental factors. In this study, we collected and examined sediment and water quality data from Lake Taihu, China. Three monitoring stations were established in three lake zones to obtain continuous time series data of the water quality and meteorological variables. We used these data to develop three turbidity models based on long short-term memory (LSTM). The three zones differed in terms of environmental factors related to turbidity: in West Taihu, the Lake Center, and the mouth of Gonghu Bay, the critical shear stress of bed sediments was 0.029, 0.055, and 0.032 N m−2, and the chlorophyll-a concentration was 23.27, 14.62, 30.80 μg L−1, respectively. The LSTM-based turbidity model developed for any zone could predict the turbidity in the other two zones. For the model developed for West Taihu, its performance to predict the turbidity in the local zone (i.e., West Taihu) was inferior to that for the other zones; the reverse applied to the models developed for the Lake Center and Gonghu Bay. This can be attributed to the complex hydrodynamics in West Taihu, which weakens the learning of LSTM from the time series data. This study explores the feasibility of the development of a universal LSTM-based turbidity model for Lake Taihu and promotes the application of machine learning algorithms to large shallow lakes.

Effect of Canopy Gaps on Growth and Water Use Efficiency of Seedling of Vulnerable Species, Hopea sangal Korth

Aims: Hopea sangal Korth. is listed as vulnerable species and recently its remnant habitat was rediscovered in the degraded forest near the springs area in East Java. In the forest, the regeneration of the seedling and saplings understory is affected by the heterogeneity in environmental factors especially various light levels due to the canopy gaps. H. sangal is considered a shade-tolerant species, hence the establishment of the seedling in its natural habitat occurs under the closed canopy. This study aimed to understand the Water Use Efficiency (WUE) and Relative Growth Rate (RGR) of Hopea sangal Korth seedling grown in two different levels of tree canopy shades.
 Study Design: This research was using a completely randomized design, with 9 replications.
 Place and Duration of Study: This study was conducted in the Purwodadi Botanic Garden, East Java, between March – July.
 Methodology: We used seedling of H. sangal collected from the field in Pasuruan which were planted in plastic pots and acclimatized to obtain seedling of a similar size. The microclimate was measured weekly using solarimetri and sling psychrometer at 08.00; 10:00; 12:00; 14:00 and 16:00. RGR was measured by harvesting the seedling and whole plant WUE was measured using the gravimetric method.
 Results: We observed the light level during the periodic opening of the canopy gap was significantly different at a specific time (8-12 am), with the highest light intensity at 10.00 of 333.57 µmol photon m-2s-1. The canopy shades differences did not affect the WUE of seedling (P = 0.333), meanwhile, there was a significant difference in the seedling RGR (P = 0.025). The seedling that were grown under a higher gap and received higher light intensity periodically during a day had higher RGR than those under a closed canopy. The WUE has a positive relationship with the RGR of seedling (R2 = 0.5; P < 0.05).
 Conclusions: This study suggested that the H. sangal is one of the shade-tolerant species capable of responding to gap-opening sunlight. The study also showed RGR of the seedling positively correlated with the WUE of plants, indicating that the seedling could use the water supply efficiently to grow rapidly.

Estimating spatial and temporal variation in ocean surface pCO2 in the Gulf of Mexico using remote sensing and machine learning techniques

Research on the carbon cycle of coastal marine systems has been of wide concern recently. Accurate knowledge of the temporal and spatial distributions of sea-surface partial pressure (pCO2) can reflect the seasonal and spatial heterogeneity of CO2 flux and is, therefore, essential for quantifying the ocean's role in carbon cycling. However, it is difficult to use one model to estimate pCO2 and determine its controlling variables for an entire region due to the prominent spatiotemporal heterogeneity of pCO2 in coastal areas. Cubist is a commonly-used model for zoning; thus, it can be applied to the estimation and regional analysis of pCO2 in the Gulf of Mexico (GOM). A cubist model integrated with satellite images was used here to estimate pCO2 in the GOM, a river-dominated coastal area, using satellite products, including chlorophyll-a concentration (Chl-a), sea-surface temperature (SST) and salinity (SSS), and the diffuse attenuation coefficient at 490 nm (Kd-490). The model was based on a semi-mechanistic model and integrated the high-accuracy advantages of machine learning methods. The overall performance showed a root mean square error (RMSE) of 8.42 μatm with a coefficient of determination (R2) of 0.87. Based on the heterogeneity of environmental factors, the GOM area was divided into 6 sub-regions, consisting estuaries, near-shores, and open seas, reflecting a gradient distribution of pCO2. Factor importance and correlation analyses showed that salinity, chlorophyll-a, and temperature are the main controlling environmental variables of pCO2, corresponding to both biological and physical effects. Seasonal changes in the GOM region were also analyzed and explained by changes in the environmental variables. Therefore, considering both high accuracy and interpretability, the cubist-based model was an ideal method for pCO2 estimation and spatiotemporal heterogeneity analysis.

The drivers of the Asian longhorned beetle disaster show significant spatial heterogeneity

The Asian longhorned beetle (ALB), Anoplophora glabripennis (Motschulsky), is one of the most dangerous pests in forestry worldwide and has exhibited an accelerating expansion trend. To date, the effects of the spatial heterogeneity in environmental factors on the ALB disaster have not been assessed. We collected data on the incidence rates and driving factors of the ALB in 666 counties and districts of China’s Central Plains in 2008. The driving factors include meteorological elements (such as temperature, wind speed and precipitation), socioeconomic elements (such as gross regional product and population density), and ecological elements such as vegetation cover. A geographically weighted regression (GWR) model was used to analyze the spatial differentiation of the impacts of environmental factors on the occurrence of the ALB. The results showed that the effects of temperature, wind speed, precipitation and population density on the ALB occurrence were significant in terms of spatial heterogeneity, and the significant regions were concentrated in northwestern China. Temperature and precipitation had the most extensive impacts on the ALB occurrence on a geospatial scale. This study highlights the spatially varying nature of environmental factors that affect the ALB occurrence. Given the future climate change scenario, extreme weather events in local areas will be more frequent and severe, potentially affecting the ALB occurrence in local areas. We suggest that to prevent and control ALB infestation, it is necessary to consider the spatial differences in environmental factors and implement different measures in different regions according to local conditions.

Importance and strength of environmental controllers of soil organic carbon changes with scale

Spatial heterogeneity in environmental factors on the land surface moderates exchanges of water, energy, and greenhouse gases between the land and the atmosphere. However, appropriately representing this heterogeneity in earth system models remains a critical scientific challenge. We used a large dataset of environmental factors (n = 31) representing soil-forming factors, field observations of soil organic carbon (SOC) (n = 6213), and a machine-learning algorithm (Cubist) to analyze the scaling behavior of SOC across the conterminous United States. We found that various environmental factors are significant predictors of SOC stocks at different spatial scales. Out of the 31 environmental factors we investigated, only 13 were significant predictors of SOC stocks at spatial scales ranging from 100 m to 50 km. Overall, topographic variables had higher influence at finer scales, whereas climatic variables were more important at coarser scales. The model performance worsened with increasing scale or the spatial resolution of prediction (R2 = 0.38–0.65). The strength of environmental controls (median regression coefficient) on SOC weakened with scale, and we represented them using mathematical functions (R2 = 0.38–0.98). Both the mean and variance of SOC stocks decreased linearly with increasing the scale in soils of the conterminous United States. Fitted linear functions accounted for 81% and 82% of the variability in the mean and variance of SOC, respectively. We also found linear relationships among mean and high-order moments of SOC (R2 = 0.51–0.97). Improved understanding of the scaling behavior of SOC stocks and their environmental controllers can improve earth system model benchmarking and may eventually improve representation of the spatial heterogeneity of land surface biogeochemistry.

Explaining long-term inter-individual growth variation in plant populations: persistence of abiotic factors matters.

An unanswered question in ecology is whether the environmental factors driving short-term performance also determine the often observed long-term performance differences among individuals. Here, we analyze the extent to which temporal persistence of spatial heterogeneity in environmental factors can contribute to long-term inter-individual variation in stem length growth. For a natural population of a long-lived understorey palm, we first quantified the effect of several environmental factors on stem length growth and survival. We then performed individual-based simulations of growth trajectories, in which we varied, for two environmental factors: (1) the strength of the effect on stem length growth and (2) the temporal persistence. Short-term variation in stem length growth was strongly driven by light availability. Auto-correlation in light availability and soil pH increased simulated variation in stem length growth among 20-year-old palms to levels similar to the observed variation. Analyses in which we varied both the strength of the effect on stem length growth and the temporal persistence of the environmental factors revealed that a large fraction of observed long-term growth differences can be explained, as long as one of these effects is strong. This implies that environmental factors that are relatively unimportant for short-term performance can still drive long-term performance differences when the environmental variation is sufficiently persistent over time.

How do heterogeneity in vegetation types and post-fire age-classes contribute to plant diversity at the landscape scale?

Heterogeneity in environmental factors (e.g. soils) and disturbances (e.g. fire) are important drivers of plant diversity but the relative strength of their effects may be scale dependent. Given that fire regimes are often manipulated to maintain plant diversity at the landscape scale, detecting and disentangling fire from environmental effects at this scale is important. In SE Australia prescribed burning and wildfires have created mosaics of post-fire age-classes in vegetation ranging from moist forests to sclerophyllous heathlands along a gradient of decreasing productivity (rainfall and soil fertility). This provided us with an opportunity to investigate the effects of both environmental and disturbance heterogeneity on plant diversity at the landscape scale. Our survey units were mosaics (approx. 100ha) of varying heterogeneity in the numbers of vegetation types (surrogate for environment) and post-fire age-classes (disturbance). As predicted, we found that plant diversity increased as the number of vegetation types in mosaics increased and as mean annual rainfall increased from heathlands to moist forests. There was also a weaker positive relationship between plant diversity and the number of post-fire age-classes in mosaics. The effect of post-fire age-classes on plant diversity was much stronger, however, when each vegetation type was considered separately, with species composition more sensitive to post-fire ages-classes in the moist forests at higher productivity compared with sclerophyllous heathlands at lower productivity. The effectiveness of mosaic burning in maintaining plant species diversity in environmentally diverse landscapes is therefore stronger at the small scale, within vegetation types, where the influence of environmental factors is minimised.

Soil moisture dynamics within soil profiles and associated environmental controls

Knowledge of soil moisture dynamics within soil profiles can improve hydrologic process modeling. The heterogeneity of environmental factors (e.g., topographic attributes, soil properties, land use types and antecedent precipitation) influences soil moisture dynamics. In this study, we measured the soil moisture content at 10-cm depth intervals between the soil surface and the soil–bedrock boundary at 39 sites within a small catchment in 2008 and 2009; we primarily analyzed the environmental controls on soil moisture dynamics over the entire soil profile (0–10, 10–20, 20–30, 30–40, 40–50, and >50cm). Soil properties, terrain attributes, land use types and precipitation were evaluated as the potential controls. Given the high co-dependence of environmental factors, partial least squares regression (PLSR) was used to understand the links between soil moisture dynamics and the 14 selected environmental indices. The results show that PLSR considerably reduced the dimensionality of the soil moisture data. The first two components explained 73.5%, 69.9%, 65.7%, 60.1%, 51.5% and 47.6% of the soil moisture variability at the measurement depths of 0–10cm, 10–20cm, 20–30cm, 30–40cm, 40–50cm and >50cm, respectively. Antecedent precipitation dominates the first and second positive components of the PLSR models at all of the measurement depths. The first-order controls were determined by calculating the Variable Influence on Projection (VIP). The VIP values revealed that the soil moisture in the top layer (0–10cm) is controlled by antecedent precipitation. In the intermediate layer (10–30cm), the first-order controls of soil moisture are antecedent precipitation, soil thickness, topographic wetness index, and soil texture; while in the deeper layer (>30cm), antecedent precipitation, soil thickness, topographic wetness index, relative elevation and slope gradient control soil moisture dynamics. The results indicate that soil moisture is closely associated with environmental factors in measurement catchments at various soil layers and provide a deeper understanding of hydrological processes.

Estimating nitrogen fluxes at the European scale by upscaling INTEGRATOR model outputs from selected sites

Abstract. A comparison was made between upscaled model results of nitrogen (N) fluxes to air and water from 450 sites within the EU-27 and results derived for the entire EU-27 area using the model INTEGRATOR. The 450 sites were selected using stratified random sampling, dividing the EU-27 into 150 strata and selecting three sites at random within each stratum. The strata were based on important environmental factors influencing N fluxes. Hierarchical divisive cluster analysis was used to reduce the numerous combinations of environmental factors to the required total of 150, such that the heterogeneity of environmental factors within strata was as small as possible. Modelled NH3, N2O and NOx emissions and N leaching/runoff obtained were scaled up from the 450 sites to the entire EU-27 and were within 10% of results obtained by running the model for the whole of the EU-27 using about 36 500 sites. This implies that a reliable estimate of N fluxes for EU-27 can be made by upscaling results of the 450 selected sites suggesting that dramatic reduction in computation time can be achieved without substantial deterioration of results

Susceptible genes of type 2 diabetes and their disease predictive power in Chinese population

Type 2 diabetes mellitus is influenced by the environmental factors and genetic factors. Due to the heterogeneity in environmental factors as well as genetic background, the susceptibility of type 2 diabetes mellitus in different ethnic populations is totally different, with low susceptibility in Caucasians, while modest susceptibility in Chinese Hans. The recent success of genetics have identified 50 susceptible genes of type 2 diabetes in the Caucasians by biology candidate study, linkage study as well as genomewide association study, among which some loci such as KCNJ11, TCF7L2, TCF2 genes have been replicated successfully in our Chinese Hans populations with the odds ratio (OR) ranging from 1.14-1.31. Besides, by replicating the susceptible genes specific for the East Asian, 6 loci showed significance in our Chinese samples, with KCNQ1 showing the strongest association with type 2 diabetes. Furthermore, we have been searching for new susceptibility genes in Han Chinese. We also performed a positional cloning study and found that one of the nominal associations was located in the gene NOS1AP. We also used GWAS to identify susceptibl eg enes of the Chinese and identified eight novel hits by collaboration with East Asian groups. However, although nearly 50 susceptible genes for type 2 diabetes were identified so far, the prediction effects were still limited. Thus the utility of both genetic and environmental factors will be worthy to explore in the disease prediction.

Local-scale environmental variation generates highly divergent lineages associated with stream drainages in a terrestrial salamander, Plethodon caddoensis

Spatial and temporal heterogeneity in environmental factors can have profound effects on diversification in species that are tightly linked to their environments. The Caddo Mountain Salamander ( Plethodon caddoensis) inhabits a unique physiographic section of the Ouachita Mountains in central North America, a region in which Pleistocene climatic fluctuations have been implicated in driving lineage diversification in two other closely related salamanders. We examined P. caddoensis to determine whether it was similarly impacted by historic climatic changes and test whether physiographic features unique to the area also contributed to its diversification. We found that P. caddoensis is composed of four highly divergent, geographically distinct lineages that abut one another along an east–west axis. Phylogeographic structure was significantly related to both geographic distance and stream drainages, indicating that connectivity of streams and stream-associated habitats (e.g., talus) influence patterns of interpopulation gene flow. Lineages originated during the Middle Miocene and population size decreased in all lineages during the Pleistocene. Surface Geology and precipitation were the most important variables predicting the species distribution. Our results show that the unique physiographic features of the area coupled with species response to climatic factors have driven lineage diversification and phylogeographic structure in P. caddoensis. Variation in responses to historic climatic fluctuations among salamander species in this region underscore the importance of integrating species ecology with other factors such as geology and hydrology in order to better understand the effects of climate change on species with close associations to their environments.

Azimuthal variations in xylem structure and water relations in cork oak (Quercus suber)

Azimuthal variations in xylem conductivity and transpiration can occur in trees and may be due to heterogeneity in environmental factors. In cork oak (Quercus suber L.), it can be hypothesized that such modifications may be more pronounced because the insulating layer of bark is harvested every 9–10 years, thus cambial cells will be exposed to fluctuations in the microenvironment. To investigate whether xylem structure and water relations differed around the stems of mature cork oak, sap flow per section and xylem structure were measured on the northern (N) and southern (S) sides of nine trees during three months in Portugal, using the Trunk Sector Heat Balance method. Crown size was measured on both sides of each tree and increment wood cores were extracted from the sites where sap flow was measured in five trees. Wood moisture content, earlywood (EW) vessel size and density were measured and theoretical hydraulic conductivity for individual vessels (Lth) was calculated along the N and S stem radial profiles. No significant differences in crown size between the two sides of the tree were found, but sap flow was higher on the S side of the tree in May only. No differences in wood moisture content were observed along the length of each wood core throughout the heartwood. Significant differences in vessel size occurred, with a greater diameter and surface area on the N side of the tree, and consequently Lth was significantly greater. These conduit diameters on the S facing side of the tree may be smaller in response to a combination of signals and trade-offs due to the heterogeneous air and soil environment around the tree.

Relationships between spatial environmental heterogeneity and plant species diversity on a limestone pavement

No empirical studies have examined the relationship between diversity and spatial heterogeneity across unimodal species richness gradients. We determined the relationships between diversity and environmental factors for 144 0.18 m2 plots in a limestone pavement alvar in southern Ontario, Canada, including within‐plot spatial heterogeneity in soil depth, microtopography and microsite composition. Species richness was unimodally related to mean soil depth and relative elevation. Microsite heterogeneity and soil depth heterogeneity were positively correlated with species richness, and the richness peaks of the unimodal gradients correspond to the maximally spatially heterogeneous plots. The best predictive models of species richness and evenness, however, showed that other factors, such as ramet density and flooding, are the major determinants of diversity in this system. The findings that soil depth heterogeneity had effects on diversity when the effects of mean soil depth were factored out, and that unimodal richness peaks were associated with high spatial heterogeneity in environmental factors represent significant contributions to our understanding of how spatial heterogeneity might contribute to diversity maintenance in plant communities.

Chloroplast DNA phylogeography and cytotype geography in autopolyploid Plantago media

In order to gain insight into the causes of parapatric diploid and tetraploid distributions in Plantago media chloroplast DNA (cpDNA) restriction site polymorphism was studied in 36 European populations. Parapatric distributions are often explained by adaptive differences between cytotypes to an underlying heterogeneity in environmental factors. Alternatively, such distribution patterns may be explained nonadaptively, through frequency‐dependant production of hybrids with low fitness. However, nonadaptive explanations have been neglected in polyploid literature. In this study nine chloroplast haplotypes were found. Their phylogeny suggests that tetraploids arose at least three times from diploids. In general, related haplotypes were also geographically clustered, although there were some marked geographical discontinuities. In the Pyrenees, diploids and tetraploids carried diverged haplotypes throughout their parapatric ranges. At the contact zone the level of cpDNA introgression in a mixed diploid‐tetraploid population was low. It is discussed that the cpDNA phylogeography supports the nonadaptive hypothesis that parapatric cytotype distributions may be explained by postPleistocene range expansions followed by mutual minority cytotype exclusion, due to hybrid unfitness.