A spatial prioritization method for identifying potential eco-risk distributions of heavy metals in soil and birds.

Geochemical approaches are popular for evaluations based on heavy metal concentrations in sediments or soils for eco-risk assessment. This study proposes a systematic geochemical approach (SymGeo) to explore six heavy metals in topsoils and bird tissues and organs of the target birds. We assume that the proposed approach based on field-collected heavy metals in topsoils and feathers can predict the areas with the potential risk of the heavy metals in birds. Finite mixture distribution modeling (FMDM) was used to identify background values of the heavy metal concentrations in topsoil. A spatial enrichment factor (EF), potential contamination index (PCI), contamination degree (Cod), and potential ecological risk index (PRI) based on FMDM results for topsoil, and a potential risk index (PRIbird) of heavy metals in the birds, were utilized for systematic prioritization of high eco-risk areas. Using multiple EF, PRI, and Cod results and multiple PRI-based maps of the heavy metals in feathers, we systematically prioritized risk areas where there is a high potential for heavy metal contamination in the birds. Our results indicate that heavy metal concentrations in the feather, liver, and kidney are not spatially cross-autocorrelated but are statistically significantly correlated with some heavy metals in topsoil due to external and internal depositions. Further, multiple EF, Cod, and RI distributions for topsoil, along with the PRI of the feather, showed that adequate coverages for potential risk for birds were greater than 71.05% in the top 30% and 84.69% in the top 20% potential eco-risk priority area of heavy metals in bird liver and kidney. Hence, our proposed approach suggests that assessments of heavy metals in bird feathers and topsoils without bird organs can be utilized to identify spatially high-risk areas. The proposed approach could be improved by incorporating water and sediment samples to enhance the crowdsourcing and the species-specific data.

Exploring hybrid consensus models to assess roadkill.

Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUCKDE = 0.944, AUCME = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran's I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots.

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework.

Real-time identification of irrigation water pollution sources and pathways (PSP) is crucial to ensure both environmental and food safety. This study uses an integrated framework based on the Internet of Things (IoT) and the blockchain technology that incorporates a directed acyclic graph (DAG)-configured wireless sensor network (WSN), and GIS tools for real-time water pollution source tracing. Water quality sensors were installed at monitoring stations in irrigation channel systems within the study area. Irrigation water quality data were delivered to databases via the WSN and IoT technologies. Blockchain and GIS tools were used to trace pollution at mapped irrigation units and to spatially identify upstream polluted units at irrigation intakes. A Water Quality Analysis Simulation Program (WASP) model was then used to simulate water quality by using backward propagation and identify potential pollution sources. We applied a "backward pollution source tracing" (BPST) process to successfully and rapidly identify electrical conductivity (EC) and copper (Cu2+) polluted sources and pathways in upstream irrigation water. With the BPST process, the WASP model effectively simulated EC and Cu2+ concentration data to identify likely EC and Cu2+ pollution sources. The study framework is the first application of blockchain technology for effective real-time water quality monitoring and rapid multiple PSPs identification. The pollution event data associated with the PSP are immutable.

Fabrication of Multilayer Borophene on Insulator Structure.

The X-ray photoelectron spectroscopy spectra indicate the peak of BB bonds, implying that the elemental boron structure might be formed after the process. The multilayer ß-borophene is directly observed by transmission electron microscopy (TEM) and the lattice parameters are valid. The middle SiNx layer also can be identified in TEM image. Furthermore, the 1.61 eV bandgap of the multilayer ß-borophene is announced in this study.

Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management.

Thermodynamic sensitivity of ammonia oxidizers-driven N2O fluxes under oxic-suboxic realms.

In terrestrial ecosystems, the nitrogen dynamics, including N2O production, are majorly regulated by a complex consortium of microbes favored by different substrates and environmental conditions. To better predict the daily, seasonal and annual variation in N2O fluxes, it is critical to estimate the temperature sensitivity of different microbial groups for N2O fluxes under oxic and suboxic conditions prevalent in soil and wetlands. Here, we studied the temperature sensitivity of two groups of ammonia oxidizers, archaea (AOA) and bacteria (AOB), in relation to N2O fluxes through both nitrification and nitrifier-denitrification pathways across a wide temperature gradient (10-55 °C). Using square root theory (SQRT) and macromolecular rate theory (MMRT) models, we estimated thermodynamic parameters and cardinal temperatures, including maximum temperature sensitivity (TSmax). The distinction between N2O pathways was facilitated by microbial-specific inhibitors (PTIO and C2H2) and controlled oxygen supply environments (oxic: ambient level; and suboxic: ∼4%). We found that nitrification supported by AOA (NtA) and AOB (NtB) dominated N2O production in an oxic climate, while only AOB-supported nitrifier-denitrification (NDB) majorly contributed (>90%) to suboxic N2O budget. The models predicted significantly higher optimum temperature (Topt) and TSmax for NtA and NDB compared to NtB. Intriguingly, both NtB and NDB exhibited significantly wider temperature ranges than NtA. Altogether, our results suggest that temperature and oxygen supply control the dominance of specific AOA- and AOB-supported N2O pathways in soil and sediments. This emergent understanding can potentially contribute toward novel targeted N2O inhibitors for GHG mitigation under global warming.

Soil microbiome feedback to climate change and options for mitigation.

Microbes are a critical component of soil ecosystems, performing crucial functions in biogeochemical cycling, carbon sequestration, and plant health. However, it remains uncertain how their community structure, functioning, and resultant nutrient cycling, including net GHG fluxes, would respond to climate change at different scales. Here, we review global and regional climate change effects on soil microbial community structure and functioning, as well as the climate-microbe feedback and plant-microbe interactions. We also synthesize recent studies on climate change impacts on terrestrial nutrient cycles and GHG fluxes across different climate-sensitive ecosystems. It is generally assumed that climate change factors (e.g., elevated CO2 and temperature) will have varying impacts on the microbial community structure (e.g., fungi-to-bacteria ratio) and their contribution toward nutrient turnover, with potential interactions that may either enhance or mitigate each other's effects. Such climate change responses, however, are difficult to generalize, even within an ecosystem, since they are subjected to not only a strong regional influence of current ambient environmental and edaphic conditions, historical exposure to fluctuations, and time horizon but also to methodological choices (e.g., network construction). Finally, the potential of chemical intrusions and emerging tools, such as genetically engineered plants and microbes, as mitigation strategies against global change impacts, particularly for agroecosystems, is presented. In a rapidly evolving field, this review identifies the knowledge gaps complicating assessments and predictions of microbial climate responses and hindering the development of effective mitigation strategies.

A cell hybridization-based method of generating recombinant rabbit monoclonal antibodies for detecting cytokines.

Recombinant rabbit monoclonal antibodies (rabbit rAbs) have shown promise in various biomedical fields. However, it is challenging and costly to generate rabbit rAbs using traditional techniques. Here we describe a convenient and cost-effective method. Using this method, we generated rabbit rAbs against mouse soluble IL-6 receptor α with affinities in the range of 10-9 to 10-12 M. The presented method is suitable for industrial and academic scientists looking to customize rabbit rAbs for their research.

Rapid injection linear dichroism for studying the kinetics of biological processes.

Linear dichroism is defined as the differential absorbance of linearly polarized light oriented in two orthogonal directions by an aligned sample. The measurement of a linear dichroism (LD) spectrum of a sample provides two key pieces of structural information. First, that the sample and the chromophores within the sample are able to align. Second, given knowledge of the transition polarization directions of the chromophores, the orientation of the chromophores within the aligned sample can be resolved. It has been shown that LD can provide unique information on the structure of some of the more challenging biomolecular complexes. This has included macromolecular protein and peptide fibers such as actin, tubulin, and amyloids as well as protein-membrane complexes and DNA-protein complexes. Much of this work has been enabled by the development of a low volume Couette flow cell that efficiently aligns long molecules in solution. However, the current Couette system is inherently complex to assemble for each experiment and hence not suited to measurement of rapid reactions. In this paper we detail the development of the first rapid injection LD cell. The system utilizes a conventional stopped-flow injection system coupled to a modified low volume Couette cell, where a narrow bore capillary replaces the normal solid central rod. The system is shown to have similar optical characteristics to the conventional LD Couette flow cell but with the added benefit of a much shorter dead time (0.60 s compared to ~60). The rapid injection Couette cell has been used to measure the degradation of DNA by DNA exonuclease I, providing data that would not be available using a conventional system.

Assessing the influence of environmental niche segregation in ammonia oxidizers on N2O fluxes from soil and sediments.

Understanding the environmental niche segregation of ammonia-oxidizing archaea (AOA) and bacteria (AOB) and its impact on their relative contributions to nitrification and nitrous oxide (N2O) production is essential for predicting N2O dynamics within an ecosystem. Here, we used ammonia oxidizer-specific inhibitors to measure the differential contributions of AOA and AOB to potential ammonia oxidization (PAO) and N2O fluxes over pH (4.0-9.0) and temperature (10-45 °C) gradients in five soils and three wetland sediments. AOA and AOB activities were differentiated using PTIO (2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide), 1-octyne, and acetylene. We used square root growth (SQRT) and macromolecular rate theory (MMRT) models to estimate cardinal temperatures and thermodynamic characteristics for AOA- and AOB-dominated PAO and N2O fluxes. We found that AOA and AOB occupied different niches for PAO, and soil temperature was the major determinant of niche specialization. SQRT and MMRT models predicted a higher optimum temperature for AOA-dominated PAO and N2O fluxes compared with those of AOB. Additionally, PAO was dominated by AOA in acidic conditions, whereas both AOA- and AOB-dominated N2O fluxes decreased with increasing pH. Consequently, net N2O fluxes (AOA and AOB) under acidic conditions were approximately one to three-fold higher than those observed in alkaline conditions. Moreover, structural equation and linear regression modeling confirmed a significant positive correlation (R2 = 0.45, p < 0.01) between PAO and N2O fluxes. Collectively, these results show the influence of ammonia oxidizer responses to temperature and pH on nitrification-driven N2O fluxes, highlighting the potential for mitigating N2O emissions via pH manipulation.

Visual Cu2+ Detection of Gold-Nanoparticle Probes and its Employment for Cu2+ Tracing in Circuit System.

Highly sensitive, simple and reliable colorimetric probe for Cu2+-ion detection was visualized with the L-cysteine functionalized gold nanoparticle (LS-AuNP) probes. The pronounced sensing of Cu2+ with high selectivity was rapidly featured with obvious colour change that enabled to visually sense Cu2+ ions by naked eyes. By employing systemic investigations on crystallinities, elemental compositions, microstructures, surface features, light absorbance, zeta potentials and chemical states of LS-AuNP probes, the oxidation-triggered aggregation effect of LS-AuNP probes was envisioned. The results indicated that the mediation of Cu2+ oxidation coordinately caused the formation of disulfide cystine, rendering the removal of thiol group at AuNPs surfaces. These features reflected the visual colour change for the employment of tracing Cu2+ ions in a quantitative way.

Environment is associated with chytrid infection and skin microbiome richness on an amphibian rich island (Taiwan).

Growing evidence suggests that the origins of the panzootic amphibian pathogens Batrachochytrium dendrobatidis (Bd) and Batrachochytrium salamandrivorans (Bsal) are in Asia. In Taiwan, an island hotspot of high amphibian diversity, no amphibian mass mortality events linked to Bd or Bsal have been reported. We conducted a multi-year study across this subtropical island, sampling 2517 individuals from 30 species at 34 field sites, between 2010 and 2017, and including 171 museum samples collected between 1981 and 2009. We analyzed the skin microbiome of 153 samples (6 species) from 2017 in order to assess any association between the amphibian skin microbiome and the probability of infection amongst different host species. We did not detect Bsal in our samples, but found widespread infection by Bd across central and northern Taiwan, both taxonomically and spatially. Museum samples show that Bd has been present in Taiwan since at least 1990. Host species, geography (elevation), climatic conditions and microbial richness were all associated with the prevalence of infection. Host life-history traits, skin microbiome composition and phylogeny were associated with lower prevalence of infection for high altitude species. Overall, we observed low prevalence and burden of infection in host populations, suggesting that Bd is enzootic in Taiwan where it causes subclinical infections. While amphibian species in Taiwan are currently threatened by habitat loss, our study indicates that Bd is in an endemic equilibrium with the populations and species we investigated. However, ongoing surveillance of the infection is warranted, as changing environmental conditions may disturb the currently stable equilibrium.

Scientists' warning of threats to mountains.

Mountains are an essential component of the global life-support system. They are characterized by a rugged, heterogenous landscape with rapidly changing environmental conditions providing myriad ecological niches over relatively small spatial scales. Although montane species are well adapted to life at extremes, they are highly vulnerable to human derived ecosystem threats. Here we build on the manifesto 'World Scientists' Warning to Humanity', issued by the Alliance of World Scientists, to outline the major threats to mountain ecosystems. We highlight climate change as the greatest threat to mountain ecosystems, which are more impacted than their lowland counterparts. We further discuss the cascade of "knock-on" effects of climate change such as increased UV radiation, altered hydrological cycles, and altered pollution profiles; highlighting the biological and socio-economic consequences. Finally, we present how intensified use of mountains leads to overexploitation and abstraction of water, driving changes in carbon stock, reducing biodiversity, and impacting ecosystem functioning. These perturbations can provide opportunities for invasive species, parasites and pathogens to colonize these fragile habitats, driving further changes and losses of micro- and macro-biodiversity, as well further impacting ecosystem services. Ultimately, imbalances in the normal functioning of mountain ecosystems will lead to changes in vital biological, biochemical, and chemical processes, critically reducing ecosystem health with widespread repercussions for animal and human wellbeing. Developing tools in species/habitat conservation and future restoration is therefore essential if we are to effectively mitigate against the declining health of mountains.

Surfactant-free purification of membrane proteins with intact native membrane environment.

In order to study the structure and function of a protein, it is generally required that the protein in question is purified away from all others. For soluble proteins, this process is greatly aided by the lack of any restriction on the free and independent diffusion of individual protein particles in three dimensions. This is not the case for membrane proteins, as the membrane itself forms a continuum that joins the proteins within the membrane with one another. It is therefore essential that the membrane is disrupted in order to allow separation and hence purification of membrane proteins. In the present review, we examine recent advances in the methods employed to separate membrane proteins before purification. These approaches move away from solubilization methods based on the use of small surfactants, which have been shown to suffer from significant practical problems. Instead, the present review focuses on methods that stem from the field of nanotechnology and use a range of reagents that fragment the membrane into nanometre-scale particles containing the protein complete with the local membrane environment. In particular, we examine a method employing the amphipathic polymer poly(styrene-co-maleic acid), which is able to reversibly encapsulate the membrane protein in a 10 nm disc-like structure ideally suited to purification and further biochemical study.

A hybrid scheme for comparing the effects of bird diversity conservation approaches on landscape patterns and biodiversity in the Shangan sub-watershed in Taiwan.

This work utilizes bird survey data, regression modeling, land-use modeling and landscape metrics to evaluate the effects of various spatial bird diversity conservation approaches on land-use allocation, land-use patterns, and biodiversity in the Shangan sub-watershed in central Taiwan. A survey of the distribution of species revealed that bird species are concentrated in the central and western parts of the sub-watershed. The results obtained using a Shannon-Weaver diversity regression model suggest that diversity of land-use increases the diversity of bird species. Logistic regression results verify that socio-economic factors determine the potential advantages of designating a particular type of land-use in certain parts of the study area. The results of land-use simulation modeling indicate that the eastern and southwestern areas of the sub-watershed will change most frequently between 2007 and 2017. Additionally, increasing the areas to protect bird diversity will effectively increase the patch size, habitat core area, edge effect and habitat connectivity. The Shannon-Weaver diversity regression model shows that protecting bird species diversity in large areas increases bird diversity. The proposed modeling approach is an effective tool that provides useful information for ecological planning and policymaking related to watersheds.

Monitoring and estimating scale-dependent hierarchical relationships between Sicyopterus japonicus density and stream habitat features in different seasons in northern Taiwan.

Biological and physical processes operate collaboratively through spatial or temporal scales to form ecological patterns, which are considered as a key element for understanding the natural liens within an ecosystem. Given the importance of scaling in ecological inference, this study elucidates how physical and biological variables under or within scales interact with each other. Density of Sicyopterus japonicus and environmental variables are examined and quantified at 70 stream sections distributed among 14 reaches in the Datuan stream catchment of northern Taiwan during the fall and winter of 2007, as well as the spring and summer of 2008. Hierarchical linear regression analysis indicates that S. japonicus density and habitat features are related on two levels, i.e., sections within reaches and reaches within streams throughout the year. Moreover, parameter uncertainty is represented by the confidence interval, which is calculated by the variance-covariance matrix of hierarchical linear model (HLM) parameters. According to HLM results, environmental variables at the section level (water depth and current velocity) and the reach level (stream width, water temperature, stream slope, soil erosion index) influence S. japonicus density. Although S. japonicus density varies significantly among reaches and sections within reaches, cross-level interaction may not always influence the distribution, processes and activities of S. japonicus throughout the year. The HLMs of S. japonicus density associated with stream features describe thoroughly multiple processes and the activities of S. japonicus across scales and within scales during different seasons. The annual HLM results represent the overall biological and physical patterns of the Datuan stream annually, explaining why they do not reflect seasonal associations or explain S. japonicus-related activities and environmental features of the stream.

Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods.

This study attempts to determine the scale-dependent hierarchical spatial variation and longitudinal distributions of Sicyopterus japonicus year round. The distribution of S. japonicus in the Datuan Stream in northern Taiwan was surveyed during the fall and winter 2007, as well as the spring and summer of 2008. The spatial structure of S. japonicus density was modeled using geostatistics. The longitudinal distributions of S. japonicus density were then estimated using kriging and hydrology distance with nested variogram models. Variography results indicate that nested variogram models could reflect the hierarchical structure in the spatial variation of seasonal S. japonicus density, with the small, median, and large ranges representing three nested scales. Models for the four seasons were consistent in that they shared the same shape of variogram models with various ranges and sill values. This model shape consistency implies stationary spatial correlations in the longitudinal fish distribution across the four seasons. The Kriging geostatistical method based on the multiple scales nested variogram models also provided robust estimates of S. japonicus densities at unsampled sections. We conclude that S. japonicus densities exhibit hierarchical patterns and variation in the four seasons along the study stream. Geostatistical methods with a nested variograms and hydrological distance are a highly effective means of delineating the hierarchical structure in longitudinal patterns of S. japonicus density in each season, providing estimates of the S. japonicus density for hierarchically structured spatial distributions and expanding knowledge of S. japonicus beyond the limits imposed by spatial and temporal scales.

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation.

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.