Changing Environmental Conditions Research Articles

Climate change adaptation through traditional Buffalo knowledge: learning reflection from the Blackfoot indigenous community

ABSTRACT This paper highlights the potential of Indigenous Buffalo knowledge as a valuable resource for adapting to the impacts of climate change. Buffalo, particularly in regions where they have been traditionally reared, have played a crucial role in Indigenous cultures and livelihoods. Indigenous communities have developed an intimate understanding of buffalo behaviour, ecological interactions, and their relationship with changing environmental conditions. This traditional Buffalo knowledge encompasses observations, practices, and Indigenous knowledge systems passed down through generations, providing insights into adaptive strategies that can be applied in the face of climate change. Following decolonial and relational theoretical frameworks, we used deep listening to learn how traditional Buffalo knowledge contributed to climate change adaptation strategies in Indigenous communities; how does integrating traditional Buffalo knowledge into climate change adaptation policies and practices contribute to the resilience of vulnerable communities? What challenges and opportunities are associated with incorporating traditional Buffalo knowledge into mainstream climate change adaptation efforts?

PEMENUHAN GIZI DAN PENINGKATAN PENDAPATAN MASYARAKAT DENGAN BUDIDAYA SAYURAN DI LAHAN PEKARANGAN

Fulfilling people's food needs faces many obstacles, especially due to changes in environmental conditions, such as climate change, land conversion, and the increasing number of cases of pest attacks and plant diseases which cause a decrease in crop yields. The solution offered is to use yard land for cultivating plants. Apart from being able to be used for daily consumption, the harvest from the yard can also be sold as a side business for family members. The community service program aims to assist the target community in increasing their knowledge and skills in optimally utilizing yard land for cultivating vegetable crops. The method used is community empowerment with activities: counseling, training and participatory mentoring of target community groups. The results of the implementation of the community service program show an increase in community knowledge and skills in cultivating vegetable plants in their yards. Planting vegetable plants in the yard can be a source of food, medicine and environmental beauty, as well as increasing people's income.

Rational Combination of Selenium Application Rate and Planting Density to Improve Selenium Uptake, Agronomic Traits, and Yield of Dryland Maize

Exogenous selenium application could effectively improve the selenium absorption of crops affected by different climatic conditions due to changes in the planting environment and planting conditions. We planted maize at planting densities of 67,500 plants ha−1 (D1) and 75,000 plants ha−1 (D2). Five selenium fertilizer gradients of 0 mg m−2 (Se0), 7.5 mg m−2 (Se1), 15.0 mg m−2 (Se2), 22.5 mg m−2 (Se3), and 30.0 mg m−2 (Se4) were applied to investigate the response of the plants to selenium fertilizer application in terms of the gradient selenium absorption and substance accumulation. With the increase in the amount of selenium fertilizer applied, more of the selenium fertilizer will be absorbed and transported from the leaves to the grains, and the selenium content of the grains will gradually increase and exceed the selenium content of leaves. Under the D2 density in 2022, the selenium content of the grains under Se1, Se2, Se3, and Se4 treatments increased by 65.67%, 72.71%, and 250.53%, respectively, compared with that of Se0. A total of 260.55% of the plants showed a gradient of grain > leaf > cob > stalk from the Se2 treatment, and the overall selenium content of the plants increased first and then decreased. Under the D1 density, compared with the Se0, the dry matter mass of the Se1, Se2, Se3, and Se4 treatments significantly improved by 5.84%, 1.49%, and 14.26% in 2021, and significantly improved by 4.84%, 3.50%, and 2.85% in 2022. The 1000-grain weight under Se2, Se3, and Se4 treatments improved by 8.57%, 9.06%, and 15.56% compared to that under the Se0 treatment, and the yield per ha under the Se2, Se3, and Se4 treatments was 18.58%, 9.09%, and 21.42% higher than that under Se0 treatment, respectively. In addition, a reasonable combination of selenium application rate and density could improve the chlorophyll content and stem growth of dryland maize. This lays a foundation for the efficient application of selenium fertilizer and provides an important reference.

Recent shortening of the mature tropical cyclone stage over the western North Pacific

AbstractThis study investigates long‐term trends related to the mature stage duration of tropical cyclones (TCs) over the western North Pacific (WNP) during June–November between 1982 and 2021. There is a significant shortening in the mean duration of the WNP mature TC stage, which is defined as the period when a TC is within 5 kt of its lifetime maximum intensity. This shortening is induced by a significant (weak) poleward migration in the starting (ending) location of the WNP mature TC stage, which can be further explained by changes in environmental conditions. From 1982 to 2021, there have been significant increases in maximum potential intensity and 700–500‐hPa relative humidity over most of the WNP, which has broadly expanded the TC‐favorable area poleward. Consequently, WNP TCs can start their mature stages and reach their lifetime maximum intensities at higher latitudes. By contrast, there are only weak changes in 850‐hPa relative vorticity and 850–200‐hPa vertical wind shear (VWS). Given the dominant role of VWS in modulating the TC weakening rate, the TC‐suppressive area over the subtropical WNP has shown lesser changes, thus leading to insignificant changes in the ending location of the mature TC stage.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958–2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000–2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Transgenerational salinity tolerance plasticity in rice: Unraveling nongenetic phenotypic modifications and environmental influences.

Transgenerational plasticity in plants enables rapid adaptation to environmental changes, allowing organisms and their offspring to adapt to the environment without altering their underlying DNA. In this study, we investigated the plasticity of transgenerational salinity tolerance of rice plants using a reciprocal transplant experimental strategy. Our aim was to assess whether nongenetic environment-induced phenotypic modifications and transgenerational salinity affect the salinity tolerance of progeny while excluding nuclear genomic factors for two generations. Using salt-tolerant and salt-sensitive rice genotypes, we observed that the parentally salt-stressed salt-sensitive genotype displayed greater growth performance, photosynthetic activity, yield performance, and transcriptional responses than did the parentally nonstressed salt-sensitive plants under salt stress conditions. Surprisingly, salt stress-exposed salt-tolerant progeny did not exhibit as much salinity tolerance as salt stress-exposed salt-sensitive progeny under salt stress. Our findings indicate that the phenotypes of offspring plants differed based on the environment experienced by their ancestors, resulting in heritable transgenerational phenotypic modifications in salt-sensitive genotypes via maternal effects. These results elucidated the mechanisms underlying transgenerational plasticity in salinity tolerance, providing valuable insights into how plants respond to changing environmental conditions.

Computational design of non-porous pH-responsive antibody nanoparticles

AbstractProgramming protein nanomaterials to respond to changes in environmental conditions is a current challenge for protein design and is important for targeted delivery of biologics. Here we describe the design of octahedral non-porous nanoparticles with a targeting antibody on the two-fold symmetry axis, a designed trimer programmed to disassemble below a tunable pH transition point on the three-fold axis, and a designed tetramer on the four-fold symmetry axis. Designed non-covalent interfaces guide cooperative nanoparticle assembly from independently purified components, and a cryo-EM density map closely matches the computational design model. The designed nanoparticles can package protein and nucleic acid payloads, are endocytosed following antibody-mediated targeting of cell surface receptors, and undergo tunable pH-dependent disassembly at pH values ranging between 5.9 and 6.7. The ability to incorporate almost any antibody into a non-porous pH-dependent nanoparticle opens up new routes to antibody-directed targeted delivery.

Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China

Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection.

Dune behavior in the Source Area of the Yellow River under climate changes observed from various remote sensing datasets

Sand dunes are a landscape feature with a quick response time to climate change and human influences (e.g. grazing, greening projects, and fixation structures). Their migration rates and their development can help to gather information about changing environmental conditions over time. The Source Area of the Yellow River (SAYR), located upon the Tibetan Plateau, is highly complex with topographical, hydrological, and climatological influences on active dunes, making it a good study area for these interactions. Based on remote sensing datasets, spanning the last 54 years, 415 dunes were mapped for migration rate calculations. Further, climate data from ERA-5 reanalysis and a local climate station was used to assess their changes within a changing climate. Generally, dune migration rates are rather slow with an average of 3.62 m y-1. In accordance, the averaged resultant drift potential (RDP) values are lower than 10 m3/s−3(−|-). Further, we assessed the density development of the main active barchan dune field in direct premise of the Yellow River. Throughout the past 54 years, we observed the emergence of more than 5 new barchans per square kilometer. This increase is likely attributed to higher sand flux from the Yellow River, which has resulted from increased discharge due to declining snowfall and rising precipitation levels.

Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an R2 value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831.

The capacity of sentinel species to detect changes in environmental conditions and ecosystem structure

Abstract A major obstacle to preventing and reversing biodiversity loss in the Anthropocene lies in the scarcity of tools and data for monitoring the health and trajectory of ecosystems. Sentinel species can provide insight into unobserved ecosystem change, but it is unclear how effective sentinels are due to the local, context‐dependent nature of past research. Here, we present the first global evaluation on the effectiveness of sentinel species as indicators of ecosystem change. We conducted a meta‐analysis on 372 case studies to identify the ecological and methodological factors that correlate with the most effective sentinel species. Sentinel performance did not vary consistently across taxa or system; instead, sentinels that were more directly linked to ecosystem change due to their trophic role as predators were more effective. In addition, sentinel responses that were measured on a shorter timescale were more effective at indicating ecosystem change. Policy Implications. These results contribute to the longstanding debate on “what makes a good sentinel” and demonstrate the importance of both ecological and methodological factors when selecting sentinels to detect ecosystem change. For example, sentinel species which are trophically linked and measured on short timescales may be more effective for managers seeking to monitor ecosystem change than other species. By identifying effective traits for the use of sentinel species, scientists and policymakers will be able to develop rapid and adaptable management plans in response to global change.

Archaeology in the Fourth Dimension: Studying Landscapes with Multitemporal PlanetScope Satellite Data

AbstractFor the last seven years, PlanetScope satellites have started near-daily imaging of parts of the Earth’s surface, making high-density multitemporal, multispectral, 3-m pixel imagery accessible to researchers. Multitemporal satellite data enables landscape archaeologists to examine changes in environmental conditions at time scales ranging from daily to decadal. This kind of temporal resolution can accentuate landscape features on the ground by de-emphasizing non-permanent signatures caused by seasonal or even daily changes in vegetation. We argue that the availability of high spatial and temporal resolution multispectral imagery from Planet Inc. will enable new approaches to studying archaeological visibility in landscapes. While palimpsests are discrete overlapping layers of material accumulation, multitemporal composites capture cyclical and seasonal time and can be used to interpret past landscape histories at multiple scales. To illustrate this perspective, we present three case studies using PlanetScope imagery in tropical environments on the Indian Ocean islands of Madagascar, Mauritius, and Zanzibar.

EcoSonicML: Harnessing Machine Learning for Biodiversity Monitoring in South African Wetlands

Biodiversity monitoring, particularly in a country as diverse as South Africa with its extensive migratory bird population, presents significant challenges. This challenge becomes even more pronounced in environments with a multitude of coexisting bird species, notably wetlands, which serve as crucial breeding and feeding grounds for various avian species. This research will address this challenge by designing a cost-effective sound-based sensor system capable of deployment in diverse wetland ecosystems. The primary aim is to aid in the monitoring of bird species by detecting their presence and distribution and then transmitting this valuable data to a central base station. To assess the system’s feasibility and performance, a series of experiments were conducted at the Rondevlei Nature Reserve in Cape Town, South Africa. These experiments focused on the sensor’s capacity to accurately identify avian species while maintaining robustness in varying environmental conditions. The results yielded promising outcomes, demonstrating the successful identification of bird species. Furthermore, the system exhibited reliability across different weather conditions, positioning it as a viable choice for long-term deployment in wetland environments. Beyond species detection, this project also delved into practical aspects of data transfer and storage efficiency, ensuring the system’s suitability for real-world applications. Modularity was another crucial consideration, simplifying maintenance and upgrades. Moreover, a preliminary cost analysis indicated the cost-effectiveness of the system compared to commercial alternatives. The integration of climate sensors into the monitoring system was explored as a future direction. This addition holds the potential to provide a more comprehensive approach to environmental monitoring by incorporating climate data into the analysis. Such a holistic approach can further enrich our understanding of bird behaviour in relation to changing environmental conditions. The findings of this research have significant implications for avian conservation and ecological studies, particularly in the unique context of South Africa. This project introduces an affordable and practical tool for monitoring bird species in wetland habitats, offering valuable insights into the preservation and management of these critical ecosystems.

From route to dive: multi-scale habitat selection in a foraging tropical seabird

Comprehending how environmental variability shapes foraging behaviour across habitats is key to unlocking insights into consumer ecology. Seabirds breeding at high latitudes are exemplars of how marine consumers can adapt their behaviours to make use of predictable foraging opportunities, but prey tends to be less predictable in tropical oceanic ecosystems and may require alternative foraging behaviours. Here we used GPS and time-depth recorder loggers to investigate the foraging behaviour of central placed adult red-footed boobies (Sula sula rubripes), a tropical seabird that forages in oceanic waters via diving, or by capturing aerial prey such as flying fish in flight. Dive bout dynamics revealed that red-footed boobies appeared to exploit denser, but more sparsely distributed prey patches when diving further from the colony. Furthermore, although we found no evidence of environmentally driven habitat selection along their foraging routes, red-footed boobies preferentially dived in areas with higher sea surface temperatures and chlorophyll-a concentrations compared to conditions along their foraging tracks. This multi-scale variation implies that habitat selection differs between foraging routes compared to dive locations. Finally, red-footed booby dives were deepest during the middle of the day when light penetration was greatest. Ultimately, we highlight the importance of gaining insights into consumer foraging across different ecosystems, thereby broadening understanding of how animals might respond to changing environmental conditions.

Changes in water conservation and possible causes in the Yellow River Basin of China during the recent four decades

As water quality declines and water resources become scarce, water conservation emerges as the central challenge of water resource management and sustainable development in the watershed ecosystem. The Yellow River, known as the “mother river” of China, of which the water conservation function has received significant public attention in the changing environmental conditions. In the study, the InVEST water yield model was utilized to evaluate the spatiotemporal dynamics of water conservation; the MK trend test and Sen’s slope were used to examine the trends of precipitation and water conservation, respectively. Additionally, the response of water conservation to climate, land use, and soil changes during the period 1981–2020 was also discussed. The results showed that under the comprehensive influence of various factors, the water conservation of the YRB showed a decreasing and then increasing trend, which was presumed to be associated with the Grain for Green Project. Water conservation showed an increasing trend in the upstream area of the study area while decreasing in the lower reaches in recent decades. The changes in water conservation function in the YRB resulted from the comprehensive influence of climate factors, soil conditions, and land use types. Besides, woodland and grassland provide the highest water conservation capacity, which initially increased and then decreased with the increase of slope. The spatial analysis emphasized the critical role of water conservation in the Yellow River Basin, particularly highlighting the source area (above Lanzhou Station), the southern tributaries of the Wei River, and the upper reaches of the Yiluo River as significant water conservation zones. These areas should be considered crucial and given priority in regional water resource management and ecological protection efforts. The findings of this study could provide a theoretical foundation for ecological protection and water security in the YRB, which are essential for sustainable development.

Investigating a batch-flow photocatalytic LED system for diclofenac removal in wastewater treatment plants: Assessing the influence of reaction conditions on photocatalytic efficiency

This work introduces an innovative batch-flow photocatalytic LED system aimed at efficiently removing diclofenac from wastewater. The study highlights the novelty of fabricating TiO2-loaded carbon fiber photocatalysts through a hydrothermal process, exhibiting a blue shift in their absorption spectrum and enhancing their photon absorption capability. A crucial design feature is the spectral matching between the LED light source and the photocatalyst, which is critical for achieving high photodegradation efficiency. A significant innovation of our study is the testing of the system under real conditions at an operational sewage treatment plant. Here, the designed system demonstrated high efficacy in degrading diclofenac across various treatment stages, highlighting its robustness and adaptability to changing environmental conditions. The highest degradation efficiency was achieved with sewage from the secondary settling tank, where nearly 90 % efficiency was observed after 6 h of irradiation. Moreover, the use of renewable energy sources emphasizes the system's sustainability, presenting a greener alternative to traditional wastewater treatment methods. The investigation not only showcases the system's effectiveness in diclofenac degradation but also its adaptability and stability under real-world conditions, marking a significant advancement in sustainable wastewater treatment technologies.

Dispersal and connectivity in increasingly extreme climatic conditions.

While climate change has been shown to impact several life-history traits of wild-living animal populations, little is known about its effects on dispersal and connectivity. Here, we capitalize on the highly variable flooding regime of the Okavango Delta to investigate the impacts of changing environmental conditions on the dispersal and connectivity of the endangered African wild dog (Lycaon pictus). Based on remote sensed flood extents observed over 20 years, we derive two extreme flood scenarios: a minimum and a maximum flood extent, representative of very dry and very wet environmental periods. These conditions are akin to those anticipated under increased climatic variability, as it is expected under climate change. Using a movement model parameterized with GPS data from dispersing individuals, we simulate 12,000 individual dispersal trajectories across the ecosystem under both scenarios and investigate patterns of connectivity. Across the entire ecosystem, surface water coverage during maximum flood extent reduces dispersal success (i.e., the propensity of individuals to disperse between adjacent subpopulations) by 12% and increases dispersal durations by 17%. Locally, however, dispersal success diminishes by as much as 78%. Depending on the flood extent, alternative dispersal corridors emerge, some of which in the immediate vicinity of human-dominated landscapes. Notably, under maximum flood extent, the number of dispersing trajectories moving into human-dominated landscapes decreases by 41% at the Okavango Delta's inflow, but increases by 126% at the Delta's distal end. This may drive the amplification of human-wildlife conflict. While predicting the impacts of climate change on environmental conditions on the ground remains challenging, our results highlight that environmental change may have significant consequences for dispersal patterns and connectivity, and ultimately, population viability. Acknowledging and anticipating such impacts will be key to effective conservation strategies and to preserve vital dispersal corridors in light of climate change and other human-related landscape alterations.

Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods

Monitoring tools are needed to maximise living systems' ability to mitigate emissions and adapt to changing environmental conditions. Therefore, it is important to be able to predict the fundamental fluxes in crops, in this case vineyards, such as sensible heat flux (H), latent heat flux (LE) and carbon dioxide flux (CO2), in order to know their capacity to adapt to the environmental effects of climate change. In this study, Linear Regression (LR), Elastic Net (EN) regression, K-Nearest Neighbours (KNN), Gaussian-Process (GP), Decision Tree (TREE) Regression, Random Forest (RF) Regression, XGBoost (XGB) Regression, Support Vector Regression (SVR) and Multi-layer Perceptron (MLP) models have been applied to predict fundamental fluxes of an eddy-covariance station from conventional meteorological parameters. These models reproduced well the estimations of three output parameters from the eddy-covariance station. The performance of each predictive model was evaluated using Root-Mean-Squared Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE) and the coefficient of determination (R2). The findings indicate that for the variable H, the GP model outperformed the SVR and all other models, achieving an R2 value of 0.99. Conversely, the SVR demonstrated superior performance for the variables LE and CO2, with R2 values of 0.96 for both. In summary, these findings suggest that the three models proposed show a robust performance in the prediction of the studied fluxes, underlining their versatility and adaptability to the various environmental conditions of the vineyard.

Naïve and induced tolerance of 15 amphibian populations to three commonly applied insecticides

Human impacts on ecological communities are pervasive and species must either move or adapt to changing environmental conditions. For environments polluted by contaminants, researchers have found hundreds of target pest species evolving increased tolerance, but we have substantially fewer cases of evolved tolerance in non-target species. When species do evolve increased tolerance, inducible tolerance can provide immediate protection and favor the evolution of increased tolerance over generations via genetic assimilation. Using a model larval amphibian (wood frogs, Rana sylvatica), we examined the tolerance of 15 populations from western Pennsylvania and eastern New York (USA), when first exposed to no pesticide or sublethal concentrations and subsequently exposed to lethal concentrations of three common insecticides (carbaryl, chlorpyrifos, and diazinon). We found high variation in naïve tolerance among the populations for all three insecticides. We also discovered that nearly half of the populations exhibited inducible tolerance, though the degree of inducible tolerance (magnitude of tolerance plasticity; MoTP) varied. We observed a cross-tolerance pattern of the populations between chlorpyrifos and diazinon, but no pattern of similar MoTP among the pesticides. With populations combined from two regions, increased tolerance was not associated with proximity to agricultural fields, but there were correlations between proximity to agriculture and MoTP. Collectively, these results suggests that amphibian populations possess a wide range of naïve tolerance to common pesticides, with many also being able to rapidly induce increased tolerance. Future research should examine inducible tolerance in a wide variety of other taxa and contaminants to determine the ubiquity of these responses to anthropogenic factors.

A Facile Solution Phase Synthesis of Peptide Assemblies as Functional Moiety for Antimicrobial and Anticancer Peptides

In recent years, human being are affected by various types of cancer and are highly microbial infected due to changes in environmental conditions and hence, the recovery from this cause of disease using peptide-based drugs as a function of both activities like antimicrobial peptides (AMPs) and anticancer peptides (ACPs). Herein, we report the solution phase peptide synthesis using α-amino acids such as alanine, phenylalanine and lysine. The structural parameters of the polypeptides were investigated by circular dichroism, FT-IR, MALDI-TOF ESI-mass, NMR, etc. Water-soluble hydrophilic-based synthesized polypeptides possess various biological applications throughout, especially antibacterial and anticancer activities–short reaction process, less cost of chemicals, minimum glassware and no hazardous chemicals involved. Multiple species have innate immune systems that contain antimicrobial peptides (AMPs). Most of them have been proved to possess dual action, both as antibacterial and anticancer peptides, making them most promising alternative to conventional compounds used today to treat these infections. Present approach offers unique opportunities for developing highly potent and focused antibacterial and anticancer peptide-based drugs.