ALBERT

Description: Citizen science and informed citizens have become fundamental in providing the first records and accounts about the expansion of numerous non-indigenous species. However, implementing a successful citizen science campaign can be expensive and particularly difficult for aquatic species. Here, we demonstrate how a low-cost citizen science campaign and its outreach plan in social and traditional media enabled to track the expansion of the Atlantic blue crab Callinectes sapidus Rathbun, 1896 along the coast of Algarve (southern Portugal, Europe). We describe the outreach strategy and a cost-benefit analysis of the first year of the citizen science campaign. Social media platforms allowed us to reach a significant number of citizens (over 31,500 clicks in Facebook publications), while traditional media gave national visibility to the citizen science campaign and biological invasions. In only 1 year, we documented the spread of the invasive Atlantic blue crab across the entire 140 km of the Algarve coast with 166 valid observations referring to 1747 specimens, submitted by 62 citizen scientists. We spent 0 € on the citizen science campaign, but considering the time invested in the campaign the cost would have summed up to 3,751 €, while the total minimum cost for one scientist to go to the field and retrieve the equivalent information would have exceeded 11,000 €. We used free online tools of communication to obtain the records about the Atlantic blue crab, instead of a dedicated web platform or mobile app, and handled social media accounts ourselves, which saved us at least 18,815 €. The citizen science campaign revealed that the Atlantic blue crab is unequivocally established in southern Portugal and that females appear to exhibit summer migrations to coastal areas to spawn as in the native area. Overall, our low-cost citizen science campaign effectively documented the rapid spread of a marine invasive species while providing some insights into its ecology. Our strategy can be easily replicated and implemented elsewhere in the world to tackle the ever-growing problem of biological invasions while increasing the scientific literacy of local populations.

Description: In the domain of globalization, the global value chain (GVC) is formed across diverse economies. Owing to the expansion of economic globalization and the resultant global trade liberalization, the geographical isolation of production and consumption has resulted in the transfer of polluting emissions from advanced economies to developing states. Developing economies, falling in the middle and low end of the GVC, have to strive hard to expand their production in the GVC, while simultaneously addressing the consequent environmental damage and attending to the core environmental concerns of sustainable growth, energy preservation and drastic cuts in CO2. This paper addresses how manufacturing subsidiaries can improve their low-carbon innovation ability and help to achieve a sustainable competitive advantage through the embedded GVC. Data were gathered employing interview and survey methods, 31 manufacturing companies and 56 enterprise groups across 16 Chinese provinces were selected as the case study. A partial least square structural equation modelling was adopted to analyse the responses. The results indicate that the embedded GVC significantly enhances low-carbon innovation capability and promotes a sustainable competitive advantage. This study proposes that companies should continuously identify high-quality resources from the GVC and discover ways of integrating internal and external carbon innovation resources to form innovation capabilities.

Description: Sustainable development remains unattainable unless we move to reduce the negative impact of economic factors on environmental quality. It is noteworthy to provide new evidence on whether and how the empirical association between export diversification, agricultural value-addition, renewable energy, and regulatory quality with greenhouse gas (GHG) emissions evolved in Asian countries from 1996 to 2014. The study examines the relationships between these variables using current panel data techniques. The econometric procedure includes second-generation cointegration and unit root tests together with a novel Method of Movements Quantile Regression (MMQR). This approach offers an asymmetric relationship between the variables and is very robust to outliers compared to traditional quantile regression. The empirical outcomes show that export diversification, renewable energy, and regulatory quality are significantly and negatively associated with GHG emissions. In contrast, agricultural value-added in Asia has become a source of increased GHG emissions. Our findings are also robust with alternate specifications, including fully modified, dynamic and fixed effect regressions. This study will help policymakers for diversifying their export portfolio while ensuring a sustainable environment in Asia.

Description: In recent decades, degradation and loss of the world’s forest ecosystems have been key contributors to biodiversity loss and future climate change. This article analyzes plant diversity, biomass, carbon sequestration potential (CSP), and the net primary productivity (NPP) of four vegetation types viz., Dense mixed forest (DMF); Open mixed forest (OMF); Teak plantation (TP), and Sal mixed forest (SMF) in the dry tropical forest ecosystem of central India through remote sensing techniques together with physical ground observations during 2013–2018. The total C storage in trees varied from 16.02 to 47.15 Mg ha−1 in studied vegetation types with the highest in DMF and lowest in OMF. The total C storage in stem wood, branches, and foliage falls in the range of 52.93–78.30%, 9.49–22.99%, and 3.31–12.89% respectively. The total standing biomass varied from 83.77 to 111.21 Mg ha−1 and these variations are due to different vegetation types, with the highest in DMF followed by TP, SMF while the lowest was estimated in OMF. The net primary productivity (NPP) [aboveground (AG) + belowground (BG)] varied from 7.61 to 9.94 Mg ha−1 yr−1 with mean values of 8.74 Mg ha−1 yr−1 where AG shares a maximum contribution of 77.66%. The total biomass production was distributed from 64.09 to 82.91% in AG and 17.08–35.91% in BG components. The present study outlines that the studied forest ecosystem has the substantial potential of carbon sequestration and a great possibility of mitigating local and global climate change.

Description: Climate change and human activities are having increasing impacts on the global water cycle, particularly on streamflow. Current methods for quantifying these impacts are numerous and have their merits and limitations. There is a lack of a guide to help researchers select one or more appropriate methods for attribution analysis. In this study, hydrological modeling, statistical analysis, and conceptual approaches were used jointly to develop a methodological options framework consisting of three modules, to guide researchers in selecting appropriate methods and assessing climatic and anthropogenic contributions to streamflow changes. To evaluate its effectiveness, a case study in the Upper Yangtze River Basin (UYRB) of China was conducted. The results suggest that the SWAT-based method is the best approach to quantify the influences of climate change and human activities on streamflow in the UYRB. The comprehensive assessment indicates that climate change is the dominant cause of streamflow changes in the UYRB, and the contribution of climate change, indirect human activities, and direct human activities to streamflow changes is about 7:1:2. The proposed framework is efficient and valuable in assisting researchers to find appropriate methods for attribution analysis of streamflow changes, which can help to understand the water cycle in changing environments.

Description: Urbanization and seasonality strongly influence the bacterial composition of the soil. However, aquatic environments such as rivers are understudied owing to their high dynamics and therefore rules relating to more static habitats such as lentic or terrestrial environments may be limited. Here, we compared the spatiotemporal patterns of bacterioplankton communities in the Zhangxi river along a gradient of urbanization using 16S ribosomal DNA identification. The alpha and beta diversity of bacterioplankton showed no significant response to watershed urbanization. A significant difference in predicted functional profiles of the bacterioplankton community was also revealed between the wet and dry seasons. The bacterioplankton community assembly was driven by both deterministic and stochastic processes. Stochasticity was one of the most vital processes affecting the bacterioplankton communities in both wet and dry seasons, explaining over 50% variation in the community by the null model analysis. Bacterioplankton co-occurrence patterns in the river changed with the seasons. More notably, the composition of bacterioplankton communities was inconsistent with alternations of the spatial distance offering meaningful implications for interactions between zero-radius operational taxonomic units and the dynamics of the bacterioplankton communities in surface water. In summary, we found clear patterns of seasonal variations in the bacterioplankton community structures.

Description: Antibiotics resistance genes (ARGs) are mainly caused by the extensive use and abuse of antibiotics and have become a global public health concern. Owing to the development of high-throughput sequencing, metagenomic sequencing has been widely applied to profile the composition of ARGs, investigate their distribution pattern, and track their sources in diverse environments. However, the lack of a detailed transmission mechanism of ARGs limits the management of its pollution. Hence, it’s essential to introduce how to utilize the metagenomic data to obtain an in-depth understanding of the distribution pattern and transmission of ARGs. This review provides an assessment of metagenomic data utilization in ARG studies and summarizes current bioinformatic tools and databases, including ARGs-OAP, ARG analyzer, DeepARG, CARD, and SARG, for profiling the composition of ARGs and tracking the source of ARGs. Several bioinformatic tools and databases were then benchmarked. Our results showed that although SARG is a good database, the application of two or more bioinformatic tools and databases could provide a comprehensive view of ARG profiles in diverse environmental samples. Finally, several perspectives were proposed for future studies to obtain an in-depth understanding of ARGs based on metagenomic data. Our review of the utilization of metagenomic data together with bioinformatic tools and databases in ARG studies could provide insights on exploring the profiles and transmission mechanism of ARG in different environments that mitigate the spread of ARGs and manage the ARGs pollution.

Description: This study attempts to analyze the time-varying pattern between the exchange rates, stock market return, temperature, and number of confirmed COVID-19 cases in G7 countries caused by the COVID-19 pandemic. We have implemented our analysis using wavelet coherence and partial wavelet coherence (PWC) on independent variables from January 4, 2021 to July 31, 2021. This paper contributes to the earlier work on the same subject by employing wavelet coherence to analyze the effect of the sudden upsurge of the COVID-19 pandemic on exchange rates, stock market returns, and temperature to sustain and improve previous results regarding correlation analysis between the above-mentioned variables. We arrived at the following results: 1) temperature levels and confirmed COVID-19 cases are cyclical indicating daily temperatures have a material bearing on propagating the novel coronavirus in G7 nations; 2) noteworthy correlations at truncated frequencies show that a material long-term impact has been observed on exchange rates and stock market returns of G7 and confirmed COVID-19 cases; 3) accounting for impact of temperature and equity market returns, a more robust co-movement is observed between the exchange rate returns of the respective nations and the surge in COVID-19 cases; and 4) accounting for the influence of temperature and exchange rate returns and the increase in the confirmed number of coronavirus-infected cases and equity returns, co-movements are more pronounced. Besides academic contributions, this paper offers insight for policymakers and investment managers alike in their attempt to navigate the impediments created by the coronavirus in their already arduous task of shaping the economy and predicting stock market patterns.

Description: Sustainable development and reducing environmental pressure are major issues that concern developed as well as developing countries. Although researchers widely use carbon dioxide emissions and ecological footprint within the scope of environmental degradation, a more comprehensive ecological indicator is needed to assess environmental sustainability. In this context, the load capacity factor enables a comprehensive environmental sustainability assessment through the simultaneous analysis of biocapacity and ecological footprint. However, there are few studies analyzing the determinants of load capacity factor and this study aims to fill this gap for Indonesia. Using the recently developed Fourier quantile causality test, this study investigates the impact of income, export diversification, non-renewable and renewable energy consumption on the load capacity factor for Indonesia during 1965Q1–2014Q4. The results show unidirectional causality from non-renewable energy consumption to the load capacity factor at all quantiles, while income, export diversification, and renewable energy are the causes of environmental quality at middle and higher quantiles (within 0.5, 0.7, and 0.9). Most importantly, renewable energy and export diversification increase the load capacity factor and thus support environmental quality. In contrast, an increase in income and consumption of non-renewable energy reduces the load capacity factor. These results highlight the importance of renewable energy and export diversification for the sustainable development of Indonesia.

Description: Invasive species pose one of the greatest threats to global biodiversity. Early detection of invasive species is critical in order to prevent or manage their spread before they exceed the ability of land management groups to control them. Optical remote sensing has been established as a useful technology for the early detection and mapping of invasive vegetation populations. Through the use of airborne hyperspectral imagery (HSI), this study establishes a target detection methodology used to identify and map the invasive reed Phragmites australis subsp. australis within the entire extent of Îles-de-Boucherville National Park (Quebec, ON, Canada). We applied the Spectral Angle Mapper (SAM) target detection algorithm trained with a high accuracy GNSS ground truth data set to produce a park-wide map illustrating the extent of detected Phragmites. The total coverage of detected Phragmites was 26.74 ha (0.267 km2), which represents 3.28% of the total park area of 814 ha (8.14 km2). The inherent spatial uncertainty of the airborne HSI (∼2.25 m) was accounted for with uncertainty buffers, which, when included in the measurement of detected Phragmites, lead to a total area of 59.17 ha (0.591 km2), or 7.26% of the park. The overall accuracy of the Phragmites map was 84.28%, with a sensitivity of 76.32% and a specificity of 91.57%. Additionally, visual interpretation of the validation ground truth dataset was performed by 10 individuals, in order to compare their performance to that of the target detection algorithm. The overall accuracy of the visual interpretation was lower than the target detection (i.e., 69.18%, with a sensitivity of 59.21% and a specificity of 78.31%). Overall, this study is one of the first to utilize airborne HSI and target detection to map the extent of Phragmites over a moderately large extent. The uses and limitations of such an approach are established, and the methodology described here in detail could be adapted for future remote sensing studies of Phragmites or other vegetation species, native or invasive, at study sites around the world.