Sampling Period Research Articles

Lessons learned during a 12-year monitoring project with the endangered Magdalena River turtle (Podocnemis lewyana): hunting pressure, habitat degradation, and methodological considerations.

Turtle species in the Family Podocnemididae, including the Colombian endemic and critically endangered Magdalena River Turtle Podocnemis lewyana, characteristically present low recapture rates that preclude estimation of population parameters using maximum likelihood modeling. In our 12-year monitoring project with this species, we evaluated changes in relative abundances, proportions of sex/size classes, and individual body sizes and body conditions in a population in four channels in the middle Magdalena River drainage. We also inspected for associations between trends in changes in these variables and differences in hunting pressure and habitat degradation. To inspect for temporal and spatial demographic dynamics, we estimated variation in relative abundances using the Catch Per Unit Effort index, the total number of turtles captured over an entire 5-day sampling period using ten baited funnel traps. Relative abundances and the proportions of sex/size classes were different between sites and years. We found a significant decline in the proportion of females and juveniles over time, along with evidence that the females still present were smaller in body size. Our results support the hypothesis that hunting eliminates adult females from these sites, perhaps also translating into a reduction in recruitment. The lack of evidence of generalized declines in body condition of all size classes suggests that habitat degradation might contribute less to the population declines in this region. Our results also illustrate that even when recapture rates are low, monitoring turtles via standardized trapping may yield insights into the population's conservation status that other relative abundance indices cannot.

Sustained Spread of HIV-1 CRF55_01B in Its Place of Initial Origin: Dynamics and Hotspots.

Shenzhen, a city with a substantial mobile population, was identified as the first discovered region of HIV-1 CRF55_01B and epicenter of its severe epidemic. During the implementation of venue-based behavioral interventions and the "treat-all" policy, discerning the spread patterns and transmission hotspots of CRF55_01B is imperative. In this study, 1,450 partial pol sequences, with demographic information, were collected from all newly diagnosed CRF55_01B infections in Shenzhen from 2008 to 2020. Molecular networks were constructed using the maximum likelihood and time-resolve phylogenies. Transmission rates, effective reproduction numbers (Re) of clusters and viral dispersal were evaluated using Bayesian inference. In total, 526 sequences formed 114 clusters, including seven large clusters. The status and size of clusters were strongly correlated with age, ethnicity, occupation and CD4+ T cell counts. The transmission rates of clusters were significantly higher than the national epidemic estimate. Four large clusters had Re exceeding 1 at the end of sampling period. Immigrants from Guangdong and Hunan, along with local residents, were identified as the transmission hubs, with heterosexual men being the main source and MSM being the main destination. The virus exhibited a high movement frequency from individuals aged 30-49 years toward diverse age groups. This study demonstrated the hidden CRF55_01B transmissions continued despite current combined interventions in Shenzhen, and special at-risk individuals susceptible to infection or transmission were identified, potentially serving as targets for more effective prevention and control of the local epidemic, thereby mitigating cross-regional spread nationwide due to population migration.

Surveillance Metrics and History of the COVID-19 Pandemic in Central Asia: Updated Epidemiological Assessment.

This study updates the COVID-19 pandemic surveillance in Central Asia we conducted during the first year of the pandemic by providing 2 additional years of data for the region. The historical context provided through additional data can inform regional preparedness and early responses to infectious outbreaks of either the SARS-CoV-2 virus or future pathogens in Central Asia. First, we aim to measure whether there was an expansion or contraction in the pandemic in Central Asia when the World Health Organization (WHO) declared the end of the public health emergency for the COVID-19 pandemic on May 5, 2023. Second, we use dynamic and genomic surveillance methods to describe the history of the pandemic in the region and situate the window of the WHO declaration within the broader history. Third, we aim to provide historical context for the course of the pandemic in Central Asia. Traditional surveillance metrics, including counts and rates of COVID-19 transmissions and deaths, and enhanced surveillance indicators, including speed, acceleration, jerk, and persistence, were used to measure shifts in the pandemic. To identify the appearance and duration of variants of concern, we used data on sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID). We used Nextclade nomenclature to collect clade designations from sequences and Pangolin nomenclature for lineage designations of SARS-CoV-2. Finally, we conducted a 1-sided t test to determine whether regional speed was greater than an outbreak threshold of 10. We ran the test iteratively with 6 months of data across the sample period. Speed for the region had remained below the outbreak threshold for 7 months by the time of the WHO declaration. Acceleration and jerk were also low and stable. Although the 1- and 7-day persistence coefficients remained statistically significant, the coefficients were relatively small in magnitude (0.125 and 0.347, respectively). Furthermore, the shift parameters for either of the 2 most recent weeks around May 5, 2023, were both significant and negative, meaning the clustering effect of new COVID-19 cases became even smaller in the 2 weeks around the WHO declaration. From December 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t test of speed equal to 10 became entirely insignificant for the first time in March 2023. Although COVID-19 continues to circulate in Central Asia, the rate of transmission remained well below the threshold of an outbreak for 7 months ahead of the WHO declaration. COVID-19 appeared to be endemic in the region and no longer reached the threshold of a pandemic. Both standard and enhanced surveillance metrics suggest the pandemic had ended by the time of the WHO declaration.

Asymmetric spillovers and resilience in physical and financial assets amid climate policy uncertainties: Evidence from China

Climate policy is essential for fostering sustainable development and addressing climate-related issues, while climate policy uncertainty (CPU) introduces complex challenges in providing a consistent framework for action. In this study, we delve into the spillover dynamics among China's domestic CPU, foreign CPU, physical assets, and financial assets across various market scenarios, through a quantile-based connectedness method and evidence from China. The findings reveal: (1) The spillover dynamics are asymmetric across quantiles and time, and the COVID-19 pandemic influences the total connectedness. (2) The complexity of spillover patterns in physical assets is likely contingent upon market conditions, policy sources, asset types, and industrial-chain positions, while the spillovers of financial assets are quantile-dependent and time-varying, with the agricultural equity market showing relative resilience to the pandemic. (3) There is no significant Granger causality in quantiles between domestic CPU and foreign CPU under the entire sample period, and the rankings regarding the spillovers from CPUs to assets vary across market conditions. Implications for policymakers, investors, and firms are provided.

How does carbon emissions trading scheme affect the investment efficiency of companies? Evidence from China

Carbon emissions trading scheme (ETS) will change the internal and external operating environment of related companies, increase new carbon reduction costs, and may affect their investment behavior and decision-making. This study constructs the policy effect evaluation model to evaluate the net effect of China's ETS pilot policy on the investment efficiency of related companies in high-carbon industries. This study indicates that: first, China's ETS pilot policy has reduced the inefficient investment of related companies by 11.40% during the sample period. Second, innovation ability plays an enhanced moderating effect in the inhibitory effect of China's ETS pilot policy on the inefficient investment of related companies in the short term, but the effect is weakened in the long-term. Third, the policy effect is relatively obvious for non-state-owned companies and companies with more institutional shareholdings. This research has important reference value for the continuous improvement of China's national carbon market, and the steady transformation from simple investment-driven to investment-efficiency-driven economic development in China.

Momentum and reversal strategies with low uncertainty

We propose augmented momentum and reversal trading strategies that incorporate considerations of market entry timing. Market entry timing is determined by two criteria: (1) low uncertainty of the model used to forecast trading return, and (2) a positive forecasted return. Utilizing daily data from the Chinese stock market, we demonstrate that while canonical momentum and reversal trading strategies typically yield negative returns and skewness, our augmented strategies consistently exhibit positive returns and skewness. Additionally, we show that implementation of the two criteria does not significantly diminish trading opportunities, with identified market entry days evenly distributed throughout the sample period.

Assessing the impact of green patents on pollution reduction: A micro-level study of Chinese industrial firms

As global warming and environmental degradation escalate, policymakers worldwide increasingly advocate for the development of green patents. However, there is ongoing debate regarding the effectiveness of green patents in actual pollution reduction. Some studies suggest that firms may engage in green patenting activities to align with government and market expectations rather than achieving substantive breakthroughs in pollution reduction. In light of this, it is crucial to examine the impact of green patents on pollution reduction. This study employs a PSM-DID model to analyze the impact of green patents on pollution emission intensity using plant-level data from Chinese industrial firms. The results demonstrate that with an average increase of 1% in the number of green patents, the industrial waste gas emission intensity and industrial wastewater discharge intensity decreased by 4.74% and 8.68%, respectively. Furthermore, pollution treatment facilities were found to be more effective than green patents during the sample period. On average, the contribution of waste gas treatment facilities and wastewater treatment facilities in pollution reduction is 3.33% and 9.79% higher than that of green patents. These findings suggest that firms should adopt a balanced approach when making decisions on pollution reduction. Further analysis shows that the pollution reduction effect of green patents is partly at the expense of the total factor productivity of firms. This trade-off highlights the need for policy interventions to support firms in integrating green technologies without compromising productivity. This study underscores the emphasizes of transitioning from end-of-pipe pollution control to green production processes to achieve sustainable development in China.

One sampler to see it all: The use of APIStrips for beehive characterization and pesticide residue evaluation based on mass spectrometry

Environmental monitoring is crucial for assessing the overall state of the ecosystems in terms of contaminant impact and chemical landscape. The use of honey bee (Apis mellifera) colonies considerably eases the sampling activities, as honey bees are exposed to a wide range of substances that are transported and accumulated within the beehives. In this work, combining low-resolution and high-resolution mass spectrometry, the APIStrip passive sampler has been employed to evaluate the presence of pesticide residues and the overall characterization of beehive environments. A total of 180 APIStrips have been deployed in 10 Danish apiaries, located in different landscapes, during a five-month sampling period. The targeted methodology for pesticide analysis was based on gas and liquid chromatography coupled with triple quadrupole mass spectrometry, covering 430 pesticide residues. A total of 29 pesticide residues were identified (fluopyram and azoxystrobin being the most frequently detected), with remarkable differences in the pesticide load between apiaries. For its part, the use of non-targeted approaches through liquid chromatography coupled with an Orbitrap mass spectrometer allowed the detection of unknown compounds that were specific of certain environments. Natural products such as eupatilin and gnaphaliin, which are derived from plant sources, were present exclusively in one of the apiaries. Additionally, the detection of drimane sesquiterpenoids, including compounds potentially originating from the Aspergillus genus, suggests the capability of APIStrips to early detect fungal contamination within beehives. This dual approach of low- and high-resolution mass spectrometry maximizes the analytical potential of APIStrips as a tool capable of detecting a wide range of substances with implications for both agricultural practices and ecological health.

Time-varying exchange rate pass-through over 2005–2021 using dynamic model averaging

This study reexamines the time-varying structure of exchange rate pass-through (ERPT) by employing the dynamic model averaging (DMA) method. DMA is a novel empirical methodology that allows both parameters and predictors of the model to change over time, thereby addressing model uncertainty in determining time-varying ERPT degrees. We apply DMA to quarterly time series data spanning from 1996:1–2021:3 for each of the 39 advanced and emerging economies in the sample. The findings reveal a shift in short run ERPT dynamics prompted by the global crisis in 2008-09. While ERPT levels are initially low, they have been rising steadily since the onset of the pandemic across all advanced countries. Conversely, the ERPT levels are approximately twice as high in emerging economies, exhibiting a U-shaped pattern over the sample period.

Assessment of illicit drug use in Seoul, the capital of South Korea for 21 days by wastewater-based epidemiology

22 illicit drugs were monitored in Seoul, the capital of South Korea for 21 days using wastewater-based epidemiology to assess the drug usage patterns for the first time by region and social status. Among the targeted compounds, 10 were detected, with consistent detection of methamphetamine in samples over the entire sampling period. In addition, ketamine had the highest estimated consumption rate at 47.5 mg/day/1000 people, followed by methamphetamine at 12.5 mg/day/1000 people. Methamphetamine and MDMA(3,4-methylenedioxymethamphetamine) exhibited relatively stable daily patterns, with coefficients of variation of 7.03 %, and 13.4 % respectively. Furthermore, no statistically significant differences were observed between weekends and weekdays for all compounds (Mann-Whitney Rank Sum test, p > 0.05). Statistically significant regional differences in drug consumption were observed for methamphetamine, MDMA, and ketamine (Mann-Whitney Rank Sum test, p < 0.05). These differences were found to be related to average annual income and educational levels.

The dynamic impact of network attention on natural resources prices in pre-and post-Russian-Ukrainian war

Natural resources prices have been greatly affected by the Russian-Ukrainian war. Investors also pay special attention to the price of natural gas and oil. This paper introduces the Thermal optimal path (TOP) method and the Long Short-Term Memory (LSTM) model to investigate the dynamic lead-lag relationship between investor attention and natural resources prices for crude oil and natural gas.We find that during the war, the ability of investor attention to lead the prices of crude oil and natural gas become significantly stronger. Adding investor attention to the model significantly improves price prediction in the full sample. In addition, after the sample period is segmented according to the TOP method, the forecasting performance is greatly improved compared to the full sample. These findings indicate that traders and companies can incorporate investor attention into price predictions to further improve their trading strategies.

Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China

Based on the theories of synergetic and evolutionary economics, we build a theoretical model of the co-evolutionary mechanisms of the composite systems of science and technology resources and high-tech industries in China. According to the established index system of the composite systems, we calculated the steady-state solution of the co-evolve and drew the potential function curve of science and technology resources and high-tech industries in China during 2010–2022 using the Haken model. Our empirical results show that high-tech industries are the order parameter of co-evolve. During the sample period, there is a synergistic growth effect of two-way promotion between science and technology resources and high-tech industries in China. However, within the system, the positive feedback mechanism of science and technology resources to high-tech industries in China is not perfect, and the allocation of science and technology resources needs to be further optimized, with a low degree of system order and large room for improvement. In addition, the endowment of science and technology resources and the development degree of high-tech industries in China are different in the eight comprehensive economic zones, and their co-evolvement is different. Therefore, by combining the advantages and allocating the elements of regional science and technology resources, we can better leverage the comparative advantages of high-tech industries in China and improve the degree of co-evolve in the system.

Infection Rates and Characterisation of Rickettsia africae (Rickettsiaceae) Detected in Amblyomma Species from Southern Africa.

Tick-borne rickettsioses are considered among the oldest known vector-borne zoonotic diseases. Among the rickettsiae, Rickettsia africae is the most reported and important in Africa, as it is the aetiological agent of African tick bite fever (ATBF). Studies describing the prevalence of R. africae in southern Africa are fragmented, as they are limited to small geographical areas and focused on Amblyomma hebraeum and Amblyomma variegatum as vectors. Amblyomma spp. ticks were collected in Angola, Mozambique, South Africa, Zambia and Zimbabwe during the sampling period from March 2020 to September 2022. Rickettsia africae was detected using the ompA gene, while characterisation was conducted using omp, ompA, ompB and gltA genes. In total, 7734 Amblyomma spp. ticks were collected and were morphologically and molecularly identified as Amblyomma eburneum, A. hebraeum, Amblyomma pomposum and A. variegatum. Low levels of variability were observed in the phylogenetic analysis of the R. africae concatenated genes. The prevalence of R. africae ranged from 11.7% in South Africa to 35.7% in Zambia. This is one of the largest studies on R. africae prevalence in southern Africa and highlights the need for the inclusion of ATBF as a differential diagnosis when inhabitants and travellers present with flu-like symptoms in the documented countries.

Event-based model predictive control with two-phase predictive detection

This paper proposes an event-based model predictive control for continuous-time systems in the presence of bounded disturbances. A two-phase predictive event detection strategy is proposed. In the first phase, a conservative time interval is calculated based on the worst possible system behavior caused by disturbances. The optimal control sequence is applied during this interval without sampling. After the first phase, periodic samplings and detections with adjustable sampling period are then executed in the second phase until finding the triggering instant. This strategy has the advantages in terms of reducing the sampling frequency and triggering rate. The maximum number of samplings can be adjusted to balance computational cost and sensing cost. Sufficient conditions are provided for recursive feasibility and closed-loop stability.

Recovery of both volatile fatty acids and ammonium from simulated wastewater: Performance of membrane contactor and understanding the effects of osmotic distillation

Membrane Contractor (MC) is a separation method that has had growing interest because of its recovery performance and comparably lower energy consumption. Herein, a two-stage recovery MC system was investigated to recover volatile fatty acids (VFAs) and ammonium from simulated wastewater. The MC achieved the total VFA recovery of 77 % ± 3 %, 82 % ± 5 %, and 74 % ± 8 %, with 0.1, 0.3, and 0.5 M NaOH permeate solutions, respectively. The 0 M NaOH permeate recovered only 38 % ± 2 % of the VFAs due to the osmotic distillation occurring in the opposite direction (permeate to feed) of the VFA transport. Despite the initial pH of the feed solution, osmotic distillation was similar when the permeate was maintained at 0.5 M NaOH. The vapor pressure changes at each sampling period showed high correlation with the water transported (R2=0.958). Ammonium recovery was not significantly different when the pH was maintained while increasing the molarity of the H2SO4 permeate, likely due to the high vapor pressure of ammonia gas. Multi-criteria decision analysis was used to determine the optimal operation conditions for MC operation. The results of this study would encourage further exploration of MC technologies for efficiency recovery of VFA and ammonium from wastewater.

Spatial and Temporal Distribution Characteristics and Cytotoxicity of Atmospheric PM2.5 in the Main Urban Area of Lanzhou City.

PM2.5, as one of the most harmful pollutant in the atmospheric environment and population health, has received much attention. We monitored PM2.5 levels at five sampling sites in the Lanzhou City and collected PM2.5 particles from two representative sites for cytotoxicity experiment. The cytotoxicity of PM2.5 samples on A549 cells and migration ability of the cells were respectively detected by Cell Counting kit-8 (CCK-8) assay and scratch assay. We detected the levels of cellular inflammatory factors and oxidative damage-related biochemical indexes. RT-qPCR was used to detect the mRNA levels of NF-κB and epithelial-mesenchymal transition (EMT)-related genes. We found that the Lanlian Hotel station had the highest PM2.5 annual average concentration. The annual average concentration change curve of PM2.5 showed a roughly "U"-shaped distribution during the whole sampling period. The cytotoxicity experiment showed the viability of A549 cells decreased and the scratch healing rate increased in the 200 and 400μg/mL PM2.5-treated groups. We also found 400μg/mL PM2.5 induced changes in the mRNA levels of NF-κB and EMT-related genes, the mRNA levels of IKK-α, NIK, and NF-κB in the 400μg/mL PM2.5 group were higher than those in the control group. The mRNA levels of E-cadherin decreased and α-SMA increased in the 400μg/mL PM2.5 groups, and the mRNA levels of Fibronectin increased in the 400μg/mL PM2.5 groups. Moreover, we found hydroxyl radical scavenging ability and T-AOC levels were lower, and LPO levels were higher in the 200 and 400μg/mL PM2.5 groups, and the SOD activity of cells in the 400µg/mL PM2.5 group decreased. And compared with the control group, the levels of TNF-α were higher in the 200 and 400μg/mL PM2.5 groups and the levels of IL-1 were higher in the 400μg/mL PM2.5 group. The results indicated that the cytotoxicity of atmospheric PM2.5 was related to oxidative damage, inflammatory response, NF-κB activity and EMT.

Association of microbial community structure with gill disease in marine-stage farmed Atlantic salmon (Salmo salar); a yearlong study

BackgroundUnderstanding the relationship between resident microbiota and disease in cultured fish represents an important and emerging area of study. Marine gill disorders in particular are considered an important challenge to Atlantic salmon (Salmo salar) aquaculture, however relatively little is known regarding the role resident gill microbiota might play in providing protection from or potentiating different gill diseases. Here, 16S rRNA sequencing was used to examine the gill microbiome alongside fish health screening in farmed Atlantic salmon. Results were used to explore the relationship between microbial communities and gill disease.ResultsMicrobial community restructuring was observed throughout the sampling period and linked to varied drivers of change, including environmental conditions and severity of gill pathology. Taxa with significantly greater relative abundance on healthier gills included isolates within genus Shewanella, and taxa within family Procabacteriaceae. In contrast, altered abundance of Candidatus Branchiomonas and Rubritalea spp. were associated with damaged gills. Interestingly, more general changes in community richness and diversity were not associated with altered gill health, and thus not apparently deleterious to fish. Gross and histological gill scoring demonstrated seasonal shifts in gill pathology, with increased severity of gill damage in autumn. Specific infectious causes that contributed to observed pathology within the population included the gill disorder amoebic gill disease (AGD), however due to the uncontrolled nature of this study and likely mixed contribution of various causes of gill disease to observed pathology results do not strongly support an association between the microbial community and specific infectious or non-infectious drivers of gill pathology.ConclusionsResults suggest that the microbial community of farmed Atlantic salmon gills undergo continual restructuring in the marine environment, with mixed influences upon this change including environmental, host, and pathogenic factors. A significant association of specific taxa with different gill health states suggests these taxa might make meaningful indicators of gill health. Further research with more frequent sampling and deliberate manipulation of gills would provide important advancement of knowledge in this area. Overall, although much is still to be learnt regarding what constitutes a healthy or maladapted gill microbial community, the results of this study provide clear advancement of the field, providing new insight into the microbial community structure of gills during an annual production cycle of marine-stage farmed Atlantic salmon.

Return and Volatility Connectedness between Agricultural Tokens and US Equity Sectors

This study investigates the return and volatility interconnectedness between emerging digital assets, specifically agricultural tokens, and U.S. equity sectors using the TVP-VAR model. Analyzing data from August 7, 2020, to January 2, 2024, the findings indicate modest interconnections between agricultural tokens and U.S. sectors, with time-varying behavior. Notably, return interconnectedness is generally stronger than volatility, except at the sample period's outset, where volatility dominates. Return spillovers predominantly drive the connectedness system, though agricultural tokens uniquely act as net recipients of both return and volatility spillovers, while U.S. equity sectors mainly transmit spillovers. Optimal portfolio analysis, utilizing portfolio weights and hedge ratios, reveals that incorporating agricultural tokens offers portfolio diversification benefits and enhances hedging performance. Investors are advised to frequently adjust portfolios to maximize diversification and hedging gains. These findings provide significant portfolio implications for policymakers, market participants, and investors.

Effect of the addition of an ultraviolet B light bulb for 6 months to the enclosure of client-owned cockatiels (Nymphicus hollandicus) on serum 25-hydroxyvitamin D concentrations

BackgroundThe objective of this study was to determine the effects of ultraviolet B (UVB) radiation on the serum concentration of 25-hydroxyvitamin D [25(OH)D] in pet cockatiels (Nymphicus hollandicus) living in their home environments. MethodsThirty client-owned cockatiels were allocated to an intervention group, consisting of exposure to a commercial coil fluorescent bulb, or a control group, with no exposure to supplemental lighting. Blood samples were collected at baseline (Day 0) and 6 months after simple randomization for measurement of 25(OH)D (Day 180). A linear mixed model was used to determine if serum 25(OH)D concentrations were influenced by time, group, age, or sex. ResultsAt baseline, median 25(OH)D concentration for the whole study population was 13nmol/L (range, 0.0-15.0nmol/L). At the end of the study, median 25(OH)D concentration for the whole study population was 6.5 nmol/L (0.0-15.0nmol/L), for the UVB group was 3 nmol/L (0.0-8.0nmol/L) and for the control group was 7.5nmol/L (6.0-11.0nmol/L). There was a statistically significant 50% reduction in 25(OH)D concentrations from the baseline to the final sampling period for the overall population, but no differences by group, age, or sex. Conclusion and clinical relevanceThe addition of a UVB bulb for 6 months to the enclosures of cockatiels did not increase their serum 25(OH)D concentrations. There was an unexplained decrease in both groups when comparing Day 0 to the Day 180 sampling period. The 50% attenuation in the UVB lighting group, although not statistically significant, may be clinically relevant and deserves further investigation.

Some biological aspects of Chacunda gizzard shad, Anodontostoma chacunda (Hamilton, 1822) from the Bay of Bengal, Bangladesh

Anodontostoma chacunda is a commercial fish species in the market and is usually consumed in fresh, smoked, and dried forms. This study investigated the population structure, length-weight relationships (LWR), condition factor (KF), size at sexual maturity (Lm), and breeding season of A. chacunda in the Bay of Bengal, Bangladesh, using the data collected from January to December 2020. A total of 1061 individuals were collected in the sampling period with the help of fishers. Total length (TL) and body weight (BW) were measured using a measuring board and an electronic balance. The TL ranged from 12.5 to 26.7 cm. The allometric coefficient (b) value was 3.34, indicating positive allometric growth in this species. The fish is in good condition, evidenced by its condition factor (KF) ranging between 0.88 and 1.73. Maturity size ranged from 15.40 to 16.80 cm (TL) based on three distinguished models. Spawning time lasts from January to May and from September to December. The peak spawning time was December of A. chacunda in the Bay of Bengal. This information could prove valuable for fisheries researchers and biologists, facilitating efforts toward the conservation and sustainable management of this species.