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The population and importance of camels in human life have improved in recent years. As genetics and genomics are becoming a more comprehensive section of life sciences, studying genetic/genomic aspects of the camelid nutrition, milk production, reproduction, immunity, disease and racing ability has become common in recent years. This study was conducted based on text mining and scientometrics techniques. To employ relevant information from Web of Science (WoS), a search strategy was developed to retrieve the “genetics or genomics” and words/phrases related to “camelids”. The statistical population of this study included 3830 publications over a period of 50 years (1971–2020). A total of 3830 publications were retrieved that included 3269 research articles (85.35% of the publications) and 224 review articles (5.85% of the publications). The most frequent subject groups were “Veterinary Sciences” including 862 publications. In total, articles on camelid genomics were published in 1345 journals. Moreover, 127 countries contributed to these 3830 publications, with the USA being the leading country both in number of publications and international collaboration. During the 1971–2010 time period, the phrases “MERS-CoV” and “coronavirus” did not exist in the literature at all, while in the last decade, with 140 and 63 times (5.36% and 2.41%, respectively), they were the most frequent keywords. In general, the most important topics studied from the perspective of camelid genomics have been population genetics and pathogens and their diagnosis, as well as camelid immunity. Future studies should pay special attention to the specificity of camelid genomics for hosting the coronavirus. Furthermore, the special structure of humoral immunity in camels makes this section attractive in immunogenetics research.

The population and importance of camels in human life have improved in recent years. As genetics and genomics are becoming a more comprehensive section of life sciences, studying genetic/genomic aspects of the camelid nutrition, milk production, reproduction, immunity, disease and racing ability has become common in recent years. This study was conducted based on text mining and scientometrics techniques. To employ relevant information from Web of Science (WoS), a search strategy was developed to retrieve the “genetics or genomics” and words/phrases related to “camelids”. The statistical population of this study included 3830 publications over a period of 50 years (1971–2020). A total of 3830 publications were retrieved that included 3269 research articles (85.35% of the publications) and 224 review articles (5.85% of the publications). The most frequent subject groups were “Veterinary Sciences” including 862 publications. In total, articles on camelid genomics were published in 1345 journals. Moreover, 127 countries contributed to these 3830 publications, with the USA being the leading country both in number of publications and international collaboration. During the 1971–2010 time period, the phrases “MERS-CoV” and “coronavirus” did not exist in the literature at all, while in the last decade, with 140 and 63 times (5.36% and 2.41%, respectively), they were the most frequent keywords. In general, the most important topics studied from the perspective of camelid genomics have been population genetics and pathogens and their diagnosis, as well as camelid immunity. Future studies should pay special attention to the specificity of camelid genomics for hosting the coronavirus. Furthermore, the special structure of humoral immunity in camels makes this section attractive in immunogenetics research.

Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian generalised surplus production models will be used in order to estimate the stock status of fourteen Demersal fish species targeted by small-scale fisheries in Sistan and Baluchestan, Iran. The model was estimated using Markov chain Monte Carlo (MCMC) and Gibbs Sampling. Model parameter estimates were evaluated by the formal convergence and stationarity diagnostic tests, indicating convergence and accuracy. They were also aligned with existing parameter estimates for fourteen species of the other locations. This suggests model reliability and demonstrates the utility of Bayesian models. According to estimated fisheries' management reference points, all assessed fish stocks appear to be overfished. Overfishing considered, the current fisheries management strategies for the small-scale fisheries may need some adjustments to warrant the long-term viability of the fisheries.

Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian generalised surplus production models will be used in order to estimate the stock status of fourteen Demersal fish species targeted by small-scale fisheries in Sistan and Baluchestan, Iran. The model was estimated using Markov chain Monte Carlo (MCMC) and Gibbs Sampling. Model parameter estimates were evaluated by the formal convergence and stationarity diagnostic tests, indicating convergence and accuracy. They were also aligned with existing parameter estimates for fourteen species of the other locations. This suggests model reliability and demonstrates the utility of Bayesian models. According to estimated fisheries' management reference points, all assessed fish stocks appear to be overfished. Overfishing considered, the current fisheries management strategies for the small-scale fisheries may need some adjustments to warrant the long-term viability of the fisheries.

Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian generalised surplus production models will be used in order to estimate the stock status of fourteen Demersal fish species targeted by small-scale fisheries in Sistan and Baluchestan, Iran. The model was estimated using Markov chain Monte Carlo (MCMC) and Gibbs Sampling. Model parameter estimates were evaluated by the formal convergence and stationarity diagnostic tests, indicating convergence and accuracy. They were also aligned with existing parameter estimates for fourteen species of the other locations. This suggests model reliability and demonstrates the utility of Bayesian models. According to estimated fisheries' management reference points, all assessed fish stocks appear to be overfished. Overfishing considered, the current fisheries management strategies for the small-scale fisheries may need some adjustments to warrant the long-term viability of the fisheries.