Antarctic coastal polynyas are biological hotspots in the Southern Ocean that support the abundance of hightrophic-level predators and are important for carbon cycling in the high-latitude oceans.In this study,we exam...Antarctic coastal polynyas are biological hotspots in the Southern Ocean that support the abundance of hightrophic-level predators and are important for carbon cycling in the high-latitude oceans.In this study,we examined the interannual variation of summertime phytoplankton biomass in the Marguerite Bay polynya(MBP)in the western Antarctic Peninsula area,and linked such variability to the Southern Annular Mode(SAM)that dominated the southern hemisphere extratropical climate variability.Combining satellite data,atmosphere reanalysis products and numerical simulations,we found that the interannual variation of summer chlorophyll-a(Chl-a)concentration in the MBP is significantly and negatively correlated with the spring SAM index,and weakly correlated with the summer SAM index.The negative relation between summer Chl-a and spring SAM is due to weaker spring vertical mixing under a more positive SAM condition,which would inhibit the supply of iron from deep layers into the surface euphotic layer.The negative relation between spring mixing and spring SAM results from greater precipitation rate over the MBP region in positive SAM phase,which leads to lower salinity in the ocean surface layer.The coupled physical-biological mechanisms between SAM and phytoplankton biomass revealed in this study is important for us to predict the future variations of phytoplankton biomasses in Antarctic polynyas under climate change.展开更多
We consider the problem of population estimation using capture-recapture data, where capture probabilities can vary between sampling occasions and behavioural responses. The original model is not identifiable without ...We consider the problem of population estimation using capture-recapture data, where capture probabilities can vary between sampling occasions and behavioural responses. The original model is not identifiable without further restrictions. The novelty of this article is to expand the current research practice by developing a hierarchical Bayesian approach with the assumption that the odds of recapture bears a constant relationship to the odds of initial capture. A real-data example of deer mice population is given to illustrate the proposed method. Three simulation studies are developed to inspect the performance of the proposed Bayesian estimates. Compared with the maximum likelihood estimates discussed in Chao et al. (2000), the hierarchical Bayesian estimate provides reasonably better population estimation with less mean square error;moreover, it is sturdy to underline relationship between the initial and re-capture probabilities. The sensitivity study shows that the proposed Bayesian approach is robust to the choice of hyper-parameters. The third simulation study reveals that both relative bias and relative RMSE approach zero as population size increases. A R-package is developed and used in both data example and simulation.展开更多
基金The Key Research&Development Program of the Ministry of Science and Technology of China under contract No.2022YFC2807601the National Natural Science Foundation of China under contract Nos 41941008 and 41876221+3 种基金the Fund of Shanghai Science and Technology Committee under contract Nos 20230711100 and 21QA1404300the Impact and Response of Antarctic Seas to Climate Change funded by the Chinese Arctic and Antarctic Administration under contract No.IRASCC 1-02-01Bthe National Key Research and Development Program of China under contract No.2019YFC1509102the Shanghai Pilot Program for Basic Research—Shanghai Jiao Tong University under contract No.21TQ1400201。
文摘Antarctic coastal polynyas are biological hotspots in the Southern Ocean that support the abundance of hightrophic-level predators and are important for carbon cycling in the high-latitude oceans.In this study,we examined the interannual variation of summertime phytoplankton biomass in the Marguerite Bay polynya(MBP)in the western Antarctic Peninsula area,and linked such variability to the Southern Annular Mode(SAM)that dominated the southern hemisphere extratropical climate variability.Combining satellite data,atmosphere reanalysis products and numerical simulations,we found that the interannual variation of summer chlorophyll-a(Chl-a)concentration in the MBP is significantly and negatively correlated with the spring SAM index,and weakly correlated with the summer SAM index.The negative relation between summer Chl-a and spring SAM is due to weaker spring vertical mixing under a more positive SAM condition,which would inhibit the supply of iron from deep layers into the surface euphotic layer.The negative relation between spring mixing and spring SAM results from greater precipitation rate over the MBP region in positive SAM phase,which leads to lower salinity in the ocean surface layer.The coupled physical-biological mechanisms between SAM and phytoplankton biomass revealed in this study is important for us to predict the future variations of phytoplankton biomasses in Antarctic polynyas under climate change.
文摘We consider the problem of population estimation using capture-recapture data, where capture probabilities can vary between sampling occasions and behavioural responses. The original model is not identifiable without further restrictions. The novelty of this article is to expand the current research practice by developing a hierarchical Bayesian approach with the assumption that the odds of recapture bears a constant relationship to the odds of initial capture. A real-data example of deer mice population is given to illustrate the proposed method. Three simulation studies are developed to inspect the performance of the proposed Bayesian estimates. Compared with the maximum likelihood estimates discussed in Chao et al. (2000), the hierarchical Bayesian estimate provides reasonably better population estimation with less mean square error;moreover, it is sturdy to underline relationship between the initial and re-capture probabilities. The sensitivity study shows that the proposed Bayesian approach is robust to the choice of hyper-parameters. The third simulation study reveals that both relative bias and relative RMSE approach zero as population size increases. A R-package is developed and used in both data example and simulation.
