Current global warming is particularly pronounced in the Arctic and arthropods are expected to respond rapidly to these changes. Long-term studies of individual arthropod species from the Arctic are, however, virtuall...Current global warming is particularly pronounced in the Arctic and arthropods are expected to respond rapidly to these changes. Long-term studies of individual arthropod species from the Arctic are, however, virtually absent. We examined butterfly specimens collected from yellow pitfall traps over 14 years （1996-2009） at Zackenberg in high-arctic, north-east Greenland. Specimens were previously sorted to the family level. We identified them to the species level and examined long-term species-specific phenological responses to recent summer wanning. Two species were rare in the samples （Polaris fritillary Boloria polaris and Arctic blue Plebejus glandon） and statistical analyses of phenological responses were therefore restricted to the two most abundant species （Arctic fritillary, B. chariclea and Northern clouded yellow Colias hecla）. Our analyses demonstrated a trend towards earlier flight seasons in B. chariclea, but not in C. hecla. The timing of onset, peak and end of the flight season in B. chariclea were closely related to snowmelt, July temperature and their interaction, whereas onset, peak and end of the flight season in C. hecla were only related to timing of snowmelt. The duration of the butterfly flight season was significantly positively related to the temporal overlap with floral resources in both butterfly species. We further demonstrate that yellow pitfall traps are a useful alternative to transect walks for butterfly recording in tundra habitats. More phenological studies of Arctic arthropods should be carded out at the species level and ideally be analysed in context with interacting species to assess how ongoing climate change will affect Arctic biodiversity in the near future [Current Zoology 60 （2）： 243-251, 2014].展开更多
Literate computing environments,such as the Jupyter(i.e.,Jupyter Notebooks,JupyterLab,and JupyterHub),have been widely used in scientific studies;they allow users to interactively develop scientific code,test algorith...Literate computing environments,such as the Jupyter(i.e.,Jupyter Notebooks,JupyterLab,and JupyterHub),have been widely used in scientific studies;they allow users to interactively develop scientific code,test algorithms,and describe the scientific narratives of the experiments in an integrated document.To scale up scientific analyses,many implemented Jupyter environment architectures encapsulate the whole Jupyter notebooks as reproducible units and autoscale them on dedicated remote infrastructures(e.g.,highperformance computing and cloud computing environments).The existing solutions are stl limited in many ways,e.g.,1)the workflow(or pipeline)is implicit in a notebook,and some steps can be generically used by different code and executed in parallel,but because of the tight cell structure,all steps in the Jupyter notebook have to be executed sequentially and lack of the flexibility of reusing the core code fragments,and 2)there are performance bottlenecks that need to improve the parallelism and scalability when handling extensive input data and complex computation.In this work,we focus on how to manage the workflow in a notebook seamlessly.We 1)encapsulate the reusable cells as RESTful services and containerize them as portal components,2)provide a composition tool for describing workflow logic of those reusable components,and 3)automate the execution on remote cloud infrastructure.Empirically,we validate the solution's usability via a use case from the Ecology and Earth Science domain,illustrating the processing of massive Light Detection and Ranging(LiDAR)data.The demonstration and analysis show that our method is feasible,but that it needs further improvement,especially on integrating distributed workflow scheduling,automatic deployment,and execution to develop as a mature approach.展开更多
文摘Current global warming is particularly pronounced in the Arctic and arthropods are expected to respond rapidly to these changes. Long-term studies of individual arthropod species from the Arctic are, however, virtually absent. We examined butterfly specimens collected from yellow pitfall traps over 14 years （1996-2009） at Zackenberg in high-arctic, north-east Greenland. Specimens were previously sorted to the family level. We identified them to the species level and examined long-term species-specific phenological responses to recent summer wanning. Two species were rare in the samples （Polaris fritillary Boloria polaris and Arctic blue Plebejus glandon） and statistical analyses of phenological responses were therefore restricted to the two most abundant species （Arctic fritillary, B. chariclea and Northern clouded yellow Colias hecla）. Our analyses demonstrated a trend towards earlier flight seasons in B. chariclea, but not in C. hecla. The timing of onset, peak and end of the flight season in B. chariclea were closely related to snowmelt, July temperature and their interaction, whereas onset, peak and end of the flight season in C. hecla were only related to timing of snowmelt. The duration of the butterfly flight season was significantly positively related to the temporal overlap with floral resources in both butterfly species. We further demonstrate that yellow pitfall traps are a useful alternative to transect walks for butterfly recording in tundra habitats. More phenological studies of Arctic arthropods should be carded out at the species level and ideally be analysed in context with interacting species to assess how ongoing climate change will affect Arctic biodiversity in the near future [Current Zoology 60 （2）： 243-251, 2014].
基金partially funded by the European Union's Horizon 2020 research and innovation programme by the project CLARIFY under the Marie Sklodowska-Curie grant agreement No 860627by the ARTICONF project grant agreement No 825134+2 种基金by the ENVRI-FAIR project grant agreement No 824068by the BLUECLOUD project grant agreement No 862409by the LifeWatch ERIC.
文摘Literate computing environments,such as the Jupyter(i.e.,Jupyter Notebooks,JupyterLab,and JupyterHub),have been widely used in scientific studies;they allow users to interactively develop scientific code,test algorithms,and describe the scientific narratives of the experiments in an integrated document.To scale up scientific analyses,many implemented Jupyter environment architectures encapsulate the whole Jupyter notebooks as reproducible units and autoscale them on dedicated remote infrastructures(e.g.,highperformance computing and cloud computing environments).The existing solutions are stl limited in many ways,e.g.,1)the workflow(or pipeline)is implicit in a notebook,and some steps can be generically used by different code and executed in parallel,but because of the tight cell structure,all steps in the Jupyter notebook have to be executed sequentially and lack of the flexibility of reusing the core code fragments,and 2)there are performance bottlenecks that need to improve the parallelism and scalability when handling extensive input data and complex computation.In this work,we focus on how to manage the workflow in a notebook seamlessly.We 1)encapsulate the reusable cells as RESTful services and containerize them as portal components,2)provide a composition tool for describing workflow logic of those reusable components,and 3)automate the execution on remote cloud infrastructure.Empirically,we validate the solution's usability via a use case from the Ecology and Earth Science domain,illustrating the processing of massive Light Detection and Ranging(LiDAR)data.The demonstration and analysis show that our method is feasible,but that it needs further improvement,especially on integrating distributed workflow scheduling,automatic deployment,and execution to develop as a mature approach.
