Big data opportunities and challenges for assessing multiple stressors across scales in aquatic ecosystems

摘要: Aquatic ecosystems are under threat from multiple stressors, which vary in distribution and intensity across temporal spatial scales. Monitoring assessment of these have historically focussed on collection physical chemical information increasingly include associated observations biological condition. However, ecosystem is often lacking because the scale quality frequently fail to match those available measurements. The advent high-performance computing, coupled with new earth observation platforms, has accelerated adoption molecular remote sensing tools assessment. To assess how emerging science can be applied study stressors a large (ecosystem) facilitate greater integration approaches among different scientific disciplines, workshop was held 10–12 September 2014 at Sydney Institute Marine Sciences, Australia. Here we introduce conceptual framework for assessing using sources big data critique range big-data types that could support models stressors. We define as any set or series data, either so complex, it becomes difficult analyse traditional analysis methods.