Limited recovery following a massive seagrass decline in subarctic eastern Canada.

Over the last few decades, there has been an increasing recognition for seagrasses' contribution to the functioning of nearshore ecosystems and climate change mitigation. Nevertheless, seagrass ecosystems have been deteriorating globally at an accelerating rate during recent decades. In 2017, research into the condition of eelgrass (Zostera marina) along the eastern coast of James Bay, Canada, was initiated in response to reports of eelgrass decline by the Cree First Nations of Eeyou Istchee. As part of this research, we compiled and analyzed two decades of eelgrass cover data and three decades of eelgrass monitoring data (biomass and density) to detect changes and assess possible environmental drivers. We detected a major decline in eelgrass condition between 1995 and 1999, which encompassed the entire east coast of James Bay. Surveys conducted in 2019 and 2020 indicated limited changes post-decline, for example, low eelgrass cover (<25%), low aboveground biomass, smaller shoots than before 1995, and marginally low densities persisted at most sites. Overall, the synthesized datasets show a 40% loss of eelgrass meadows with >50% cover in eastern James Bay since 1995, representing the largest scale eelgrass decline documented in eastern Canada since the massive die-off event that occurred in the 1930s along the North Atlantic coast. Using biomass data collected since 1982, but geographically limited to the sector of the coast near the regulated La Grande River, generalized additive modeling revealed eelgrass meadows are affected by local sea surface temperature, early ice breakup, and higher summer freshwater discharge. Our results caution against assuming subarctic seagrass ecosystems have avoided recent global declines or will benefit from ongoing climate warming.

A call for an ethical framework when using social media data for artificial intelligence applications in public health research.

Advancements in artificial intelligence (AI), more precisely the subfield of machine learning, and their applications to open-source internet data, such as social media, are growing faster than the management of ethical issues for use in society. An ethical framework helps scientists and policy makers consider ethics in their fields of practice, legitimize their work and protect members of the data-generating public. A central question for advancing the ethical framework is whether or not Tweets, Facebook posts and other open-source social media data generated by the public represent a human or not. The objective of this paper is to highlight ethical issues that the public health sector will be or is already confronting when using social media data in practice. The issues include informed consent, privacy, anonymization and balancing these issues with the benefits of using social media data for the common good. Current ethical frameworks need to provide guidance for addressing issues arising from the use of social media data in the public health sector. Discussions in this area should occur while the application of open-source data is still relatively new, and they should also keep pace as other problems arise from ongoing technological change.