Distribution, species composition and management implications of seed banks in southern New England coastal plain ponds

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Biological Conservation 142 (2009): 1350-1361, doi:10.1016/j.biocon.2009.01.020.Buried seeds that germinate during periods of low water or water level drawdown can play important roles in shaping plant community composition, community dynamics and species richness in ecosystems with fluctuating water levels. Northeastern US coastal plain ponds have fluctuating water levels and contain a characteristic shoreline flora that contains many rare plants. The objectives of this study were to: (1) test whether geographically distant ponds in Cape Cod and Martha’s Vineyard had distinct seed banks, (2) determine if hydrologic status as permanent and ephemeral ponds led to differences in seed banks, and (3) examine seed diversity and seed abundance across gradients of shoreline elevations and sediment characteristics. Viable seeds of 45 plant species were identified from 9 ponds. Native species dominated pond-shore seed banks and made up 89 to 100% of all species. There was high overlap in seed bank composition across hydrological classes and geographic regions. One hydrological class captured 73-76% of total species and one geographical region captured 69-78% of the total species recovered from the entire suite of seed bank samples. Seeds were relatively evenly distributed along the shorelines of ephemeral ponds but seed diversity and abundance were lower at low elevations in permanent ponds. Results suggest that strategies to protect pond shorelines to capture maximum diversity of coastal plain pond plants contained in pond sediment seed banks should be implemented across pond hydrologic classes and across a wide geographic area. Shoreline seed distributions indicate that ground-water withdrawals or climate changes that lower pond water levels in permanent ponds will reduce the diversity and abundance of plants recovered from seed banks by shifting water levels to a shoreline zone of high sediment organic matter where seed densities are lower. This effect will be much less in ephemeral ponds where seed diversity and abundance on pond bottoms was high.This study was funded by the Massachusetts Environmental Trust and the Barnstable Water Company

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages