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Despite the remarkable expansion of laboratory studies, robust estimates of single species CO 2 sensitivities remain largely elusive. We conducted a meta-analysis of 20 CO 2 exposure experiments conducted over 6 years on offspring of wild Atlantic silversides ( Menidia menidia ) to robustly constrain CO 2 effects on early life survival and growth. We conclude that early stages of this species are generally tolerant to CO 2 levels of approximately 2000 µatm, likely because they already experience these conditions on diel to seasonal timescales. Still, high CO 2 conditions measurably reduced fitness in this species by significantly decreasing average embryo survival (−9%) and embryo+larval survival (−13%). Survival traits had much larger coefficients of variation (greater than 30%) than larval length or growth (3–11%). CO 2 sensitivities varied seasonally and were highest at the beginning and end of the species' spawning season (April–July), likely due to the combined effects of transgenerational plasticity and maternal provisioning. Our analyses suggest that serial experimentation is a powerful, yet underused tool for robustly estimating small but true CO 2 effects in fish early life stages. 

Abstract 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. 

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.