Selecting Indicator Portfolios for Marine Species and Food Webs: A Puget Sound Case Study

Ecosystem-based management (EBM) has emerged as a promising approach for maintaining the benefits humans want and need from the ocean, yet concrete approaches for implementing EBM remain scarce. A key challenge lies in the development of indicators that can provide useful information on ecosystem status and trends, and assess progress towards management goals. In this paper, we describe a generalized framework for the methodical and transparent selection of ecosystem indicators. We apply the framework to the second largest estuary in the United States – Puget Sound, Washington – where one of the most advanced EBM processes is currently underway. Rather than introduce a new method, this paper integrates a variety of familiar approaches into one step-by-step approach that will lead to more consistent and reliable reporting on ecosystem condition. Importantly, we demonstrate how a framework linking indicators to policy goals, as well as a clearly defined indicator evaluation and scoring process, can result in a portfolio of useful and complementary indicators based on the needs of different users (e.g., policy makers and scientists). Although the set of indicators described in this paper is specific to marine species and food webs, we provide a general approach that could be applied to any set of management objectives or ecological system

A trio of gamma-ray burst supernovae: GRB 120729A, GRB 130215A/SN 2013ez, and GRB 130831A/SN 2013fu

We present optical and near-infrared (NIR) photometry for three gamma-ray burst supernovae (GRB-SNe): GRB 120729A, GRB 130215A/SN 2013ez, and GRB 130831A/SN 2013fu. For GRB 130215A/SN 2013ez, we also present optical spectroscopy at t − t0 = 16.1 d, which covers rest-frame 3000–6250 Å. Based on Fe ii λ5169 and Si ii λ6355, our spectrum indicates an unusually low expansion velocity of ~4000–6350 km s-1, the lowest ever measured for a GRB-SN. Additionally, we determined the brightness and shape of each accompanying SN relative to a template supernova (SN 1998bw), which were used to estimate the amount of nickel produced via nucleosynthesis during each explosion. We find that our derived nickel masses are typical of other GRB-SNe, and greater than those of SNe Ibc that are not associated with GRBs. For GRB 130831A/SN 2013fu, we used our well-sampled R-band light curve (LC) to estimate the amount of ejecta mass and the kinetic energy of the SN, finding that these too are similar to other GRB-SNe. For GRB 130215A, we took advantage of contemporaneous optical/NIR observations to construct an optical/NIR bolometric LC of the afterglow. We fit the bolometric LC with the millisecond magnetar model of Zhang & Mészáros (2001, ApJ, 552, L35), which considers dipole radiation as a source of energy injection to the forward shock powering the optical/NIR afterglow. Using this model we derive an initial spin period of P = 12 ms and a magnetic field of B = 1.1 × 1015 G, which are commensurate with those found for proposed magnetar central engines of other long-duration GRBs

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