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Drivers and rates of stock assessments in the United States.

ABSTRACT: Fisheries management is most effective when based on scientific estimates of sustainable fishing rates. While some simple approaches allow estimation of harvest limits, more data-intensive stock assessments are generally required to evaluate the stock's biomass and fishing rates relative to sustainable levels. Here we evaluate how stock characteristics relate to the rate of new assessments in the United States. Using a statistical model based on time-to-event analysis and 569 coastal marine fish and invertebrate stocks landed in commercial fisheries, we quantify the impact of region, habitat, life-history, and economic factors on the annual probability of being assessed. Although the majority of landings come from assessed stocks in all regions, less than half of the regionally-landed species currently have been assessed. As expected, our time-to-event model identified landed tonnage and ex-vessel price as the dominant factors determining increased rates of new assessments. However, we also found that after controlling for landings and price, there has been a consistent bias towards assessing larger-bodied species. A number of vulnerable groups such as rockfishes (Scorpaeniformes) and groundsharks (Carcharhiniformes) have a relatively high annual probability of being assessed after controlling for their relatively small tonnage and low price. Due to relatively low landed tonnage and price of species that are currently unassessed, our model suggests that the number of assessed stocks will increase more slowly in future decades.

SUBMITTER: Neubauer P

PROVIDER: S-EPMC5947900 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Drivers and rates of stock assessments in the United States.

Neubauer Philipp P Thorson James T JT Melnychuk Michael C MC Methot Richard R Blackhart Kristan K

PloS one 20180511 5

Fisheries management is most effective when based on scientific estimates of sustainable fishing rates. While some simple approaches allow estimation of harvest limits, more data-intensive stock assessments are generally required to evaluate the stock's biomass and fishing rates relative to sustainable levels. Here we evaluate how stock characteristics relate to the rate of new assessments in the United States. Using a statistical model based on time-to-event analysis and 569 coastal marine fish ...[more]

PMID: 29750789

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Project description:Prompt bystander cardiopulmonary resuscitation (CPR) improves the likelihood of surviving an out-of-hospital cardiac arrest. Large regional variations in survival after an out-of-hospital cardiac arrest have been noted.To determine whether regional variations in county-level rates of CPR training exist across the United States and the factors associated with low rates in US counties.We used a cross-sectional ecologic study design to analyze county-level rates of CPR training in all US counties from July 1, 2010, through June 30, 2011. We used CPR training data from the American Heart Association, the American Red Cross, and the Health & Safety Institute. Using multivariable logistic regression models, we examined the association of annual rates of adult CPR training of citizens by these 3 organizations (categorized as tertiles) with a county's geographic, population, and health care characteristics.Completion of CPR training.Rate of CPR training measured as CPR course completion cards distributed and CPR training products sold by the American Heart Association, persons trained in CPR by the American Red Cross, and product sales data from the Health & Safety Institute. RESULTS During the study period, 13.1 million persons in 3143 US counties received CPR training. Rates of county training ranged from 0.00% to less than 1.29% (median, 0.51%) in the lower tertile, 1.29% to 4.07% (median, 2.39%) in the middle tertile, and greater than 4.07% or greater (median, 6.81%) in the upper tertile. Counties with rates of CPR training in the lower tertile were more likely to have a higher proportion of rural areas (adjusted odds ratio, 1.12 [95% CI, 1.10-1.15] per 5-percentage point [PP] change), higher proportions of black (1.09 [1.06-1.13] per 5-PP change) and Hispanic (1.06 [1.02-1.11] per 5-PP change) residents, a lower median household income (1.18 [1.04-1.34] per $10 000 decrease), and a higher median age (1.28 [1.04-1.58] per 10-year change). Counties in the South, Midwest, and West were more likely to have rates of CPR training in the lower tertile compared with the Northeast (adjusted odds ratios, 7.78 [95% CI, 3.66-16.53], 5.56 [2.63-11.75], and 5.39 [2.48-11.72], respectively).Annual rates of US CPR training are low and vary widely across communities. Counties located in the South, those with higher proportions of rural areas and of black and Hispanic residents, and those with lower median household incomes have lower rates of CPR training than their counterparts. These data contribute to known geographic disparities in survival of cardiac arrest and offer opportunities for future community interventions.

Project description:BackgroundForests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine.ResultsDuring the study period (2000-2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change.ConclusionsLand use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.

Project description:Importance:US life expectancy has not kept pace with that of other wealthy countries and is now decreasing. Objective:To examine vital statistics and review the history of changes in US life expectancy and increasing mortality rates; and to identify potential contributing factors, drawing insights from current literature and an analysis of state-level trends. Evidence:Life expectancy data for 1959-2016 and cause-specific mortality rates for 1999-2017 were obtained from the US Mortality Database and CDC WONDER, respectively. The analysis focused on midlife deaths (ages 25-64 years), stratified by sex, race/ethnicity, socioeconomic status, and geography (including the 50 states). Published research from January 1990 through August 2019 that examined relevant mortality trends and potential contributory factors was examined. Findings:Between 1959 and 2016, US life expectancy increased from 69.9 years to 78.9 years but declined for 3 consecutive years after 2014. The recent decrease in US life expectancy culminated a period of increasing cause-specific mortality among adults aged 25 to 64 years that began in the 1990s, ultimately producing an increase in all-cause mortality that began in 2010. During 2010-2017, midlife all-cause mortality rates increased from 328.5 deaths/100 000 to 348.2 deaths/100 000. By 2014, midlife mortality was increasing across all racial groups, caused by drug overdoses, alcohol abuse, suicides, and a diverse list of organ system diseases. The largest relative increases in midlife mortality rates occurred in New England (New Hampshire, 23.3%; Maine, 20.7%; Vermont, 19.9%) and the Ohio Valley (West Virginia, 23.0%; Ohio, 21.6%; Indiana, 14.8%; Kentucky, 14.7%). The increase in midlife mortality during 2010-2017 was associated with an estimated 33 307 excess US deaths, 32.8% of which occurred in 4 Ohio Valley states. Conclusions and Relevance:US life expectancy increased for most of the past 60 years, but the rate of increase slowed over time and life expectancy decreased after 2014. A major contributor has been an increase in mortality from specific causes (eg, drug overdoses, suicides, organ system diseases) among young and middle-aged adults of all racial groups, with an onset as early as the 1990s and with the largest relative increases occurring in the Ohio Valley and New England. The implications for public health and the economy are substantial, making it vital to understand the underlying causes.

Dataset Information

Drivers and rates of stock assessments in the United States.

Publications

Drivers and rates of stock assessments in the United States.

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