Mortality estimate of Chinese mystery snail, Bellamya chinensis (Reeve, 1863) in a Nebraska reservoir

BackgroundAccording to the 2014 Surgeon General’s Report (SGR), ‘5.6 million (American) youth currently aged 0–17 years of age will die prematurely of a smoking-related illness.’ Advocates cite this number as evidence that smoking will exact an enormous toll for decades to come. This paper examines whether the projected toll accurately portrays smoking’s likely future burden.MethodsThe SGR estimate, using 2012 state-specific data, can be closely approximated using national data by multiplying the population ages 0–17 by 2012 smoking prevalence among adults ages 18–30, and multiplying that by 32%, the 1996 estimate by the Centers for Disease Control and Prevention of the probability of future smoking-attributable mortality among young adult smokers. Repeating this process using 2018 data estimates the number of future deaths of youth ages 0–17 in 2018. A hypothetical estimate for 2024 assumes continuation of the 2012–2018 smoking prevalence decrease.FindingsBased on 2012 data, the estimated number of youth alive in 2012 who will die prematurely from smoking is 5.31 million. With lower young adult smoking prevalence in 2018, the future smoking-related mortality estimate is 3.66 million. For 2024, the estimate is 2.54 million.ConclusionsThe SGR estimate depended on assumptions that no longer held a few years later. Yet advocates for youth smoking prevention cite it frequently today. Considerations such as this paper’s calculations, decreasing smoking initiation rates, increasing cessation, better screening for and treatment of smoking-related diseases, and smoking’s increasing social unacceptability suggest that smoking’s death toll for today’s youth will be far lower than contemporary estimates. How much lower is virtually impossible to estimate.

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How does seasonal variability in growth, recruitment, and mortality affect the performance of length-based mortality and asymptotic length estimates in aquatic resources?

ICES Journal of Marine Science ◽

10.1093/icesjms/fss163 ◽

2012 ◽

Vol 70 (2) ◽

pp. 329-341 ◽

Cited By ~ 10

Author(s):

Marc Hufnagl ◽

Klaus B. Huebert ◽

Axel Temming

Keyword(s):

Seasonal Variability ◽

Total Mortality ◽

Growth Parameter ◽

Brown Shrimp ◽

Aquatic Resources ◽

Frequency Distributions ◽

Mortality Estimate ◽

Species Specific ◽

Method Bias ◽

General Guidance

Abstract Hufnagl, M., Huebert, K. B., and Temming, A. 2013. How does seasonal variability in growth, recruitment, and mortality affect the performance of length-based mortality and asymptotic length estimates in aquatic resources? – ICES Journal of Marine Science, 70: 329–341. We tested the sensitivity of eight methods for estimating total mortality from size frequencies (modified Wetherall; Powell; Beverton and Holt; Jones and van Zalinge; Hoenig; Ssentongo and Larkin; seasonal and non-seasonal Length Converted Catch Curve) to violations of basic assumptions, such as seasonal growth, mortality, recruitment and variable asymptotic length L∞ or growth parameter K. For each method, bias was estimated by simulating length frequency distributions with different combinations of known L∞, Z and K values, calculating θ (Z/K) and L∞ estimates, and comparing the true input with the estimated output values. Input mortality was generally underestimated by all methods and in 27% of all simulations no method provided estimates within θ ± 1. Spring recruitment especially negatively influenced the mortality estimate. A decision tree was developed that provides general guidance in selecting appropriate methods despite violated assumptions, but species-specific case studies are recommended. An example of a species-specific study is provided for the brown shrimp, Crangon crangon. Despite inherent limitations for all methods, the results illustrate that estimates of θ and Z for brown shrimp can be improved substantially by selecting suitable methods and correcting for observed bias.

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