Search

Robust analyses of noisy, stage-structured, irregularly spaced, field-scale data incorporating multiple sources of variability and nonlinear dynamics remain very limited, hindering understanding of how small-scale studies relate to large-scale population dynamics. We used a novel, complementary Bayesian and frequentist state-space model analysis to ask how density, temperature, plant nitrogen, and predators affect cotton aphid (Aphis gossypii) population dynamics in weekly data from 18 field-years and whether estimated effects are consistent with small-scale studies. We found clear roles of density and temperature but not of plant nitrogen or predators, for which Bayesian and frequentist evidence differed. However, overall predictability of field-scale dynamics remained low. This study demonstrates, stage-structured state-space model analysis incorporating bottom-up, top-down, and density-dependent effects for within-season (nearly continuous time), nonlinear population dynamics. The analysis combines Bayesian posterior evidence with maximum-likelihood estimation and frequentist hypothesis testing using average one-step-ahead residuals. [ABSTRACT FROM AUTHOR]

The article focuses on the study that investigates the combined effects of multiple plant parasites on plant performance in the U.S. According to the author, the effects of multiple plant parasites can either be additive or non-additive. The study cites two statistical methods which are usable for testing additive effects including the natural history of the plant-parasite system. The study determined that no evidence has been found for nonlinearities in the relationship between cumulative parasite effect to plant performance. It is suggested that understanding of plant performance is important in plant-parasite interactions.

Background: To estimate changes in the cost and utilization of Medicare among beneficiaries over age 65 who have been impacted by a natural disaster, we merged publically available county-level Medicare claims for the years 2008-2012 with Federal Emergency Management Agency (FEMA) data related to disasters in each U.S. County from 2007 to 2012.Methods: Fixed-effects generalized linear models were used to calculate change in per capita costs standardized by region and utilization per 1000 beneficiaries at the county level. Aggregate county demographic characteristics of Medicare participants were included as predictors of change in county-level utilization and cost. FEMA data was used to determine counties that experienced no, some, high, and extreme hazard exposure. FEMA data was merged with claims data to create a balanced panel dataset from 2008 to 2012.Results: In general, both cost and utilization of Medicare services were higher in counties with more hazard exposure. However, utilization of home health services was lower in counties with more hazard exposure.Conclusions: Additional research using individual-level data is needed to address limitations and determine the impacts of the substitution of services (e.g., inpatient rehabilitation for home health) that may be occurring in disaster affected areas during the post-disaster period. [ABSTRACT FROM AUTHOR]