United States Geological Survey

About: United States Geological Survey is a government organization based out in Reston, Virginia, United States. It is known for research contribution in the topics: Population & Groundwater. The organization has 17899 authors who have published 51097 publications receiving 2479125 citations. The organization is also known as: USGS & US Geological Survey.

Land use alters the resistance and resilience of soil food webs to drought

Abstract: A study shows that soil food webs directly help mitigate the effects of drought on soil nutrients. The fungal-based food webs of grassland were more resistant to bouts of drought than the bacterial-based food webs of intensively managed wheat, and retained more carbon and nitrogen in the soil.

Reproductive disruption in fish downstream from an estrogenic wastewater effluent.

Abstract: To assess the impact of an estrogenic wastewater treatment plant (WWTP) effluent on fish reproduction, white suckers (Catostomus commersoni) were collected from immediately upstream and downstream (effluent site) of the city of Boulder, CO, WWTP outfall. Gonadal intersex, altered sex ratios, reduced gonad size, disrupted ovarian and testicular histopathology, and vitellogenin induction consistent with exposure to estrogenic wastewater contaminants were identified in white suckers downstream from the WWTP outfall and not at the upstream site. The sex ratio was female-biased at the effluent site in both the fall of 2003 and the spring of 2004; the frequency of males at the effluent site (17–21%) was half that of the upstream site (36–46%). Intersex white suckers comprised 18–22% of the population at the effluent site. Intersex fish were not found at the upstream site. Chemical analyses determined that the WWTP effluent contained a complex mixture of endocrine-active chemicals, including 17β-estradiol (E2) 1...

Model averaging and muddled multimodel inferences

Abstract: Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.