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Full-Text Articles in Life Sciences

Escherichia Coli O157:H7 In Free-Ranging Deer In Nebraska, David G. Renter, Jan M. Sargeant, Scott E. Hygnstrom, Jeff D. Hoffmann, Jerry R. Gillespie Jan 2001

Escherichia Coli O157:H7 In Free-Ranging Deer In Nebraska, David G. Renter, Jan M. Sargeant, Scott E. Hygnstrom, Jeff D. Hoffmann, Jerry R. Gillespie

School of Natural Resources: Faculty Publications

In order to determine the prevalence and distribution of the human pathogen, Escherichia coli O157:H7, in free-ranging deer, hunters were asked to collect and submit fecal samples from deer harvested during a regular firearm season (14–22 November 1998). Prior to the season, 47% of the hunters with permits in the southeastern Nebraska (USA) study area indicated a willingness to participate in the study. Approximately 25% of successful hunters in the area submitted deer fecal samples. Escherichia coli O157:H7 was cultured from four (0.25%) of 1,608 total samples submitted. All of the fecal samples that were properly identified (1,426) and all …


Toxicant Impacts On Density-Limited Populations: A Critical Review Of Theory, Practice, And Results, Valery E. Forbes, Richard M. Sibly, Peter Calow Jan 2001

Toxicant Impacts On Density-Limited Populations: A Critical Review Of Theory, Practice, And Results, Valery E. Forbes, Richard M. Sibly, Peter Calow

Valery Forbes Publications

Most natural populations experience some density dependence, and longterm average rates of population growth are likely to be close to zero (i.e., steady state). An essential question, therefore, is how and to what extent do density-dependent effects influence the responses of populations to toxicant impacts? Here we consider three general types of interaction between density dependence and toxicant effects: additive, less than additive, and more than additive. If we know enough about the life-history dynamics of an organism and how its life-history traits are affected by density and toxicant exposure, we should be able to use life-history models to predict …