Identifying factors that influence fish movement is a key step in predicting how populations respond to environmental change. Using mark–recapture (four species) and trap capture (eight species) data, we examined relationships between three attributes of movement and 15 ecological variables. The probability of emigrating from a reach was positively related to intermittency (one species) and body size (one species) and negatively related to distance from the mainstem creek (two species) and habitat complexity (one species). The number of fish moving upstream through traps was positively related to increases in flow (five species), day length (three species), and water temperature (two species); the number moving through downstream traps was positively associated with increases in flow (three species). Distance moved was greater for fish moving through unsuitable reaches (one species). Floods have a pervasive effect on fish movement, and human activities that affect flows will have widespread implications. The importance of other factors varies interspecifically, which may translate into variation in persistence and colonization rates. For example, species that exhibit reach fidelity in complex habitats may increase movement if habitats are homogenized. These species may suffer population declines because of the cost of increased movement and may ultimately be replaced by ecological generalists.

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The sos package provides a means to quickly and flexibly search the help pages of contributed packages, finding functions and datasets in seconds or minutes that could not be found in hours or days by any other means we know. Its findFn function accesses Jonathan Baron's R Site Search database and returns the matches in a data frame of class "findFn", which can be further manipulated by other sos functions to produce, for example, an Excel file that starts with a summary sheet that makes it relatively easy to prioritize alternative packages for further study. As such, it provides a very powerful way to do a literature search for functions and packages relevant to a particular topic of interest and could become virtually mandatory for authors of new packages or papers in publications such as The R Journal and the Journal of Statistical Software.

Velvetleaf has been a major concern of Southern cotton growers, yet information on its competitiveness and seed production in cotton is lacking. Experiments were conducted in 1997 and 1998 at the Central Crops Research Station in Clayton, NC, to evaluate density-dependent effects of velvetleaf interference and seed production dynamics in conventional tillage cotton. Velvetleaf at densities of up to 3.5 plants m−1 of row did not influence cotton height until at least 4 wk after planting. Velvetleaf height increased as plant density increased throughout the season in 1997, but it was not affected until 9 wk after planting in 1998. Because of differences in environmental conditions, velvetleaf and cotton achieved maximum height later in 1998 than in 1997; however, velvetleaf seed production and cotton yields were higher in 1998 than in 1997 regardless of velvetleaf density. Velvetleaf density had no effect on the fresh weight, dry weight, and stem diameter of velvetleaf plants in 1997. But in 1998, all these parameters decreased significantly with increasing velvetleaf density. Velvetleaf seed production in 1998 was nearly twice that in 1997. Averaged over velvetleaf densities, the greatest number of seed were produced between nodes 6 and 20 in 1997 and between nodes 1 and 10 in 1998. In both years, cotton yield loss increased with velvetleaf density. Maximum yield loss was 84% at 3.5 velvetleaf plants m−1 of row. Yield losses of 5 and 10% were caused by 0.2 and 0.4 velvetleaf plants m−1 of row (1,930 and 4,110 plants ha−1), respectively, in 1997 and by 0.03 and 0.08 velvetleaf plants m−1 of row (360 and 850 plants ha−1), respectively, in 1998. To understand better the applicability of these results, we hypothesized that environmental variation caused differences in measured responses between 1997 and 1998. Therefore, kriging methods were used to fit correlations between observed rainfall and growing degree days (GDD) each year at the experiment site. Results based on climate data from 4 yr at 110 sites indicated that inference space was higher for GDD than for rainfall. The conditions observed at the experiment site in 1997 were deemed most appropriate for the recommendations made in the surrounding area.

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The availability of precise data on TV ad consumption fundamentally changes this advertising medium, and allows many techniques developed for analyzing online ads to be adapted for TV. This chapter looks in particular at how results from the emerging field of online ad quality analysis can now be applied to TV.