You can probably use dot plot for this purpose. The dot plot as a representation of a distribution consists of group of data points plotted on a simple scale. Dot plots are used for continuous, quantitative, univariate data. Data points may be labelled if there are few of them. Dot plots are one of the simplest statistical plots, and are suitable for small to moderate sized data sets. They are useful for highlighting clusters and gaps, as well as outliers.

The Hmisc package extends most lattice functions to better incorporate confidence intervals.

Here's an example of how you would use it. Note that we combine the data you want to plot here into a data.frame so we can use a proper formula symtax

mm<-data.frame(a,stdError, fish=names(a))
library(lattice)
library(Hmisc)
Dotplot(fish~Cbind(a, a-stdError, a+stdError), mm,
xlab="mean catch", ylab = "species",las =2)