Software Listing of Author : "Patrick J. Sullivan"

This program is set up to determine the standard error of the density given three choices for n (e.g. the number of transects: n1, n2, n3), a range of the number of bins per transect and the assumed cluster variance. See the section on cluster sampling for more details on this method.

The cluster variance (s2clu) and the standard error of the estimated average number per bin (SE(rho.bar)) may be found as:

where:

Pi is the agregate density in cluster i;
rho.bar is the average number per bin over all clusters;
mi is the number of elements (bins) in cluster i, i = 1, , n;
n is the number of clusters in the simple random sample of clusters (transects); and
m.baris the estimated average number of elements (bins) per cluster (transect), such that

The cluster variance, which is...

  • Platform: WinOther

Many types of noise can be eliminated during data collection or post-processing. Noise removal during data collection is preferable. Noise that cannot be removed will increase Sv measurements and bias in situ TS measurements, leading to errors in fish density or biomass estimates.

Some instruments output noise levels at 1 m depth and this should be recorded. We recommend that authors report the noise levels or the SNR at the deepest depth included in the analyses. Remember that noise levels at 1 m are different if expressed as TSu or Sv data. This is because Sv values include a term for the equivalent beam angle and sampling volume whereas uncompensated target strength (TSu) values do not (compare Equations 3 and 6). Therefore, the difference between TSu and Sv data is depth dependent. Note that TS refer to the target...

  • Platform: WinOther

Ping interval determines the rate at which acoustic energy is sent into the water column. In choosing a ping interval, the goal is to select the fastest rate that will not cause shadow bottoms in the data. To avoid a shadow bottom originating from the "thirdd-deDt bottom return, the minimum ping interval

  • Platform: WinOther

TSNZ and SVNZ vs. Depth Graph is designed as an easy-to-use and small utility that creates a graph based on input values.

Values you need to input are temperature, noise level, frequency and others. TSNZ and SVNZ vs. Depth Graph is developed in the Java programming language and can be run on Windows, Mac OS X, and Linux.


  • Platform: WinOther
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