clus.str.lf: Statistical Comparison of Length Frequencies from Stratified...

In fishmethods: Fishery Science Methods and Models

Statistical Comparison of Length Frequencies from Stratified Random Cluster Sampling

Description

Statistical comparison of length frequencies is performed using the two-sample Kolmogorov & Smirnov test. Randomization procedures are used to derive the null probability distribution.

Usage

clus.str.lf(group = NULL, strata = NULL, weights = NULL,
 haul = NULL, len = NULL, number = NULL, binsize = NULL,
 resamples = 100)

Arguments

group	vector containing the identifier used for group membership of length data. This variable is used to determine the number of groups and comparisons. Identifier can be numeric or character.
strata	vector containing the numeric identifier used for strata membership of length data. There must be a unique identifier for each stratum regardless of group membership.
weights	vector containing the strata weights (e.g., area, size, etc.) used to calculate the stratified mean length for a group.
haul	vector containing the variable used to identify the sampling unit (e.g., haul) of length data. Identifier can be numeric or character.
len	vector containing the length class. Each length class record must have associated group, strata, weights, and haul identifiers.
number	vector containing the number of fish in each length class.
binsize	size of the length class (e.g., 5-cm, 10, cm, etc.) used to construct the cumulative length frequency from raw length data. The formula used to create bins is trunc(len/binsize)*binsize+binsize/2. If use of the raw length classes is desired, then binsize=0.
resamples	number of randomizations. Default = 100.

Details

Length frequency distributions of fishes are commonly tested for differences among groups (e.g., regions, sexes, etc.) using a two-sample Kolmogov-Smirnov test (K-S). Like most statistical tests, the K-S test requires that observations are collected at random and are independent of each other to satisfy assumptions. These basic assumptions are violated when gears (e.g., trawls, haul seines, gillnets, etc.) are used to sample fish because individuals are collected in clusters . In this case, the "haul", not the individual fish, is the primary sampling unit and statistical comparisons must take this into account.

To test for difference between length frequency distributions from stratified random cluster sampling, a randomization test that uses "hauls" as the primary sampling unit can be used to generate the null probability distribution. In a randomization test, an observed test statistic is compared to an empirical probability density distribution of a test statistic under the null hypothesis of no difference. The observed test statistic used here is the Kolmogorov-Smirnov statistic (Ds) under a two-tailed test:

Ds= max|S1(X)-S2(X)|

where S1(X) and S2(X) are the observed cumulative proportions at length for group 1 and group 2 in the paired comparisons.

Proportion of fish of length class j in strata-set (group variable) used to derive Ds is calculated as

p_j=\frac{\sum{A_k\bar{X}}_{jk}}{\sum{A_k\bar{X}}_k}

where A_k is the weight of stratum k, \bar{X}_{jk} is the mean number per haul of length class j in stratum k, and \bar{X}_k is the mean number per haul in stratum k. The numerator and denominator are summed over all k. Before calculation of cumulative proportions, the length class distributions for each group are corrected for missing lengths and are constructed so that the range and intervals of each distribution match.

It is assumed all fish caught are measured. If subsampling occurs, the numbers at length (measured) must be expanded to the total caught.

To generate the empirical probability density function (pdf), length data of hauls from all strata are pooled and then hauls are randomly assigned without replacement to each stratum with haul sizes equal to the original number of stratum hauls. Cumulative proportions are then calculated as described above. The K-S statistic is calculated from the cumulative length frequency distributions of the two groups of randomized data. The randomization procedure is repeated resamples times to obtain the pdf of D. To estimate the significance of Ds, the proportion of all randomized D values that were greater than or equal to Ds is calculated (Manly, 1997).

Data vectors described in arguments should be aggregated so that each record contains the number of fish in each length class by group, strata, weights, and haul identifier. For example,

group	stratum	weights	tow	length	number
North	10	88	1	10	2
North	10	88	1	12	5
North	10	88	2	11	3
North	11	103	1	10	17
North	11	103	2	14	21
.	.	.	.	.	.
.	.	.	.	.	.
South	31	43	1	12	34
South	31	43	1	14	3

To correctly calculate the stratified mean number per haul, zero tows must be included in the dataset. To designate records for zero tows, fill the length class and number at length with zeros. The first line in the following table shows the appropriate coding for zero tows:

group	stratum	weights	tow	length	number
North	10	88	1	0	0
North	10	88	2	11	3
North	11	103	1	10	17
North	11	103	2	14	21
.	.	.	.	.	.
.	.	.	.	.	.
South	31	43	1	12	34
South	31	43	1	14	3

Value

results	list element containing the Ds statistics from the observed data comparisons and significance probabilities.
obs_prop	list element containing the cumulative proportions from each group.
Drandom	list element containing the D statistics from randomization for each comparison.

Author(s)

Gary A. Nelson, Massachusetts Division of Marine Fisheries [e-mail]

References

Manly, B. F. J. 1997. Randomization, Bootstrap and Monte Carlos Methods in Biology. Chapman and Hall, New York, NY, 399 pp.

Seigel, S. 1956. Nonparametric Statistics for Behavioral Sciences. McGraw-Hill, New York, NY. 312 p.

See Also

clus.lf

Examples

data(codstrcluslen)
clus.str.lf(
group=codstrcluslen$region,strata=codstrcluslen$stratum,
 weights=codstrcluslen$weights,haul=codstrcluslen$tow,
 len=codstrcluslen$length,number=codstrcluslen$number,
 binsize=5,resamples=100)

fishmethods documentation built on April 27, 2023, 9:10 a.m.