Chapter 5 — benthic infauna

2011 Mugu Lagoon Benthic Infauna Report                                                                                                 9 

MBC Applied Environmental Sciences, 3000 Red Hill Avenue, Costa Mesa, CA 92626 [phone]

 

Utah State University Western Center of Monitoring and Assessment of Freshwater Ecosystems. 

2009. 

http://wmc2.bnr.usu.edu:8080/examples/servlets/LoginSession.html. 

Accessed 

February 23, 2009. 

 

VCWPD. See Ventura County Watershed Protection District 

 

Ventura County Watershed Protection District. 2011. 2005-2011 annual rainfall totals rain gauge 

station 017C, Port of Hueneme- Oxnard Sewer Plant. Historic Hydrologic Data Webpage: 

http://www.vcwatershed.net/hydrodata/php/rain_year.php?wy=2006&order=site_id&forma

t=html. Downloaded, 31 October 2011.  

 

 

 

 

 

 

 

 

 

 

 

 

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                                                                                                             Appendix A 

Analysis Methods 

 

 

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Appendix A. Analysis Methods 

 

 

Summary statistics developed from the biological data included the number of individuals, 

number of species, and Shannon-Wiener (Shannon and Weaver 1962) species diversity (H') 

index. The diversity equation is as follows: 

 

Shannon-Wiener 

 

  

 

where: 

H’ 

= 

species 

diversity 

 

 

 

 

n

j

  = 

number of individuals in the j

th

 species 

 

 

 

 

S  = 

total number of species 

 

 

  N 

= 

number 

of 

individuals 

 

Cluster Analysis  

 

 

Infauna data were subjected to log transformations (when necessary) and classified 

(clustered) using NCSS 2000 Hierarchial Clustering (Hintze 1998). Cluster analysis provides a 

graphic representation of the relationship between species, their individual abundance, and 

spatial occurrence among the stations sampled. In theory, if physical conditions were identical at 

all stations, the biological community would be expected to be identical as well. In practice this is 

never the case, but it is expected that the characteristics of adjacent stations would be more 

similar than those distant from one another. The dendrogram shows graphically the degree of 

similarity (and dissimilarity) between observed characteristics and the expected average. The 

two-way analysis utilized in this study illustrates groupings of species and stations, as well as 

their relative abundance, expressed as a percent of the overall mean. Two classification analyses 

are performed on each set; in one (normal analysis) the sites are grouped on the basis of the 

species which occurred in each, and in the other (inverse analysis) the species are grouped 

according to their distribution among the sites. Each analysis involves three steps. The first is the 

calculation of an inter-entity distance (dissimilarity) matrix using Euclidean distance (Clifford and 

Stephenson 1975) as the measure of dissimilarity. 

 

 Euclidean 

Distance: 

 

 

 

 

where:  D  = 

Euclidean distance between two entities 

 

 

 

 

x

1

  = 

score for one entity 

 

 

 

 

x

2

  = 

score for other entity 

 

 

  n 

= 

number 

of 

attributes 

 

 

The second procedure, referred to as sorting, clusters the entities into a dendrogram 

based on their dissimilarity. The group average sorting strategy is used in construction of the 

dendrogram (Boesch 1977). In step three, the dendrograms from both the site and species 

classifications are combined into a two-way coincidence table. The relative abundance values of 

each species are replaced by symbols (Smith 1976) and entered into the table. In the event of 

extreme high abundance of a single species, abundance data are transformed using a natural log 

transformation [ln(x)]. 

 

 

(

)

Dx

x

=

−

⎣⎢⎢

⎦⎥⎥

∑

1

2

1

 

′ = −

=

∑

H

n

N

s

n

N

j

j

j

1

ln

(

)

D

x

x

n

=

−

⎡

⎣

⎢

⎢

⎤

⎦

⎥

⎥

∑

1

2

1

1 2

/

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