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  • Marine ecology practice 2: Eelgrass bed communities

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  • Summary

    • 　A landscaped community dominated by large plants (seaweeds and seagrasses) that inhabit the intertidal to subtidal zones of the sea is called a "seagrass bed". Among seaweed beds, those dominated by seed plants (such as eelgrass) are called "eelgrass beds". Eelgrass beds are inhabited by a wide variety of animals, and their abundance is large. Each animal species uses eelgrass beds for various purposes such as habitat, feeding, shelter, and spawning grounds, resulting in complex interactions among species.

      　In this practice, eelgrass bed animals will be sampled in the eelgrass beds of Lake Akkeshi and Akkeshi Bay, which have different environments, using the same method. By measuring and comparing their biomass and species diversity, we will understand how the eelgrass bed communities are formed and how they fluctuate.

  • Survey

    • 【What to prepare】

      Outdoors: shovel, large sieve (1 mm mesh), plastic bags (Ziplock), carrying bucket (Toslon), field book (preferably water-resistant), sled net, cooler with ice, box glasses, insect repellent spray, insect repellent net

      Laboratory: trays, small sieves, tweezers, petri dishes, filter paper

      Data analysis: calculator, laptop computer

       

      【Field survey】

      1. In the morning, we will go to the eelgrass beds on the east and north shores of Akkeshi Bay and the north and east shores of Lake Akkeshi by small boats (outboard motors) and cars. We will be divided into 4 groups, each group will take a boat and collect animals in the eelgrass beds by sled net.

      2. While waiting on the boat, we will observe eelgrass species on the tidal flat portion of the eelgrass bed. A detailed explanation will be given at the site.

       

      【Indoor work】

      3. After returning to the laboratory, sorting is done to separate organisms into taxonomic groups and count the number of individuals. Identification of taxonomic groups will be made to the point where they can be recognized by external morphology, using pictorial books or other sources.

      4. Based on the data obtained, we will statistically analyze the variation and similarity of the species diversity of animals in the eelgrass bed (see documents below).

      5. If you have time to spare, dissect and examine the stomach contents of typical fish and shrimp species. Based on this, I will try to draw a diagram of the food web in the eelgrass beds.

      6. Based on these results, we will discuss what factors influence and determine the community structure and food web of eelgrass beds.

  • Analysis

    • 【Species diversity analysis methods】

      In this case, we will seek the following variables and compare them between locations in different environments.

      １：Species richness: total number of species occurring in one sample.

      ２：Diversity index: Use Simpson's diversity index

      ３：Relative priority curve

      ４：Analysis of community similarity

    •  【Diversity Analysis】

      　Even if a community has the same number of species, the relative proportion of individuals of each species (evenness) will give a different impression of the community's richness. The diversity index is an indicator of community diversity based on two factors: "species richness" and "evenness of species composition", calculated from the number of species and the number of individuals of each species. Two commonly used indexes are shown below.
       

      Shannon-Weiner Diversity Index

       

      Simpson's Diversity Index

       

      In both cases, S is the number of species and pi is the percentage of the total population that is occupied by species i (relative dominance).

    • A numerical example is shown below. It can be seen that even though the number of species is the same, the diversity index differs due to differences in evenness.

      Figure 1

    • 【Analysis of community similarity】

      The similarity index is a continuous expression of the difference between two communities, with 100% for the case where the species composition and the number of individuals of each species occurring in the two communities are exactly the same and 0% for the case where they are completely different (not a single common species). Although many different methods have been developed, we will use the Bray-Curtis similarity index shown in the equation below.


      Sjk: The similarity between the jth sample and the kth sample

      Yij: Number of individuals of the ith species in the jth sample (present and coverage)

      The similarity index is a value obtained between pairs of every two communities, so for a data set consisting of n samples, a similarity of n(n-1)/2 is obtained. The representation of this in the form of a match table is called a similarity matrix.

      Many methods have been developed to visually represent similarity relationships between communities based on similarity matrices. There are two main types of methods: 1) clustering (cluster analysis), which classifies (divides) clusters according to the magnitude of similarity, and 2) ordination, which plots the differences in similarity between clusters in a 2-dimensional (3-dimensional) space as the difference in distance. Although knowledge of multivariate statistical analysis is necessary to understand the characteristics, advantages, and disadvantages of each method, here we will classify communities by cluster analysis, which is relatively simple. Cluster analysis arranges the locations of communities so that more similar communities are located closer together. There are many ways to do this, but in this article, we will introduce the linkage method using group-average linkage, which can be done by hand calculation.

    • Figure 3

  • Actual example (data in table below)

    • １：In the original 4x4 similarity matrix, sample 2 and 4 have the highest similarity [S(2,4)=68.1%], so we first concatenate these two

      ２：Create a 3x3 similarity matrix combining data from 2 and 4 S(1,2&4)=[S(1,2)+S(1,4)]/2=38.9%, S(3,2&4)=[S(3,2)+S(3,4)]/2=55.0%

      ３：Since S(3,2&4) is maximum in the 3x3 group matrix, we then concatenate these two

      ４：Finally, concatenate 1 and 2&3&4 S(1,2&3&4)=[S(1,2) + S(1,3) + S(1,4)]/3=25.9%

      Data Tables for Community Structure

      Figure 4

      Similarity matrix (before concatenation)

      Figure 5

      Similarity matrix (after concatenating 2&4)

      Figure 6

      Similarity matrix (after concatenating 2&3&4)

      Figure 7


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