1. Dariyus Z Kabraji MARS 6300 Project
Analysis of Hawaiian Invertebrate Communities With Respect to Seagrass Sediment Cores
Dariyus Z Kabraji
MARS 6300 Project

2. Seagrasses Marine plants from the Alismatales family
Halophila hawaiiana
Halophila decipiens
Marine plants from the Alismatales family
Primary producers in marine ecosystems
They are engineering species, i.e. capable of altering their habitat
They attract a variety of species, offer protection from predators, food for herbivores, support epiphytes
Images obtained from the UH Botany Department

3. Given Data
Seagrass sediment cores collected from eight different locations on Oahu. Core samples were chilled in formalin to preserve the species.
Sediment core types affected by seagrass presence, or absence. Bare sediment selected as control.
Species data: 66 species (quantitative) from 8 locations, 2 seagrass species per location.
Environmental variables: 6 sediment textural groups-presence (categorical) from 8 locations , 2 seagrass species per location.

4. Kaneohe Bay Sandbar
Kaneohe Bay Sampan Channel
Kahala
Maunalua Bay 1
Waikiki
Wailupe
Maunalua Bay 3
Maunalua Bay 2

5. Objective
Characterize the Hawaiian seagrass infaunal invertebrate community
Confirm that the presence of seagrasses affect the abundance and diversity of associated invertebrates by comparing invertebrates within sediment core samples beneath the seagrass species to samples from a bare habitat.
Hypotheses:
Abundance of invertebrate species will vary according to the textural group of the sediment cores.
There will be significant evidence of competition between the worms and crustacean species.

6. Data Matrix Locations: Sp = Kaneohe Bay Sampan Channel
Sp = Kaneohe Bay Sandbar
M1 = Maunalua Bay Niukuki
M2 = Maunalua Bay Paiko
M3 = Maunalua Bay
Kuli’ou’ou beach park
Wk = Waikiki
Wa = Wailupe beach park
KH = Kahala
First attempt: arranged a full matrix with genus and species only.
Suffix to locations: h = Halophila hawaiiana Sediment cores
d = Halophila decipiens Sediment cores
b = bare surfaces

7. Data Summary 1056 cells in main matrix Percent of cells empty = 69.223
Matrix total = E+06
Matrix mean = E+03
Variance of totals of species = E+09
CV of totals of species = %
Skewness Kurtosis
Averages:

8. Data Summary Outliers: 3
ENTITY AVERAGE STANDARD
RANK NAME DISTANCE DEVIATIONS
1 Leptochelia dubia
65 Paragrubia vorax
66 Munna acarina

9. Direct NMS-Texture groups
Compared full species abundance with sediment core consistencies observed in various locations:
STRESS IN RELATION TO DIMENSIONALITY (Number of Axes)
Stress in real data Stress in randomized data
50 run(s) Monte Carlo test, 50 runs
Axes Minimum Mean Maximum Minimum Mean Maximum p

10. Direct NMS-Texture groups
Compared full species abundance with sediment core consistencies observed in various locations:
MANTEL TEST RESULTS: Mantel`s asymptotic approximation method
= r = Standardized Mantel statistic
E+02 = Observed Z (sum of cross products)
E+02 = Expected Z
E+01 = Variance of Z
E+01 = Standard error of Z
E+01 = t
Ho: no relationship between matrices
= p (type I error)

11. Data Transformations
As per the instructions in data manipulation and transformation for count data, a constant (1) can be added so fx = log(0+1)
The logarithmic transformation f(x) is conducted since there are large amounts of zero values and a high degree of variation between the samples.
Outliers: 2
ENTITY AVERAGE STANDARD
RANK NAME DISTANCE DEVIATIONS
1 Leptochelia dubia
2 Armandia intermedia

12. Data Modification
Relativization by maximum conducted on column species
General Relativization conducted as a test run in case there is considerable noise generated by the rarer species in relativization by maximum.
Since the data is not normal, conducting NMDS.

13. Data Modification (General Relativization)
STRESS IN RELATION TO DIMENSIONALITY (Number of Axes)
Stress in real data Stress in randomized data
50 run(s) Monte Carlo test, 999 runs
Axes Minimum Mean Maximum Minimum Mean Maximum p
p = proportion of randomized runs with stress < or = observed stress
i.e., p = (1 + no. permutations <= observed)/(1 + no. permutations)
Conclusion: a 2-dimensional solution is recommended.

14. Phoxichillidiidae sp. A
Species Diversity
H. hawaiiana only
H. decipiens only
Bare sediment only
Amphliochidae sp. B
Oligochaeta sp. B
Cirratulidae sp. A
Elasmopus spp.
Spionidae sp. A
Amphipoda sp. A
Eursiroides diplonyx
Isopoda sp. A
Photis spp.
Smaragdia bryanae
Phoxichillidiidae sp. A
Aspidosiphon spp.
Sipuncula sp. C
Sipuncula sp. B
Halophila hawaiiana did not support greater species biodiversity, even though it is an endemic species

15. Relation between Crustaceans and Worm Species
Split the matrix into crustacean species data and annelid, phoronid, nemerted, sipunculate data.
2 matrices, 33 species each
STRESS IN RELATION TO DIMENSIONALITY (Number of Axes)
Stress in real data Stress in randomized data
50 run(s) Monte Carlo test, 50 runs
Axes Minimum Mean Maximum Minimum Mean Maximum p
p = proportion of randomized runs with stress < or = observed stress
i.e., p = (1 + no. permutations <= observed)/(1 + no. permutations)

16. Relation between Crustaceans and Worm Species
MANTEL TEST RESULTS: Mantel`s asymptotic approximation method
= r = Standardized Mantel statistic
E+02 = Observed Z (sum of cross products)
E+02 = Expected Z
E+00 = Variance of Z
E+00 = Standard error of Z
E+01 = t
Ho: no relationship between matrices
= p (type I error)

17. Further Analysis
Obtain the original datasets of leaf biomass and rhizome biomass.
Conduct correlation studies between them, and ordination with the species data to determine species abundance with respect to seagrass abundance.
Also conduct MRPP with species data against environmental variables.

18. Data source
Matthew Spielman’s Thesis: ‘Benthic Community Structure of Hawaiian Seagrass Habitats’ (2011).