1. Stranding Patterns in Stenella spp.
Carissa Cabrera

2. Objective
The goal of this project is to determine which variable influences strandings most significantly in 3 species of Stenella dolphins in the MHI from
Age
Season of stranding
Island of stranding
Sex
Hypotheses & Predictions
Ho: no parameters influence stranding patterns
Ha: at least 1 parameter influences patterns
Season?
Methods: NMDS
Spinner dolphin (Stenella longirostris)
Striped dolphin (Stenella coerualba)
Spotted dolphin (Stenella attenuata)

3. Dataset Description 3 species of stenella: 45 strandings
spinner (s. longirostris)
striped (s. coerualba)
spotted (s. attenuata)
45 strandings
in the MHI
Variables
Age (calf, yearling, subadult, adult)
Sex (male, female)
Island (Big Island, Oahu, Maui, Kauai)
Season (Winter, Spring, Summer Fall)
M1:
frequencies of strandings by species
16 samples:
Combinations of island & season
24 species:
Combinations of species + age + sex
LSAF: longirostris, subadult, female
CAF: coerualba, adult, female
ACM: attenuata, calf, male
M2: categorical by island (Big Island = 1, Maui = 2, Oahu = 3, Kauai = 4) categorical by season (winter = 1, spring = 2, summer = 3, fall = 4)

4. Dataset Processing Number of species discarded: Final sample size
Species without known answers to the variables in question  eliminated Lanai
Empty rows: Maui_Spring, Maui_Fall, Kauai_Spring, Kauai_Fall
Empty columns: LYM, CCM, CCF, CYM, CYF, ACM, ACF, AYM, AYF, ASAF, AAF
No outliers
General Relativization by columns p = 1: since columns totals were different & I’m using Sorensen distance measure
Final sample size
12 samples
12 species
2 environmental variables

5. Dataset Exploration
Uncorrelated environmental variables because they are categorical
Islands:
Big island = 1
Maui = 2
Oahu = 3
Kauai = 4
Season:
Winter = 1
Spring = 2
Summer = 3
Fall = 4
Skewness = 2+ with 75% empty cells
Transformations are unable to resolve this, even log transform
I want to explore patterns in the data
Best to use a nonparametric ordination  NMDS

6. Dataset Analysis: Setup
Data already relativized
community data, 5 variables

7. Results Interpretation: Number of Axes
Criterion 1: stress reduced by at least 5 per axis Criterion 2: p value < 0.05
Final stress: with 5 dimensions
Clarks Rule of Thumb:
<5 is ideal

8. Results Interpretation: Scree Plot
Axis 1 & 2 & 3 insignificant because they fall within simulated data
Axis 4 & 5 significant, falls below randomized data

9. Results Interpretation
Coefficient of determination  axis 1 explains the most
Orthogonality: axes are independent
41.0% of the variance is explained by 3 axes
Axes 4? 5?

10. Results Interpretation
Axis 3: island gradient?
Oahu high
Maui middle
Kauai & Big Island lower
Axis 3: season gradient?
Fall higher end
Winter lower end
Axis 1 & 3
Can’t overlay environmental variables with species because they aren’t quantitative
Problematic
Stress value <1 so conclusions can be drawn from the ordination, but contradicting criteria results imply caution
Winter = 1
Spring = 2
Summer = 3
Fall = 4
Big island = 1
Maui = 2
Oahu = 3
Kauai = 4

11. Results Interpretation: strongest relationships
Adult female spinner dolphins exhibit a strong relationship with axis 3
Tau = 0.545
Strong negative relationship with axis 1 for adult male spinner dolphins
Tau =
Islands seem to be grouped together.
Exception: 1 big island sample?

12. Discussion: the method
1. What do these results mean for the hypotheses/predictions you proposed?
Using p values, I would accept Ho
Using minimum stress, I would say there are significant patterns in the data, but not what was predicted
Not season  may be island?
2. What did this exercise teach you regarding your overarching analysis methods and objective?
Emphasis of ordination as data exploration rather than hypothesis testing
Despite stress values and axes significance, I would not feel comfortable drawing definite or even broad conclusions about my data

13. Discussion: next steps
What do you propose to do for your re-analysis?
MRPP to evaluate whether strandings by groups of seasons are distinctly different from one another
This is common in other species of marine mammals like California Sea Lions
What would be the next steps for this study
Quantitative environmental variables would create a more useful ordination plot to confirm if species vary by environmental variables
Coordinates? Latitude
A way to make stranding date quantitative? Date  proportion of year?