Quantifying Diving Data from - PowerPoint Presentation

1. Quantifying Diving Data from Christmas Shearwaters (CHSH)Ilana Nimz: Mars 6300Dataset: Time-depth-recorder (TDR) raw data 1. Date2. Time of dive (H:M:S, recorded every second underwater)3. Pressure (dBar, every second underwater)Collected from 8 CHSH over 33 tag-days

2. Objective: Quantify Diving of Christmas Shearwaters (CHSH)Hypothesis:CHSH are diving exclusively during daylight hours (civil twilight a.m. to civil twilight p.m.)Prediction: CHSH dive more frequently in the late afternoon and evening, prior to civil twilight Approach: Summarize dive profiles from TDR raw data Standardize time across 3 months of tagging (Jun-Aug) by dividing 24hrs into 4 time blocks 1) Twilight + 3.5 hrs, 2) Middle of Day, 3) Twilight -3.5 hrs, 4) NightStart by exploring correlations between depth measurements and dive frequency via ordination

3. Dataset DescriptionMain_CHSHsummary.wk1 (main matrix) 125 samples & 5 variables Samples: Bird-Tag Day-Time Block (Twilight, Morning, Daytime, Evening) Variables: Dives per Hour: # dives/time-block hrs Maximum Depth: meters Average Maximum Depth: meters Median Maximum Depth: meters %CV Max Depth: metersSecond_CHSHgroups.wk1 (Second matrix)Samples:Bird-Tag Day- Time BlockGrouping Variables:Time: Morning 1, Daytime 2, Evening 3, Twilight 4Bird: Bird # (1-8)

4. Dataset Processing Outliers: “No… outliers given cutoff of 2.0 SD from the grand mean”Empty samples: 500 cells in main matrix; % empty =   32Samples discarded: All night samples were empty- 26 discardedSome morning samples empty- 12 discardedData transformations / relativizations:-Attempted to normalize w/ log transform- skew and kurtosis reduced but not normal range for all variables, so Non-parametric route!- Using Median to describe non-parametric data: removed average depth column -Give variables the same weight General Relativization (will relativize during ordination)Describe your sample size: Sample total= 85: 19 Morning, 33 Mid-day, 33 Evening

5. Dataset ExplorationWeaker correlation with dives/hrand Depth: Max, Med, CVTau= 0.605, 0.4271, 0.604 Max depth & Median depth Strongest Positive Correlation Tau= 0.6951Weakest correlation between Med max and CV Tau = 0.3567Identifying correlations:Dives/hr and Depth metrics*Data not normal, so used Kendall Tau correlation0.60 0.43 0.600.70 0.60 0.36

6. Settings used in the analysisDistance: Rel SorensenRandomizations:999Dataset AnalysisNMS_DiveMaxAvgMedCV_RelSor_999                                                  Ordination of plots    in metrics  space.         85 plots           4 metrics          The following options were selected:ANALYSIS OPTIONS         1. REL.SOREN. = Distance measure         2.          6 = Number of axes (max. = 6)         3.        250 = Maximum number of iterations         4.     RANDOM = Starting coordinates (random or from file)         5.          1 = Reduction in dimensionality at each cycle         6. NO PENALTY = Tie handling (Strategy 1 does not penalize                         ties with unequal ordination distance,                         while strategy 2 does penalize.)         7.       0.20 = Step length (rate of movement toward minimum stress)         8.   USE TIME = Random number seeds (use time vs. user-supplied)         9.         50 = Number of runs with real data        10.        999 = Number of runs with randomized data        11.         NO = Autopilot        12.   0.000010 = Stability criterion, standard deviations in stress                         over last 200 iterations.OUTPUT OPTIONS        14.        YES = Write distance matrix?        15.         NO = Write starting coordinates?        16.         NO = List stress, etc. for each iteration?        17.        YES = Plot stress vs. iteration?        18.        YES = Plot distance vs. dissimilarity?        19.        YES = Write final configuration?        20.  UNROTATED = Write varimax-rotated, principal axes, or unrotated scores for graph?        21.        YES = Write run log?        22.        YES = Write weighted-average scores for metrics ?------------------------------------------------------------------------------      1500 = Seed for random number generator.

7. Results Interpretation1 significant axis 2.07 = final stress for 1-dimensional solution *Excellent!(lower stress with fewer “species”)Minimum stress real data > Minimum randomized stressp-value = 0.024  (23+1 / 999+1)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-------------------------------------------------------------------- 1 2.070 26.608 57.040 0.000 23.970 57.052 0.0240 2 1.145 1.442 1.683 0.000 1.975 40.936 0.2280 3 0.764 0.946 1.296 0.008 1.104 2.161 0.1840 4 0.706 0.834 1.089 0.012 1.030 2.065 0.1320 5 0.645 0.810 1.086 0.039 0.986 1.635 0.1020 6 0.651 0.825 1.166 0.055 0.955 2.308 0.0950--------------------------------------------------------------------p = proportion of randomized runs with stress < or = observed stressi.e., p = (1 + no. permutations <= observed)/(1 + no. permutations)Conclusion: a 1-dimensional solution is recommended.

8. Results Interpretation Scree PlotStress very low at 1st axis- real dataHigh variability ofRandomized stressIn 1st axis Overlap: 23 times, randomized > real

9. Results Interpretation Coefficient of Determination (% of Variance):Report Orthogonality: N/A- only 1 axisCoefficients of determination for the correlations between ordinationdistances and distances in the original n-dimensional space:            R SquaredAxis   Increment   Cumulative 1       .998        .998Extremely high amount of variance (99.8%) explained with 1 axis

10. Results InterpretationStrongest correlation indicates Med Max is explaining axis- negative correlation with axiscorrel_DiveMaxMedCVPearson and Kendall Correlations with Ordination Axes   N= 85Axis:               1                               r    r-sq   tau    dives pe    .351   .123   .019max dep     .082   .007  -.390median m   -.402   .161  -.525%CV         .768   .590   .328Most significant correlation with axis: Median DepthMax & Median Max had strong negative influenceCV weak positive influence on axis 1Not much influence from dive frequency

11. Discussion – MethodWhat do these results mean for the hypotheses / predictions you proposed ?-Accept hypothesis: CHSH dive exclusively during the day Need further analysis to test predictions, but NMS was good exploration of patterns prior to testing temporal frequency and depth with a grouping test-Max depth and Median Max depth strongest influences-Frequency of dives not as strong as depth measures in axis

12. Discussion – Next Steps What do you propose to do for your re-analysis?MRPP- test the prediction to identify when during daylight hours CHSH are diving more frequently What would be the next steps for this study?Look at time under water and time between divesCompare to other shearwater species