Final Technical Report: - Southwest Fisheries Science Center - NOAA

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avoid these problems, we decided to use ArcGIS only as an optional method for viewing model outputs and extracting information from geospatial images. The primary software would be hosted on a website and would not require any specialized software on the user’s computer. Because of their long experience in developing web-based data servers for marine mammal research, the Duke Team agreed to take the lead in developing this web-based SDSS software. Furthermore, Duke University volunteered to initially host the SDSS software on their website. Subsequent to the March 2007 user’s workshop, the Duke Team developing the SDSS decided to use Google Earth as the primary visualization tool within the SDSS software. Most of the specifications for the SDSS were developed at the 2007 Users Workshop in La Jolla. The primary recommendations of the users were: • Nobody likes to read manuals. Therefore, we should try to minimize the need for a user manual by testing the SDSS software on naïve users to develop a user-friendly interface. • Absolute population density estimates are the highest priority model products. Relative population density and probability of occurrence are the second- and third-best options, respectively. • We should obtain peer-review of the bootstrap approach for estimating the CVs of density estimates within the scientific community. • We should allow survey effort and sightings from input datasets to be displayed on all maps of model output. • We should allow the user to set the categories and extents for figure legends. • We should provide spatial plots of user-specified upper and lower confidence limits for density or probability of occurrence estimates as an output option. • We should provide a session history tool to record user choices. • Software documentation should provide a stepwise explanation of everything from model development to the extraction of model results from the SDSS. • We should provide a complete list of model assumptions, caveats, and limitations. • The users prefer a single, peer-reviewed model per species/species group per region and time period rather than a collection of alternative models. • Contingent upon future funding, the SDSS development team should send the user community regular updates of relevant changes to the software. 40
4.1 Oceanographic Data Interpolation 4.0 Results and Accomplishments 4.1.1 Comparison of Interpolation Methods An example of thermocline depth interpolations in the ETP, calculated from 933 CTD and XBT profiles collected in 2006, shows basic differences between the interpolation methods (Fig. 9). Minimum curvature, radial basis function and, to a lesser extent, inverse distance squared tend to produce isolated areas of high or low values (i.e., bull’s eyes). These interpolation methods have slightly lower residuals than other methods because local changes in the observed values are captured. Local polynomial interpolation tends to produce extreme highs or lows beyond the edge of the sampled area; this problem was minimized by using a first-order polynomial. Kriging results in the fewest number of bull’s eyes, but has higher residuals. In spite of these obvious visual differences, residuals at individual sample points are very similar for all interpolation methods (Fig. 10 shows inverse distance squared and kriging, for example; r 2 =0.94 between single jackknife residuals of the two interpolation methods and r 2 =0.93 and 0.99 between single and daily jackknife residuals for the two methods). This result suggests that the residuals comprise measurement error more than error introduced by the interpolation method. In addition, the magnitude and spatial pattern of residuals does not change substantially with the jackknife procedure (Fig. 10). Results using other variables in both the ETP and CCE are similar (California Current surface chlorophyll from 2005 is shown in Fig. 11). Kriging was selected as the best method for interpolating the oceanographic data collected on cetacean and ecosystem assessment surveys. This decision was based on the prevalence of its use in geostatistical spatial mapping and the fact that patterns of variability in the data are used directly in the kriging process through the fitted variogram model. It is likely that manipulation of parameters for the inverse distance squared or local polynomial methods could have produced interpolated fields very similar to the kriged fields. 4.1.2 Yearly interpolated fields of habitat variables Initally, yearly fields were created at a very high resolution: 0.05 degree (5 km) for the CCE and 0.10 degree (10 km) for the ETP. In both cases, these resolutions are much smaller than the spacing of sample points. We found that kriging at this resolution results in fine-scale artifacts of two types: 41
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U N I R A P E D T T E M D S E N TA
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C O L A N IO T A N U N C I EA .S. D
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This report was prepared under cont
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Table of Contents Acronyms and Abbr
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C.1 Journal Publications ..........
Page 12 and 13: Figure 18. Encounter rate models bu
Page 14 and 15: List of Tables Table 1. Summary of
Page 16 and 17: Appendix B, Table B-6. Summary of m
Page 18 and 19: Acknowledgements This project was f
Page 20 and 21: of these choices as possible and us
Page 22 and 23: Although our models include most of
Page 24 and 25: 2.0 Background The Navy and other m
Page 26 and 27: comparison is based on a separate s
Page 28 and 29: Figure 1. Transects (green lines) s
Page 30 and 31: used to evaluate the ability of sum
Page 32 and 33: 3.1.5 Mid-trophic Sampling with Net
Page 34 and 35: variable. Interpolated maps of the
Page 36 and 37: Table 2. Variogram model results. A
Page 38 and 39: “number of individuals” as the
Page 40 and 41: Encounter Rate and Group Size Model
Page 42 and 43: were built separately for each of t
Page 44 and 45: Expanding models to the entire U.S.
Page 46 and 47: Table 4. Summary of the weighted ef
Page 48 and 49: Consistent with Barlow and Forney (
Page 50 and 51: incorporation of geographic coordin
Page 52 and 53: overwhelming majority of “Bryde
Page 54 and 55: the genera Ziphius and Mesoplodon.
Page 56 and 57: Table 7. Geographically stratified
Page 58 and 59: above, and the data themselves, are
Page 60 and 61: (hereafter “summer”). SWFSC has
Page 64 and 65: Figure 9. Thermocline depth (m) obs
Page 66 and 67: Attempts to adjust search parameter
Page 68 and 69: CAMMS 1991 PODS 1993 ORCAWALE 1996
Page 70 and 71: 4.2 Modeling Framework : GLM and GA
Page 72 and 73: 4.2.4 Conclusions Regardings Modeli
Page 74 and 75: Table 10 cont. Comparison of the si
Page 76 and 77: Table 11 cont. Comparison of the si
Page 78 and 79: Figure 15. The transect lines used
Page 80 and 81: A) Striped dolphin 10 km B) Short-b
Page 82 and 83: dependent. For example, the number
Page 84 and 85: Figure 19. Predicted average densit
Page 86 and 87: ecosystem, eastern spinner dolphins
Page 88 and 89: Table 13. Variables selected for mo
Page 90 and 91: Table 14. Starting and final AIC va
Page 92 and 93: Table 17. Ratios of observed to pre
Page 94 and 95: species represented in the acoustic
Page 96 and 97: 0.29 (Risso’s dolphin) to 3.20 (n
Page 98 and 99: Figure 25. Sample 2005 validation p
Page 100 and 101: Table 18. (continued) 1991 1993 Nor
Page 102 and 103: Blue whales had the greatest deviat
Page 104 and 105: Table 20. Abundance (number of anim
Page 106 and 107: Table 22. Proportion of deviance ex
Page 108 and 109: Figure 27. Average density (AveDens
Page 110 and 111: Table 23. Effective degrees of free
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We attempted to build encounter rat
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5.0 Conclusion The field of predict
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6.0 Transition Plan The models of c
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needed to ensure that the SDSS rema
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Administrative Report LJ-07-02. NOA
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Forney KA, Barlow J (1993) Prelimin
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Redfern JV, Barlow J, Ballance LT,
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Appendix A: Detailed Model Results
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Table A-1 (continued) Blue whale 19
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Figure A-1a. Striped dolphin 108
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Figure A-1c. Risso’s dolphin 110
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Figure A-1e. Northern right whale d
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Figure A-1g. Sperm whale 114
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Figure A-1i. Blue whale 116
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Figure A-1k. Baird’s beaked whale
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Figure A-2. Predicted average densi
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Figure A-2b. Short-beaked common do
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Figure A-2d. Pacific white-sided do
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Figure A-2f. Dall’s porpoise 126
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Figure A-2h. Fin whale 128
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Figure A-2j. Humpback whale 130
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Figure A-2l. Small beaked whales 13
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Table B-2. Summary of model validat
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Table B-10. Summary of model valida
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Figure B-1. Predicted yearly and av
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Figure B-1. b) Eastern spinner dolp
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Figure B-1. c) Whitebelly spinner d
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Figure B-1. d) Striped dolphin 168
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Figure B-1. e) Rough-toothed dolphi
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Figure B-1. f) Short-beaked common
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Figure B-1. g) Bottlenose dolphin 1
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Figure B-1. h) Risso’s dolphin 17
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Figure B-1. i) Cuvier’s beaked wh
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Figure B-1. j) Blue whale 180
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Figure B-1. k) Bryde’s whale 182
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Figure B-1. l) Short-finned pilot w
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Figure B-1. m) Dwarf sperm whale 18
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Figure B-1. n) Mesoplodon beaked wh
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Figure B-1. o) Small beaked whales
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Figure B-2. Predicted average densi
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Figure B-2. (cont.) c) Whitebelly s
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Figure B-2. (cont.) g) Bottlenose d
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Figure B-2. (cont.) k) Bryde’s wh
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Figure B-2. (cont.) o) Small beaked
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Ferguson MC (2005) Cetacean Populat
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Barlow J, Kahru M, Mitchell BG (In
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Memorandum NMFS-SWFSC-374, U.S. Dep
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