Final Technical Report: - Southwest Fisheries Science Center - NOAA

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above, and the data themselves, are not independent, making the development of analytical methods for estimating variance an intractable, if not impossible, process. It is not realistic to account for all sources of uncertainty when estimating the variance in population density estimates. Furthermore, due to the large range in the magnitude of uncertainty introduced by each of the sources described above, it is not necessary to quantify the uncertainty associated with every source in order to derive a relatively accurate estimate of overall uncertainty. Rather, estimation of the uncertainty contributed by the dominant sources is often sufficient. In our analyses, the greatest source of uncertainty is inter-annual variability in actual population density due to movement of animals within or outside of the study areas. We focus on this source of uncertainty to produce estimates of variance or standard error for the population density estimates in the California Current and ETP ecosystems. In the SDSS, we provide variance estimates at two spatial scales, the grid cell and the user-defined polygon. Estimating uncertainty at the scale of a grid cell was briefly mentioned in Section 3.5. It involves the following two steps: 1. Computation of gridded population density estimates throughout the study area for each survey year using the methods outlined in Sections 3.3 and 3.5. 2. Computation of the variance in population density estimates among survey years for each grid cell. To estimate the variance in the density estimates for any given polygon, the same annual grids of density predictions are used, average density is computed for the polygon in each year, and the variance in the resulting density estimates is computed across years using standard statistical formulae. Lower and upper 90% lognormal confidence limits for species density are calculated from the estimated polygon variance. 3.7 Inclusion of Prey Indices from Net-Tow and Acoustic Backscatter Data in Models For many SWFSC cetacean and ecosystem assessment surveys, only physical and biological oceanographic data are available for use in cetacean-habitat models. Currently, it is unknown whether these oceanographic data are adequate proxies for the abundance of cetacean prey or whether prey indices should be directly included in habitat models. To explore whether oceanographic data are adequate proxies of cetacean prey, we tested how well our direct measurements of cetacean prey abundance (38 kHz acoustic backscatter data collected by a Simrad EQ-50 echosounder during cetacean and ecosystem assessment surveys conducted in the ETP from 1998 to 2000) could be predicted from basic oceanographic data. 36
We developed GAMs to relate oceanographic variables, such as surface temperature and salinity, thermocline depth and strength, and surface chlorophyll, to the following acoustic backscatter variables: mean backscatter throughout the water column, mean backscatter near the surface, and vertical variability of backscatter. These backscatter variables are related to the density and vertical distribution of small fish and krill-sized organisms. Explained deviance in the GAMs was generally about 25%, although results for individual years were higher. These results suggest that oceanographic variables are not perfect proxies for prey abundance and, therefore, the backscatter variables should be used directly in the models. We built cetacean-habitat models using mid-trophic prey indices to determine whether predictor variables comprised of oceanographic measurements, mid-trophic prey indices, or a combination of both improves model fit and predictive power. Mid-trophic prey indices were derived from manta and bongo net-tow samples and from acoustic backscatter data. Oceanographic, net-tow, and acoustic backscatter data from which noise was removed were only available for four years of surveys: 2003 and 2006 in the ETP and 2001 and 2005 in the CCE. Species modeled in each ecosystem varied and were selected based on sample size (Table 9). We developed GAMs to model the expected number of sightings of each species; group size models could not be developed because sample sizes were too small. Table 9. Number of segments containing a sighting and the total number of sightings used to build mid-trophic models in the ETP and CCE. ETP Number of Number of segments segments containing a Total containing a Total Species sighting sightings Species sighting sightings Striped dolphin 46 109 Striped dolphin 19 24 Short-beaked Short-beaked common dolphin Eastern spinner 25 64 common dolphin 38 103 dolphin 40 83 Dall's porpoise 24 94 Bryde's Whale 16 26 Blue whale 17 22 Number of unique Number of unique segments 111 segments 95 3.8 Seasonality Ideally, comprehensive shipboard surveys would be conducted year-round in the CCE to better assess seasonal patterns in the distribution and abundance of cetaceans. However, weather constraints often prohibit shipboard surveys during the winter and spring (hereafter “winter”), and therefore most of our shipboard line-transect data were collected during summer and fall 37 CCE
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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
Page 8 and 9: Table of Contents Acronyms and Abbr
Page 10 and 11: 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 60 and 61: (hereafter “summer”). SWFSC has
Page 62 and 63: avoid these problems, we decided to
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
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Figure 27. Average density (AveDens
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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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