Final Technical Report: - Southwest Fisheries Science Center - NOAA

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ecosystem, eastern spinner dolphins in the ETP and Dall’s porpoises in the CCE, and two large baleen whale species, Bryde’s whales in the ETP and blue whales in the CCE. The data were collected on the David Starr Jordan, a <strong>NOAA</strong> research vessel, from July to early December in 2003 and 2006 in the ETP and in 2001 and 2005 in the CCE. Four models were built for the number of sightings of each species using all data available in each ecosystem. Models differed in the candidate predictor variables. The only candidate variable common to all models was Beaufort sea state, which was used to account for the increased difficulty of detecting cetaceans at higher sea states (Barlow et al. 2001). Oceanographic models were built using depth of the seafloor (depth), sea surface temperature (SST), sea surface salinity (SSS), mixed layer depth (MLD), and the natural logarithm of surface chlorophyll concentrations (CHL). During the years for which unbiased acoustic backscatter data were available, only manta tows were available to develop net-tow indices in the ETP and only bongo tows were available in the CCE. Indices from each tow type were developed using the same technique. Details of the technique can be found in Vilchis and Ballance (2005); hence, we only provide a brief synopsis here. The SWFSC net-tow database contains 1,869 manta and 835 bongo tow records, which are comprised of abundance and distribution data for hundreds of taxonomic categories. A majority of the taxa occur only once; hence, data matrices have a high dimensionality and many zeroes. To mitigate these analytical challenges, species were consolidated into families. In addition, data were standardized to represent percent dominance on a per station basis, and rare taxa were removed (those contributing less than 0.5% of mean dominance at all stations). The combined reduction in dimensionality resulted in matrices with 15 and 28 families for manta and bongo samples, respectively. Hierarchical clustering and multidimensional scaling methods were used to group fish families into categories based on similarity using Bray-Curtis measures. In our models, we used only indices that had pair-wise correlations less than 0.5 and that were greater than zero for at least 17 daily transects. Candidate predictor variables in net-tow models for the ETP were the combined abundance of Polynemidae, Mugilidae, Gerridae, Carangidae, Clupeidae and Engraulidae (manta1), the combined abundance of Gonostomatidae and Myctophidae (manta2), and the combined abundance of Phosichthydae, Nomeidae, Scombridae, Coryphaenidae, Exocoetidae and Hemiramphidae (manta3). Candidate variables in the CCE were the combined abundance of Myctophidae, Stomiidae, Phosichthydae and Bathylagidae (bongo1), the combined abundance of Sebastidae and Paralichthyidae (bongo2), the combined abundance of Paralepidae, Gonostomatidae and Sternoptychidae (bongo3), the abundance of Cephalopods (bongo4), and total zooplankton volume caught (bongo5). 64
Candidate predictor variables derived from acoustic backscatter data, the Svmean and NASC, are highly correlated; consequently, we only used Svmean in our acoustic backscatter models. Because our acoustic backscatter data were collected during daytime surveys (when vertically migrating prey are deep), we only used the 0-500 m integrated values, which included the deepest recorded depths. <strong>Final</strong>ly we built a combined model in which candidate predictor variables were derived from the variables selected in the other three models. Variables were selected using an automated forward/backward stepwise approach based on Akaike’s Information Criterion (AIC). Comparison of the four models was also based on AIC values, as well as explained deviance and temporal ratios of the number of observed to predicted sightings. Maps of the predicted number of sightings were interpolated using exponential distance weighting (decay = 250 km and neighborhood = 500 km for the ETP, decay = 100 km and neighborhood = 200 km for the CCE). Short-beaked common dolphins were unique in each ecosystem in that none of the mid-trophic variables were selected in combined models. Also, only Beaufort sea state was selected in the net-tow and acoustic backscatter models for the ETP (Table 13). Although Svmean was selected in the acoustic backscatter model for the CCE, it was not selected in the combined model (Table 13). Short-beaked common dolphins specialize in cool, upwelling habitat in the ETP (Reilly and Fiedler 1994). Our analyses of the effect of extent on dolphin-habitat models (see Section 4.3) suggest that the same variables define short-beaked common dolphin habitat in the CCE. Hence, it is possible that this habitat is so well defined by oceanographic measurements that the data about mid-trophic species we used are not needed to improve habitat models for short-beaked common dolphin. It is possible that other mid-trophic species data, such as fine resolution acoustic backscatter indices, would improve the models. Oceanographic and combined models produced very similar results for Bryde’s whales in the ETP (Fig. 22 and Tables 14, 15, and 16). The only variable added to the combined model was the abundance of Phosichthydae and Myctophidae. Expected prey for Bryde’s whales include species in the families Clupeidae, Engraulidae, and Scombridae as well as euphausiids and pelagic crabs (Vilchis and Ballance 2005). The lack of congruence between the manta tow index selected in the model and the expected prey species for Bryde’s whales may explain why the manta tow index does not have a strong influence on the predictions from the combined model. 65
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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 ..........
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Figure 18. Encounter rate models bu
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List of Tables Table 1. Summary of
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Appendix B, Table B-6. Summary of m
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Acknowledgements This project was f
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of these choices as possible and us
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Although our models include most of
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2.0 Background The Navy and other m
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comparison is based on a separate s
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Figure 1. Transects (green lines) s
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used to evaluate the ability of sum
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3.1.5 Mid-trophic Sampling with Net
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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 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 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
Page 112 and 113: We attempted to build encounter rat
Page 114 and 115: 5.0 Conclusion The field of predict
Page 116 and 117: 6.0 Transition Plan The models of c
Page 118 and 119: needed to ensure that the SDSS rema
Page 120 and 121: Administrative Report LJ-07-02. NOA
Page 122 and 123: Forney KA, Barlow J (1993) Prelimin
Page 124 and 125: Redfern JV, Barlow J, Ballance LT,
Page 126 and 127: Appendix A: Detailed Model Results
Page 128 and 129: Table A-1 (continued) Blue whale 19
Page 130 and 131: Figure A-1a. Striped dolphin 108
Page 132 and 133: Figure A-1c. Risso’s dolphin 110
Page 134 and 135: 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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