Final Technical Report: - Southwest Fisheries Science Center - NOAA

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Expanding models to the entire U.S. West Coast All of our initial west coast analyses (e.g., scale evaluation, seasonal predictions, etc.) were based on models developed using survey data collected only in California waters in 1991, 1993, 1996, and 2001, because Oregon and Washington waters were not surveyed in 1991 and 1993 and it was important to capture the greatest degree of inter-annual variability possible. Using four years of California-only data provided the most robust data set for construction of models, model validation, and other associated analyses. However, the inclusion of waters off Oregon and Washington in the final West Coast Spatial Decision Support System (SDSS) required a new approach to model selection, because the pseudo-jackknife cannot be used when regional coverage is unequal, and the varying survey extent could result in biased models. Therefore, we explored alternate 'best model' selection criteria for models encompassing the entire West Coast study area. First, we compared key predictor variables and associated functional shapes of independent models built with California only vs. Oregon and Washington data. Based on the similarities of the variables and their functional forms, we concluded that we could combine the datasets for model building without introducing bias. This approach has the advantage of maximizing sample sizes and building models based on a broader range of environmental conditions. We then selected the five models that minimized AIC, and chose the best model based on non-AIC criteria applied to each individual survey year and the collective data set. These criteria included density ratios (line-transect derived density divided by predicted density) and a visual evaluation of spatial patterns in the model compared to the sighting data. For evaluation purposes, we built nested models for six species using only the California survey data. The species selected represented a broad range of habitat preferences: short-beaked common dolphin (Delphinus delphis), Risso’s dolphin (Grampus griseus), northern right whale dolphin (Lissodelphis borealis), Dall’s porpoise (Phocoenoides dalli), fin whale (Balaenoptera physalus), and humpback whale (Megaptera novaeangliae). Models constructed for California waters using these methods were similar or identical to those selected using the pseudo-jackknife procedure; therefore, this alternate selection process was used for the final West Coast model development. Two candidate 'pre-final' models were developed for each species: one built only with remotely sensed habitat variables and another built with a combined set of in situ and remotely sensed predictor variables (“combined” models). Habitat predictor variables Predictor variables for the remotely-sensed models included sea surface temperature (SST), the coefficient of variation (CV) of SST within a 6x6 pixel (1,109 km 2 ) box (to serve as a proxy for frontal regions; Becker 2007), water depth, bathymetric slope, distance to the 2,000 m 22
isobath, and Beaufort sea state. Distance to the 2,000 m isobath was added to the list of predictors because sighting plots suggested that this variable could potentially improve model performance for some species (e.g., sperm whale, Physeter macrocephalus, and Baird’s beaked whale, Berardius bairdii) that are generally found only in slope or deep waters. This variable was coded to indicate whether the location was deeper (-) or shallower (+) than the 2,000 m isobath. Beaufort sea state affects the probability of detecting animals (Barlow et al. 2001), and the average observed sea state value on each segment was included as a continuous predictor variable in our models in order to account for sighting conditions. In addition to the variables used for the remotely-sensed models, the combined models included three potential predictors derived from data collected in situ: sea surface salinity, the natural logarithm of surface chlorophyll concentration, and mixed layer depth, measured as the depth at which the water temperature was 0.5C less than at the surface. Remotely sensed measures of SST and CV(SST) were used in the combined models because the remotely-sensed CV(SST) was found to be more effective at characterizing frontal regions than our in situ CV(SST) measures (Becker 2007), and SST measures performed similarly. The in situ data were derived in one of two ways. Salinity was sampled continuously along the transect and segmentspecific estimates were obtained by averaging values within 5 km of the mid-point of each transect segment included in the analysis. Chlorophyll and mixed layer depth were measured much less frequently, and a linear interpolation between nearby stations did not accurately capture values at the edges of the study area or when samples were sparse, causing 'bull’s eye' effects in estimated cetacean density. Therefore, the data were first contoured (see Section 3.2) to provide a 2-D surface of estimated chlorophyll and mixed-layer depth values, and segment mid-point values were extracted from the contour grid using the Surfer 8.0 (Golden Software, Inc) Residuals feature. Density Estimation Segment-specific density estimates were derived by incorporating the predicted values for encounter rate and group size into the standard line-transect equation (Buckland et al. 2001) as described by Becker (2007) and in Section 3.3.1. We relied on published values of detection probability (f(0) and g(0)) for each species as estimated from the same survey data used for model development (Barlow 2003). Published values for many species were stratified by group size and, for purposes of estimating densities, we incorporated weighted f(0) and g(0) values based on the number of small and large groups observed during the surveys (Becker 2007, Table 4). All final model predictions were made using the average observed Beaufort sea state for conditions 0-5 during the SWFSC cruises. This is appropriate because it corresponds to the conditions for which the line-transect parameters f(0) and g(0) were estimated (Barlow 2003). For Dall’s porpoise and small beaked whales, published f(0) and g(0) values were available only for Beaufort conditions of 0-2. Model predictions for this species and guild were made using the average observed Beaufort sea state for conditions 0-2. 23
Page 1 and 2: U N I R A P E D T T E M D S E N TA
Page 3: C O L A N IO T A N U N C I EA .S. D
Page 6 and 7: 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 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 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
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species represented in the acoustic
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0.29 (Risso’s dolphin) to 3.20 (n
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Figure 25. Sample 2005 validation p
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Table 18. (continued) 1991 1993 Nor
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Blue whales had the greatest deviat
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Table 20. Abundance (number of anim
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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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