Final Technical Report: - Southwest Fisheries Science Center - NOAA

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species represented in the acoustic backscatter data are not currently available. Hence, the Svmean is simply an estimate of the total fish and zooplankton from 0 to 500 m. Improvements in acoustic backscatter indices may be obtained from analyses that relate acoustic signatures to specific prey species. The effect of including data about mid-trophic species distributions in cetaceanhabitat models was species specific. Substantial improvements were not noticed for short-beaked common dolphins in either ecosystem, for Bryde’s whales in the ETP, or for blue whales in the CCE. However, mid-trophic indices did appear to provide additional information about species distributions for striped dolphin in both ecosystems, eastern spinner dolphin in the ETP, and Dall’s porpoise in the CCE. In addition to the improvements to the mid-trophic indices suggested above, a more conclusive understanding about the effect of mid-trophic species data may be obtained with the addition of more data. When interpreting our results, it is important to bear in mind the small sample size available for our analyses. We have found that models using small samples sizes can be unstable, particularly in dynamic ecosystems such as the CCE (see the CCE resolution analyses in Section 4.3). Hence, our results must be further explored using a longer time series of data, which will increase sample sizes and expand the range of habitat conditions included in the models. 4.6 Seasonal Predictive Ability of Models 4.6.1 Model performance Although results varied by species, we found that both model type (GAM/GLM) and data source (remotely sensed/in situ) exhibited similar performance (Becker 2007). This conclusion is based on 1) the type and form of predictor variables included in the models, 2) ASPE values, 3) ratios of line-transect derived densities divided by predicted densities for the total study area, and 4) plots of predicted species densities and sightings from the survey data. Given sufficient sample size (ideally greater than 100 sightings), GAMs and GLMs built with remotely sensed measures of SST and CV(SST) performed as well, and in some cases better, than models built with analogous in situ measures. It is likely that models built with remotely sensed data are more appropriate for some species than others, particularly those species that exhibit a strong association to SST. We found satellite-derived estimates of sea surface temperature variance to be more effective at characterizing frontal activity due to their ability to measure heterogeneity in two dimensions. The predictive ability of cetacean-habitat models was affected by the level of complexity of the oceanographic environment, because more data were required to parameterize models for species that inhabit diverse environments. 72
4.6.2 Seasonal Predictive Ability Results indicated that inter-annual variability in environmental parameters can explain part of the variation in the seasonal distribution patterns of some cetacean species, particularly for species with large numbers of sightings during the summer survey periods (Becker 2007). Seasonal geographic patterns in ranked species density were captured for three of the five species considered. Density plots for Dall’s porpoise (Fig. 24) illustrate a species for which summer models were effective at predicting the southward shift of animals during winter. However, the predictions for northern right whale dolphins demonstrate that extreme over-predictions can result in the areas off northern California where waters were cooler during winter than observed during the summer surveys (dark blue shading in Fig. 24B). Additional surveys are required to fully characterize environmental variability and improve predictive performance sufficiently to apply these models quantitatively. In particular, model input data must include the full range of conditions for the temporal/spatial period they are predicting, i.e. cold-water conditions during winter. If possible, future seasonal model development and evaluation should also include a broader range of cold-season oceanographic conditions to characterize inter-annual variation. A final complication is that some cetaceans found in the CCE during the warm season are migratory and nearly absent in the cold season. For these reasons, we did not make any predictions of cetacean densities in one season from data that were collected in another. 4.7 Model Validation Data from the novel 2005 (CCE) and 2006 (ETP) SWFSC cetacean surveys were used to validate the final encounter rate and group size models constructed using data from 1991-2001 for the CCE and from 1986-2003 for the ETP. To assess the models’ fit to the validation data set and to examine the inter-annual variability in model predictions, density was predicted separately for each survey year. Methods used to evaluate model fit included visual inspection of geographic contour plots of the annual density predictions and computation of geographically stratified ratios of observed to predicted density. 4.7.1 California Current Ecosystem Models When the CCE models built using 1991-2001 survey data were used to predict density across all survey years (1991-2005), density ratios (density calculated using standard line-transect methods divided by density predicted by the habitat model) ranged from 0.62 (Baird’s beaked whale) to 1.44 (northern right whale dolphin) (Table 18). Density ratios for the novel year (2005) predictions were more variable, ranging from 73
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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
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Table 2. Variogram model results. A
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“number of individuals” as the
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Encounter Rate and Group Size Model
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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 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 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
Page 136 and 137: Figure A-1g. Sperm whale 114
Page 138 and 139: Figure A-1i. Blue whale 116
Page 140 and 141: Figure A-1k. Baird’s beaked whale
Page 142 and 143: 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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