Final Technical Report: - Southwest Fisheries Science Center - NOAA

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(hereafter “summer”). SWFSC has conducted aerial surveys during the winter in portions of the CCE, but the aerial survey data contain too few sightings to build predictive environmental models. However, they can be used as test data to evaluate whether models constructed for summer using the extensive shipboard sighting data are able to predict distribution patterns in other seasons. This comparison required the development and evaluation of a separate set of models that rely on remotely-sensed environmental variables instead of in situ shipboard data. Predictive ability across seasons was estimated by applying the summer models to remotely sensed environmental data for winter and assessing performance based on winter aerial survey data (Becker 2007). This approach provided the advantages of a robust data set for construction of models (the shipboard data) and a more comprehensive seasonal data set (the aerial survey data) for examination of seasonal predictions. Initially, we developed cetacean-habitat models for the CCE study area using multi-year (1991-2001) summer ship survey data and remotely sensed oceanographic data. GLMs and GAMs for both cetacean encounter rates and group sizes were developed for the ten species with the greatest number of sightings to provide the most robust environmental models: striped dolphin, short-beaked common dolphin, Risso’s dolphin, Pacific white-sided dolphin, northern right whale dolphin, Dall’s porpoise, sperm whale, fin whale, blue whale, and humpback whale. Prior to evaluating the across-season predictive ability of the final shipboard models, we examined the performance of models built with remotely sensed SST data vs. analogous in situ measurements. Predictor variables included a combination of temporally dynamic, remotely sensed environmental variables (SST and measures of its variance, the latter serving as a proxy for frontal regions) and geographically fixed variables (water depth, bathymetric slope, and a categorical variable representing oceanic zone). For this comparison, we constructed a separate set of GAMs and GLMs by replacing the satellite data with analogous in situ data collected during the shipboard surveys. The in situ GAMs and GLMs with the highest predictive ability were selected based on the pseudo-jackknife cross validation procedure described above (Becker 2007, see Section 3.5). To compare model performance by type (GAM or GLM) and data source (satellite or in situ), we re-fit each of the final models to a commonly shared dataset using all segments available for the species-specific SST resolution (i.e., segments for which both remotely sensed and in situ data were available) and calculated ASPE for each encounter rate and group size model. We also used paired encounter rate and group size predictions from each model type (GAM/GLM) and data source (satellite/in situ) to estimate density by species for the total study area and compared these to density estimates derived by standard line-transect analyses of the sighting data. Aerial survey data collected off California during winter 1991-1992 (see Section 3.1) were used to assess the across-season predictive ability of the final summer shipboard models. We selected five species that are known to be present year-round and had sufficient sightings 38
during the winter aerial surveys to evaluate the models: short-beaked common dolphin, Risso’s dolphin, Pacific white-sided dolphin, northern right whale dolphin, and Dall’s porpoise. Differences in platform-specific biases for ship vs. aerial surveys (e.g., the proportion of diving animals missed) prevented a direct quantitative comparison of estimated densities from aerial and shipboard surveys. For this reason the winter predictions can only be considered relative densities. To evaluate the between-season predictive ability of our final shipboard models, we used a nonparametric Spearman rank correlation test, as well as visual inspection of predicted and observed distributions by species. To enable a rank analysis, the study area was geographically stratified into six biogeographic regions. Predictive ability was based on a comparison of the models’ ranked predicted values across biogeographic strata to those derived from the actual survey data for each species’ encounter rate, group size, and density. Results from the Spearman rank correlation tests were also compared to results obtained when the models were used to predict data from the shipboard surveys that were used for model building, as well as to a “null” model, defined as the density derived from summer shipboard surveys without consideration of environmental data. To qualitatively evaluate the models’ predictive ability, density estimates for each segment were smoothed on a grid resolution of approximately 12 km, and the resultant predictions of distribution and density were compared with actual sightings made during the winter aerial surveys. 3.9 Model Output and Visualization Software Although the models of cetacean density we develop can be viewed as hard copy (see Appendices A and B) or as digital graphics, the real value of models can only be realized if they are interactively accessible via a geographically based software system. Two SERDP projects, ours (SI-1391) and a sister project at Duke University (SI-1390) are both developing geospatial habitat models for cetaceans. Their project covers the Atlantic Coast and the Gulf of Mexico and our project covers the Pacific Coast (CCE) and the ETP. The Navy has expressed their desire for models of all areas to be accessible with a single software system. Consequently, we have been coordinating closely with the Duke team in developing what we call a Spatial Decision Support System (SDSS) for viewing cetacean habitat models and obtaining desired output from those models. Our SERDP team has met four times with the Duke SERDP team and with potential Navy users of the SDSS system to design it: 7-9 June 2004 at Duke University, 20-21 June 2005 at the SWFSC in La Jolla, California, 22-23 March 2007 in La Jolla, California, and 17-18 June 2008 in Durham, North Carolina. Initially, ArcGIS was chosen as the software package to form the foundation of our SDSS system. In meeting with Navy users, however, we discovered that there are problems with standardization of versions and access to upgrades within the Navy. To 39
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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
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 58 and 59: above, and the data themselves, are
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
Page 108 and 109: 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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