Final Technical Report: - Southwest Fisheries Science Center - NOAA

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were built separately for each of these ecosystems with a model that was built using pooled data from both ecosystems. Modeling methodology followed Redfern et al. (2008), but only the 60km resolution was used. 3.5 Model Selection Model validation using an independent data set is an integral part of building robust cetacean-habitat models (Forney 1997 and 2000, Becker 2007). In this analysis, final models for the CCE and the ETP were selected using a two-part process in which models were initially built using stepwise variable selection based on the available SWFSC survey data through 2003. Candidate models were then evaluated in terms of their predictive capabilities when applied to data from the novel 2005 (CCE) and 2006 (ETP) SWFSC cetacean surveys (see 3.1.1 Marine Mammal Surveys). Predictions and overall model performance were compared to identify the best models. A collection of quantitative and qualitative methods were used to compare models. Average squared prediction error (ASPE) was used to assess each model’s prediction accuracy across all segments (n) within the entire study area, where Prediction accuracy was addressed in a spatial context using ratios of observed to predicted number of sightings (for the encounter rate models) or group size within each geographic stratum. These geographic strata were defined to be large enough to encompass a sufficient number of observations for a meaningful comparison of model predictions, yet environmentally distinct in terms of the biological and physical processes that determine habitat. In addition to examining the observed-to-predicted ratios themselves, we computed the sum of absolute deviations of the observed-to-predicted ratios, defined as observed 1 , predicted where the sum is taken over all geographic strata used in model evaluation. For both the ASPE and observed-to-predicted ratio computations, the Beaufort sea state variable was set to the observed value to generate encounter rate and group size predictions. Explained deviance, the likelihood analogue of explained variance, was used to assess each model’s fit to the assumed distribution for the data. Model complexity was evaluated by examining the number of predictor variables selected and their associated degrees of freedom, in conjunction with visual inspection 20
of the smooth functions relating the effects of each predictor variable to the response variable. <strong>Final</strong>ly, density predictions derived from the encounter rate and group size models were plotted on a map of the study area and the spatial distribution was evaluated by eye. Following model selection and validation, the best models were then re-fit to the additional year of data to parameterize the final predictive models. Details on the methods used to select and validate our final models within each geographic region are provided below. 3.5.1 California Current Ecosystem Models In preparation for model selection and validation using the 2005 west coast survey data, in situ models built at scales of 2 and 10 km for the scale analyses (see Section 3.4 Model Scale: Resolution and Extent) were compared to 5 km models built with remotely sensed data (Becker 2007). For each species, we compared key predictor variables and associated functional shapes, study area density ratios (density calculated using standard line-transect methods divided by density predicted by the habitat model), standard errors (SE) of density ratios, and average squared prediction errors (ASPE). We found that the models built with remotely sensed data performed as well or better than the models built with in situ data. However, for some species the in situ oceanographic variables had a large effect on one or both response variables (encounter rate and group size) relative to the other predictors. Based on these analyses, we developed two sets of CCE models at the 5 km scale: 1) a set that included only remotely sensed habitat variables, and 2) a set that included a combination of in situ and remotely sensed predictor variables. These two types of models were subsequently compared to develop and finalize models on a species-specific basis. Initial Model Selection and Evaluation Process Initial models for both the in situ and remotely sensed data sets were selected using a “pseudo-jackknife” cross-validation approach (Becker 2007). Specifically, three data sets were constructed by excluding one of the four survey years available for model building (1991, 1993, 1996, and 2001). [Note: Data collected during 1993 were included in all model combinations because 1993 was the year with the warmest mean sea surface temperatures and was considered essential to capture the observed inter-annual variability in oceanographic conditions.] Each model was then used to predict the excluded year, and ASPE was calculated. This process of cross validation on all model combinations produced four ASPE values for each of the six initial models (three encounter rate models and three group size models). The paired models with the lowest sum of ASPE values (i.e. with lowest prediction errors across all survey years) were selected as the best overall models. Group size and encounter rate models were constrained to be paired because preliminary analyses indicated that variable selection was not independent; an increase in animal densities (e.g., with higher sea surface temperature) could be reflected in either a higher encounter rate or larger groups, and this effect varied among years. If models were built from different yearly subsets, this could result in the loss or overrepresentation of one or more variables, causing bias. 21
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 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
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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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