Final Technical Report: - Southwest Fisheries Science Center - NOAA

(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
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