Final Technical Report: - Southwest Fisheries Science Center - NOAA

above, and the data themselves, are not independent, making the development of analytical
methods for estimating variance an intractable, if not impossible, process.
It is not realistic to account for all sources of uncertainty when estimating the variance in
population density estimates. Furthermore, due to the large range in the magnitude of
uncertainty introduced by each of the sources described above, it is not necessary to quantify the
uncertainty associated with every source in order to derive a relatively accurate estimate of
overall uncertainty. Rather, estimation of the uncertainty contributed by the dominant sources is
often sufficient. In our analyses, the greatest source of uncertainty is inter-annual variability in
actual population density due to movement of animals within or outside of the study areas. We
focus on this source of uncertainty to produce estimates of variance or standard error for the
population density estimates in the California Current and ETP ecosystems.
In the SDSS, we provide variance estimates at two spatial scales, the grid cell and the
user-defined polygon. Estimating uncertainty at the scale of a grid cell was briefly mentioned in
Section 3.5. It involves the following two steps:
1. Computation of gridded population density estimates throughout the study area for
each survey year using the methods outlined in Sections 3.3 and 3.5.
2. Computation of the variance in population density estimates among survey years for
each grid cell.
To estimate the variance in the density estimates for any given polygon, the same annual
grids of density predictions are used, average density is computed for the polygon in each year,
and the variance in the resulting density estimates is computed across years using standard
statistical formulae. Lower and upper 90% lognormal confidence limits for species density are
calculated from the estimated polygon variance.
3.7 Inclusion of Prey Indices from Net-Tow and Acoustic Backscatter Data in Models
For many SWFSC cetacean and ecosystem assessment surveys, only physical and
biological oceanographic data are available for use in cetacean-habitat models. Currently, it is
unknown whether these oceanographic data are adequate proxies for the abundance of cetacean
prey or whether prey indices should be directly included in habitat models. To explore whether
oceanographic data are adequate proxies of cetacean prey, we tested how well our direct
measurements of cetacean prey abundance (38 kHz acoustic backscatter data collected by a
Simrad EQ-50 echosounder during cetacean and ecosystem assessment surveys conducted in the
ETP from 1998 to 2000) could be predicted from basic oceanographic data.
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