Final Technical Report: - Southwest Fisheries Science Center - NOAA

2.0 Background
The Navy and other military users of the marine environment are required to assess the
impact of their activities on marine mammals to comply with the Marine Mammal Protection
Act, the Endangered Species Act, and the National Environmental Policy Act. The number of
marine mammals that might be impacted by Navy activities must be estimated in any such
Environmental Assessment or Environmental Impact Statement. However, existing marine
mammal density data are typically estimated for areas that are much larger than the area of
interest for a naval exercise. For example, the Navy might be interested in knowing the number
of whales and dolphins in a portion of their Southern California Offshore Range (SCORE), and
density estimates are only available collectively for all of California’s offshore waters.
Stratification to estimate density in smaller areas is not effective because the number of sightings
is typically not sufficient to make an estimate. Clearly, a method is needed to estimate cetacean
density on a finer geographic scale. Also, marine mammal densities are known to change as a
function of the oceanographic variables that define their habitat, and historical densities might
not be the best estimates of current or projected density. There is therefore a need to predict
marine mammal density based on measured or projected oceanographic conditions. In addition
to their need for absolute estimates of marine mammal density (the expected number of animals
per square km), the Navy also could use relative measures of marine mammal density in
selecting among alternative sites for their training activities.
The development of tools for the statistical analysis of geographic distribution and
abundance has accelerated recently, as evidenced by special issues of two journals dedicated to
this subject (Ecological Modelling 2002, Vol. 157, Issues 2-3 and Ecography 2002, Vol. 25,
Issue 5). Although Generalized Linear Models (GLMs) are still commonly used (Martínez et al.
2003), there is a growing recognition that species abundances should not be expected to vary
linearly with habitat gradients (Austin 2002, Oksanen and Minchin 2002). There is growing
acceptance of non-linear habitat relationships including Huisman-Olff-Fresco and Gausian
models (Oksanen and Minchin 2002) as well as non-parametric Generalized Additive Models
(Guisan et al. 2002, Wood and Augustin 2002). Active areas of current research in this field
include methods of model selection such as ridge regression (Guisan et al. 2002), dealing with
spatial autocorrelations (Keitt et al. 2002, Wood and Augustin 2002), and investigations of the
appropriate scale for modeling (Dungan et al. 2002).
The development of spatially explicit methods of analyzing cetacean line-transect data
has increased rapidly in recent years (see review by Redfern et al. 2006). Reilly (1990) used
multivariate analysis of variance to examine the relationship of dolphin distributions to
environmental variables in the ETP. Reilly and Fiedler (1994) and Fiedler and Reilly (1994)
used canonical correspondence analysis (CCA) to quantitatively determine the relationship
2