Cetacean-habitat modeling, although still in the early stages of development, represents a potentially powerful tool for predicting cetacean distributions and understanding the ecological processes determining these distributions. Marine ecosystems vary temporally on diel to decadal scales and spatially on scales from several meters to 1000s of kilometers. Many cetacean species are wideranging and respond to this variability by changes in distribution patterns. Cetacean-habitat models have already been used to incorporate this variability into management applications, including improvement of abundance estimates, development of marine protected areas, and understanding cetacean-fisheries interactions. We present a review of the development of cetacean-habitat models, organized according to the primary steps involved in the modeling process. Topics covered include purposes for which cetacean-habitat models are developed, scale issues in marine ecosystems, cetacean and habitat data collection, descriptive and statistical modeling techniques, model selection, and model evaluation. To date, descriptive statistical techniques have been used to explore cetacean-habitat relationships for selected species in specific areas; the numbers of species and geographic areas examined using computationally intensive statistic modeling techniques are considerably less, and the development of models to test specific hypotheses about the ecological processes determining cetacean distributions has just begun. Future directions in cetacean-habitat modeling span a wide range of possibilities, from development of basic modeling techniques to addressing important ecological questions.

Many users of the marine environment (e.g. military, seismic researchers, fisheries) conduct activities that can potentially harm cetaceans. In the USA, Environmental Assessments or Environmental Impact Statements evaluating potential impacts are required, and these must include information on the expected number of cetaceans in specific areas where activities will occur. Typically, however, such information is only available for broad geographic regions, e.g. the entire West Coast of the United States. We present species−habitat models that estimate finer scale cetacean densities within the eastern Pacific Ocean. The models were developed and validated for 22 species or species groups, based on 15 large-scale shipboard cetacean and ecosystem assessment surveys conducted in the temperate and tropical eastern Pacific during the period from 1986 to 2006. Model development included consideration of different modeling frameworks, spatial and temporal resolutions of input variables, and spatial interpolation techniques. For the final models, expected group encounter rate and group size were modeled separately, using generalized additive models, as functions of environmental predictors, including bathymetry, distance to shore or isobaths, sea surface temperature (SST), variance in SST, salinity, chlorophyll, and mixed-layer depth. Model selection was performed using cross-validation on novel data. Smoothed maps of species density (and variance therein) were created from the final models for the California Current Ecosystem and eastern tropical Pacific Ocean. Model results were integrated into a web-interface that allows end-users to estimate densities for specified areas and provides fine-scale information for marine mammal assessments, monitoring, and mitigation.

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Generalized linear and generalized additive habitat models were used to predict cetacean densities for 10 species in an 818 000 km 2 area off California. The performance of models built with remotely sensed oceanic data was compared to that of models built with in situ measurements. Cetacean sighting data were collected by the Southwest Fisheries Science Center on 4 systematic line-transect surveys during the summer and fall of 1991, 1993, 1996, and 2001. Predictor variables included temporally dynamic, remotely sensed environmental variables (sea surface temperature and measures of its variance) and more static geographical variables (water depth, bathymetric slope, and a categorical variable representing oceanic zone). The explanatory and predictive power of different spatial and temporal resolutions of satellite data were examined and included in the models for each of the 10 species. Alternative models were built using in situ analogs for sea surface temperature and its variance. The remotely sensed and in situ models with the highest predictive ability were selected based on a pseudo-jackknife cross validation procedure. Environmental predictors included in the final models varied by species, but, for each species, overall explanatory power was similar between the remotely sensed and in situ models. Cetacean-habitat models developed using satellite data at 8 d temporal resolution and from 5 to 35 km spatial resolution were shown to have predictive ability that generally met or exceeded models developed with analogous in situ data. This suggests that the former could be an effective tool for resource managers to develop near real-time predictions of cetacean density.KEY WORDS: Cetacean density · Habitat modeling · GAM · GLM · California Current · Remote sensing · Whale · Dolphin · Porpoise Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 413: 2010 Ferguson et al. 2006). Although some relied on satellite data to investigate cetacean-habitat associations (e.g. Waring et al. 1993, Jaquet & Whitehead 1996, Moore et al. 2002, satellite data typically were used to augment in situ data or when equipment failure precluded the collection of along-track data (Davis et al. 1998, Baumgartner et al. 2001, Davis et al. 2002, Hamazaki 2002. However, satellite data provide synoptic spatial coverage in near real-time, and this can be an important advantage if remotely sensed data are as effective at capturing species-environment relationships as in situ data. To date, there have been no direct comparisons of cetacean habitat models based solely on in situ and solely on remotely sensed oceanic variables.Generalized linear models (GLMs) and generalized additive models (GAMs) have been used effectively to model cetacean sighting rates (Hedley et al. 1999, Forney 2000 and cetacean density (Ferguson et al. 2006) as a function of environmental variables. Cetacean densities are typically estimated by line-transect surveys and generally result in estimates for large geogr...

While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time-scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine-scale and ephemeral | 1099MANNOCCI et Al. | INTRODUCTIONHighly mobile marine animals such as marine mammals, seabirds, sea turtles and fish are unevenly distributed in the ocean. Ecologists have long sought to understand and predict their patterns of distributions, particularly for commercially valuable species subject to exploitation (Lehodey, Bertignac, Hampton, Lewis, & Picaut, 1997) and for protected species vulnerable to incidental harm (Reilly, 1990). They often employ species distribution models (SDMs) that statistically relate distribution patterns to environmental conditions by linking animal observations to environmental variables. SDMs have been successfully used to examine many ecological, management and conservation questions (Elith & Leathwick, 2009). In particular, they have been widely used to explain and predict distribution patterns of highly mobile marine animals in a variety of ecosystems (Benson et al., 2011;Forney, Becker, Foley, Barlow, & Oleson, 2015;Hartog, Hobday, Matear, & Feng, 2011;Mannocci et al., 2014).It has become apparent that the hierarchical structure of processes in the marine environment drives the distribution and movement patterns of marine animals at multiple spatio-temporal scales (Benoit-Bird, Battaile, Nordstrom, & Trites, 2013;Fauchald, Erikstad, & Skarsfjord, 2000;Fauchald & Tveraa, 2006;Fritz, Said, & Weimerskirch, 2003;Pinaud & Weimerskirch, 2005) (Figure 1). At fine scales, animals track ephemeral prey patches that extend over tens of metres to satisfy their energy requirements (Goldbogen et al., 2008;Heaslip, Iverson, Bowen, & James, 2012 (Benson et al., 2011;Hobday & Hartog, 2014;Tew Kai & Marsac, 2010). At broad scales, animals associate with persistent water masses and current systems that extend over thousands of kilometres and delimit their geographic ranges or migration routes (Jaquet, Whitehead, & Lewis, 1996;Reygondeau et al., 2012;Shillinger et al., 2008). Thus, the distributions of highly mobile marine animals appear determined by both short-term ocean variability and persistent patterns of longer-term ocean climate.Researchers use a variety of methods to obtain synoptic data on marine animal distributions and the marine environment at a wide range of spatial and temporal extents ( Figure 2, see Appendix S1 in Supporti...