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.

Abstract. At a landscape scale, the combined influence of biotic and abiotic factors may determine the distribution patterns of large herbivores in African savanna ecosystems. Herbivores foraging in these ecosystems may become nutritionally stressed during an annual dry season when both forage quality and quantity are reduced. Additionally, the locations of water sources may impose a landscape-scale constraint on dry-season herbivore distributions. We used logistic regression to analyze 13 years of aerial census data collected in the Kruger National Park (KNP), South Africa, and evaluated hypotheses regarding the relative influences that surface water, forage quality, and forage quantity exert on the dryseason, landscape-scale distribution patterns of eight herbivore species. Hypotheses regarding the degree of correlation between species' distributions and distance to water were developed using previous observations of species' relative water dependence. We also developed hypotheses regarding species' responses to the trade-off that may occur between surface-water constraints and nutritional requirements when either forage quality or quantity is reduced. In general, we expect an increase in species' mean distance to water as a result of individuals mitigating limitations in nutritional requirements (i.e., intake quality or quantity) by foraging farther from water. Our analyses suggest that the trade-off between nutritional requirements and surface-water constraints that species face varies according to the species' water dependence, size, and gut morphology. Of the four grazers considered in our analyses, waterbuck distributions appear to be constrained primarily by surfacewater availability. Distributions of buffalo, a large ruminant grazer, suggest that individuals face a trade-off between nutritional requirements and surface-water constraints when forage quantity is reduced. Alternatively, distributions of wildebeest, a smaller ruminant grazer, suggest that individuals face this trade-off when access to high-quality forage is limited. In comparison to buffalo and wildebeest, the strength of this trade-off is moderate for zebra, a nonruminant similar in size to wildebeest, when either forage quality or quantity is reduced. Distribution patterns for browsers are characterized by a weak relationship with distance to water, as expected for these relatively water-independent species. Population densities relative to forage quality confound exploration of this trade-off for mixed feeders.

Marine spatial planning provides a comprehensive framework for managing multiple uses of the marine environment and has the potential to minimize environmental impacts and reduce conflicts among users. Spatially explicit assessments of the risks to key marine species from human activities are a requirement of marine spatial planning. We assessed the risk of ships striking humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), and fin (Balaenoptera physalus) whales in alternative shipping routes derived from patterns of shipping traffic off Southern California (U.S.A.). Specifically, we developed whale-habitat models and assumed ship-strike risk for the alternative shipping routes was proportional to the number of whales predicted by the models to occur within each route. This definition of risk assumes all ships travel within a single route. We also calculated risk assuming ships travel via multiple routes. We estimated the potential for conflict between shipping and other uses (military training and fishing) due to overlap with the routes. We also estimated the overlap between shipping routes and protected areas. The route with the lowest risk for humpback whales had the highest risk for fin whales and vice versa. Risk to both species may be ameliorated by creating a new route south of the northern Channel Islands and spreading traffic between this new route and the existing route in the Santa Barbara Channel. Creating a longer route may reduce the overlap between shipping and other uses by concentrating shipping traffic. Blue whales are distributed more evenly across our study area than humpback and fin whales; thus, risk could not be ameliorated by concentrating shipping traffic in any of the routes we considered. Reducing ship-strike risk for blue whales may be necessary because our estimate of the potential number of strikes suggests that they are likely to exceed allowable levels of anthropogenic impacts established under U.S. laws.

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.

Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered "measured data"), but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS) to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE), observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.

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...

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Aim Changes in abundance and shifts in distribution as a result of a warming climate have been documented for many marine species, but opportunities to test our ability to forecast such changes have been limited. This study evaluates the ability of habitat‐based density models to accurately forecast cetacean abundance and distribution during a novel year with unprecedented warm ocean temperatures caused by a sustained marine heatwave. Location California Current Ecosystem, USA. Methods We constructed generalized additive models based on cetacean sighting and environmental data from 1991 to 2009 for eight species with a diverse range of habitat associations. Models were built with three different sets of predictor variables to compare performance. Models were then used to forecast species abundance and distribution patterns during 2014, a year with anomalously warm ocean temperatures. Cetacean sighting data collected during 2014 were used to assess model forecasts. Results Ratios of model‐predicted abundance to observed abundance were close to 1:1 for all but one species and accurately captured changes in the number of animals in the study area during the anomalous year. Predicted distribution patterns also showed good concordance with the 2014 survey observations. Our results indicate that habitat relationships were captured sufficiently to predict both changes in abundance and shifts in distribution when conditions warmed, for both cool‐ and warm‐temperate species. Main conclusions Models built with multidecadal datasets were able to forecast abundance and distribution in a novel warm year for a diverse set of cetacean species. Models with the best explanatory power did not necessarily have the best predictive power. Also, they revealed species‐specific responses to warming ocean waters. Results have implications for modelling effects of climate change on cetaceans and other marine predators.