Many whale populations have been slow to increase in numbers following cessation of commercial whaling, and one of the reasons for this is human-induced mortality. There were 21 blue whale (Balaenoptera musculus) strandings along the California coast during 1988 to 2007, of which 8 were confirmed as ship strikes. Eleven other whale species have been hit by ships, including fin whales (Balaenoptera physalus), humpback whales (Megaptera novaeangliae), and gray whales (Eschrichtius robustus). Entanglement in fishing nets and lines can also lead to drowning, which has resulted in the death of thousands of cetaceans. There is great concern that underwater noise from human sources, such as seismic activity, Navy sonar, vessel noise, and offshore energy can cause stranding, impair hearing, mask calls, or result in area avoidance. These anthropogenic impacts are a major source of mortality to large whales. There is an urgent need for a comprehensive conservation strategy and we propose near real-time predictions of the probability of whale occurrence to help limit anthropogenic activities to times and areas of lower risk to whales. Our team has access to the largest satellite tracking dataset for whales in the world. The advantage of telemetry data is that it provides an animal’s eye-view of its movements and habitat preferences, and is not limited to the spatial scale and resolution of standard survey designs. This telemetry data will be combined with satellite-derived oceanographic data to create predictive habitat models. These products will allow large multi-species whale hotspots to be identified, and provide a near real time tool for determining risk to whales. Managers and users of ecosystem services could identify the time or location at which there would be the lowest probability of whale occurrence, and ultimately the lowest risk to whales. This creates a building block towards developing a decision support system for distribution to marine users, developers, and planners. The development of our near real-time tool will involve analyzing multi-year satellite-derived tracks of blue, fin, humpback and gray whales in the California Current System. A switching state-space model will be applied to the raw Argos satellite tracks of these whales to provide temporally regularized position estimates and to infer their behaviors. We have already applied this to the blue whale data set, and will apply the same procedure to the fin, humpback and gray whale tracks so that position and behavioral estimates will be comparable for all four species. Satellite-derived environmental data will then be extracted for the time and location of each whale position. This will include sea surface temperature, chlorophyll-a concentration, and sea surface height. Additional variables derived from satellite products will also be used, such as primary productivity, temperature fronts, Ekman upwelling, geostrophic currents, and eddy kinetic energy. We will then use an ensemble of habitat modeling techniques including generalized additive and generalized linear mixed models, and resource selection functions to quantify and predict the probability of whale occurrence based on environmental parameters. The habitat models for each whale species will be calculated from daily to weekly satellite products to generate a near real-time product that predicts large whale occurrence in the California Current System. This tool will be hosted by NOAA/SWFSC and will be transferred to the NOAA Southwest Regional Office. It can then be merged with products describing anthropogenic activities, and developed in the future as a decision support system for managers, the Navy, offshore energy industry, and other marine users to reduce the risk of impact to Federally protected resources such as whales. Our project will support NASA’s objectives by assisting management of marine protected species under a changing climate and facilitating compliance with legal mandates.