Effective conservation of endangered North Atlantic right whales Eubalaena glacialis requires information about their spatio-temporal distribution. Understanding temporal distribution is particularly important, because a portion of the population migrates between high-latitude summer feeding grounds off the northeastern US and Canadian Maritimes coasts and lower-latitude calving and wintering grounds off the southeastern US coast (SEUS). Here, we modeled SEUS residence patterns using photo-identification data from coastal South Carolina, Georgia, and Florida from 7 winter seasons (2004/2005-2010/2011). We used multistate open robust design models to evaluate effects of reproductive status, demographic group, and environmental conditions on SEUS residence. Model estimates accounted for temporal variation and imperfect detection and provided probabilities of entering the SEUS, staying in the SEUS, and being sighted in the SEUS. We also derived estimates for residence time and seasonal abundance. We observed staggered arrival and departure patterns and demographic differences in residence patterns that are characteristic of a differential migration strategy. Calving females arrived ear liest and, in most seasons, had mean residence periods more than twice as long as other demographic groups. Conversely, adult males arrived the latest and had the shortest residence times. Within-season detection was positively influenced by survey effort, and overall seasonal mean (± SE) detection rate estimates ranged from 0.83 ± 0.08 for non-calving adult females to 0.98 ± 0.02 for calving females. Results provide insights into right whale behavior, biology, and temporal distribution in the SEUS and can be used to evaluate spatially and temporally dynamic management measures.

Transportation industries can negatively impact wildlife populations, including through increased risk of mortality. To mitigate this risk successfully, managers and conservationists must estimate risk across space, time, and alternative management policies. Evaluating this risk at fine spatial and temporal scales can be challenging, especially in systems where wildlife–vehicle collisions are rare or imperfectly detected. The sizes and behaviors of wildlife and vehicles influence collision risk, as well as how much they co‐occur in space and time. We applied a modeling framework based on encounter theory to quantify the risk of lethal collisions between endangered North Atlantic right whales and vessels. Using Automatic Identification System vessel traffic data and spatially explicit estimates of right whale abundance that account for imperfect detection, we modeled risk at fine spatiotemporal scales before and after implementation of a vessel speed rule in the southeastern United States. The expected seasonal mortality rates of right whales decreased by 22% on average after the speed rule was implemented, indicating that the rule is effective at reducing lethal collisions. The rule's effect on risk was greatest where right whales were abundant and vessel traffic was heavy, and its effect varied considerably across time and space. Our framework is spatiotemporally flexible, process‐oriented, computationally efficient and accounts for uncertainty, making it an ideal approach for evaluating many wildlife management policies, including those regarding collisions between wildlife and vehicles and cases in which wildlife may encounter other dangerous features such as wind farms, seismic surveys, or fishing gear.