Individual movement modeling expands the power of migratory species observations: North Atlantic right whale case study

Abstract Understanding a population's distribution depends on observing the presence and movement of individuals throughout their range. For highly mobile marine species, these observations typically rely on high effort monitoring programs. Tracking enough individuals to understand trends in movement behavior is not always logistically feasible, and animals are less likely to be observed in migratory or transitional habitats. To optimize observation data we built a Brownian bridge movement model to generate spatially and temporally explicit space use estimations of individuals along tracks created from intermittent sightings of North Atlantic right whales from 1980 to 2022. Right whales can be identified by unique callosities and markings, providing a noninvasive opportunity to link sightings to individual movement. This model generated location probability estimates of medium‐ and large‐scale movements in biologically plausible habitats. A total of 351,214 d of occurrence distributions were calculated from 67,840 sightings attributed to 806 individuals, representing more than a five‐fold increase in individual spatial information. From 1980 to 2022, the model generated space use estimates for at least one whale on 75.7% of days, compared to the underlying visual sightings data, which only provided observations on 38.7% of days. Model outputs compared to tracks of tagged whales demonstrated proficiency estimating space use across regions. The occurrence distributions produced depict known changes in seasonal and decadal space use and estimate transitional space use where observations are sparse. These methods improve spatial distribution predictions in intermittently observed animals and expand the utility of stationary sightings without constraining predictions to historical relationships with the environment.