Models Incorporating Non‐Stationarity Improve Detection of Climate‐Driven Range Shifts in Odontocetes

ABSTRACT Aim Climate change is causing distributional shifts in many species globally. identifying and anticipating these shifts is critical to understanding ecosystem impacts and implementing successful management strategies. species distribution models ( SDMs ) are useful tools often employed to describe current and changing habitat use, particularly for marine predators. However, most SDMs assume the statistical relationships between species and their environment are temporally static, which may not be true. We examined how incorporating temporal variability improved SDM performance and estimated range shifts for six Odontocete species. We used a high performing model to quantify changes in Odontocete distribution over a 24‐year period. Location Waters of the United States, east coast, from Florida to Nova Scotia. Methods We utilised nearly 1.4 million kilometres of line transect survey data collected from 1997 to 2020 along the East Coast of the United States to evaluate changes in the distribution of six Odontocete species. We assessed six model specifications of generalise additive models that varied in the extent of temporal and spatial variability incorporated. Results We found that the best performing model specifications included temporally dynamic species–environment relationships and temporally dynamic spatial terms. These model specifications identified significant poleward range shifts in all species for which we had sufficient data across their range. In contrast, model specifications which only included static terms performed poorly and identified limited or no spatial shifts. Main Conclusions These results advance our predictive capabilities from static species–environment relationships for marine predators and demonstrate the importance of carefully considering assumptions and model specifications when modelling changes to distributions. The odontocete range shifts we identified are likely to have substantial ecosystem impacts, and the framework we present offers a diagnostic approach for modelling and identifying range shifts in other wide‐ranging species.