Models incorporating non-stationarity improve detection of climate-driven range shifts in odontocetes

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 utilized nearly 1.4 million kilometers of line transect survey data collected from 1997-2020 along the east coast of the United States to evaluate changes in the distrib..., Refer to the associated manuscript for details.
, , # Models incorporating non-stationarity improve detection of climate-driven range shifts in odontocetes

[https://doi.org/10.5061/dryad.0000000dm](https://doi.org/10.5061/dryad.0000000dm)

## Description of the data and file structure

Line transect survey data of 6 species of odontocetes collected from 1997 - 2020 on the eastern seaboard of North America from Florida to Nova Scotia.

### Files and variables

#### File: src_public_2.zip

**Description:** contains all source files used for analyses. Additional README is contained within the folder.

#### File: data_public_2.7z

**Description:** contains all data used for analyses. Additional README is contained within the folder.

## Code/software

R (any version) is the only software needed to read these files.,