Evaluating the validity of using sea surface temperature as an alternative to bottom temperature in American lobster habitat suitability modeling

Oceanographic information is critical for understanding the capacity of a given habitat to support a certain species. Habitat Suitability Index (HSI) models are widely used to describe the relative suitability of an area by defining the relationship between key environmental variables and the species’ abundance. The Gulf of Maine (GOM) region provides essential habitat for American lobster (Homarus americanus) and has been identified as one of the most rapidly warming ocean ecosystems in the world. Understanding how climate change may influence American lobster habitat is imperative in planning for an uncertain future and informing the lobster industry and management. However, a lack and lag of bottom temperature (BT) data availability has impeded the development and operationalization of projected lobster suitable habitat. In this study, we first examined the ability of sea surface temperature (SST) in representing the spatiotemporal variability of BT in the GOM. We then compared the suitability index (SI) curves and HSI maps generated by each temperature dataset. Finally, we assessed the correlation level of the predicted HSI values with lobster abundance. Our study demonstrated that the SST-based model effectively captures the general spatial patterns of suitable habitats predicted by the BT-based model. However, the performance of the SST-based model when compared to the BT-based model was less consistent across seasons and between eastern and western GOM regions, which may be attributed to the seasonal stratification and nearshore physical oceanographic dynamics. The SST-based HSI model had a slightly higher correlation with observed abundance data at most depth intervals and particularly in recent fall periods. Our findings suggest that in less stratified regions, SST can serve as a reasonable alternative to BT and may even provide a more responsive indicator of warming. Leveraging data-rich SST in lobster HSI models can enhance the operationalization of habitat projections and support timely management responses under these conditions. However, in stratified regions, reliance on SST alone may underestimate suitable habitat. As ocean stratification may intensify under climate change, further evaluation of the alternative validity is warranted. The methodology and analysis framework of this study can also be applied to other species and areas where limited BT observations constrain habitat modeling and stratification is short-lived, allowing for more prompt identification of and response to climate impacts.