Applying Deep Learning to Quantify Drivers of Long-Term Ecological Change in a Swedish Marine Protected Area.

ABSTRACT: In this study, we trained an object-detection model to classify 17 benthic invertebrate taxa in archived footage of a study site on the northern west coast of Sweden (a wall section of the Koster Fjord) within the Swedish marine protected area Kosterhavet National Park. The model displayed a mean average precision score of 0.738 and was applied to footage from 1997 to 2023, generating a dataset of 72,369 occurrence records. The dataset was used to quantify depth distributions and abundance trends of both individual taxa and functional groups over time. Depth distributions for 15 of 17 taxa occurred at depths ≥ 45 m. Distributions of 11 taxa aligned with empirical observations, and for the remaining six taxa, we propose expanded depth distributions in the area. Abundances over time signific

SUBMITTER: Nilsson CL 

PROVIDER: S-EPMC12404701 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature