Melissa Ann Collier

The paper uses machine learning to analyze skin lesions and rake marks in bottlenose dolphins to assess disease vulnerability and environmental influences on health.

The paper shows density increases network connectedness in wild animal populations, but effects saturate, with stronger spatial than social impacts.

This study develops a data-driven epidemiological model for bottlenose dolphins to understand disease spread and improve outbreak preparedness by analyzing spatial and contact structures.

This study examines how social breathing behavior in bottlenose dolphins influences respiratory virus transmission risk by analyzing contact patterns and demographic differences.

This paper develops a machine learning model to detect skin lesions in bottlenose dolphins and estimates lesion prevalence, finding high model accuracy and a positive relationship between lesion presence and gregariousness.