Dante Horemans

ORCID

0000-0003-0106-8122

Research Project

Our research focuses on ecological forecasting and understanding broader ecological dynamics in estuarine and coastal systems. We study both primary producers and higher trophic species using laboratory, in situ, and remote sensing data, coupled with state-of-the-art hybrid numerical modeling approaches. Specifically, we integrate process-based models to identify driving factors and feedback mechanisms within dynamic systems, and combine them with machine learning and satellite remote sensing to improve applicability and predictive capacity.

We have focused on forecasting harmful algal blooms (HABs) through the development of hybrid numerical models that predict bloom occurrence in the Chesapeake Bay based on physical-biogeochemical environmental conditions. We examined how machine learning model complexity (Horemans et al., 2023) and coupling of processed-based models with machine learning (Horemans et al., 2024, 2025) affect the accuracy of our forecasts. Using these insights, we generate daily forecasts of the HAB species Prorocentrum minimum, and more recently including multiple HAB species that are of stakeholders’ concern. These forecasts are accessible through the operational Chesapeake Bay Environmental Forecasting System (https://www.vims.edu/cbefs.

Current Students

• M.S. Peichen Huang (entered 2025)

Current Student Committees:

• Colin Hawes (VIMS)

Selected Publications

Horemans, D. M. L., Lin, J. C., Friedrichs, M. A. M., St-Laurent, P., Hood, R. R., & Brown, C. W. (2025). The effect of collinearity between observed and model derived training variables on estuarine algal species distribution models. Ecological Informatics, 90, 103225. https://doi.org/10.1016/J.ECOINF.2025.103225

Horemans, D. M. L., Friedrichs, M. A. M., St-Laurent, P., Hood, R. R., & Brown, C. W. (2024). Evaluating the skill of correlative species distribution models trained with mechanistic model output. Ecological Modelling, 491, 110692. https://doi.org/10.1016/J.ECOLMODEL.2024.110692

Horemans, D. M. L., Friedrichs, M. A. M., St-Laurent, P., Hood, R. R., & Brown, C. W. (2023). Forecasting Prorocentrum minimum blooms in the Chesapeake Bay using empirical habitat models. Frontiers in Marine Science, 10, 433. https://doi.org/10.3389/fmars.2023.1127649