Machine Learning
Using machine learning algorithms, we build models that can forecast species distributions and ecosystem responses under various future scenarios.
Explore our projectsPast climate conditions are main drivers shaping the genetic diversity of global marine forests, providing key conservatio...
Marine life across the globe organizes into 6 functionally similar trophic communities, driven by temperature and depth.
Future climate change could cause up to 10.26% loss in seagrass biomass, stressing the critical need to fulfill the Paris ...
Climate change is projected to severely reshape Azorean coastal marine biodiversity by the end of century.
Data science reveals region-specific drivers for hazardous Portuguese man o' war blooms
Flawed machine learning led to inaccurate seagrass conservation priorities.
Projections indicate that climate change will drive a significant spatial redistribution of seagrass species globally.
Climate change threatens to disrupt these underwater havens, but we can take action. Our new research provides a roadmap ...
Potential climate-induced range shifts and losses in biomass, emphasizing the urgency of international climate agreements ...
A database structured under Darwin Core standards, offering a stable, straightforward and flexible framework for sharing b...
A project to enhance aquaculture sustainability and suitability while fostering interdisciplinary collaboration.
A project aimed at investigating the effects of climate change on key marine forest species, such as kelp, fucoids and col...