Coordinator: Jorge Assis (email@example.com)
Scope: The course covers the interactions and potential impacts of global climate changes (past, ongoing and future) on different levels of marine biodiversity. It is mostly hands-on oriented, with a strong component on biodiversity and climate data acquisition, management and visualisation (e.g., the new Representative Concentration Pathway scenarios of climate change), as well as on ecological modelling using state of the art mechanistic and correlative approaches (e.g., machine learning algorithms).
Target audience: The course is targeted to the fields of marine biology, ecology, conservation and evolution. Students must be fluent in English and have some basic knowledge on marine ecology, statistics and R computing language (although not mandatory). Students are highly encouraged to bring their own datasets (if data are not available, the professor will provide his own data).
6 h of lectures + 24h of computer lab classes + 50 h of independent work.
Independent study will be based on hands-on tutorials of R programming language. This will be of Problem learning, following practical classes, and aims to the development of an individual written work addressing a relevant research question.
Registration on electives (except field and Lab methods) are dealt with directly with the Serviços Académicos. The MBM coordination will not interfere in that process. Be attentive that if you enrol in a subject and later on you want to change you are subject to a small penalty, so think well beforehand on what are your best options.
1. One final exam (multiple choice; e.g., moodle environment).
2. One individual written work about the interactions OR impact of global climate changes (past OR future) in one of the different levels of biodiversity and ecological group. This can be the projection of future range shifts, predicting marine invasion processes or identifying the main climatic drivers of evolution and diversification. Students can bring their own bioclimatic / biodiversity datasets.