For thousands of years, we have been changing the environment that surrounds us to satisfy our needs and to make our life safer, easier and more prosperous. However, the increasing magnitude of alterations that we impose on our environment, from building dams to deforestation, impacts the biosphere at every level. Many organisms are unable to adapt at a sufficient rate, resulting in a growing list of endangered species, extinctions and loss of biodiversity. To avoid that, it is crucial that we understand the process of adaptation and its limits in a changing environment.
The problem of adaptation to a changing environment is central to many studies from ecology, evolution and genetics, both theoretical and experimental.
Despite multiple approaches, however, our current understanding of this problem is not satisfactory, and we are unable to estimate the impact of our activities.
One of the main complications stems from the fact that each discipline has been developing its own models and methods, relying on different sets of assumptions. Even though connected by the same problem, there is a severe lack of communication between disciplines.
While attempts to synthesize approaches and re-unite fields are relatively frequent, their relevance may be temporary. As all the research fields are quickly expanding, and more models are being developed, review articles can quickly be outdated. It is challenging to keep track of all the developments, especially across different fields.
Therefore, we created EcoEvoModels - an extendable database of models of adaptation to a changing environment. The models are entered into the database with detailed and structured information about their parameter space, timescales, assumptions, and prediction. The database grows with the expanding fields - new models are added continuously.
The database will be supplemented by a glossary of terms to prevent confusion when the terminology differs between fields.
This database will grow with the expanding fields - new models can be added even after the end of this project, which will ensure the long-term relevance of my work. The database will be advertised among researchers of relevant fields; adding one's own model into the database will increase its visibility. Furthermore, I will search for empirical studies of adaptation to a changing environment that could support predictions generated by the models.