The objective of this project was to understand evolution on different timescales. On the generation-to-generation timescale, we are able to predict evolution given information about parental phenotypes and the environment. However, on a longer time scale, such as those that represented in historical records and the fossil record, we are much less able to predict the diversity of forms that we observe. Macroevolution.abc hopes to bridge our gap in understanding evolutionary processes by elevating an understudied group called bryozoans, to a model system in which to link short-term and long-term evolutionary processes. Why are bryozoans especially good for this purpose?
Bryozoans are widely distributed in today's oceans, meaning that even when we study fossils from the deep past, we have contemporary analogues to help us understand ancient forms. They are sessile, benthic and calcify, characteristics that make them very fossilizable in the (shallow and deep) past. They are also clonal and polymorphic, traits that allow us to infer how fecund they are (or have been in the past), how they compete for space (or have competed in the past). This duality (contemporary and fossils) of observation of factors important for evolution (fitness and competition) renders bryozoans a perfect model system for the purpose of macroevolution.abc.
Bryozoans are, however, severely understudied. They are marine foulers and are an annoyance, perhaps, to leisure craft owners and companies that own marine installations. Some species have been prospected for chemicals that may be useful in cancer treatments. This said, bryozoans themselves do not have a high direct utilitarian value for the general public. However, what they can teach us about evolution is potentially applicable to problems as diverse as pandemics (e.g. using tools of inferences developed in evolutionary biology) to conservation management and climate change ecology (e.g. using ecological inference to suggest management practices).
There were two main themes in macroevolution.abc. The first was to infer past population and diversification dynamics. To do so, we developed machine-learning approaches to compile past occurrences of bryozoans available in the literature; collected new field data; and developed new statistical models to understand the contribution of ecological competition in the past to such dynamics. The second was to understand what contributed to the evolutionary success (or failure) of different groups of species with varying characteristics. To do so, we sequenced more 1000 different species of bryozoans, inferred their phylogenetic relationships, developed deep-learning approaches to be able to measure phenotypic traits, including those pertaining to fitness (e.g. fecundity).
In summary, to use bryozoans for answering big evolutionary questions, this project seeks to infer the evolutionary relationships among species of bryozoans all over the world and to study how they have evolved by collecting and analysing data on several different time scales (generational, decadal, tens of thousands of years and millions of years).
