Science storytelling

In August, I was privileged to attend a storytelling workshop with Denise Withers, as part of the genomics entrepreneurship program. Denise is convinced that every research proposal can be boiled down to a 3 minute sales pitch that your neighbor can understand, though it may take a lot of hard work. Denise spends her time coaching scientists and entrepreneurs to do just that.

These days, it isn’t enough to have excellent science in a proposal for funding or an evaluation report. Eventually, administrators and politicians also need to be convinced that we deserve to get a slice of their scarce financial resources. Research proposals increasingly need to demonstrate impact. This is some steps removed from direct project outputs, with all the risk and uncertainty involved, so scientists are understandably hesitant to ‘promise the moon’, while at the same time feeling the pressure to do just that. The funding agency may then appear be “playing Civilization”: starting at level  3 and investing 10 units in science should automatically result in reaching level 4. But real life is not like that, is it?

Science is characterized by the unexpected, so rather than a problem, this hurdle allows the characters to shine … if we tell the story well. Seeing the story in our research work requires zooming out from the detailed focus in which we spend our daily lives. Developing a logic model and a theory of change for the project are steps which can help us develop the overview. Doing it as a team may enhance mutual understanding of the various project activities, how they are connected, and any unstated assumptions. It can be built into the funding proposal to provide ongoing evaluation. And towards the projects’ end, we scientists turn into heroes having overcome insurmountable obstacles, to reach places where we perhaps didn’t expect to get to, and having changed the world just a little bit… And this, more than the 100 page report and the 10 papers, is what evaluators (and your neighbor) will remember. Although the 100 pages and the 10 papers are still needed.

Example: A story of the AdapTree Project in three minutes, based on logic models and theory of change.

Due to climate change, the seed sources being planted in our forest are becoming increasingly mismatched to the climates they will experience. This mismatch can be addressed by moving seed over the landscape where needed. This requires adjusting the rules of what can be planted where. However, we must first know which seed sources are best adapted to given sites, both now and in the future.

We reduced the scope of this problem to the two provinces of BC and AB and two important commercial species, interior spruce and lodgepole pine. However, we still can’t plant every seed source on every planting site and wait thirty years for the results. Instead, we looked at the underlying patterns of adaptation in the genome of these species.

Because of the enormous size of conifer genomes, we zoomed in on >25,000 candidate genes. These genes were then sequenced in 500 natural seedlots from all over to find genotypic variation at the base-pair level. The adaptive value was revealed by growing plants of the same seedlots in controlled environments simulating present and future climates.

We then developed a SNP chip to evaluate adaptive genetic potential in orchard seed lots, whose top quality seed is used by preference in tree planting. Using that, we can evaluate the ecological risks of moving seed sources over the landscape or ‘assisted migration’, including the status quo. This allows policy makers to update seed transfer guidelines as climate changes. A socio-economic research component ensures that stakeholders can make informed decisions regarding new strategies.

The main outcome of AdapTree is information about the adaptive potential of seed sources regarding present and near-future climates.  The resulting impact, long-term, would be reduced plantation failures, increased forest productivity, and resilient forest-dependent communities. A short term outcome is the development of sampling and research methods that are transferable to other seed sources, species and climates.


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