Summer 2019 conferences

Conference season is in full swing and the Aitken lab is travelling near and far to share what we’ve been up to.

Here’s a list of recent and upcoming conference talks by lab members and associates.

Evolution June 21-25

Combining exome capture and pool-seq: Lessons from three conifer species. Brandon Lind.

Evolution of phenology along elevation gradients: insights from different modeling approaches. Ophélie Ronce, Isabelle Chuine, Julie Gauzere, Sylvain Delzon, Luis-Miguel Chevin

Western Forest Genetics Association Conference June 24-26

Phenotypic and genomic patterns of climate adaptation in western larch to assess assisted migration strategies with climate change. Beth Roskilly, Brandon Lind, Mengmeng Lu, Sam Yeaman, Sally N. Aitken

A forest of information: Comparing phenotypic, genomic and climatic data for managing climate adaptation. Colin R. Mahony, Ian R. MacLachlan, Brandon M. Lind, Jeremy B. Yoder, Tongli Wang, Sally N. Aitken

SMBE July 21-25

Patterns of genetic diversity around protein-coding exons and conserved non-coding elements are explained by strong selective sweeps in mice. Tom Booker. Poster session.

Canadian Forest Genetics Association Conference Aug 19-23
Does local adaptation to drought need to be considered in assisted gene flow strategies for Douglas-fir reforestation? Rafael Candido Ribeiro. August 20 5pm poster session.

IUFRO World Congress Sep 29 – Oct 5

Does local adaptation to drought need to be considered in assisted gene flow strategies for Douglas-fir reforestation? Rafael Candido Ribeiro. In session “B2a Trees on the move: range shifts, potential for genetic adaptation and assisted migration”


The Homebrew Series: Inferring demographic history with ABC, by Joane Elleouet and Sally Aitken

Want to know about the history of the populations you’re studying? Joane Elleouet and Sally Aitken see how far Approximate Bayesian Computation (ABC) and your sequencing method of choice can take you in a new paper in Molecular Ecology Resources.

20 years ago, Tavaré et al. used ABC to estimate that the time to coalescence for the human population based on the Y chromosome was about 157,300 years ago (but!) and evolutionary biologists were off to the races. Now ABC is a common tool in the field with many software implementations to choose from. A lot else has happened in the last 20 years – like huge advances in genotyping technology. Even for non-model species, we can now get lots of genomic data for cheaper than ever using reduced representation library sequencing methods like genotyping-by-sequencing and GBS.

But how well does ABC perform with RRL data and different demographic models? What are its limitations? How do you make the best choices for your sequencing efforts? To answer these questions, Jo simulated data sets for 4 kinds of demographic models and 5 types of sequencing efforts and performed ABC on those datasets. She compared different model’s performance with

  • phased and unphased data, (phasing doesn’t help)
  • data from lots of short reads vs fewer, longer sequences (lots of short reads just as good)
  • different times since colonization, (depends on parameter value and demographic model)
  • tradeoffs between number and individuals and sampling depth at different sequencing error rates, (go for more individuals over depth)
  • and compared ABC to an SFS method. (similar)

As far as the different demographic models they consider, they find that ABC can be used with data from reduced representation library sequencing methods to precisely infer very simple demographic models, but not complex ones.

Here’s what Aitken Lab members had to say after reading the paper:

What’s your takeaway from this paper?

Reader 1: This paper provides several rules of thumb for inferring demographic events from incomplete, fragmented genomic data. Demographic models should be kept as simple as possible, and numerous short sequences from many individuals is preferable to fewer long sequences from a small sample of the population.

Reader 2: You’d better know what kind of demographic history your population has before you start trying to estimate parameters!

Reader 3: Estimating demographic parameters with ABC has limitations even with very simple demographic models

What’s the coolest thing about this paper?

R1: Improving techniques to infer the ancient demographic history of any species you like, not just model species.

R2: I didn’t realize how hard inferring demographic history is, even with so much genomic data. The extensive simulations are really impressive and convincing.

R3: Adding a realistic component by testing the effect of sequencing depth and error

What questions are you left with after reading this paper?

R2: How often do researchers know the “right” model of demographic history to try to infer? Whether or not to include migration or how many populations there have been? How do people figure this out?

R3: What if summary statistics lead to a too drastic loss of data? is there a better way to summarize the data while keeping crucial information?



The Homebrew series: Novel climates in BC, by Colin Mahony et al.

The year got off to a flying start in the Aitken lab, with a Forest Ecology and Management publication from our own Colin Mahony and his collaborators. Basically, if everyone else’s year started on a rocket, it’d better be running on clean energy. Following on his recent Global Change Biology publication, Colin shows us that human induced climate change is no joke to our beloved BC forests, and the future might be more grim than previously anticipated by forest managers. Colin shows that novel climates, climates that are currently not experienced anywhere in BC, might arise in BC by mid-century, and that these unprecedented conditions may fall under the radar with the current use of the BEC projection model.

At the moment, the best way to decide what forest tree species to plant where is to project ourselves forward about 50 years (about the time it takes for forestry trees to become fully mature) and determine what climate will be occurring then in the area of interest. Tree species and provenances can then be selected from BC areas with the closest contemporary climate. This is all done through the BEC system, which groups BC climatic conditions in an ecologically meaningful set of units.

The crucial question is: what if there is no place in BC that matches the predicted future climate of an area? Should we expand our range to all of North America? Well, Colin et al. actually show that some mid-century novel climates of BC have no equivalent in other parts of North America either…

These results highlight the limitations of the BEC projection model: 2 of the measures of the method, analog similarity and ensemble agreement, are actually diagnostic of novel climates at high values. In other words, projections of future BEC climates can be erroneous in novel climates, in particular by creating an appearance of reduced climate disruption and uncertainty. In providing a map of where novel climates are likely, this research points to regions where BEC projections are reliable from areas where other analytical approaches are required.

Here’s what lab members reading the paper had to say about it:

What’s our takeaway from this paper?

We don’t know what we think we know about future climate/ecology predictions in lowlands, coastal regions, and NE BC, because the climates there are unlike all others in North America. Forest management practices should rely on a species-specific basis, and be adapted with new management approaches—not found in other BEC zones’ practices.

“The best match is not necessarily a good match.” Depending on the analog pool, amount of climate change, and particular decision framework you use, you may find that the best climate analog is nothing like the actual climate. If I’m picking my pants from the kids section, the biggest ones are the best fit, but I can still only get one leg in them!

What’s the coolest thing about this paper?

Standardizing changes in climate by interannual variablility is a really cool way of expressing their ecological relevance, and if you want to know more about this smart method, check out Mahony et al. 2017. Also, despite the uncertainties in predicting the future of ecosystems under climate change, this study presents a fine-scale adjustment on what was known and on what was expected for British Columbian forests under climate change. The provided information will be key to reducing the risks of resource losses in the province.

Figures 2 and 3 are such awesome figures. They clearly and concisely explain a concept that can be difficult to get your head around.

What question are we left with after reading this paper?

What would the scenario of novel climates be on a global scale using the same approach?

What management methods should we use in areas where novel climates are predicted to occur?

Is Colin’s middle name Reginald? Rudolphus? Royce? An enduring mystery…

I didn’t really understand the pros and cons of the linear novelty detection method vs random forests on first read and was pretty confused trying to interpret the random forests figures. I’d love to see a talk breaking down the random forests method and results in isolation. Or maybe I should just read the supplementary material?

Reading climate novelty papers like this makes me want a tool that lets me plug in coordinates, a year and an emissions scenario and get a map of climate analogs and their associated ecosystems.


An interview with Jill Hamilton

Last year, Aitken lab alum Jill Hamilton published a paper in the American Journal of Botany, Genetic and morphological structure of a spruce hybrid (Picea sitchensis × P. glauca) zone along a climatic gradient.  I interviewed Jill about her paper and got the story behind the research last August. Listen to the interview:

Interview with Jill Hamilton

Jill is currently a postdoctoral fellow with Dr. Janice Cook at the University of Alberta where she is part of large genomics projects on both white spruce and lodgepole/jack pine. She will be joining Johanna Schmitt’s lab at UC Davis this summer.