Characterizing a conifer species range expansion using genomic and tree ring data Joane Elleouet Sally Aitken.

Characterizing a conifer species range expansion using genomic and tree ring data
Joane Elleouet Sally Aitken

Context In my favorite book, Ocean Sea (Oceano mare), one of the characters, Professor Bartleboom, is a scientist. For his research he spends his days on the beach watching this line separating the sea from the earth in an attempt to understand the end of the ocean. And this is part of his broader, life-long research theme that is the limits of nature. The seaside is constantly moving, showing oscillations on several time scales with waves and tides, and parameter and process that apply in the middle don’t hold anymore on the edge.

Context The limit I am interested in is the limit of the forest. Tree species range limits, and fronts of expansion. I want to understand the processes that shape them and their genetic effect on populations.

Context “Bottleneck” effect Allele surfing Excoffier and Ray 2008
The genetics of range expansions has already been studied through simulations or experiments: We know that in a stepping stone colonization process each founding event is associated with a decrease in genetic diversity due to a demographic bottleneck. Likewise simulations have shown that some rare alleles at the front of expansion, represented as the red beads on the figure, can become prevalant by chance, surfing the wave of expansion and increasing differentiation. This process has been experimentally shown in 2 d expansion with bacteria as you can see on the bottom figure. Excoffier and Ray 2008

Effect of long-distance dispersal
Context Effect of long-distance dispersal Bialozyt et al. 2006 But all those processes can be altered by the characteristics of organisms. In the case of trees, the frequency of ldd events has been discussed: it might explain pace of exp, and the more there are the more patchy and diverse landscape is. Also life history traits of tree species (long gen time) can anneal bottlneck effects. Those studies are all simulation studies though. Effect of life history traits Annuals Trees Austerlitz et al. 2000

Picea Sitchensis Context > Study system
A L A S K A Picea Sitchensis To bring empirical knowledge to the question of tree expansion dynamics and its genetic effects I focused on the case of spatial expansion choosing Sitka spruce as a study system. Let me briefly introduce the species first SS happens to have the steepest genetic cline of climate adaptation, across its 22 degrees of latitudinal range.

Context > Study system
A L A S K A A L A S K A Picea Sitchensis

2. Demographic processes?
Context > Study system > 1.Pace of colonization Objectives: Pace of colonization?  dendrochronology 2. Demographic processes? Approximate Bayesian computation & genomic data analysis Genetic consequences for populations? Today I will only present the 2 first points

Objectives: Pace of colonization?  dendrochronology
Context > Study system > 1.Pace of colonization Objectives: Pace of colonization?  dendrochronology With on top: -ages -play the video -tree ring profiles -tree rings of all 10 first years

Context > Study system > 1.Pace of colonization
With on top: -ages -play the video -tree ring profiles -tree rings of all 10 first years

But I wanted a more quantitative description

Needle and bark DNA tissue
Context > Study system > 1.Pace of colonization Needle and bark DNA tissue Tree cores 1 2 3 4

1. Dendro to date forest establishment 2. Going to be useful when doing genetic analyses. Understanding how linear forest expansion is actually helps understanding pace of adaptation at a pop scale.

Direction of colonization
Context > Study system > 1.Pace of colonization 1. Dendro to date forest establishment 2. Going to be useful when doing genetic analyses. Understanding how linear forest expansion is actually helps understanding pace of adaptation at a pop scale. Direction of colonization

Estimated time of canopy closure
Context > Study system > 1.Pace of colonization 1. Dendro to date forest establishment 2. Going to be useful when doing genetic analyses. Understanding how linear forest expansion is actually helps understanding pace of adaptation at a pop scale. Estimated time of canopy closure

2016 Context > Study system > 1.Pace of colonization

Expansion = linear at large scale, patchy at small scale
Context > Study system > 1.Pace of colonization It is possible to link tree ring patterns to time of canopy closure in recently established forests Expansion = linear at large scale, patchy at small scale Colonization is ongoing 1. Dendro to date forest establishment 2. Going to be useful when doing genetic analyses. Understanding how linear forest expansion is actually helps understanding pace of adaptation at a pop scale.

2. Demographic processes?
Context > Study system > 1.Pace of colonization > 2.Demographic processes Objectives: Pace of colonization?  dendrochronology 2. Demographic processes? Approximate Bayesian computation & genomic data analysis Genetic consequences for populations? Good idea of pattern of expansion on Kodiak but we lose the signal as forests are oder (and canoty gets older than a tree can be) so to infer on a longer timescale we need other methods -> genetics.

Context > Study system > 1. Pace of colonization > 2
Context > Study system > 1.Pace of colonization > 2.Demographic processes

Context > Study system > 1.Pace of colonization > 2.Demographic processes Time of expansion? present t

Context > Study system > 1.Pace of colonization > 2.Demographic processes Time of expansion? Bottleneck severity? present t

Context > Study system > 1.Pace of colonization > 2.Demographic processes Time of expansion? Bottleneck severity? Bottleneck duration? present t

Context > Study system > 1.Pace of colonization > 2.Demographic processes Time of expansion? Bottleneck severity? Steady growth? present t

Context > Study system > 1.Pace of colonization > 2.Demographic processes Time of expansion? Gene flow? Bottleneck severity? Steady growth? present t

Method: ABC (Approximate Bayesian Computation)
Choose models Prior distribution of model parameters mod1 mod2 mod3 mod4 Simulate datasets Sample DNA from populations of interest extract dataset 3800 unlinked SNPs

Choose models Prior distribution of model parameters mod1 mod2 mod3 mod4 summary statistics calculate distance Simulate datasets summarized simulated datasets K H FST π summarized obseved dataset Sample DNA from populations of interest extract dataset 3800 unlinked SNPs

Choose models Prior distribution of model parameters infer posterior distributions mod1 mod2 mod3 mod4 summary statistics calculate distance Simulate datasets summarized simulated datasets select best simulated datasets K H FST π summarized obseved dataset Sample DNA from populations of interest extract dataset output model fit 3800 unlinked SNPs mod1 mod2 mod3 mod

Choose models Prior distribution of model parameters infer posterior distributions mod1 mod2 mod3 mod4 summary statistics calculate distance Simulate datasets summarized simulated datasets select best simulated datasets K H FST π summarized obseved dataset Sample DNA from populations of interest extract dataset output model fit 3800 unlinked SNPs mod1 0 mod mod mod

Choose models Prior distribution of model parameters infer posterior distributions mod1 mod2 mod3 mod4 summary statistics calculate distance Simulate datasets summarized simulated datasets select best simulated datasets K H FST π mod5 summarized obseved dataset Sample DNA from populations of interest extract dataset output model fit 3800 unlinked SNPs mod1 0 mod mod mod mod

Context > Study system > 1.Pace of colonization > 2.Demographic processes present t

Context > Study system > 1.Pace of colonization > 2.Demographic processes m1-2 m1-2 Pop size 1 present t ini. pop size Pop size 2

initial gene flow was stronger than current gene flow
Context > Study system > 1.Pace of colonization > 2.Demographic processes initial gene flow was stronger than current gene flow several migration sources More work to be done on ABC before moving to more complex scenarii

Demographic processes
Context > Study system > 1.Pace of colonization > 2.Demographic processes > 3.Future directions What’s next Demographic processes wider geographic area explore alternative models and datasets Genetic consequences for populations?  link past demography to genetic diversity and patterns of selection

Acknowledgements Supervision: Sally Aitken
Amazing field assistants: Christine Chourmouzis, Bert Terhart, Ian McLachlan, Sally Aitken, Jon Degner, Vincent Hanlon Precious help at UBC: Pia Smets, Elissa Sweeney-Bergen Alaskan experts: Keith Coulter, Karl Potts, Stacy Studebaker, Tash Shaheed, Bill Pyle, Ed Berg, John Morton

Choose models mod1 mod2 mod3 mod4 mod5

Characterizing a conifer species range expansion using genomic and tree ring data Joane Elleouet Sally Aitken.

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Characterizing a conifer species range expansion using genomic and tree ring data Joane Elleouet Sally Aitken.

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Presentation on theme: "Characterizing a conifer species range expansion using genomic and tree ring data Joane Elleouet Sally Aitken."— Presentation transcript:

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