Presentation: macroevolutionary perspective on adaptation to environmental change

I’ve just posted a presentation I gave at the Evolutionary Potential in Marine Populations at the AWI station in Sylt to SlideShare. The notes that go with the slides follow below.

 

Slide 1

  • different talk than most in this workshop
  • deeper back in time, coarser picture
  • many species and overall patterns

Slide 2

  • these folks did the hard work

Slide 3

  • title says adaptation to environmental change => focus is on temperature
  • this is how sea surface temperature evolved during the Cenozoic

Slide 4

  • instead of using fossils, our goal was to see if similar conclusions could be achieved using phylogenies
  • starting from phylogeny and values for sea surface temperature affinities at tips

Slide 5

  • inference about past using models of evolutionary change
  • this way we can study how evolution of thermal affinities relate to figure below
  • since the phylogeny includes speciation events (bifurcations) we can relate niche evolution to diversification

Slide 6

  • main goal is exploration of techniques
  • these are the specific questions we set out to answer

Slide 7

  • our two model systems

Slide 8

  • first question

Slide 9

  • evolutionary change in continuous character usually modeled using simple diffusion model
  • graph => several simulations under same rate
  • parameter => rate of change => sigma^2

Slide 10

  • bigger sigma^2
  • optimize the model => rate of change is quantified (estimated)

Slide 11

  • to answer question whether niches evolve faster when climate is changing
  • we subdivided the tree into upward, downward and stable trends in SST
  • optimize diffusion model with 3 different rates
  • how does sigma^2 compare between conditions

Slide 12

  • big difference between sigma^2 of stable vs the other two
  • more evolution in warming and cooling periods => looks promising
  • model is a substantially better fit than the null model with only one identical rate for the regimes
  • how accurate are these estimates => don’t know => simulations being done to find out

Slide 13

  • Codium is very different story
  • likelihood surface as flat as a pancake
  • not enough information to solve the parameter optimization problem

Slide 14

  • next question => adaptation

Slide 15

  • model derived from diffusion model
  • selection in addition to diffusion
  • rate of diffusion sigma^2
  • selective force (measured by alpha) towards an optimum value (in our case temperature optimum)

Slide 16

  • we’re going to try and find out whether optimum theta differs between warming, stable and cooling
  • sigma squared and alpha are kept constant at their ML estimates

Slide 17

  • model with selection does a better job at explaining evolution of SST preferences in both cases
  • Dictyota => very strange result => higher optimum for cooling periods than for warming periods
  • potential reasons: (1) flat likelihood surface with slightly better fit for this, (2) shaky molecular clock
  • Codium did optimize nicely this time
  • somewhat more reasonable order of values although 120º for stable condition is problematic

Slide 18

  • does the profile predict the adaptation optimum at a fine scale?
  • does this predict the pattern of SST evolution better than models in which there is no such association?

Slide 19

  • new procedure that permits testing these sorts of questions
  • skip the details (1) it is based on the same type of model as before, (2) not all parameters were automatically optimized, (3) SST optimum was varied through time following profile
  • I’ve been having some unanticipated problems with the matrix calculations involved in the optimization => work in progress

Slide 20

  • last question => are speciation-extinction dynamics influenced by niche evolution

Slide 21

  • work stems from my interest in diversity patterns
  • typical diversity patterns: well-characterized LDG
  • many possible explanations => focus here is on species turnover and how rates of diversification relate to the niche

Slide 22

  • seaweeds don’t follow general rules => bimodal diversity pattern
  • do same evolutionary processes hold or is diversification faster in temperate habitats?

Slide 23

  • Codium is suitable case study with similar diversity map

Slide 24

  • evolution of SST affinities traced along phylogeny
  • clade 3: almost half of all species in young clade, only 25 Ma
  • seems to be associated with move from temperate into tropics
  • logical question: is diversification faster in tropics

Slide 25

  • model of diversification dynamics in which diversification is function of SST

Slide 26

  • optimum value of beta => positive association between SST and diversification
  • higher rates in tropics
  • so process seems similar to other organisms and reasons for bimodal diversity pattern has to be sought elsewhere

Slide 27

  • that’s what we found for Codium

Slide 28

  • no such thing for Dictyota => constant diversification explains it better

Slide 29

  • previous test only checked for very simple relationship between SST and diversification
  • many other types of relationships you could imagine
  • for example one could expect that clades whose niches are more evolvable manage to diversify more rapidly
  • we do seem to find that in Dictyota
  • split phylogeny up in major clades
  • positive relationship between rate of SST evolution and diversification

Slide 30

  • slope very deviant from that simulated under null model

Slide 31

  • lineages with many allopatric sister species along latitudinal thermal gradient diversify more rapidly
  • we seem to have a situation where clades that some clades manage to speciate more often along the latitudinal thermal gradient than others
  • clades that do, diversify more rapidly, probably because their presence in both temperate and more tropical habitats permits further radiation in those habitats

Slide 32

  • there are definitely caveats to the approach proposed here

Slide 33

  • overview of some caveats

Slide 34

  • for me, three conclusions emerge from these experiments
  • results are very taxon specific => very little generality in what we find
  • lots of uncertainties and sometimes simply not enough data to even get the models to optimize => these techniques can be a piece of the puzzle but with them alone we’re never going to get a fly-on-the-wall perspective of what happened during evolutionary history
  • it’s going to take a lot of intimate face-to-face time with my computer to get a better understanding of how far we can take these methods
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