In my previous post I simulated binary morphological trait data to evaluate the prevalence of cryptic diversity for morphologically complex and simple organisms. Here I aim to do the same thing for measurable (continuous) traits, which are often more abundant in algae  (i.e., there are more measurable traits than discrete traits).

In addition, I want to look in more detail at how directional selection may influence the diagnosability of species. This is relevant because it is well known that habitat can have a profound effect on algal phenotypes. Finally, I will investigate how habitat-induced phenotypic plasticity affects species diagnosability. As before, I will tackle this problem with simulations of morphological trait evolution (see last week’s post).

Simulation 1: Effect of number of traits on species diagnosability

With the first set of simulations, I want to check if last week’s conclusion that more complex lineages have a lower prevalence of cryptic species is also valid for measurable (continuous) traits. To do this, I simulated the evolution of continuous morphological traits evolve along a species tree. The simulation protocol is as follows:

  1. Simulate a Yule species tree (pbtree from phytools package) and rescale to have root-to-tip length of 1.
  2. Simulate evolution of the desired number of traits along the tree. I simulated under a simple diffusion process (Brownian motion model, σ2 = 1.0) using OUwie.sim from the OUwie package for this. Seems like using a bazooka to kill a mosquito, but the choice for OUwie will become clear below.
  3. The result of the previous step is a set of trait values for each species.
  4. Loop through all species pairs and see how many can be distinguished from one another based on the trait values.

This overall procedure is similar to what I did for discrete traits, but there are a couple of important differences…

First, it’s no longer possible to count the number of distinct morphologies. Traits that vary along a continuous scale will never be exactly the same so the concept of “unique morphology” doesn’t make sense anymore.

Second, I needed to come up with a way to have a realistic amount of intraspecific variation of the continuous traits in the generated datasets. The simulations return only a single trait value for each species. To solve this, I looked at my Halimeda morphometric datasets and noticed that the standard deviation of traits is typically about 15% of the mean value for those traits. So, to get variation of intraspecific trait values, I used a normal distribution with the simulated trait value as the mean and 15% of this value as the standard deviation. Not a particularly elegant way of simulating phenotypic variance in populations, but good enough for the purpose…

Lastly, for step 4 of the procedure, we need to calculate the percentage of species that can be distinguished from one another. This is easy for discrete traits (the character combinations of the two species are either identical or different), but quite difficult for continuous traits. How different do two species need to be to call them morphologically distinguishable? I decided to sample 20 values from the distribution of each trait (i.e., the normal distribution explained in the previous paragraph). This is an attractive solution because it is equivalent to constructing a morphometric dataset by taking measurements of all traits on 20 randomly selected samples from each species. Then, I compared the two species trait by trait. If one (or more) of the traits had non-overlapping ranges, the species were considered as distinguishable. In fact, I used the range between the 2.5 and 97.5 percentile of the sampled trait values to allow for a tiny bit of overlap. If there was overlap between the ranges of all traits, the species were considered indistinguishable.

Now let’s get back to the simulations. I started by running a simulation for 10 traits and 20 traits to see if simple organisms are harder to distinguish from each other than complex organisms. The number of taxa in the simulated trees was varied between 10 and 100 and the outcome was summarized into a boxplot. Remember that we previously saw that the number of species does not affect the percentage of distinguishable species, so a boxplot suffices to summarize the results. Here are the results:

boxplots1

As expected, the percentage distinguishable species is higher for complex organisms (72.5 % for organisms with 20 characters) than for simpler organisms (54.2% for organisms with 10 characters). This is congruent with what we found for discrete characters.

Simulation 2: Effect of habitat-induced selection on the phenotype

The second thing I wanted to look at is how selection on morphological traits would influence how easy it is to do distinguish species based on morphological traits. In this second set of simulations, I followed this procedure:

  1. Simulate a Yule species tree (pbtree from phytools package) and rescale to have root-to-tip length of 1.
  2. Simulate in which of five possible habitats the species reside. This is done by “simulation mapping” of a discrete trait with 5 states (representing 5 habitats) using the sim.history function in phytools. The rate of the Markov process controlling habitat evolution was set at 0.3 and it was enforced that all habitats are occupied at the end of the simulation.
  3. Simulate evolution of the desired number of traits along the tree.
    1. Half of the traits were simulated as before (no selection, simple Brownian motion model, σ2 = 1.0).
    2. The other half of the traits were simulated under directional selection, with an Ornstein-Uhlenbeck model that evolves towards different optimal trait values depending on which habitat the lineage in question occupies. Parameter values were α = 0.5, σ2 = 1.0 and θ = [1, 3, 5, 7, 9]. In other words, if a lineage is in habitat #1, the trait will be pulled towards the optimal value of θ1 = 1 with a strength of α = 0.5. For habitat #4, this would become a pull towards θ4 = 7 of the same strength α. The state at the root of the tree (θ0) was set at 5 (i.e., the median of the θ vector).
    3. OUwie.sim from the OUwie package was used to carry out the simulations.
  4. As before, the result of the previous step is a set of trait values for each species.
  5. Loop through all species pairs and see how many can be distinguished from one another based on the trait values, again using the procedures described above.

Here’s what came out of this simulation:

boxplots2

Pretty cool. There’s an increase of how many species can be distinguished from each other in both cases. While the increase from 54.2 to 59.7 for the 10-character situation is obviously not significant, the increase from 72.5 to 85.8 for the more complex organisms certainly is. I had not expected this result. I had expected a decrease. After all, habitat selection drives morphological traits to certain “optimum values”, and such traits would thus not contribute to distinguishing between species that live in the same habitat.

The reasoning above is true, but incomplete. Only 50% of the characters are driven towards optimum values while the other 50% evolve free from selective forces. Selection subdivides the morphologies into five habitat-specific categories, thereby subdividing the species distinguishability problem into five smaller sub-problems (one for each habitat). These smaller subproblems are easier to solve with the remaining characters that are not under selection, leading to an overall increase of species distinguishability compared to the simulation without selection.

Simulation 3: Effect of phenotypic plasticity in response to habitat

Clearly, selection is only part of the story. So far, I have assumed that every species lives in a single habitat. In most organisms, and this is certainly true for algae, one also has species that live in multiple environments and feature adaptive morphological plasticity in response to those environments.

The effect of plasticity in response to habitat is harder to simulate using the type of approach I’ve chosen, but here’s the simulation design I came up with:

  1. Simulate a Yule species tree (pbtree from phytools package) and rescale to have root-to-tip length of 1.
  2. Simulate which of five habitats the species live in as in the previous simulation.
  3. Simulate a binary trait to create lineages with and without phenotypic plasticity.
    1. Perform “simulation mapping” of a binary trait along the tree, where one state denotes plastic and the other non-plastic. This was done with sim.history (phytools).
    2. For simplicity and to avoid difficulties associated with plastic species returning to non-plastic, I forced the root state to be non-plastic and only allowed changes from non-plastic to plastic. The latter was achieved by setting the plastic to non-plastic rate to 10–10. The non-plastic to plastic rate was 1.0.
    3. I also forced the fraction of plastic and non-plastic species to be similar (at least 1/3 plastic and at least 1/3 non-plastic) by repeating the simulation mapping until this condition was met.
  4. Simulate evolution of the desired number of traits along the tree.
    1. Half of the traits were simulated without selection (Brownian motion model, σ2 = 1.0).
    2. The other half of the traits were simulated under directional selection with an Ornstein-Uhlenbeck model as described above (simulation 2).
    3. The difference with the simulation above is that lineages that show phenotypic plasticity were assumed to occupy all five habitats. For these lineages, five separate evolutionary tracks were simulated, i.e. one towards the optimum of each habitat.
    4. OUwie.sim from the OUwie package was used to carry out the simulations.
  5. The result of the previous step is a set of trait values for each species.
  6. Loop through all species pairs and see how many can be distinguished from one another based on the trait values, again using the procedures described above.

What’s different from before is that instead of having one mean trait value per species, we now end up with five mean trait values for plastic species (because they were simulated along 5 evolutionary tracks towards different optima). So I sampled 4 values from each of the corresponding five distributions (normal, mean = simulation outcome, standard deviation = 15% of mean). This resulted in 20 trait measurements for comparison to other species in step 6.

Here are the results:

boxplots3

Neat. The species distinguishability clearly drops from the condition with selection and without plasticity (59.7 to 48.6% for the simpler organisms and 85.5 to 69.7% for the more complex organisms). In other words, plasticity has a strongly negative effect on the potential to recognize species based on their morphology. Any advantages brought about by habitat selection (i.e. subdivision of the species distinguishability problem into sub-problems) are completely wiped out by the presence of species that have distinctive morphologies in the different habitats they inhabit.

Wrapping up

That was an interesting set of experiments. Let me just recapitulate the most important results:

  1. Species from character-poor lineages are more difficult to distinguish from one another than species from character-rich lineages.
  2. Selection towards habitat-specific phenotypic optima increases rather than decreases our ability to distinguish between species.
  3. Habitat-determined phenotypic plasticity within species greatly reduces the likelihood that one can distinguish between species based on morphology, even in complex organisms.

Obviously, these are just a handful of simulations, and I don’t expect these results to be valid across a wider range of parameter settings. For example, I would expect that point 2 may not hold if a greater proportion of characters are under selection. I would also anticipate that the relative importance of the drift (σ2) and directional (α) components of the Ornstein-Uhlenbeck model may change things. Perhaps I will explore this further for another post. Or you could do it yourself.

You can download the code for these simulations from here.

These results are also presented in a paper that is about to appear in Journal of Phycology. [UPDATE: This paper is now out here. A PDF is available here]

Algae have the annoying tendency to show high levels of cryptic diversity, i.e. with distinct species being morphologically indistinguishable. This has been shown repeatedly by first assessing species boundaries using DNA work or crossing studies, and subsequently comparing these species boundaries with morphological features.

I’ve always been interested in how morphological complexity of organisms relates to their tendency to produce cryptic species. When we found cryptic diversity in Pseudochlorodesmis, a genus in which the algal body is utterly simple, we argued that this may be due to its simplicity: “From a strictly morphological point of view, it is simple to conceive that the potential prevalence of cryptic diversity within any given taxon is a function of its morphological complexity. For example, if the morphology of the members of the taxon can be scored as a set of X binary characters, and morphological species boundaries are defined by a minimum of one character difference, the maximum number of morphologically determinable species increases exponentially with the number of characters available (N = 2X). In other words, for a higher taxon containing a given number of species, chances of encountering cryptic diversity increase dramatically with decreasing morphological complexity.” (Verbruggen et al. 2009 J. Phyc. 45: 726-731)

Of course, the N = 2X is a theoretical maximum, and I wouldn’t expect all theoretically possible morphologies to be produced in the course of the evolution of a lineage. To look at this in some more detail, I’ve done a few simulations. This approach consists of generating phylogenetic trees containing a number of species, and subsequently letting a set of traits (i.e. morphological characters) evolve along this phylogeny at a rate that corresponds to those measured for a real algal morphometric dataset. The result of this exercise is a set of values for each trait for each species in the phylogeny. Those can then be compared with each other to evaluate how many unique morphologies there are and how many of the species can be reliably distinguished from one another morphologically.

First, I wanted to quantify how fast your average discrete morphological trait evolves in algae. So I took one of my morphological datasets for Halimeda (mostly unpublished, but similar in nature to Verbruggen et al. 2005 J. Phyc. 41: 606-621) and a corresponding phylogenetic tree of the species in that dataset. The tree is a chronogram, which was rescaled to have a root-to-tip path length of 1. Five of the variables in the dataset are discrete, and I calculated the rate of the Markov process for these using the fitDiscrete function in the geiger package for R. Here are the results:

> print(mkr)
   perwall perfusions    secinfl     secper   segundul
 5.0208161  1.0331563  0.8157761  5.4067015  0.1252521

Cool. The evolutionary rates of the traits vary quite a bit. I decided to start the simulations with the lowest of these rates (I used 0.1), and then increase the rate later on.

Here’s a breakdown of the simulation function:

    • simulate Yule tree (pbtree from phytools package)
    • rescale tree to have root-to-tip length of 1
    • simulate the evolution of the desired number of traits along the tree (rTraitDisc from ape package)
    • count the number of unique morphologies (trait combinations) produced during the simulation
    • loop through all species pairs and score how many are distinguishable from each other (two species are considered distinguishable if they have at least one trait that differs between them)

This procedure was repeated for trees containing different numbers of species (from 10 to 400), with the number of unique morphologies and the fraction of distinguishable species pairs being retained at each step. Now let’s plot some results…

unique_morphologies

That’s quite spectacular. At this rate of trait evolution you get MUCH fewer unique morphologies than there are species. For organisms with 20 traits, you get only about 50 unique morphologies even though there are 400 species. That’s a lot of cryptic diversity. For organisms with 10 traits, the situation is even worse and only about 20 unique morphologies are produced for 400 species. Okay, now let’s plot the percentage of distinguishable species pairs…

perc_disting_spp

The blue triangles (20 characters) clearly lie above the golden dots (10 characters), reflecting that organisms with lower morphological complexity have fewer unique morphologies and are harder to distinguish from one another. In other words, lower morphological complexity leads to higher levels of cryptic diversity. That was expected, but nice to see it confirmed in the simulation.

Another interesting feature of this graph is that there is no relationship between the number of taxa in the tree and the percentage of distinguishable species (flat lines). At first, this seemed counterintuitive to me. When given a certain amount of time to diversify (one time unit from root to tips), and with a fixed rate of morphological evolution, shouldn’t more diverse lineages have more species that look the same? Actually, no, because trees with more species have a higher total tree length. The root-to-tip distance is still the same, but you have more lineages that add to the total tree length and thus to the total amount of evolution in the morphological trait. So those flat lines do make sense.

I’ve also tried these simulations with different rates of evolution:

disting_vs_rate

Clearly, traits with higher rates are better at distinguishing between species than slower traits, reducing the number of cryptic species in a lineage.

In conclusion, let me just wrap up the core results from this exercise:

    1. There are substantially fewer unique morphologies than there are species.
    2. Character-poor lineages produce fewer unique morphologies than character-rich lineages.
    3. Lineages with fast-evolving traits feature less cryptic diversity than those with slow-evolving traits.

Because of these results, we can expect cryptic diversity to abound, especially in character-poor lineages. As such, for any given algal taxon, we should expect to be unable to distinguish between at least some and possibly many of its species based on morphology alone.

Some of these results are presented in a paper that is about to appear in Journal of Phycology. [UPDATE: This paper is now out here. A PDF is available here]

You can download the code for these simulations from here.

Two PhD scholarships are available in my lab.

1. Molecular systematics of siphonous green algae

You will revise the species-level systematics of the siphonous green algae (orders Bryopsidales and Dasycladales) using a combined molecular and morphological approach. Sanger sequencing and high throughput sequencing will be used to generate multi-gene datasets from which species boundaries are to be inferred. Morphological features that match with the species boundaries will be identified. You will also be involved in resolving the higher-level phylogeny of the siphonous green algae using a phylogenomics approach. The project involves field work, microscopy, DNA sequencing and molecular phylogenetics.

2. Algal evolutionary stoichiogenomics

You will study genomes, transcriptomes and proteomes of algae to investigate their utilization of key elements (e.g. N, S, trace elements). The resulting stoichiometries will be studied in a phylogenetic context to infer the evolutionary dynamics of element utilization. You will interpret the evolutionary patterns in a paleoenvironmental context and test specific hypotheses about the inheritance of elemental stoichiometry during plastid endosymbiosis. The project involves generation of molecular data (if desired) and comparative evolutionary bioinformatics. Computational skills are a key selection criterion for this position.

To be successful you will:

  • satisfy the requirements for a PhD degree at the University of Melbourne (http://goo.gl/VGRgQ)
  • have had exposure to molecular biology and bioinformatics
  • have a strong interest in algal biology and systematics (position 1) or bioinformatics (position 2)
  • evidence strong oral and written communication skills

To find out more about the lab, go to http://www.phycoweb.net

To apply, send your CV, a representative piece of English writing (e.g. MSc thesis), and the names and contact information of two or more references (at least one former supervisor) to heroen.verbruggen@unimelb.edu.au. Please indicate which position you want to be considered for. Informal enquiries are welcome.

Note: The systematics position has been filled. If you’re interested in stoichiogenomics, let me know, that one’s still open.

These are the notes for a presentation I just uploaded to SlideShare. I gave this as a seminar at the University of Melbourne last Tuesday and at LaTrobe University two days later.

 

Slide 1

  • introduce

Slide 2

  • outline of the talk

Slide 3

  • student Dioli Payo
  • genus Portieria
  • pretty thallus shape – well-described with fractals
  • only two species known worldwide
  • her goal was to look at population structure of the species

Slide 4

  • her sampling localities in the Philippines

Slide 5

  • sequenced rapidly evolving marker from mt genome
  • we applied GMYC to the data
  • this is a quick ‘n dirty method to detect species boundaries
  • she found 21 species instead of 1
  • these are distinct species that have been separated for millions of years
  • species are cryptic => impossible to distinguish morphologically
  • they have limited distribution ranges, often a single island or bay

Slide 6

  • what does this mean globally?
  • our global sampling is not nearly as good
  • we’re at 50 species and counting
  • difficult to extrapolate but it could be well over 100 spp

Slide 7

  • we have a situation where much of what we think we know about species diversity is wrong
  • not only the case for Portieria, we know this is true for many algae, although perhaps not as spectacularly high diversities
  • what does this mean…
  • as a taxonomist to describe => they all look identical
  • every conservation decision that has ever been made that involves seaweeds needs to be revisited
  • more work for me at all levels: (1) difficult to study biodiversity patterns in meaningful way, (2) cannot trust a single species record from the literature or from online databases, (3) much denser sampling is needed in the field and DNA sequencing for every single specimen

Slide 8

  • move on to biodiversity
  • focus on understanding processes => diversification
  • geographic and ecological dimensions

Slide 9

  • our approach consists of this

Slide 10

  • our approach for the more visually inclined
  • start with phylogeny calibrated in geological time

Slide 11

  • add information about contemporary species
  • in this case macroecological: sea surface temperature

Slide 12

  • 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 13

  • these are the three model systems we’ve developed
  • very dense global sampling
  • starting to get to grips with what the species are and where they occur

Slide 14

  • start with geographic patterns of diversification

Slide 15

  • we aimed for general patterns, not individual case studies
  • hence focus on entire order of brown algae, the Dictyotales
  • you see some of the genera illustrated here

Slide 16

  • they are Olivier’s pet group so we know a lot about them
  • distributed worldwide across tropics and temperate water
  • we have > 2000 barcoded or accurately identified specimens belonging to 236 species
  • gives us pretty good idea of the distributions of the species

Slide 17

  • we want to know…
  • we have …
  • so we need a window into the past to see what happened

Slide 18

  • as explained before, models of evolutionary change offer a solution
  • relevant evolutionary events are parameters in the model, which is then optimized
  • with optimized model, we can infer things about the evolutionary events and estimate the ancestral situation
  • for biogeography => relevant parameters relate to how species move around
  • simple form with areas A/B
  • explain parameters for dispersal-extinction-cladogenesis
  • generalize to more areas
  • what it can do => phylogeny + current distribution => biogeographic history

Slide 19

  • we did this for Dictyotales
  • simple subdivision of world in three biogeographic regions: northern temperate, tropical, southern temperate
  • remember colors

Slide 20

  • change to Preview (cf. next page)

Slide 21

  • zoom in on terminal species, legend corresponds to colors in slide 19
  • reconstructed ancestral states are also there
  • show example of speciation associated with S to N shift
  • show example of speciation within region
  • base of Dictyoteae: temperate southern hemisphere
  • some lineages stay there (e.g. Dilophus)
  • at base of Dictyota more generalist
  • gives rise to a mixture of tropical and temperate lineages
  • top lineage: origin is tropical, moves into N temp on several occasions
  • next lineage down: all temperate, with S origin, dispersing into N
  • lineage all the way at bottom: starts in tropics, moves into S, later moves from S into N

Slide 22

  • tree is great to look at specific cases but doesn’t global picture
  • these are summary graphs
  • dispersal rate through time => 3 types are substantially higher than others
  • movement out of tropics
  • movement from S to N

Slide 23

  • put this in perspective
  • slide shows decreasing SST through Cenozoic
  • narrowing tropical belt
  • more temperate habitat opening up in S and N
  • movement from tropics to temperate
  • north is major sink because there was almost no temperate habitat => tropics and S feed into N

Slide 24

  • move on to macroecological correlates of diversification

Slide 25

  • case study Halimeda
  • diversity map => high diversity in tropics, with a few species in temperate habitat
  • so where is the origin? tropics or temperate
  • how often to niche shifts between temperate and tropical occur?

Slide 26

  • we have lots of DNA barcodes
  • we get SST for localities using satellite imagery
  • we get an idea of SST affinities of species
  • how do affinities evolve?

Slide 27

  • similar methods as before => model optimized
  • every tip is species
  • color gradient shows SST affinities
  • tropical origin
  • marker conservatism for tropical SST in clades 2-5
  • conservatism lostin clade 1 => 4 transitions into temperate
  • in perspective: show time frame and correspondence to narrowing tropics

Slide 28

  • do these modes of speciation and the shifting niches have implications for the distribution of biodiversity on the planet?

Slide 29

  • typical diversity patterns: well-characterized => bell-shaped around tropics
  • many possible explanations
  • my goal is to provide macroevolutionary perspective
  • higher species turnover in tropics => higher rate of diversification

Slide 30

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

Slide 31

  • Codium is suitable case study with similar diversity map

Slide 32

  • 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

Slide 33

  • logical question: is diversification faster in tropics

Slide 34

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

Slide 35

  • 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 36

  • so why is Codium richer in colder water?
  • probably due to historical causes
  • origin is in temperate waters and a lot of the branches remain in those temperate waters
  • it appears that the genus has only invaded the tropics recently and that, because of that, the majority of species is still in temperate water

Slide 37

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

Slide 38

  • 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
  • slope very deviant from that simulated under null model

Slide 39

  • 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 40

  • so, we saw that evolvability of the macroecological niche leads to more rapid diversification
  • where does that evolvability come from?

Slide 41

  • student Vanessa Marcelino was studying the evolution of microhabitat traits and macroecological traits in Halimeda
  • she decided to investigate in more detail whether there could be an interaction going on between micro and macro
  • Halimeda is mostly tropical and of tropical origin
  • found in different habitats on coral reef
  • exposed wave-swept and more sheltered e.g. reef slope but also lagoon
  • one could expect that SST evolution is faster for species in exposed microhabitats because they experience more extreme environments (low tide, wave action, etc)

Slide 42

  • compare model in which rate of SST evolution is constant with one in which it depends on whether or not species lives in exposed habitat

Slide 43

  • 2-rate model performs considerably better
  • difference in AIC 7.6 => integrated across uncertainty in exact pattern of evolution of microhabitat preference
  • lineages from exposed habitat 4.3x faster
  • so, it appears that microhabitat specializations can be exaptations for macroecological shifts

Slide 44

  • wrap up
  • for speciation, no “one rule fits all” => examples of everything you can imagine (allopatric vs. within region, associated with niche shift vs. conservatism)
  • for distributions, some patterns did come out => tropics act as source, with confirmation of “out of the tropics” hypothesis for Dictyotales; north is major sink because so recent
  • for diversification, all kinds of things going on: (1) simple relation with historical effect in Codium, (2) role of evolvability in Halimeda and Dictyota, (3) I think the evolvability aspect may emerge as a general pattern as more taxa are studied
  • reach out => (1) better models can be designed, (2) evolutionary dimension is applicable to any problem that any biologist is working on

Slide 45

  • these folks did the hard work

Slide 46

  • funding agencies
  • collaborators and collectors => due to the dense sampling that we need, lots of samples are required, and we could not do what we do if it wasn’t for all these people volunteering their time

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

I’ve just uploaded new versions of Maxent Model Surveyor and MatrixGradients to my website.

Two minor changes have been introduced in version 1.07 of Maxent Model Surveyor. First, users can now specify the amount of memory that Maxent can use with the -jm flag. Second, I’ve updated the parser of the Maxent options file, in which Maxent flags can be specified. The parser now prints out a warning if the user tries to change options for which MMS doesn’t allow user control. In addition, it will terminate the program if the user attempts to manually toggle a predictor or a species. More information is provided on the website about what to do if you want to exclude predictors a priori and how to specify flags in the Maxent options file (I’ve included an example file). Thanks to Mark Andersen for bringing these issues to my attention.

MatrixGradients is a perl script that draws colored matrices in which the colors correspond to the values in the matrix. It is now in version 1.02, which includes the option to transform values in the matrix to be plotted. This is useful if many values in the matrix are close to the maximum or minimum value and you want to exaggerate the color differences in that part of the values range. This is illustrated below for a matrix in which many values are close to zero, but with a few values that are considerably higher (up to 22.4). If plotted without transformation (left panel), the entire matrix is green, with just one red value, i.e. not very informative. If a log10 transformation is applied to the same matrix, a much clearer picture of what happens in the near-zero values emerges (right panel) simply because the color gradient is compressed near zero. The new version can also print the values in the matrix as shown in the figure.

MatrixGradients transformation

When I took up my fellowship in March, I posted the project proposal of the fellowship on my website. My main reason for doing this was to let others know what I planned to work on in the years to come.

In the last few weeks, there’s been a lot of talk on blogs and twitter about making project proposals public and many of the reasons that were mentioned made sense to me. So I’ve taken the time to dig up some older projects and put them on my web site along with some recently submitted proposals.

So, what are my reasons for making them public…

  1. I’ve had good experience in the past sharing unpublished data of mine with others. On pretty much every occasion this has led to fruitful collaboration. I’m hoping posting my project proposals may also lead to some interesting interactions with others working in the same field. Seriously… I can use the help.
  2. I’m also hoping that it may help me to stay focused on the goals of my projects. This has not always been the case in the past and having the proposals out in the open will perhaps generate a stronger sense of responsibility to stick to the plan.
  3. Science and innovation would progress at a faster rate if people openly shared ideas and information and stopped duplicating work simply because they’re unaware others are working on the same thing. Me posting my proposals is a tiny step in that direction.
  4. Last but not least, I’ve learned a lot from studying other people’s proposals. By posting mine, others can now learn from them. They can learn from my mistakes – not all of them got funded – and perhaps identify what made those that did get funded better.

That’s it. I hope something good will come of this.

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