Research focus #1: Using statistical model comparison to advance biogeography beyond the dispersal/vicariance debate
​Matzke, Nicholas J. (2015). Ancient Forests and Peripatetic Primates: Alan de Queiroz's The Monkey's Voyage. Reports of the National Center for Science Education, 25(2), 1-8. March-April 2015. (Journal)
Matzke, Nicholas J. (2015). Review of: Michael Heads (2014), Biogeography of Australasia: A Molecular Analysis. Quarterly Review of Biology, 90(3), 327-328. September 2015. (JSTOR)
Alford, Justine (2014). "Researcher Finds Support For One Of Darwin's Controversial Theories." IFLScience. October 2, 2014.
Hannah Osborne (2014). "Darwin's Theories on Invasive Species and Jump Dispersal Confirmed by Experts." International Business Times. October 2, 2014.
The most famous debate in historical biogeography is "dispersal versus vicariance." When closely-related species are allopatric (living in different areas), separated by some barrier, is this because of rare dispersal events across the barrier, or because of "vicariance," the idea that the ancestral species was widespread, and its range was broken up by the formation of the barrier?
Darwin was a dispersalist, as were many biologists who accepted the fixity of continents. Once it was accepted that continents move, this seemed to provide a ready explanation for many puzzling distributions, such as the distribution of southern beeches (Nothofagus) in South America and Australasia. Vicariance principles were combined with cladistics to produce "vicariance biogeography," the dominant (although debated) perspective in historical biogeography for several decades.
Although the debate was well-known (even called "interminable"), less recognized was the fact that computer methods for inferring biogeographical history on phylogenies actually took strong positions in this debate. The programs DIVA (Dispersal-Vicariance Analysis) and Lagrange-DEC (Dispersal-Extinction-Cladogenesis), each used in hundreds of publications, had a strong tendency to infer widespread ancestors, which then broke up by vicariance.
I wondered what would happen if these models included a process like founder-event speciation, which occurs when rare dispersal across a barrier results in "instantaneous" (geologically speaking) genetic isolation and founding of a new lineage. The new model was called DEC+J (the J is for "jump dispersal"), and it turns out that DEC+J very often (not always) outperforms the traditional DEC model. This was expected for island clades, but it turns out to be common on continents as well. This work was published in Systematic Biology (Matzke 2014), and is already well-cited.
This was the first major application of statistical model choice in historical biogeography, and I view it as the beginning, not the end, of a research program. There are very likely better models out there, so what is required is creative thinking about what processes are likely to be important in explaining biogeography, and careful work creating the models and testing them against data.
Other models I have included in BioGeoBEARS are DIVALIKE, BAYAREALIKE, and +J, +x (dispersal depends on distance), +n (dispersal depends on environmental distance), +w (dispersal depends on a user-specified dispersal modifier matrix; unlike previous uses of such matrices, in a +w model, the scaling of the matrix is modified by a free parameter, w).
These are all special cases of the supermodel implemented in my R package BioGeoBEARS. Check it out at PhyloWiki.
In all cases, the point is to convert traditional, often informal, evaluations of different programs/models/hypotheses into formal statistical comparisons, using probabilistic methods and the standard tools of statistical model comparison (Burnham and Anderson, 2002; e.g., Likelihood Ratio Test, AIC, AICc).
Additional new developments in BioGeoBEARS include: (1) a full-likelihood trait-based dispersal model (where dispersal probability depends on a discrete trait which is itself evolving; the rates of trait evolution, and the modifier on dispersal probability depending on the trait value, are both estimated); and (2) Biogeographical Stochastic Mapping, which gives users a better way to count the number of events of different types that are recorded in their data, including uncertainty in both event type, and timing. Please see PhyloWiki for more.
​Matzke, Nicholas J. (2014). "Model Selection in Historical Biogeography Reveals that Founder-event Speciation is a Crucial Process in Island Clades." Systematic Biology, 63(6), 951–970.
Dupin, Julia; Matzke, Nicholas J.; Sarkinen, Tiina; Knapp, Sandra; Olmstead, Richard; Bohs, Lynn; Smith, Stacey (2015). Bayesian estimation of the global biogeographic history of the Solanaceae. Journal of Biogeography, accepted September 4, 2016.
Burnham, Kenneth P., Anderson, David R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer.
Research focus #2: Linking phylogenies and species distribution modeling
My DECRA (Discovery Early Career Researcher Award, DECRA) has been funded by the Australian Research Council (ARC) from 2015-2018, in order to develop a Bayesian hierarchical model to jointly infer ecological niche models for living species, and the evolutionary models describing how these niche models evolve on the phylogeny.
The basic argument for doing this is presented at right. Basically, traditional ancestral niche estimation techniques use niche model parameters estimated for each species individually. These parameters are then take as "data", and they are run through phylogenetic models (e.g. Brownian motion) in order to estimate ancestral niche parameters on the phylogeny.
The advantages of joint modelling, by contrast, could include the following. (1) If related species have related niche preferences, they should inform each other, particular in the case (ideally, inferred from the data), where niche is strongly conserved, and the locality data informing the niche model in one species is limited. (2) A joint modelling approach would produce ancestral niche estimates that automatically include the uncertainty due to uncertainty in estimating the niche of each living species. (3) Having a model accounting for evolutionary conservation of the niche will provide a basis for expanding the modelling to include other factors (dispersal limitation; competitive exclusion of sister species, etc.) that often are confounded with environment as predictors of species ranges.
A fuller argument for this approach is given in Lawing & Matzke (2014). However, while it is easy to state the theoretical advantages of a joint modelling approach, actual implementation is quite complex, as it involves combining the complexities of Species Distribution Modelling with the complexities of phylogenetic continuous trait models. It is therefore a fantastic area to get into!
Recent development: I recently co-organized a symposium at Evolution 2016 in Austin, Texas, entitled "Putting Evolution Into Ecological Niche Modelling." To see a summary of the session, please see PhyloWiki.
Lawing, Michelle; Matzke, Nicholas (2014). "Conservation paleobiology needs phylogenetic methods." Ecography. 37(11), 1109–1122.
Research focus #3: Integrating new sources of information into phylogenetics and dating
Putting Fossils in Trees: With Bayesian phylogenetic methods, it is possible to integrate new sorts of information into the estimation of evolutionary trees, and the dates on those trees. A major example of this is "tip-dating", where, rather than merely using fossils as date calibrations for nodes on a molecular tree, the fossils, and their dates, are included in an overall total-evidence dating analysis. I have a number of publications using and testing these new methods. In 2014, I co-organized a special symposium at the Society for Vertebrate Paleontology meeting, called "Putting Fossils in Trees." This symposium was well-received, and led to a special issue of Biology Letters on the topic (with the same name).
For example, the eight trees at left come from Figure 1 of Matzke and Wright (2016), Biology Letters. We compare various Bayesian models and methods (Figure 1b-1h) in their ability to estimate the dated tree of fossil Canidae (dog family). Our "ground-truth" tree, Figure 1a, is derived from major monographs on fossil Canidae by Wang and Tedford. (If the term "ground-truth" seems too strong, it is also fair to represent the tree as an approximation of received expert opinion on the phylogeny and dates of fossil dogs.) Some model inferences closely match the basic conclusions of Wang and Tedford, but other ones are not so close, even though the dataset is about as high-quality as we can hope to get in paleontological cases.
The major difference is due to choices about tree priors, so this paper highlights the importance of these decisions for the resulting date estimates.
Using Relative Dating Information: "Cross-bracing": In collaboration with Patrick Shih, we developed a method to use the fact that paralogous mitochondrial, chloroplast, and nuclear proteins are often going to "co-diverge" with speciation events in the multicellular organisms they inhabit. We used this fact to use multicellular plant fossil calibrations at many points in the molecular tree -- "cross-bracing" (see left). In a PNAS paper, we found that this strategy somewhat improved the resolution of our dating, and we obtained some evidence that age of cyanobacteria may not be as old as conventionally thought. While dating analyses extrapolated to pre-Cambrian times are full of hazards, we thought our approach took a step forward in terms of including additional knowledge in the dating analysis, and are working on exploring the approach further (Shih et al., 2016).
Our paper was summarized in PNAS by Olga Zhaxybayeva; the figure at left is from that summary.
Matzke, Nicholas J.; Wright, April (2016). Inferring node dates from tip dates in fossil Canidae: the importance of tree priors. Biology Letters, 12(8), 20160328.
Shih, Patrick M.; Matzke, Nicholas J. (2013). "Primary endosymbiosis events date to the later Proterozoic with cross-calibrated phylogenetic dating of duplicated ATPase proteins." Proceedings of the National Academy of Sciences, 110(30), 12355-12360.
Zhaxybayeva, Olga (2013). "Anciently duplicated genes reduce uncertainty in molecular clock estimates." Proceedings of the National Academy of Sciences, 110(30), 12168–12169.
Shih, Patrick; Hemp, James; Matzke, Nicholas J.; Fischer, Woodward (2016). Crown group Oxyphotobacteria postdate the rise of oxygen. Geobiology. Published, July 8, 2016.