Taste of quals: understanding evolution through quantitative trait loci

This is the second post with examples of questions and answers from qualifying exams given in the graduate program of Ecology, Evolution and Systematics at UMSL. Here is another sample of my own quals, in this case, a question for a minor in Evolution. If you’d like to catch up with this discussion, read the initial post “The ultimate grad student guide to survive (and pass) qualifying exams“, and the first question/answer example on incomplete lineage sorting and species delimitations. I’ll post two more examples in the near future, from the quals of our dear peer, Robbie Hart. Stay tuned!

I remember that during my oral exam, my committee asked me to define epigenetics, which I do define in my text as you’ll see as you read, but I don’t coin the definition. So, here is another advice, make sure you give short and straightforward definitions for all concepts you use.

What are Quantitative Trait Loci and how are they relevant to the study of evolution?

The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here for a | the density of hairs (trichomes) that occur on a plant leaf. Inbred parents that differ in the density of trichomes are crossed to form an F1 population with intermediate trichome density. b | An F1 individual is selfed to form a population of F 2 individuals. c | Each F2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). Each RIL is homozygous for a section of a parental chromosome. The RILs are scored for several genetic markers, as well as for the trichome density phenotype. In c, the arrow marks a section of chromosome that derives from the parent with low trichome density. The leaves of all individuals that have inherited that section of chromosome from the parent with low trichome density also have low trichome density, indicating that this chromosomal region probably contains a QTL for this trait. Figure and legend taken from Mauricion 2001, Nature Genetics

The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here for a | the density of hairs (trichomes) that occur on a plant leaf. Inbred parents that differ in the density of trichomes are crossed to form an F1 population with intermediate trichome density. b | An F1 individual is selfed to form a population of F 2 individuals. c | Each F2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). Each RIL is homozygous for a section of a parental chromosome. The RILs are scored for several genetic markers, as well as for the trichome density phenotype. In c, the arrow marks a section of chromosome that derives from the parent with low trichome density. The leaves of all individuals that have inherited that section of chromosome from the parent with low trichome density also have low trichome density, indicating that this chromosomal region probably contains a QTL for this trait. Figure and legend taken from Mauricion 2001, Nature Genetics

Phenotype is the assemblage of observable characteristics, or traits, manifested by one individual as a result of the interaction between genes and the environment. Quantitative traits are phenotypic characteristics mediated by more than one gene (i.e. present polygenic control) (Erickson et al. 2004). Quantitative trait loci (QTL) are the several gene loci determining the expression of quantitative traits (Avise 2004). For instance, five QTLs determine morphological variation of male genitalia in Drosophila montana (Schafer et al. 2011), more than 800 QTLs are responsible the variation of 35 distinct traits in tomato, Solanum lycopersicum (Semel 2006), and few QTLs were described regulating the foraging choices in honey bees (Rüppell et al. 2004). The genetic base of ecologically and evolutionarily relevant traits has been described with QTL analysis. Evolution operates through heritable phenotypic variation, driving adaptation and diversity (Mauricio 2001). Describing QTLs supports the genetic background for understanding what determines phenotypic variation of quantitative traits, and how such variation is selected and fixed in populations (Erickson et al. 2004).

QTLs provide insights on the genetic mechanisms regulating phenotypic patterns, such as dominance, pleiotropism, epistasis or environmental interactions (Erickson et al. 2004, Avise 2004). Hybrids of S. lycopersicum with elevated reproductive fitness presented more overdominant (ODO) QTLs (Semel 2006). It seems that ODO QTLs (i.e. loci presenting heterozygous alleles with dominant expression over all homozygous alleles) were the genetic mechanism causing hybrids of Solanum sp. to present heterosis, a phenomenon in which hybrids outperform the most fit inbred parental lineage (Semel 2006). QTLs are also involved in pleiotropism, when a locus mediates the expression of multiple traits, and epistasis, when one locus suppresses the expression of alleles in a different locus (in an analogous way of dominance) (Phillips 1998). More than 60% of the phenotypic variation of body weight and fat accumulation in mice can be explained by QTLs in pleiotropy or epistasis (Brockmann et al. 2000). Moreover, the interaction of QTLs with environmental conditions explains phenotypic plasticity (i.e. habitat-dependent adaptive phenotype) in both barley and aphid populations (Tétard-Jones et al. 2011).

The basic procedure for QTL mapping in plants and animals is: 1) selection of two parental lineages that differ in the allele affecting a common trait; 2) generation of an F1 population by mating parents; 3) parental alleles are shuffled by creating a mapping population (F2); 4) traits are quantified and multilocus genotypes are identified (Mauricio 2001). Erickson et al. (2004) define three difficulties in identifying QTLs: 1) the genetic markers employed; 2) how the crosses of lineages are designed and 3) the magnitude of the QTL effect. For instance, if genetic markers are dominant, it will be harder to tell apart the effects of homozygotes dominants and heterozygotes. Random crossing of parental lineages might bias QTL identification towards alleles with large effects, but rare in natural populations (Pérez-Pérez et al. 2010). QTL identification is also biased towards the magnitude of its effect (i.e. genetic variance explained by the QTL) (Erickson et al. 2004); which is an issue in the presence of confounding factors, such as genotype-environment interactions, low heritability and imprecise estimation of genotypes and phenotypes (Erickson et al. 2004). One can overcome these problems by applying large sample sizes, adequate type and number of genetic markers, and carefully designed crosses. However, the current genomic era, with increasing number of whole sequenced genomes, overwhelms such problems by providing more markers, refining genetic maps and improving crosses due to reduction in genotyping costs (Mauricio 2001). QTL analysis detects and describes the regions of the genome responsible for the phenotypic variation under selection, shedding light on the mechanisms of evolution of complex traits.

References

Avise, J. 2004. Molecular markers, natural history, and evolution. 2nd edition. Sinauer Associates, Sunderland. 684 pp.

Brockmann, G. A., J. Kratzsch, C. S. Haley, U. Renne, M. Schwerin, and S. Karle. 2000. Single QTL Effects, Epistasis, and Pleiotropy Account for Two-thirds of the Phenotypic F2 Variance of Growth and Obesity in DU6i x DBA/2 Mice. Genome Research:1941–1957.

Erickson, D. L., C. B. Fenster, H. K. Stenøien, and D. Price. 2004. Quantitative trait locus analyses and the study of evolutionary process. Molecular Ecology 13:2505–2522.

Mauricio, R. 2001. Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology. Nature Reviews Genetics 2:370–381.

Pérez-Pérez, J. M., D. Esteve-Bruna, and J. L. Micol. 2010. QTL analysis of leaf architecture. Journal of Plant Research 123:15–23.

Phillips, P. C. 1998. The language of gene interaction. Genetics 149:1167–1171.

Rüppell, O., T. Pankiw, and R. E. Page. 2004. Pleiotropy, epistasis and new QTL: the genetic architecture of honey bee foraging behavior. The Journal of Heredity 95:481–491.

Schäfer, M. A., J. Routtu, J. Vieira, A. Hoikkala, M. G. Ritchie, And C. Schlötterer. 2011. Multiple quantitative trait loci influence intra-specific variation in genital morphology between phylogenetically distinct lines of Drosophila montana. Journal of Evolutionary Biology 24:1879–1886.

Semel, Y. 2006. Overdominant quantitative trait loci for yield and fitness in tomato. Proceedings of the National Academy of Sciences 103:12981–12986.

Tétard-Jones, C., M. A. Kertesz, and R. F. Preziosi. 2011. Quantitative trait loci mapping of phenotypic plasticity and genotype-environment interactions in plant and insect performance. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences 366:1368–1379.

Taste of Quals: Incomplete lineage sorting and species delimitation

This is a continuation of the post “The ultimate grad student guide to survive (and pass) qualifying exams“, in which you can find helpful advice collected from several grad students that were successful (or not so much) in their qualifying exams. As promised, here is the first sample of a quals question and answer under the format of the Ecology, Evolution and Systematics graduate program of the University of Missouri St Louis. The question and answer bellow is from my own qualifying exam, back in 2012, and it is supposed to be a question for a population biology major, but it could easily be systematics also. I didn’t edit anything from the version I sent to my committee, so if you find some wrong, that’s what my committee received! :P

What is meant by incomplete lineage sorting, and how does it affect assessments of relationship and species delimitation?

Figure 01: Hypothetical species tree and gene trees exemplifying a case of incongruent tree topology due to incomplete lineage sorting. The species tree for taxa A, B, C and D is showed at the top. Genes were sampled from species A, B, C and D, and are represented by the lines in the species tree. The respective gene trees are represented in the bottom. The gene represented by the continuous black line is a case of incomplete lineage sorting (first tree in the bottom line). If a tree is constructed based on the branching pattern of this gene, species B will share a common ancestor with species C more recently than with species A, which is the opposite prediction based on the species tree. The gene trees represented by the grey and the dashed lines have the same topology of the species tree, exemplifying cases of congruence among trees. Figure and legend adapted from Edwards (2009).

Figure 01: Hypothetical species tree and gene trees exemplifying a case of incongruent tree topology due to incomplete lineage sorting. The species tree for taxa A, B, C and D is showed at the top. Genes were sampled from species A, B, C and D, and are represented by the lines in the species tree. The respective gene trees are represented in the bottom. The gene represented by the continuous black line is a case of incomplete lineage sorting (first tree in the bottom line). If a tree is constructed based on the branching pattern of this gene, species B will share a common ancestor with species C more recently than with species A, which is the opposite prediction based on the species tree. The gene trees represented by the grey and the dashed lines have the same topology of the species tree, exemplifying cases of congruence among trees. Figure and legend adapted from Edwards (2009).

The branching pattern of a phylogeny tells the history of how species and genes evolved through time (Edwards 2009). This history can be constructed, for instance, by the comparison of morphological traits or DNA sequences. In the latter case, the number of nucleotide substitutions accumulated in the DNA gives an estimate of when the operational taxonomic units under comparison shared the same ancestor. However, the history of species and genes can differ from each other, generating incongruent trees (Pamilo and Nei 1988). Incongruence between trees can happen because species and genes may not have branched at the same time, or in other words, lineages may have failed to sort out at the same time speciation happened, a process named incomplete lineage sorting (Maddison 1997). Under the point of view of population genetics, when there is incomplete lineage sorting the coalescence time of genes and speciation time are different. Such difference in the time to coalesce means that if time is traced backwards in a branch of a phylogenetic tree, genes will not coalesce at the same time that speciation events will happen. Since the time needed to DNA sequences coalesce, or the time needed for such sequences to converge to a common ancestor (Charlesworth 2009), can be longer than speciation events, incomplete lineage sorting can also be referred as deep coalescence (Maddison 1997). When trees are constructed based on molecular sequences with incomplete lineage sorting, gene trees and species trees will present different topologies, showing distinct branching patterns, and influencing on the interpretations of species relationships and definitions. Figure 01 represents what a species tree and a gene tree looks like when incomplete lineage sorting is present.

There are two types of scenarios under which incomplete lineage sorting is more likely to happen: 1) large effective population size (Ne) (i.e. wide phylogenetic branches) and/or 2) few generations to divergence (short phylogenetic branches) (Maddison 1997). The effective population size (Ne) is the number of individuals in a population with equal probability to contribute with gametes for the next generation (Wright 1931, Avise 2004). The concept of Ne was first developed to compose predictions on the fate of genes in a population over time (Wright 1931), revealing how random sampling of allele frequencies in a population (i.e. genetic drift) influences the rate of evolutionary change (Charlesworth 2009). Genetic drift can also be viewed as a matter of statistical sampling error of alleles in a population, which is inversely related to the sample size (i.e. Ne) (Avise 2004). The effects of genetic drift are remarkable in small Ne, denoting that the chance of losing alleles at every generation is high. Therefore, incomplete lineage sorting is more likely to occur when ancestral populations present large Ne, since the action of genetic drift will not be significant, increasing the chance that alleles will not coalesce at the same time speciation occurs (Nichols 2001). Thus, trees with wide branches (i.e. small Ne) are less likely to present incomplete lineage sorting (Maddison 1997).

Figure 02: Probabilities (π) of survival of two or more founding lineages through time. Probability curves for populations of various sizes (N) are shown. Figure and legend adapted from Avise (2004).

Figure 02: Probabilities (π) of survival of two or more founding lineages through time. Probability curves for populations of various sizes (N) are shown. Figure and legend adapted from Avise (2004).

Although lineage persistence is correlated with Ne, it is improbable that a lineage is able to persist for more than 4 Ne generations (Nichols 2001, Avise 2004). Figure 02 shows the probability of survival of lineages through time, depending on Ne. Transposing Figure 02 to a phylogenetic tree, it is possible to interpret that the wider (i.e. larger Ne) and the shorter (i.e. few generations) branches are, the higher the chances lineages will fail to sort out before speciation events (Maddison 1997, Maddison and Knowles 2006). Thus, divergence time, the number of generations taken until speciation, is the second contributing factor for incomplete lineage sorting occurrence. Conceptually, gene trees and species trees are not the same (Pamilo and Nei 1988), because even though both trees describe evolutionary histories, the former refers to orthologous genes (i.e. segregated by speciation), while the latter refers to evolutionary pathways of species, meaning that incongruence among these trees might not be considered as odd (Pamilo and Nei 1988). The probability of congruence among species trees and gene trees (P) can be derived as a direct function of the number of generations (T) using the equation P = 1 – 2/3e-T. In the equation, T is the number of generations between the more ancient and the more recent divergences, and it is given by the formula T = t/2(Ne), where t is the number of generations (Pamilo and Nei 1988, Rosenberg 2002, Figure 03). Large values of Ne and small values of t will reduce T, approximating the value of the term 2/3e-T to 1, and reducing the probability of congruence among topologies.

Figure 03: Relationship between the probability of congruent topology between species tree and gene trees (P) and intermodal branch length (T). Figure and legend adapted from Pamilo and Nei (1988).

Figure 03: Relationship between the probability of congruent topology between species tree and gene trees (P) and intermodal branch length (T). Figure and legend adapted from Pamilo and Nei (1988).

If gene and species trees disagree due to incomplete lineage sorting, one can question what the consequences are for defining species and interpreting the relationships among them. The consequences are very straightforward, fitting in tree broad scenarios: 1) gene trees retrieve erroneous species trees, with unrealistic representations of taxa relationships, and/or 2) absence of reciprocal monophyly, meaning that alleles will be more related within paraphyletic than within monophyletic clades (i.e. contains a common ancestor and all its descendants) (Avise 2004). Incomplete lineage sorting causing uncertainty in species definitions was investigated by Heckman et al. (2007), who tested the phylogenetic hypothesis of eight species identity for mouse lemurs of Madagascar. Phylogenetic analysis of a single mitochondrial DNA (mtDNA) locus defined eight species for the genus of mouse lemurs, Microbeus, adding six new species to the group (Yoder et al. 2000). However, a multilocus analysis can provide stronger evidence for species divergence (Maddison 1997, Maddison and Knowles 2006, Zachos 2009). Applying a multilocus approach, Heckman et al. (2007) obtained incongruence when comparing trees obtained from mtDNA sequences and segregated nuclear loci. Monophyletic clades recovered from mtDNA sequences showed polyphyletic (i.e. clade derived from at least two ancestors) in trees retrieved from nuclear DNA data (Heckman et al. 2007). The incongruence is rooted in the fact that mtDNA has smaller Ne than nuclear DNA, and the latter is phylogenetically less informative than the former, due to lower mutation rates (Avise 2004). The authors attributed the mechanism of such incongruence to incomplete lineage sorting, since the species at the polyphyletic clade share polymorphisms at every nuclear locus analyzed, indicating that during Microcebus diversification, mtDNA haplotypes, but not nuclear alleles, sorted out before speciation (Heckman et al. 2007). However, when authors concatenated all gene sequences, they retrieved a tree with better support and resolution, shedding light to an alternative of how to deal with incomplete lineage sorting and obtain more reliable phylogenetic trees, a topic further discussed in this essay (Heckman et al. 2007).

The influence of incomplete lineage sorting in the interpretation of species relationships was investigated when genomes of humans and other primates were compared (Patterson et al. 2006). Genetic divergence between humans and chimpanzees varies between less than 84% and more than 147%, suggesting that incomplete lineage sorting might be the reason for lower divergence in some loci (Patterson et al. 2006). When the orangutan genome is added to the comparison, it reveals that incomplete lineage sorting happened approximately 1% of the time along the evolutionary history of these three species (Hobolth et al. 2011). More interestingly, in 0.8% of the genome, humans are more close to orangutans than they are to chimpanzees, and the later is more close to orangutans in 0.6% of the genome (Hobolth et al. 2011). The occurrence of incomplete lineage sorting in the phylogeny of these species can be explained by the fairly large Ne for the human-chimpanzee ancestor populations (Hobolth et al. 2007). Incomplete lineage sorting was also pointed out as the cause of incongruence when comparing the trees retrieved from the genome of species composing the Drosophila melanogaster complex (Pollard et al. 2006). Even though the phylogenetic analysis with full genome data of the four species in the complex generated a tree with better support, it was observed widespread incongruence among nucleotide and amino acid substitutions, insertions and deletions (i.e. indels), as well as gene trees (Pollard et al. 2006). It seems that species in the D. melanogaster complex suffered a rapid speciation event (i.e. low T value), which contributed to the maintenance of ancestral polymorphisms in the recently diverged species (Pollard et al. 2006). Despite that Pollard et al. (2006) successfully point out incomplete lineage sorting as the reason of incongruence among species and gene trees, the study does not attempt to control or incorporate such information to better understand the phylogenetic relationships among species.

When testing phylogenetic hypothesis, especially for recently diverged taxa, it is recommended to use approaches that can overcome the problems of misinterpretations due to retention of polymorphisms from ancestral lineages. The use of many genes sampled from each species was one of the first approaches suggested to deal with the absence of reciprocal monophyly among genes and species trees (Takahata 1989, Sanderson and Shaffer 2002). Also attempting to consider the effects of incomplete lineage sorting when retrieving consistent phylogenies, Maddison and Knowles (2006) reconstructed species trees using simulated of nucleotide sequences and their respective gene trees. They concluded that for shallow species trees (i.e. rapid species divergence) increasing the number of loci raises the chance of sampling various models of evolution, providing a more accurate species tree (Maddison and Knowles 2006). A systematic investigation of how multiple genes can improve phylogenetic inferences and solve problems of incongruence was conduced by Rokas et al. (2003), who analyzed trees recovered from 106 orthologous genes from eight yeast species of the genus Saccharomyces. High probability of incongruence was widespread among the analyzed genes, regardless if trees were retrieved from single or concatenated genes (Rokas et al. 2003). However, trees generated from at least 20 concatenated genes had bootstrap support above 95%, overwhelming the problems of inconsistency obtained by single genes (Rokas et al. 2003).

It has been suggested that phylogeny can be more well described by a statistical distribution (Maddison 1997). Considering species phylogeny as a probabilistic event, maximum likelihood has also been applied to obtain the species tree that offers the highest probability of finding the observed gene trees (Maddison 1997, Carstens and Knowles 2007, Wu 2011). The phylogenetic relationships of species from the genus Melanoplus of montane grasshoppers was better described by estimating species tree probabilistically from gene trees (Carstens and Knowles 2007). The five species in the genus, M. montanus, M. oregonensis, M. marshalli and M. triangularis, recently radiated in the Pleistocene, present distinct morphology and distribution, but have unresolved species relationships (Carstens and Knowles 2007). Five alleles per species, one mitochondrial and four nuclear, were sampled to generate gene trees using maximum likelihood. Trees were also generated considering the probability of incomplete lineage sorting, by applying a model of stochastic loss of lineages through genetic drift, elaborated as a function of Ne and number of generations to divergence (t) (Carstens and Knowles 2007). In this study, the method for obtaining the species tree proved to be consistent when applying the same procedures to simulated nucleotide sequences (Carstens and Knowles 2007). The best estimated phylogenetic species tree had high accuracy and support in comparison to previously obtained phylogenies (Carstens and Knowles 2007).

Incomplete lineage sorting is a widespread phenomenon and can provide useful insights on the population size of ancestors, speed of species divergence, as well as comparative information on how different genes evolved through time, shedding light on how different selection pressures acted on genomes through the evolutionary time (Nichols 2001). The failure of lineages to sort out along evolutionary history is associated with reduced Ne and rapid species divergence. The use of multiple loci of both mitochondrial and nuclear origins seems to provide enough evolutionary variability to reproduce consistent species phylogenies. Although incomplete lineage sorting can mess phylogenetic inferences, when such phenomenon is recognized, and strategies that reduce problems of tree congruence are incorporated, the evolutionary history of species can be revealed with more accuracy. Considering how incomplete lineage sorting, among other factors, can generate incongruent evolutionary histories, Maddison (1997) makes an insightful analogy about phylogenetic trees and electrons. In physics, there is a probability associated with the presence of electrons around the nucleus of an atom, meaning that electrons can be found in more than one place at once. So can phylogenies. Depending on the genes sampled, phylogenetic history can be found in different places at the same time. Thus, the same way electrons can be described as a probabilistic cloud of occurrence around an atom, a phylogeny can be viewed as a diffuse cloud of gene histories (Maddison 1997). The history of how species evolved through time, and appropriate hypothesis tests on species relationships can only be successfully achieved if the chance of occurrence of incomplete lineage sorting is considered and properly incorporated in the phylogenetic inferences.

References:

Avise, J. 2004. Molecular markers, natural history, and evolution. Sinauer Associates, Sunderland. 684 pages, 2nd edition.

Carstens, B. C., and L. L. Knowles. 2007. Estimating species phylogeny from gene-tree probabilities despite incomplete lineage sorting: an example from Melanoplus grasshoppers. Systematic Biology 56:400–411.

Charlesworth, B. 2009. Fundamental concepts in genetics: Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics 10:195–205.

Edwards, S. V. 2009. Is a new and general theory of molecular systematics emerging? International Journal of Organic Evolution 63:1–19.

Heckman, K. L., C. L. Mariani, R. Rasoloarison, and A. D. Yoder. 2007. Multiple nuclear loci reveal patterns of incomplete lineage sorting and complex species history within western mouse lemurs (Microcebus). Molecular Phylogenetics and Evolution 43:353–367.

Hobolth, A., J. Y. Dutheil, J. Hawks, M. H. Schierup, and T. Mailund. 2011. Incomplete lineage sorting patterns among human, chimpanzee, and orangutan suggest recent orangutan speciation and widespread selection. Genome Research 21:349–356.

Hobolth, A., O. F. Christensen, T. Mailund, and M. H. Schierup. 2007. Genomic relationships and speciation times of human, chimpanzee, and gorilla inferred from a coalescent hidden Markov model. PLoS Genetics 3:e7.

Maddison, W. P. 1997. Gene trees in species trees. Systematic Biology 46:523–536.

Maddison, W. P., and L. L. Knowles. 2006. Inferring phylogeny despite incomplete lineage sorting. Systematic Biology 55:21–30.

Nichols, R. 2001. Gene trees and species trees are not the same. Trends in Ecology & Evolution 16:358–364.

Pamilo, P., and M. Nei. 1988. Relationships between gene trees and species trees. Molecular Biology and Evolution 5:568–583.

Patterson, N., D. J. Richter, S. Gnerre, E. S. Lander, and D. Reich. 2006. Genetic evidence for complex speciation of humans and chimpanzees. Nature 441:1103–1108.

Pollard, D. A., V. N. Iyer, A. M. Moses, and M. B. Eisen. 2006. Widespread discordance of gene trees with species tree in Drosophila: Evidence for Incomplete Lineage Sorting. PLoS Genetics 2:e173.

Rokas, A., B. L. Williams, N. King, and S. B. Carroll. 2003. Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature 425:798–804.

Rosenberg, N. A. 2002. The Probability of Topological Concordance of Gene Trees and Species Trees. Theoretical Population Biology 61:225–247.

Sanderson, M. J., and H. B. Shaffer. 2002. Troubleshooting molecular phylogenetic analyses. Annual Review of Ecology and Systematics:49–72.

Takahata, N. 1989. Gene genealogy in three related populations: consistency probability between gene and population trees. Genetics 122:957–966.

Wright, S. 1931. Evolution in Mendelian Populations. Genetics 16:97–159.

Wu, Y. 2011. Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood. International Journal of Organic Evolution 66:763–775.

Yoder, A. D., R. M. Rasoloarison, S. M. Goodman, J. A. Irwin, S. Atsalis, M. J. Ravosa, and J. U. Ganzhorn. 2000. Remarkable species diversity in Malagasy mouse lemurs (primates, Microcebus). Proceedings of the National Academy of Sciences of the United States of America 97:11325–11330.

Zachos, F. E. 2009. Gene trees and species trees–mutual influences and interdependences of population genetics and systematics. Journal of Zoological Systematics and Evolutionary Research 47:209–218.

The ultimate grad student guide to survive (and pass) qualifying exams

***Updated on 11/24/2014***

phd0111

Most qualifying exam stories come with the same take home message: it’s the worse moment in the life of a PhD student. My story is no different than the others: several times I considered just walking out the door, heading to the airport and taking the first flight back home; all I put in my stomach in the last three days of the quals process were 24 cans of coke and a giant bag of dinosaur shaped nuggets; I would work 15-20 hrs a day, and often question myself if it was enough; some days I wouldn’t work at all, because my brain just refused to, and the struggle with the endless guiltiness was even worse; I couldn’t sleep the night before my oral exam; during the exam I was so tired and got so nervous that I couldn’t think straight, and said “I don’t know”, s-e-v-e-r-a-l times; at the end, I passed, and I cried (a lot). Not tears of happiness though, those were intentional tears of relief, to wash away tons of stress and personal pressure.

If you’re about to take your quals and just read the above, you probably hate me for being such a Debbie Downer. I’m sorry about that, but I needed to highlight the general negativeness around qualifying exams so you can understand the point I want to make with this post: the hardest part of quals isn’t the tons of papers you have to read, or endless hours working, or deciding how to structure your arguments…the hardest part is to manage your levels of self confidence. If you cannot trust yourself, you can trust me and the other graduate students that contributed with several suggestions to this post. I’ll try my best so our mistakes don’t become yours, and summarize here good and safe strategies for doing well on qualifying exams, as well as the most common self-trapping strategies.

Think about the characters in the Hollywood classic “The good, the bad, and the ugly” when trying to understand how you can win the quals war looking as pretty as Clint Eastwood would, or how you could fail by choosing a bad stratagem, or letting the ugly side of your own self doubts make your life harder than it should be, and even drive you to failure. The advices here are mainly towards the system of qualifying exams at the Ecology, Evolution and Systematics program at the University of Missouri St Louis, however they can be useful to graduate students taking qualifying exams in different areas and institutions as well. The quals in our the department is far from being easy, but it is a fair and very reasonable process. It’s important to highlight here that the qualifying exam structure varies tremendously across graduate programs. At least in the fields of Ecology and Evolution, the common component of all exams is an oral examination with a committee (as far as I know). However, the written part of the quals exams goes from exams lasting a few hours, days, months, or no written component at all. PhD students in our program have one month to write down the answers for five questions: two four page long essays on major theoretical fields that your dissertation fits in, and three shorter, one page long essays on minor, or satellite topics. The written part goes to a committee composed by faculty members who will read the answers and discuss them with the student during in a meeting, which makes up the oral exam of the quals process. The student’s advisor is left out of the entire process, in order to avoid conflict of interests. One of the most distinctive characteristics of our quals at UMSL is that while working on our questions, we are allowed to brainstorm with other people. Hence, you are free to discuss your questions with other people, as long as you use your own words when writing the answers. A solo and silent qualifying structure is common elsewhere.

good_the_bad_and_the_ugly

The good, or Eastwood-style strategies for success:

1) Get your life ready beforehand. If you don’t want to end up like me, eating dinosaur nuggets for three days in a row, stock some provisions beforehand. As if you’re preparing yourself for war or a long hard winter, make sure you have enough food, caffeine and whatever keeps you going. Crock pot-borne food will be your best friends. Warn family, friends and significant others that you’ll be in a on the edge/cave-man mode for a while. They’ll have to bear with a bipolar version of you that, on the blink of an eye, goes from a cold working machine to a highly emotional type that cries watching diaper commercials.

2) Make a work and rest schedule and stick to it. Set up the order of the questions you will answer, and a time frame for each. Include an order of tasks: read -> write -> revise -> break -> next question. The transition from reading to writing is very important, I personally struggle with start writing even after reading more than enough, which is why respecting the schedule is essential.

3) Plan on finishing before the deadline. My deadline was on a Friday. I finished on Tuesday, took a brain break on Wednesday, and revised half on Thursday and half on Friday. Taking a break before doing a final review allows you to set your brain free from your own text, and do a somewhat unbiased review. Sometimes the oral exam is scheduled only a few days after you send the responses to the examination committee –  hence, you want to rest and take it easy at the very end.

4) Tackle the hardest first. If you leave the hardest and the longest parts to the second half of the process, your tiredness and emotional state will affect your progress.

5) Put some endorphin in your blood stream, at least twice a week. Best way of doing it: exercise. Bike to the library, Brainstormwalk around the block, do some jumping jacks, yoga, walk to the coffee shop that is two blocks away…It doesn’t matter how, just find a way to boost your endorphins levels, it’ll help to clean up your head, control your stress and improve your concentration.

6) Brainstorm with your colleagues. Papers shouldn’t be your only learning resource. To me, one of the coolest things in the academic environment is to be able to knock on the door across the hallway and discuss whatever you want with your colleagues. Take advantage of the intellectual environment around you, and learn how to use it in your favor, if you aren’t doing it already.

7) Read about writing. As any method of communication, there are clearly stablished writing techniques out there. My favorite read on the topic is “Gopen and Swan, 1990. The Science of Scientific Writing. American Scientist“. Duke University has a free-web based course on scientific writing: https://cgi.duke.edu/web/sciwriting/index.php.

8) Beat the myth of the professor-enemy. Students of the world: your teachers are not your enemies. I’ve been taking a teacher training course at UMSL this fall, and we discuss a lot about how to be the student’s “best friends” through out their educations journey. However, even when their mentors try and are supportive, students often don’t even acknowledge that their professors are their more powerful allies. Dr Patty Parker, our department chair, pointed out the following after reading the first version of this post: “In general, the faculty completely believe in the students and want them to do well. It may feel like someone is “out to get you” but that is never the case, in my experience. In general, the examiners are supportive of the students and want them to succeed, and understand that everyone is different and responds differently to the particular form of nervousness that comes with qualifying exams. I guess that is my main point: the faculty on the examination committee are humans, too, with feelings and strengths and weaknesses. Remember that we, too, would struggle to formulate strong responses to these same questions. Remember that we are sitting there when someone asks a question, asking ourselves whether we could answer it and how we would answer it.  I am usually extremely impressed with the poise of our students and how they can respond to questions that I think I would struggle to answer“.

 The bad, or guaranteed failure (or partial failure) strategies, or two ways of shooting your own foot:

Trying to do multiple things at the same time during your quals is a bad idea. Photo from: http://christinabakerkline.files.wordpress.com/2010/03/multitasking1.jpg

Trying to do multiple things at the same time during your quals is a bad idea. Photo from: http://christinabakerkline.files.wordpress.com/2010/03/multitasking1.jpg

1) Multitasking. During your quals, ALL you will do will be your quals. NOTHING ELSE. You should engage in single priority mode. That’s the main reason the quals in our department have been moved to the winter break – there’s the downside of kind of missing all the holiday parties, but the very very very positive side that there’s little overlap with field seasons and conferences, which mostly happen over the summer. Focusing and not multitasking may sound obvious to you, but people don’t follow this rule more often than you’d think, and all cases I’m aware of people that have failed (or partially failed) qualifying exams did something else during that month, which includes distractions from both professional and personal life. So, be careful with this one.

2) Answering “it depends”. For my own qualifying exam, I received the following question for a minor in Conservation Biology: “If scientists readily adopted the phylogenetic species concept and this concept became accepted by policy-makers, how might that impact the U.S. Endangered Species Act?“. The phylogenetic species concept considers species as the smallest monophyletic units in a phylogeny, hence species are irreducible clusters grouped by unique shared characters and ancestry. The U.S. Endangered Species Act (ESA) of 1973 provides legal means for the conservation of wildlife endangered or in threat of extinction, and the ecosystems upon which they depend. The ESA original definition of species included “any subspecies of fish, wildlife or plants”, and having a major flaw of not specifying the species concept under which endangered and threatened taxa are recognized. My answer strategy on that was: “It depends.”. Bad mistake. My arguments were that adopting the phylogenetic species concept in the ESA could be beneficial for giving a standard operational unit for policy makers, besides considerably reducing the number of species in the list, which can be an advantage when resources are limited; however, by adopting the phylogenetic species concept, the ESA would ignore that species are complex evolutionary entities, and should be treated as so. I concluded saying that the species concept adopted should be context dependent. Bad idea again. The problem here was, by being so on the fence I: 1) didn’t prepare myself well enough to defend either side of what I was proposing; and 2) gave my committee the chance to ask me questions that went in any possible direction. I was also told that as a scientist I should be able to give a single answer when policy makers ask my opinion: “People out there want one answer, and it’s your responsibility to be able to provide that single answer”. I still don’t have experience enough to judge these words, but here is my message: if you are asked to give your opinion on something, even if you really believe the answer is “it depends”, pick a side for your own sake.

imposter

 The ugly, or the dangerous lack of self confidence:

Have you heard of the impostor syndrome? If you are in your first years in grad school, I bet two phalanges from my right hand that you have it. Impostor syndrome is a term that was coined to describe several types of feelings related to problems with self-acceptance. It’s that constant feeling of being a fraud that comes with a fear of being caught – “what if everybody finds out that I actually know nothing”. It is constantly accompanied by thoughts like: “I’ll never be as good as Mary Jane, or John Smith”. You’re not alone when it comes to feeling like an impostor, but it’s up to you to make your way out of it. You can find out how here and here. As I said earlier, the hardest part of the qualifying exam process is to manage your self confidence. If you are going through the quals process, you earned your place in hell, and you know it wasn’t easy getting there. Think about it.

I’ll be posting two examples of qualifying exam answers in the near future. There’s a lot of anxiety around thinking about what type of questions are asked, and how in depth one should answer these questions. I got the ok from our Department Chair to post examples of questions and answers, and hope you can take advantage of them. Stay tuned.

If you have any other suggestion that I didn’t cover in this post, please post a comment :) !

Not all frogs jump alike – the evolution of landing in frogs

Well, at least they don’t land alike – some prefer a nose-dive style! A group of researchers led by Dr. Rick Essner, from the Southern Illinois University Edwardsville, have recorded the jumping styles of different frogs in slow-motion and found that some frogs, more specifically the ones belonging to the Leiopelmatidae family, don’t know how to land like most frogs. Interestingly, Leiopelmatidae is the basal-most living frog family, indicating frogs first learned how to jump, and only later in their evolutionary history did they develop a way to land that didn’t involve a head or belly flop. Here is a link to their paper.

The Leiopelmatidae:

frogs1

The frogs we are accustomed to seeing, and that we used to chase when we were kids, have a typical jump that works like this: first there is a propulsion to get the body off ground, then half-way through, the body and limbs will flex in preparation for landing. This mid-air flexion is what prevents them from a head-first collision.

Screen Shot 2014-11-04 at 6.23.07 PM

All frogs (order Anura) can be divided in two main classifications, the basal-most Leiopelmatidae and all other frogs, Lalagobatrachia (Frost et al, 2006). These two groups diverged around 225 million years ago (Roelans and Bossuyt, 2005). The Leiopelmatidae were particularly interesting for this study because according to Dr. Essner they “retained central and behavioral features that are evolutionary informative”. Dr. Essner and his group already knew that these basal frogs swim differently than others. They do a trot-like rather than a kick-like swim. This trot-like style is characterized by asynchronous movement of the hindlimb, while in the kick-like one, frogs extend and flex both their hindlimbs together, which is what all other frogs do. That suggested to the researches that maybe there were other differences in how these frogs moved. So, they set out to test how they jumped and landed. They analyzed slow-motion video footage from five species, three basal leiopelmatidae, Ascaphus montanus, the Rocky Mountain tailed frog, Leiopelma pakeka and L. hochstetteri; and two lalagobatrachians, Bombina orientalis and Lithobates pipiens.

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Amphibian Tree of Life, including caecilians, salamanders and all frogs. Not the first frog family is Leiopelmatidae. From http://www.livescience.com/11286-amphibian-tree-life.html

The Lalagobatrachia frogs they observed all had a similar jumping pattern where the “aerial phase [is] characterized by mid-air body and limb rotation in preparation for landing. […] Limb recovery involves protraction, adduction, and extension of the forelimbs, placing them in position to absorb impact forces”. We can call the lalagobatrachians derived frogs, a reference to their more recent placement in the Anuran phylogeny.  The Leiopelamtidae, however, didn’t come programed to flex their hindlimb mid-air, and therefore, land in a belly-flop, abdomen (and sometimes nose) first, and skid to a stop. Like in this video from their study:

Poor guy, but I don’t blame you if you replay that video a couple of times.

Such a simple maneuver, you would think, to flex you limbs before you have to skid your way through a stop. Maybe the art of jumping and landing had evolved together. Apparently not in frogs. The fact that the most basal lineages can’t perform such maneuver indicates that frogs first evolved how to jump, and the landing skills were only developed much later on, in the ancestrals of the lalagobatrachian frogs. According to the authors: “The switch to lalagobatrachian landing and swimming behavior appears to have involved a simple evolutionary change in the timing of limb muscle motor patterns, shifting the onset of hindlimb flexors to an earlier point in the stride cycle.” There seems to be no difference in the morphology of these frogs that could influence how they land, and what makes a difference is simply the timing of their limbs flexion.

“All else being equal, if A. montanus shifted the onset of recovery so that flexion began at mid-flight it would land on its limbs like other frogs.” – Essner et al.

It is worth mentioning that these basal frogs are tiny, as you can see in the picture below where the for is next to a dime. Their smaller size probably helps in their rough landing. They also have large, shield-shaped cartilages, which could soften the uncontrolled landing.

Ascaphus montanus next to a dime. Photo from http://www.fotolog.com/origen_dela_vida/14648227/

Ascaphus montanus next to a dime. Photo from http://www.fotolog.com/origen_dela_vida/14648227/

By now you could be thinking: how did jumping evolve, and is there any relation of how these frogs differ in how they land to primitive terrestrial fishes, or did jumping evolve independently more than once? Well, we don’t know it, but Dr. Essner and his collaborators are currently investigating how jumping involved in anurans.

A very important point to be taken from their work is that when looking at morphological traits to understand evolutionary history, we tend to ignore behavioral aspects that may involve multiple ways of using the same available structures. This paper proves that to make an engine work, it takes much more than just having the right tools.

For more information, read the article: Essner, Richard L, Daniel J Suffian, Phillip J Bishop, and Stephen M Reilly. 2010. “Landing in Basal Frogs: Evidence of Saltational Patterns in the Evolution of Anuran Locomotion.” Naturwissenschaften 97 (10): 935–39. doi:10.1007/s00114-010-0697-4.

Photo courtesy of Dr. Essner.

Photo courtesy of Dr. Essner.

Interbreeding, introgression and human evolution: Neanthertal cousins responsible for high altitude adaptation in Tibetans

Tibetans ability to survive in the mountains, where there is 40% less oxygen than at sea level, was donated by Denisovans, which are Neanthertals close relatives. Photo: Lhasa girl, Gaelle Morand, from http://www.planet-mag.com/2011/home/editors/winners-portrait-slideshow/

Tibetans ability to survive in the mountains, where there is 40% less oxygen than at sea level, was donated by Denisovans, which are Neanthertals close relatives. Photo: Lhasa girl, Gaelle Morand, from http://www.planet-mag.com/2011/home/editors/winners-portrait-slideshow/

When I think about Tibetans, what first comes to my mind is the expression of enlightenment in their faces. Maybe because to survive at 14,000 ft of elevation, one needs to have something else, which can be either lots of wisdom or an especial adaptation craved in the genes. In a recent study published in Nature, researches found that this something else that makes Tibetans so successful at colonizing high elevation areas are haplotypes donated by Denisovan hominins through DNA introgression. In a multi-national collaboration, Huerta-Sanchez and colleagues investigated the genetic variation of the gene EPAS1, linked to adaptation to low oxygen levels in high altitudes. When ordinary, non-adapted to high altitude, people are exposed to environments with low concentration of oxygen (hypoxia) the body enters in a compensatory mode. Hemoglobin levels increase, the number of red blood cells boosts, the heart starts to overwork in order to deliver oxygen to all demanding tissues, and finally, blood pressure ramps up to the risk of heart failure and damage to the peripheral circulation. All that doesn’t happen to Tibetans though. Their hemoglobin levels aren’t boosted because of the low oxygen levels, in fact they present similar adaptations of other mammalian species that live in high altitude, such as pigs and antelopes: they have thin walled pulmonary vascular structure, which translates into high gas exchange efficiency, and their blood flows at a higher velocity, meaning that tissues get their oxygen delivered even when the supply is low. All these anatomic and physiologic variations have a direct implication on reproductive success, since women that lack high-altitude adaptation usually have miscarriages due to eclampsia, or fetal heart failure. Given such a tuned phenotype-environment adaptation, one can ask how evolution of altitude adaptation in the Tibetan population took place. In order to disentangle this evolutionary history, Huerta-Sanchez and colleagues put their bet on using SNPs (see bellow) to understand the genetic variability of one gene, EPAS1, a transcription factor associated with the activation of several other genes regulated by oxygen concentration.

Thanks to Denisovans, Tibetans physiology make them well equipped to survive hypoxia. Photo by Lynn Johnson, Nat Geo. http://news.nationalgeographic.com/news/2014/07/140702-genetics-tibetan-denisovan-altitude-science/

Thanks to Denisovans, Tibetans physiology make them well equipped to survive hypoxia. Photo by Lynn Johnson, Nat Geo. http://news.nationalgeographic.com/news/2014/07/140702-genetics-tibetan-denisovan-altitude-science/

Data from Single Nucleotide Polymorphisms (SNPs) analysis have been contributing to understand how altitude adaptation took place. A SNP is a single nucleotide difference in a DNA sequence. These unique changes on the basic building blocks of genes can be associated to several phenotypic differences detectable between populations of the same species. Human arrival in the Tibetan plateau took place in the Last Glacial Maximum, around 25 thousand years ago. Since then, about 1,100 generations of Tibetans have been surviving under high-elevation-related hypoxia – sufficient time for fixation of alleles that confer altitude adaptation. Just looking at the gene EPAS1, Tibetans have shown to present a remarkable SNP diversity when compared to their closely related ethnic group, the Han Chinese, showing the fastest allelic change observed in any human genome to date – how impressive! But, what is the deal with populations that are not highly differentiated, but present considerable difference in the frequency at which specific mutations happen, like Tibetans and Han Chinese for EPAS1 SNPs? Huerta-Sanchez and colleagues hypothesized that the source of variation may come from donor populations. They first tried to understand how so much variation in such a short genomic region evolved, by testing two models of selection that simulate how EPAS1 haplotype diversity evolved: 1) selection under standing variation, assuming that Tibetans already had the beneficial haplotype when they colonized high altitude environments; or 2) selection under de novo mutation, which predicts that beneficial haplotype showed up and was fixed in the population after establishment in high altitudes. Surprisingly, the high haplotype diversity found in Tibetan EPAS1 couldn’t be explained by neither of the models of evolution, which supported the hypothesis that a donor population contributed to the fast EPAS1 diversification: in other words, DNA introgression lead to adaptation.

Haplotype network. Each pie chart is a haplotype, and colors within each pie chart represent the proportions of individuals from all populations that share the same haplotype. The Tibetan haplotypes are closer to the Denisovan than they are from any other modern human population a pattern expected under introgression. Figure and legend adapted from Huerta-Sanchez 2014.

Haplotype network. Each pie chart is a haplotype, and colors within each pie chart represent the proportions of individuals from all populations that share the same haplotype. The Tibetan haplotypes are closer to the Denisovan than they are from any other modern human population a pattern expected under introgression. Figure and legend adapted from Huerta-Sanchez 2014.

Work inside the Denisova cave in Siberia, where Denisovan, Neanthertal and modern humans took shelter from the cold generation after generation, during thousands of years. Photo from: http://ngm.nationalgeographic.com/2013/07/125-missing-human-ancestor/gallery-interactive

Work inside the Denisova cave in Siberia, where Denisovan, Neanthertal and modern humans took shelter from the cold generation after generation, during thousands of years. Photo from: http://ngm.nationalgeographic.com/2013/07/125-missing-human-ancestor/gallery-interactive

By searching for possible donor populations in several genomic databases, the authors found that Tibetans shared several haplotypes with the Denisovan individuals, popularly known as being the Neanthertal cousins. The Denisovan fossil record, even though only composed as whole by two phalanges and two teeth, have rendered amazing insights into hominin evolution since their discovery four years ago, in a cave in Siberia. What Huerta-Sanches and co-authors visualized when they looked at the haplotype networks to understand the relationships between Denisovan, Tibetan and 26 other modern human populations haplotype diversity of the EPAS1 gene was striking: the Tibetan haplotypes are closer to the Denisovan than they are from any other modern human population, a pattern only expected under introgression. Adaptation to high altitude amongst Tibetans may have been facilitated by gene flow from other hominins that may already have been adapted to those environments. This fantastic finding leads to an infinite network of questions: What is the relationship between Tibetans and other human populations adapted to high altitudes, like Basques in the Pyrenees and several ethnic groups in the Andes?  How does the genetic variation of other hypoxia-related genes look like? Are Tibetans good marathonists? How culturally different were Homo sp. populations interbreeding around 30 thousand years ago, did they speak the same language, how different they looked, how long did they interbred for? Should Tibetans thank the DNA donation by creating a new holiday called National Denisovan Day? Well, I suppose this is one of the beauties of science, one question answered, so many more to go.

Thanks to Dr Hughes, who chose this paper to be discussed in the seminar lead by him about Next Generation Sequencing at the Bio Department of the University of Missouri St Louis!

Huerta-Sanchez et al. 2014. Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA, Nature 512, 194–197. 

The role of dispersal in Neotropical avian diversity

In a paper published in Nature last month by Brian T. Smith (American Museum of Natural History) and collaborators argue that the strongest predictors of avian speciation in the Amazon are the amount of time a species lineage has endured in the landscape, and how well a bird can move through that landscape. Their results suggest that the dispersal abilities of the birds and how long their lineage has persisted are important drivers of the high biodiversity in the Amazon.

The authors start the introduction by reminding us that we, scientists, usually link the biodiversity of the Neotropics to two major hypotheses:

1) large-scale landscape changes that generate bio-diversification by population fragmentation followed by isolation, and

2) the formation of a geographically structured landscape matrix on which diversification occurred.

The first, commonly known as vicariance, involves reconfigurations of the landscape, such as the separation of continents by plate tectonics, the uplift of mountains or the formation of large rivers. Since the study involves the avian fauna of the Neotropic region, the large-scale events considered by the authors that could drive biodiversity patterns are the Andean mountain uplift, and the formation of the (very large) Amazonian rivers. This first hypothesis is easier to understand: big mountain or rivers separate populations, which can no longer exchange genes and start differentiating from one another to the point where the different sides will have completely separate evolutionary futures.

The second hypothesis involves organisms’ ability to persist in a structured landscape, which does not necessarily need to change. In this case, allopatric speciation would follow dispersal events, and thus, organism-specific abilities to persist and disperse in the landscape are the principal drivers of speciation. Species with lower dispersal abilities have a lower chance of navigating the landscape and, therefore, tend to accumulate higher genetic differentiation between populations. Higher differentiation, in turn, leads to higher speciation rates.

Figure 1 from Smith et al. 2014. Main landscape barriers and data points in the Neotropics.

Figure 1 from Smith et al. 2014. Main landscape barriers and data points in the Neotropics.

To test these two hypotheses, the authors used 2,500 individuals from 27 widespread bird lineages in the Neotropics. To prevent biases of current taxonomic limitations, authors considered lineages instead of species, i. e., they used monophyletic groups as their definition of a lineage instead of going by current taxonomic nomenclature.

They looked at relatively recently diversified lineages that have their distribution interrupted by the Andes, the Isthmus of Panama and large rivers of the Amazon Basin (the Amazon, Madeira and Negro rivers).

To get around hypothesis 1, the authors tested whether the timing of divergence events were congruent with a single episode of vicariance associated with barrier formation, the Andean uplift. To test hypothesis 2, they compared the different dispersal abilities of lineages to their diversification rate. The idea being that species with lower dispersal abilities accumulate higher genetic differentiation between populations, which, in turn, leads to higher speciation rates. The measures of dispersal are based on “foraging stratum (a measure of dispersal ability linked to the behavior of birds: canopy, high dispersal ability or understory, low dispersal ability) and niche breadth (an indirect measure of dispersal ability based on habitat preference)”.

Birds included in the study. Bird drawings from Smith et al. (2014), originally from del Hoyo et al. (2013) Handbook of the Birds of the World.

Birds included in the study.
Bird drawings from Smith et al. (2014), originally from del Hoyo et al. (2013) Handbook of the Birds of the World.

What their genetic data indicate is that there was not a single divergence event, but rather between 9 and 29, and the timing of these events were not synchronous. Most of the species diversity originated during the Pleistocene, i.e. after the Neogene formation of the landscape matrix. If any of the vicariance events predicted to affect speciation (Andean uplift, Isthmus of Panama, Amazonian rivers formation) had been the source of the diversification, the lineage divergence time would be synchronous, since they were being affected by the same event, considering these are relatively recently divergent species. However, wouldn’t only older divergence events be affected by old vicariance events? How well we can test this is entirely dependent on how well the old phylogenetic node divergences can be estimated. In the paper, the authors acknowledge that they “… do not reject the possibility that the initial geographical isolation of populations at deeper phylogenetic scales was due to vicariance associated with the Andean orogeny or with the emergence of other landscape features”.

“Although highly suggestive of multiple dispersal events, this variation could be explained by a single vicariant event associated with the Andean uplift if the dispersal restrictions imposed by the barrier were heavily dependent on dispersal ability, such as was reported for a taxonomically diverse group of marine organisms isolated by the formation of the Isthmus of Panama. In a similar fashion, the emerging Andes could have first become a barrier for bird lineages with low dispersal abilities, with fragmentation of the distributions of more dispersive lineages occurring later. However, we detected no significant associations between dispersal abilities and divergence times across the Andes and the Isthmus of Panama that would support a model of ecologically mediated vicariance for these barriers.”

What about hypothesis 2? They found that whether a bird lineage inhabits canopy or understory affected the species diversity of that lineage. Since they used foraging strata as a proxy for dispersal ability, this result corroborates with the idea that dispersal-limited lineages (occupying forest understory) are significantly more diverse. The longer a lineage has persisted through time was also a good predictor of species diversity, i.e., older lineage accumulated more differentiation.

“The accumulation of bird species in the Neotropical landscape occurred through a repeated process of geographical isolation, speciation and expansion, with the amount of species diversity within lineages influenced by how long the lineage has persisted in the landscape and its ability to disperse through the landscape matrix.”

All in all, the paper doesn’t refute the vicariance hypothesis, but highlights the role of dispersal. These findings add to the ever-increasing pile of possible explanations for the higher diversity of the tropics and its heated discussion.

Smith, Brian Tilston, John E McCormack, Andrés M Cuervo, Michael J Hickerson, Alexandre Aleixo, Carlos Daniel Cadena, Jorge Pérez-Emán, et al. 2014. “The Drivers of Tropical Speciation.” Nature, September. doi:10.1038/nature13687.

Mastering scariness: the mechanisms behind hooding and growling in cobras

Snake charming is a very popular and ancient performance in Africa and Asia, which takes advantage of the natural defensive behavior of cobras of forming a hood.

Snake charming is a very popular and ancient performance found in Africa and Asia, in which flute players takes advantage of the natural defensive behavior of cobras of forming a hood. Picture from: http://cdn.fansided.com/wp-content/blogs.dir/75/files/2013/07/snake-charmer.jpg

The second Ultimate Vert Bio Challenge is a warm up for Halloween, about one of the most terrifying, albeit amazing, creatures in nature: Cobras! These reptiles found their place in the animal kingdom hall of fame due to snake charming, a very ancient and popular performance in African and Asian countries,  in which a flute player pretends to hypnotize a cobra. What snake charmers actually do is take advantage of the defensive behavior called hooding, which cobras naturally perform by standing vertically, flaring the neck laterally and compressing it dorsoventrally. But, precisely, what adaptations in the skeleton and musculature of the cobras allow them to perform such a scarring defensive hooding display? When comparing X-rays of king cobras displaying hooding to cobras in a relaxed state, one is able to see how, in order to flare the hood, these snakes can rotate the ribs in two planes, frontal and transverse. The rotating movement of the ribs allow these bones to protract (move towards the head), and elevate (flatten and move dorsally), anchoring the muscles associated to the hood. Rib rotation is initiated by contraction of two muscles in the head, followed by contraction of intercostal muscles to support the protracted and elevated ribs. How long cobras can keep up with the defensive display depends on the amount of visual stimuli, or how threatened they feel, as well as intra- and inter- specific variation. However, there is evidence from laboratory observations that they are able to maintain the hood flared for at least 10 min, and up to 80 min!

Young BA, Kardong KV. 2010. The functional morphology of hooding in cobras.J Exp Biol. 213, 1521-8.

Young BA, Kardong KV. 2010. The functional morphology of hooding in cobras.J Exp Biol. 213, 1521-8.

I wouldn't hold a king cobra for a million dollars...wait, maybe for that money I would..but I definitely wouldn't smile while doing it like this guy does.  Picture from: http-//static.panoramio.com/photos/large/100885070.jpg

I wouldn’t hold a king cobra for a million dollars…wait, maybe for that money I would..but I definitely wouldn’t smile while doing it like this guy does. Picture from: http-//static.panoramio.com/photos/large/100885070.jpg

If you think hooding is enough to make cobras one of the most frightful creatures out there, you probably haven’t seen a video of a cobra hooding and growling at the same time. Yes, growling. Super laud nasty scary growling. Check out the video bellow:

Most snakes are able to produce hissing-like vocalizations at a frequency of 7,500 Hz, whereas cobras’ vocalizations lie at much lower frequencies, around 700 Hz, which is what characterizes them as growlers. The production of low frequency sound is possible due to the presence of a structure called tracheal diverticula. These are sacs associated to the trachea, which work as low frequency resonating chambers for the air flushed down the respiratory passageway. Interestingly, the only snake that has tracheal diverticula and is also able to growl, is the cobra’s favorite snack, the mangrove rat snake. This is considered to be a case of vocal Batesian mimicry, in which the mangrove rat snake mimics the vocalization of the more threatening cobras. The venom of mangrove rat snake is not toxic to humans, whereas cobras can inject up to 7 ml of venom in a single bite, and can kill a person in less than half an hour. We’re aware that cobras are predated by honey badgers (because they just don’t care), but I wonder what was the actual evolutive pressure through time to select for such a nasty defensive apparatus! Any thoughts?

Just to prove that King cobras can also look cute! Picture from: http://www.snaketype.com/wp-content/uploads/king_cobra_200-623x200.jpg

Just to prove that King cobras can also look cute! Picture from: http://www.snaketype.com/wp-content/uploads/king_cobra_200-623×200.jpg

 

Zombies of the ocean: the mechanism behind shark tonic immobility

Shark in tonic immobility state.

Shark in tonic immobility state.

My true passion in science is ecology and evolution of host-parasite systems. However,vertebrate evolution was what really caught my attention when I first started to study biology. Just based on the number of fans the movie Jurassic Park has, I’m sure I’m not alone with my fascination by vertebrate biology and evolution. Luckily, I got the chance to TA the Vertebrate Biology Lab at UMSL, which is an anatomy lab that I try to teach in an evolutionary, ecological and behavioral context. This Fall, I’ve decided to spice things up, and proposed to the students what I called the “Ultimate Vert Bio Challenge”. The idea here is to get our brains around some of the coolest, but, complex and most times under studied, facts involving vertebrates. In this first challenge, students had to try to explain the mechanism involved on shark tonic immobility (TI), a very popular topic referred to as ‘shark hypnosis’ or ‘zombie sharks’ in the media, and recently featured on Discovery Channel’s shark week (see video bellow).

Tonic immobility is assumed to be a behavioral strategy of preys – but, what does it mean when a predator presents the same type of behavior? Figure from the book Epossumondas Plays Possum, by Salley and Stevens.

Tonic immobility is assumed to be a behavioral strategy of preys – but, what does it mean when a predator presents the same type of behavior? Figure from the book Epossumondas Plays Possum, by Salley and Stevens.

TI is a behavioral strategy found in several species of vertebrates, such as  rabbits, chickens, hummingbirds, opossums, lizards, humans, and even in invertebrates, such as the red-flour beetle. In terrestrial vertebrates, TI is characterized as an unlearned and reversible behavior, in which the animal involuntarily enters a dead-like state characterized by motor inhibition. It is a behavioral display commonly associated with stress and fear responses to predators – hence a very widespread strategy among prey species. If TI is a response to predation, why the heck sharks, one of the sea’s top predators, can also be induced into a TI state? The TI mechanism is somewhat understood in terrestrial vertebrates: it involves activity of the hypothalamic-adrenal-axis, production of corticosteroids and muscle contraction. In contrast, in sharks and other elasmobranchs, TI is characterized by muscle relaxation. It is known that sharks experience physiological stress when in TI, due to high levels of carbon dioxide in the blood caused by inefficient ventilation while immobilized and turned upside down. However, the precise mechanism of TI in sharks has yet to be determined.

To get some insights on the possible mechanistic pathway of this phenomena, I got in touch with Dr Stephen Kajiura, the PI of the Elasmobranch Research Laboratory, at the Florida Atlantic University. Dr Kajiura mentioned that the consensus is that we just don’t know what the precise mechanism is. When I asked him to speculate what he believes the mechanism could be, he stated: “Since it (TI) works when the animal is flipped upside down, I would suspect that the mode of action is initiated by the vestibular system.  Another option is that the position causes blood flow to the brain to be compromised causing the animal to pass out.  In the wild, these animals are only likely to be flipped upside-down when being mated and it would probably be adaptive to be somewhat passive during that procedure to avoid being damaged by the mate’s teeth.” Another fact frequently pictured in shark TI videos are divers rubbing the animal’s snout with metal gloves, to stimulate the shark’s Ampullae of Lorenzini (AOL), an electro-receptive sensory system. This often misleads us to believe that AOL disruption is somehow the mechanism behind TI. Dr Kajiura explains that “it is possible to flip the sharks in the absence of any metal glove and get the same result.  AOL detect changes in electric fields so the shark may be momentarily confused by the metal glove, which might help to get it flipped upside-down, but remaining in TI is accomplished without any metal.  Again, we flip sharks with just our bare hands and get the same result so AOL are not likely the mechanism“. What is your hypothesis about the mechanism responsible for turning sharks into zombies?

Thanks to Dr Stephen Kajiura for kindly answering my questions so promptly!!

Long field seasons: how to prepare for one

Planning for a long field season next summer? Here is some advice for you. 

Recently, Leticia Soares wrote a post giving advice to students who are planning their first field season. Well, let’s be honest, we all could learn a thing or two (or a gazillion, in my case) about having a successful field season. Together, we decided that this was a topic worth extending, and we invited a few friends from the University of Missouri – St. Louis (UMSL) to give us (and you) some extra advice. In a previous post, Robbie Hart gave us some food for thought while in the field. In this post, you can read Mari Jaramillo‘s tips on how to plan for long periods in the field. She is a PhD candidate who works with avian malaria in the Galapagos islands. That’s right, she works in the Galapagos!! (sigh). Mari is a student in Dr. Patricia Parker’s lab at UMSL, and you can read more about her work at the end of this post.  

Taken at Tortuga Bay, Santa Cruz Island.

Taken at Tortuga Bay, Santa Cruz Island.

If you are lucky, field work doesn’t only take place during summer. Depending on the nature of your project you might need to stay at the field for extended periods of time, which for a field biologist is not hard at all. The hardest thing is probably leaving; you may be so comfortable you may want to make it your home…

But at some point you ought to know when you have collected enough data. No need to start crying and pouting though, the preliminary analysis of these data will point you in the right direction in future field seasons needed to complete your project.

Planning for extended field seasons is not that different from shorter ones, there’s just a lot more of it! Start thinking way ahead of time about the things that may take a while to get and be proactive about it. Lists are crucial! Ask yourself what things are indispensable for your research, for your assistants and for yourself and write these things down on a field or personal notebook. Also, you and your advisor will be glad if you check the list, item by item, with them or with your teammates that have been to the field site before. You could also send a list of personal items to your assistants and colleagues so they too are prepared for the field conditions and make sure they know about things that they are going to live without, like fresh water or electricity. Now, it doesn’t matter where and for how long you are going if all items in your list are checked off, you are good to go! And if you didn’t include it in your list, after all the scrutiny…

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…the truth is you will likely be fine without it.

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Field conditions and protocols are different from place to place; make sure you get acquainted with the rules and regulations of the different parks or reserves that you will be working at. Embrace the rules! You may find some of these rules are a pain in the %#$, but there is usually a pretty good reason behind them. Most of my field experience comes from work in the Galapagos Islands. These islands are a world icon and for that reason the park rules are more strict and extensive than anywhere else I have ever been. But I wouldn’t worry; there is a whole lot to enjoy as a scientist in these islands that no one else ever gets to experience!

The stars of the Pacific sky. Credit: Jeisson Zamudio.

The stars of the Pacific sky. Credit: Jeisson Zamudio.

If your work involves being away and isolated for long periods of time, you need to think survival!

Cover yours and everyone else’s basic needs and you will have a happy team! This means: food and water, a well-equipped first aid kit, a comfortable and warm place to sleep, a stove, gas or fuel and cooking equipment, duct tape (YES! Duct tape is a must!), rope, and never forget the matches!! I usually take a bunch of lighters and carry them in Ziploc bags in different places. Trust me, you do not want your field team to be eating cold food for two and a half months! This leads me to something I forgot to mention (and my advisor reminded me of), notice I said a ‘bunch of lighters’, not just one? Always take a spare, especially for items that are important for your work!! There are certain places in the Galapagos where you can head to do field work and find yourself in real isolation; it may take hours (and hundreds of dollars) for boats to get there, if an important piece of equipment brakes you’ll be glad to have a spare one!

Also, make your own plan of what to do in case something unusual happens or in case of an emergency and make sure everyone knows that plan. When the basics are covered, give yourself and your team a place to talk about the research each day. I usually break the group into two-people teams that go out and work all day to come back to camp before sunset. We may or may not have a cooking schedule (I’ve recently learned big groups alaways need schedules), but we usually eat dinner together, talk about how the day went and plan for the next day.

Some field experiences may be overwhelming, especially if it is the first time in a new place or leading a big group of people. You’re usually very busy and constantly planning for the next step… but I guess my best word of advice would be to stop and look around. I mean, really look around. You may be working with a single species but give yourself time to observe its surroundings, its habitat and its interactions with other organisms. Field work is a whole learning experience on its own, take advantage of it. And learn from others too, listen to other people’s ideas and suggestions; some people may surprise you with their creativity.

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Lastly, know that things never go exactly as planned. When this happens, IMPROVISE!

Even if that means adding sea water to the rice because you forgot to bring the salt, holding your arm up next to the roof drain at 3am to collect rain water for cooking because they told you there would be water up in the hut and there isn’t, or brushing your teeth with noodle water. Aah! All the good things about field work!

 

 

About Mari Jaramillo: I am an Ecuadorian biologist and have been doing field work in the Galapagos since 2008. I began as a field assistant in different projects with PhD students from Australia and Germany. I eventually ended up working with Dr. Sharon Deem, DVM, and Dr. Patricia Parker in a project under the Wildcare Center for Avian Health in the Galapagos Islands of the Saint Louis Zoo. Then I was awarded one of the scholarships for two Ecuadorian students established by Dr. Parker, Dr. Hernán Vargas and The Peregrine Fund to complete a master’s degree working with the Galapagos hawk. My master’s project (at UMSL) studied the impacts of ungulate (mainly goat) eradication on the diet of the Galapagos hawk on Santiago Island. This project required me to lead big groups of people to an uninhabited island for long periods of time (up to 2 1/2 mo) and very hard work. For my PhD I switched back to work with avian diseases. I’d like to break down the disease dynamics of avian malaria in this somewhat isolated archipelago to understand which are the main players in transmission and what is its effect on the endemic avifauna. However, I return to Santiago often to lead field seasons for the long term monitoring of the hawk population run by Dr. Parker in collaboration with Dr. Vargas and others (GNP, CDF).

Getting your statistician side out of the closet

anxiety3Ecology is a science that demands from researchers a decent amount of mathematical thinking and good analytical skills.  To be fair, these are must have traits for all of us working in this data-rich era. Despite the obvious mathematical reasoning that comes with studying how organisms and populations thrive, interact and evolve, most ecology graduate programs don’t provide a formal mathematical training for students, thought advanced stats and programming courses are offered in most departments out there. I see this trend as a “lets go straight to what matters” type-of-strategy for learning and teaching analytical methods in ecology graduate programs – which works, but is this the best strategy? I believe the lack of a more traditional training on the basic stuff, such as algebra and probability theory, makes it really hard for early-career ecologists to get their statistics skills developing in a steep learning curve. Fortunately, there are ways to overcome that – and the sooner the better to start going around these limitations through working on improving math and programming skills.

As an ecologist ‘under development’, I believe the first way to get around the limitations in our analytical training is by losing the fear of math: in other words, get the puppy face off and go rough my friend, throw yourself in the mud, and have fun trying to walk on very slippery terrain until you become a pro at doing so. My inspiration for writing this post comes from my recent experience as an ecologist in an environmetrics conference: Graybill/ENVR Conference  – Modern  Statistical Methods for Ecology. The Graybill Conference is hosted every year by the Department of Statistics of the Colorado State University, and it’s a great opportunity to get to know people that are the actual developers of the statistical approaches we apply in ecology and evolution. Some topics discussed in the conference were hierarchical modeling, occupancy modeling, modeling spatial data, latent variable modeling, and estimating species diversity taking phylogenetics into account. As any other ordinary grad student in Ecology, I also didn’t receive a formal mathematical training, besides undergrad level calculus zillions of years ago. Hence, I definitely wasn’t able to understand most talks as thoroughly and completely as I (probably) would in an ecology-related conference. However, I was indeed able to scoop enough information that will help me to improve my work in progress–and that’s exactly what I was looking for. If you’re a grad student in ecology, and frequently find yourself trying to answer questions that would take advantage of a more advanced statistical approach, keep an eye on environmetrics meetings and workshops, as these might be a handy resource for you.

If this post inspired you, check out these links:

I’ll leave you with a remarkable quote from S. J. Gould in the book “The Mismeasure of Man”, which always inspires me to go beyond in my learning process, in an attempt to understand this beautiful thing called nature.

“We naturally favor, and tend to overextend, exciting novelties in vain hope that they may supply general solutions or panaceas–when such contributions really constitute more modest (albeit vital) pieces of a much more complex puzzle.”