The so-called grad school life

Field prep for chronic medical conditions


Got field work and a medical condition? Well, you are not alone. Christina Baer, PhD candidate at the University of Missouri–St. Louis, shares great advice on how to prepare for a successful field season that accommodates your needs. We are hoping this post will not only be useful to someone with a medical condition, but also to any field crew-member trying to find out how to support a colleague. Please add a comment if you have any tips related to the topic!

In addition to being a field biologist, I also happen to be a Type 1 diabetic. I’ve just started my ninth year of field work, so it hasn’t slowed me down, but when you have a chronic medical condition, you definitely need to make some extra preparations. I’ve done field work in two very different situations: within driving distance of my home in the US and at field stations in Costa Rica. When I do field work, I’m walking around collecting data on caterpillars, so preparing for field work while living at home is the same as preparing for a hike.

Doing field work while living at a field station in a foreign country for ten or eleven weeks is a little more complicated. The field stations I’ve stayed at are comparable to Girl Scout summer camp—I’m not actually camping, but the only place that has constant air conditioning is the computer server room. Based on my experience, these are some of the preparations you should make before heading off for a long field season.

Disclaimer: I’m working on my PhD, not my MD—I am not a medical professional.

  1. Make sure your medical condition is more or less under control. This is important not just for your safety but also everyone else’s, and the success of the field work.
  2. Do a little worst-case scenario planning. What will happen if your symptoms get worse, you need to replace your medication, or you need to see a doctor? The field work I’ve done has been close to towns, so this would be relatively simple if it ever came up. But I also know researchers who work on remote mountains that they reach on foot or by horse. If you’re doing something like that, replacing medication will be more complicated than driving to the pharmacy.
  3. Pack backup supplies. If you have daily medications or supplies, take at least 1-2 weeks of extra supplies. These are not just important for emergencies, but also if you need to extend your field season. If that happens, the last thing you’ll want to be doing is filling prescriptions. If you have medical equipment, bring at least one extra of each, along with extra batteries and whatnot.

Bringing extras can be really important: one year, I left my insulin on a table in the dining hall at the same time a course group was leaving, and someone decided to send it along with them “just in case”. When I came back for lunch, I discovered that my insulin was headed halfway across Costa Rica. We got it back a week later (minus its cooling pack), but I would have been seriously inconvenienced if I hadn’t had extras.

The one downside to being well-prepared is that all those supplies can take up a lot of space, and packing space is always at a premium. All the science supplies are non-negotiable, so it usually comes down to medical supplies or clothes. Take the supplies. Even if you’ll only have three sets of field clothes to wear in rotation for three months, take the supplies.


Packing for Costa Rica: The red rectangle in front shows my diabetes supplies. The green rectangle in back shows my clothes.

  1. Share information. Make sure people know what your medical condition is, that your medication is yours (see above), and whether it needs to be kept cold, dry, etc. If you’re traveling abroad, make sure you can describe your medical condition and medication to others. When I started teaching myself Spanish, some of the first things I looked up were “diabetic”, “insulin”, and how to ask for a refrigerator or ice. (Since so much medical terminology has its roots in Latin or is recently created, this probably won’t be hard. “Diabetic” and “insulin” are simply diabético/a and insulina in Spanish.)
  2. Follow medical advice in the field. Even if it’s not about your “real” medical problems. On a field course I took, a professor with some chronic health problems got dehydrated and didn’t drink his rehydration salts because they taste nasty. He developed a urinary tract infection, was carried out of our field site on a stretcher, and taken to the hospital in an ambulance. Not fun.

I hope this information is useful to some of you. If you have questions, feel free to contact me at, although I can’t promise to have answers.


Christina Baer is a PhD candidate in the Marquis Lab at the University of Missouri-St. Louis. Her research interests include plant-insect interactions, natural history, and community ecology, so she’s doing her dissertation research on how tropical caterpillars build shelters to protect themselves from predators and parasitic insects. She wants to be a professor when she grows up.


Coming back with a 3-Min competition

It’s been a while since we have posted, we know. In the past year, one of us had two offspring (Maria is officially Dr. Pil, mother of Sebastião), and I’m in homestretch of my one and only offspring, my dear dissertation. The truth is: I miss the blog, I miss writing without peer-review manuscript prep or grant purposes, I miss talking about science that fascinates me, and I miss rambling about academia.

3MT-logoMy ‘return post’ will be about a recent experience I had – a 3-MIN thesis competition! I signed in almost instantaneously after I read the email from grad school about the competition. Don’t get deceived, I was prompt to take part in it not because I love competitions, but because I did a 2-min ‘lightening talk’ on a meeting in the past, and it was an absolute disaster. I needed to try again.

I won’t say my performance was successful, but it was definitely better than the first time I tried giving these types of very (very) short talks. I started preparing my 3-MIN talk by watching the presentations of  the past winners of the worldwide 3-MIN Thesis Competition. Yes, there’s such a thing, and one can learn a lot by seeing the strategies people come up with to tell a complex story of years of research within such a short amount of time. The four strategies I noticed were:

1st. Impactful opening and closing statements. You need to start with something that catches people’s attention and end with a statement that will leave the audience thinking about your take home message.

2nd. Clear presentations very often use numbering/listing to organize ideas – this makes the content very easy to follow.

3rd. You need to make people relate to your research. No matter how much you think your research topic is important (and I bet it is), you lose your audience if you don’t articulate it on a way that people from different backgrounds relate to.

I wrote a speech with these three observations on mind. The core of my dissertation is to use historical biogeography to determine turnover rates of avian haemosporidian assemblages in the West Indies. “Say what?,” you may think, if you are not the N=very limited number of people who work with what I work. Below, I share how I adapted my speech. I didn’t win the competition, but I’m happy with the fact that I made it to the finals.

Time traveling with bird malaria parasites in the West Indies islands

by Letícia Soares

Time traveling is not a privilege of science fiction characters. In my research, I used the geologic history of islands in the West Indies as a time machine to determine how the distribution of bird malaria parasites changes over evolutionary time scales. World wide, 3 billion people live at risk of malaria, and a half million people die of it every year. Bird and human malaria parasites are closely related through evolution. In fact, malaria parasites are also a threat to bird populations, and the disease has lead to the extinction of at least 10 unique bird species in Hawaii. With that said, my research 1) determines how rapidly these parasites can jump from one population to another, and 2) how the disease spreads over time scales of millennia. Bird populations in islands are the perfect study model to understand the evolution of host and parasite interactions, because birds canʼt go to the doctor to get treated, and we can see how parasites and hosts evolve in a natural system. 2 thousand years ago, the Earth was going through a glaciation, and sea levels were very low, causing some islands in the West Indies to be connected by landbirdges that were once covered by sea water. During that time, birds could move across islands that, today, are isolated by sea. I used this history of past connection as a natural isolation experiment to determine how long it takes to observe changes in 1) the type and 2) the frequency of parasites infecting these populations. I searched for the parasite DNA on the blood samples of birds, and 5 thousand blood samples across 21 islands later, I found that within 2 thousand years, there is a complete turnaround on the malaria parasite strains and the frequency they occur in these bird populations. This fast turnaround indicates that within 2 thousand years, birds may evolve resistance to parasites and parasites may evolve alternative ways to exploit birds. My research shows that birds and malaria parasites in nature are like Alice in Wonderland, in the sense that for one of them to get somewhere else, it needs to run at least twice as fast than the other.

Taste of quals: on the relationship between diversification and key innovations

This is the last post on the series “Taste of Quals” with examples of questions and answers given in the qualifying exams of the Ecology, Evolution and Systematics program at the University of Missouri St Louis. In this post you’ll find another answer written by Robbie Hart (find more about him here and here) in which he explains why, when and how key innovations can be associated to evolutionary diversification. The reason why I love this text and think it’s a great read is that even though this was written back in 2010, and since then the ‘omics’ era has overloaded us with tons of data, we are yet to understand several of the relationships between innovations and diversification that Robbie mentions. Enjoy the read!

Describe the relationship between diversification and key innovations

For half a century, rapid evolutionary diversifications have been conceptually linked to key innovations. Especially in the case of adaptive radiations, features associated with the diverging lineage have been held to have a causal effect in the diversification. However, the generality of this phenomenon has come under criticism from empirical, theoretical, and philosophical standpoints. Recent, careful studies have utilized detailed phylogenies and quantitative comparisons with null models, connected proposed innovations to genetic and developmental mechanisms, and examined replicated cases of radiations, to move the concept of the key innovation beyond “plausible suggestions” (1).


In the Simpsonian model of evolution, the essence of diversification is entry into a new adaptive zone, or way of life complete with characteristic adaptive pressures (2). Entry is only possible if three criteria of access are met: physical or geographic access, ecological access (the zone must be empty of competitors), and evolutionary or morphological access. To a large part, Simpson’s definitions have been retained. The first two criteria are now often lumped as ‘ecological opportunity’ (3, 4, 5) in contrast with the final criteria, now often termed a ‘key innovation’. Of the many definitions given for key innovation (Hunter lists seven (6), a small subset), Galis gives one that is in accord with modern ideas of macroevolution: “A key innovation is an innovation which opens up a new character space (or breaks constraints) that potentially allows the occupation of more niches” (7). Key innovations, then, open up adaptive space and expose many characters of an organism to a diverse new suite of selective pressures.

Other definitions accentuate different aspects of key innovations, but there are two that especially contrast with this definition. The first is a definition of key innovations in a phylogenetic context taken to mean any trait responsible for increasing diversification (8). The emphasis is explicitly on tempo rather than mode. Analyses of proposed key innovations differentiate this tempo and various mode definitions more or less (or not at all). The second is the concept of correlated progression, which proposes that coordinated suites of traits are the key features (6). Correlated progression is in clear contrast with the ‘constraint-breaking’ definition of Galis; but not incompatible with the tempo-only definition. At its root, correlated progression is getting at the individuation of key innovations, discussed in more detail below. For now, I’ll say that increasing phylogenetic resolution may ‘smear’ what we think of as one key innovation into several – a concept that has similarities to that of correlated evolution.

Necessary and sufficient conditions for diversification

The biggest potential problem with the concept of key innovations, the idea that they are a necessary and sufficient condition for an adaptive radiation, is easily dispelled. Examples showing the contingency of any causation between key innovations and diversification include the drilling radula in naticid gastropods. The drilling radula is an excellent candidate for a key innovation – it opened the door to many opportunities for specialized predation on shelled bivalves, and was associated with a major diversification of naticid lineages. However, the same feature was found to have evolved previously in the Triassic, and quickly disappeared for the extent of the Jurassic (9). Perhaps the most famous adaptive radiation of all, that of Rift Valley lake cichlids, has been attributed in part to the key innovation of recruiting the pharyngeal jaw for food processing, freeing the oral jaws for diversification (10) – a nice example of a constraint-breaking key innovation. However, Embiotocids also have pharyngeal jaws and Tilapiine cichlids share both structural and behavioral traits with the diverse clades of cichlids, but neither the Embiotocids nor Tilapiine cichlids have undergone a comparable adaptive radation (7).These examples illustrate that key innovations per se are at best necessary but not sufficient to spur diversification. This is the case even when it is combined with a an open niche, as with the Triassic drilling naticids.

Andean lupines, which show record levels of fast speciation and morphological change without identified key innovations or ecological opportunity (3), and plethodontid salamanders, which similarly exhibit an incredible burst of lineage diversification, but without key innovations, particular ecological opportunity, or even significant morphological difference (11), are two examples that bring the necessity of key innovations for rapid diversification into serious doubt. Hunter defends key innovations, however, as important conditions for radiations:
“ ‘environmental challenge’ cannot be sufficient to produce a radiation because, in the absence of [available ecological space ability to use that space via key innovations], any environmental challenge that destroys a species’ habitat is likely to result in extinction not radiation” (12).

All of this raises the question – are adaptive radiations even something that we need key innovations to explain? Are they a “thing” at all? Raup, Gould, Schopf and Simberloff published one of the early null models for phylogenetic diversification, and questioned the notion that there deterministic forces like key innovations significantly structured phylogenetic diversity at a macroevolutionary timescale (13). More recent work is often carefully tested against null models (1, 11, 12) , but criticisms on that basis remain (5), including a recent and suggestive model showing that many of the same phylogenetic patterns interpreted as adaptive radiations could in fact be produced by cryptic mass extinctions (14). Of course, sometimes the comparison with a null model finds no differences pervasive enough to indicate key innovations. This is the case for passerine birds (11), and presumably many more unpublished, as suggested by Donoghue (8)).

Conceptual criticisms: individuation, methodology, diversity patterns

Cracraft (15) offers a comprehensive critique of key innovations, citing them for failing on three themes: ontological (non-individuated ‘innovations’), methodological (failure to trace genetic and developmental mechanisms), and empirical (inappropriate comparisons with clade diversity measures). The first point questions whether a proposed evolutionary novelty is in fact a single character with an individuated identity independent of the observer, or whether it is merely a typological construct. Cracraft offers the example of avian flight, a frequently proposed key innovation that certainly offers access to a new adaptive zone, and a host of structures to differentiate and elaborate. However, flight requires a host of characters, stretching back across 50 million years of evolution to Archaeopteryx or before; it’s hard to argue that this amalgamation is a discrete innovation.

Nevertheless, Bond and Oppel could be doing just that when they write “If a key innovation is defined as the appearance of a new capability that facilitates the proliferation of the lineage that possesses it, then one or more characters may contribute to the key innovation. In the case of character complexes, a key innovation is not functional and therefore not present until all of its components are present. Thus, the key innovation appears at the point in a group’s phylogeny where the last of the functionally linked suite of characters appears” (1). Theirs is more a definitional assertion than an argument. Donoghue has a slightly different take on the issue – working with a tempo definition (Bond and Oppel’s was based on apomorphy) he notes that with increasing phylogenetic resolution many traits that researchers formerly lumped as a key innovation are now best understood as “sequences of character change, no one element of which can cleanly be identified as … responsible for shifting diversification rate”(8). Donoghue, pursuing Cracraft’s methodological theme, notes that this shift in what we think of as a key innovation often leads to surprising developmental features as the first steps towards key innovations – for instance, at the root of the compound feature “macrophyllous leaves” we find overtopping growth.

Photo by Alan Cressler.

Yucca moths in yucca flower. Photo by Alan Cressler.

Pellmyr and Krenn’s work on yucca moths (16) elegantly addresses Cracraft’s methodological theme (as well as the ontological and to some extent the empirical). Building off of a large body of prior research, they identify a unique limb (the yucca moth tentacle); associate it with the adaptive radiation of yucca moths, show that it is integrally related to the mechanism driving that radiation, ie. the pollinating co-evolution with yucca1; convincingly argue for the developmental origin of the tentacle as a heterotopic expression of a proboscis element; hypothesize on a genetic basis for the change; and show that it occurred as an abrupt change at the base of the pollinating yucca moth clade.

Finally, Cracraft’s empirical theme explores the difficulty in connecting an evolutionary novelty causally with patterns of species diversity, including problems of: assuming that higher taxa are comparable, making an arbitrary choice of a certain rank of taxa to compare, ignoring counterexamples, correlating a proposed key innovation and number of species, and qualifying clades as “diverse” or “not diverse” rather than quantifying diversity. This final theme has its roots in the very nature of key innovations. They are novelties, unique or at least rare, and this makes it extremely difficult to test causation. “Suppose that we agree wings are a key adaptation of bats. How can we show that they are responsible for there being ca. 870 species as against, say, 87 or 8,700?” (Raikow 1988 qtd in (10)). Cracraft specifically criticizes the comparative- functional argument as based on a correlation driven by an arbitrary choice of taxonomic or phylogenetic level of comparison. This is an issue that shares some conceptual similarity to the concern about null models. Bond and Opell (1) attempt to address the empirical theme by identifying unbalanced bifurcations in a semiautomated, a priori way across a well-resolved tree of all spiders, and only then locating the functional innovations at the nodes that emerge as exceptional; however their basic procedure remains qualitatively what Cracraft criticizes: “to qualify as a key innovation, our analysis requires that a feature: (1) be a synapomorphy; (2) be functionally advantageous; and (3) be capable of facilitation a change or an expansion of adaptive zone [and be associated with one side of an unbalanced bifurcation].”

Another way to address the empirical theme is through replicated examples of key innovations driving adaptive radiations. Unfortunately, replicated adaptive radiations themselves are few, in part because of the necessary conditions for geographic replicates. Islands and island- like habitats are promising; but candidate ‘archipelagos’ must be small enough to be replicated2 and at the same time large enough to show any speciation at all3. Caribbean anoles (17), Galapagos snails (4, 18), Mesozoic semionotid lake fish (19), and Hawaiian spiders (20) are rare examples; but are not strongly associated with key innovations (although toe pads and dewlaps have both been suggested for anoles (21)). Rift Valley lake cichlids, are one of the few examples of a replicated adaptive radiation with a fairly well-supported key innovation – the pharyngeal jaw (10).


Even the classic cichlid example, however, demonstrates the highly contingent nature of the connection between diversification and key innovations. The failure to radiate of closely related groups that share the pharyngeal jaw and other innovations with the species-rich groups of cichlids (7); phylogenetic reconstructions that show sexual selection, habitat selection, and trophic diversification as each driving a separate mini-radiation (10); and the possibility that much of the cichlid speciation may be due to peripatric speciation in geologically ephemeral satellite lakes and multiple colonizations from riverine lineages (19, 22) all emphasize this contingency.

Haplochromis (Pundamilia) nyererei, one of the species that is part of the outstanding species pool of Lake Vitoria, which contains more than 500 cichlid taxa.

Haplochromis (Pundamilia) nyererei, one of the species that is part of the outstanding species pool of Lake Vitoria, which contains more than 500 cichlid taxa.

De Queiroz (23) formalizes this contingency by breaking it into three parts: the effect of other taxa; the effect of other traits; and the effect of the environment. ‘Other taxa’ may be seen as more or less equivalent to Simpson’s ‘ecological access’ – one reason that a key innovation could arise and not lead to adaptive radiation is lack of an empty niche. De Queiroz advances image-forming eyes as a possible example of this contingency; the first three clades (vertebrates, arthropods, and cephalopods) in which this innovation emerged all diversified greatly; but it has subsequently evolved 15 times in various taxa without spurring comparable radiations (23). A less hypothetical example is presented in Hawaiian tertragnathid spiders, where ecomorph niches are present on an island or volcano either through immigration or diversification (20). No ecomorph is represented by two sympatric species, so a niche filled by immigration will presumably not spur radiation.

‘Other traits’ gets at individuation and complexes of traits. Functional modules may constrain evolution of their parts (24); and key innovations may often be, or result from, decoupling features from modules to, in effect, add parameters for diversification – this is seen in a number of key innovations from cichlid jaws (7) to avian flight musculature (6). At the same time; features that have a proximal connection to fitness (eg. macrophyllous leaves) may spring from from distal innovations (eg. overtopping growth) (8). Some distal innovations (baupläne) may offer much better ways of ‘solving’ evolutionary ‘problems’ than others – Donoghue gives the example of repeated convergence on ‘tree’ life forms in plants that seem to have not spurred radiations or been particularly efficient until it occurred in lineages with bifacial cambium.

Finally, ‘environment’ is clearly a key contingency; and is often associated with other biogeographic factors that may swamp patterns of adaptive radiation driven by key innovations; through patterns produced by other mechanisms of speciation (as in the possible peripatric speciation of cichlids) (22); ‘tier II’ or ‘tier III’ historical phenomena such as species selection and mass extinction (perhaps responsible for the disappearance of the Tertiary drilling naticids (9)); or environmental changes such as global CO2 change (which affects the ‘keyness’ of the C4 innovation (23)) and climatic cycles (which drove repeated local mass extinctions of radiating semionotid lake fish (19)).

The relationship between diversification and key innovations could, perhaps, be summed up in one word: ‘contingent’. I’m inclined to think of this a more of a ‘profound insight’ than a ‘truism’ (23); and agree with the forecasts of de Queiroz (23), Donoghue (8), and Losos (4); that a careful study of how key innovations can drive diversification is rich ground for evolutionary insight, even lacking a strict deterministic framework.


1 The related Raven-Ehrlich hypothesis of key antagonistic/defensive coevolutionary synchronization driving adaptive radiation through escalation has received somewhat equivocal support (25, 26).

2 Continent-scale comparisons are of course limited in number.

3 One of the most intriguing patterns to emerge from studies of island speciation is a threshold island area, below which no inferred speciation events are seen (18).

Works cited

1. Bond JE, Opell BD (1998) Testing Adaptive Radiation and Key Innovation Hypotheses in Spiders. Evolution 52:403.

2. Simpson G (1953) Major Features of Evolution (Columbia University Press, New York).

3. Hughes C, Eastwood R (2006) Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the Andes. Proceedings of the National Academy of Sciences of the United States of America 103:10334-9.

4. Losos JB (2010) Adaptive Radiation, Ecological Opportunity, and Evolutionary Determinism. The American naturalist 175.

5. Masters J, Rayner R (1998) Key innovations? Trends in Ecology & Evolution 13:281.

6. Hunter JP (1998) Key innovations and the ecology of macroevolution. Trends in Ecology and Evolution 13:31-36.

7. Galis F (2001) in Character Concept of Evolutionary Biology, Wagner GP, pp. 581-605.

8. Donoghue MJ (2005) Key innovations, convergence, and success: macroevolutionary lessons from plant phylogeny. Paleobiology 31:77-93.

9. Fürsich FT, Jablonski D (1984) Late Triassic Naticid Drillholes: Carnivorous gastropods gain a major adaptation but fail to radiate. Science 224:78-80.

10. Danley PD, Kocher TD (2001) Speciation in rapidly diverging systems: lessons from Lake Malawi. Molecular ecology 10:1075-86.

11. Kozak KH, Weisrock DW, Larson A (2006) Rapid lineage accumulation in a non- adaptive radiation: phylogenetic analysis of diversification rates in eastern North American woodland salamanders (Plethodontidae: Plethodon). Philosophical transactions of the Royal Society of London. Series B, Biological sciences 273:539-46.

12.  Ricklefs RE (2003) Global diversification rates of passerine birds. Proceedings. Biological sciences / The Royal Society 270:2285-91.

13. Raup DM, Gould SJ, Schopf TJ, Simberloff DS (1973) Stochastic models of phylogeny and the evolution of diversity. Journal of Geology 81:525-542.

14. Crisp MD, Cook LG (2009) Explosive radiation or cryptic mass extinction? Interpreting signatures in molecular phylogenies. Evolution 63:2257-65.

15. Cracraft J (1990) in Evolutionary Innovations, Nitecki MH, pp. 21-44.

16. Pellmyr O, Krenn HW (2002) Origin of a complex key Innovation in an Obligate Insect-Plant Mutualism. Science 99:5498-5502.

17. Losos JB (1998) Contingency and Determinism in Replicated Adaptive Radiations of Island Lizards. Science 279:2115-2118.

18. Losos JB, Parent CE (2010) in The Theory of Island Biogeography Revisited, Ricklefs RE, Losos JB (Princeton University Press, Princeton), pp. 416-438.

19.  McCune AR, Thomson KS, Olsen PE (1984) in Evolution of Fish Species Flocks, Echelle A, Kornfield I, pp. 27-44.

20. Gillespie R (2004) Community Assembly Through Adaptive Radiation in Hawaiian Spiders. Science 303:356-359.

21. Jackman T, Losos JB, Larson A, de Queiroz K (2000) in Molecular Evolution and Adaptive Radiation, Givnish TJ, Sytsma KJ (Cambridge University Press), pp. 535-557.

22. Genner MJ et al. (2007) Evolution of a cichlid fish in a Lake Malawi satellite lake. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 274:2249-57.

23. De Queiroz A (2002) Contingent predictability in evolution: key traits and diversification. Systematic biology 51:917–929.

24. Wagner GP, Pavlicev M, Cheverud JM (2007) The road to modularity. Nature reviews. Genetics 8:921-31.

25. Agrawal AA, Lajeunesse MJ, Fishbein M (2008) Evolution of latex and its constituent defensive chemistry in milkweeds (Asclepias): a phylogenetic test of plant defense escalation. Entomologia Experimentalis et Applicata 128:126-138.

26. Berenbaum MR, Favret C, Schuler MA (1996) On defining “key innovations” in an adaptive radiation: cytochrome p450S and Papilionidae. The American Naturalist 148:S139-S155.

Hope that's you now, after having read all these posts on how to nail your quals!

Hope that’s you now, after having read all these posts on how to nail your quals!

Taste of quals: do phylogenetics and conservation biology walk side by side?

Photo by Letícia Soares.

Robbie Hart writes on the relationships between conservation biology and phylogenetic trees, systematics and species concepts. Photo by Letícia Soares.

Here I am again, with another example of a qualifying exam question, from the Ecology, Evolution and Systematics program of the University of Missouri-St Louis. This time, I’ll post a sample from the quals of Robbie Hart, a PhD candidate (very soon PhD to be) in our program. Robbie’s quals answers were pointed out by a former faculty faculty member as one of the best through out years of evaluating the quals from several student cohorts. When I told Robbie the great things I heard on the quality of his answers, I asked him if I could post a few of them in the blog, and that was his reaction:

Robbie Hart gives us a sample of his own qualifying exam. He is also a pro when it comes to crack nuts. Courtesy of Robbie Hart.

“Awwwww don’t make me blush! That’s certainly a nice complement, though it seems unlikely! Quals was upsetting and difficult for me as it is for everyone…and it involved an early version of dropbox eating one of my answers. […]. I found them [the answers], but can barely understand them now. I think I’ve spent too long in the field. I’ll share with you […] my excessively wordy evolution major and my superficial and incomplete conservation bio minor.”

So here it is, a conservation biology minor question, answered by Robbie Hart. If you want to catch up with this topic on how to prepare yourself for qualifying exams in ecology and evolution, check out our previous posts!

What information in classifications and phylogenies may help – or hinder – efforts in conservation?

To prioritize conservation actions, one must first ask the existential question of conservation biology: ‘what are we trying to conserve when we protect biodiversity?’. One answer to this question is based in utility to humans: the goal is ecosystem services (1), and in an uncertain world, continued diversity conserves ‘option value’ – net benefit of keeping various possibilities open (2). Another is based on evolutionary history – an organismal lineage is seen as taking a certain amount of time to evolve, and a loss of that lineage (extinction) is lost evolutionary time (3,4). Both of these may be seen as preserving distinctive features of organisms; most modern approaches to quantifying biodiversity take genetic diversity as a proxy for a multitude of unknown (and perhaps unknowable) ‘features’ (1,2). Phylogenies and classifications, therefore, are central to setting the units of conservation, as they are both maps of the diversity to be conserved.

Species are historically the units of conservation for the public, scientists and legislators. However, their very importance may make them especially unstable categories – driven by biological evidence, legislative criteria, or the adoption of different species concepts, species number may change drastically, often leading to confusion or changes in prioritization (“taxonomy as destiny”(5), also see (6, 7, 8)). Different species concepts may work better for the different taxonomic goals of listing and management (7), and even the quality of the species level as a uniquely real grouping has been called into question as another just another lump in the continuum (6,9). Infra-specific groupings have fared even less well; they are subject to differing taxonomic cultures across different taxa, and have little relation even to genetic subdivisions of species (10). Higher taxa are also commonly used, and to some advantage: they offer deeper insight into loss of evolutionary history, and they are potentially more stable than specific and infraspecific levels. It could be argued that evolutionary taxa sensu Simpson are to some extent based on features themselves. However, the arbitrary nature of the higher divisions make them less suitable for quantitative, comparative analysis (1,3).

In light of these troubles, other units have been proposed for conservation. Units may be ‘management’, consisting of any population groups differing in allele frequency (1); ‘designatable’, designed with pragmatic policy issues in mind (11); ‘evolutionarily significant’, defined either as historically isolated (12), reciprocally monophyletic (1), or more broadly defined (13); or any of a large set of distinct or partially overlapping terms. As classifications, these terms share an unfortunate dichotomy – a group is either a unit, or not (13).

Phylogenetic diversity methods move beyond this dichotomy and treat distinctness or originality as a continuum. Methods are similarly diverse here, but generally apportion to each organism the amount of tree for which they are responsible. This offers a detailed look at exactly how much phylogenetic history is lost with each species that goes extinct; and is a measure with significant stability to taxonomic revision. This method can be extended in various ways: to probabilistic measures that take into account each sister node’s threat levels (14); or combined with complementarity principles to quantify hotspots of phylogenetic endemism (15, 16).

In the past, taxonomies and classifications have posed hindrances to conservation efforts. Newer phylogenetic diversity methods show great promise in moving past dichotomous categories and quantifying the threat to the shared evolutionary history of organisms. The virtue and immediacy of these are highlighted by studies showing that nonrandom extinction can pose a particularly severe threat to evolutionary history (4, 17).

Works cited

1. Crozier RH (1997) Preserving the information content of species: genetic diversity, phylogeny, and conservation worth. Annual Review of Ecology, Evolution, and Systematics 28:243-268.

2. Faith DP (1994) Phylogenetic pattern and the quantification of organismal biodiversity. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 345:45-58.

3. Avise JC, Johns GC (1999) Proposal for a standardized temporal scheme of biological classification for extant species. Proceedings of the National Academy of Sciences of the United States of America 96:7358-63.

4. Vamosi JC, Wilson JR (2008) Nonrandom extinction leads to elevated loss of angiosperm evolutionary history. Ecology Letters 11:1047-53.

5. May RM (1990) Taxonomy as destiny. Nature 347:129–130.

6. Isaac NJ, Mallet J, Mace GM (2004) Taxonomic inflation: its influence on macro ecology and conservation. Trends in Ecology and Evolution 19:464-9.

7. Mace GM (2004) The role of taxonomy in species conservation. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 359:711-9.

8. Baker RJ, Bradley RD (2006) Speciation in Mammals and the Genetic Species Concept. Journal of mammalogy 87:643-662.

9. Mishler BD (2009) in Contemporary Debates in Philosophy of Biolgoy, Ayala FJ, Arp R (Wiley-Blackwell), pp. 110-122.

10. Zink RM (2004) The role of subspecies in obscuring avian biological diversity and misleading conservation policy. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 271:561-4.

11. Green DM (2005) Designatable Units for Status Assessment of Endangered Species. Conservation Biology 19:1813-1820.

12. Moritz C (2002) Strategies to protect biological diversity and the evolutionary processes that sustain it. Systematic biology 51:238-54.

13. Crandall KA, Bininda-Emonds OR, Mace GM, Wayne RK (2000) Considering evolutionary processes in conservation biology. Trends in Ecology and Evolution 15:290-295.

14. Faith DP (2008) Threatened species and the potential loss of phylogenetic diversity: conservation scenarios based on estimated extinction probabilities and phylogenetic risk analysis. Conservation Biology 22:1461-70.

15. Rosauer D, Laffan SW, Crisp MD, Donnellan SC, Cook LG (2009) Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular ecology 18:4061-72.

16. Faith DP, Reid CA, Hunter J (2004) Intergrating Phylogenetic Diversity, Complementarity and Endemism for Conservation Assessment. Conservation Biology 18:255-261.

17. Purvis A (2000) Nonrandom Extinction and the Loss of Evolutionary History. Science 288:328-330.


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.


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! 😛

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.


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***


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.


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:

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:

Trying to do multiple things at the same time during your quals is a bad idea. Photo from:

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.


 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 🙂 !

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…


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


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.


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.”

Field work’s yin and yang, lessons from China

Following up our “Field preparation” series, Robbie Hart from the Missouri Botanical Garden in St. Louis gives us some extra advice on how to prepare for the unforeseen during your field time. Thanks, Robbie, for this great post!

Robbie Hart is a 7th-year Ph.D. candidate at UMSL. He’s spent about half of his time since coming to St. Louis away at his field site in Himalayan China, monitoring the effects of climate change on Rhododendron flowering time along a gradient 2600-4100 m above sea level. He’s now writing up his dissertation and working at the Missouri Botanical Garden, where he continues to focus on climate change impacts on high-elevation Himalayan plants. There’s more about his work, and some pictures of his field sites at


Planning is a feedback loop.

Having a set packing list is important when you’re traveling out of the range of Amazon 2-day shipping. Even more vital is a set methodology when you’re trying to collect data while exhilarated, exhausted, exposed to the elements, or all of the above. However, recognize that planning ahead, while essential, is uninformed by the potent realities of how things actually work in practice. Maybe you can’t actually sample 100 trunks without walking across a contested international border. Maybe the idea of a straight-line transect which seemed doable from the perspective of a map doesn’t seem as realistic when you’re staring down a cliff. Ultimately, you’ll never be able to plan perfectly for fieldwork until the project is actually complete, and the final product will always be a compromise between what you did and what you now know you should have done. Don’t fight it, because this is inescapable – just be a little flexible, a little firm, and find the point of compromise that works for your project.
There’s a book by Trevor Legget called ‘Zen and the Ways’, where he talks about two terms one encounters in Japanese martial arts: isshin and zanshin. I’m fairly certain I’m butchering them, but I see isshin (‘one-heart’) as a single-minded focus, an in-the-moment ‘zenning out’ on the task at hand. This is certainly how I get through the taxing or difficult periods of data collection in the field, and I think it’s true of others. There just isn’t another way to sit in a hailstorm for another four hours trying to write with frozen fingers, or to make it up that last mountain pass with a press full of collections on your back. Zanshin(‘remaining heart’) is a wider awareness, meta-level thinking about what you’ve done, why you’ve done it, and what you’re going to do.
Perhaps true samurai, or tenured faculty, can always maintain the right balance of isshin and zanshin. For me, it’s harder – it’s easy to get stuck in just getting the planned work done. Equally, it can also be a trap to constantly be questioning yourself or changing methods, and end up with data that’s not comparable, not efficiently collected, or not collected at all. I think it can be important to plan in times to stop and cultivate zanshin. In the evenings, or those break days that Leticia mentioned (in her previous post to the Naked Darwin), take some time over your well-deserved beer to evaluate and evolve your plans. During the work days, focus on getting things done, and file away those nagging doubts for the appropriate time.


Some rules of thumb which probably hold true no matter how your plan evolves
Back up your data. If you can’t get it in the cloud, make two or three digital copies and keep them in physically separated locations (keydrives, camera cards, etc.). If you can’t do that, make physical copies. You’re never going to get that year back if all of the data you collected during it goes up in smoke.

Don’t be afraid to ask questions. It’s a new field site, country, species, discipline, culture, method, or trail. Someone (or maybe almost everyone) knows more than you do. Ask for advice! I’m always scared to do this, and it always, always is worth it.

Don’t just take data, take metadata. Take much more than you think you need. Whether it’s in a fieldbook, or going through and putting tags on your photos, don’t underestimate your power to forget things in a day or a year. You *will* be grateful that you wrote down that person’s full name, detailed your custom designed sampling scheme, drew a map of where that nest is, or took a photo of your altimeter between every photo you took a photo of a species on your alpine transect. Data is your friend. Metadata is your friend with benefits.

Remember your limits, and those of others with you, and communicate about them. These aren’t always the safest conditions. Just because you can’t catch your breath and are feeling dizzy, doesn’t mean that the team member ahead of you knows that you’re getting mountain sickness. Alternately, just because you’re feeling tired but can totally make that last push to collect another sample doesn’t mean that everyone on your team can.


View from my rooftop on Yunnan, China

Yulong Mountain, Robbie’s field site

Rhododendron racemosum – 2800 meters above sea level

Rhododendron racemosum – 2800 meters

Rhododendron impeditum – 3800 meters

Rhododendron impeditum – 3800 meters

Courtesy of Robbie Hart.