Predicting the evolutionary response of populations to climate change

For this month’s research highlight in Evolutionary Applications, I chose to focus on recent approaches to studying evolutionary responses to climate change:

“Given the increasingly unpredictable weather patterns associated with global climate change, a key aim of current research is to predict whether and how populations will be able to respond. Major advances are being made through a combination of natural, long-term studies and experimental approaches, for example by experimentally manipulating the environment and measuring phenotypic and/or genetic/genomic change. The first issue of Evolutionary Applications this year (Volume 7, Issue 1), under the lead of guest editors Andrew Hendry and Juha Merilä, brought together perspectives from a number of researchers on the leading edge of climate change research. This included multiple synthetic reviews discussing the challenges of teasing apart evolutionary change from more ‘plastic’ responses to environmental perturbation, as well as reviews of the current empirical evidence for and against key theoretical predictions. Overall, the volume highlights that, although data on the evolutionary potential of populations in response to climate change are accumulating, we are still far from where we hope to be in terms of predictive ability, especially given the complexity of most environments (Merilä and Hendry 2014).

Such biological and environmental complexity can be disentangled through the use of experimentally manipulated microcosms, and these studies provide a powerful tool for differentiating correlation from causation and investigating interactive, synergistic effects among multiple factors. Researchers in this field are moving beyond tests of increasing temperature to examine more complex factors, including environmental fluctuations and multiple abiotic or biotic stressors (Jeffs and Leather 2014; Vasseur et al. 2014). Recent work by Jennifer Lau and collaborators manipulated both the atmospheric CO2 concentration and the competitive environment of experimental Arabidopsis thaliana populations. They found that, while populations showed little direct response to increased CO2, there were strong indirect effects of the abiotic environment on the population level response to competitor-mediated selection (Lau et al. 2014). This type of indirect effect of climate change on populations would be overlooked in studies focused solely on climate-specific adaptations. Similarly, the effect of climate change at the community level can often be more striking than effects on a single population or species (Bailey et al. 2014). Sarah Evans and colleagues examined adaptation of soil bacterial communities after a decade of drying and rewetting stress and found that community composition was significantly altered relative to plots experiencing normal precipitation, especially in favor of those taxa exhibiting increased stress tolerance (Evans and Wallenstein 2014). The outcome of adaptation to changing environmental conditions can also differ in very meaningful ways among populations or genotypes. Romain Gallet and coauthors recently evolved replicate populations of Escherichia coli under extreme shifts in pH over 2,000 generations and found adaptation to the new pH in all replicates but evidence for niche width expansion in only two of the four replicates. This latter finding emphasizes the importance of incorporating the evolution of plasticity into predictions regarding climate-mediated adaptation and also highlights the very different evolutionary trajectories that populations may take in response to the same selection pressure (Gallet et al. 2014).

Although these experimental approaches offer important insight to the processes underlying climate-mediated evolutionary change, an understanding of whether a population can respond does not clearly translate into an understanding of whether it will respond. As such, there is still great need for long-term studies from natural populations. Katie Becklin and colleagues have examined changes in leaf physiology across seven conifer species by comparing samples preserved in middens (debris piles) from five time points since the last glacial maximum. Their data suggest physiological adaptations of leaves to climate change, such as decreased stomatal conductance, were influenced by evolutionary history but were not the primary determinant of shifts in community composition (Becklin et al. 2014). Finally, combining both natural and experimental datasets can offer a unique perspective on our ability to predict the effects of climate change. Sean Menke and collaborators have compared the results of long-term experimental data with those from natural populations to demonstrate that, although ant community composition has shifted along an elevational gradient over time in the natural populations, there has been relatively little change over the course of experimental warming (Menke et al. 2014). This work highlights the potential limitations of microcosm studies in isolation, given the biological complexity of populations and communities over space and time. However, these limitations can be overcome in part with biologically meaningful replication of experimental plots. This is nicely demonstrated by a recent study of wetland seed banks from multiple latitudes across two continents in which species diversity of communities from southern latitudes were found to be less affected by experimental warming than those from more northern latitudes (Baldwin et al. 2014).

Together, these studies demonstrate the need to incorporate both biotic and abiotic complexity into models and empirical studies in order to fully understand the resilience of populations and communities in the face of climate change. The impressive range of recent approaches, from experimental manipulation of multiple factors, to seminatural studies of communities spanning a wide range of abiotic conditions, to tests of species-level adaptations from preserved specimens across millions of years, has allowed great strides in our understanding of both if and how populations might respond to the increasingly unpredictable environment.”

Bailey, J. K., M. A. Genung, I. Ware, C. Gorman, M. E. Van Nuland, H. Long, and J. A. Schweitzer 2014. Indirect genetic effects: an evolutionary mechanism linking feedbacks, genotypic diversity and coadaptation in a climate change context. Functional Ecology 28:87–95.

Baldwin, A. H., K. Jensen, and M. Schönfeldt 2014. Warming increases plant biomass and reduces diversity across continents, latitudes, and species migration scenarios in experimental wetland communities. Global change biology 20:835–850.

Becklin, K. M., J. S. Medeiros, K. R. Sale, and J. K. Ward 2014. Evolutionary history underlies plant physiological responses to global change since the last glacial maximum. Ecology Letters, doi: 10.1111/ele.12271.

Evans, S. E., and M. D. Wallenstein 2014. Climate change alters ecological strategies of soil bacteria. Ecology letters 17:155–164.

Gallet, R., Y. Latour, B. S. Hughes, and T. Lenormand 2014. The dynamics of niche evolution upon abrupt environmental change. Evolution, doi: 10.1111/evo.12359.

Jeffs, C. T., and S. R. Leather 2014. Effects of extreme, fluctuating temperature events on life history traits of the grain aphid, Sitobion avenae. Entomologia Experimentalis et Applicata 150:240–249.

Lau, J. A., R. G. Shaw, P. B. Reich, and P. Tiffin 2014. Indirect effects drive evolutionary responses to global change. New Phytologist 201:335–343.

Menke, S. B., J. Harte, and R. R. Dunn 2014. Changes in ant community composition caused by 20 years of experimental warming vs. 13 years of natural climate shift. Ecosphere, 5:art6.

Merilä, J., and A. P. Hendry 2014. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evolutionary Applications 7:1–14.

Vasseur, D. A., J. P. DeLong, B. Gilbert, H. S. Greig, C. D. Harley, K. S. McCann, V. Savage, T.D. Tunney, and M. I. O’Connor 2014. Increased temperature variation poses a greater risk to species than climate warming. Proceedings of the Royal Society B: Biological Sciences, 281:20132612.

Predicting evolution

For this month’s research highlights in Evolutionary Applications I thought I’d take a look at some of the recent work addressing the predictability of Evolution.

“Whether evolution is predictable becomes a key question when deciding how to translate and apply evolutionary theory to solve real-world problems. Mutations arise by chance, and therefore, it seems fair to question if we can predict when, where, and how a population will respond to a given selection pressure. One of the many exciting results from Richard Lenski’s long-term experimental evolution of Escherichia coli bacterial populations was the clear demonstration that, given a set of so-called ‘potentiating’ mutations, populations followed the same evolutionary trajectory again and again as the experiment was ‘replayed (Blount et al. 2008). In their new paper, Diana Blank and collaborators have taken a similar approach by experimentally evolving E. coli mutants with major gene deletions to test if and how the mutants are able to regain function (Blank et al. 2014). By measuring both the likelihood of recovery and the type of mutations underlying recovered function, the authors uncover a relationship between regulatory versus structural mutations and metabolic function, demonstrating a clear and predictable link between molecular change and functional innovation.

The ability to predict how a population will respond to selection pressure is further aided by a good understanding of the ‘fitness landscape’ (the relationship between genotype and reproductive success). If the effect of every possible mutation, and interactions among mutations, on an organism’s fitness was known, we would have a much higher probability of successfully predicting the evolutionary trajectory in response to a given selection pressure such as drug treatment or insecticide use. Recent work by Ashley Acevedo and colleagues used next-generation sequencing to reveal the mutation landscape of poliovirus and to explore the fitness change associated with thousands of mutations across the landscape (Acevedo et al. 2014). This work sets the stage for generating and testing predictions regarding the evolution of RNA viruses, taking us a step closer to designing ‘evolution-proof’ treatment strategies.

The predictability of evolution at the phenotypic level can also be observed in natural populations, in particular when multiple populations are evolving in response to similar selection pressure. For example, a recent study by Rüdiger Riesch and coauthors examined change across nine species of New World livebearing fish that independently colonized toxic, sulfur spring environments (Riesch et al. 2014). They discovered that, as predicted by evolutionary theory, each of the 22 populations evolved toward larger offspring size and fewer offspring number after colonization, emphasizing that parallel adaptation to a specific environment can lead to repeatable evolution at the phenotypic level across both populations and species.

Finally, a good working knowledge of the mutational landscape and genotype–phenotype map can be extremely useful when employing adaptive laboratory evolution to generate organisms of applied interest. Artificial selection has been around since prehistoric times, but recent advances in, for example, stress-induced and transposon mutagenesis as well as experimental evolution have allowed for the rapid generation of organisms with desired traits. These techniques have recently been employed to select for acid- and temperature-tolerant strains of hydrogen-producing photosynthetic bacteria (Cai and Wang 2014) improved carotenoid production in yeast (Reyes et al. 2014) and reduced ethanol content of fermenting wine (Tilloy et al. 2014). For example, 200 generations of artificial selection for reduced ethanol and enhanced glycerol yields of Saccharomyces cerevisiae were enough to generate new strains showing little to no decrease in fermentation activity (Tilloy et al. 2014). In comparison with the speed at which mutants can be generated by engineering and mutagenesis (e.g., Cai and Wang 2014), this method may seem old-fashioned. However, Valentin Tilloy and collaborators argue that given the feasibility of classic methods and the public hesitation surrounding genetic modification, artificial selection still holds great promise for the generation of commercially relevant organisms (Tilloy et al. 2014). The success of such ‘directed evolution’ will of course depend on our ability to choose the appropriate selective pressures, as emphasized by Luis Reyes and coauthors who recently evolved yeast strains under oxidative stress with the goal of selecting for increased production of carotenoids with known antioxidant properties (Reyes et al. 2014).

These recent studies highlight that our ever-increasing understanding of evolutionary trajectories along the fitness landscape, uncovered by the genotype–phenotype map, is allowing both for an increased ability to predict the outcome of evolution and for the improved application of directed evolution in generating desirable phenotypes. Furthermore, the use of next-generation sequencing to generate single nucleotide fitness landscapes will offer a resolution that was once only dreamed of (Wright 1932).”

Acevedo, A., L. Brodsky, and R. Andino 2014. Mutational and fitness landscapes of an RNA virus revealed through population sequencing. Nature 505:686–690.

Blank, D., L. Wolf, M. Ackermann, and O. K. Silander. 2014. The predictability of molecular evolution during functional innovation. Proceedings of the National Academy of Sciences USA, doi: 10.1073/pnas.1318797111.

Blount, Z. D., C. Z. Borland, and R. E. Lenski 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences USA 105:7899–7906.

Cai, J., and G. Wang 2014. Photo-biological hydrogen production by an acid tolerant mutant of Rhodovulum sulfidophilum P5 generated by transposon mutagenesis. Bioresource technology 154:254–259.

Reyes, L. H., J. M. Gomez, and K. C. Kao 2014. Improving carotenoids production in yeast via adaptive laboratory evolution. Metabolic engineering 21:26–33.

Riesch, R., M. Plath, I. Schlupp, M. Tobler, and R. Brian Langerhans 2014. Colonisation of toxic environments drives predictable life-history evolution in livebearing fishes (Poeciliidae). Ecology letters 17:65–71.

Tilloy, V., A. Ortiz-Julien, and S. Dequin. 2014. Reducing ethanol and improving glycerol yield by adaptive evolution of Saccharomyces cerevisiae wine yeast under hyperosmotic conditions. Applied and Environmental Microbiology, doi: 10.1128/AEM.03710-13.

Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the Sixth International Congress on Genetics 1:356–366.

What’s been keeping me busy?

What a whirlwind of a few months it’s been! Stay tuned, as we have some great new data rolling out and a couple of new review/synthesis papers. My lack of blogging recently (which I hope to remedy) is also the result of too many papers to review, lots of travel to and from (with the added complication of being disconnected from London by a washed out train track) – including a recent trip to Lisbon to discuss grapevine trunk disease, and various other projects. One such project which I have thoroughly enjoyed is starting a new “research highlights” section for Evolutionary Applications.

In my opinion, Evol Apps is a great journal that was founded by my good friend, Michelle Tseng just after she finished her PhD in Curt Lively’s lab at Indiana University. At the time, there was no good venue for application of evolutionary theory and no journal aimed at exemplifying how very applicable it can be to everyday problems! I am very happy to be involved with the journal, especially now that it has moved to open access.

The only downside of my taking the time every month to put together a research highlight is that it will no doubt take away from ability to update this blog as much as I would like to (although I will still try). Therefore, I’ve decided to share my first research highlight below – see digital, PDF version here.

“This past year was full of noteworthy evolutionary applications, including a strong focus on the use of combination therapy to slow or stop the spread of drug resistance. In particular, the application of evolutionary theory to resistance of cancers was a hot issue, with a special issue of Evolutionary Applications (volume 6, issue 1), a Nature Reviews Cancer piece about the importance of life-history trade-offs to our understanding of cancer progression (Aktipis et al. 2013), a Nature review article on genomic instability of cancer (Burrell et al. 2013), a Jacques Monod conference on ‘Ecological and evolutionary perspectives in cancer,’ and the 2nd International Biannual Evolution and Cancer Conference. This blossoming interest is uncovering the roles of evolutionary processes in shaping the development, spread, and virulence of cancers, but is also highlighting the current gap in our ability to translate evolutionary theory into successful treatment. An elegant example of the great potential of this approach is the recent work by Shi and coauthors identifying two core resistance pathways in metastatic melanomas (Shi et al. 2014). The researchers found that drug resistance of these cancers occurred most often via two distinct ‘drug escape pathways,’ explaining 70% and 22% of resistance among disease-progressive tissues. These data act as strong justification for the use of combination treatment to target both BRAF inhibitor resistance pathways simultaneously to block these common routes of drug escape.

Similar approaches are being applied to tackle the current antibiotic crisis, as has been reviewed in a recent Nature Reviews Genetics article (Palmer and Kishony 2013). The authors point out the importance of using optimal combinations of antibiotics, ideally chosen based on negative cross-resistance and antagonistic interactions among resistance mechanisms. However, a recent paper by Pena-Miller and colleagues, combining experimental and genomic techniques with mathematical modeling, highlights that caution must be employed when employing combination treatments, as the use of synergistic antibiotics can in fact increase pathogen load under suboptimal treatment durations or subinhibitory drug concentrations (Pena-Miller et al. 2013). A similar warning has now been raised for combination treatment of malaria due to the discovery of high association among resistance alleles in multiple regions of China (Ding et al. 2013). Furthermore, a clear understanding of epistasis among resistance mutations is key to predicting the spread of multidrug-resistant bacteria, as has been nicely demonstrated by Borrell et al. (2013). They found that combinations of drug resistance mutations in Mycobacterium smegmatis showing a competitive fitness advantage in vitro were also the most frequently observed combinations found in multidrug-resistant clinical isolates of human tuberculosis in South Africa.

The application of evolutionary theory to development of combined treatments is certainly not limited to human disease. Experimental evolution of the green chlorophyte, Chlamydomonas reinhardtii, was used by Lagator and coauthors to demonstrate the utility of combined herbicides for slowing the evolution of resistance in agricultural settings (Lagator et al. 2013). However, their data come with a similar word of warning: using these combinations at low doses can select for both increased rates of resistance and a more general resistance mechanism relative to single herbicide treatment.

This small subsample of recent research published in Evolutionary Applications and other high-profile journals on combination treatment emphasizes the power of applying evolutionary theory to treatment design, both in terms of creating more ‘evolution-proof’ combinations and in predicting potential negative consequences of this approach. We look forward to future research on the topic and continued monitoring of treatments designed in the light of evolutionary theory.”

References cited:

Aktipis, C. A., A. M. Boddy, R. A. Gatenby, J. S. Brown, and C. C. Maley 2013. Life history trade-offs in cancer evolution. Nature Reviews Cancer 13:883–892.

Borrell, S., Y. Teo, F. Giardina, E. M. Streicher, M. Klopper, J. Feldmann, … S. Gagneux 2013. Epistasis between antibiotic resistance mutations drives the evolution of extensively drug-resistant tuberculosis. Evolution, Medicine, and Public Health 2013:65–74.

Burrell, R. A., N. McGranahan, J. Bartek, and C. Swanton 2013. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501:338–345.

Ding, S., R. Ye, D. Zhang, X. Sun, H. Zhou, T. F. McCutchan, and W. Pan. 2013. Anti-folate combination therapies and their effect on the development of drug resistance in Plasmodium vivax. Scientific Reports 3:????–????.

Lagator, M., T. Vogwill, A. Mead, N. Colegrave, and P. Neve 2013. Herbicide mixtures at high doses slow the evolution of resistance in experimentally evolving populations of Chlamydomonas reinhardtii. New Phytologist 198:938–945.

Palmer, A. C., and R. Kishony 2013. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nature Reviews Genetics 14:243–248.

Pena-Miller, R., D. Laehnemann, G. Jansen, A. Fuentes-Hernandez, P. Rosenstiel, H. Schulenburg, and R. Beardmore 2013. When the most potent combination of antibiotics selects for the greatest bacterial load: the smile-frown transition. PLoS biology 11:e1001540.

Shi, H., W. Hugo, X. Kong, A. Hong, R. C. Koya, G. Moriceau, … R. S. Lo 2014. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discovery 4:80–93.

Why I dropped out of psychology and became an evolutionary biologist, Part II: Evolution is happening, and it matters.

At about the same time that I was getting very frustrated by my psychology courses, I was taking an Evolution lab course (taught by the ingenious Janis Antonovics) where the theories I had been reading about first began to take shape. It was my first taste of why evolution mattered to me and also of how I could test it experimentally – yes, with controls!

Happy birthday to Charles Darwin. I wonder if he could have imagined how useful his work would be?

During one of our modules (or “labs” as I called them before becoming a Brit), Janis had each of us sample and test our own gut flora for antibiotic resistance. I won’t go into the details of how we did this (it is kind of gross), but instead explain why. When antibiotics are prescribed to kill off an infection caused by one pesky microbe, the entire microbial community is perturbed. What we are doing is imposing very strong selection against all sensitive bacteria in our body, and therefore selection for any bacteria that are able to survive. During treatment, replicating bacterial cells that do not carry resistance to the antibiotic you just took will be killed (want to know how?). This means you are left with much fewer, and a lower diversity of microbes; many of which positively influence your health. This was recently demonstrated using a multi-omic approach [1], where the researchers followed the microbial dynamics of a single patient taking antibiotics (for a nice synthesis of the work see here). They show that, as predicted, microbial diversity in the gut drops during the course of treatment and does not necessarily recover to its previous state after treatment.

One common way bacteria survive antibiotic treatment is by halting reproduction. This works because many antibiotics affect only replicating cells, and therefore any cells in the population that are just hanging about in stationary phase, the so-called “persister cells,” are temporarily resistant. However, just to emphasize how little we know about one of the most important evolutionary phenomena affecting human health: bacteria break even this rule. A recent study out this month in Science [2] has found that a bacterium closely related to the one that causes tuberculosis is able to reproduce, albeit slowly, in the presence of an antiobiotic to which it is not “resistant” (according to the current definition). The ability of this bacterial population to persist in the face of antibiotics is because identically genetic cells are in fact diverse in their behavior; specifically in production of an enzyme with which the antibiotic interacts. This means that some cells, just by chance, are able to survive and reproduce but that their future generations of offspring are just as likely to be killed by the antibiotic as any other cell in the population. In other words, in the absence of heritable genetic change, the population is unable to respond to selection and will not evolve resistance (at least using this mechanism). Unfortunately, this also means that the population will be able to persist and, if it gets lucky, a mutation conferring heritable resistance will pop up. A similar result, albeit without the amazing microfluidics, was found in 1997 [3].

If this result holds true for many more bacterial species, it would suggest yet another way in which our current army of antibiotics are likely to fail. So what does this mean in terms of finishing your course of antibiotics like all good children should? Well, we don’t know. We need data. I could rant about this, but instead I will refer you to a great talk by one of the experts, Andrew Read.

Okay, so evolution happens every time you take your antibiotics. But what about those microbes that aren’t picking fights with their human hosts? Are natural bacterial populations evolving as rapidly? Of course they are! I recently monitored changes in bacterial and bacteriophage populations living within horse chestnut tress in a park near Oxford and found that the bacteria were rapidly evolving resistance against their local phages, and the phages were responding by overcoming this resistance – all within the course of a single season (Paper in review now, so stay tuned). There are also many many examples of bacteria that have evolved incredible adaptations to changing or hostile environments. For example, bacteria are now known to thrive under the Antarctic ice, in thermal vents reaching up to 235°F, and in heavy metal environments. The resilience and adaptability of bacteria is staggering, and we are now learning how bacterial evolution can work in our favor, for example by turning toxic compounds into pure gold, storing our data, protecting against the spread of dengue fever, or making our food.

Rapid bacterial evolution also has major consequences for the fitness of macroscopic organisms with which they interact; our microbiota have important roles in our health (as I blogged about previously), plants associated with salt- and drought-tolerant Rhizobia can increase their fitness under harsh conditions, and emerging disease is often associated with bacterial acquisition of toxins or virulence genes. For example, the strain of Pseudomonas syringae that causes bleeding canker of horse chestnut trees acquired genes that allow it to thrive on woody tissue, presumably allowing for the host shift and subsequent spread.

Much of the great evidence for rapid evolution comes from microbes because of their short generation times and large population sizes, but this certainly does not mean the patterns we observe are restricted to microscopic beings. Indeed, one need look no further than the size of our watermelons and dogs to realize the speed at which an eukaryotic population can response to selection – especially when it is imposed artificially. During my PhD research with Curt Lively, I was able to show that trematode parasites can impose strong selection on populations of their snail hosts over only a few generations in the lab. We evolved experimental snail populations in tanks with and without the sterilizing trematodes and found that, over the five year experiment, trematodes adapted to specifically infect the most common snail genotypes in the tanks and subsequently drove the frequency of these types down [4]. And evidence for such rapid responses during experimental evolution is building. Rowan Barrett recently showed that stickleback populations can evolve cold-tolerance within three generations and Anurag Agrawal demonstrated rapid evolution (over only four years) of flowering time in experimental field populations that were affected by or protected from insect herbivores.

So whether it’s microbes, plants, or animals, there is no question as to whether populations evolve in response to the environment they are in and the species with which they are interacting. Evolution happened, is happening, and will continue to happen. I study evolutionary processes because I believe an understanding of how populations respond to selection is the only way we will be able to produce enough food, fight disease, and protect natural populations.

1 Pérez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, Eismann K, Otto W, Rojo D, Bargiela R, von Bergen M, Neulinger SC, Däumer C, Heinsen FA, Latorre A, Barbas C, Seifert J, Dos Santos VM, Ott SJ, Ferrer M, & Moya A (2012). Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut PMID: 23236009

2Wakamoto Y, Dhar N, Chait R, Schneider K, Signorino-Gelo F, Leibler S, & McKinney JD (2013). Dynamic persistence of antibiotic-stressed mycobacteria. Science (New York, N.Y.), 339 (6115), 91-5 PMID: 23288538

3 Thompson, J. K., et al. “Mutations to antibiotic resistance occur during the stationary phase in Lactobacillus plantarum ATCC 8014.” Microbiology 143.6 (1997): 1941-1949.

4 Koskella, B. and C.M. Lively. 2009. Evidence for negative frequency-dependent selection during experimental coevolution of a freshwater snail and a sterilizing trematode. Evolution, 63(9): 2213-2221.

Why I dropped out of psychology and became an evolutionary biologist

Every few months or so, I go through a period of wondering why I am doing what I am doing (as a scientist, that is). It usually happens when I am talking to, or listening to a talk by, another scientist who is studying the mechanism underlying a specific feature of biology. For example, the exact mutations underlying a given disease, the specific cause of a newly emerging disease, or the cascade of interacting hormones that influences a plant’s response to the environment. In other words, when I hear other researchers who are trying to answer questions that have clear-cut answers.

Evolutionary biology is a very stimulating, but often unsatisfying field of study. There are no answers, per se, because we are working to explain phenomena or patterns that are almost certainly the result of multiple interacting factors (and not always in the same way!) We are seeking to describe how selection in the past led to the evolution of the phenotypes we see in the present and ultimately to predict how populations will evolve in response to particular selective forces in the future. Therefore, most of the action either happened well before we were born or will happen well after we are dead, and we are unlikely to find out whether we are “right.”

So why bother? Well, every researcher will have a different answer to this question. But for me, there are (at least) three very exciting reasons. First, because evolution is happening all around us, all the time – we just need to know how to look. Second, because of the new inroads being laid that allow us to understand how our and other genomes evolved millions of years earlier. And third, because experimental evolution allows us to explicitly test how evolution works and what its limits might be. Over the next few posts, I will highlight some of my favorite new work from each of these research avenues and (hopefully) explain why being an evolutionary biologist is not nearly as futile of a task as it might first appear.

Okay, so what does any of this have to do with my decision to change degrees while I was at UVA? Well I thought I wanted to study human psychology; in particular, the role that biology plays in shaping our behavior (oh if my former self could have known that microbes play a role in this, she’d have been too excited to sleep!). I sat through some really great courses, and found the whole field incredibly fascinating. By my third year I had hypotheses… lots of them. I wanted answers. Unfortunately, every paper I read used statistics to attempt to account for confounding factors – like socioeconomic status or how much support a child received from their parents – and I found this very unsatisfying indeed. I wanted to test hypotheses directly. I needed control. Of course, I agree that it is unethical to randomly assign one twin to be raised in one way and another in a different way (or to teach a sick child to be terrified of rabbits), but how else can we satisfy our curiosity? And that is why I dropped out of psychology and became an evolutionary biologist.

Some of my favorite psychology studies:

Two classic studies exploring human cruelty:

1) Stanley Milgrim’s experiment where subjects give electric shocks to others despite their screams.

For a really interesting take on this, with some new insight, I highly recommend the Radiolab podcast:

2) The Stanford prison experiment.

Photo credit:
Photo credit:

3) Why was psychology so much cooler before the advent of ethics committees? (Thanks to Jason Mansel for sharing this!)

4) The broken window effect: exploring when crime begets crime (and this one has great control treatments!)
Keizer, K., Lindenberg, S., & Steg, L. (2008). The Spreading of Disorder Science, 322 (5908), 1681-1685 DOI: 10.1126/science.1161405

And good coverage of the study: