Sunday, August 30, 2015

Tinkering with the Axial Skeleton: Insights from an Evolutionary “Supermodel”

Tinkering with the Axial Skeleton: Insights from an Evolutionary “Supermodel”
By Windsor Aguirre

Fishes are extremely diverse and exceed all other living vertebrate groups combined in terms of species numbers. With this great taxonomic diversity comes a great diversity of forms. From seahorses and hatchet fishes to eels and the common mola, from tiny gobies and miniature tetras to giant groupers and the great pirarucu, fishes come in all shapes and sizes (Fig. 1).

Fig. 1. A. Common mola (courtesy of B. Adult male miniature tetra Iotabrycon praecox from western Ecuador. White scale bar is 10mm (from Aguirre et al., 2014a). C. Pirarucu (courtesy of

A major theme in the variability of fish form is the evolution of elongation. Extreme elongation has evolved repeatedly in different groups of fishes (and other vertebrates, for that matter), and is strongly associated with changes in the axial skeleton (Fig. 2), especially increases in the number of vertebrae (e.g., Ward & Brainerd, 2007; Ward & Mehta, 2010). Advances in evo-devo are facilitating the study of the molecular mechanisms responsible for changes in body form and axial patterning. For example, there has been great progress on deciphering the molecular mechanisms through which the number of body segments is regulated and the identity of body segments is established in vertebrates (Gomez et al., 2008; Gomez & Pourquié, 2009; Mallo et al., 2010). Moreover, several recent studies present candidate genes for the evolution of vertebral number in fishes (e.g., Ward & Mehta, 2010; Kimura et al., 2012; Berner et al., 2014). Although much remains to be learned, progress is definitely being made.

Fig. 2. Radiographs showing examples of variation in body form and vertebral number in fishes (courtesy of the Smithsonian Institution).

But what about the early stages of the evolution of body elongation? What are the selective pressures at play early on that guide fish with typical body forms to evolve body elongation? How do the structural changes originate, how are developmental pathways modified, and what are the functional consequences along the way? That old friend of evolutionary biologists, the threespine stickleback (Gasterosteus aculeatus), may provide some insights.     

I have been studying the threespine stickleback since the early 2000’s, when my former Ph.D. advisor, Mike Bell, formally introduced me to them. Although there are many aspects of their morphology that have attracted the interest of scientists, body shape has been a standout. Body shape variation is substantial within this species and correlates strongly with ecology. Oceanic ancestral populations differ greatly from the resident freshwater populations that they have established throughout the world. Resident freshwater populations also differ substantially from one another. Along one axis of diversification in freshwater environments, resident populations in streams and shallow lakes (benthics) evolve relatively deep bodies, whereas resident populations in deep lakes (limnetics) evolve more elongated, streamlined bodies.  This shape variation has been characterized repeatedly in different geographic areas and is relatively well understood (e.g., Walker, 1997; Spoljaric & Reimchen, 2007; Aguirre, 2009). Is this variation in body form at the microevolutionary level associated with changes in the axial skeleton as seen across broad taxonomic ranks in fishes? Does vertebral number increase in more elongate limnetics?

Since this is the threespine stickleback we are talking about, an evolutionary “supermodel” (Gibson, 2005), there is of course some previous research on the subject that suggests that body form indeed covaries with variation in the axial skeleton in the predicted direction (e.g., Reimchen and Nelson, 1987; Ahn, 1988). To explore the evolution of body form and vertebral architecture in stickleback in greater detail, my students and I set up a study to address four basic questions:
  1. Is there sexual dimorphism in vertebral number? Stickleback are sexually dimorphic for many traits, and males and females can experience very different selective demands. They are also known to differ significantly in body form, and Reimchen and Nelson (1987) previously reported differences between sexes in total vertebral number, with males having more vertebrae.
  2. Is there a significant difference in vertebral number among anadromous (oceanic), benthic/stream, and limnetic populations of threespine stickleback? If so, we expected limnetics to have more vertebrae than the other ecomorphs.
  3. Is there body region specificity in terms of where vertebral number changes? Fish have two major types of vertebrae, abdominal (or precaudal) vertebrae and caudal vertebrae. These have different forms and serve different purposes, so it makes sense that where you add or subtract vertebrae may matter functionally.
  4. Finally, is there an association between vertebral number and body shape within ecomorphs? That is,do more elongate individuals within a particular ecomorph tend to have more vertebrae than more deep-bodied individuals within the same ecomorph?
The project started with a survey of nine populations of threespine stickleback sampled from Alaska: three limnetic populations, three benthic populations, and three anadromous populations. The initial results were published in 2014 (Aguirre et al., 2014b). We have since expanded to 20 populations by adding samples of four more limnetic populations and seven stream populations. The expanded data is currently being prepared for publication in combination with results of common garden experiments to examine the heritability of vertebral number in these stickleback populations.

Body shape data were collected using geometric morphometric methods, which allow for very precise measurement and visualization of shape differences among specimens. We then took the same specimens to the Field Museum of Natural History (Chicago), where they were X-rayed to obtain vertebral number data (Fig. 3).  Details of the methods of analysis are available in Aguirre et al. (2014b).

Fig. 3. Radiograph showing method for counting vertebral number.

Was there sexual dimorphism in vertebral number? We only have data on nine of the populations for this, but the result was consistent enough to draw a conclusion without the need to survey all 20 populations. Surprisingly, we did not detect a significant difference in total vertebral number between sexes as Reimchen and Nelson (1987) did. Geographic variation or differences in statistical power between studies may account for the difference. However, we did see a consistent and statistically significant difference in the proportion of abdominal to caudal vertebrae between the sexes, with females having more abdominal vertebrae and males having more caudal vertebrae (Fig. 4). The difference was not very large but the trend was the same in every population for which data were collected.  This is consistent with a transformation of vertebral identities resulting in an expansion in the abdominal region in females, perhaps to increase the volume available for packing eggs. Stickleback are small fish and the abdomen of gravid females can exhibit fairly extreme distension, suggesting that an expansion of the abdominal region in females might increase fitness by allowing them to pack a greater number of eggs in the abdomen.  This was previously suggested by Lindsey (1962, 1975) and similar patterns have been reported for other types of fish (Hart & McHugh, 1944; Hastings, 1991), although it is not clear whether this is widespread in fishes.
Fig. 4. Sexual dimorphism in vertebral number among Alaskan threespine stickleback. Mean number of abdominal vertebrae is plotted against mean number of caudal vertebrae, coded by sex for the nine populations for which data are available. Note the consistent pattern of divergence between sexes. In every population males have more caudal vertebrae and fewer abdominal vertebrae than females, although total vertebral number did not differ significantly between sexes.

Is there a significant difference in vertebral number among anadromous (oceanic), benthic/stream, and limnetic populations of threespine stickleback? Yes. Using samples of males from all 20 populations surveyed, limnetics have significantly more vertebrae than populations of the other ecomorphs (Fig. 5A). The difference is only 0.5-0.6 more vertebrae on average – not very large, but statistically significant and fairly consistent with the exception of one limnetic population, Lynne Lake, which was an outlier for every variable measured. Whether vertebrae are changing in length as well is something that we are currently examining. There was also fairly obvious body region specificity in terms of where vertebral number changed. It was the number of caudal vertebrae that increased in limnetics and accounted for the greater number of vertebrae overall. This is consistent with the previously documented expansion of the caudal region of the body in limnetics, suggesting covariation between body form and vertebral number as observed at broader taxonomic ranks.

Fig. 5. Vertebral number variation among male Alaskan stickleback. Points are population sample means. A) Variation in mean total vertebral number. B) The mean number of abdominal vertebrae plotted against the mean number of caudal vertebrae. Notice that limnetics have more vertebrae than the other ecomorphs and that it is caudal vertebral number that specifically increases in limnetics.

Finally, is there an association between vertebral number and body shape within ecomorphs? Again, the answer is yes. We conducted a discriminant function analysis (DFA) on the body shape data to classify individuals as being relatively benthic-/stream-shaped (treating benthic lake and stream populations as one ecomorph because of their morphological and ecological similarity) or relatively limnetic-shaped, and then calculated the mean DF score along this shape-type axis for each population. Populations with more extreme mean scores along the axis had more pronounced benthic/stream (positive side) or limnetic (negative side) body shapes. Mean population DF score based on the body shape data was strongly correlated with total vertebral number within both ecomorphs (r = -0.888 and -0.811 for benthic/stream populations and limnetic populations, respectively), indicating that body shape was correlated with variation in vertebral number in the same way within both ecomorphs (Fig. 6).
Fig. 6. Association between total vertebral number and body shape within ecomorphs. Anadromous samples not included, and benthic and stream samples pooled as one ecomorph.

Where do we go from here?

A major goal of future research will be to try to decipher whether variation in vertebral number matters functionally at this scale. Vertebral number and body shape covary in stickleback, so it is possible that differences in vertebral number per se do not matter functionally; perhaps the trait being selected for is the change in body shape, with selection for more elongate bodies and an expanded caudal region in limnetics resulting in a correlated increase in the number of caudal vertebrae. However, maybe the number of vertebrae itself does matter. Inasmuch as increasing the number of vertebrae, for fish with similar body shapes, may result in an increase in flexibility, changes in vertebral number may impact burst-swimming performance since rapidly bending the body is an important part of the escape response of fishes. This was suggested previously by Brainerd and Patek (1998), although much more work on this issue is needed, including in the threespine stickleback.

How are differences in vertebral number associated with vertebral length? Vertebral length decreases from the anterior to the posterior region of the vertebral axis (unpublished data), suggesting that more and shorter vertebrae in the caudal region is functionally advantageous. Are there patterns to how vertebral length changes in fish differing in vertebral number? Are there uniform changes throughout the body axis, or does there tend to be body region specificity in how vertebral lengths are modified?

Finally, do these small changes in vertebral number really matter beyond the threespine stickleback? How do we connect the results from this study to the evolution of extreme body elongation in vertebrates? This is a trickier question, but a potential direction for future research is suggested when one looks at variation among the threespine’s relatives. Gasterosteus aculeatus is not the only stickleback. There are other genera of stickleback, and – as is the case across fishes – body elongation is a major theme in the evolution of body form among the Gasterosteidae and their closest relatives (Fig. 7). The ninespine stickleback, Pungitius, is more elongate than Gasterosteus, and this elongation is associated with a slight increase in vertebral number. Then there is the fifteenspine stickleback, Spinachia spinachia, which exhibits extreme body elongation. A beautiful fish with a remarkable body form, it looks like the cartoon stick-figure version of a stickleback, and exhibits a substantial increase in vertebral number. Continue to the closest known relatives of the gasterosteids, Aulorhynchus and Aulichthys, and even more extreme elongation and greater increases in vertebral number are observed. How are these extreme changes in morphology achieved? How did extreme elongation evolve in these fishes? Can comparative analyses across these related lineages differing in body elongation and vertebral phenotypes illuminate our understanding of the evolution of vertebrate body form? Time will tell… 

Fig. 7. Radiograph illustrating phenotypic variation associated with elongation in gasterosteids (Gasterosteus, Pungitius, and Spinachia) and their relatives (Aulorhynchus).

Literature Cited:
  • Aguirre, W.E. 2009. Microgeographical diversification of threespine stickleback: body shape-habitat correlations in a small, ecologically diverse Alaskan drainage. Biological Journal of the Linnean Society 98:139-151.
  • Aguirre, W.E., R. Navarrete, P. Calle, and G.C. Sanchez-Garces. 2014a. First Record of Iotabrycon praecox Roberts 1973 (Characidae) in the Santa Rosa River, southwestern Ecuador. Checklist 10:382-385.
  • Aguirre, W.E., Walker, K., Gideon, S. 2014b. Tinkering with the axial skeleton: vertebral number variation in ecologically divergent threespine stickleback populations. Biological Journal of the Linnean Society 113:204-219.
  • Ahn, D. 1998. Factors controlling axial variation in the threespine stickleback, Gasterosteus aculeatus (Teleostei: Gasterosteidae): pattern of natural variation and genetic/developmental mechanisms. DPhil Thesis, University of Michigan.
  • Berner, D., Moser, D., Roesti, M., Buescher, H., Salzburger, W. 2014. Genetic architecture of skeletal evolution in European lake and stream stickleback. Evolution 68: 1792–1805.
  • Brainerd, E.L., Patek, S.N. 1998. Vertebral column morphology, C-start curvature, and the evolution of mechanical defenses in tetraodontiform fishes. Copeia 1998: 971–984.
  • Gibson, G. 2005. The synthesis and evolution of a supermodel. Science 307:1890-1891.
  • Gomez, C., Ozbudak, E.M., Wunderlich, J., Baumann, D., Lewis, J., Pourquié, O. 2008. Control of segment number in vertebrate embryos. Nature 454: 335–339.
  • Gomez, C., Pourquié, O. 2009. Developmental control of segment numbers in vertebrates. Journal of Experimental Zoology 312B: 533–544.
  • Hart, J.L., McHugh, J.L. 1944. The smelts (Osmeridae) of British Columbia. Bulletin of the Fisheries Research Board of Canada 64: 1–27.
  • Hastings, P.A. 1991. Ontogeny of sexual dimorphism in angel blenny, Coralliozetus angelica (Blennioidei:Chaenopsidae). Copeia 1991: 969–978.
  • Kimura, T., Shinya, M., Naruse, K. 2012. Genetic analysis of vertebral regionalization and number in medaka (Oryzias latipes) inbred lines. G3: Genes, Genomes, Genetics 2: 1317–1323.
  • Lindsey, C.C. 1962. Experimental study of meristic variation in a population of threespine sticklebacks, Gasterosteus aculeatus. Canadian Journal of Zoology 40: 271–312.
  • Lindsey, C.C. 1975. Pleomerism, the widespread tendency among related fish species for vertebral number to be correlated with maximum body length. Journal of the Fisheries Research Board of Canada 32: 2453–2469.
  • Mallo, M., Wellik, D.M., Deschamps, J. 2010. Hox genes and regional patterning of the vertebrate body plan. Developmental Biology 344: 7–15.
  • Spoljaric, M.A., Reimchen, T.E. 2007. 10 000 years later: evolution of body shape in Haida Gwaii three-spined stickleback. Journal of Fish Biology 70: 1484–1503.
  • Walker, J.A. 1997. Ecological morphology of lacustrine threespine stickleback Gasterosteus aculeatus L. (Gasterosteidae) body shape. Biological Journal of the Linnean Society 61: 3–50.
  • Ward, A.B., Brainerd, E.L. 2007. Evolution of axial patterning in elongate fish. Biological Journal of the Linnean Society 90: 97–116.
  • Ward, A.B., Mehta, R.S. 2010. Axial elongation in fishes: using morphological approaches to elucidate developmental mechanisms in studying body shape. Integrative and Comparative Biology 50: 1106–1119.

Tuesday, August 25, 2015

High enthusiasm and low r-squared.

I am copying my title from a review by David Houle in the journal Evolution that savaged the 1998 book Asymmetry, Developmental Stability, and Evolution. Among many criticisms in the review was the general point that a lot of attention was being directed toward research on asymmetry when, in reality, it explained relatively little of the variation in ecological and evolutionary processes. The goal of this post is to suggest some other research areas of high enthusiasm whose low explanatory power suggests they are perhaps over-represented and unduly-lauded by granting agencies, by weekly scientific magazines, by the general public, and by many students.

“Negative reviews often give a frisson of pleasure to the reader” was the line that headed the last paragraph of Houle’s review. Although this post might be interpreted as a negative indictment of several lines of research, I will try to be sufficiently polite that any frisson derived therefrom is only minor. The key point to remember is that I am not suggesting these research areas are not useful or interesting, merely that their exaggerated popularity might belie their ultimate utility. Of course, if you actually work on these topics, please do not think I am criticizing the value of your work.

1. The tyranny of the gene.

Much attention is directed these days toward finding the “gene for” this or that. Indeed, papers that identify particular genes and confirm their function tend to get a lot of attention at conferences, in the weekly publications, and (partly for these reasons) in granting panels. You can see the appeal – such work gets right down to the specific part of the genome that is having a particular effect on the phenotype. However, the amount of time and effort put into trying to find the “gene for” something is often not worth the insight gained, as has recently been pointed by a number of authors, including Rockman (2011) and Rausher and Delph (2015). More importantly, such efforts will typically be futile given that nearly all adaptation is the result of many genes of modest-to-small effect. That is, the well-characterized and clearly-important genes (e.g., EDA, PITX1, MC1R, and so on) are actually exceptions, the focus on which can detract us from the vast majority of the variance that needs to be explained. I have previously summarized some of my main arguments on this topic (Hendry 2013) and I summarize them briefly here.

a) Current genomic methods are strongly biased toward the detection of large-effect genes. In particular, investigators tend to search around for some gene that is explaining a lot of something and then they focus subsequent efforts in that direction. The reader of published work on these genes often doesn’t realize that the incredibly strong ascertainment bias means that the elegant examples are really exceptions to the general rule that genes of large effect are very rare.  

b) Even large-effect genes usually explain only a small fraction of the variation in a trait. Sure some of those genes (e.g., EDA) do explain a lot of the variation but most other “large effect” genes explain much less – often less than 15%. That is, the majority of trait variation typically remains unexplained even by large-effect genes.

Just one example of how genes of large effect explain less than half the variance in traits. This figure is from Hendry (2013) based on data from Rogers et al. (2012).
c) Most studies focus on traits rather than adaptation per se. Yet adaptation to a given environment is the result of many traits, each of which is potentially influenced by many genes. Indeed, objective genome scans often recover hundreds to thousands of genes or genomic regions contributing to adaptation in different environments. As a result, the variation in ADAPTATION (fitness) explained by any given gene – even the large-effect ones noted above – is typically vanishingly small.

(Of course if someone is willing to search for – and, better yet find – some genes of massive effect in my study populations, I promise to sing a different song as we published your findings in Nature/Science.)

2. Parallel evolution is not very parallel.

A very large number of papers claim to demonstrate parallel evolution at the phenotypic level – that is, similar phenotypes evolve in similar habitats. I am continually irked, however, by the fact that such inferences often stem simply from a statistically-significant main effect for the “habitat” term (lake vs. stream, dry vs. wet, high-elevation vs. low-elevation, high-predation vs. low-predation, cave vs. surface, etc., etc.) in a statistical model. However, when one looks closely at the population means, one almost invariably finds that some populations in a given habitat diverge in the opposite direction. This means that, in most cases, evolution is a mix of both parallel and non-parallel components. We (Krista Oke, Caroline Leblond, and myself) have performed a meta-analysis of more than 100 papers focusing on fishes and found that the amount of variation explained by the habitat term ranges from very low to very high, which means that the parallelness of evolution is equally variable. Instead of testing for – and asserting – parallel evolution, authors should QUANTIFY precisely how parallel and non-parallel different aspects of evolution are. Parallel evolution isn’t an either-or situation, it is instead a continuum, and we need to know where a given system falls on that continuum. Moreover, I would argue that the non-parallel aspects of evolution are often more interesting than the parallel aspects, because we are poised to learn something new rather than just confirming something we already suspected.

Studies of parallel evolution in fishes ranges from near-perfect parallelism (near 1) to near perfect non-parallelism (near 2). These data are from an early version of the analysis conducted by Oke, Leblond, and Hendry. For details, contact A Hendry.
3. Animal personalities and behavioral syndromes.

Behavioral ecology has recently been seduced by the idea that individual animals have “personalities” manifest as correlated behavior across contexts/situations, such as being bold (as opposed to shy) in the presence of both predators and mates. A related idea is that behavioral traits are often correlated with each other, such that some individuals are bold/social whereas others are shy/non-social. These twin concepts have swept through the field and now seem to foremost in the minds of many students. My reading of the literature, however, is that the variance explained by these phenomena is typically very low. For instance, investigators that find a significant repeatability of individual behavior through time (or across contexts) will often conclude that this variation indicates personality. However, these significant p values are often associated with very small effect sizes. In fact, in nearly all cases, data on the behavior of a set of individuals at one time point (or context) would allow you to predict less than half of the variation in behavior for those same individuals at another time point (or context). I am not saying that personalities and syndromes are not real or interesting, merely that their near-monopoly on modern behavioral ecology is not necessarily deserved given their limited explanatory power.

Any value less than 0.7 means that less than half of the variance in the behavior at one time can be explained by variance in behavior at another time. From Hendry (2015) based on data from Bell et al. (2009). 
(Of course, I happen to be engaged in several exciting studies of animal personalities/syndromes and their importance in natural populations.)

4. Biodiversity and ecosystem function.

Hugely important and influential in ecology is the relationship between biodiversity (species diversity, phylogenetic diversity, functional diversity) and ecosystem function (e.g., productivity, nutrient cycling, decomposition, etc.). These relationships are important because they help to provide a justification for preserving biodiversity per se given that they suggest "more is better" independent of the specific species. (Otherwise we could simply assemble our ideal set of species that fill the roles we need and forget about the rest.) Partly due to the applied nature of this inference, the defense of biodiversity and its preservation has shifted in many circles to arguments surrounding its role in “ecosystem services.” And yet the variance in ecosystem function explained by biodiversity is often quite small, with a typical value being about 20%. This means that, while biodiversity does often correlate with ecosystem function, something else must be much more important. This realization means that arguments for the preservation of biodiversity can’t devolve simply to arguments about ecosystem function – they must instead continue to tout the many other benefits of biodiversity.

Some typically messy biodiversity-ecosystem function relationships (from Loreau et al. 2001).


In addition to all of the above, the effect size (e.g., r-squared) for a given term in a given statistical model will often be overestimated. First, even two variables with no true correlation will return a non-zero r-square simply by chance – and the magnitude of this bias will increase with decreasing sample size. As a result, estimates of weak effects are inflated by error (for an illustration see Jiang et al. 2013). Second, and related to the first point, most analyses fail to account for error in the estimation of the individual data points that make up the regression (that is, they do not appropriately propagate the error). As a result, confidence in the estimated r-squared is greater than it should be (e.g., Morrissey and Hadfield 2012). Third, correlation does not equal causation and so the causal part of a given correlation might be much lower than the estimated effect size. See my earlier post about “Faith's Conjecture", which states that “any correlation from which a causal relationship might be inferred (the thing on the x axis influences the thing on the y axis) can be inverted (the things on the x and y are switched) to lead to a new causal inference”. Fourth, many studies show that effect sizes decrease through time as studies are replicated, which is called – among other things – “regression toward the mean”.

On a positive note.

This post might be interpreted in the depressing sense that we can’t explain much in ecology or evolution and that the cherished relationships on which we focus so intensively and that we tout so loudly are really a waste of time. However, I would instead like to end focus with a more positive message.

In general, it seems that the variance explained by a given factor in ecology is often quite small – about 2.5-5.4% - as revealed by Moller and Jennion’s (2002) meta-analysis of meta-analyses. Peck et al. (2003) later argued that the more important question was “How much variation is NOT explained” and came up with an answer of “roughly half.” Together, these two analyses suggest that ecological and evolutionary variation is multifactorial and that, if we are to explain much of the variation, we need to look beyond single causation. Stated another way, we shouldn’t focus so much effort on single explanatory factors that explain relatively little of the total variance but we should instead embrace multi-factorial causality that can explain much more.

From Moller and Jennions (2002).

Reflecting on the above four research areas for which proponents show high enthusiasm but the data indicates low explanatory power, I am reminded of David Queller’s (1995) response to Gould and Lewontin’s anti-adaptationist rhetoric: “That’s well said, but let’s get back to our field work.” 

Saturday, August 15, 2015

The Heartbreak of Salmon Migration

[This post is by Erika Eliason - I am just putting it up for her. APH]

It’s the most wonderful time of the year. BBQs, ice cream, cabin trips… and the upriver sockeye salmon spawning migration in British Columbia, Canada is in full swing! Each year millions of sockeye salmon return to the Fraser River to migrate to the same stream where they were born in order to spawn. The upriver migration is a biological wonder. Salmon cease feeding in the ocean, so upriver swimming, morphological changes, and spawning behaviours are fueled entirely by energy stores. The fish develop impressive secondary sexual characteristics during the migration, turning bright red and green, and males develop a pronounced dorsal hump and kype (hooked jaw). Some populations travel over 1,000 km in just a few weeks – traversing rapids, and avoiding predators and fisheries gear. Sockeye salmon have a single opportunity to reproduce and naturally die shortly after spawning (semelparity). Consequently, an individual fish that does not make it back to the spawning ground will have zero lifetime reproductive success. 
Male sockeye salmon migrating upstream. Pic 1 - photo credit: M. Casselman
Our research team has spent the past couple of weeks on the side of the Fraser River, tagging and collecting sockeye salmon for our experiments. Unfortunately, it is hot. Very hot. As in, we’re wearing bathing suits instead of waders and diving into the river to cool off every chance we get. There is simply not enough beer and ice cream to beat the heat.

Conditions have been warm and dry all across the west coast of North America for several months. Fraser River temperatures have been up to 4–5°C warmer than usual (DFO Ewatch), which is alarming salmon biologists and managers. Elevated river temperatures have been repeatedly correlated with high mortality during the spawning migration, and there are grave concerns that the number of fish turning up on the spawning grounds in 2015 could be catastrophically low. 
Healthy salmon on the spawning ground look like this. Pic 2 – photo credit: M. Casselman
When temperatures soar, salmon can start to look like this. Pic 3 – photo credit: A. Teffer
A fair amount of research has been conducted on the thermal tolerance and physiological capacity of Fraser River sockeye salmon which can be used to help inform management. First of all, high fidelity to natal streams has resulted in many genetically and geographically distinct sockeye salmon populations across the Fraser River watershed, and reproductively isolated salmon populations are hypothesized to be locally adapted to their environmental conditions (Fraser et al., 2011; Taylor, 1991). Fraser River sockeye salmon populations with more challenging migrations arrive at the mouth of the river with fewer eggs, higher body energy stores, a more streamlined body shape, high swimming performance,  greater aerobic scope, higher cardiac scope, and larger hearts with an enhanced oxygen supply (Crossin et al., 2004; Eliason et al., 2011; Eliason et al., 2013b). So populations with difficult migrations appear to be prepared for the challenge ahead, despite never having previously experienced the upriver migration conditions.
Fig 1: Aerobic scope as a function of upriver migration distance for 8 populations of Fraser River sockeye salmon. Data from Eliason et al., 2011; 2013b. 

In addition, sockeye salmon populations encounter varying temperatures depending on when they enter the river and where they spawn. Functional thermal tolerance for each population appears to be tailored to the typical environmental temperatures encountered (Eliason et al., 2011). For example, Weaver Creek sockeye salmon typically migrate upstream in the cool fall months, and they have a correspondingly lower functional thermal tolerance compared to populations that migrate in warm summer temperatures. Chilko sockeye salmon have the highest and broadest thermal tolerance of all the Fraser River populations studied to date. They enter the Fraser River in the middle of the summer and encounter peak river temperatures while migrating through the most challenging sections of the river, but spend the final third of their migration traveling up a cool glacial river to finally spawn in or adjacent to a glacial lake at ~1,200 m in elevation. Collectively, these results provide compelling, though not conclusive, evidence that Fraser River sockeye salmon populations are locally adapted to their upriver migratory environment.
Fig 2: Aerobic scope as a function of temperature for 6 populations of Fraser River sockeye salmon. Temperature frequency histograms indicate the typical temperatures encountered by each population over 14 years of historical data (1995-2008). Data from Eliason et al., 2011. 
Functional thermal tolerance has been determined for 7 populations so far (Eliason et al., 2011; 2013b) and management agencies are able to use this information to predict how the different populations may fare in a given year. If river temperatures exceed the functional thermal tolerance for populations currently in the river, fisheries can be shut down to enable more fish to arrive on the spawning grounds. This year, we expect many of the returning salmon to be from the Chilko population. This presents us with an interesting natural experiment – how will the super-high-temperature-tolerant population cope with this exceptionally warm year? How will the other co-migrating populations with lower thermal tolerance fare? Tagging experiments from our research group will link migration rates and migration success with river temperature, disease profiles, and population differences.
Fig 3: Number of years (between 1996 and 2008) that en route mortality exceeded 50% for a given population. Note that en route loss exceeded 50% for none of the years for the Chilko population, whereas it was greater than 50% in every year for the Weaver population. Data from Hinch and Martins, 2011. 
This work is currently being expanded in a few different directions. On the one hand, I’m interested in the physiological mechanisms that determine thermal tolerance. Cardiac function appears to be the primary factor limiting functional thermal tolerance in salmon (Eliason et al., 2013a; Farrell et al., 2009). Specifically, swimming performance and aerobic capacity decline at warm temperatures due to an inability of the heart to deliver sufficient oxygen to the working muscles (Eliason et al., 2013a). Indeed, Chilko sockeye salmon hearts have an enhanced ability to use adrenaline, which increases cardiac capacity and protection and likely confers a higher thermal tolerance compared to other populations (Eliason et al., 2011). We’re currently trying to understand what other cellular and molecular mechanisms are important for maintaining cardiac function at high temperature.

We’re also interested in the broader implications of these climate-driven ecological changes on the population and community dynamics. Pacific salmon are keystone species in the Pacific Northwest. How do fluctuations in their abundance or migration timing impact the larger community? Pacific salmon are not just ecologically, but also economically and culturally important fish species. What are the economic, social and cultural implications for humans? Almost all of our work thus far has focused on how ecology is shaping evolution in salmon, but it’s very interesting to also consider how evolutionary change might influence ecology in this system.

I’m frequently asked 2 questions about this work:

If Chilko sockeye salmon are so great, why don’t we just spread their genes all over the Fraser River watershed to save the salmon? This question gets me riled up every time. The research outlined above is focused on a single, brief time period in the life cycle of salmon. The upriver migration only lasts ~4 weeks and thus represents ~2% of the lifespan of a sockeye salmon. The other 98% of the time is spent on spawning grounds, incubating as eggs, rearing in their natal lake as fry, migrating downstream to the ocean as smolts, and feeding and growing in the ocean for a couple of years as sub-adults and adults. The environmental conditions and selection pressures vary widely across the life cycle and among habitats. It would be foolish to assume that traits are fixed across all life stages (i.e. no phenotypic plasticity), or that traits beneficial for one environment are advantageous in all environments. In fact, Chilko sockeye salmon eggs incubate in a cool alpine lake or stream, and studies have shown that Chilko eggs have a correspondingly lower thermal tolerance compared to populations that incubate at warmer temperatures (Whitney et al., 2013, 2014). Given the broad environmental heterogeneity across salmonid habitats, and the uncertain future with respect to ongoing climate change, we should be focusing on preserving biodiversity and maintaining sufficient genetic and phenotypic variability within the species.

Will Fraser River sockeye salmon adapt fast enough to keep pace with climate change? The short answer? I don’t know. The long answer? Fraser River temperatures have increased by ~2°C over the last 60 years (Patterson et al., 2007), and are expected to continue to increase along the same trajectory. In order to cope with warming river temperatures, salmon will need to undergo some combination of behavioural and physiological adaptation.
Fig 4: Yearly maximum Summer Fraser River Temperatures. Data from Patterson et al., 2007.
Behaviourally, salmon can avoid the warm water temperatures by entering the river when temperatures are cooler (Reed et al., 2011) or by hiding out in deep, cold lakes until conditions become more favourable. However, energetic and timing constraints can limit these strategies. High Fraser River flows during the freshet in the spring prohibit passage, and many sockeye salmon spawning grounds freeze over in the fall and winter. In addition, spawning dates are highly conserved within a population to match incubation and emergence timing with optimal conditions for fry. 

The other option is to increase their physiological thermal tolerance, by improving cardiorespiratory performance at high temperature for example. Some salmonid populations do have the capacity to cope with warm temperatures – redband trout in Oregon can encounter summer river temperatures between 24 and 30°C (Rodnick et al., 2004). Hatchery-reared rainbow trout in southern Western Australia have undergone passive selection over 19 generations to increase thermal tolerance (Chen et al., 2015). Studies have shown that rapid evolution can occur in salmon populations over just 11–30 generations (Hendry et al., 2000; Quinn and Adams, 1996; Quinn et al., 2000). However, 11–30 generations is a fairly long time, since Fraser River sockeye salmon typically have a 4 year life cycle; we’re talking about 44–120 years.

All told, salmon are remarkably resilient. So it’s not all doom and gloom, and I wouldn’t count Fraser River sockeye salmon out just yet.

Chen, Z., Snow, M., Lawrence, C.S., Church, A.R., Narum, S.R., Devlin, R.H., Farrell, A.P., 2015. Selection for upper thermal tolerance in rainbow trout (Oncorhynchus mykiss Walbaum). The Journal of Experimental Biology 218, 803-812.

Crossin, G.T., Hinch, S.G., Farrell, A.P., Higgs, D.A., Lotto, A.G., Oakes, J.D., Healey, M.C., 2004. Energetics and morphology of sockeye salmon: effects of upriver migratory distance and elevation. Journal of Fish Biology 65, 788-810.

Eliason, E.J., Clark, T.D., Hague, M.J., Hanson, L.M., Gallagher, Z.S., Jeffries, K.M., Gale, M.K., Patterson, D.A., Hinch, S.G., Farrell, A.P., 2011. Differences in thermal tolerance among sockeye salmon populations. Science 332, 109-112.

Eliason, E.J., Clark, T.D., Hinch, S.G., Farrell, A.P., 2013a. Cardiorespiratory collapse at high temperature in swimming adult sockeye salmon. Conservation Physiology 1, 10.1093/conphys/cot1008.

Eliason, E.J., Wilson, S.M., Farrell, A.P., Cooke, S.J., Hinch, S.G., 2013b. Low cardiac and aerobic scope in a coastal population of sockeye salmon Oncorhynchus nerka with a short upriver migration. Journal of Fish Biology 82, 2104-2112.

Farrell, A.P., Eliason, E.J., Sandblom, E., Clark, T.D., 2009. Fish cardiorespiratory physiology in an era of climate change. Canadian Journal of Zoology 87, 835-851.

Fraser, D., Weir, L., Bernatchez, L., Hansen, M., Taylor, E., 2011. Extent and scale of local adaptation in salmonid fishes: review and meta-analysis. Heredity 106, 404-420.

Hendry, A.P., Wenburg, J.K., Bentzen, P., Volk, E.C., Quinn, T.P., 2000. Rapid evolution of reproductive isolation in the wild: Evidence from introduced salmon. Science 290, 516-518.

Hinch, S.G., Martins, E.G., 2011. A review of potential climate change effects on survival of Fraser River sockeye salmon and an analysis of interannual trends in en route loss and pre-spawn mortality. Cohen Commission Technical Report 9, Vancouver, B.C., pp 1-134.

Patterson, D.A., Macdonald, J.S., Skibo, K.M., Barnes, D.P., Guthrie, I., Hills, J., 2007. Reconstructing the summer thermal history for the lower Fraser River, 1941 to 2006, and implications for adult sockeye salmon (Oncorhynchus nerka) spawning migration. Canadian Technical Report of Fisheries and Aquatic Sciences 2724, 1-43.

Quinn, T.P., Adams, D.J., 1996. Environmental changes affecting the migratory timing of American shad and sockeye salmon. Ecology 77, 1151-1162.

Quinn, T.P., Unwin, M.J., Kinnison, M.T., 2000. Evolution of temporal isolation in the wild: Genetic divergence in timing of migration and breeding by introduced Chinook salmon populations. Evolution 54, 1372-1385.

Reed, T.E., Schindler, D.E., Hague, M.J., Patterson, D.A., Meir, E., Waples, R.S., Hinch, S.G., 2011. Time to evolve? Potential evolutionary responses of Fraser River sockeye salmon to climate change and effects on persistence. PLoS ONE 6, e20380.

Rodnick, K.J., Gamperl, A.K., Lizars, K.R., Bennett, M.T., Rausch, R.N., Keeley, E.R., 2004. Thermal tolerance and metabolic physiology among redband trout populations in south-eastern Oregon. Journal of Fish Biology 64, 310-335.

Taylor, E.B., 1991. A review of local adaptation in Salmonidae, with particular reference to Pacific and Atlantic salmon. Aquaculture 98, 185-207.

Whitney, C.K., Hinch, S.G., Patterson, D.A., 2013. Provenance matters: thermal reaction norms for embryo survival among sockeye salmon Oncorhynchus nerka populations. Journal of Fish Biology 82, 1159-1176.

Whitney, C.K., Hinch, S.G., Patterson, D.A., 2014. Population origin and water temperature affect development timing in embryonic sockeye salmon. Transactions of the American Fisheries Society 143, 1316-1329.

Saturday, August 1, 2015

How to be a Postdoc.

I just participated in a career mentoring session at a scientific conference: Stickleback 2015 organized by Mike Bell. Many of the questions were related to the transition between graduate school and a career – essentially encompassing the postdoc period. I found myself saying a lot of things that I had intended to put in my next “How To …” post on postdocs (earlier posts are listed at the end). So, right after the session ended, I went off to a coffee shop to bang out a first draft.

An important point at the outset – as in my previous “How To …” posts – is that the suggestions I give aren’t universal truths. The reality is that postdoctoral positions and the gains derived from them will vary among countries, universities, disciplines, PIs, and the postdocs themselves. I will try to note some of these distinctions but I am sure I will forget some – please let me know what I have missed. Also, the postdoc you choose and the way in which you implement it should depend on your career goals and thus the types of skills, expertise, and experience that you need. For instance, such decisions depend on whether you want to pursue a career in government, the private sector, or various types of universities (primary undergraduate, research intensive, etc.). And, of course, your career path might not benefit much from a postdoc anyway.
Many career routes are possible: From

Postdoctoral positions are often the most rewarding, creative, and productive time of your career. You don’t have any of the limitations and constraints of a graduate student: you are already experienced and knowledgeable in research and you don’t have the same annoying and time-consuming non-research requirements (qualifying exams, classes, etc.). At the same time, you don’t have any of the non-research responsibilities (committees, committees, committees) of a faculty member. Now is the time when you can fully (or mostly) dedicate yourself to research and let your creativity and originality have (almost) free reign. Thus, a first important rule is POSTDOC AS LONG AS POSSIBLE. Never again can you be so free, so creative, and so inspired. Of course, there are exceptions when the postdoctoral position or project is very restrictive or just not very fun or you are very stressed about the future (but you needn’t be – as I will explain in a later post on “How to Get a Faculty Position”). Moreover, at some point, it might look bad to have been a postdoc for too long, perhaps somewhere around 6 years - depending on the discipline. Yet, I think that most faculty look back on their postdocs as a truly formative and fun time of their career. So let’s get to it.

Long postdoc periods are common: From

How to get a postdoc

The first necessity is usually money – one can rarely do a postdoc without decent funding for salary and for research. Funding options fall into several categories: competitive external fellowships, institutional/programmatic postdocs, and targeted project-based postdocs. (In writing these options out, I realize that I did one of each of them.) External postdoctoral fellowships (mine was from the Natural Sciences and Engineering Research Council of Canada – NSERC) are typically the most flexible, giving you the maximum range of options because it is less likely to tie you in advance to a specific university, lab, or project. Moreover, the PI should allow you more flexibility because they are not paying your salary out of their grant – and for the same reason, they might have more money to contribute to the research. So, it is a great idea to apply for these fellowships – frequently and widely. Institutional/programmatic postdoctoral positions are less common but often quite rewarding – mine was the “Darwin Fellowship” at UMASS Amherst – because you are often expected to take on a leadership role (sometimes with a bit of teaching) that begins to prepare you to be a faculty member. They can be great for building a range of collaborations and projects and for establishing a large network of colleagues. Project-based postdocs (mine was on Darwin’s finches with Jeff Podos at UMASS Amherst) are probably the most common option, especially in the U.S. These are usually advertised by the PI with whom you interview. The PI then funds your salary to do a particular project set out in advance. One advantage here is that the project is usually associated with a brand-new grant for which they have lots of money to spend. A limitation is the lack of flexibility and dependence on the PI funding you.


How to choose a postdoc

People often choose graduate schools largely for personal reasons – proximity to family, good weather, good skiing, good music, good surfing, etc. Although these considerations can also play into picking a postdoc position, they are more likely to be subsumed by decisions made with an eye to career advancement. That is, people typically want their postdoc to help them take their research to the next level and – in essence – get them the best possible job. So how does one make the choice of postdoctoral position beyond the first concern of making sure that some money is available? Several strategies are possible. All of them can work but they present different opportunities and risks.

Work with a famous PI. Famous PIs tend to be famous for a good reason – they do good work and the people in their labs are usually successful. If you can get into one of these labs, then you are likely to have good funds, good projects, and good career prospects owing to your good work, your “success by association,” and the contacts you will get. Of course, these positions can be hard to obtain because famous PIs usually have a lot of applicants and can afford to be picky. In addition, not all famous PIs are good supervisors or good for the careers of their postdocs – so you need to do your homework on how well the former postdocs in the lab have done. But, in the case of “good” famous PIs, this is probably the best route to career advancement. You will have to make sure you can demonstrate that your success is not simply a function of the famous PI, however – you have to be a good independent scientist (more on this point below).

Work with a cool system. Many graduate students work on study systems that lack key resources – such as annotated genomes, experimental manipulability, or good ecological background knowledge. These students are often so annoyed by such limitations that they choose to do their postdoc work with a better-developed “model system” – threespine stickleback! This can be a great way to conduct more sophisticated and advanced research, but it can also be difficult to establish an identity for yourself in a crowded research area with many researcher that are already well-established. One can also run afoul of the problem of having to stay on the cutting edge of research methodologies, which are expensive and often difficult to develop. In essence: you might be a big fish, but in a pond full of much bigger fish you will still look small.

Work with a fun lab. Some labs have all the fun. They are exciting (cool projects even if they aren’t publishing a lot in those weekly periodicals), dynamic (numerous energetic people, invigorating weekly lab meetings), and fun (they party hard at conferences, have heated but respectful debates, have fun retreats, and the like). These labs are always great to be a part of but sometimes aren’t the best way to career advancement (although they can be).

Follow your muse. Sometimes you just have your own ideas and you really want to pursue them: a novel study system, a novel method, a bizarre question. In this case, you need to pitch your
 idea to as many people as possible to find one who will let you forge your own way in their lab. This is generally a high-risk but potentially high-reward route. That is, you are less likely to publish a lot in fancy journals, but you also have the potential to do something creative and new that is totally yours and that can really change the way we think about the world or do science. That is, you can end up being by far the biggest fish in a very cool pond that is newly discovered. And, even if that doesn’t happen, at least you know you went your own way and on your own merits. And, of course, if you succeed, then you are clearly independent, successful, motivated, passionate, and creative (see comments below).

Importantly, these are not mutually exclusive options. In fact, a famous PI working on a model system can have a fun lab with a lot of money in which they will let you follow your own muse. And those people are ………………………..

Use your postdoc to “finish” your PhD

As noted earlier, many people expect their postdoc to be what “puts them over the top” or “takes them to the next level.” This might well be the case but it is a delayed payoff. Instead, research during your current postdoc rarely will be what gets you your next position. The reason is that most postdoctoral positions are too short for you to have any publications from the work by the time you are applying for your next position. Thus, people who do only a single 2-year postdoc are going to be chosen for an interview based on the publication record from their PhD, not their postdoc. Certainly the promise of your postdoctoral work (how good the project looks on paper, who the supervisor is, some preliminary data) will help, especially during an interview, but you won’t have many (or, more commonly, any) publications from your postdoc by the time you are applying for your next position. Hence, it is critical to use your postdoctoral time to finish up work you had been doing previously: publish all those PhD chapters, continue those side projects you started, write that review paper you had been thinking about. (Although it might seem that your postdoc work will be so much better you’re your PhD work that it isn’t worth finishing up the earlier stuff, you need to resist the grass-is-greener syndrome.) These will be the things that get you your next position. Your current postdoc will be what gets you tenure!!!!!! (Note also that this can be harder in a project-based postdoc.)

Establish your identity

In the panel discussion that prompted me to finally write this post, almost all of the panelists forged their current career trajectories during their postdocs. In short, postdocs are when you really establish the sort of work you want to do, the questions you want to ask, the collaborations you want to develop, and – more generally – the type of scientist you want to be. In addition to this maturation of yourself as a scientist, establishing an identity is also important from a practical career-advancement perspective. For instance, job search committees often spend time debating whether or not an applicant’s publications really reflect their own abilities or whether they instead reflect the abilities of the supervisor. Thus, it is great if you can generate some first-authored papers that do not have a “silverback” author on them – and the same is true during your PhD. These publications help to confirm that you can drive a research agenda and do good work independently of established mentors. In addition, search committees will want to see that you have a long-term plan in mind. Thus, you want to establish a research plan that is integrated, comprehensive, creative, exciting, and cohesive (completely different projects are OK as long as you have a body of work – with or without side projects – that builds to a greater whole). Your postdoc is the time to do this - indeed it is also the time you have to write those “research plans” that search committees want to see, and the time you have to construct compelling and exciting hour-long seminars that show not only what you have done but what you plan to do and how it all (or at least a bunch of it) fits together into a reasonably cohesive research agenda.

Related to this, it is generally a good idea to switch labs – and ideally universities and even countries – between your PhD and your postdoc. Doing so helps with all of the above points – and, of course, it broads your perspectives and knowledge and helps you to see a given problem from multiple angles. Also, this switch is sometimes required by particular search committees, departments, universities, or even countries. However, it isn’t absolutely essential in all cases. For instance, sometimes you have started something amazing with your PhD that you can really take to the next level only by continuing on in the same lab where you already know what you are doing, you have the resources and support, and you can most easily take the next step. Staying in the same lab, or at least the same institution, is sometimes also important for personal reasons, such as family.

Build collaborations – but don’t get carried away

Continuing the above themes, one way to build an identity, show creativity and independence, explore new directions, and generally have a good time is to build collaborations. This statement holds true during graduate school but even more so during your postdoc: now is the time to seek links with labs employing sophisticated methodologies (various -omics!), with people having important skills (bioinformatics, theory, stats) or good ideas, with complementary systems (stickleback, guppies, and finches!), and so on. But you have to be careful. First, you will want to work with people you like personally – it can be miserable to be stuck in a project with someone you can’t stand (or, much more likely in my case, someone who can’t stand you). Second, you would ideally have some collaborative projects that will generate first-authored papers for you. First-authored papers are vastly more important for getting a job than are co-authored papers. It will not benefit your career if you accumulate a bunch of co-authored papers at the expense of first-authored papers. And collaborations take time – so don’t start them just because you think you should be collaborating more. Third – and related to the above points – you need to be a GOOD collaborator. For instance, you shouldn’t be the one who holds up the project; that builds bad blood and annoyance, and if you get a reputation as a bad collaborator that is very bad for your job prospects (word does get around!). Fourth, some of the best collaborations emerge organically or by chance, such as during late-night conversations over beer. Of course, you will likely also want to seek out collaborators with particular skills and contact them specifically about collaborations. Both approaches can work.

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With so many papers out there, you need to promote your work if it is to be noticed. From

Network, network, network

Collaborations are increasingly common: From
In the old days, maybe 50 years or so ago, so few journals existed in a given discipline that everyone in the field would read (or at least see) all of the same papers. This made further promotion of your work somewhat unnecessary. Now, however, so many journals are publishing so many papers that each scientist in a given discipline reads (or even sees) only a small fraction of the papers in that discipline. In this high-volume era, it is essential to further promote your work – especially as a postdoc hoping to get a permanent position. You need to get your work and yourself in front of as many people as possible and do your best to explain the importance and excitement of your work while acknowledging the importance of related work (especially that conducted by the person you are talking to). A key route to this networking is to attend scientific meetings in your field and give talks. Posters can work too, but at the postdoc stage you should be presenting orally as much as possible. Job interviews often hinge primarily on the research presentation, so you need to get practice. You might also “pre-impress” potential colleagues and employers who might be in the audience (word gets around here too). Also, try to attend social events at meetings and actively seek out and talk energetically to as many people as possible in your field. These sorts of interactions can make a difference in cases where search committees are debating among various candidates to interview.

Networking is hard for shy people who just won’t be comfortable becoming a social butterfly. However, it is great to try to interact as much as you can – it will almost always be rewarding. (And note that sometimes the bigwig you are talking too can be just as shy.) In addition, shy people can sometimes promote their work and careers remotely, such as through email and social media. More generally, social media is an effective means of promoting your work and yourself regardless of where you fall on the shy–bold continuum. Blogs (as long as they are good and regular) can raise your profile, and Twitter (or equivalent outlets) can get your name and papers and ideas to a wide audience within your specific field and within science more generally. Yet, in a day with limited minutes, doing your science can trade off with promoting your science. So it is worth asking yourself: just how many new Twitter followers would it take to make up for not publishing a first-authored paper. Many thousands certainly. In addition, a big social media profile is really only good for a career in academia if you can back it up with good science. (See the quirky paper on the Kardashian Index for scientists with a social media presence out of proportion to the influence of their actual research.)  

A strong social media presence should be backed up with a strong research record. From


A postdoc is a stepping-stone, and in life generally you don't step onto a stepping-stone without some picture of what the *next* stepping-stone will be, and perhaps the next one after that, and where the path overall is leading.  In other words, use your postdoc strategically; it should be crafted to connect you to the skills, the systems, the people, the institutions, etc., that will facilitate your future path.  The coolest, most fun, most interesting postdoc is not very useful if it is a stepping-stone in a direction that is not ultimately the direction in which you want to go.  Of course it's hard to know and plan at this level when you've just finished your PhD, but try.

*Written by Ben Haller who felt the post needed a conclusion. His suggested text was so good, I just put it in verbatim. Thanks also to the other panelists at the career mentoring session at Stickleback 2015: Katie Peichel, Matt Wund, Ionna Katsiadaki, Juha Merila, and Windsor Aguirre.

Previous "How to" posts

Links to other blog posts with advice for postdocs

Dynamic Ecology


The Professor Is In

The Trophic Link

I will add more as people suggest them

A 25-year quest for the Holy Grail of evolutionary biology

When I started my postdoc in 1998, I think it is safe to say that the Holy Grail (or maybe Rosetta Stone) for many evolutionary biologists w...