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 was a concept called the Adaptive Landscape. The reason for such exalted status is that the adaptive landscape was then – and remains – the only formal quantitative way to predict and interpret an adaptive radiation of few organisms into many. I was heavily indoctrinated into this framework - as my postdoc was at UBC during precisely the time when Dolph Schluter was writing his now-classic book The Ecology of Adaptive Radiation.

Adaptive landscapes come in several forms (e.g., genotype based, allele frequency based, phenotype based), and the one we are concerned with here is the “phenotypic adaptive landscape.” Perhaps the first serious description of this landscape was the one presented by George Gaylord Simpson in his 1953 book The Major Features of Evolution. In essence, Simpson’s landscape depicts phenotype combinations (e.g., population average values for two important traits, such as beak size and beak shape for birds) and the expected fitness associated with a population having those mean values. The resulting landscapes are expected to be “rugged,” with various peaks of high fitness separated by valleys of low fitness. The idea is that a lineage (e.g., of birds) is expected to diversify phenotypically into multiple species that each occupy one of those different high-fitness peaks, with few individuals having phenotypes in the low-fitness combinations between the peaks. From one species thus comes many in a predictable way by different species matching their traits to particular resources or habitats that represent high-fitness peaks on the adaptive landscape,

This adaptive landscape idea remained largely conceptual, or heuristic, until Lande (1976, 1979) figured out how to represent it mathematically. Then a bit later, Lande and Arnold (1983) showed how to link the adaptive landscape to formal estimates of natural selection at the phenotypic level – thus providing a means for formally estimate the landscape. This theoretical work spurred several decades of intense interest in attempting to quantify adaptive landscapes – or parts of them – for particular adaptive radiations. Yet the effort required turned out to be rather extreme for several reasons. Most critically, perhaps, the full landscape can be specified only if one knows the fitness of individuals across the entire range of phenotypic space (e.g., from small beaks to large beaks for every possible beak shape value from pointy to blunt). Yet, almost by definition, individuals with phenotypes of low fitness should be rare in nature -  because they are constantly selected against; and so the fitness of phenotypes between species tend to be unknown in nature.

The Holy Grail

Owing to this problem of “missing phenotypes,” as well as other difficulties, I would argue that no formal adaptive landscape – in the Lande sense – existed for any natural radiation of organisms at the time I started my postdoc. Yet its predictive promise made it the Holy Grail of the time.

Although no adaptive landscapes had been formally estimated, some studies got part way there (see the Appendix below on “other landscapes I have known and loved”). As one example, Benkman (2003) measured the performance of crossbills on different types of conifer cones and used these estimates and other information to construct an “individual fitness landscape” spanning the range of phenotypes – that is, the expected fitness of an individual having each possible combination of beak trait values. As had been predicted, the landscape had peaks of high fitness near the average phenotypes of the different crossbill types – and those peaks were separated by phenotypes with low fitness – yet something was missing.

This figure is from my 2017 book.

In particular, a formal adaptive landscape relates MEAN phenotypes for a population (assuming a particular phenotypic variance) to the expected MEAN fitness of that population, which requires a particular conversion – as the gif below (made by Marc-Olivier Beausoleil) illustrates. When this conversion takes place, the fitness peaks tend to sink and the fitness valleys tend to rise – because the adaptive landscape averages fitness across all of the phenotypes in a population, which inevitably span a range of fitnesses on either side of a peak (or valley). That is, because a population adapted to a fitness peak will have some variation in phenotypes, the off-peak individuals will reduce mean population fitness relative to a population where all individuals were identical and had phenotypes EXACTLY on the peak. The inevitable is that adaptive landscapes based on population means are always smoother than those based on individual fitness values. As an example, the individual fitness peaks for crossbills shown above tend to disappear if converted to a formal adaptive landscape based on population means (C. Benkman pers. comm.)

So, as time went on, a formal adaptive landscape for an adaptive radiation in nature remained elusive.

My Galapagos Dreams

In 2002, I had the opportunity to visit Galapagos at the behest of Jeff Podos (UMASS Amherst) who had just received an NSF Career Grant that could fund my participation. I was really excited and read all of the classic books by Lack (1947), Bowman (1961), and Grant (1998). Yet, I still had no real practical experience with Galapagos or even with bird research. Thus, on my first visit, I decided to not actually conduct any of my own research but rather learn about the system – both by helping Jeff with his research and also simply walking around in the field making natural history observations that might motivate future experiments. A highlight from that year was the afternoon I spent in the field with the deacons of all things finch, Peter and Rosemary Grant.

One of the major efforts of Jeff’s team was to capture finches at a focal site (El Garrapatero), measure their beak traits, band them, and then relate those traits to song features and mating. Within a year, I had encouraged an increasing emphasis on using the banded birds to track inter-annual survival. When it was clear that this effort would be productive, I set the goal of – once and for all – estimating the adaptive landscape – in the formal way – for Darwin’s finches at this site.

My first step in this effort was to use the extremely-variable population of Geospiza fortis (the medium ground finch) at El Garrapatero to measure selection across their phenotypic distribution. This population had long been suggested to be bimodal in its beak size distribution, with somewhat distinct “large” and “small” beak morphs. My idea was that the fitness landscape should show a valley between the two morphs, such that intermediate birds have lower fitness – a selective function called disruptive selection. Using two years of data, low-and-behold, disruptive selection between the beak modes (and stabilizing selection around each mode) is what we found. This initial discovery was exhilarating but (1) we had only two years of data, (2) we had a potentially imprecise measure of fitness (survival over one year), and (3) we were looking at only one species.

This figure is from my 2017 book.

It took another 10 years of accumulated data – contributed by many team members and collaborators working at El Garrapatero – to solve the first of those problems. In particular, Marc-Olivier Beausoleil (graduate student of Rowan Barrett at McGill) was able to compile years of the most intensive data collection for G. fortis to show that, yes, disruptive selection was always working to maintain some separation of the two morphs (large and small) – and that the intensity of this selection could be explained by weather conditions. Specifically, disruptive selection was strongest when dry periods followed wet periods – probably because many fledglings were produced during wet periods which then increased competition (and hence mortality) during subsequent dry periods.

But these landscapes were still at the level of the individual (rather than population mean), and they still showed only one species, whereas the Geospiza radiation at this location has four species: the small ground finch (Geospiza fuliginosa), the medium ground finch (Geospiza fortis), the large ground finch (Geospiza magnirostris), and the cactus finch (Geospiza scandens). When would we have enough data for this more comprehensive effort.

After 17 years of data collection that included more than 3400 individual birds, we set out to give it a try. For a fitness surrogate, we chose longevity, which Peter and Rosemary had previously shown was strongly correlated with total fitness (note: we could not track reproductive output, such as the number of fledglings, in our population). Marc-Olivier Beausoleil led this effort and first calculated an individual fitness landscape, relating the fitness of individual birds to their individual phenotypes across the entire dataset. As expected, and as shown in other studies (crossbills, African seedcrackers, and more – see Appendix), the individual fitness landscape was rugged, showing clear peaks and valleys. Further, the average phenotypes of each species (and the two morphs of G. fortis) were situated fairly close – in phenotypic space – to the estimated peaks of high fitness.

Would this reasonable and logical landscape hold up when the individual fitness landscape was converted to a formal population mean adaptive landscape. We held our breadth – and I was skeptical given how no study of an adaptive radiation had been comfortable doing so before. As expected, the landscape was smoother and a peak or two sank to the point of obscurity: yet, remarkably, the adaptive landscape still had peaks and valleys and the mean phenotypes of each species (or G. fortis morph) was reasonably close to a peak of mean fitness. IT WORKED – and it only took 20 years of effort!

So, what did we learn from all this work. Well, for starters, one can – with enough effort – estimate a formal adaptive landscape for a real adaptive radiation in nature. Second, these landscapes do have the expected peaks and valleys even when using MEAN phenotypes and fitness – they are rugged indeed! Third, evolution of the various species and morphs seems to follow the estimated landscape, with about as many phenotypic modes (species or morphs) as estimate peaks and with the mean trait values of each close to a different peak. Yet some deviations from these expectations were also seen – note in the figure aboe how the circles (mean phenotypes) are always displaced a bit off the peak. At this point, we expect the deviations arise from our incomplete fitness surrogate: longevity. Perhaps, for instance, the birds that live the longest had the fewest offspring (or, stated the other way around, birds that have the fewest offspring live the longest) – as has been documented in some species.

Was it worth it?

The first ten years of effort were accompanied by high optimism that “this could work.” But, then, as various constraints and funding issues came into play, fatalism set in: “well, it was a nice idea but impractical.” Then I kind of forgot about it for several years while the data stream stayed alive – until Marc-Olivier came in and cleaned up the database and applied his statistical wizardry to G. fortis. Then hope sprang again that we could do the full adaptive landscape and here we are. Despite the effort, I think the accomplishment by the entire team of field organizers, crews, funders, and analyzers is quite remarkable given that – to my knowledge – this is the first formal adaptive landscape estimated for an natural radiation of local species. It has been 25 years coming for me – but we made it.

I should note in closing that the adaptive landscape is more like a Rosetta Stone than a Holy Grail. First, different evolutionary biologist would likely search for different Holy Grails – but, of course, there can be only one. Second, the phenotypic adaptive landscape links phenotypes, fitness, and evolution – and thus is something of a translation between these traits.

Finally, I suppose the real Holy Grail is to link not just phenotypes and fitness across all species in a radiation (or part of a radiation) but to also integrate individual genotypes. Did I mention that Rowan Barrett’s team (with Marc-Olivier) have more than 400 individual whole genomes for these birds – via collaborators in Switzerland (Daniel Wegmann). Stay tuned to this channel!

 

Appendix: Some other landscapes I have known (and loved) for vertebrates:

1.  1. Smith (1993) estimated a single-trait individual fitness landscape for African seedcrackers, which showed disruptive selection between two beak size morphs – and thus inspired my initial analysis testing for the same thing in G. fortis at El Garrapatero.

2.  2. Schluter and Grant (1984) used seed size distributions on different Galapagos islands to estimate the beak sizes of Darwin’s finches that would be expected to evolve. This landscape was inspiring for our own efforts on finches – in part because it examined all of the ground finch species – as opposed to only single species.

3. Martin and Wainwright (2013) used hybridization between pupfish species to increase phenotypic variation and then estimate individual fitness landscapes in natural environments – albeit in enclosures. Fortunately, hybridization in our finches inflates the variation naturally – precluding the need to perform artificial crosses (which would be impossible anyway).

4.   

4. 4. Arnegaard et al. (2014) performed a similar study the pupfish work by hybridizing threespine stickleback populations and testing their fitness in artificial ponds. The additional innovation in this second study was to link the traits in question to genomic regions.

5.   5. Stroud et al. (2023) estimated fitness landscapes through time for a community (although not a radiation) of Anolis lizards on a very small island.

Predicting Speciation?

(posted by Andrew on behalf of Marius Roesti)

Another year is in full swing. What will 2024 hold for us?

Nostradamus, the infamous French astrologer, is well known for his projections into the future. Some 500 years ago, he also made a series of predictions for 2024. He wrote, for instance, “Red adversary will become pale with fear, putting the great Ocean in dread”, and “The dry earth will grow more parched, and there will be great floods when it is seen".¹ These vague predictions that lack a rigorous rationale and are quite open to interpretation (have a go at it!) are probably better called “prophecies”. In contrast, the scientific enterprise has led to a very different quality of predictions. For example, today's astronomy predicts with stunning precision a total solar eclipse for parts of North America on 8 April this year. According to NASA, "... the first location in continental North America that will experience totality is Mexico’s Pacific coast at around 11:07 a.m. PDT. (...) The eclipse will exit continental North America on the Atlantic coast of Newfoundland, Canada, at 5:16 p.m. NDT". ² Based on NASA's past predictive success we can confidently mark this eclipse in our calendars. As of today, however, meteorology cannot predict whether the weather conditions will indeed allow us to witness the eclipse from Earth. In this respect, a lot of uncertainty remains.

In many life situations, we base our decisions and actions on their predicted consequences. Yet, predictions are also indispensable in fundamental sciences where interests center on understanding how our universe works, rather than assisting with practical life problems. We recently asked some colleagues why we want to predict things in science and often heard something like "because this will tell us whether we got it right or not". – This made us think. If true, what does this mean for a field of research still struggling with making accurate predictions?

(And now for some shameless self-promotion!...)

This question marked the starting point for our essay³, which now appeared online and will be part of a special volume on "Speciation" in CSH Perspectives in Biology. To start, it seems essential to distinguish between two types of scientific predictions because they fundamentally differ in their function and value: in fundamental research, we are mainly interested in "causal predictions", not "correlational predictions". We then identify the three fundamental challenges for making accurate causal predictions in speciation research and discuss which of them are theoretically surmountable. Don't despair, there is hope! We also outline how these and further insights (more in the paper!) could shape future speciation research – namely toward a Standard Model – as well as related research in ecology and evolution.

Indeed, although tailored to speciation research, our essay connects with and easily translates to scientific disciplines beyond this field. We believe there are benefits for many of us in a deep consideration of scientific predictability, whether empiricists, theoreticians, or folks drawn to the philosophy of science. Perhaps even Nostradamus would have found interest in this read. While the fundamental topics we discuss in the essay are not new, we use an integrative and somewhat unorthodox approach – including a thought experiment with an Orrery and a Speciation Machine (see figure below) – to hopefully stimulate not only vivid and fun but also fruitful discussions. In fact, our (many) discussions on predictability have led us to organize a symposium on this topic at this year's joint ESEB/Evolution conference in Montreal (stay tuned for it!).

Our paper well complements and extends Andrew Hendry's recent paper on "Prediction in Ecology and Evolution"⁴, and these two papers may provide a nice back-to-back read for students, journal clubs, or coffee-break discussions. Below is the abstract of our paper. Should you have trouble accessing it in full, don't hesitate to let me know and I can email you a copy (you should also be able to access the full paper via my personal homepage).

And folks in North America, don't forget to mark 8 April in your calendars for an astronomical spectacle because it won’t happen there again for the next 9 years (3278 days, to be precise)!

References

1. Nostradamus, M. Les Prophéties. Lyon, 1555.

2. From https://science.nasa.gov/eclipses/future-eclipses/eclipse-2024/where-when/ (accessed 28 January      2024)

3. Roesti M, Roesti H, Satokangas I, Boughman J, Chaturvedi S, Wolf JBW, Langerhans RB. Predictability, an Orrery, and a Speciation Machine: Quest for a Standard Model of Speciation. Cold Spring Harbor Perspectives in Biology 2024 Feb 12:a041456. doi: 10.1101/cshperspect.a041456. Epub ahead of print. PMID: 38346860.

4. Hendry A. Prediction in ecology and evolution, BioScience, Volume 73, Issue 11, November 2023, Pages 785–799, https://doi.org/10.1093/biosci/biad083

Sticklestock center

"There are two kinds of readers.  Those who have read the Lord of the Rings.  And those who are going to."

There are two kinds of biologists.  Those who have worked with stickleback. And those who are going to.  

If you count yourself in the former category, we have good news. You can now buy stickleback through a non-profit center.  The Stickleback Stock Center (aka Sticklestock) at the University of Connecticut provides eggs, juveniles, adults, cell cultures, and associated microbes for research and education. We can do microinjection of CRISPR so you don't have to. You can place orders here:

https://www.sticklestock.com/store-1

If you count yourself in the latter category, even better news: not only can you buy stickleback for research, but the sticklestock.com website provides detailed protocols to help you get started with husbandry, breeding, field work, lab work, spatial transcriptomics, and more, through our protocols page:

https://www.sticklestock.com/protocols

Our goal is to facilitate adoption of this useful research model organism, both by supplying animals (or tissues), and advice to ease the transition.

Why have a stock center? A number of reasons motivated us to start this initiative. 

First, stickleback are highly seasonal animals that breed in mid-spring (depending on where you go). If you want embryos another time of year, you are out of luck. By offering a stock center we hope to provide a year-round source of samples.  

Second, field work is expensive and time-consuming. You need flights, lodging, rental cars, permits, traps. You need to ship fish which can turn into a major hassle when crossing international borders.  We typically do field work at just one location at a time (e.g., this summer I'll go to Vancouver Island for two weeks), which makes it hard to create crosses between geographically far-flung populations. A stock center provides a cost effective alternative, saving researchers time, expense, and reducing carbon footprints.

Third, when we study wild populations we are studying a moving target. For example, my lab has worked on Gosling Lake on Vancouver Island for 20 years now. During that time the population has seen a major evolutionary change leaving genome-wide alterations in allele frequencies, and large allele frequency change at the gene I care most about (spi1b). A stock center population may also evolve, adapting to culture enviroments in aquaria, but provides a stable genotype for future study. Moreover, multiple labs can readily study the sample genotypes facilitating collaboration and replication.

Fourth, we rarely have the option to work with populations that have genome sequences before we ever begin. The stock center plans to work primarily with populations where we already have some whole genome sequence data, or acquire some early on during culture. This allows us to offer our customers genotype-guided choices of populations to work with, an unusual opportunity for a wild vertebrate model system.

We hope the stock center grows and provides a sustained resource for stickleback researchers, current and future. If you are thinking of trying out stickleback research, get in touch! Only through sustained orders will we convince funding agencies that we have the capacity to become a self-sustaining program that can provide long-term support to the community. 

Prediction In Ecology And Evolution

I recently published a paper titled Prediction in Ecology and Evolution in BioScience. I was pretty sure the paper would get a lot of attention as I had six reviewers who provided more than 20 single-spaced pages of comments. In all cases, the reviewers were very interested and sincerely wanted to help improve the paper. Most of the criticisms focused on (1) what I should have paid more attention to, or (2) what I should have excluded. After publishing the paper, I immediately started getting emails making similar suggestions. This inspired me to start an exchange of ideas.

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To start, I provide the abstract of my paper, which is freely accessible on the journal website through the end of 2023. Then we will move to some commentaries/criticisms/suggestions. 

Prediction In Ecology and Evolution - by Andrew P. Hendry   

Prediction is frequently asserted to be the sine qua non of science, but prediction means different things to different people in different contexts. I organize and explain this diversity by addressing five questions. What does it mean to predict something? To answer this question, I describe concepts of prediction as prophecy, diagnosis, history, repeatability, and fate. What are we trying to predict? Here, I describe how predictions vary along several axes: general to specific, qualitative to quantitative, relative to absolute, point to range, and continuous to discontinuous. Where do predictions come from? In this case, I focus on deductive versus inductive reasoning. How do we test predictions? The answer here is not straightforward and I discuss various approaches and difficulties. How good are predictions? Not surprisingly, it depends on what is being predicted and how we judge success. Importantly, I do not espouse a “best”way to approach prediction but, rather, I outline its diverse manifestations so as to help organize practical thinking on the topic.

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1. From Predicting an Eclipse to Predicting Speciation

By Marius Roesti - University of Bern

In a separate post, Marius asks if we can move from a machine predicting an eclipse (use of an orrery to do so in the movie Pitch Black is shown below) a "machine" for predicting speciation in the post is here.

Excerpt from the post: In many life situations, we base our decisions and actions on their predicted consequences. Yet, predictions are also indispensable in fundamental sciences where interests center on understanding how our universe works, rather than assisting with practical life problems. We recently asked some colleagues why we want to predict things in science and often heard something like "because this will tell us whether we got it right or not". – This made us think. If true, what does this mean for a field of research still struggling with making accurate predictions?

The paper on which the post is based.

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2. Assessing Predictability in Ecology and Evolution

Daniel Ortiz-Barrientos, The University of Queensland, d.ortizbarrientos@uq.edu.au

 “We never actually prove any proposition in science; nor do we accept any proposition as ‘true,’ ‘finally true,’ or even ‘probable.’ But we do accept some propositions (or theories) as better tested, or better corroborated, than others.” - Lakatos (1978)

The paper “Predictability in Ecology and Evolution” (PIEE, Hendry 2023) comprehensively frames our understanding of the diverse applications of prediction in ecology and evolution. However, an emphasis on predictive ability can lead us to overestimate foresight given the complex and contingent nature of biological systems (Mayr 1961). Integrating a structured categorization of prediction in ecology and evolution with tailored best practices could add to the paper’s grounded perspective (Tables 1-2).

The taxonomy of PIEE encompasses concepts such as prophecy and repeatability and avoids a one-size-fits-all approach. This approach suits the complex context and aims of prediction in our research fields. However, predictions range on a continuum from universal principles to specific forecasts, each with distinct utilities and limitations (Table 1). This spectrum reflects the trade-offs among realism, precision, and generality (Levins 1966). General principles provide theoretical guides but can oversimplify nonlinear dynamics (Gould and Lewontin 1979), and detailed quantitative predictions often fail when extended beyond their inductive scope.

Practices like replicating studies, comparing models, and providing effect sizes (Table 2) should capture the relevant contexts we need for proper interpretation without demanding impossible generalities. Such a goal can ultimately reflect the role of contingency in evolution (Wiens and Donoghue, 2004). Also, conveying historical context and information on the sensitivity of evolutionary processes to initial conditions can help. In general, categorizing prediction into types that teach us about our own practice can prevent us from presuming universal accuracy while keeping our fundamental goal of finding rules in biology.

In conclusion, while the PIEE paper sheds light on the diverse role of prediction in ecology and evolution, it might overstate the predictive power achievable for researchers, given the complex realities of nature. Combining a structured prediction framework (Table 1) with adaptable methodologies (Table 2, Lakatos, 1978), can strengthen PIEE’s string conceptual foundation. It should also help us embrace unpredictability and sharpen our scientific rigor. Recognizing intrinsic limitations, as highlighted by Mayr (1961), can equip researchers with epistemic humility to navigate ecological and evolutionary complexity pragmatically.

References

Gould, S. J., & Lewontin, R. C. (1979). The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proceedings of the Royal Society of London. Series B. Biological Sciences, 205(1161), 581-598.

Hendry, A. P. (2023). Prediction in ecology and evolution. BioScience, 73(3).

Lakatos, I. (1978). The Methodology of Scientific Research Programmes (Philosophical Papers Volume 1). Cambridge University Press.

Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421-431.

Mayr, E. (1961). Cause and effect in biology. Science, 134(3489), 1501-1506.

Wiens, J. J., & Donoghue, M. J. (2004). Historical biogeography, ecology and species richness. Trends in Ecology & Evolution, 19(12), 639-644.

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 YOUR COMMENT HERE!

A rejected analogy

Analogies can be useful ways of explaining complicated ideas - but they can also be problematic. Reviewers of a recent paper were having trouble understanding a rather intricate idea we were presenting. Thus, on revision, I attempted an analogy. I liked the analogy and found it helpful but - whether unfortunately or not - it didn't make it into the final paper. It was left on the cutting room floor, so to speak. Still, I kind of like it and so provide it here - with context.

Led by Sarah Sanderson, we recently conducted several studies that tested the hypothesis that populations living in areas with low levels of a limiting nutrient would show compromised performance for traits depending on that nutrient. Specifically, we first tested whether fish populations living in water with very low calcium levels would show reduced levels of calcium in their scales. This work was possible because several native species can be found across an environmental gradient in calcium levels, from "high" in the St. Lawrence to "low" in the Ottawa River (Figure  below).

To cut a long story short, we found NO EVIDENCE that the populations in low-calcium water had lower levels of calcium in their scales. The figure below shows that - for a given change in water calcium - scale calcium levels changed hardly at all.  At one level, this made sense - because calcium is what makes scales strong - and strong scales aid defense from predators and other environmental stressors. Yet experimentally exposing fish to similarly low calcium levels has been shown to compromise various aspects of performance - so how were these fish maintaining high-quality scales?

Our next hypothesis was that these fish really really need high-quality scales - and so prioritize that function when faced with low calcium availability. If so, they might show reduced functionality of other calcium-dependent traits - and so we next looked at various aspects of the skeleton. Again, we found no evidence that fish in low-calcium water show any compromise in this calcium-dependent structure. This result is summarized below, where the results for scale calcium from the earlier paper are in red and the results from various skeleton measurements are in green and blue. 

 

It seems, then, that populations in low-calcium water have somehow evolved to be better at obtaining calcium and/or building calcium-dependent structures - perhaps a classic case of "counter-gradient variation" (which would have to be confirmed using common-garden rearing). Yet, even in cases of counter-gradient variation, something else is often compromised - that is "Darwinian Demons" who are great at everything are unlikely. 

So where are the compromises then? We could always look at other calcium-dependent structures (although scales and skeletons are the most obvious) or perhaps the effort required to obtain calcium in low-calcium water has other costs, such as to growth or survival. But this is could be a wasted effort because ....

And now we come to the alternative idea for which I generated the analogy. Here it unfolds as written out in the draft version. The first paragraph is the analogy that was axed later and the rest of it was also modified for the final published paper. Perhaps you will like it or maybe you will hate it but, regardless, I bet you will remember it - even if only for short time.

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Perhaps trade-off payments during adaptation are “front loaded”

"We start with an analogy. When purchasing a mortgage for a home, the purchaser incurs an additional cost (beyond the cost of the home itself) in interest payments to the lender (usually a bank) – and most mortgages are structured such that those interest payments are front-loaded. As a result, payments early in a mortgage include a lot of interest payments (on the money loaned by the bank), whereas payments late in a mortgage almost entirely reduce the principal (because the interest was paid earlier). Thus, if one examines mortgage payments early on, this additional cost is apparent – and is a trade-off associated with purchasing a home via a mortgage. Later on, however, examining mortgage payments would suggest this cost was minimal – because the interest payments have become very small. In short, a strong trade-off (interest payments) can be evident early in the process of adaptation (getting a home) but are not evident later in the process (because they were paid early on).

From this analogy, we suggest that the costs of adaptation to a limiting nutrient might be absent in the present because they have been paid via selective mortality in the past. When an environment changes to become more stressful or difficult (e.g., colonizing an environment with limited resources, like calcium), selection for improved tolerance to that stressor is expected to be “hard” (as opposed to “soft”) – that is, by increasing mortality rates (Brady et al., 2019a). This increased mortality represents a cost in the form of “selection load” that stays high until evolution better adapts the population for the new stressful conditions. If population sizes are low during this period, an additional cost can be incurred through inbreeding that exposes “genetic load” (Crow, 1970). Once the population adapts and increases in abundance, however, it has paid those costs of selection (removing maladaptive alleles) and inbreeding (“purging” recessive deleterious mutations); and, hence, might perform better than the ancestral population in both environments. Indeed, populations adapted to stressful environments can show higher fitness (or at least not lower fitness) in all environments – both in laboratory adaptation studies and in field experiments (Reed et al., 2003; Rolshausen et al., 2015).

Applying this last scenario to our study system, adaptation to low-calcium water might have been extremely difficult at first – generating substantial mortality and strong selection. This expectation is supported by experiments that expose naïve fish to low-calcium water (Baldwin et al., 2012; Iacarella & Ricciardi, 2015) and by the failure of Ponto-Caspian invaders to colonize low-calcium water (Iacarella & Ricciardi, 2015; Jones & Ricciardi, 2005; Palmer & Ricciardi, 2005). During this initial period, trade-offs would be expected. During the period of intensive adaptation, selection would tend to remove individuals that showed the strongest trade-offs – or that suffered the most from them. Once the population passed through this period and became reasonably well adapted to the stressful conditions, the result could be a locally-adapted populations able to maintain homeostasis without incurring large costs. Time will tell whether invaders in the system will be able to pass through this same bottleneck.

We are not here suggesting that this selection cost is high every time a high-calcium fish population colonizes low-calcium water. Instead, a long history of native fishes occupying a diversity of calcium environments has probably maintained a pool of standing variation that facilitates rapid adaptation to new calcium conditions. The costs paid during this rapid adaptation would presumably be lower than the cost paid the first time that adaptation proceeded – that is, the first time a high-calcium fish lineage successfully colonized low-calcium conditions. Subsequently, alleles suitable for adaptation to low-calcium conditions might persist within the species as a whole – even when not in low-calcium water. An analog to this situation could be the ability of marine threespine stickleback to adapt repeatedly and rapidly to new freshwater habitats via standing genetic variation in the marine population that persists via gene flow from past and present freshwater populations (Roberts Kingman et al. 2021; Roesti et al., 2014; Schluter & Conte, 2009). We can see considerable value in applying these ideas to other systems where some fishes can occupy a broad diversity of habitats without obvious costs, whereas other fishes cannot."

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Histories of Stickleback Research - Tom Reimchen

Retrospection - by Prof. Tom Reimchen (University of Victoria, BC, Canada)

I was a second year undergraduate student at the University of Alberta in 1967 and interested in evolution but indecisive as to majoring in biology or geology. Students were given an opportunity to assist graduate students in summer projects.  I applied with Ric Moodie who was doing his PhD on Haida Gwaii (then called the Queen Charlotte Islands) studying the evolution of a giant black stickleback at Mayer Lake (I had read about stickleback in the journal Evolution). Other students applied for this position as well but I got the job. Ric later told me later that I was the only applicant who expressed no interest in sport fishing but a strong interest in evolution, so he hired me.

Ric’s research exposed me to methods of studying variation in natural populations. One of the techniques involved gill-netting cutthroat trout from the lake, extracting the stickleback from the stomachs and then measuring their size and various defense traits including the bony lateral plates on the side of the fish.  Ric got the impression that stickleback with seven lateral plates, which comprised the modal (most common) phenotype in the population, were captured by trout at a lower rate than stickleback with fewer or greater number of plates. He suspected that fish with different numbers of plates differed in their behaviour  and that this variation might account for the trends in trout stomach contents.  He assigned me the task of locating stickleback nests in the shallows of this large lake, after which we would capture the territorial males. Ric scored the males for nuptial colour, number of lateral plates on both sides, and body size. I did not really understand the rationale for this but it gave me the first exposure to the idea that individual phenotypes differing by a single lateral plate might be acting in different ways.

Over the three summers that I worked as Ric’s assistant, I found many nests and Ric scored many male stickleback. Ric thought it might be interesting to sample some of the neighbouring lakes to see if there were any other interesting stickleback. We hiked into the interior of the island across Sphagnum bogs and through forests using survey maps and compass bearings occasionally missing lakes altogether. One of these isolated lakes was much smaller than Mayer Lake, lacked littoral vegetation, and was appropriately named Drizzle Lake.  As we walked along the shoreline, speculating on whether there would be any fish in this small lake, I found a dead stickleback on the shore that looked superficially like the giant black Mayer Lake stickleback. I thought Ric had dropped a Mayer Lake stickleback to fool me into thinking that we had discovered another example of gigantism. He assured me that he had done no such thing. The genetic work years later showed that this was an independent origin of gigantism from that at Mayer Lake. On inspection of this dead stickleback, we could see that it had only 4 lateral plates, lower than the lowest plate count that Ric had ever seen at Mayer Lake, and we concluded (naively) that there would be minimal predation in the lake.

In my free time, I hiked to some additional unsampled lakes. One of these was the small Boulton Lake that lacked predatory fish. I put some traps in, waited a few hours, and when I checked them, to my absolute amazement, I found that most of the stickleback had no pelvis and many were missing some of the dorsal spines. Excitedly I returned to Mayer Lake to tell Ric who told me that such a stickleback had not been previously found in North America or Europe. We continued these lake surveys and got stickleback from 22 lakes, and many of the populations were distinctive to each lake. Such variability across such short distances exceeded the known morphological diversity of stickleback throughout Canada and Europe. The provincial environmental agency got wind of our ‘discoveries’ and asked us to recommend three lakes with unusual stickleback, one of which would be established as an Ecological Reserve for long term protection of the entire watershed and opportunity for research. We proposed Mayer Lake, where Ric was doing his study, Drizzle Lake, with the other giant stickleback in the north of the island, and Boulton Lake, which had stickleback with a missing pelvis. The government made a decision that Drizzle Lake would be established as an Ecological Reserve as it had the fewest administrative conflicts with other agencies. I was happy with this decision as the undisturbed lake was remote with no road access and had a small old log cabin near the lake that would provide a living place if I ever were to do research on the fish in this lake.

In May 1970, after completing my Zoology undergraduate degree at the University of Alberta, I convinced my friend Joe Rasmussen to come with me to Haida Gwaii for several weeks to sample more lakes for stickleback. During this trip, we discovered a small acidic bog pond with stickleback that were very unusual: not only were the lateral plates missing but the entire trunk was covered in a unique dinoflagellate parasite. We called this locality Rouge Lake as the nuptial colour of the males was outlandishly red;  it turned out with later studies to be also very atypical with respect to its genetic structure. On this expedition, I also made detailed collections of the Boulton Lake stickleback and saw that the relative expression of the pelvic girdle differed from one part of this lake to another, variation that I was later able to relate to spatial differences in the predation landscape.

During the spring of 1970, I started looking at samples of the giant stickleback from Drizzle Lake and noticed that some of the anterior lateral plates underlay the basal support structures for the dorsal and pelvic spines. One atypical fish had spines that were easily laterally deflected from their erect position. This fish was missing one of the lateral plates and it immediately became clear that the plates would buttress the dorsal and pelvic spines from lateral forces exerted during predator manipulation. I shelved my idea about this for several years, returning to it in the late 1970’s and  eventually publishing these observations in 1983 (Evolution 37: 931-946 - Figure 1).  

Figure 1: Relationships between lateral plates and spine supports.

Ric Moodie suggested I read EB Ford’s recent book on ecological genetics. This completely hooked me on studying adaptation and measuring selection in real time in the field. Fortunately, the University of Alberta had just hired Kennedy McWhirter, who was Ford’s colleague at Oxford. I took the courses Ecological Genetics and Population Genetics from Kennedy and this further cemented my interest in this emerging discipline. Kennedy encouraged me to do graduate work at the University of Liverpool in the UK where Arthur Cain and Philip Shepard had recently arrived from Oxford and were developing a graduate program in Ecological Genetics.  My subsequent four years in the UK resulted in a DPhil on the ecology and genetics of two sibling species of intertidal gastropods. This research greatly emphasized to me the importance of spatial and temporal scale for interpreting polymorphic variation within and among populations, an approach that would structure my subsequent studies on stickleback.

During my graduate program, I became friends with Paul Handford and Graham Bell who were both at Oxford;  over several years, we hatched an outlandish research plan in which we would go to Drizzle Lake on Haida Gwaii, and with our partners, live in an old log cabin, and undertake a multi-year study on the giant stickleback from the lake;  Paul would focus on the behavioural adaptations of the fish, Graham on the demography of the population and myself on predator-prey interactions in relation to phenotypic variability. Paul joined me on Haida Gwaii in late 1975 and 1976. Paul, who had just finished a post-doc on songbird dialect in the dry mountains of Argentina, did not take well to living in a small dark cabin in the midst of a wet and cool rainforest. Graham at this time had just finished his DPhil at Oxford and with Sue, his partner, initiated the process of immigrating to Canada. They got as far west as Edmonton where Paul was staying. It was clear that the logistics of potentially six of us living at Drizzle Lake were unreasonable. Paul and Graham got sensible and each got jobs, Paul at the University of Western Ontario and Graham at McGill University.

Figure 2a. Haida Gwaii with Drizzle Lake and research cabin (inset)

Not as well-anchored in reality, I took up residency at the Drizzle Lake cabin in 1976 (Figure 2a) and began the research program. I wanted to extend principles from some of my thesis work involving polymorphic traits and felt that sources of mortality and microsite adaptations might also be operating with traits that were continuous, such as lateral plates, rather than only discontinuous traits like the colour of  intertidal snails. With a $2000 support from the Ecological Reserves Unit (BC government) as well as similar amounts from Joseph Nelson at the University of Alberta, my partner Sheila Douglas and I equipped the 80 year old Drizzle Lake cabin with solar panels, a small wind generator, lights, microscopes as well as a ‘portable’ Osborne computer. In 1985, I was successful at getting an NSERC operating grant that allowed us to continue the research. 

Figure 2b. The lab at Drizzle Lake.

Our year round residency at Drizzle Lake from 1976 to 1985 and then summer residency from 1986 to 1990 yielded substantial evidence of repeatable temporal and spatial variation in the predation regime and the potential influence on the selection landscape affecting the stickleback population. Within several years, we had identified over 20 species of predators on stickleback in the lake, including 16 species of avian piscivores as well as resident salmonids (Figure 3). Most of these predators differed seasonally and they differed as to where they foraged in the lake and what size classes of stickleback they consumed. This high diversity of predators was not because the lake was a predator ‘hotspot’ but rather, evidence for this diversity emerged only as a consequence of the extended time duration (multiple years, multiple seasons) of the study. I summarized these data in a chapter for an Oxford publication that Mike Bell and Susan Foster put together in 1994. 

Figure 3: Predator assemblage at Drizzle Lake.

During the research program, Sheila and I circumnavigated Haida Gwaii several times and sampled about 800 lakes, ponds and streams, of which about 15% had stickleback, all of which were morphologically different from each other. Each lake offered a distinctive set of biophysical parameters (predators, diet, parasites, lake morphometry, spectra, etc) that had the potential of structuring the selective landscape (Figure 4).

Figure 4: Geographical survey of Haida Gwaii for stickleback with representative examples of habitats. Symbols: blue- stickleback present, black- stickleback absent

I was able to recruit some excellent students  including Carolyn Bergstrom on the role of asymmetry in defensive traits, Patrik Nosil on fluctuating selection and Mark Spoljaric on plasticity and predictability of body shape. We summarized the adaptive radiation of these 100+ allopatric populations in 2013 (Evolutionary Ecology Research 15: 241–269). Essentially, the selective landscape at each locality was defined by the relative importance of puncturing, compression or grappling piscivores combined with water spectra (reaction distance) and lake size (Figure 5).

Figure 5: Summary of major processes influencing the selective landscapes of Haida Gwaii stickleback defense structures. 

While it was clear to me that large differences in morphology, from fully-armoured to unarmoured among and within these populations, were adaptive; it remained ambiguous whether this represented genetic variation or plasticity. Axel Meyer had recently shown a major role of adaptive plasticity in jaw and skull morphology of central American cichlids, and such plasticity could not be ruled out for these divergent Haida Gwaii populations.  In 1989, I asked a new graduate student, Patrick O’Reilly, to examine the mtDNA of some of the most divergent populations of stickleback, including the unarmoured stickleback in small acidic ponds that I discovered back in 1970. It was reassuring that these unarmoured fish had a very distinctive mtDNA haplotype from most other stickleback populations (although it was similar to those in Japan), and were potentially relictual but this did not resolve whether the unarmoured phenotype was heritable. Subsequently, advances in DNA sequencing techniques allowed David Kingsley at Stanford to develop genome-wide SNP arrays for stickleback. My post-doc, Bruce Deagle, was able to use the arrays to show extensive genomic differentiation among the morphologically divergent populations, including three different lake-stream species pairs (Proc. Roy. Soc.  279 : 1732 1277), although this did still not rule out adaptive plasticity for divergence in morphological traits.

The repeated evidence for adaptive differentiation among and within populations would have more conceptual context if I had some  experimental data that allowed estimates of strength of selection and rates of change. In the mid-1980’s, I sketched out a plan to transplant limnetic giant stickleback with robust armour from a large lake into a small ‘barren’ fishless pond that differed in multiple ecological axes from the source lake. Ideally, I wanted to create a shift in the ecological theatre involving predation landscape (salmonids/birds to macroinvertebrates), trophic regime (limnetic/plankton to benthic/macrobenthos), spectral regime (dystrophic/heavily stained to eutrophic/non-stained waters) and water chemistry (lower to higher conductivity), expecting that subsequent generations might reveal phenotypic changes in the direction predicted from the differences I observed in the geographical surveys (Figure 6). Eventually, I identified four suitable barren ponds that generally met the criteria and in 1992 initiated the first of the transplant experiments. 

Figure 6: Transplant experiment from Mayer Lake with giant stickleback into a barren roadside pond.

The colonists successfully reproduced, yielding numerous generations that I sampled over time. In 2007, an outstanding student (Steven Leaver) began a graduate program with me and photographed and measured all the samples for meristic and metric traits. The results produced striking evidence for shifts in all defense and trophic traits over nine generations, all in the direction predicted. Some of the traits shifted in the first generation, consistent with adaptive plasticity, and other traits shifted across generations, more consistent with genetic changes (Biol. J. Linnean Society. 107:494-509 - Figure 7).  David Kingsley offered to do whole-genome sequences and I sent him 56 stickleback, including the source and transplant populations as well as representative stickleback from morphologically divergent Haida Gwaii populations. I needed a post-doc to do the genomics of these samples and fortunately Katie Peichel encouraged David Marques to apply. Remarkably skilled, David was able to complete the analyses of these fish identifying trait-specific genetic markers across the genome. These results gave novel insight into opsin evolution, as well as evidence for genome-wide shifts, all in the direction that were predicted by the genomic differences among the allopatric populations differing in ecological conditions. I feel this paper (Marques et al. 2017, Nature, Ecology and Evolution) has been the most substantive to emerge from my research program as it exemplifies the efficacy of natural selection and predictability of evolutionary changes among populations in remarkably few generations. This theme greatly contrasts to that of the famous orator, Stephen J. Gould, who concluded in 1985 that “….some geographic variation within a species is clearly adaptive, but much is a non-adaptive product of history."

Figure 7: Results of phenotypic shifts in the transplant population relative to the original colonists after 8 generations.

One of the important concepts  that emerged during our many years in the ecological theatre at Drizzle Lake and Haida Gwaii was the persistent reminder that inter- and intra population variability in defense morphology could not be reliably understood without context to age-specific sources of mortality. Presence of multiple species of piscivores that differed spatially and seasonally, as well as in their foraging and prey capture behaviour, creates an opportunity for diversifying selection on the traits that can differ spatially within populations and fluctuate over short periods of time. The tendency of researchers, including Ric and myself in the early formative years, to classify populations as either with or without predators was simply wrong. There were localities without predatory fish but there were none that lacked one or more species of piscivorous birds or macroinvertebrates (Figure 8). 

Figure 8: Routine morning observations of Common Loons foraging on stickleback at Drizzle Lake. Inset shows adult stickleback that escaped after capture by a Common Loon. These exhibit different lateral plate phenotype than uninjured fish and have elevated fitness relative to modal phenotypes (Evolution 2023, 77(4), 1101–1116).

Researchers, editors and reviewers still uncritically accept the flawed dichotomous characterization of the predation landscape (yes/no or high/low) despite the lack of evidence to warrant the dichotomy. While It is unlikely that  future researchers will embrace my approach -  living in an old cabin at the study lake for year-round observations on the abundance and behaviour of the predator assemblage,  I am optimistic for the future. New technologies, such as hi-resolution field cameras and e-DNA  will hopefully contribute to the understanding of the subtle but real temporal and spatial variability in selective landscapes, the motivation for the extended field studies on Haida Gwaii stickleback.

Sheila Douglas putting in the work.