Friday, May 2, 2025

Tales from the Hill

Contributed by Dan Bolnick


On April 30 2025 (the same day NSF froze all funding actions), about 20 colleagues and I were crisscrossing Capitol Hill in Washington DC meeting with staff of our Senators and Representatives (and occasionally the congressfolk themselves). I was there  with a delegation from the American Institute of Biological Sciences (AIBS) to ask Congress to support science in the US through continued funding at or above recent levels. Allocating funding, after all, is where Congress’ primary power lies. But in addition to our “ask”, we also came to our meetings with an “offer”. We would ask the staff, “what can we do to help the Representative or Senator push through science-friendly policy?”  That offer of help changed the nature of our meetings  – the aides began giving us specific advice, questions, and requests.  Here, I want to convey some of the lessons I learned from those conversations. I’ll give the short social-media post ‘executive summary’ version, then elaborate below in a longer post.




Quick version:

When asked about what we scientists can do to help federal funding of science, Congressional aides suggested the following:

1)        Send your representatives specific, personal, heart-warming stories of the benefits of science funding, and also examples of the harm done by cuts to science. Be sure it connects with everyday lives in ways anyone can relate to.

2)        Engage in science communication and public outreach to get the wider public excited about science, and concerned about cuts.

3)        Focus your letter writing and phone calling wisely, on those in power (GOP), and on those who need to be persuaded (GOP). So those of you in “red states” are most important in advocating for science. Find common ground with opponents of science funding to try to persuade them.

4)        Your reps (especially democrats) are cut off from information coming from federal agencies under the executive branch. They are learning about science cuts from us, or from the media, so when you learn about breaking examples of terminated grants, interference in free speech, etc, call or mail your reps to inform them.

5)        You can ghost write material (Dear Colleague Letters, Oversight Letters, questions for committees and hearings) that your representative might find useful, and their staff are not qualified to write.

6)        There is hope: there remains bipartisan support for science in Congress, they just need the spine to stand up for their beliefs. Hearing from constituents who support science, and hearing the benefits of NSF and harms from cutting it, helps strengthen their resolve.






Now, the long version…

First, a brief explanation about the AIBS Congressional Visits Day. The AIBS offers a two day training event in Washington DC each year, which I very highly recommend. This event covers some basics of science communication, the federal budget process, and how to develop a ‘script’ for meetings with congressfolk or their aides to succinctly convey your “ask”. I found the training itself to be very useful, a nice mix of presentations about do’s and don’t’s, coupled with active group work, presentation, and giving feedback on each others’ scripts. The event was attended by a couple dozen scientists – mostly graduate students and postdocs – with a strong field and organismal biology representation.  At the end of the training, we split up into delegations by geographic region and had a full day of meetings with our representatives. Congressional offices tend to only book meetings with their own constituents, so my group (myself from Connecticut, plus two Massachusetts and one New Hampshire residents) met with Senators’ staff from our three states, and staff from our particular House districts. Other AIBS attendees from elsewhere in the country met with staff from southern, east coast, Midwest, southwest, mountain, and west coast representatives.

As a member of the New England delegation, we had it rather easy: every one of our Senators and Representatives had previously voted for the CHIPS and Science act (e.g., funding NSF at nearly $12 billion dollars). New Hampshire is very much a swing state where we thought it was important to clearly convey how important science funding is. There, we went with our script, which had a central “ask” (please protect science in the US) with three pillars to it (funding, protecting free speech and inquiry, and protecting the people who do science). 

But for the most part, I felt a bit guilty taking 20 minutes of a busy congressional aide’s time to argue for something they were likely to do anyway. So we pivoted to giving more time to the “offer”. And that’s where things began to get interesting. Here are some of the responses that we got, when we asked what we as scientists (and you, too) can do to help congress help science:

1. Tell the reps stories.  

This was the most universal theme.  “Stories, stories, stories,” said one aide.  Ultimately, lawmaking and funding are about how constituents feel, and making their constituents feel heard and supported.  And a key rule in politics is that emotionally resonant stories are far more effective at winning hearts and minds, than numbers can be. So, call or write to your congressperson with stories about how science funding is benefitting their constituents. This gives your representative a supply of stories that they can draw on in conversations with skeptical colleagues, on speeches, in press conferences.

The stories need to be something that they, and a random constituent, would value. If I just say that the money makes it possible for me to study fish speciation, that’s not going to strike a non-biologist as valuable. So, what kinds of stories connect for a politician? Stories that relate to their constituents’ jobs, workforce training, income, health, safety, and quality of life. To some extent, that means applied research producing innovations that help small businesses or agriculture or fisheries, conservation, health care advances, technology, patents. Applications. Now, many of us don’t actually pursue applied research (I sure don’t). But  “basic” science (or what I prefer to call discovery science) is nevertheless a great engine for producing unexpected benefits. You might not have any applications from your work – yet. But might you in the future? I’ll give a couple examples of stories that I prepared for my congressional meetings, just for a flavor, at the end of this post.

Even if you aren’t doing applied work you can tell personal stories about employment. I told my House Representative’s aide that I had brought in $X in research funding, that we largely spent employing students and research technicians, generating over 250 jobs in my district over the years. Those employees spent their income within the Representative’s district. And when they did research they often bought supplies from local businesses. So those research dollars coming into the Representative’s district are spent several times over, contributing to the local economy.

Workforce training is important to representatives also. Academics sometimes complain that we train more PhDs than there are faculty jobs to receive them. But that’s a strength, not a weakness – those ‘excess’ PhDs may be the most important thing we do, because they go on and use their skills in data analysis and communication to contribute to the US economy. As an example, I brought a brilliant student into the US from another country, and after she graduated with a PhD in biology she used her statistical savvy to work as a data scientist, first at AirBnB, then Oculus, and now as a leading data scientist at Meta, contributing to the growth of our tech economy. Then, there are all the undergraduates who we work with in the classroom, lab, and field, who go on to use the skills we teach them in do work as doctors, pharmacists, epidemiologists, start-up company owners, K-12 teachers, and far more. Stories of these former students’ later careers convey the key role NSF funding has in creating an educated workforce needed for the modern economy.

That said, statistics do help sometimes. An aide for one senator with a strong foreign-affairs focus clearly took notes when I gave her specific numbers on how US spending on science R&D compared to Chinese spending on R&D, and the opposing trends (US cutting R&D, China increasing it dramatically). Another took notes when I mentioned specific numbers about grant funding received and number of people employed. Still others liked a colleague’s numbers about the seafood trade deficit ($20 billion, twice the total spending on NSF). I find this report on the economic value of federal R&D funding especially compelling: https://aura.american.edu/articles/report/Preliminary_Estimates_of_the_Macroeconomic_Costs_of_Cutting_Federal_Funding_for_Scientific_Research/28746446?file=53480237 from the Institute from Macroeconomic & Policy Analysis.



 

2. Engage in public outreach and science communication.

Congressional support of science funding will pretty much always be as strong, or as tepid, as public support for science. Many of us have struggled, at one time or another, to explain to our relatives (or, friends) what we do, and why it is interesting. If your own relatives don’t understand your work’s value, they can at least appreciate that it is giving you some income (I hope). But now imagine a random tax payer who is equally skeptical of the value of your work, but actively dislikes the idea of their tax dollars going to your salary. We need that random person to see the value in your science, so that they are motivated enough to write to their own representatives in support of science funding. Or, at least, not object to science funding. To bring that random person on board, we need effective science communication aimed at the public. Often that might take the form of articles or OpEds in local papers, local news channels, which reach the constituents in your own district. Convince people that grants aren’t wasteful, that the money is going to something that generates something they value. You might value knowledge for its own sake, but don’t assume others do, so think about value also in terms of economic growth, jobs, health, safety, conservation. One congressional aide emphasized these “tangible impacts” multiple times, connecting science to benefits that non-scientists will feel in their “everyday lives”.




3. Focus advocacy efforts wisely.

One of the things that surprised me is how focused Congressfolk are, on their own constituents.  If you want to schedule a meeting with them, or their staff, you must live in their district. It’s not enough to have information relevant to their district. Nor is it enough to do research or spend funds in their district. Therefore, our New England delegation met with exclusively Democratic Senators and Representatives (we didn’t have anyone from Maine along). So we were preaching to the choir (a phrase that at least two aides used, which felt a tiny bit like a rebuke for wasting their time). But those same aides emphasized that what we really should be doing is pushing our peers in swing states and Republican-represented states to write letters, call, and arrange similar meetings. Focus our advocacy efforts on the people currently in power, who chair committees and have a majority of votes. The Democrats we met with seemed to feel nearly as helpless as we do.  Also focus advocacy on the anti-science representatives who need to hear from constituents who disagree with them. So, we really need those of you who are in Republican-controlled districts to do some of the heavy lifting here. This isn’t just folks with Republican senators or house members; you may have a Democratic Congressional representative but a Republican governor (looking at you, New Hampshire) who can be pressured to express concerns to his party members in Washington DC. The rest of us can help out by working with our colleagues to hone a message, help you practice your delivery. But, it is crucial that our red-state colleagues be especially vocal. And given the current climate of fear of reprisals, we really need to encourage our senior colleagues who are retired, or nearing retirement, to lead the charge here. They don’t have to fear having a grant cancelled, or their employer targeted, so have far more freedom to speak their minds.

When reaching out to a more skeptical audience, there are some things to keep in mind. First, tell stories that have emotional and economic resonance (see point 1, above). Second, find common ground. There’s always something that your opposite will value, that you can draw on.  One member of our delegation works on population genetics of marine organisms, and could point to the seafood trade deficit: we spend 20 billion dollars more on buying foreign seafood, than we export. That’s twice the entire NSF budget! So if we can spend a bit of NSF money to improve our seafood industry by better matching genotypes of aquaculture organisms (e.g., oysters) to warming water climates, that’s an investment that can improve economic productivity and cut into the trade deficit. Republican politicians are currently obsessed with efficiency, so we can make the point that grant terminations are downright inefficient. If you build a house, and stop just before you put the roof on because you want to stop spending money, that’s not savings, that’s wasting the funds you’ve already put in, without seeing any of the benefit that would later accrue. Hunters and gun lobbyists value nature. And everyone values health. If you are willing to go down a slightly jingoistic path, you can raise alarms about how US government funding for scientific research & development is declining as a share of the economy, whereas in China it increased by over 8% just last year. They are on track to spend 2.6% of their budget on R&D, compared to our 0.6%. Lots of republicans are worried about geopolitical competition from China, and a powerful argument for them is to invoke memories of Russian achievements during the Sputnik area, and how that spurred scientific funding that allowed the US to dominate in science globally. Argue that this is a new Sputnik moment. Find that common ground and exploit it to explain why science funding, and freedom of inquiry, yield benefits that your interlocutor wants. 

The good news is, after our congressional visits day we reconvened with a few of our colleagues from other regions of the US, for dinner.  They reported that even their Republican representatives were broadly supportive of continuing NSF, NIH, and NOAA funding. During the last Trump administration, he proposed a 50% cut to NSF, but congress ignored that request and passed a budget that gave NSF a slight increase.

 


4. Pass along breaking information

I was astonished to hear that federal agency staff have been largely prohibited from communicating with Democratic members of Congress. This means that our Democratic representatives are mostly in the dark about grant terminations, prohibited speech, and other political interference within science funding agencies. They only know what they learn through the news (which is often significantly behind on breaking events in science), and from what their constituents tell them. So please, if you have specific evidence of something that congress should know about (a terminated grant, impounded funds,  grant programs being mothballed), let your representatives know about it. It may be that they hear it first from you. For example, some of the staff we talked to did not know that the NSF only awarded half the usual number of Graduate Research Fellowships – that’s over 1000 of our best students who normally would have received salaries to pursue innovative science. That’s less income for their districts, and less economic growth as a result. Other staff did not know that grant programs earmarked by congress had nevertheless been archived, an illegal impoundment of Congressionally-allocated funds. So, every grant termination, every cancelled review panel, let your representatives know! This is especially important because the US Constitution gives Congress the “power of the purse” to determine spending levels. In practice, the White House is using a strategy called “impoundment control” (which, I am told is illegal, though I am not an expert on this point) in which they simply drag their feet on actually spending allocated funds. Congress needs to know of specific examples of impoundment (and the harm it causes) if they are to pursue protecting their constitutional role in government.

 

5. Ghost write

Your congressional representative has a small staff of mostly 20 year olds, very few of whom have any scientific training. Those aides don’t have the expertise, nor the time, to provide their boss with detailed text that can be used to move forward legislation on science topics. But you do. We were told by an aide to Elizabeth Warren, that we are welcome to provide ghost-written documents that the Senator could use. For example, within the Senate, and within the House, there are “Dear Colleague Letters” that express a representative’s intent to support funding for a particular item in the budget, at a particular amount. For instance, there is currently a Dear Colleague Letter within the House of Representatives written by Representatives Neguse and Fitzpatrick calling for 9.9 billion in funding for NSF – that’s a shade more than the last budget, though less than the CHIPS and Science Act of 2023 ($11.96 billion), and is probably our best case scenario. These Ddear colleague letters get signed by other reps, as a way of gauging the level of support for a topic before a vote. These are an important tool for selecting subjects to legislate and fund, and building political consensus. What surprised me was that Warren’s aide said they are willing to see drafts of Dear Colleague Letters written by us, their constituents, which they might then use. In a congressional office that is thin on science expertise and short on time, this can be a powerful tool for moving forward congressional action on a topic you care about. Similarly, we constituents can ghost write Oversight Letters, which congressfolk can send to federal agencies asking for information and explanations. Finally, when there is an upcoming Congressional hearing on a topic, your Senator or Representative has a chance to ask hard questions of people like NSF or NIH directors, DOGE staff, or whoever is being grilled that day. You can submit questions that you would like your representative to ask during these hearings, of people who are testifying or questions they might pose to their fellow representatives.


 

These are grim times for science to be sure. US federal funding of science has been a cornerstone of nearly a century of global scientific leadership and economic growth. That leadership is quickly being squandered and once lost may not be regained (for example, Germany never recovered the global scientific leadership it had before World War 2, though now many US scientists are looking wistfully across the Atlantic). But let’s not cede the field uncontested. You have allies in Congress and in government, you simply need to give them the information and support that they need to be effective at achieving our common goals. That requires time and effort from you, but what better use of your time could there be at this juncture in history?

 


 

 

 

Example Story 1: 35% of deaths involve tissue damage called fibrosis. This is a build up of scar tissue in or around organs such as lungs, heart, or liver. I didn’t set out to study fibrosis. I began as an evolutionary biologist studying how fish split into new species. While pursuing NSF-funded research about evolution of fish in small coastal lakes, we stumbled on something unexpected: fish in some lakes experience severe fibrosis, fusing together all their organs. In other lakes, the same fish species shows no fibrosis at all, or can recover from prior fibrosis (whereas in humans the disease is irreversible). Today, my NIH-funded research team uses this same small fish to study the genetics and cellular causes of fibrosis, hoping to  discover tools to prevent, or reverse, this pervasive immune disease.  This is an example of how NSF funding for discovery-oriented research, driven by mere curiosity, can yield benefits that may launch new therapies and ultimately improve human well-being. The combination of federal investment in discovery-oriented research at NSF, and applied research at NIH, is a powerful combination that drives innovation for long-term economic growth in the US, and to improve health and well-being of your constituents.

 

Example Story 2: Funding science at NSF benefits your constituents and the nation. I’ll use my own team as an example: In 2 decades as a professor I’ve brought my university $XX million in funding, from NSF, NIH, and private foundations. That money generates new knowledge – for instance, I study evolutionary biology but this led me to research on an immune pathology contributing to millions of deaths in the US. But the funds have a much wider impact. We buy research supplies from local businesses and equipment from start-ups. My lab alone has employed over 250 people – students, postdocs, technicians whose income contribute to the local economy. And the skills they learn in the process contributes to the CT workforce: my students have become statisticians, doctors, brewers, epidemiologists, K-12 STEM teachers, and start-up owners. 

 

Example Story 3: Let me give you an example of how NSF funding for discovery-focused science has benefitted your constituency.  My student ------- moved to CT where she won a prestigious NSF graduate research fellowship to study evolutionary biology.  This fellowship gave her freedom to explore different research directions, and she eventually started research on yeasts that live on apples in nearby orchards. In the process of studying how yeasts evolve to attack particular apple varieties, she unexpectedly found a poorly known species of yeast. In the lab, she bred it for cider production, creating a new commercial product we are bringing to market with LongView Cider House at Rodgers Orchards in New Britain. NSF support for her salary (which she spent living in your district) made it possible for her to help a family farm and a start-up company in your district. NSF grants and fellowships can often have this kind of unanticipated benefit, and contribute to economic growth and prosperity.  A recent study by the Macroeconomic Policy Institute confirms what we have long known, that public funding of science has large economic benefits. Conversely, they found that “A 25 percent cut to public R&D spending would reduce GDP by an amount comparable to the decline in GDP during the Great Recession”. NSF is facing the possibility of 55% budget cuts. NIH faces 45% cuts. This year NSF awarded only half as many Graduate research fellowships. Postdoc fellowships are getting cut. Grants are being revoked arbitrarily. We need your support for maintaining, and growing, science funding, for the good of your constituents’ prosperity, health, quality of life, and yes, education and new knowledge. 

 

Saturday, March 23, 2024

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.



Friday, February 23, 2024

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

Tuesday, February 20, 2024

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. 


Thursday, November 9, 2023

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-BarrientosThe 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!


Tales from the Hill

Contributed by Dan Bolnick On April 30 2025 (the same day NSF froze all funding actions), about 20 colleagues and I were crisscrossing Capit...