“Nobody goes there anymore. It’s too crowded.” — Yogi Berra

“You can observe a lot by watching.” — also Yogi Berra

One of the formative papers I read in my first year of graduate school was a lament about the state of the field I was trying to enter. It was a 1998 essay by Doug Futuyma, “Wherefore and wither the naturalist?” published in, appropriately, The American Naturalist. Futuyma was concerned with the future of naturalists, or natural historians, people with granular knowledge of organismal taxonomy and life histories. He reports results from a survey taken at the 1993 Evolution meetings, in which the general sentiment was that (1) natural history knowledge is important and valuable, but (2) it is increasingly hard to come by, probably because (3) it isn’t, on its own, a viable basis for a career in science. This is a problem because, as Futuyma explains, even if with think that “conceptualization”—forming and testing hypotheses about the processes of evolution and ecology—is the “real” work of science, we can’t do that work without a foundation of natural history knowledge.

Worries about the state of natural history probably also reflect our personal anxieties. On the one hand, there’s a fear that one’s own natural history knowledge isn’t valued in hiring, grant funding, or promotion decisions. On the other hand, there’s the fear that one knows less natural history than one ought—and that ecologists and evolutionary biologists working today, as a group, know less natural history than their predecessors did. I know the latter very personally, having first taken over an angiosperm systematics course in a region—southern California—wildly different from the Pennsylvania woods where I learned plant identification; and then watched not one but two more knowledgable (and confident) senior colleagues retire. It is weird to find myself the senior botanist in a department where, before winning tenure, I was at best the third-most adept plant taxonomist.

These worries, both the personal and the systemic, are recurring themes among ecologists and evolutionary biologists—Futuyma cites a precedent from 1950, and a paper slated for the September 2026 issue of The American Naturalist cites Futuyma and then a series of reiterating articles that bridge the three decades between them. That newest paper, by Hari Sridhar, Joyshree Chanam, and Priyanka Hariharan, finds a fresh perspective on our natural history nervousness, however, by drawing on an impressive scientific oral history project to describe how natural history supports fundamental research in ecology and evolutionary biology.

Sridhar, Chanam, and Hariharan are collaborators on Reflections On Papers Past, an oral history project interviewing the authors of influential papers in ecology and evolutionary biology how those papers, and the projects they report, came to be. It’s a truly impressive undertaking on its own—the list of interviews covers the breadth of the field, from Anurag Agrawal (discussing “Transgenerational induction of defenses in animals and plants”) to Marlene Zuk (“Heritable true fitness and bright birds: a role for parasites?”). You could build a course around them all, but it might actually be too much to fit into a single semester.

In the new article, Sridhar, Chanam, and Hariharan perform a qualitative survey of all those interviews, with an eye to the role of natural history in the origins of the papers described. The result is a systematic version of prior discussions like Futuyma’s, with copious examples of three major contributions natural history makes to ecology and evolutionary biology: as inspiration for systematic investigations (“spark”), as the basis to design effective experiments and relevant models (“scaffold”), and as data for systematic analysis (“substrate”). That is, familiarity with the life history of specific organisms is, first, how a biologist identifies surprising or intriguing phenomena; second, how she plans a project to study what she’s noticed; and, third, a potential source for data she can use in that study.

This taxonomy of contributions (a natural history of natural history?) puts a somewhat different spin on natural history anxiety. The “spark” experience is available to anyone who spends time with an organism or in a particular place—or who works with mentors who have spent that time and can impart some expertise—and the “scaffold” often follows as a result of pursuing systematic study. As I started up my doctoral research, my dissertation advisor took me on a couple of day trips into the mountains outside Moscow, Idaho, to look for possibly interesting organisms as I cast about for a research focus. Today I know as much as I do about the foibles of Joshua trees and their pollinators because I spent multiple spring semesters in the desert, trying (and mostly failing) to make various experimental plans work. Between my renewed familiarity with the Mojave since I moved to Los Angeles, and time spent leading class field trips or just wandering on my own in the natural landscapes of Southern California, I feel moderately secure in my source of potential sparks, and satisfied that I can build up the scaffold on a new system if and when a spark arrives.

Natural history as “substrate” feels both more and less secure than either of the other contributions. On the one hand, digitization of natural history collections and the growth of crowdsourcing platforms—especially iNaturalist, for plant-focused folks like me—means that I have more and easier access to natural history in substrate form than any of my scientific forbears. But digital sources are only as reliable as the servers that host them, and a lot more natural history of this sort is encoded in physical specimens that have yet to be digitized, field guides and treatises that may be long out of print … and, yes, the personal experience of long-practicing field botanists and entomologists and wildlife biologists whose day jobs may have little or nothing to do with that knowledge (or were eliminated in an ideological purge). Digitization doesn’t mean that we don’t still need physical natural history collections, even as the institutions hosting them cut costs and corners.

Then, too, the real limiting factor for accessing all three contributions of natural history is that perpetual source of human but especially academic anxiety: time. It takes time to get to know an organism or a natural community to the point that you can see the variation that sparks interest. It takes time to work with a study system and build operational knowledge as a scaffold for research. It takes time to become comfortable navigating and handling even fully online, digitized natural history—and much more time to learn its limitations, or how to bridge its gaps with offline records, specimens, and taxonomic guides. Time is a function of funding for education, for student stipends and instructor salaries, and for the facilities that let us make extended stays at field sites, and funding is nothing if not unevenly distributed. Sridhar, Chanam, and Hariharan close with this thought:

The conditions that promote the operation of background natural history (which we discussed above)—such as flexibility in the conduct of research, the availability of supporting communities of natural history expertise, and the resources needed for long-term research as well as to leverage preexisting natural history information for new studies—are more present in certain geographies and institutional settings (e.g., those supported by Global North resources) than others.

We natural historians stand on the shoulders of so much prior work to understand biodiversity, and we have easier access to much of that prior work than ever before in human history. But that foundation of prior work needs constant maintenance and renewal—and even more, it needs expansion to truly accommodate the global community of people who love to notice the world around them. Natural history is dying; long live the natural historians.

References

Boldgiv B, et al. 2025. Global natural history infrastructure requires international solidarity, support, and investment in local capacity. Proc. Natl. Acad. Sci. U.S.A. 122(6): e2411232122, doi.org/10.1073/pnas.2411232122

Futuyma DJ. 1998. Wherefore and whither the naturalist? The American Naturalist. 151(1):1-6. doi.org/10.1086/286097

Sridhar H, J Chanam, P Hariharan. 2026. Spark, scaffold, substrate: The multiple hidden roles of natural history in ecology and evolutionary biology. The American Naturalist. doi.org/10.1086/741683

Former NIH Program Officer Elizabeth Ginexi has detailed the many concerning rule changes, and helped lead a general call for people to provide comment on the proposals, from their perspectives as members of the American public, as federally funded scientists, and as representatives of scientific organizations. These comments are supposed to inform OMB’s decision to implement, revise, or scrap the proposed rule changes — and, more likely, provide an evidentiary basis for pushback by Congress and in lawsuits that seem very likely to be necessary.

I’ve signed on to statements by the scholarly societies in which I’m a member, and I’ve consulted a bit on some of the language in those; and the California State University system, my employer, also has a comment in process. Today I also submitted a comment from me, personally. Individual comments are limited to 5,000 characters, and specificity is key, so I focused in on a recurring theme I saw in the changes — restrictions on scientists’ ability to communicate. I’m posting the text of my comment here as an independent record, and in case any readers want some additional motivation or inspiration for their own comments. Comments on the proposal are still open until 11:59pm (Eastern time) on Monday, 13 July, and I’d encourage you to contribute if at all possible — you can do so here.

I am commenting on the proposed Regulation for Federal Financial Assistance in my capacity as Principal Investigator of a research lab supported with current federal funding, and with a career-long history of work on federally funded research. I won Honorable Mention from the National Science Foundation’s Graduate Research Fellowship program, and my doctoral research benefitted from a NSF Doctoral Dissertation Improvement Grant; I went on to postdoctoral research on a NSF-funded collaborative project, and as faculty at California State University Northridge, my lab has received funding from both NSF and the Bureau of Land Management. I am concerned that the proposed Regulation would impede one of the most basic tasks of scientific work: communication.

Communication among scientists, and between scientists and the American public, is essential to scientific discovery, and to unlock its benefits for American society. The proposed Regulation places new restrictions on the use of grant funds for conference attendance (§200.432) and professional society membership (§200.454), severely restricts the use of grant funds for publication costs and open access (OA) fees (§200.461), severely restricts public communication and outreach by grant-funded researchers (§200.421), and prohibits the use of grant funds in connection with sweepingly defined “issue advocacy” (§200.450). These restrictions would hamstring scientific discovery and prevent scientists with federal funding from communicating results of their research to the taxpayers who supported it. That is, they could, ironically, prevent scientists from doing the very things that federal research funding pays us to do.

Conference attendance and professional society memberships let scientists exchange ideas, especially projects in progress, in less formal settings where discussions can be frank and free-flowing. At the Evolution meetings in June, I presented work supported by NSF, and a graduate student in my lab presented her BLM-supported thesis project. Both of us fielded questions and suggestions that will help us improve the work as it continues. This conference was also my first opportunity recruit prospective new graduate students and a postdoctoral scholar whose work would be supported by a NSF award I had just received. Because I was at the meeting, I could talk to prospects in person, get to know them, and make my “pitch” well ahead of the recruitment and hiring process.

Publication costs and open-access fees are also essential for basic research. Articles published in peer-reviewed journals are the primary way scientists learn about each other’s work, so we can build on it, refute it, or simply learn from it. Open access (OA) publication — in which articles are posted online, free of charge to the reader — makes research available to the widest possible community, including the American public. The cost of organizing peer review and publication is, however, not zero, and OA fees paid by authors are a solution (if an imperfect one) to cover these costs. NSF now mandates OA publication of articles reporting research work funded by NSF grants, so rules impeding the use of grant funds for OA fees are at odds with an existing agency mandate as well as the fundamental processes of science.

Finally, communication of research results to the American public is a civic duty for scientists working with the support of government funding. Taxpayers have a right to know what discoveries are produced by the investments federal agencies make on their behalf, through research grants. NSF recognizes this right through the OA publication mandate, and by including public communication and outreach as important research activities. Researchers are often the best qualified people to speak on topics of public interest, when those topics touch on their expertise. As just one example, I have written an Op-Ed and given media interviews advocating in favor of energy development in the Mojave Desert, which impinges on the habitat of species I study as part of my federally funded research — but which can also help to protect those species if planned carefully.

Thus, all of these provisions in the proposed Regulation are concerning to me because they limit activities that improve the effectiveness of federally supported research, and could prevent federally supported scientists from communicating their results to the American public or contributing expertise to important matters of public concern. I would ask that all of them be withdrawn, and that the proposed Regulation be withdrawn in full, or rewritten from the ground up in conversation with the relevant Congressional committees, who can speak to the purpose of legislation that grants all funds administered by OMB.

Thank you for your consideration,
Jeremy B. Yoder
Associate Professor of Biology
California State University Northridge

A little knowledge of natural history is a dangerous thing. This is, first, because knowing a few things — the names of some common wildflowers, or the songs of some neighborhood birds — begets curiosity about more things, and soon enough you’ve gone from installing an identification app on your smartphone, to ordering a field guide, to pricing binoculars. But, second, it’s dangerous because once you know a little natural history, you start to notice living things are often not where you expect them.

Should the violet-blue flowers of a jacaranda really be shading the upper slopes of the Griffith Park ridgeline, overlooking the urban sprawl of Los Angeles? Can that really be an eastern gray squirrel chattering at you from the bushes in suburban Seattle? Why on Earth is a honeybee visiting the flowers of a Joshua tree, which offer no nectar and very little pollen?

The most immediate answer for all of these is, because humans put them there. Jacarandas and eastern gray squirrels and honeybees are just some of the species we’ve carried to new habitats for our own purposes. But in all three cases these organisms are not going about human-directed business — they’ve gotten away from the places they were introduced, to make a living on their own terms. And the questions they prompt apply much more broadly.

The ecologist Dan Janzen was struck by those questions, not while walking through a city full of species from other continents, but while contemplating a Costa Rican forest. The hundred square kilometers of Santa Rosa National Park had, Janzen estimated, 650 species of plants, 250 species of birds, 58 species of mammals, and thousands of species of insects. All that tropical biodiversity worried him, he wrote in 1985, because he’d realized that his observations of those many and varied species might not tell him much about their evolutionary history, especially their shared evolutionary history.

The vast majority of species Janzen could observe at Santa Rosa had geographic ranges well beyond the park’s boundaries, and probably had their origins as distinguishable species elsewhere. Was it more likely, Janzen reasoned, that the predators and prey, or hosts and parasites, or plants and pollinators interacting at Santa Rosa had those interactions because they had adapted specifically to each other — or because, when they encountered each other at Santa Rosa, they already had traits that made their interactions possible?

Janzen called this second possibility ‘ecological fitting’, and he realized it would be the rule, rather than the exception, for any moderately widespread species. As a species disperses beyond its place of origin, it establishes local populations in the places where it finds resources, and perhaps an escape from predators or competitors. There might be many other places it could establish if not for a lack of suitable prey, or the presence of a particularly virulent parasite. For the most part, the species is not evolving to fit the places it establishes — it’s establishing where it already fits well enough.

Ecological fitting is a concept most natural historians now encounter at the very start of their careers, though we don’t usually call it that; we call it ‘invasive species.’ Any species humans have introduced into new habitats, either accidentally or on purpose, has done some ecological fitting to establish in those new habitats. The ones we identify as ‘invasive’ have done ecological fitting so well that they overrun the locals. Thinking about these species as examples of ecological fitting makes every hike and nature walk into a litany of the ecologically fitted, from European starlings schooling in the air above the cornfields around my childhood home in rural Pennsylvania, to California poppies growing among roadside wildflowers in Mediterranean France.

Ecological fitting is a problem for the ecologist or the evolutionary biologist, because it means that what we see in a single field trip, or even many field seasons, may not reflect the specific conditions that shaped the species we study. Janzen explicitly linked ecological fitting to the “punctuated equilibrium” model of evolution proposed by Stephen Jay Gould and Niles Eldredge, in which species spend long stretches of their existence with a fairly stable form, behavior, and habitat needs, with the kinds of changes that define new species occurring (relatively) quickly, in “punctuational” bursts. Those periods of stability between big changes are periods of ecological fitting, in which the species finds enough resources and environments to which it is already reasonably well adapted, and so does not experience strong natural selection, or dramatic changes in population size, that might facilitate big, species-redefining changes.

There’s a connection here to another of Gould’s big ideas, his extended argument against the “adaptationist programme” in evolutionary biology. Just as Gould and Richard Lewontin argued that we cannot assume any particular feature of an organism is specifically adapted to its present-day function — because organisms evolve via more processes than natural selection, and because the uses of body parts and behaviors can change with ecological and anatomical context — ecological fitting implies that, however well two species may work together in the present day, we cannot assume that the traits and behaviors necessary for the interaction are the result of that specific interaction.

We also cannot assume that they represent the best possible versions of those interactions. A hummingbird would probably do well enough drinking from any number of tubular red flowers, and a scarlet monkeyflower would probably be adequately pollinated by a Black-chinned hummingbird or an Anna’s hummingbird — or even, as a fallback, a visiting bee. In all likelihood the hummingbird’s traits and behaviors have adapted, not in response to its interaction with one species of monkeyflower, but in response to the general availability of red, tubular flowers full of nectar across the bird’s full geographic range. Or, vice-versa, the monkeyflower’s color and shape reflect the presence of hummingbirds and hummingbird-sized animals with hummingbird-like attraction to the color red, interest in nectar, and ability to drink from a tubular flower.

For ecologists and evolutionary biologists, ecological fitting is a reminder to humility in the face of the living world’s complexity. We can travel the globe, or spend years learning to know a single field site, and only see a little of it directly. Contemplating the ecological fitting of Santa Rosa National Park certainly seems to have put Janzen in such a mood: at the end of his essay he pivots, rather abruptly, to personal reflection:

Widespread species are not adapted to their habitats, they just are. In fact it can be argued that most members of most widespread species are quite maladapted to their habitats. As anyone knows who has suffered a setback in life, you don’t have to be well-adapted to survive. You just have to survive. We are all asymmetrical pegs in square holes.

It’s not possible or practical to expect we’ll achieve a perfect understanding of how the living world works, and how it came to be. But by the same token, perhaps with time and patience, we can understand it well enough.

Perhaps the most Seattle thing I’ve done, in a year I’ve spent more time in Seattle than LA, is attempt to make First Contact with the Capitol Hill murder.

Seattle has an unusually high density of urban crows — the suburb of Bothell has a particularly huge co-roosting population, but they’re out in force across the greater metro area — and human residents are quite fond of them. Crows recognize and remember humans who treat them well (or badly!), and urban crows have a lower baseline fear of bipedal apes that makes them particularly amenable to friendly overtures. University of Washington biologist Carl Bergstrom has gone as far as to recommend befriending crows, and suggests starting with an offering of shelled unsalted peanuts.

So when I happened to see shelled unsalted peanuts on offer in a corner of the QFC produce section, I bought a bag. I spend most Seattle workdays on my laptop at the table in the apartment’s dining area, with my back to the sliding glass doors that let out onto the balcony. I laid out some peanuts on the little table on the balcony and, soon enough, heard visitors outside.

I’ve been in and out of town since that first visit, and so not able to keep the peanuts available continuously. It’s not clear how much the crows recognize or remember me through the balcony door; and so far I haven’t remarked any distinguishing features that let me identify individuals. They come singly or in groups of as many as four, and often there are others who swoop by to consider the peanuts.

They are still quite skittish if C or I are standing too close to the balcony doors, but they’re pretty bold when I’m seated at my laptop with my back turned. I do think they know that one or both of us are the source of the peanuts. It’s not uncommon, now, for a crow to land on the balcony while my back is turned and then call — maybe asking for a refill? A couple times crows have remained perched on the railing while I grab a handful of peanuts, flitted away to a nearby rooftop or tree just at the moment I open the balcony door to step out. From there, they’ve watched me lay the peanuts out on the table, go back inside, and shut the door before flying back down for more snacks.

So it’s a process, and I think we’re building rapport. I don’t have much more time in Seattle before I start the spring semester in LA, but I have a feeling that the next time I’m back in town, crows will be ready for me.

This has been, for fairly obvious reasons, a hard year. Most of the institutions that define what I do for a living have been weakened to the point of desperation, if not outright dismantled — some inviting the vandals in, some forcibly invaded and hollowed out. It was a year in which I tried to explain to students that most of the research projects on our campus were under threat, threw together a local pro-science satellite rally, squinted at the text of the US Code to no discernible effect, wrote plaintive op-eds for the newspaper I read back in high school, tried to plan for what I’d do if immigration enforcement agents demanded entry to my classroom, and pressed on with projects whose long-term future is uncertain at best.

And yet, by any objective measure, it’s been a very fortunate year. I ran the Boston Marathon, and had a pretty good time of it, even if it wasn’t a personal-best time. I hosted a collaborator for a guest seminar on campus, and we took a day to drive out to the desert and see wildflowers, including blooming Joshua trees. I landed a sabbatical semester for the fall, with plans that let me spend most of my time with C in Seattle, from June onwards. We road-tripped north through Yosemite and Crater Lake National Parks, both first-time visits for me, and then took a long week in New England and New York City. I spent most of another week in Georgia for the Evolution Meetings, where I gave a high-pressure plenary that seems to have gone over pretty well.

I saw one student-led project from writeup to submission, review, and acceptance for publication next year; as well as the publication of a pretty big discovery about Joshua tree physiology and an opinion article I’d been working on for a while. I spent a week in New Haven with a friend and collaborator at Yale, where I started building my first-ever analysis package for R, and we made major progress on another long-gestating project. While I was in-state I also drove up to the University of Connecticut to see a bunch of old friends and collaborators and give a seminar, which seems to have gone over pretty well. And I spent two gray, rainy weeks of Pacific Northwest December on a writing retreat at the Friday Harbor research station on San Juan Island, where I wrote a fair chunk of a new project proposal.

So, strictly speaking, it was a pretty good year, professionally and personally.

It was also a year punctuated with wonderful experiences in the natural world. When we pulled over to photograph the view on the way out of Yosemite Valley, I spotted a new-to-me lupine with spectacular yellow and pink flowers on the roadside bank. During our New England trip, C booked a spur-of-the-moment cruise out of Bar Harbor to see puffins on a nearby rookery island; when we got back to dry land we rented e-bikes and rode into Acadia National Park, and we caught a brief glimpse of beavers at work on a lodge.

Later in the summer, on the return leg of a hike to Snow Lake in the Alpine Lakes Wilderness, we turned onto a spur trail for a view over the lake, only to realize there was a collared pika watching us from atop a boulder just a couple yards away. On an October trip to Vancouver, C took me to a spot he remembered in the heart of Stanley Park, where so many tourists have offered birdseed to the chickadees that you can stand by the trail, hand outstretched, and the birds will fly down and alight on your palm to see if you’ve brought them snacks.

So many of these moments were that much better because I got to share them with C. Shuttling between LA and Seattle hasn’t always been ideal, and truly the best benefit of my sabbatical is how much of it we got to spend in the same location, taking adventures where inspiration struck rather than having to plan around interstate travel.

We hiked to mountain lakes, and foraged for blackberries in a Seattle city park — which I baked into pie the same day. We joined friends for movie nights and dinners, in board game cafes and at neighborhood bars. We planned for grocery trips and coordinated workday schedules and watched increasingly obscure iterations of The Traitors.

Lots of other folks made it a good year, many in ways that don’t leave much photographic evidence. The Los Angeles Frontrunners’ regular group runs define much of my social week in SoCal, and the extended time in Seattle has let me settle in with that city’s Frontrunners, too.

There were some nice moments of connection with family, which I don’t get in LA — I have cousins in Seattle, and my more immediate family on the east coast, and we checked in with all of them.

The east coast trip for sabbatical work also included a lot of quality time with friends from multiple stages of a scientific career that has taken me between different states and even into another country. Extended time with a current friend and collaborator in New Haven, and the UConn visit included the first time in years seeing folks I’ve known since my first year of graduate school.

There were moments of broader community connection, too. Over months walking through Capitol Hill I got to feel like I knew a neighborhood on foot, and established some regular stops where baristas or cashiers got to know me. C and I joined the local iteration of historic nationwide protests against our thuggish government, and a month later I watched Seattle elect an experienced, serious progressive for Mayor, alongside a city council that will, maybe, help make progress happen.

This experience of 2025 reflects, really, a lot of privilege and a lot of very good luck. As I look forward to the new year it feels incredibly fragile, and I can see the weak points, where breaks are most likely — the precarity of research funding for my lab; the challenges of dear, personal relationships that cross state lines; the mounting obstacles to teaching students how to find scientific fact and reasoning in a sea of online slop; the looming threat of new and greater violence and vandalism from the people with the most power in this country.

Many of these weak points, though, are also where I draw the most strength. I’m grounded in my scientific work and my classroom engagement with students. I take strength from the people I can call on to go for a run or get on a phone call. I find some relief in the focused challenge of chasing a bird with my camera — a brown creeper that circled a tree trunk just out of reach for long minutes, or a belted kingfisher that taunted me from one end of Friday Harbor’s marina to the other.

And I find perspective in numinous visions of mountains on the horizon — Mount Roosevelt rising over Snow Lake, or Mount Baker resolving into view as the San Juan Islands ferry sails clear of the clouds on the way into Anacortes.

Any one of these moments seems too brief to matter, as light as a chickadee perched on my fingers, flitting away as quickly as it landed. This year, though, they added up to what I needed. I have real hope — I have to hope — that there will be more in 2026.

Well, here’s the funny thing: If I’d been a reviewer on that paper, I’d have said that statement in the abstract was an error. I’d probably also have said that it was a dangerous one.

I suspected right away that this description of the survey results was incomplete, at best. Indeed, the text of the C&EN article has a distinctly different spin than the headline or the paper’s abstract: “When asked if they felt they were forced to modify their manuscript with text they thought to be incorrect, 22% of survey respondents said yes.” Authors making a change they thought to be incorrect is not actually the same as introducing an error! It may simply reflect peer reviewers disagreeing with an author’s interpretation of results in the paper under review, and the editor siding with the reviewers in that agreement.

This is the degree to which authors can be “forced” to make changes they don’t like to a paper — when expert reviewers ask for a change and the editor who oversees the review process decides that the paper will only be accepted with that change made. Authors are free to take their paper to another journal if they feel strongly enough about the disagreement, though this is not the path of least resistance. It’s a pain to start review over from scratch at a new journal, and if you want to publish sooner, your quickest route is to try to do what reviewers ask even if you’d rather not.

Does Bordignon’s paper have a softening from its abstract to its actual text that parallels the C&EN shift from “errors” to “changes they thought to be incorrect”? I have, unfortunately, no subscription access to Accountability in Research. Fortunately, Bordignon followed best practices and provided supporting materials on Zenodo that include the survey questions. The relevant one is Q13: “During the peer-review process of your own papers, have you ever made changes to your manuscript that you thought were incorrect (i.e. introducing an error), but you did them in response to pressure from reviewers?”

That is indeed more complex than the interpretation in the paper’s abstract. There’s a difference between “changes … you thought were incorrect” and “introducing an error,” despite the parenthetical offering the latter as an example of the former. Speaking as an author of peer-reviewed papers, “introducing an error” would certainly be a change I “thought was incorrect.” But! I have absolutely had reviewers ask me to make changes I considered incorrect, and made them — because the reviewers and I disagreed on a point where there isn’t a clear factual answer.

Many such questions arise in the design and execution of a scientific project and the interpretation of its results. Reviewers have asked me for changes to things like figure design, or the use of specialized terminology, or how I communicate the strength of confidence in a statistical test result — or even what conclusions I could legitimately draw from the data. I have strong opinions about writing and figure design, and I know all too well they’re not universal; but there may also be ambiguities in choosing methods and statistical tests. Some examples, off the top of my head:

• Using frequentist versus Bayesian methods — Yes, there are specific cases where this choice can change your risks of a false positive; but in many situations, frequentist methods and their Bayesian equivalents will give the same qualitative answer, and which you trust more is down to personal preference.
• Modeling a process via detailed simulation versus a more abstract analytic model — Here again, there are pros and cons to each choice. A simulation can give you granular control of complex details; an analytic model can give you an elegant description of relationships between the variables that are most important. (Many papers present simulations and analytic models of the same process for precisely this reason.)
• Displaying variation in treatment groups — There are many, many options for comparing the central tendency and variation of measurements organized by treatments. Which you choose depends on your sense of what will optimize clarity, emphasize between-group differences, or even specifically indicate when groups would differ significantly in a standard t-test. For the most part, though, there are no truly wrong choices, so long as the resulting figure isn’t actually deceptive.
• Selecting “candidate loci” from a genome-scan for association with a trait or environment — In genome-wide association studies testing thousands or millions of loci, even after you raise the threshold of “significance” to account for multiple testing, you may have many, many more loci associated with the trait or environment of interest than you can investigate individually. You can triangulate with other data or analyses to narrow the list, but how you choose a subset for further study is simply a judgement call.

The whole point of peer review is that there will be questions like this, on which reviewers and I may disagree even if we’re all experts in a narrowly defined field. If an editor feels reviewers’ opinions are better aligned with the consensus of our field, I can either accept that judgement or find a venue where I think I’m more likely to find agreement with my own way of thinking. However this is the real value that I get from peer review, as an author: Even when I’m sure I’m in the right, I may actually be wrong! Peer reviewers are there to tell me when I’m at risk of being wrong.

(Does peer review always accomplish this? Of course not. We’re all fallible humans muddling through a terrifying and unknowable universe. But the goal is that multiple of us are more effective at the muddling than any one of us alone.)

So anyway, Bordignon asked research chemists a question that encompasses both “did peer review force you to say something false in a paper” and “did peer review require you to change a paper in a way you didn’t like but couldn’t defend to an editor” — then foregrounded only the former, much more alarming version of that question in reporting that 22% of her survey participants said “yes.” I think I’ve spent enough text explaining why I think that choice in itself is an error. Why is it a dangerous error?

In short, it’s dangerous because endangering public trust in the scientific process — of which peer review is a critical part — has higher stakes than at any point in my scientific career. The Trump Administration is framing an ongoing assault on research funding and academic freedom as campaign for “gold-standard” science, which is not so much “spin” in the old political sense as an outright lie: placing political apparatchiks in positions to decide what research is funded and how its results are reported. The Secretary of Health and Human Services — who has no business remaining in that role — is using his national platform and institutional power to tell people that science isn’t trustworthy. The Environmental Protection Agency is revising the actual evidence out of its evidence-based understanding that climate change poses a threat to human health.

Work that undermines the credibility of peer review is ammunition for these ascendant campaigns against public health and environmental protection. That’s not to say no one should criticize scientific processes or publishing — there’s plenty of that to be done, and self-critique is how science gets better. But now, more than ever, ensuring such criticisms rest on a rock-solid foundation of evidence is a matter of life and death. Claiming that peer reviewers have made as many as 1 in 5 scientists add errors to their work doesn’t rise to the standard we desperately need at this moment.

I am not a fan of large-language model chatbots. I’m enough of a hater, in fact, that I don’t like calling ChatGPT and its LLM-powered kin “AI” — they are a particular class of products of a particular form of machine learning, which guess the correct response to a query as informed by associations between words and phrases in vast volumes of training text. One of the most painful lessons of the last couple of years, I think, is that what I’ve just described turns out not to be anywhere as close to “intelligence” as it appears.

I digress; the distinction is important, but it’s not exactly my point. My point is that it recently dawned on me that the LLM chatbots are the latest iteration of a now multi-decade process of big tech companies trying to fit the whole Internet into a box that they own and control.

The first version of this was search — Google and its less successful competitors wanting to be the one website you went to before you went to any other website, so you would see the ads they’d placed alongside their search results. This seemed like a good bargain, since Google was very good at indexing and ranking the rest of the Internet to help find the external sites most relevant for any given query.

Then came social media, mostly Facebook and original-flavor Twitter. Social networks initially gave you links to other websites, selected by people who you knew personally, or who you followed for their expertise or taste (broadly defined, in both cases). This meant you could learn about things to read or watch on other websites, rather than just finding the things you searched for. It was, like search, initially a pretty good bargain for some ads in the sidebars or even mixed directly into the posts from people you’d chosen to follow.

The original bargain of both search and social sites — ads, alongside links to other things of interest — broke down because both Google and Facebook could show more ads to people who stayed on their platform instead of following links to other sites. So search sites built in “summaries” on frequently-searched topics, up to and including just straight-up showing you information pulled from external sites on the search results page. Social networks tried to get people to publish directly on their platforms (I’m old enough to remember when Facebook was recruiting news organizations to publish articles as Facebook posts), and eventually down-ranked or outright hid external links in favor of posts within the platform (the platform formerly known as Twitter is now infamous for this).

None of this is news to the kind of Extremely Online people who may have read this far, inasmuch as we’ve all watched it happen. It’s paradigmatic enshittification: build something valuable, then make it difficult to leave, and extract as much money as possible from the fact that you’ve drawn a big online audience into the walled garden of your search engine or your social network.

What does it have to do with LLM chatbots? It’s this: they are, or aspire to be, the ultimate walled garden.

The models behind LLM chatbots are trained on as much freely-available text as OpenAI and their peers can obtain. “Training” a LLM summarizes that huge corpus of texts into associations among words and phrases, so the chatbot has a basis to choose a likely-to-be-correct response to any given text input. In other words, the model is an attempt to reduce the training corpus into a single thing that you interact with through their chatbot interface. ChatGPT provides dutiful caveats about checking results with external sources, but the idea of these things is very much that their chatbot can give you all of the information you’d otherwise find in an extended search over the open web. The goal is that you never have to leave the platform.

The value in this, from the perspective of the chatbot purveyors, is not (yet) to sell your attention to ads on the chatbot page or (yikes) somehow folded into the responses. Their business model so far is that you’ll pay to chat with the bot. Which means the fundamental function of the model is to compress the open Internet into a box small enough that it can be sold back to you. Whatever other pie-in-the-sky visions the chatbot makers have for the technology — or wish to sell to investors — this is what the product is right now.

On the one hand, this is antithetical to the reasons I want to use the Internet, as a researcher. I don’t want my sources of information filtered through an algorithm I can’t parse or control — the LLM training — and summarized by the unreliable narrator of a chatbot, any more than I would write a review article using only notes taken by a student assistant. I want to be able to read the actual sources, dig up the earlier works they cite, and root around in the supporting data if I’m so inclined. (But, it occurs to me, this may not seem like such an obvious shortcoming to corporate executives who experience “research” mostly as briefings and memos assembled by their assistants?)

On the other hand, even if the LLM behind a chatbot could perfectly communicate the contents of the training corpus, it would still be an attempt to put that training corpus — the training corpus they scraped from the open Internet — in a box and sell it back to you. After watching the rise and demise of previous attempts to build walls around the garden of the Internet, I don’t know why I’d ever accept that bargain.

We spent this June more on the road than otherwise. C drove down from Seattle over Memorial Day weekend, and after I spent a workweek packing for an extended stay on Puget Sound, we road-tripped north. We only had a three-day weekend, but we strung together some sightseeing stops along the way.

We had an overnight stay in Yosemite National Park, where I finally saw the Yosemite Valley — and not one but two new-to-my-camera lupine species, and a very cooperative nuthatch, the headline image for this post.

From Yosemite we drove north, and detoured for Crater Lake, which I’ve never seen at all. There were still multiple feet of snow at the summit visitor center; we crunched over it for photos, perused the gift shop, bought a couple coffees, and drove on.

The weekend after arriving in Seattle, we flew cross-country to Boston. C had wedding invitation from an old college friend — a lovely ceremony on a farm-turned-event-venue in rural New Hampshire. From there we drove our rental car toward Bar Harbor, where we’d booked a bed and breakfast for most of the week. We detoured en route to take the cog railway up Mount Washington, a precipitous trip up to a summit where the winds were so strong we could barely stand upright for a photo at the top.

Arriving in Bar Harbor that night, we parked at the bed and breakfast and didn’t get into the car again until we left three days later. We could walk from town right into Acadia National Park, and over our time there we hiked from the B&B to the summit of Cadillac Mountain and back, took a boat tour past lighthouses to a seabird rookery full of Atlantic puffins, and rented electric bikes to ride almost the entire length of the wide graveled carriage roads that wind through the National Park.

From Bar Harbor, we drove back to Boston, dropped the rental car, and took the train to New York. Over two nights there, we caught a couple of shows — Cabaret on the Friday, a transcendently silly adaptation of The Pirates of Penzance on Saturday — and then flew back to Seattle. I had three working days in town before flying back east for the Evolution meetings, and got back from that jaunt just in time for Seattle Pride, on the final weekend of the month.

It was a good month of travel; it’s good to finally be home.

I have a new op-ed at LNC|LancasterOnline, making the case for the “curiosity-driven” science that the National Science Foundation has supported through much of my adult life — and which is under dire threat from cuts proposed by the Trump Administration and the budget bill under consideration in Congress right now. In it, I discuss some of the possible long-term applications of my lab’s research on Joshua trees, which seem at first like they might be too weird to tell us anything helpful in daily life:

Joshua trees’ pollination by a single species of moths serves as an informative contrast to plant and pollinator interactions that play out in more common, more complicated situations, such as pollination of fruit and nut trees by wild insects and domestic honeybees. By studying Joshua trees’ simplified system, my graduate mentor learned how cooperative relationships like pollination stay cooperative, with two different species trading resources and services, even when they have incentives to cheat.

My lab has also studied how weather influences Joshua trees’ very sporadic annual flowering. Most years, they either flower prolifically, or not at all — and until recently we didn’t know what made the difference between bloom or bust. For that project, we developed software to use machine learning models trained with easy-to-collect data, which may work well for many other species of plants. That could be helpful in agriculture, or in planning to protect rare plant species, or even in understanding how the environment limits where different species can live.

But — and this is the important part — work with Joshua trees and hundreds of other peculiar creatures in distant habitats is important even if it doesn’t pan out into applied discoveries.

Of course, none of these applications of Joshua tree research may pan out. For every Thermus aquaticus [the hot-spring bacterium whose chance discovery enabled modern genetic research] there are hundreds of scientific projects that end in nothing more than a peer-reviewed research article and some fond memories of fieldwork. But we need those hundreds of curiosity-driven studies to find that one lucky, world-changing discovery.

This column is a new iteration of the connection I made with my old hometown newspaper thanks to the Science Homecoming campaign, and anticipates the McClintock Letters initiative rolling out later this month. Go read the whole thing, and — please — call your congresspeople to ask that they preserve funding for NSF’s support of curiosity-driven science.

Monday, I ran the 129th Boston Marathon. It’s something like 15 and a half years after my first marathon, and it’s taken me that entire time to get to Boston.

I ran my first marathon back in October 2009, in Portland. I’d tried a spring half marathon and survived it, and found a simple enough training plan to work my way up to a 26.2 mile (42km) distance, running through the rolling Palouse hills beyond the University of Idaho campus in Moscow, Idaho. Portland went well enough that I signed up for another the next year, and another the year after that.

For most of a decade I did about one marathon a year, with the exception of a possibly over-ambitious 2012. I trained to survive the distance but not really to manage my pace, and generally didn’t come close to hitting the time I’d need to qualify for an entry to Boston — sort of a universal benchmark for marathon performance. Boston is an old race, and a big one, and its organizers ration space on the race course by setting a minimum performance standard to apply for a registration. The closest I came was 3:14:30 on the 2015 Twin Cities Marathon, 9 and a half minutes too long for my age bracket.

Then I moved to LA, and started running with the Los Angeles Frontrunners. Social workouts are a huge improvement over solo, it turns out, and they come with a lot of peer pressure — the Frontrunners have many veterans of Boston, and conversation at group runs usually turns to upcoming race plans. I started choosing my annual marathons with an eye to course speed — net downhills, comfortable running surfaces — and training for speed and strength and pacing in a systematic way.

I ran the lovely gentle downhill course of the 2023 Mountains 2 [sic] Beach Marathon from Oxnard to Ventura, California, in 31 whole seconds less than the Boston threshold; and then that wasn’t enough to win a registration. A “Boston qualifying” time lets you apply to register, but then if more people apply than the race can accommodate, the organizers set a lower cutoff threshold for registration. In 2023 I needed to be about five minutes faster for that second cut.

I came back around the next June, for the Light at the End of the Tunnel Marathon, on a rails-to-trails path through Snoqualmie Pass above Seattle, Washington — green and cool in the shadow of the Cascades, and a nice gentle downhill grade. That, and my training, got me to 3:02:21, fast enough to survive the second-round cut when I applied for Boston in the fall.

Training up for a marathon run in late April was going to be challenging anyway — who wants to start workout runs in December? — and between the natural and political disasters of this spring, I did not turn in the my best preparatory performance, by a fairly long shot.

The Boston course, through a series of small New England towns to downtown Boston, turns out to be very pretty but sort of brutal. It starts with a long descent that makes an over-fast start feel like no effort, then transitions to gentle but relentless rolling hills cresting to a big climb at “Heartbreak Hill” around mile 21. And it’s all on pavement; I trained on much tougher topology in Griffith Park in Los Angeles, but almost all of it nice soft trails. So by the top of that last big climb my legs were dead and I didn’t have much left to give. I walked a fair chunk of the home stretch — though I did manage to save enough effort for a jog on the final mile, and my finish time was far from my worst-ever. (Though also, this was the first event in a while where I’ve been in the slower 50% of my age bracket!)

I’m happy I did it, though. The course was lined with cheering crowds truly the whole way, a level of community support I’ve hardly ever seen. (Maybe in the Twin Cities?) And Boston is convenient for family — Mom and Dad drove up from Lancaster County, and my brother and sister-in-law drove in from the suburbs with my nephews, and of course C flew out to almost literally catch me at the finish.

Once you’re in shape to run the distance, running a marathon is mostly about pace management, knowing what effort you can give and metering it out. That’s more or less what I’ve ended up doing with my preparation for Boston 2025; the result is far from my best marathon performance, good enough as a race experience. The real work, and the accomplishment, was getting to the start line.