The research team, led by Olivier Lepais of BIOGECO at the University of Bordeaux and INRAE, together with Maya Gonzalez of ISPA and Marie-Lise Benot, set out to answer a question that population geneticists have rarely tackled at the scale of an entire ecological community. Most studies of demographic history examine a single species at a time, inferring past population trajectories from patterns of genetic variation. But species that share a habitat are exposed to the same storms, the same shifting sands, and the same episodes of habitat loss. If past environmental changes were strong enough, the argument goes, they should leave comparable genetic fingerprints across many unrelated species at once. Testing that idea required a sampling effort of unusual breadth: the researchers developed species-specific microsatellite markers for eight dune plants and genotyped 3,116 individuals collected along the French Atlantic coast.

Microsatellites, also known as simple sequence repeats, are stretches of DNA in which a short motif of two to six base pairs is repeated over and over. Because the number of repeats changes relatively quickly through mutation, microsatellites are superb recorders of recent demographic events. Allelic richness, the number of distinct repeat-length variants circulating in a population, responds within generations to changes in effective population size, the genetically meaningful size of a population that determines how fast diversity is lost. The catch is that this fast mutation rate also means microsatellites saturate over longer timescales, blurring signals from the deeper past. To recover that deeper history, the team turned to the DNA sequences flanking each microsatellite, where point substitutions accumulate far more slowly. Heterozygosity at these flanking sites preserves a memory of ancient population sizes that the rapidly evolving repeat tracts have long since overwritten.

Combining the two marker types in a single inference framework is what gives the study its temporal reach. The researchers built a simple demographic model in which each population underwent a single change in size at some point in the past, and they used coalescent simulations to generate genetic data under a wide range of scenarios: different ancient and modern effective population sizes, different timings of the change, and different mutation rates. These simulated datasets were then compared with the observed genetic data using approximate Bayesian computation, or ABC, a family of methods that sidesteps the need for an analytical likelihood by simulating millions of datasets and retaining those that resemble the real one. In a further refinement, the team employed ABC random forests, a machine learning approach in which summary statistics computed from the genetic data act as predictors of the underlying demographic parameters, allowing the inference to weigh which combinations of statistics carry the most information.

The results were reassuringly clear on the quantities that matter most. Recent effective population sizes were well recovered, informed chiefly by allelic richness at the microsatellite repeats, while ancient effective population sizes were reliably estimated from heterozygosity in the flanking sequences. The timing of the demographic event proved harder to pin down for any single species, but the combined summary statistics, which mix microsatellite variation with flanking substitutions, sharpened the estimates considerably. This division of labor between fast and slowly mutating markers is the technical heart of the paper, and it demonstrates that sequencing-based microsatellite genotyping can do double duty: the same sequencing reads that yield repeat counts also yield the surrounding nucleotide variation needed to anchor the timeline.

What emerged from the analysis was a demographic signature shared across the community. Most of the eight species showed a strong and roughly synchronous decline in effective population size, with the timing of the crash estimated at between 135 and 450 years before the present. That window overlaps squarely with the Little Ice Age, the cold period lasting roughly from the fourteenth to the nineteenth century during which the North Atlantic storm belt intensified. For the Aquitaine coast, historical and geological records document exactly what intensified storminess means: waves of sand mobilization, known as sand drift, that buried vegetation and rolled inland across the coastal plain. Previous work on late Holocene sand invasion along this coast, including studies of the Médoc peninsula and the wider Aquitaine basin, has mapped these dune incursions and linked them to cold climate events. The genetic data now provide a biological mirror of that geomorphological story.

The mechanism the researchers propose is habitat loss. Open sand dune vegetation, the early-successional communities that colonize bare, mobile sand, depends on exactly the disturbance regime that storms once provided. But when storm-driven sand drift became too intense, it likely wiped out most of the open dune habitat rather than creating it, squeezing the specialist plants into a narrow strip along the shoreline where they remain today. A community-level contraction of habitat translates into a community-level contraction of effective population size, and that is precisely the synchronous decline the genomes record. Because the eight species are ecologically distinct, with different life histories and dispersal strategies, their shared demographic trajectory is best explained by a shared external driver rather than by any species-specific process.

The study also carries a cautionary note for conservation. Effective population size, not census count, determines a population’s capacity to maintain genetic diversity and adapt to changing conditions, and the classic 50/500 benchmarks for short- and long-term viability are measured in these terms. A historical crash that reduced effective population sizes across an entire community constrains the evolutionary options of every member species, even those that appear locally abundant today. The authors argue that this legacy of past habitat loss will limit how sand dune plants respond to future environmental change, including the ongoing transformation of European dunes by stabilization, afforestation, and development. Restoration efforts, other researchers have argued, must specifically consider species that require open and early-successional dune habitats, and the new genetic evidence underscores why: these species may already be running on reduced genetic capital accumulated through centuries of contraction.

Methodologically, the paper joins a growing movement in population genetics toward joint analysis of different mutation classes at the same loci. Earlier work on rear-edge oak populations by members of the same team showed that combining microsatellite repeat variation with flanking sequence substitutions could illuminate complex demographic histories involving gene flow and vicariance. The present study extends that approach from a single species to a whole community, and from a proof of concept to a comparative framework. The researchers also made their raw data available, depositing low-coverage whole genome sequences used for marker development in the European Nucleotide Archive, alongside extensive supplementary analyses that test how marker number, sample size, and mutation rate estimates affect the reliability of the demographic inference.

For ecologists and conservation biologists, the broader message is that genomes can serve as community-level archives. When multiple species sharing a landscape tell the same demographic story, with the same timing and the same direction of change, the case for a common environmental cause becomes compelling in a way that single-species studies cannot achieve. The dune plants of southwest France, it turns out, all recorded the same stormy centuries in their DNA. Reading that record required 3,116 genotyped individuals, hundreds of newly developed markers, and a simulation framework capable of extracting timing from a mixture of fast and slow mutations, but the payoff is a demonstration that past climate-driven habitat loss left a coherent, measurable signature across an entire plant community, one that continues to shape its capacity to face the changes still to come.

Subject of Research: Shared demographic history of coastal sand dune plant communities inferred from microsatellites and flanking sequence variation

Article Title: Joint analysis of microsatellites and flanking sequences shows shared demographic response of coastal sand dune plant communities to past environmental changes

Article References: Lepais, O., Gonzalez, M., & Benot, M.-L. (2026). Joint analysis of microsatellites and flanking sequences shows shared demographic response of coastal sand dune plant communities to past environmental changes. Heredity. https://doi.org/10.1038/s41437-026-00881-2

Image Credits: AI Generated

DOI: 10.1038/s41437-026-00881-2

Keywords: population genetics, microsatellites, flanking sequences, approximate Bayesian computation, effective population size, coastal sand dunes, Little Ice Age, habitat loss, community ecology, Aquitaine coast, plant conservation, demographic history

The chain of causality that makes such reconstructions possible begins far beyond Earth. The Sun’s magnetic field, dragged outward by the solar wind, fills the heliosphere, the vast protective bubble surrounding the solar system. Galactic cosmic rays, high-energy particles arriving from outside, must fight their way through this magnetic shielding to reach Earth. When the Sun is magnetically active, the shielding strengthens and fewer cosmic rays penetrate; when the Sun quiets, the floodgates loosen. Upon entering the atmosphere, cosmic rays collide with nitrogen, oxygen and argon, spawning showers of secondary particles that forge rare radionuclides such as carbon-14 and beryllium-10. Carbon-14, once formed, is incorporated into carbon dioxide, absorbed by trees during photosynthesis and permanently archived in annual growth rings. Because a magnetically active Sun suppresses cosmic ray influx, the abundance of these isotopes in wood and ice is inversely correlated with solar activity, giving scientists a decipherable record stretching back thousands of years.

Deciphering it, however, is harder than it sounds. Previous reconstruction efforts relied on statistical regressions that mapped isotope-derived quantities onto sunspot numbers, and those regressions carried hidden dangers. As the new study’s authors, led by Chitradeep Saha of the University of Reading, point out, even a regression with an impressively high correlation coefficient can misfire if the data contain non-linearities, zero-level offsets, or uneven variance across amplitudes. The most notorious failure mode appears during grand minima, the extended intervals when solar activity collapses, such as the Maunder minimum of the seventeenth century. When regression equations calibrated on modern data are extrapolated to these unusually quiet conditions, they can yield sunspot numbers below zero. Values are often simply clipped to zero, but that crude fix distorts cycle averages and inflates estimates of the Sun’s total irradiance, which in turn muddies attempts to understand the Sun’s influence on past climate.

The Reading-led team, which also includes Mathew Owens, Mike Lockwood and Luke Barnard, together with colleagues at ETH Zurich, the University of Lancashire, the University of Oslo, Lund University and the University of Groningen, took a different path. Rather than inverting the physics with a regression, they ran it forward, over and over, in a Monte Carlo framework. The method, implemented in publicly released code under the name PRISM, begins by generating an ensemble of thousands of hypothetical sunspot cycles drawn from statistical priors: cycle amplitudes sampled from a log-normal distribution, cycle lengths from a Gaussian distribution centred on 10.5 years, and a random start offset for each window of time. Each trial cycle is then passed through a sequence of two semi-empirical forward models that translate sunspot number into open solar flux, the total magnetic flux threading the outer boundary of the corona, and then into the heliospheric modulation potential, a quantity describing how much energy cosmic rays lose as they traverse the heliosphere.

The forward models rest on decades of established solar physics. The first couples sunspot number to the emergence of new magnetic flux through a empirically optimised source function, balanced against a phase-dependent loss rate derived from solar cycles 13 through 24. The second model computes the modulation potential from the open flux together with the tilt and polarity of the heliospheric current sheet, following a formulation calibrated by Owens and colleagues in 2024. Crucially, the open solar flux evolves with memory: it accumulates from past sunspot activity and decays through magnetic reconnection, introducing a hysteresis that makes the inverse problem fundamentally non-unique. Multiple distinct sunspot histories can produce statistically indistinguishable modulation records, which is precisely why simple deterministic inversions break down.

To handle that non-uniqueness, the team employed Approximate Bayesian Computation, a statistical technique that sidesteps the need for an explicit likelihood function. In each sliding ten-to-fifteen-year window, ten thousand Monte Carlo realisations of sunspot cycles are propagated through the forward models and compared directly against the observed modulation potential using a weighted Euclidean distance. The best two percent of candidates, some two hundred realisations, are retained as samples from the approximate posterior distribution, and their spread provides rigorous, quantified uncertainty bounds reported as 68 percent highest-density intervals. Because the sunspot cycle amplitudes are constrained to be non-negative by construction, the resulting reconstruction can never produce the negative values that haunted earlier regression-based approaches, and it requires no post-hoc correction.

To test the method, the researchers applied it to two annual-resolution records of the modulation potential. The first, spanning 1845 to 2020, was derived from geomagnetic observations of open solar flux by Owens and colleagues. When used as the inversion target, the method recovered sunspot numbers and open flux in close agreement with the direct instrumental record maintained by SILSO, with a mean absolute error of just 19.25 megavolts, about three percent of the mean modulation potential. That success validated the technique and justified applying it to the second, far longer dataset: a radiocarbon-based modulation potential record covering 971 to 1932, reconstructed by Nicolas Brehm of ETH Zurich and colleagues from tree-ring carbon-14 measurements. Before feeding the tree-ring record into the inversion, the team cross-calibrated it against the geomagnetic record over their overlapping decades, applying an additive correction of 65.76 megavolts that statistical tests showed produced a near-perfectly symmetric, homoskedastic residual distribution.

The millennial-scale reconstruction that emerged is rich with detail. It captures the familiar grand minima and maxima of the past thousand years, including the Maunder minimum, the Spörer minimum, the Dalton minimum, and the double-peaked grand maximum the Sun passed through between 1900 and 2020. Crucially, the reconstructed open solar flux never falls to zero. Even during the deepest quiet of the Spörer minimum, the unsigned open flux dropped only to about 1.21 times ten to the fourteen webers in 1443, the lowest value in the entire record and well below anything observed in the telescopic era, yet still decisively nonzero. During the Maunder minimum the flux averaged around 2.63 times ten to the fourteen webers. This confirms that the solar dynamo never fully shuts down during grand minima but instead idles in a reduced, finite activity state, a conclusion consistent with flux transport dynamo models in which meridional plasma circulation sustains weak cycles and eventually drives recovery.

The new record also documents some striking extremes at the other end of the scale. The peak annual open solar flux of the twentieth century, reached in 1958, was about 10.96 times ten to the fourteen webers, the highest since the year 1200. Only three years in the entire millennium exceeded it, the largest peaking at roughly 14.21 times ten to the fourteen webers in 981 CE, shortly after the record begins. In other words, the era of telescopic observation has sampled a large fraction, but not all, of the Sun’s true dynamic range; deeper minima and higher maxima both occurred before instruments existed to see them. The reconstruction also flags three intervals around 993, 1052 and 1279 CE where proposed Miyake events, extreme solar particle storms recorded as abrupt radiocarbon spikes, contaminate the cosmic-ray-based record, and the authors conservatively mask these windows from their results.

Beyond its intrinsic appeal as a thousand-year diary of solar magnetism, the work has immediate practical value. Annually resolved, physically consistent sunspot numbers feed directly into reconstructions of total and spectral solar irradiance, which in turn constrain climate models exploring the Sun’s role in terrestrial temperature variability over past centuries. The uncertainty-quantified cycle amplitudes during grand minima provide empirical targets for solar dynamo theorists probing the minimum operating point of the solar cycle engine. The authors note that their forward model templates were built from modern, regular solar cycles, so reconstructions of grand minimum cycles should be treated as indicative rather than definitive, and that the additive cross-calibration between the two modulation potential datasets is itself a simplification. Even so, the framework offers what the team describes as a probabilistic ensemble of physically admissible solar histories rather than a single deterministic answer, and it opens the door to pushing the same technique further back in time as longer and older cosmogenic isotope records become available. The Sun, it turns out, kept meticulous records all along; the trick was learning to read them without breaking the laws of physics.

Subject of Research: Physics-constrained reconstruction of annually resolved sunspot numbers from millennial-scale heliospheric modulation potential records.

Article Title: Physics-Constrained Reconstructions of Sunspot Number from Millennial-Scale Annual Heliospheric Modulation Potential

Article References: Saha, C., Owens, M., Lockwood, M., Barnard, L., Brehm, N., Dalla, S., Herbst, K., Muscheler, R., & Wang, J. (2026). Physics-Constrained Reconstructions of Sunspot Number from Millennial-Scale Annual Heliospheric Modulation Potential. Solar Physics, 301(9), Article 144. https://doi.org/10.1007/s11207-026-02731-0

Image Credits: AI Generated

DOI: 10.1007/s11207-026-02731-0

Keywords: sunspot number, solar cycle, heliospheric modulation potential, cosmogenic isotopes, radiocarbon, open solar flux, Maunder minimum, Spörer minimum, approximate Bayesian computation, space climate, solar dynamo, galactic cosmic rays