Submit a preprint

Latest recommendationsrsstwitter

IdTitle * Authors * Abstract * Picture * Thematic fields * RecommenderReviewersSubmission date
25 Jun 2024
article picture

Taking fear back into the Marginal Value Theorem: the risk-MVT and optimal boldness

Applying the marginal value theorem when risk affects foraging behavior

Recommended by ORCID_LOGO based on reviews by Taom Sakal and 1 anonymous reviewer

Foraging has been long been studied from an economic perspective, where the costs and benefits of foraging decisions are measured in terms of a single currency of energy which is then taken as a proxy for fitness. A mainstay foraging theory is Charnov’s Marginal Value Theorem (Charnov, 1976), or MVT, which includes a graphical interpretation and has been applied to an enormous range topics in behavioral ecology (Menezes , 2022). Empirical studies often find that animals deviate from MVT, sometimes in that they predictably stay longer than the optimal time. One explanation for this comes from state based models of behavior (Nonacs 2001)

Now Calcgano and colleagues (2024) set out to extend and unify foraging models that include various aspects of risk to the foragers, and propose using a  risk MVT, or rMVT. They consider three types of risk that foragers face, disturbance, escape, and death. Disturbance represents scenarios where the forager is either physically interrupted in their foraging, or stops foraging temporarily because of the presence of a predator (i.e. a fear response). Such a disturbance can be thought of as altering the gain function for resources acquired while foraging in the patch, allowing the rMVT to be applied in a familiar way with only a reinterpretation of the gain function.  In the escape scenarios, foragers are forced to leave a patch because of predator behavior, and therefore artificially decrease their foraging time as compared with their desired foraging time. Now, optimization can be calculated based on this expected time foraging, which means that in effect the forager compensates for the reduced time in the patch by modifying their view of how long they will actually forage.

Finally they consider scenarios where risk may result in death, and further divide this into two cases, one where foraging returns are instantaneously converted to fitness, and another where they are only converted in between foraging bouts. This represents an important case to consider, because the total number of foraging trips now depends on the rate of predator attack. In these scenarios, the boldness of the forager is decreased and they become more risk-averse.

The authors find that under the disturbance and escape scenarios, patch residence time can actually go up with risk. This is in effect because they are depleting the patch less per unit time, because a larger fraction of time is taken up with avoiding predators. In terms of field applications, this may differ from what is typically considered as risk, since harassment by conspecifics has the same disturbance effect as predator avoidance behaviors.

Most experiments on foraging are done in the absence of risk or signals of risk, i.e. in laboratory or otherwise controlled environments. The rMVT predictions deviate from non-risk scenarios in complex ways, in that the patch residence time may increase or decrease under risk. It is also important to note that foragers have evolved their foraging strategies in response to the risk profiles that they have historically experienced, and therefore experiments lacking risk may still show that foragers alter their behavior from the MVT predictions in a way that reflects historical levels of risk.


Calcagno, V.,  Grognard, F., Hamelin, F.M. and  Mailleret, L. (2024). Taking fear back into the Marginal Value Theorem: the risk-MVT and optimal boldness. bioRxiv, 2023.10.31.564970, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology.

Charnov E. (1976). Optimal foraging the marginal value theorem. Theor Popul Biol. 9, 129–136.

Menezes, JFS (2022).The marginal value theorem as a special case of the ideal free distribution. Ecological Modelling 468:109933.

Nonacs, P. 2001.  State dependent behavior and the Marginal Value Theorem. Behavioral Ecology 12(1) 71–83.

Taking fear back into the Marginal Value Theorem: the risk-MVT and optimal boldnessVincent Calcagno, Frederic Grognard, Frederic M Hamelin, Ludovic Mailleret<p>Foragers exploiting heterogeneous habitats must make strategic movement decisions in order to maximize fitness. Foraging theory has produced very general formalizations of the optimal patch-leaving decisions rational individuals should make. On...Adaptation, Behavior & Social Evolution, Evolutionary Ecology, Evolutionary Theory, Life HistoryStephen Proulx2023-11-03 13:25:16 View
31 May 2024
article picture

Cross-tolerance evolution is driven by selection of heat tolerance in Drosophila subobscura

Evolution of cross-tolerance: a mechanism to cope with climate change?

Recommended by ORCID_LOGO based on reviews by Marina Stamenkovic-Radak and 1 anonymous reviewer

Understanding how populations evolve under thermal stress and how this process shapes the response of other stress responses is an important research topic in the context of thermal adaptation and climate change. In a thermal experimental evolution study in Drosophila subobscura, Castañeda (2024) addressed the correlated responses to selection for increasing knockdown temperature in different resistance traits, either directly related to thermal stress (e.g. knockdown time at different temperatures and CTmax) or not (e.g. desiccation and starvation resistance). 

The author found that the evolution of higher knockdown temperature did in fact lead to correlated responses in other stress traits. While such correlations might be expected for the thermal stress traits measured (knockdown time and CTmax), it was perhaps less expectable for desiccation and starvation resistance. However, the general occurrence of correlated evolutionary responses between stressors has been previously described, namely in Drosophila (e.g. see Bubliy and Loeschcke 2005), pointing to a possible genetic link between distinct (thermal) stress traits. 

There are however some features that make the findings of this study rather appealing. First, the evidence that the correlated stress responses depend on the intensity of thermal selection (i.e. the warming rate) and on the sex of the organisms. Second, correlated patterns of both desiccation and starvation resistance highlight the possibility of the evolution of a cross-tolerance response, which might positively impact on population ability to evolve under sustained stressful environments (Rodgers and Gomez Izasa 2023). However, it is important to point out that the correlated patterns between these two resistance traits (desiccation and starvation) were not exactly consistent. In fact, the negative correlated response observed for female starvation resistance is thought provoking and argues again a general scenario of cross-tolerance. 

While these findings are a step forward for a more multifaceted understanding of thermal adaptation in the context of stressful environments, they also highlight the need for further studies of thermal adaptation namely 1) addressing the underlying physiological and genomic mechanisms that link male and female heat tolerance and the response to other stress resistance traits (namely starvation resistance); 2) testing the extent to which cross-resistance patterns can be generalized to different thermal selection contexts and populations. 

In addition, this study also opens new questions considering the scope of correlated evolution to other stress traits, that might be relevant in diverse ecological scenarios. For instance, does selection towards higher heat resistance lead to correlated evolution of cold resistance? And under which circumstances (e.g. different heat selection intensities)?  In fact, the occurrence of a positive (or negative) correlation cold and heat stress responses is a topic of high interest, with relevant ecological implications particularly considering the increased thermal fluctuations in natural environments because of climate warming. Cross-tolerance between cold and heat stress responses has been described (Singh 2022, Rodgers and Gomez Izasa 2023). On the other hand, negative correlations (i.e. trade-offs) between these stress traits (Stazione et al. 2020; Schou et al 2022) can impact negatively on populations’ ability to withstand thermal variability. 

As climatic changes proceed leading to increasing environmental variability, empirical studies such as that of Castañeda (2024) are critical in the pursue for a multivariate perspective on trait evolution in scenarios of climate change adaptation. Understanding how tolerance to different environmental stressors may evolve and which factors can act as drivers of that variation will ultimately enable better forecasts of climate change effects on biodiversity in nature.


Castañeda, LE. Cross-tolerance evolution is driven by selection on heat tolerance in Drosophila subobscura. Biorxiv, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology (2024).

Bubliy, OA, Loeschcke, V. Correlated responses to selection for stress resistance and longevity in a laboratory population of Drosophila melanogaster. J Evol Biol. 18(4):789-803 (2005).

Rodgers, EM, Gomez Isaza, DF. The mechanistic basis and adaptive significance of cross-tolerance: a 'pre-adaptation' to a changing world? J Exp Biol. 226(11):jeb245644 (2023).

Schou, MF, Engelbrecht, A, Brand, Z, Svensson, EI, Cloete, S, Cornwallis, CK. Evolutionary trade-offs between heat and cold tolerance limit responses to fluctuating climates. Sci Adv. 8(21):eabn9580 (2022).

Singh, K, Arun Samant, M, Prasad, NG. Evolution of cross-tolerance in Drosophila melanogaster as a result of increased resistance to cold stress. Sci Rep. 12(1):19536 (2022).

Stazione, L, Norry, FM, Gomez, FH, Sambucetti, P. Heat knockdown resistance and chill-coma recovery as correlated responses to selection on mating success at high temperature in Drosophila buzzatii. Ecol Evol. 10(4):1998-2006 (2020).

Cross-tolerance evolution is driven by selection of heat tolerance in *Drosophila subobscura*Luis E. Castañeda<p>The evolution of heat tolerance is a crucial mechanism for the adaptive response to global warming, but it depends on the genetic variance carried by populations and on the intensity of thermal stress in nature. Experimental selection studies h...Adaptation, Experimental EvolutionPedro Simões2023-10-02 14:13:02 View
24 May 2024
article picture

mtDNA "Nomenclutter" and its Consequences on the Interpretation of Genetic Data

Resolving the clutter of naming “Eve’s” descendants

Recommended by ORCID_LOGO based on reviews by Nicole Huber, Joshua Daniel Rubin and 1 anonymous reviewer

Nature is complicated and humans often resort to categorization into simplified groups in order to comprehend and manage complex systems. The human mitochondrial genome and its phylogeny are quite complex. Many of those ~16600 base pairs mutated as humans spread across the planet and the resulting phylogeny can be used to illustrate many different aspects of human history and evolution. But it has too many branches and sub-branches to comprehend, which is why major lineages are considered haplogroups. On the highest level, these haplogroups receive capital letters which are then followed by integers and lowercase letters to designate a more fine-scale structure. This nomenclature even inspired semi-fictional literature, such as Bryan Sykes’ “The Seven Daughters of Eve” [1] from 2001 which includes fictional narratives for each of seven “clan mothers” representing seven major European haplogroups (e.g. Helene representing haplogroup H and Tara representing haplogroup T). But apart from categorizing things, humans also like to make exceptions to rules. For instance, not all haplogroup names consist only of letters and numbers but also special characters. And not everything seems logical or intuitive: the deepest split does not include haplogroup A but the most basal lineage is L0. The main letters also do not represent the same level of the tree structure, Sykes’ Katrine representing haplogroup K should not be considered a “daughter of Eve” but (at best) a granddaughter as K is a sub-haplogroup of U (represented by Ursula). This system and the number of haplogroups have not just reached a point where everything has become incredibly complicated despite supposedly simplifying categories. The inherent arbitrariness can also have serious effects on downstream analysis and the interpretation of results depending on how and on what level the authors of a specific study decide to group their individuals. 

This situation of potential biases introduced through the choice of haplogroup groupings is the motivation for the study by Bajić, Schulmann and Nowick who are using the quite fitting term “nomenclutter” in their title [2]. They are raising an important issue in the inconsistencies introduced by the practice of somewhat arbitrary haplotype groupings which varies across studies and has no common standards in place making comparisons between studies virtually impossible. The study shows that the outcome of certain standard analyses and the interpretation of results are very sensitive to the decision on how to group the different haplotypes. This effect is especially pronounced for populations of African ancestry where the haplotype nomenclature would cut the phylogenetic tree at higher levels and the definition of different lineages is generally more coarse than for other populations.

But the authors go beyond pointing out this issue, they also suggest solutions. Instead of grouping sequences by their haplogroup code, one could use “algorithm-based groupings” based on the sequence similarity itself or cutting the phylogenetic tree at a common level of the hierarchy. The analysis of the authors shows that this reduces potential biases substantially. But even such groupings would not be without the influence of the user or researcher’s choices as different parameters have to be set to define the level at which groupings are conducted. The authors propose a neat solution, lifting this issue to be resolved during future updates of the mitochondrial haplogroup nomenclature and the phylogeny. Ideally, the research community could agree on centrally defined haplogroup grouping levels (called “macro-”, “meso-”, and “micro-haplogroups” by the authors) which would all represent different scales of events in human history (from global, continental to local). Classifications like that could be provided through central databases and the classifications could be added to commonly used tools for that purpose. If everyone used these groupings, studies would be a lot more comparable and more fine-scale investigations could still resort to the sequences and the tree itself to avoid all grouping.

The experts who reviewed the study have all highlighted its importance of pointing at a very relevant issue. It will take a community effort to improve practices and the current status of this research area. This study provides an important first step and it should be in everyone’s interest to resolve the “nomenclutter”.


1. Sykes B. (2001) The seven daughters of Eve: the science that reveals our genetic ancestry. 1st American ed. New York: Norton.

2. Bajić V, Schulmann VH, Nowick K. (2024) mtDNA “Nomenclutter” and its Consequences on the Interpretation of Genetic Data. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology.

mtDNA "Nomenclutter" and its Consequences on the Interpretation of Genetic DataVladimir Bajić, Vanessa Hava Schulmann, Katja Nowick<p style="text-align: justify;">Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into h...Bioinformatics & Computational Biology, Human Evolution, Other, Phylogenetics / Phylogenomics, Phylogeography & Biogeography, Population Genetics / GenomicsTorsten Günther2023-11-20 11:16:36 View
05 Apr 2024
article picture

Does the seed fall far from the tree? Weak fine scale genetic structure in a continuous Scots pine population

Weak spatial genetic structure in a large continuous Scots pine population – implications for conservation and breeding

Recommended by ORCID_LOGO based on reviews by Joachim Mergeay, Jean-Baptiste Ledoux and Roberta Loh

Spatial genetic structure, i.e. the non-random spatial distribution of genotypes, arises in populations because of different processes including spatially limited dispersal and selection. Knowledge on the spatial genetic structure of plant populations is important to assess biological parameters such as gene dispersal distances and the potential for local adaptations, as well as for applications in conservation management and breeding. In their work, Niskanen and colleagues demonstrate a multifaceted approach to characterise the spatial genetic structure in two replicate sites of a continuously distributed Scots pine population in South-Eastern Finland. They mapped and assessed the ages of 469 naturally regenerated adults and genotyped them using a SNP array which resulted in 157 325 filtered polymorphic SNPs. Their dataset is remarkably powerful because of the large numbers of both individuals and SNPs genotyped. This made it possible to characterise precisely the decay of genetic relatedness between individuals with spatial distance despite the extensive dispersal capacity of Scots pine through pollen, and ensuing expectations of an almost panmictic population.

The authors’ data analysis was particularly thorough. They demonstrated that two metrics of pairwise relatedness, the genomic relationship matrix (GRM, Yang et al. 2011) and the kinship coefficient (Loiselle et al. 1995) were strongly correlated and produced very similar inference of family relationships: >99% of pairs of individuals were unrelated, and the remainder exhibited 2nd (e.g., half-siblings) to 4th degree relatedness. Pairwise relatedness decayed with spatial distance which resulted in extremely weak but statistically significant spatial genetic structure in both sites, quantified as Sp=0.0005 and Sp=0.0008. These estimates are at least an order of magnitude lower than estimates in the literature obtained in more fragmented populations of the same species or in other conifers. Estimates of the neighbourhood size, the effective number of potentially mating individuals belonging to a within-population neighbourhood (Wright 1946), were relatively large with Nb=1680-3210 despite relatively short gene dispersal distances, σg = 36.5–71.3m, which illustrates the high effective density of the population. 

The authors showed the implications of their findings for selection. The capacity for local adaptation depends on dispersal distances and the strength of the selection coefficient. In the study population, the authors inferred that local adaptation can only occur if environmental heterogeneity occurs over a distance larger than approximately one kilometre (or larger, if considering long-distance dispersal). Interestingly, in Scots pine, no local adaptation has been described on similar geographic scales, in contrast to some other European or Mediterranean conifers (Scotti et al. 2023).

The authors’ results are relevant for the management of conservation and breeding. They showed that related individuals occurred within sites only and that they shared a higher number of rare alleles than unrelated ones. Since rare alleles are enriched in new and recessive deleterious variants, selecting related individuals could have negative consequences in breeding programmes. The authors also showed, in their response to reviewers, that their powerful dataset was not suitable to obtain a robust estimate of effective population size, Ne, based on the linkage disequilibrium method (Do et al. 2014). This illustrated that the estimation of Ne used for genetic indicators supported in international conservation policy (Hoban et al. 2020, CBD 2022) remains challenging in large and continuous populations (see also Santo-del-Blanco et al. 2023, Gargiulo et al. 2024).


CBD (2022) Kunming-Montreal Global Biodiversity Framework.

Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014). NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data. Molecular Ecology Resources 14: 209–214.

Gargiulo R, Decroocq V, González-Martínez SC, Paz-Vinas I, Aury JM, Kupin IL, Plomion C, Schmitt S, Scotti I, Heuertz M (2024) Estimation of contemporary effective population size in plant populations: limitations of genomic datasets. Evolutionary Applications, in press,

Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA, Paz-Vinas I, et al. (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biological Conservation 248: 108654.

Loiselle BA, Sork VL, Nason J & Graham C (1995) Spatial genetic structure of a tropical understorey shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 82: 1420–1425.

Santos-del-Blanco L, Olsson S, Budde KB, Grivet D, González-Martínez SC, Alía R, Robledo-Arnuncio JJ (2022). On the feasibility of estimating contemporary effective population size (Ne) for genetic conservation and monitoring of forest trees. Biological Conservation 273: 109704.

Scotti I, Lalagüe H, Oddou-Muratorio S, Scotti-Saintagne C, Ruiz Daniels R, Grivet D, et al. (2023) Common microgeographical selection patterns revealed in four European conifers. Molecular Ecology 32: 393-411.

Wright S (1946) Isolation by distance under diverse systems of mating. Genetics 31: 39–59.

Yang J, Lee SH, Goddard ME & Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics 88: 76–82.

Does the seed fall far from the tree? Weak fine scale genetic structure in a continuous Scots pine populationAlina K. Niskanen, Sonja T. Kujala, Katri Kärkkäinen, Outi Savolainen, Tanja Pyhäjärvi<p>Knowledge of fine-scale spatial genetic structure, i.e., the distribution of genetic diversity at short distances, is important in evolutionary research and in practical applications such as conservation and breeding programs. In trees, related...Adaptation, Evolutionary Applications, Population Genetics / GenomicsMyriam Heuertz Joachim Mergeay2023-06-27 21:57:28 View
28 Mar 2024
article picture

Gene expression is the main driver of purifying selection in large penguin populations

Purifying selection on highly expressed genes in Penguins

Recommended by based on reviews by Tanja Pyhäjärvi and 1 anonymous reviewer

Given the general importance of protein expression levels, in cells it is widely accepted that gene expression levels are often a target of natural selection and that most mutations affecting gene expression levels are therefore likely to be deleterious [1]. However, it is perhaps less obvious that the strength of selection on the regulated genes themselves may be influenced by their expression levels. This might be due to harmful effects of misfolded proteins, for example, when higher protein concentrations exist in cells [2]. Recent studies have suggested that highly expressed genes accumulate fewer deleterious mutations; thus a positive relationship appears to exist between gene expression levels and the relative strength of purifying selection [3].

The recommended paper by Trucchi et al. [4] examines the relationship between gene expression, purifying selection and a third variable -- effective population size -- in populations of two species of penguin with different population sizes, the Emperor penguin (Aptenodytes forsteri) and the King penguin (A. patagonicus). Using transcriptomic data and computer simulations modeling selection, they examine patterns of nonsynonymous and synonymous segregating polymorphisms (p) across genes in the two populations, concluding that even in relatively small populations purifying selection has an important effect in eliminating deleterious mutations. 


1] Gilad Y, Oshlack A, and Rifkin SA. 2006. Natural selection on gene expression. Trends in Genetics 22: 456-461.
[2] Yang JR, Liao BY, Zhuang SM, and Zhang J. 2012. Protein misinteraction avoidance causes highly expressed proteins to evolve slowly. Proceedings of the National Academy of Sciences 109: E831-E840.
[3] Duret L, and Mouchiroud D (2000). Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Molecular Biology and Evolution 17; 68-070.

[4] Trucchi E, Massa P, Giannelli F, Latrille T, Fernandes FAN, Ancona L, Stenseth NC, Obiol JF, Paris J, Bertorelle G, and Le Bohec, C. 2023. Gene expression is the main driver of purifying selection in large penguin populations. bioRxiv 2023.08.08.552445, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology.


Gene expression is the main driver of purifying selection in large penguin populationsEmiliano Trucchi, Piergiorgio Massa, Francesco Giannelli, Thibault Latrille, Flavia A.N. Fernandes, Lorena Ancona, Nils Chr Stenseth, Joan Ferrer Obiol, Josephine Paris, Giorgio Bertorelle, Celine Le Bohec<p style="text-align: justify;">Purifying selection is the most pervasive type of selection, as it constantly removes deleterious mutations arising in populations, directly scaling with population size. Highly expressed genes appear to accumulate ...Bioinformatics & Computational Biology, Evolutionary Dynamics, Evolutionary Theory, Population Genetics / GenomicsBruce Rannala2023-08-09 17:53:03 View
04 Mar 2024
article picture

Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.).

Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.)

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Starting with the seminar paper by Lande & Arnold (1983), several studies have addressed phenotypic selection in natural populations of a wide variety of organisms, with a recent renewed interest in forest trees (e.g., Oddou-Muratorio et al. 2018; Alexandre et al. 2020; Westergren et al. 2023). Because of their long generation times, long-lived organisms such as forest trees may suffer the most from maladaptation due to climate change, and whether they will be able to adapt to new environmental conditions in just one or a few generations is hotly debated.

In this study, Oddou-Muratorio and colleagues (2024) extend the current framework to add two additional selection components that may alter patterns of fecundity selection and the estimation of standard selection gradients, namely sexual selection (evaluated as differences in flowering phenology conducting to assortative mating) and growth (viability) selection. Notably, the study is conducted in two contrasted environments (low vs high altitude populations) providing information on how the environment may modulate selection patterns in spring phenology. Spring phenology is a key adaptive trait that has been shown to be already affected by climate change in forest trees (Alberto et al. 2013). While fecundity selection for early phenology has been extensively reported before (see Munguía-Rosas et al. 2011), the authors found that this kind of selection can be strongly modulated by sexual selection, depending on the environment. Moreover, they found a significant correlation between early phenology and seedling growth in a common garden, highlighting the importance of this trait for early survival in European beech.

As a conclusion, this original research puts in evidence the need for more integrative approaches for the study of natural selection in the field, as well as the importance of testing multiple environments and the relevance of common gardens to further evaluate phenotypic changes due to real-time selection.

PS: The recommender and the first author of the preprint have shared authorship in a recent paper in a similar topic (Westergren et al. 2023). Nevertheless, the recommender has not contributed in any way or was aware of the content of the current preprint before acting as recommender, and steps have been taken for a fair and unpartial evaluation.


Alberto, F. J., Aitken, S. N., Alía, R., González‐Martínez, S. C., Hänninen, H., Kremer, A., Lefèvre, F., Lenormand, T., Yeaman, S., Whetten, R., & Savolainen, O. (2013). Potential for evolutionary responses to climate change - evidence from tree populations. Global Change Biology, 19(6), 1645‑1661.
Alexandre, H., Truffaut, L., Klein, E., Ducousso, A., Chancerel, E., Lesur, I., Dencausse, B., Louvet, J., Nepveu, G., Torres‐Ruiz, J. M., Lagane, F., Musch, B., Delzon, S., & Kremer, A. (2020). How does contemporary selection shape oak phenotypes? Evolutionary Applications, 13(10), 2772‑2790.
Lande, R., & Arnold, S. J. (1983). The measurement of selection on correlated characters. Evolution, 37(6), 1210-1226.
Munguía-Rosas, M. A., Ollerton, J., Parra-Tabla, V., & De-Nova, J. A. (2011). Meta-analysis of phenotypic selection on flowering phenology suggests that early flowering plants are favoured. Ecology Letters, 14(5), 511-521

Oddou-Muratorio S, Bontemps A, Gauzere J, Klein E (2024) Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.). bioRxiv, 2023.04.27.538521, ver. 2 peer-reviewed and recommended by Peer Community In Evolutionary Biology 

Oddou-Muratorio, S., Gauzere, J., Bontemps, A., Rey, J.-F., & Klein, E. K. (2018). Tree, sex and size: Ecological determinants of male vs. female fecundity in three Fagus sylvatica stands. Molecular Ecology, 27(15), 3131‑3145.
Westergren, M., Archambeau, J., Bajc, M., Damjanić, R., Theraroz, A., Kraigher, H., Oddou‐Muratorio, S., & González‐Martínez, S.C. (2023). Low but significant evolutionary potential for growth, phenology and reproduction traits in European beech. Molecular Ecology, Early View

Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (*Fagus sylvatica* L.).Sylvie Oddou-Muratorio, Aurore Bontemps, Julie Gauzere, Etienne Klein<p>Background: Plant phenological traits such as the timing of budburst or flowering can evolve on ecological timescales through response to fecundity and viability selection. However, interference with sexual selection may arise from assortative ...Adaptation, Evolutionary Ecology, Quantitative Genetics, Reproduction and Sex, Sexual SelectionSantiago C. Gonzalez-Martinez2023-05-02 11:57:23 View
04 Mar 2024
article picture

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

Beyond the standard coalescent: demographic inference with complete genomes and graph neural networks under the beta coalescent

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Modelling the evolution of complete genome sequences in populations requires accounting for the recombination process, as a single tree can no longer describe the underlying genealogy. The sequentially Markov coalescent (SMC, McVean and Cardin 2005; Marjoram and Wall 2006) approximates the standard coalescent with recombination process and permits estimating population genetic parameters (e.g., population sizes, recombination rates) using population genomic datasets. As such datasets become available for an increasing number of species, more fine-tuned models are needed to encompass the diversity of life cycles of organisms beyond the model species on which most methods have been benchmarked.

The work by Korfmann et al. (Korfmann et al. 2024) represents a significant step forward as it accounts for multiple mergers in SMC models. Multiple merger models account for simultaneous coalescence events so that more than two lineages find a common ancestor in a given generation. This feature is not allowed in standard coalescent models and may result from selection or skewed offspring distributions, conditions likely met by a broad range of species, particularly microbial.

Yet, this work goes beyond extending the SMC, as it introduces several methodological innovations. The "classical" SMC-based inference approaches rely on hidden Markov models to compute the likelihood of the data while efficiently integrating over the possible ancestral recombination graphs (ARG). Following other recent works (e.g. Gattepaille et al. 2016), Korfmann et al. propose to separate the ARG inference from model parameter estimation under maximum likelihood (ML). They introduce a procedure where the ARG is first reconstructed from the data and then taken as input in the model fitting step. While this approach does not permit accounting for the uncertainty in the ARG reconstruction (which is typically large), it potentially allows for the extraction of more information from the ARG, such as the occurrence of multiple merging events. Going away from maximum likelihood inference, the authors trained a graph neural network (GNN) on simulated ARGs, introducing a new, flexible way to estimate population genomic parameters.

The authors used simulations under a beta-coalescent model with diverse demographic scenarios and showed that the ML and GNN approaches introduced can reliably recover the simulated parameter values. They further show that when the true ARG is given as input, the GNN outperforms the ML approach, demonstrating its promising power as ARG reconstruction methods improve. In particular, they showed that trained GNNs can disentangle the effects of selective sweeps and skewed offspring distributions while inferring past population size changes.

This work paves the way for new, exciting applications, though many questions must be answered. How frequent are multiple mergers? As the authors showed that these events "erase" the record of past demographic events, how many genomes are needed to conduct reliable inference, and can the methods computationally cope with the resulting (potentially large) amounts of required data? This is particularly intriguing as micro-organisms, prone to strong selection and skewed offspring distributions, also tend to carry smaller genomes.


Gattepaille L, Günther T, Jakobsson M. 2016. Inferring Past Effective Population Size from Distributions of Coalescent Times. Genetics 204:1191-1206.
Korfmann K, Sellinger T, Freund F, Fumagalli M, Tellier A. 2024. Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent. bioRxiv, 2022.09.28.508873. ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology.
Marjoram P, Wall JD. 2006. Fast "coalescent" simulation. BMC Genet. 7:16.
McVean GAT, Cardin NJ. 2005. Approximating the coalescent with recombination. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360:1387-1393.

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta CoalescentKevin Korfmann, Thibaut Sellinger, Fabian Freund, Matteo Fumagalli, Aurélien Tellier<p style="text-align: justify;">The reproductive mechanism of a species is a key driver of genome evolution. The standard Wright-Fisher model for the reproduction of individuals in a population assumes that each individual produces a number of off...Adaptation, Bioinformatics & Computational Biology, Evolutionary Applications, Evolutionary Theory, Life History, Population Genetics / GenomicsJulien Yann Dutheil2023-07-31 13:11:22 View
01 Mar 2024
article picture

Rapid life-history evolution reinforces competitive asymmetry between invasive and resident species

The evolution of a hobo snail

Recommended by based on reviews by David Reznick and 2 anonymous reviewers

At the very end of a paper entitled "Copepodology for the ornithologist" Hutchinson (1951) pointed out the possibility of 'fugitive species'.  A fugitive species, said Hutchinson, is one that we would typically think of as competitively inferior.  Wherever it happens to live it will eventually be overwhelmed by competition from another species.  We would expect it to rapidly go extinct but for one reason: it happens to be a much better coloniser than the other species.  Now all we need to explain its persistence is a dose of space and a little disturbance: a world in which there are occasional disturbances that cause local extinction of the dominant species. Now, argued Hutchinson, we have a recipe for persistence, albeit of a harried kind.  As Hutchinson put it, fugitive species "are forever on the move, always becoming extinct in one locality as they succumb to competition, and always surviving as they reestablish themselves in some other locality."

It is a fascinating idea, not just because it points to an interesting strategy, but also because it enriches our idea of competition: competition for space can be just as important as competition for time.

Hutchinson's idea was independently discovered with the advent of metapopulation theory (Levins 1971; Slatkin 1974) and since then, of course, ecologists have gone looking, and they have unearthed many examples of species that could be said to have a fugitive lifestyle.  These fugitive species are out there, but we don't often get to see them evolve.  

In their recent paper, Chapuis et al. (2024) make a convincing case that they have seen the evolution of a fugitive species.  They catalog the arrival of an invasive freshwater snail on Guadeloupe in the Lesser Antilles, and they wonder what impact this snail's arrival might have on a native freshwater snail.  This is a snail invasion, so it has been proceeding at a majestic pace, allowing the researchers to compare populations of the native snail that are completely naive to the invader with those that have been exposed to the invader for either a relatively short period (<20 generations) or longer periods (>20 generations).  They undertook an extensive set of competition assays on these snails to find out which species were competitively superior and how the native species' competitive ability has evolved over time.

Against naive populations of the native, the invasive snail turns out to be unequivocally the stronger competitor.  (This makes sense; it probably wouldn't have been able to invade if it wasn't.)  So what about populations of the native snail that have been exposed for longer, that have had time to adapt?  Surprisingly these populations appear to have evolved to become even weaker competitors than they already were. 

So why is it that the native species has not simply been driven extinct? Drawing on their previous work on this system, the authors can explain this situation.  The native species appears to be the better coloniser of new habitats.  Thus, it appears that the arrival of the invasive species has pushed the native species into a different place along the competition-colonisation axis.  It has sacrificed competitive ability in favour of becoming a better coloniser; it has become a fugitive species in its own backyard.

This is a really nice empirical study.  It is a large lab study, but one that makes careful sampling around a dynamic field situation.  Thus, it is a lab study that informs an earlier body of fieldwork and so reveals a fascinating story about what is happening in the field. We are left not only with a particularly compelling example of character displacement towards a colonising phenotype but also with something a little less scientific: the image of a hobo snail, forever on the run, surviving in the spaces in between.


Chapuis E, Jarne P, David P (2024) Rapid life-history evolution reinforces competitive asymmetry between invasive and resident species. bioRxiv, 2023.10.25.563987, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology.

Hutchinson, G.E. (1951) Copepodology for the Ornithologist. Ecology 32: 571–77.

Levins, R., and D. Culver. (1971) Regional Coexistence of Species and Competition between Rare Species. Proceedings of the National Academy of Sciences 68, no. 6: 1246–48.

Slatkin, Montgomery. (1974) Competition and Regional Coexistence. Ecology 55, no. 1: 128–34.

Rapid life-history evolution reinforces competitive asymmetry between invasive and resident speciesElodie Chapuis, Philippe Jarne, Patrice David<p style="text-align: justify;">Biological invasions by phylogenetically and ecologically similar competitors pose an evolutionary challenge to native species. Cases of character displacement following invasions suggest that they can respond to th...Evolutionary Ecology, Life History, Species interactionsBen Phillips2023-10-26 15:49:33 View
23 Feb 2024
article picture

Exploring the effects of ecological parameters on the spatial structure of genetic tree sequences

Disentangling the impact of mating and competition on dispersal patterns

Recommended by based on reviews by Anthony Wilder Wohns, Christian Huber and 2 anonymous reviewers

Spatial population genetics is a field that studies how different evolutionary processes shape geographical patterns of genetic variation. This field is currently hampered by the lack of a deep understanding of the impact of different evolutionary processes shaping the genetic diversity observed across a continuous space (Bradburd and Ralph 2019). Luckily, the recent development of slendr (Petr et al. 2023), which uses the simulator SLiM (Haller and Messer 2023), provides a powerful tool to perform simulations to analyze the impact of different evolutionary parameters on spatial patterns of genetic variation. Here, Ianni-Ravn, Petr, and Racimo 2023 present a series of well-designed simulations to study how three evolutionary factors (dispersal distance, competition distance, and mate choice distance) shape the geographical structure of genealogies.

The authors model the dispersal distance between parents and their offspring using five different distributions. Then, the authors perform simulations and they contrast the correspondence between the distribution of observed parent-offspring distances (called DD in the paper) and the distribution used in the simulations (called DF). The authors observe a reasonable correspondence between DF and DD. The authors then show that the competition distance, which decreases the fitness of individuals due to competition for resources if the individuals are close to each other, has small effects on the differences between DD and DF. In contrast, the mate choice distance (which specifies how far away can a parent go to choose a mate) causes discrepancies between DD and DF. When the mate choice distance is small, the individuals tend to cluster close to each other. Overall, these results show that the observed distances between parents and offspring are dependent on the three parameters inspected (dispersal distance, competition distance, and mate choice distance) and make the case that further ecological knowledge of each of these parameters is important to determine the processes driving the dispersal of individuals across geographical space. Based on these results, the authors argue that an “effective dispersal distance” parameter, which takes into account the impact of mate choice distance and dispersal distance, is more prone to be inferred from genetic data.

The authors also assess our ability to estimate the dispersal distance using genealogical data in a scenario where the mating distance has small effects on the dispersal distance. Interestingly, the authors show that accurate estimates of the dispersal distance can be obtained when using information from all the parents and offspring going from the present back to the coalescence of all the individuals to the most recent common ancestor. On the other hand, the estimates of the dispersal distance are underestimated when less information from the parent-offspring relationships is used to estimate the dispersal distance.

This paper shows the importance of considering mating patterns and the competition for resources when analyzing the dispersal of individuals. The analysis performed by the authors backs up this claim with carefully designed simulations. I recommend this preprint because it makes a strong case for the consideration of ecological factors when analyzing the structure of genealogies and the dispersal of individuals. Hopefully more studies in the future will continue to use simulations and to develop analytical theory to understand the importance of various ecological processes driving spatial genetic variation changes.


Bradburd, Gideon S., and Peter L. Ralph. 2019. “Spatial Population Genetics: It’s About Time.” Annual Review of Ecology, Evolution, and Systematics 50 (1): 427–49.

Haller, Benjamin C., and Philipp W. Messer. 2023. “SLiM 4: Multispecies Eco-Evolutionary Modeling.” The American Naturalist 201 (5): E127–39.

Ianni-Ravn, Mariadaria K., Martin Petr, and Fernando Racimo. 2023. “Exploring the Effects of Ecological Parameters on the Spatial Structure of Genealogies.” bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology.

Petr, Martin, Benjamin C. Haller, Peter L. Ralph, and Fernando Racimo. 2023. “Slendr: A Framework for Spatio-Temporal Population Genomic Simulations on Geographic Landscapes.” Peer Community Journal 3 (e121).

Exploring the effects of ecological parameters on the spatial structure of genetic tree sequencesMariadaria K. Ianni-Ravn, Martin Petr, Fernando Racimo<p>Geographic space is a fundamental dimension of evolutionary change, determining how individuals disperse and interact with each other. Consequently, space has an important influence on the structure of genealogies and the distribution of geneti...Phylogeography & Biogeography, Population Genetics / GenomicsDiego Ortega-Del Vecchyo2023-03-31 18:21:02 View
14 Feb 2024
article picture

Distinct patterns of genetic variation at low-recombining genomic regions represent haplotype structure

Discerning the causes of local deviations in genetic variation: the effect of low-recombination regions

Recommended by ORCID_LOGO based on reviews by Claire Merot and 1 anonymous reviewer

In this study, Ishigohoka and colleagues tackle an important, yet often overlooked, question on the causes of genetic variation. While genome-wide patterns represent population structure, local variation is often associated with selection. Authors propose that an alternative cause for variation in individual loci is reduced recombination rate.

To test this hypothesis, authors perform local Principal Component Analysis (PCA) (Li & Ralph, 2019) to identify local deviations in population structure in the Eurasian blackcap (Sylvia atricapilla) (Ishigohoka et al. 2022). This approach is typically used to detect chromosomal rearrangements or any long region of linked loci (e.g., due to reduced recombination or selection) (Mérot et al. 2021). While other studies investigated the effect of low recombination on genetic variation (Booker et al. 2020), here authors provide a comprehensive analysis of the effect of recombination to local PCA patterns both in empirical and simulated data sets. Findings demonstrate that low recombination (and not selection) can be the sole explanatory variable for outlier windows. The study also describes patterns of genetic variation along the genome of Eurasian blackcaps, localising at least two polymorphic inversions (Ishigohoka et al. 2022).

Further investigations on the effect of model parameters (e.g., window sizes and thresholds for defining low-recombining regions), as well as the use of powerful neutrality tests are in need to clearly assess whether outlier regions experience selection and reduced recombination, and to what extent.


Booker, T. R., Yeaman, S., & Whitlock, M. C. (2020). Variation in recombination rate affects detection of outliers in genome scans under neutrality. Molecular Ecology, 29 (22), 4274–4279.

Ishigohoka, J., Bascón-Cardozo, K., Bours, A., Fuß, J., Rhie, A., Mountcastle, J., Haase, B., Chow, W., Collins, J., Howe, K., Uliano-Silva, M., Fedrigo, O., Jarvis, E. D., Pérez-Tris, J., Illera, J. C., Liedvogel, M. (2022) Distinct patterns of genetic variation at low-recombining genomic regions represent haplotype structure. bioRxiv 2021.12.22.473882, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology.

Li, H., & Ralph, P. (2019). Local PCA Shows How the Effect of Population Structure Differs Along the Genome. Genetics, 211 (1), 289–304.

Mérot, C., Berdan, E. L., Cayuela, H., Djambazian, H., Ferchaud, A.-L., Laporte, M., Normandeau, E., Ragoussis, J., Wellenreuther, M., & Bernatchez, L. (2021). Locally Adaptive Inversions Modulate Genetic Variation at Different Geographic Scales in a Seaweed Fly. Molecular Biology and Evolution, 38 (9), 3953–3971.

Distinct patterns of genetic variation at low-recombining genomic regions represent haplotype structureJun Ishigohoka, Karen Bascón-Cardozo, Andrea Bours, Janina Fuß, Arang Rhie, Jacquelyn Mountcastle, Bettina Haase, William Chow, Joanna Collins, Kerstin Howe, Marcela Uliano-Silva, Olivier Fedrigo, Erich D. Jarvis, Javier Pérez-Tris, Juan Carlos Il...<p>Genetic variation of the entire genome represents population structure, yet individual loci can show distinct patterns. Such deviations identified through genome scans have often been attributed to effects of selection instead of randomness. Th...Genome Evolution, Molecular Evolution, Population Genetics / GenomicsMatteo Fumagalli2023-10-13 11:58:47 View