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.

References

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.
https://doi.org/10.1111/gcb.12181
 
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.
https://doi.org/10.1111/eva.13082
 
Lande, R., & Arnold, S. J. (1983). The measurement of selection on correlated characters. Evolution, 37(6), 1210-1226.
https://doi.org/10.2307/2408842
 
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
https://doi.org/10.1111/j.1461-0248.2011.01601.x

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 https://doi.org/10.1101/2023.04.27.538521 

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.
https://doi.org/10.1111/mec.14770
 
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 
https://doi.org/10.1111/mec.17196

The reliability of published scientific papers has been the topic of much recent discussion, notably in the biomedical sciences [1]. Although small sample size is regularly pointed as one of the culprits, big data can also be a concern. The advent of high-throughput sequencing, and the processing of sequence data by opaque bioinformatics workflows, mean that sequences with often high error rates are produced, and that exact but slow analyses are not feasible.
The troubles with bioinformatics arise from the increased complexity of the tools used by scientists, and from the lack of incentives and/or skills from authors (but also reviewers and editors) to make sure of the quality of those tools. As a much discussed example, a bug in the widely used PLINK software [2] has been pointed as the explanation [3] for incorrect inference of selection for increased height in European Human populations [4].
High-throughput sequencing often generates high rates of genotyping errors, so that the development of bioinformatics tools to assess the quality of data and correct them is a major issue. The work of Gillingham et al. [5] contributes to the latter goal. In this work, the authors propose a new bioinformatics workflow (ACACIA) for performing genotyping analysis of multigene complexes, such as self-incompatibility genes in plants, major histocompatibility genes (MHC) in vertebrates, and homeobox genes in animals, which are particularly challenging to genotype in non-model organisms. PCR and sequencing of multigene families generate artefacts, hence spurious alleles. A key to Gillingham et al.‘ s method is to call candidate genes based on Oligotyping, a software pipeline originally conceived for identifying variants from microbiome 16S rRNA amplicons [6]. This allows to reduce the number of false positives and the number of dropout alleles, compared to previous workflows.
This method is not based on an explicit probability model, and thus it is not conceived to provide a control of the rate of errors as, say, a valid confidence interval should (a confidence interval with coverage c for a parameter should contain the parameter with probability c, so the error rate 1- c is known and controlled by the user who selects the value of c). However, the authors suggest a method to adapt the settings of ACACIA to each application.
To compare and validate the new workflow, the authors have constructed new sets of genotypes representing different extents copy number variation, using already known genotypes from chicken MHC. In such conditions, it was possible to assess how many alleles are not detected and what is the rate of false positives. Gillingham et al. additionally investigated the effect of using non-optimal primers. They found better performance of ACACIA compared to a preexisting pipeline, AmpliSAS [7], for optimal settings of both methods. However, they do not claim that ACACIA will always be better than AmpliSAS. Rather, they warn against the common practice of using the default settings of the latter pipeline. Altogether, this work and the ACACIA workflow should allow for better ascertainment of genotypes from multigene families.

References

[1] Ioannidis, J. P. A, Greenland, S., Hlatky, M. A., Khoury, M. J., Macleod, M. R., Moher, D., Schulz, K. F. and Tibshirani, R. (2014) Increasing value and reducing waste in research design, conduct, and analysis. The Lancet, 383, 166-175. doi: 10.1016/S0140-6736(13)62227-8
[2] Chang, C. C., Chow, C. C., Tellier, L. C. A. M., Vattikuti, S., Purcell, S. M. and Lee, J. J. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7, s13742-015-0047-8. doi: 10.1186/s13742-015-0047-8
[3] Robinson, M. R. and Visscher, P. (2018) Corrected sibling GWAS data release from Robinson et al. http://cnsgenomics.com/data.html
[4] Field, Y., Boyle, E. A., Telis, N., Gao, Z., Gaulton, K. J., Golan, D., Yengo, L., Rocheleau, G., Froguel, P., McCarthy, M.I . and Pritchard J. K. (2016) Detection of human adaptation during the past 2000 years. Science, 354(6313), 760-764. doi: 10.1126/science.aag0776
[5] Gillingham, M. A. F., Montero, B. K., Wihelm, K., Grudzus, K., Sommer, S. and Santos P. S. C. (2020) A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system. bioRxiv 638288, ver. 3 peer-reviewed and recommended by Peer Community In Evolutionary Biology. doi: 10.1101/638288
[6] Eren, A. M., Maignien, L., Sul, W. J., Murphy, L. G., Grim, S. L., Morrison, H. G., and Sogin, M.L. (2013) Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods in Ecology and Evolution 4(12), 1111-1119. doi: 10.1111/2041-210X.12114
[7] Sebastian, A., Herdegen, M., Migalska, M. and Radwan, J. (2016) AMPLISAS: a web server for multilocus genotyping using next‐generation amplicon sequencing data. Mol Ecol Resour, 16, 498-510. doi: 10.1111/1755-0998.12453

The ability of a population to adapt to a new niche is an important phenomenon in evolutionary biology. The colonisation of a new volcanic island by plant species; the colonisation of a host treated by antibiotics by a-resistant strain; the Ebola virus transmitting from bats to humans and spreading epidemically in Western Africa, are all examples of a population invading a new niche, adapting and eventually establishing in this new environment.

Adaptation to a new niche can be studied using source-sink models. In the original environment —the “source”—, the population enjoys a positive growth-rate and is self-sustaining, while in the new environment —the “sink”— the population has a negative growth rate and is able to sustain only by the continuous influx of migrants from the source. Understanding the dynamics of adaptation to the sink environment is challenging from a theoretical standpoint, because it requires modelling the demography of the sink as well as the transient dynamics of adaptation. Moreover, local selection in the sink and immigration from the source create distributions of genotypes that complicate the use of many common mathematical approaches.

In their paper, Lavigne et al. [1], develop a new deterministic model of adaptation to a harsh sink environment in an asexual species. The fitness of an individual is maximal when a number of phenotypes are tuned to an optimal value, and declines monotonously as phenotypes are further away from this optimum. This model —called Fisher’s Geometric Model— generates a GxE interaction for fitness because the phenotypic optimum in the sink environment is distinct from that in the source environment [2]. The authors circumvent mathematical difficulties by developing an original approach based on tracking the deterministic dynamics of the cumulant generating function of the fitness distribution in the sink. They derive a number of important results on the dynamics of adaptation to the sink:

• From the point where immigration from the source to the sink starts, four phases of adaptation are observed. After a short transient phase (phase 1), a migration-selection balance is reached in the sink (phase 2). After a while, thanks to the immigration of rare adapted migrants and mutation in the sink, a small fraction of the sink population exhibits a close-to-optimal phenotype. This small adapted fraction grows in frequency and mean fitness rapidly increases in the sink (phase 3). Finally, the population settles around the sink optimum (phase 4) and, hurray, the sink is now a source!

• Interestingly, in this model the evolutionary dynamics do not depend on the immigration rate. In other words, adaptation will proceed at the same rate regardless of how many immigrants invade the sink. This is because the impact of immigration on adaptation depends on the rate of immigration relative to the sink density. This ratio is actually independent of immigration in a model where the sink is initially empty, migration from the sink back to the source is negligible and without density-dependence in the sink.

• In this model, mutation is a double-edged sword. Adapted phenotypes emerge from new mutations, and under this effect alone a higher mutation rate would translate into a shorter time to establishment in the sink. However, mutations may also have deleterious effects by displacing the phenotype away from the optimum. This mutation load will be greater when individuals need to simultaneously tune a large number of phenotypes. As a consequence of these two effects of mutations, time to establishment is minimal for an intermediate mutation rate. This result emerges from Fisher’s Geometric Model, but may hold more generally for biologically plausible fitness landscapes where mutations generates both beneficial (allowing adaptation to the sink) and deleterious genotypes.

• Lastly, in Fisher’s Geometric Model, the time to establishment increases superlinearly with harshness of the sink when the sink is too harsh, and establishment may occur only after a very long time. In these harsh sinks, the adapted genotypes are very few and increase very slowly in frequency, making the second phase of adaptation much longer. Thus, and as a direct consequence of Fisher’s Geometric Model, adding a “stepping stone” intermediate environment would allow faster adaptation to the extreme environment.

In conclusion, this theoretical work presents a method based on Fisher’s Geometric Model and the use of cumulant generating functions to resolve some aspects of adaptation to a sink environment. It generates a number of theoretical predictions for the adaptive colonisation of a sink by an asexual species with some standing genetic variation. It will be a fascinating task to examine whether these predictions hold in experimental evolution systems: will we observe the four phases of the dynamics of mean fitness in the sink environment? Will the rate of adaptation indeed be independent of the immigration rate? Is there an optimal rate of mutation for adaptation to the sink? Such critical tests of the theory will greatly improve our understanding of adaptation to novel environments.

References

[1] Lavigne, F., Martin, G., Anciaux, Y., Papaïx, J., and Roques, L. (2019). When sinks become sources: adaptive colonization in asexuals. bioRxiv, 433235, ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/433235
[2] Martin, G., and Lenormand, T. (2006). A general multivariate extension of Fisher's geometrical model and the distribution of mutation fitness effects across species. Evolution, 60, 893-907. doi: 10.1111/j.0014-3820.2006.tb01169.x

Accurate information flow is central to living systems. The continuity of genomes through generations as well as the reproducible functioning and survival of the individual organisms require a faithful information transfer during replication, transcription and translation. The differential efficiency of natural selection against “mistakes” results in decreasing fidelity rates for replication, transcription and translation. At each level in the information flow chain (replication, transcription, translation), numerous complex molecular systems have evolved and been selected for preventing, identifying and, when possible, correcting or removing such “mistakes” arising during information transfer.

However, fidelity cannot be improved ad infinitum. First, because of the limits imposed by the physical nature of the processes of copying and recoding information over different molecular supports: all mechanisms ensuring fidelity during biological information transfer ultimately rely on chemical kinetics and thermodynamics. The more accurate a copying process is, the lower the synthesis rate and the higher the energetic cost of correcting errors. Second, because of the limits imposed by random genetic drift: natural selection cannot effectively act on an allele that contributes with a small differential advantage unless effective population size is large. If s <1/Ne (or s <1/(2Ne) in diploids) the allele frequency in the population is de facto subject to neutral drift processes.

In their preprint “Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans”, Bénitière, Necsulea and Duret explore the validity of this last mentioned “drift barrier” hypothesis for the case study of alternative splicing diversity in eukaryotes (Bénitière et al. 2022). Splicing refers to an ensemble of eukaryotic molecular processes mediated by a large number of proteins and ribonucleoproteins and involving nucleotide sequence recognition, that uses as a molecular substrate a precursor messenger RNA (mRNA), directly transcribed from the DNA, and produces a mature mRNA by removing introns and joining exons (Chow et al. 1977). Alternative splicing refers to the case in which different molecular species of mature mRNAs can be produced, either by cis-splicing processes acting on the same precursor mRNA, e.g. by varying the presence/absence of different exons or by varying the exon-exon boundaries, or by trans-splicing processes, joining exons from different precursor mRNA molecules.

The diversity of mRNA molecular species generated by alternative splicing enlarges the molecular phenotypic space that can be generated from the same genotype. In humans, alternative splicing occurs in around 95% of the ca. 20,000 genes, resulting in ca. 100,000 medium-to-high abundance transcripts (Pan et al. 2008). In multicellular organisms, the frequency of alternatively spliced mRNAs varies between tissues and across ontogeny, often in a switch-like pattern (Wang et al. 2008). In the molecular and cell biology community, it is commonly accepted that splice variants contribute with specific functions (Marasco and Kornblihtt 2023) although there exists a discussion around the functional nature of low-frequency splice variants (see for instance the debate between Tress et al. 2017 and Blencowe 2017). The origin, diversity, regulation and evolutionary advantage of alternative splicing constitutes thus a playground of the selectionist-neutralist debate, with one extreme considering that most splice variants are mere “mistakes” of the splicing process (Pickrell et al. 2010), and the other extreme considering that alternative splicing is at the core of complexity in multicellular organisms, as it increases the genome coding potential and allows for a large repertoire of cell types (Chen et al. 2014).

In their manuscript, Bénitière, Necsulea and Duret set the cursor towards the neutralist end of the gradient and test the hypothesis of whether the high alternative splice rate in “complex” organisms corresponds to a high rate of splicing “mistakes”, arising from the limit imposed by the drift barrier effect on the power of natural selection to increase accuracy (Bush et al. 2017). In their preprint, the authors convincingly show that in metazoans a fraction of the variation of alternative splicing rate is explained by variation in proxies of population size, so that species with smaller Ne display higher alternative splice rates. They communicate further that abundant splice variants tend to preserve the reading frame more often than low-frequency splice variants, and that the nucleotide splice signals in abundant splice variants display stronger evidence of purifying selection than those in low-frequency splice variants. From all the evidence presented in the manuscript, the authors interpret that “variation in alternative splicing rate is entirely driven by variation in the efficacy of selection against splicing errors”.

The authors honestly present some of the limitations of the data used for the analyses, regarding i) the quality of the proxies used for Ne (i.e. body length, longevity and dN/dS ratio); ii) the heterogeneous nature of the RNA sequencing datasets (full organisms, organs or tissues; different life stages, sexes or conditions); and iii) mostly short RNA reads that do not fully span individual introns. Further, data from bacteria do not verify the herein communicated trends, as it has been shown that bacterial species with low population sizes do not display higher transcription error rates (Traverse and Ochman 2016). Finally, it will be extremely interesting to introduce a larger evolutionary perspective on alternative splicing rates encompassing unicellular eukaryotes, in which an intriguing interplay between alternative splicing and gene duplication has been communicated (Hurtig et al. 2020).

The manuscript from Bénitière, Necsulea and Duret makes a significant advance to our understanding of the diversity, the origin and the physiology of post-transcriptional and post-translational mechanisms by emphasising the fundamental role of non-adaptive evolutionary processes and the upper limits to splicing accuracy set by genetic drift.

References

Bénitière F, Necsulea A, Duret L. 2023. Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.12.09.519597 

Blencowe BJ. 2017. The Relationship between Alternative Splicing and Proteomic Complexity. Trends Biochem Sci 42:407–408. https://doi.org/10.1016/j.tibs.2017.04.001

Bush SJ, Chen L, Tovar-Corona JM, Urrutia AO. 2017. Alternative splicing and the evolution of phenotypic novelty. Philos Trans R Soc Lond B Biol Sci 372:20150474. https://doi.org/10.1098/rstb.2015.0474

Chen L, Bush SJ, Tovar-Corona JM, Castillo-Morales A, Urrutia AO. 2014. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol 31:1402–1413. https://doi.org/10.1093/molbev/msu083

Chow LT, Gelinas RE, Broker TR, Roberts RJ. 1977. An amazing sequence arrangement at the 5’ ends of adenovirus 2 messenger RNA. Cell 12:1–8. https://doi.org/10.1016/0092-8674(77)90180-5

Hurtig JE, Kim M, Orlando-Coronel LJ, Ewan J, Foreman M, Notice L-A, Steiger MA, van Hoof A. 2020. Origin, conservation, and loss of alternative splicing events that diversify the proteome in Saccharomycotina budding yeasts. RNA 26:1464–1480. https://doi.org/10.1261/rna.075655.120

Marasco LE, Kornblihtt AR. 2023. The physiology of alternative splicing. Nat Rev Mol Cell Biol 24:242–254. https://doi.org/10.1038/s41580-022-00545-z

Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ. 2008. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415. https://doi.org/10.1038/ng.259

Pickrell JK, Pai AA, Gilad Y, Pritchard JK. 2010. Noisy splicing drives mRNA isoform diversity in human cells. PLoS Genet 6:e1001236. https://doi.org/10.1371/journal.pgen.1001236

Traverse CC, Ochman H. 2016. Conserved rates and patterns of transcription errors across bacterial growth states and lifestyles. Proc Natl Acad Sci U S A 113:3311–3316. https://doi.org/10.1073/pnas.1525329113

Tress ML, Abascal F, Valencia A. 2017. Alternative Splicing May Not Be the Key to Proteome Complexity. Trends Biochem Sci 42:98–110. https://doi.org/10.1016/j.tibs.2016.08.008

Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. 2008. Alternative isoform regulation in human tissue transcriptomes. Nature 456:470–476. https://doi.org/10.1038/nature07509

This is an original paper [1] addressing the question whether cultural transmission occurs in insects and studying the mechanisms of such transmission. Often, culture-like phenomena require relatively sophisticated learning mechanisms, for example imitation and/or teaching. In insects, seemingly complex processes of social information acquisition, can sometimes instead be mediated by relatively simple learning mechanisms suggesting that cultural processes may not necessarily require sophisticated learning abilities.

An important quality of this paper is to describe neatly the experimental protocols used for such typically complex behavioural analyses, providing a detailed understanding of the results while it remains a joy to read. This becomes rare in high impact journals. In a clever experimental design, individual bumblebees are trained to pull an artificial flower from under a Plexiglas table to get access to a reward, by pulling a string attached to the flower. Individuals that have learnt this task are then shown to inexperienced bees while performing this task. This results in a large proportion of the inexperienced observers learning to pull the string and getting access to the reward. Finally, the authors could then document the spread of the string pulling skill amongst other workers in the colony. Even when the originally trained individuals had died, the skill of string-pulling persisted in the colony, as long as they were challenged with the task. This shows that cultural transmission takes place within a colony. The authors provide evidence that the transmission of this behavior among individuals relies on a mix of social learning by local enhancement (bees were attracted to the location where they had observed a demonstrator) and of non-social, individual learning (pulling the string is learned by trial and errors and not by direct imitation of the conspecific). Data also show that simple associative mechanisms are enough and that stimulus enhancement was involved (bees were attracted to the string when its location was concordant with that during prior observation).

The cleverly designed experiments use a paradigm (string-pulling) which has often been used to investigate cognitive abilities in vertebrates. Comparison with such studies indicate that bees, in some aspects of their learning, may not be different from birds, dogs, or apes as they also relied on the perceptual feedback provided by their actions, resulting in target movement to learn string pulling. The results of the study suggest that the combination of relatively simple forms of social learning and trial-and-error learning can mediate the acquisition of new skills and that bumblebees possess the essential cognitive elements for cultural transmission and in a broader sense, that the capacity of culture may be present within most animals.

Can we expect behavioural innovation such as string pulling to occur in nature? Bombus terrestris colonies can reach a total of several hundreds foragers. In the experiments, foragers needed on average 5 rounds of observations with different demonstrators to learn how to pull the string. As individuals forage in a meadow full of flowers and conspecifics, transmission of behavioural innovations by repeated observations shouldn’t strike us as something impossible. Would the behavior survive through the winter? Bumblebee colonies are seasonal in northern areas and in the Mediterranean area but tropical species persists for several years. In seasonal species, all the workers die before winter and only new queens overwinter. So there is no possibility for seasonal foragers to transmit the technique overwinter. Only queens could potentially transmit it to new foragers in spring. However flowers are different in autumn and spring. Therefore, what queens have learnt about flowers in autumn would unlikely be useful in spring (providing that they can remember it). However there is no reason why the technique couldn't be transmitted from a colony to another between spring to autumn. Such transmission of new behaviour would more easily persist in perennial social insect colonies, like honeybees. Importantly, the bees used in these experiments came from a company whose rearing conditions are unknown, and only a few colonies were used for each experiment. As learning ability has a genetic basis [2-3], colonies differ in their ability to learn [4]. In this regard, the authors showed variation between individual bumblebees and between bumblebee colonies in learning ability. Hence, we would wish to know more about the level of genetic diversity in the wild, and of genetic differentiation between tested colonies (were they independent replicates?), to extrapolate the results to what may happen in the wild.

Excitingly, the authors found 2 true innovators among the >400 individuals that were tested at least once for 5 min who would solve such a task without stepwise training or observation of skilled demonstrators, showing that behavioural innovation can occur in very small numbers of individuals, provided that an ecological trigger is provided (food reward). Hence this study shows that all ingredients for the long proposed “social heredity” theory proposed by Baldwin in 1896 are available in this organism, suggesting that social transmission of behavioural innovations could technically act as an additional mechanism for adaptive evolution [5], next to genetic evolution that may take longer to produce adaptive evolution. The question remains whether the behavioural innovations are arising from standing genetic variation in the bees, or do not need a firm genetic background to appear.

References

[1] Alem S, Perry CJ, Zhu X, Loukola OJ, Ingraham T, Søvik E, Chittka L. 2016. Associative mechanisms allow for social learning and cultural transmission of string pulling in an insect. PloS Biology 14:e1002564. doi: 10.1371/journal.pbio.1002564

[2] Mery F, Kawecki TJ. 2002. Experimental evolution of learning ability in fruit flies. Proceeding of the National Academy of Science USA 99:14274-14279. doi: 10.1073/pnas.222371199

[3] Mery F, Belay AT, So AKC, Sokolowski MB, Kawecki TJ. 2007. Natural polymorphism affecting learning and memory in Drosophila. Proceeding of the National Academy of Science USA 104:13051-13055. doi: 10.1073/pnas.0702923104

[4] Raine NE, Chittka L. 2008. The correlation of learning speed and natural foraging success in bumble-bees. Proceeding of the Royal Society of London 275: 803-808. doi : 10.1098/rspb.2007.1652

[5] Baldwin JM. 1896. A New Factor in Evolution. American Naturalist 30:441-451 and 536-553. doi: 10.1086/276408