Biology subjects

Giannelli, F.

Publications and source records attributed to Giannelli, F..

6 recordsLinked to original sources

bioRxiv2025

Sex-biased dispersal drives mito-nuclear discordance in simulated populations

Reconstructing the evolutionary dynamics of natural populations requires an understanding of the geographical distribution of nuclear and mitochondrial genetic diversity. The analysis of these two genetic markers frequently discloses discordant patterns (mito-nuclear discordance) that can arise simply as a consequence of their different effective population sizes (Ne). Species-specific sex-biased dispersal may contribute to the mito-nuclear discordance observed in natural populations. However, the relative contribution of genetic drift versus sex-biased dispersal in driving mito-nuclear discordance remains insufficiently evaluated. Here, we use forward genetic simulations to address this knowledge gap. Our findings support the baseline level of mito-nuclear discordance arising from distinct genomic Ne, but show that this inherent discordance is magnified by sex-biased dispersal patterns. We demonstrate that female-biased dispersal leads to a marked spatial mismatch between mitochondrial and nuclear diversity across the simulated populations, thereby reducing the spatial concordance between mitochondrial and nuclear markers. Conversely, male dispersal patterns appear to increase, although to a reduced degree, the intrinsic level of discordance between nuclear and mitochondrial geographical marker distribution. Our results highlight the importance of integrating the intrinsic characteristics of nuclear and mitochondrial genomes and the impact of sex-biased dispersal for accurately interpreting patterns of genetic diversity and reconstructing evolutionary histories.

genomics↗

bioRxiv2024

Pervasive and recurrent hybridisation prevents inbreeding depression in Europe's most threatened seabird

Hybridisation is a double-edged sword: while it can erode distinct evolutionary lineages, it can also introduce genetic diversity and adaptive potential into dwindling populations. In the Critically Endangered Balearic shearwater (Puffinus mauretanicus), this dilemma is exacerbated by a limited understanding of the extent and consequences of hybridisation with the Yelkouan shearwater (P. yelkouan). This knowledge gap has limited the scope of science-based conservation strategies to avoid the Balearic shearwaters imminent extinction. Here, we investigate shearwater hybridisation dynamics and their effect on genome-wide diversity in the Balearic shearwater. Divergence dating, demographic modelling and admixture analyses suggest that these two poorly-differentiated shearwater lineages experienced recurrent episodes of divergence and widespread hybridisation during glacial cycles. Selection scans reveal a 500 kb region hosting an adaptive haplotype that potentially underpins interspecific differences in migratory behaviour, and which has been repeatedly introgressed between taxa. Moreover, we show that interspecific gene flow has prevented increases in homozygosity and genetic load, and through forward simulations we illustrate how it can enhance the Balearic shearwaters resilience to future population bottlenecks. Our findings illustrate how introgression can be crucial for maintaining genetic diversity in threatened taxa, and highlight the need for considering the protection of hybridisation in conservation plans.

evolutionary biology↗

bioRxiv2023

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

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 fewer deleterious mutations between divergent species lineages (known as E-R anticorrelation), pointing towards gene expression as an additional driver of purifying selection. However, estimates of the effect of gene expression on segregating deleterious variants in natural populations are scarce, as is an understanding of the relative contribution of population size and gene expression to purifying selection. Here, we analyse genomic and transcriptomic data from two natural populations of closely related sister species with different demographic histories, the Emperor penguin (Aptenodytes forsteri) and the King penguin (A. patagonicus), and show that purifying selection at the population-level depends on gene expression rate, resulting in very high selection coefficients at highly expressed genes. Leveraging realistic forward simulations, we estimate that the top 10% of the most highly expressed genes in a genome experience a selection pressure corresponding to an average selection coefficient of -0.1, which decreases to a selection coefficient of -0.01 for the top 50%. Gene expression rate can be regarded as a fundamental parameter of protein evolution in natural populations, maintaining selection effective even at small population size. We suggest it could be used as a proxy for gene selection coefficients, which are notoriously difficult to derive in non-model species under real-world conditions.

evolutionary biology↗

bioRxiv2023

Crossmodal statistical learning is facilitated by modality predictability

Statistical learning involves extracting statistical regularities from the environment. Despite the ubiquitous crossmodal relations in our environment, previous research has failed in showing crossmodal learning suggesting that statistical learning is modality-specific, occurring within but not between sensory modalities. The present study investigates under which circumstances statistical learning can occur between modalities. In the first experiment, participants viewed a stream of meaningless visual fractals and synthetic auditory stimuli. Importantly, the sequence of stimuli could be grouped into unimodal or crossmodal pairs based on their internal transitional probabilities. Using implicit and explicit measures of learning, we found that participants only learned the unimodal pairs. In the second experiment, pairs were presented in separate unimodal and crossmodal blocks. The crossmodal blocks alternated visual and auditory modalities allowing participants to anticipate the upcoming modality. This manipulation allowed significant statistical learning for the crossmodal pairs, reflected only by implicit measures. This suggests that modality transitions predictability aids correct attention deployment across sensory modalities, crucial for learning crossmodal statistical contingencies. In the third experiment, where audiovisual stimuli with semantic content were used leaving access to an amodal shared representation, participants could explicitly recognize statistical regularities between crossmodal pairs even when the upcoming modality was unpredictable. This finding suggests statistical learning between crossmodal pairs can occur when sensory-level limitations are bypassed, and when learning unfolds at an amodal level of representation. These findings challenge the view that statistical learning is strictly modality-specific, instead indicating that crossmodal statistical learning depends on attentional mechanisms and representational level. Public significance statementPrevious research has established that low-level statistical learning occurs within individual sensory modalities. However, evidence for statistical learning across modalities remains limited. This has led to the hypothesis that statistical learning operates as a modality-specific system, constrained by the perceptual properties and content of each sensory system. In this study, we demonstrate that modality transitions predictability enables crossmodal statistical learning, even in the absence of semantic information. Notably, this constraint does not apply to meaningful stimuli, which can be processed at an amodal level. Our findings broaden the classical neurobiological model of statistical learning by highlighting the roles of attentional mechanisms and representational levels in supporting crossmodal statistical learning.

animal behavior and cognition↗

bioRxiv2021

Alpha and beta rhythms differentially support the effect of symbols on visual object recognition

Hearing spoken words can enhance visual object recognition, detection and discrimination. Yet, the mechanisms that underpin this facilitation are incompletely understood. On one account, words do not bias early visual processing, but rather affect later semantic or decision-making stages. However, recent proposals suggest that words can alter early visual processes by activating category-specific priors in sensory regions. A prediction of this account is that top-down priors evoke changes in occipital areas in anticipation of visual stimuli. Here, we tested the hypothesis that neural oscillations serve as a mechanism to activate language-generated visual priors. Participants performed a cue-picture matching task where cues were either spoken words, in their native or second language, or natural sounds, while their EEG and reaction times were recorded. Behaviorally, we replicated the previously reported label-advantage effect, with images cued by words being recognized faster than those cued by natural sounds. A time-frequency analysis of cue-target intervals revealed that this behavioral label-advantage was associated with enhanced power in posterior alpha (9-11 Hz) and beta oscillations (17-19 Hz), both of which were larger when the image was preceded by a word compared to a natural sound. Importantly, object recognition performance improved with high alpha power but slowed down with enhancement of beta synchronization. These results suggest that alpha and beta rhythms play distinct functional roles to support language-mediated visual object recognition: alpha might function to amplify sensory priors in posterior regions, while beta may (re)activate the network states elicited by the auditory cue.

neuroscience↗

bioRxiv2018

Native experience modulates neural timing: plasticity in language prediction hierarchies

We investigated how native language experience shapes prediction mechanisms. Two groups of bilinguals (either Spanish or Basque natives) performed a word matching task (WMT) and a picture matching task (PMT). They indicated whether the stimuli they perceived matched with the noun they heard. Spanish noun endings were either diagnostic of the gender (transparent), or ambiguous (opaque). ERPs were time-locked to the gender-marked determiner preceding the predicted noun. In the WMT both groups showed a negative (~340 ms) effect. Basque natives displayed an earlier effect (~150 ms) for determiners preceding transparent nouns. In the PMT both groups showed an early effect (~160 ms) for determiners preceding opaque nouns. Transparent nouns' determiners elicited prediction at ~330 ms in Spanish natives, but at ~460 ms in Basque natives. It is concluded that bilinguals rely on the features of the L1 for predicting in the L2: native experience moulds prediction. Linguistic prediction is hierarchical, with different operation levels communicating at an interface stage.

neuroscience↗