Biology subjects

Besson, M.

Publications and source records attributed to Besson, M..

7 recordsLinked to original sources

bioRxiv2025

Phylogenetic signal dynamics during niche filling in food webs

Understanding how phylogenetic signal in ecological networks--the tendency for closely related species to resemble one another in ecological roles--emerges and persists remains a central challenge in community ecology. Here, we simulate food web evolution to track how the correspondence between phylogeny and trophic structure changes as communities assemble and niche space fills. By simulating trait evolution coupled with trait-matching for ecological interactions, we quantify how phylogenetic signal in trophic structure changes through time and examine how species network positions relate to their phylogenetic distinctiveness and diversification dynamics. We find that the signal declines over time, driven by emergent feedbacks between node extinction, link reorganization, and trait divergence. Species with high phylogenetic distinctiveness tend to be more specialized and occupy peripheral network positions, particularly in late-stage communities. Centrality consistently constrains diversification in intermediate consumers, emerges as a limiting factor for top predators after niche saturation, and shows nonlinear effects in basal species diversification. Applying our framework to empirical food webs from the Galapagos Islands, we find partial support for these predictions: phylogenetic signal in foraging and vulnerability roles declines with island age, but shows contrasting trends with island area and elevation. We also detect discrepancies between distance-based and clustering-based measures of phylogenetic signal, highlighting the need for robust methods to compare phylogenetic and network structures. Together, our results reveal how trophic interactions mediate the erosion of phylogenetic structure during community assembly and offer testable predictions for systems at different stages of diversification.

ecology↗

bioRxiv2024

GenoTriplo: A SNP genotype calling method for triploids

Triploidy is very useful in both aquaculture and some cultivated plants as the induced sterility helps to enhance growth and product quality, as well as acting as a barrier against the contamination of wild populations by escapees. To use genetic information from triploids for academic or breeding purposes, an efficient and robust method to genotype triploids is needed. We developed such a method for genotype calling from SNP arrays, and we implemented it in the R package named GenoTriplo. Our method requires no prior information on cluster positions and remains unaffected by shifted luminescence signals. The method relies on starting the clustering algorithm with an initial higher number of groups than expected from the ploidy level of the samples, followed by merging groups that are too close to each other to be considered as distinct genotypes. Accurate classification of SNPs is achieved through multiple thresholds of quality controls. We compared the performance of GenoTriplo with that of fitPoly, the only published method for triploid SNP genotyping with a free software access. This was assessed by comparing the genotypes generated by both methods for a dataset of 1232 triploid rainbow trout genotyped for 38,033 SNPs. The two methods were consistent for 89% of the genotypes, but for 26% of the SNPs, they exhibited a discrepancy in the number of different genotypes identified. For these SNPs, GenoTriplo had >95% concordance with fitPoly when fitPoly genotyped better. On the contrary, when GenoTriplo genotyped better, fitPoly had less than 50% concordance with GenoTriplo. GenoTriplo was more robust with less genotyping errors. It is also efficient at identifying low-frequency genotypes in the sample set. Finally, we assessed parentage assignment based on GenoTriplo genotyping and observed significant differences in mismatch rates between the best and second-best couples, indicating high confidence in the results. GenoTriplo could also be used to genotype diploids as well as individuals with higher ploidy level by adjusting a few input parameters. Author SummaryTo cultivate plants, fish and shellfish more profitable for both farmers and consumers, one can utilize individuals one can utilize individuals with three chromosome sets instead of the two found in fertile populations that are diploids. These individuals, called triploids, are generally sterile and then often exhibit higher growth and quality of products, such as seedless fruits or better flesh quality for fish and shellfish. To be able to improve performances of the sterile triploids by selective breeding, it is important to know the versions of the genes present in the three chromosome sets of triploids. Until now, few methods existed to identify these three versions, and none have been demonstrated as sufficiently effective. It is the reason why we developed the GenoTriplo software. We demonstrate in this paper the possibility to accurately genotype triploids, as well as how it can be used to reconstruct pedigree information of triploid progeny. Ultimately, we expect that it can help select for reproduction the parents that have the best triploid progeny for the traits of interest such as growth, vigour or product quality.

bioinformatics↗

bioRxiv2023

Assessing the environmental impacts of conventional and organic scenarios of rainbow trout farming in France

In France, rainbow trout (Oncorhynchus mykiss) farming traditionally used flow-through systems, which raised concerns about environmental impacts, including limited freshwater availability, and the use of ingredients from intensive agriculture and fishing. To address the growing demand for sustainable food products, there is an increasing interest in organic aquaculture. In this study, we employed an attributional life cycle assessment (LCA) to analyse the environmental impacts of rainbow trout production. We simulated conventional and organic production practices in a hypothetical fish farm to evaluate the differences in environmental impacts at the farm level. The potential impacts were calculated using a product-based functional unit (one tonne of trout) under the two production scenarios and were also expressed using a surface-based functional unit (m2y). Our life cycle impact assessment revealed that organic farming significantly reduced environmental impacts per tonne of trout in seven out of the nine selected impact categories. Notably, freshwater ecotoxicity exhibited the greatest difference, with organic systems showing a 55% decrease. The only exceptions were freshwater eutrophication and water dependence, where organic production led to higher impacts per tonne of trout. In conventional farming, emissions amounted to 14 kg of P eq./tonne, whereas in organic farming, the emissions were slightly higher (15 kg of P eq./tonne). For water dependence, one tonne of trout production in the conventional system mobilized 128 103 m3 vs. 185 103 m3 in the organic system. The environmental benefits of organic production were even more marked when using a surface-based functional unit (m2y). We demonstrated the benefits of organic trout production from an environmental perspective. However, our findings highlight the caution needed when interpreting LCA comparisons of such production systems that can be highly influenced by methodological choices such as the functional unit used.

ecology↗

bioRxiv2022

Overconfidence undermines global wildlife abundance trends

In the face of rapid global change and an uncertain fate for biodiversity, it is vital to quantify trends in wild populations. These trends are typically estimated from abundance time series for suites of species across large geographic and temporal scales. Such data implicitly contain phylogenetic, spatial, and temporal structure which, if not properly accounted for, may obscure the true magnitude and direction of biodiversity change. Here, using a novel statistical framework to simultaneously account for all three of these structures, we show that the majority of current abundance trends estimates among 10 high-profile datasets, representing millions of abundance observations, are likely unreliable or incorrect. Our new approach suggests that previous models are too simplistic, incorrectly estimating global abundance trends and often dramatically underestimating uncertainty, an aspect that is critical when translating global assessments into policy outcomes. Further, our approach also results in substantial improvements in abundance forecasting accuracy. Whilst our results do not improve the outlook for biodiversity, our framework does allow us to make more robust estimates of global wildlife abundance trends, which is critical for developing policy to protect our biosphere.

ecology↗

bioRxiv2022

Whole-genome sequencing identifies interferon induced protein IFI6 as a strong candidate gene for VNN resistance in European sea bass

BackgroundViral Nervous Necrosis (VNN) is major disease affecting of European sea bass. Understanding the biological mechanisms that underlie VNN resistance is thus important for the welfare of farmed fish and the sustainability of production systems. This study aimed at identifying key genomic regions and genes that determine VNN resistance in sea bass. ResultsWe generated a dataset of around 900,000 single nucleotide polymorphisms (SNPs) identified from whole-genome sequencing (WGS) in the parental generation in two different commercial populations (pop A and pop B) comprising 2371 and 3428 European sea bass with phenotypic records for binary survival in a VNN challenge. In each commercial population, three cohorts were submitted to the redspotted grouper nervous necrosis virus (RGNNV) challenge by immersion and genotyped on a 57K SNP chip. After imputation of WGS SNPs from their parents, QTL mapping was performed using a Bayesian Sparse Linear Mixed Model (BSLMM). We found several QTL regions on different linkage groups (LG), most of which are specific to a single population, but a QTL region on LG12 was shared by both commercial populations. This QTL region is only 127 kB wide, and we identified IFI6, an interferon induced protein at only 1.9 kB of the most significant SNP. An unrelated validation population with 4 large families was used to validate the effect of the QTL, for which the survival of the susceptible genotype ranges from 39.8 to 45.4%, while that of the resistant genotype ranges from 63.8 to 70.8%. ConclusionsWe could precisely locate the genomic region implied in the main resistance QTL at less than 1.9 kb of the interferon alpha inducible protein 6 (IFI6), which has already been identified as a key player for other viral infections such as hepatitis B and C. This will lead to major improvements for sea bass breeding programs, allowing for greater genetic gain by using marker-assisted genomic selection to obtain more resistant fish. Further functional analyses are needed to evaluate the impact of the variant on the expression of this gene.

genetics↗

bioRxiv2019

Gene-Gene interactions and pleiotropy in the brain nicotinic pathway associated with the heaviness and precocity of tobacco smoking among outpatients with multiple substance use disorders

IntroductionTobacco smoking is a major health burden worldwide, especially in populations suffering from other substance use disorders (SUDs). Several smoking phenotypes have been associated with single nucleotide polymorphisms (SNPs) of nicotinic acetylcholine receptors (nAChRs). Yet, little is known about the genetics of tobacco smoking in populations with other SUDs, particularly regarding gene-gene interactions and pleiotropy, which are likely involved in the polygenic architecture of SUDs. Thus, we undertook a candidate pathway association study of nAChR-related genes and smoking phenotypes in a sample of SUD patients.\n\nMethods493 patients with genetically-verified Caucasian ancestry were characterized extensively regarding patterns of tobacco smoking, other SUDs, and 83 SNPs from the nicotinic pathway, encompassing all brain nAChR subunits and metabolic/chaperone/trafficking proteins. Single-SNP, gene-based and SNP x SNP interactions analyses were performed to investigate associations with relevant tobacco smoking phenotypes. This included Bayesian analyses to detect pleiotropy, and adjustment on clinical and sociodemographic confounders.\n\nResultsAfter multiple adjustment, we found independent associations between CHRNA3 rs8040868 and a higher number of cigarettes per day (CPD), and between RIC3 rs11826236 and a lower age at smoking initiation. Two SNP x SNP interactions were associated with age at onset (AAO) of daily smoking. There was pleiotropy regarding three SNPs in CHRNA3 (CPD, AAO daily smoking), ACHE (CPD, HSI) and CHRNB4 (CPD, both AAOs).\n\nDiscussionDespite limitations, the present study shows that the genetics of tobacco smoking in SUD patients are both distinct and partially shared across smoking phenotypes, and involve metabolic and chaperone effectors of the nicotinic pathway.

genetics↗

bioRxiv2017

Analyzing ecological networks of species interactions

Networks provide one of the best representations for ecological communities, composed of many species with sometimes complex connections between them. Yet the methodological literature allowing one to analyze and extract meaning from ecological networks is dense, fragmented, and unwelcoming. We provide a general overview to the field of using networks in community ecology, outlining both the intent of the different measures, their assumptions, and the contexts in which they can be used. When methodologically justified, we suggest good practices to use in the analysis of ecological networks. We anchor this synopsis with examples from empirical studies, and conclude by highlighting what identified as needed future developments in the field.

ecology↗