Resultados de investigación

A) Results from stochastic simulations showing the conjugation frequency (T/D, y axis) against time (min, x axis) obtained at different cellular densities (D). Shadowed areas correspond to 1 and 2 generation times. B) Comparison between computational simulations (dots) and theoretical predictions (lines) for the conjugation frequency (x axis, T/D) obtained at different cellular densities (x axis), at different generation times (legend). The normalized estimation error obtained at different generation times is shown in the inner graph. The normalized estimation error was measured as the difference between the conjugation frequency computed and estimated, divided by the average of the two. C) Effect of growth differences between donor and transconjugant cells on the estimation of the conjugation frequency. Computed (dots) and predicted (line) conjugation frequencies (x axis, T/D) obtained at different cell concentrations (x axis), when the transconjugants experience a growth deficit with respect to the donor cells indicated in the legend. As shown in the figure, a significant deviation from the estimated values was observed only for growth deficits >50%., Peer reviewed

Ecomorphological and biogeographical data for all lark species included in the study., R code employed to conduct the analyses. Please, note that we did not incorporate in the manuscript all analyses explored in the R script; in some cases, we built similar models using different approaches and R packages (‘PANDA’, ‘DDD’, ‘laser’, ‘diversitree’) to test the consistency of our results. Thus, this is not an exhaustive set of code but serves to demonstrate the approaches employed in this study., Peer reviewed

Transconjugants per donor ([T]/[D], y axis) measured at different Recipient densities ([R] ≈ [N], x axis) in E. coli BW27783 mating in liquid LB, 1 Donor to 100 Recipients and 1 h. conjugation. Every dot corresponds to the average of 3 technical replicates., Peer reviewed

Transconjugants per donor ([T]/[D], y axis) measured at different Recipient densities ([R] ≈ [N], x axis) in E. coli BW27783 mating on solid LB-agar surfaces, 1 Donor to 100 Recipients and 1 h. conjugation time for plasmids pOX38 (IncFI) and R64 (IncI1). As in Fig 1 in the main section, the black line represents the ideal DDT regime, where different kon produce different y-intercepts. The red line represents the FDT regime, where the conjugation efficiency is just 1/τ and the dotted curve corresponds to the fitting to Eq.3. Every black dot corresponds to the average of 3 technical replicates., Peer reviewed

Attractiveness is not solely determined by a single sexual trait but rather by a combination of traits. Because the response of the chooser is based on the combination of sexual traits in the courter, variation in the chooser’s responses that are attributable to the opposite-sex courter genotypes (i.e., the indirect genetic effects (IGEs) on chooser response) can reflect genetic variation in overall attractiveness. This genetic variation can be associated with the genetic basis of other traits in both the chooser and the courter. Investigating this complex genetic architecture, including IGEs, can enhance our understanding of the evolution of mate choice. In the present study on the field cricket Gryllus bimaculatus, we estimated (1) genetic variation in overall attractiveness and (2) genetic correlations between overall attractiveness and other pre- and post-copulatory traits (e.g., male latency to sing, female latency to mount, male guarding intensity, male and female body mass, male mandible size and testis size) within and between sexes. We revealed a genetic basis for attractiveness in both males and females. Furthermore, a genetic variance associated with female attractiveness was correlated with a genetic variance underlying larger male testes. Our findings imply that males that mate with attractive females can produce offspring that are successful in terms of pre-copulatory sexual selection (daughters who are attractive) and post-copulatory sexual selection (sons with an advantage in sperm competition), potentially leading to runaway sexual selection. Our study exemplifies how the incorporation of the IGE-framework provides novel insights into the evolution of mate choice., Peer reviewed

ANOVA testing comparing the four populations at 0 and 60 minutes after mating began gave p > 0.05 in all but R388 Recipients’ growth. B) Growth curves for donor and recipients in matings using plasmid R388 (black circles, upper graph) and pKM101 (red circles, lower graph) fitting showed that doubling times were >60 minutes for all strains analyzed., Peer reviewed

Plasmid conjugation is a major route for the dissemination of antibiotic resistances and adaptive genes among bacterial populations. Obtaining precise conjugation rates is thus key to understanding how antibiotic resistances spread. Plasmid conjugation is typically modeled as a density-dependent process, where the formation of new transconjugants depends on the rate of encounters between donor and receptor cells. By analyzing conjugation dynamics at different cell concentrations, here we show that this assumption only holds at very low bacterial densities. At higher cell concentrations, conjugation becomes limited by the engagement time, the interval required between two successful matings. Plasmid conjugation therefore follows a Holling´s Type II functional response, characterized by the encounter rate and the engagement time, which represent, respectively, the density and frequency-dependent limits of plasmid transmission. Our results demonstrate that these parameters are characteristic of the transfer machinery, rather than the entire plasmid genome, and that they are robust to environmental and transcriptional perturbation. Precise parameterization of plasmid conjugation will contribute to better understanding the propagation dynamics of antimicrobial resistances., Peer reviewed

Plasmid conjugation is a major route for the dissemination of antibiotic resistances and adaptive genes among bacterial populations. Obtaining precise conjugation rates is thus key to understanding how antibiotic resistances spread. Plasmid conjugation is typically modeled as a density-dependent process, where the formation of new transconjugants depends on the rate of encounters between donor and receptor cells. By analyzing conjugation dynamics at different cell concentrations, here we show that this assumption only holds at very low bacterial densities. At higher cell concentrations, conjugation becomes limited by the engagement time, the interval required between two successful matings. Plasmid conjugation therefore follows a Holling´s Type II functional response, characterized by the encounter rate and the engagement time, which represent, respectively, the density and frequency-dependent limits of plasmid transmission. Our results demonstrate that these parameters are characteristic of the transfer machinery, rather than the entire plasmid genome, and that they are robust to environmental and transcriptional perturbation. Precise parameterization of plasmid conjugation will contribute to better understanding the propagation dynamics of antimicrobial resistances., Peer reviewed

Expanded Dataset for Two-Echelon Electric Vehicle Routing Problems (2E-EVRP) (Original data) (Zenodo). 2E-EVRP-Instances (Original data) (Zenodo)., This paper introduces a dataset generated for research on Two-Echelon Electric Vehicle Routing Problems (2E-EVRPs) with additional constraints, including time windows, simultaneous pickup and delivery (SPD), and partial deliveries. The dataset is derived from established benchmark instances from the VRP and EVRP literature and further extended using methodologies from the literature. It features diverse scenarios designed to challenge and validate solution approaches proposed for two-echelon routing algorithms under various constraints. The dataset comprises a variety of instances, ranging from small (5-15 customers) to large (100 customers), and incorporates different geographical configurations, including clustered, random, and random-clustered distributions. Key modifications include integrating satellite locations, enhanced vehicle configurations concerning electric vehicle (EV) constraints, and adjusting time windows to accommodate the two-echelon structure. The dataset also supports multiple delivery scenarios, allowing for single delivery, simultaneous pickups and deliveries (SPD), and partial deliveries, enabling researchers to test the performance of their algorithms across a range of realistic constraints., The research presented in this paper was supported by grants TED2021-129319B-I00 and PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033., Peer reviewed

R scripts to replicate the analyses from "Energyscapes pinpoint marine megafauna feeding hotspots in the Mediterranean"., European Commission EcoScope — Ecocentric management for sustainable fisheries and healthy marine ecosystems: 101000302, With funding form the Spanish government through the "Severa Ochoa Centre of Excellence" accreditation (CEX2019-000928-S)., Peer reviewed