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Martinez-Garcia, L.

Publications and source records attributed to Martinez-Garcia, L..

2 recordsLinked to original sources

Serial depletion of Baltic herring since the Viking Age revealed by ancient DNA and population genomics

Marine resource consumption has been a key component in European diet and culture since the Middle Ages, when fish consumption increased dramatically. Yet, the early origins of marine industries and the long-term ecological consequences of historical and contemporary fisheries remain debated. The Baltic Sea was home to the first "industrial" fishery [~]800 years ago targeting the Baltic herring, a species that is still economically and culturally important today. We combine modern whole genome data with ancient DNA (aDNA) to identify the first known long-distance herring trade in the region, illustrating that large-scale fish trade began during the Viking Age. We resolve population structure within the Baltic and observe demographic independence for four local herring stocks over at least 200 generations. It has been suggested that overfishing at Oresund in the 16th century resulted in a demographic shift from autumn-spawning to spring-spawning herring dominance in the Baltic. We show that while the Oresund fishery had a negative impact on the western Baltic herring stock, the existence of autumn-spawning refugia in other regions of the Baltic delayed the demographic shift to spring spawning dominance until the 20th century. Importantly, modelling demographic trajectories over time, we identify a consistent pattern of serial depletion within the Baltic that is associated with changes in fishing pressure and climate, and conclude that herring exploitation at both historical and recent intensities is not sustainable. Our results highlight the complex and enduring impacts humans have had on the marine environment well before the industrial era.

ecology↗

Discrimination of species within the Enterobacter cloacae complex using MALDI-TOF Mass Spectrometry and Fourier-Transform Infrared Spectroscopy coupled with Machine Learning tools

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may host different acquired antimicrobial resistance and virulence mechanisms and their identification are challenging. This study aims to develop predictive models based on MALDI-TOF MS spectral profiles and machine learning for species-level identification. A total of 198 ECC and 116 K. aerogenes clinical isolates from the University Hospital Ramon y Cajal (Spain) and the University Hospital Basel (Switzerland) were included. The capability of the proposed method to differentiate the most common ECC species (E. asburiae, E. kobei, E. hormaechei, E. roggenkampii, E. ludwigii, E. bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with PCA pre-processing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development dataset. Thus, we developed supervised, non-linear predictive models (Support Vector Machine with Radial Basis Function and Random Forest). The external validation of these models with protein spectra from the two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species. Similar results were obtained with the MSI database developed recently (https://msi.happy-dev.fr/) except in the case of E. hormaechei, which was more accurately identified by Random Forest. In short, MALDI-TOF MS combined with machine learning demonstrated to be a rapid and accurate method for the differentiation of ECC species.

microbiology↗