Biology subjects

Restrepo, J.

Publications and source records attributed to Restrepo, J..

6 recordsLinked to original sources

bioRxiv2026

Area use and residency behaviour at reproductive and foraging areas of green turtles from the largest rookery in the Atlantic

The conservation of migratory marine megavertebrates depends on the protection of different areas during their life cycles. Many populations have recovered following exploitation-driven declines. Green turtles exemplify successful efforts protecting reproductive populations, reducing harvesting, promoting population recoveries globally. Tortuguero, Costa Rica, the second largest rookeries for green turtles worldwide, implemented long-term conservation efforts leading to a population recovery, which is now in decline. This study investigates migratory connectivity of green turtles from Tortuguero across the Caribbean, and the role marine protected areas (MPAs) play in protecting key habitats supporting this population. Over 25 years, we satellite tagged 63 female green turtles to study their internesting and post-nesting foraging movements. Applying a state-space model, we calculated a movement-persistence index, to separate internesting and foraging periods. We calculated turtles' utilization distribution areas for both periods. To assess the protection coverage at each end of the migration, we calculated time spent within and outside MPAs. Tracked turtles spent a median of 42.3 days (SD=18.8, range=12-80) within 30 km offshore before departing the internesting area, spending just 23.0% of time within nearby MPAs. After nesting, turtles migrated to 14 different foraging areas, Miskitos Cays, Nicaragua hosted the most turtles (n=23). Mean transmission time in foraging areas was 175.6 days (SD=126.0; range=13-544). Generally, turtles foraged 58.0% of the time within MPAs boundaries; however, ~32% of tracked turtles foraged outside MPAs. Considering all known stressors, we found this population to be insufficiently protected at either end of their migratory cycle, remaining vulnerable to regional threats.

ecology↗

bioRxiv2026

What we miss shapes what we protect: sea turtles as a case study of grey literature and language bias in an evidence synthesis

Evidence-based approaches are increasingly advocated and applied in conservation to provide reliable recommendations for management and policymaking. Although substantial efforts have been undertaken to generate ecological knowledge across languages and publication types, many syntheses and meta-analyses fail to include relevant literature sources; drawing into question their reliability. To assess the extent of biases resulting from ignoring grey literature and non-English publications, we reviewed and synthesised movement and migratory connectivity data for green turtles across Southeast Asia from multiple literature types and eight languages. We identified substantial losses in connectivity information, with at least half of all locations losing more than 88% of their connections, and shifts in demographic representation and sampling techniques when grey literature was excluded from the evidence base. The exclusion of non-English literature had a comparatively smaller impact. The regional connectivity network generated through this study highlights the need for holistic reviews to develop evidence bases to underpin transboundary management of migratory species. Ignoring grey literature and, potentially, regional languages in the development may lead to misallocation of limited management resources. Holistic reviews can further support the establishment of Important Areas for migratory species and inform how we meet and report on global biodiversity commitments.

ecology↗

bioRxiv2025

Ecological insights and management implications of the global migratory connectivity of green turtles

AimGreen turtles are a widely distributed and highly migratory species, despite extensive data on the movement of green turtles, there is no global synthesis on the subject, limiting a holistic understanding of their movement. Based on three decades of published literature, we present the first global model of migratory connectivity for green turtles. LocationGlobal. Time Period1990-2022. Major Taxa StudiedGreen Sea Turtle (Chelonia mydas) MethodsWe conducted a structured literature review extracting georeferenced information on the movement of green turtles from 1990 to 2022, aggregating this information into a single connectivity model, defining sites and "metasites" as nodes of connectivity. We then evaluate the connectivity routes from nesting areas to foraging sites for each RMU, identifying those trajectories moving outside and across the boundaries of these areas. ResultsWe found an increasing number of studies assessing movement of green turtles, with a total of 113 sources of migratory connectivity information. We identified 474 sites, representing locations where green turtles were observed (124 of these being nesting sites). Migratory connections from nesting sites ranged from resident turtles never leaving the area, to rookeries linked to 13 different sites, some over 5,000 km apart. This long-distance connectivity exposes populations to threats across disparate locations. Most connections traversed national jurisdictions, including crossing different Regional Management Units Main ConclusionsWe compiled the largest available dataset describing movement of green turtles worldwide and present the first global model of their migratory connectivity. This model provides ecological insights into regional differences in life histories, identifies geographic and demographic gaps in sampling, and provides baseline information on connectivity to support transboundary management of green turtle populations. The study highlights the need for larger collaborative efforts to aggregate knowledge beyond local jurisdictions, to inform and align effective management measures to protect this threatened species.

ecology↗

bioRxiv2025

Bimodal peptide collision cross section distribution reflects two stable conformations in the gas phase

Recent high throughput applications to shotgun proteomics have shown great benefits of coupling ion mobility spectrometry (IMS) to mass spectrometry. IMS adds a separation dimension by differentiating biomolecules by their size and shape. We (and others) find that the distribution of peptide collision cross section (CCS) is often bimodal, which limits the utility of current machine learning predictions for peptide identification. Molecular dynamics simulations indicate that the peptides in the drift tube can adopt multiple stable conformations and that the two modes correspond to predominantly extended (mostly helical) and more compact (globular and less ordered) conformations. Most peptides have a charge-dependent strong preference for one of the two conformations, while some can adapt both, as evidenced by a simple geometric model of the CCS data. We suggest a novel two-valued CCS predictor allowing for multiple peptide conformations. Its integration into data-independent acquisition proteomics increases identification rates of peptides compared to single-value predictors.

bioinformatics↗

bioRxiv2024

Tesorai Search: Large pretrained model boosts identifications in mass spectrometry proteomics without the need for Percolator.

The original mass spectrometry search engines used simple algorithms for peptide identification. Recent tools improved accuracy by adding several extra components such as fragment ion intensities or retention times prediction and training target-decoy classifiers on-the-fly, leading to sometimes inconsistent results. Our study explores the impact of replacing those extra components with a deep-learning pretrained model that directly learns the complex relationship between the full spectra and associated peptide sequence, without using decoys. This simplified workflow has fewer parameters to tweak, making it easier to use and perform robustly on data from instruments and use-cases never seen during training. Surprisingly, our approach consistently identifies more peptides than FragPipe, PEAKS, and Proteome Discoverer (12%, 9%, and 21% more, respectively, across a range of datasets). Tesorai Search is also fast - 250 immunopeptidomics searches in 45 minutes - and free for academics, available as a webserver at console.tesorai.com.

bioinformatics↗

bioRxiv2019

A new high-throughput tool to screen mosquito-borne viruses in Zika virus endemic/epidemic areas

Mosquitoes are vectors of arboviruses affecting animal and human health. Arboviruses circulate primarily within an enzootic cycle and recurrent spillovers contribute to the emergence of human-adapted viruses able to initiate an urban cycle involving anthropophilic mosquitoes. The increasing volume of travel and trade offers multiple opportunities for arbovirus introduction in new regions. This scenario has been exemplified recently with the Zika pandemic. To incriminate a mosquito as vector of a pathogen, several criteria are required such as the detection of natural infections in mosquitoes. In this study, we used a high-throughput chip based on the BioMarkTM Dynamic arrays system capable of detecting 64 arboviruses in a single experiment. A total of 17,958 mosquitoes collected in Zika-endemic/epidemic countries (Brazil, French Guiana, Guadeloupe, Suriname, Senegal, and Cambodia) were analyzed. Here we show that this new tool can detect endemic and epidemic viruses in different mosquito species in an epidemic context. Thus, this fast and low-cost method can be suggested as a novel epidemiological surveillance tool to identify circulating arboviruses.

molecular biology↗