bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2025.12.08.692933

Adaptation to heat and ocean fertilization, two keys for understanding the massive Sargassum growth in the Atlantic

Velzquez-Ochoa, R.·Enriquez, S.
View original source ↗Export metadata ↓

Abstract

O_LIA floating ecosystem constituted by three genetic variants of holopelagic Sargassum has extended since 2011 throughout the tropical North Atlantic without spatial restrictions. C_LIO_LIWe characterized the differential capacity and efficiency of each variant to collect light and fix this energy in photosynthesis under variable light and temperature regimes, focusing on the description of key physiological and optical traits the differential response to light and temperature. C_LIO_LIOur results revealed metabolic adaptations of two genetic variants to the warmer conditions of the tropical Atlantic and contrasting efficiencies in light absorption and use in photosynthesis, indicative of distinct competitive abilities under growth limitations. C_LIO_LIWe concluded that the increased fertility of a warmer ocean is the most plausible explanation for the massive presence of holopelagic Sargassum in the tropical Atlantic, which also may explain the current ecological success of the opportunistic strategy of a previously rare variant. The optical and physiological descriptors documented can assist in developing quantitative models for predicting Sargassum biomass in the Atlantic. C_LI

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Velzquez-Ochoa, R., Enriquez, S.. 2025-12-10. Adaptation to heat and ocean fertilization, two keys for understanding the massive Sargassum growth in the Atlantic. https://doi.org/10.64898/2025.12.08.692933

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Floristic composition, phenology, and conservation value of four peat bogs in Bucovina, with the presence of Betula nana

This paper presents a comparative analysis of the floristic composition and site characteristics of four peat bogs in Bucovina, Romania: Poiana Stampei, Romanesti, Saru Dornei, and Gaina-Lucina. The research was based on phytosociological releves on 25 msq plots and direct field phenological observations, on six field visits from May to August 2026. Vegetation was characterised using the Braun Blanquet method, and floristic similarity between sites was assessed with the Sorensen and Bray Curtis indices. All four plots shared a common core of taxa characteristic of peatland vegetation: Sphagnum spp., Carex rostrata, Drosera rotundifolia, Eriophorum vaginatum, and Vaccinium species. Species richness was 13 taxa at Poiana Stampei, Romanesti, and Saru Dornei, and 12 at Gaina-Lucina. Romanesti and Saru Dornei showed the highest floristic similarity (descriptive values, not statistically tested, given a single releve per site), while Gaina-Lucina differed most markedly, not through species richness, which was similar across sites, but through species identity and through the presence of Betula nana, a glacial relict absent from the other sites. The results provide a descriptive basis for future research on the floristic composition and conservation of these habitats.

ecology↗
bioRxiv2026

Long-Term Surveillance Reveals Establishment of Aedes albopictus in Eastern Nebraska, USA

Aedes albopictus (Skuse), the Asian tiger mosquito, is a highly competent arboviral vector whose range has expanded substantially across the United States over the past four decades. Despite predictive models placing Nebraska within the species' climatically suitable range, its establishment status in the state has remained poorly characterized. Here, we report results from a nine-year mosquito surveillance program (2017-2025) conducted across 44 Nebraska counties in collaboration with the Nebraska Department of Health and Human Services. Ae. albopictus was detected in five counties, with sustained, annually increasing populations documented in Richardson, Douglas, and Lancaster counties. Richardson County recorded continuous detections during 2017-2025, with proportional representation rising to 60.50% of collected mosquitoes by 2025. In Douglas and Lancaster counties, temporal advancement of first seasonal detection in 2024 and 2025 provide evidence consistent with successful overwintering rather than annual reintroduction. A cumulative degree-day model predicted adult emergence in mid-May across all county-year combinations, consistently preceding trap deployment by two to seven weeks and revealing a systematic early-season surveillance gap. Generalized linear mixed-effects models indicated that trap-level detection persistence, rather than urban location, was the primary predictor of yearly Ae. albopictus positivity, suggesting that current invasion dynamics are driven by focal source populations. These findings provide strong evidence for the establishment of Ae. albopictus in eastern Nebraska and highlight the need for earlier seasonal surveillance and standardized criteria to define establishment in northward-expanding vector populations.

ecology↗
bioRxiv2026

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗