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Abstract The Great AtlanticSargassumBelt first appeared in 2011 and quickly became the largest interconnected floating biome on Earth. In recent years,Sargassumstranding events have caused substantial ecological and socio-economic impacts in coastal communities.Sargassumrequires both phosphorus (P) and nitrogen (N) for growth, yet the primary sources of these nutrients fuelling the extensiveSargassumblooms remain unclear. Here we use coral-bound N isotopes to reconstruct N₂fixation, the ultimate source of the ocean’s bioavailable N, across the Caribbean over the past 120 years. Our data indicate that changes in N₂fixation were primarily controlled by multidecadal and interannual changes in equatorial Atlantic upwelling of ‘excess P’, that is, P in stoichiometric excess relative to fixed N. We show that the supply of excess P from equatorial upwelling and N from the N₂fixation response can account for the majority ofSargassumvariability since 2011.Sargassumdynamics are best explained by their symbiosis with N₂-fixing epiphytes, which render the macroalgae highly competitive during strong equatorial upwelling of excess P. Thus, the future ofSargassumin the tropical Atlantic will depend on how global warming affects equatorial Atlantic upwelling and the climatic modes that control it. 

The Great Atlantic Sargassum Belt (GASB) first appeared in 2011 and quickly became the largest interconnected floating biome globally. Sargassum spp. requires both phosphorus (P) and nitrogen (N) for growth, yet the sources fueling the GASB are unclear. Here, we use coral–bound nitrogen isotopes from six coral cores to reconstruct N2 fixation, the primary source of bioavailable N to the surface ocean across the wider Caribbean over the past 120 years. Our data indicate that changes in N2 fixation were controlled by multidecadal and interannual changes in the supply of excess P from equatorial upwelling in the Atlantic. We show that the supply of P from equatorial upwelling and N from the N2 fixation response can explain the extent of the GASB since 2011. # Equatorial upwelling of phosphorus drives Atlantic N~2~ fixation and *Sargassum* blooms This Excel file contains time series data combining coral geochemical records (δ¹⁵N and δ¹⁸O), climate indices, Sargassum biomass, and major riverine outflows. The dataset integrates multiple spatially distributed records to examine long-term variability in nutrient dynamics, climate forcing, and ecological responses in the Caribbean and tropical Atlantic. Values that were not available or are missing are indicated as N/A. ## Column Reference Table File: Caribbean_data_for_DRYAD.xlsx | Column Name | Description | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | **Year\_CR\_Turneffe** | Calendar year of sampling for coral records from Turneffe Atoll (Belize) and Cahuita (Costa Rica). | | **Cahuita Costa Rica\_d18O\_ts** | Coral δ¹⁸O time series from Cahuita, Costa Rica (proxy for SST and freshwater input). | | **d15N\_CR** | Coral-bound δ¹⁵N from Cahuita, Costa Rica (proxy for nitrogen source/processing). | | **Turneffe Atoll\_d18O\_ts** | Coral δ¹⁸O time series from Turneffe Atoll, Belize. | | **d15N\_Turneffe** | Coral-bound δ¹⁵N from Turneffe Atoll. | | **Date\_MQ** | Sampling date for Martinique (MQ) site. | | **d18O\_MQ** | Coral δ¹⁸O from Martinique. | | **d15N\_MQ** | Coral δ¹⁵N from Martinique. | | **Year Bermuda** | Calendar year for Bermuda coral samples. | | **d15N Bermuda** | Coral δ¹⁵N from Bermuda. | | **Year\_CUBA** | Calendar year for Cuban coral records. | | **d15N\_CUBA** | Coral δ¹⁵N from Cuba. | | **d15N\_Mexico** | Coral δ¹⁵N from Mexico. | | **Year\_Tobago** | Calendar year for Tobago coral samples. | | **d15N\_Tobago** | Coral δ¹⁵N from Tobago. | | **Year AMM** | Year corresponding to Atlantic Meridional Mode (AMM) values. | | **AMM\_SST** | Sea Surface Temperature anomalies associated with the AMM. | | **AMM\_Wind** | Wind anomalies associated with the AMM. | | **AMO** | Atlantic Multidecadal Oscillation index value. | | **average\_year** | Averaged year across all coral records included. | | **AVERAGE\_rescaled** | Composite δ¹⁵N record rescaled across sites. | | **error\_propagated** | Propagated error estimate for the rescaled average. | | **AVERAGE\_rescaled\_noCR\_BM\_TB** | Rescaled δ¹⁵N average excluding Costa Rica, Bermuda, and Tobago. | | **error\_propagated2** | Propagated error for the reduced-site average. | | **Months Sargassum** | Month of Sargassum observation. | | **Monthly Sargassum biomass (tons)** | Monthly biomass estimates of pelagic Sargassum (tons). | | **Year\_SST\_SSS** | Year corresponding to SST/SSS data. | | **SST\_10-20N\_20-60W** | Sea Surface Temperature average over 10–20°N, 20–60°W. | | **SSS\_10-20N\_20-60W** | Sea Surface Salinity average over the same region. | | **U\_windstress\_10\_20N\_58\_62W** | Zonal wind stress (10–20°N, 58–62°W). | | **windspeed\_0\_20N\_20\_50W** | Mean wind speed (0–20°N, 20–50°W). | | **Geo\_u\_12\_18N\_60\_80W (CC)** | Geostrophic zonal velocity (12–18°N, 60–80°W), Caribbean Current proxy. | | **DU\_scav\_areaweight** | Dust deposition (scavenging flux, area-weighted). | | **DU\_ddep\_areaweight** | Dust dry deposition (area-weighted). | | **BC\_scav\_areaweight** | Black carbon scavenging flux (area-weighted). | | **Bc\_ddep\_areaweight** | Black carbon dry deposition (area-weighted). | | **BC\_total\_areaweight** | Total black carbon deposition (area-weighted). | | **DU\_total\_areaweight** | Total dust deposition (area-weighted). | | **Obidos\_Amazon\_m3\_s** | Amazon River discharge at Óbidos station (m³/s). | | **Ciudad Bolivar\_Orinoco\_m3\_s** | Orinoco River discharge at Ciudad Bolívar (m³/s). | | **Year Pstar** | Year corresponding to P\* (phosphorus excess) record. | | **Pstar** | Phosphorus excess (indicator of nutrient balance, micro Molar). | | **Amazon\_outflow\_date** | Date of Amazon outflow measurement. | | **Amazon\_outflow\_km3** | Amazon River outflow volume (km³). | | **Orinoco\_outflow\_date** | Date of Orinoco outflow measurement. | | **Orinoco\_outflow\_km3** | Orinoco River outflow volume (km³). | Links to other publicly accessible locations of the data: * [https://climexp.knmi.nl](http://...) Data was derived from the following sources: * Climate Explorer was used for gridded satellite-derived products (SST, SSS, windspeed, windstress) by using the geographical extent as indicated in the manuscript ## Code/Software No software was used for data analysis, and the codes used for figures and data analyses are available on GitHub ([https://github.com/marinejon/](https://github.com/marinejon/)) 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.