Ammonium uptake plasticity and allocation trade-offs may shape invasion potential of Halophila stipulacea in the Caribbean

Nutrient uptake traits shape plant competition, yet nutrient acquisition and internal allocation strategies remain poorly resolved for invasive seagrasses, limiting predictions of responses to eutrophication. Over the past two decades, the exotic seagrass Halophila stipulacea has rapidly colonized the Mediterranean and Caribbean, occasionally displacing native species. Competition for inorganic nitrogen, particularly ammonium, may therefore drive future meadow composition and functioning. Using a novel split-chamber incubation system, we compared leaf and root ammonium ( 15 NH 4 + ) uptake kinetics and internal nitrogen transfer among three native Caribbean seagrasses ( Thalassia testudinum, Halodule wrightii, Syringodium filiforme ) and the exotic H. stipulacea. We further synthesized published ammonium uptake and transfer data to contextualize our findings. Leaf uptake exceeded root uptake by nearly an order of magnitude across species. Native species exhibited high root uptake affinity and balanced internal allocation, consistent with nutrient-conservative strategies in oligotrophic enivronments. In contrast, H. stipulacea showed lower root uptake but exceptionally high maximum leaf uptake rates and strong root-to-leaf transfer at elevated ammonium, indicating opportunistic aboveground allocation under nutrient pulses. Some species displayed non-saturating uptake kinetics, suggesting uptake capacity may be underestimated under eutrophic conditions. Our synthesis revealed substantial inter- and intraspecific variability and showed 80% of seagrass species lack kinetic parameters. Native Caribbean seagrasses are likely competitively favoured under low to moderate ammonium concentrations, whereas H. stipulacea may gain a context-dependent advantage under nutrient enrichment. These findings illustrate how trade-offs between nutrient-use efficiency and flexibility shape invasion potential highlight critical mechanistic data gaps for predicting future seagrass dominance and ecosystem functioning.