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Ecological Society of America

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Tropical forests exhibit significant heterogeneity in plant functional and chemical traits that may contribute to spatial patterns of key soil biogeochemical processes, such as carbon storage and greenhouse gas emissions. Although tropical forests are the largest ecosystem source of nitrous oxide (N2O), drivers of spatial patterns within forests are poorly resolved. Here, we show that local variation in canopy foliar N, mapped by remote‐sensing image spectroscopy, correlates with patterns of soil N2O emission from a lowland tropical rainforest. We identified ten 0.25 ha plots (assemblages of 40–70 individual trees) in which average remotely‐sensed canopy N fell above or below the regional mean. The plots were located on a single minimally‐dissected terrace (km2) where soil type, vegetation structure and climatic conditions were relatively constant. We measured N2O fluxes monthly for 1 yr and found that high canopy N species assemblages had on average three‐fold higher total mean N2O fluxes than nearby lower canopy N areas. These differences are consistent with strong differences in litter stoichiometry, nitrification rates and soil nitrate concentrations. Canopy N status was also associated with microbial community characteristics: lower canopy N plots had two‐fold greater soil fungal to bacterial ratios and a significantly lower abundance of ammonia‐oxidizing archaea, although genes associated with denitrification (nirS, nirK, nosZ) showed no relationship with N2O flux. Overall, landscape emissions from this ecosystem are at the lowest end of the spectrum reported for tropical forests, consist with multiple metrics indicating that these highly productive forests retain N tightly and have low plant‐available losses. These data point to connections between canopy and soil processes that have largely been overlooked as a driver of denitrification. Defining relationships between remotely‐sensed plant traits and soil processes offers the chance to map these processes at large scales, potentially increasing our ability to predict N2O emissions in heterogeneous landscapes.

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