A common-mesocosm experiment recreates sawgrass (Cladium jamaicense) phenotypes from Everglades marl prairies and peat marshes.

PREMISE: The southern Florida Everglades landscape sustains wetlands of national and international importance. Sawgrass (Cladium jamaicense), the dominant macrophyte in the Everglades, has two phenotypes that vary in size and density between Everglades marl prairies and peat marshes. Marl prairies have recently been hypothesized to be a newly formed habitat developed after European colonization as a result of landscape-scale hydrologic modifications, implying that sawgrass marl phenotypes developed in response to the marl habitat. We examined whether sawgrass wetland phenotypes are plastic responses to marl and peat soils. METHODS: In a common-mesocosm experiment, seedlings from a single Everglades population were grown outdoors in field-collected marl or peat soils. Growth and morphology of plants were measured over 14 mo, while soil and leaf total nitrogen, total phosphorus, total carbon, and plant biomass and biomass allocation were determined in a final harvest. RESULTS: Sawgrass plant morphology diverged in marl vs. peat soils, and variations in morphology and density of mesocosm-grown plants resembled differences seen in sawgrass plants growing in marl and peat habitats in Everglades wetlands. Additionally, sawgrass growing in marl made abundant dauciform roots, while dauciform root production of sawgrass growing in peat was correlated with soil total phosphorus. CONCLUSIONS: Sawgrass from a single population grown in marl or peat soils can mimic sawgrass phenotypes associated with marl vs. peat habitats. This plasticity is consistent with the hypothesis that Everglades marl prairies are relatively new habitats that support plant communities assembled after European colonization and subsequent landscape modifications.

Intensified inundation shifts a freshwater wetland from a CO2 sink to a source.

Climate change has altered global precipitation patterns and has led to greater variation in hydrological conditions. Wetlands are important globally for their soil carbon storage. Given that wetland carbon processes are primarily driven by hydrology, a comprehensive understanding of the effect of inundation is needed. In this study, we evaluated the effect of water level (WL) and inundation duration (ID) on carbon dioxide (CO2 ) fluxes by analysing a 10-year (2008-2017) eddy covariance dataset from a seasonally inundated freshwater marl prairie in the Everglades National Park. Both gross primary production (GPP) and ecosystem respiration (ER) rates showed declines under inundation. While GPP rates decreased almost linearly as WL and ID increased, ER rates were less responsive to WL increase beyond 30 cm and extended inundation periods. The unequal responses between GPP and ER caused a weaker net ecosystem CO2 sink strength as inundation intensity increased. Eventually, the ecosystem tended to become a net CO2 source on a daily basis when either WL exceeded 46 cm or inundation lasted longer than 7 months. Particularly, with an extended period of high-WLs in 2016 (i.e., WL remained >40 cm for >9 months), the ecosystem became a CO2 source, as opposed to being a sink or neutral for CO2 in other years. Furthermore, the extreme inundation in 2016 was followed by a 4-month postinundation period with lower net ecosystem CO2 uptake compared to other years. Given that inundation plays a key role in controlling ecosystem CO2 balance, we suggest that a future with more intensive inundation caused by climate change or water management activities can weaken the CO2 sink strength of the Everglades freshwater marl prairies and similar wetlands globally, creating a positive feedback to climate change.

Water uptake of Alaskan tundra evergreens during the winter-spring transition.

PREMISE OF THE STUDY: The cold season in the Arctic extends over 8 to 9 mo, yet little is known about vascular plant physiology during this period. Evergreen species photosynthesize under the snow, implying that they are exchanging water with the atmosphere. However, liquid water available for plant uptake may be limited at this time. The study objective was to determine whether evergreen plants are actively taking up water while under snow and/or immediately following snowmelt during spring thaw. METHODS: In two in situ experiments, one at the plot level and another at the individual species level, (2)H-labeled water was used as a tracer injected beneath the snow, after which plant stems and leaves were tested for the presence of the label. In separate experiments, excised shoots of evergreen species were exposed to (2)H-labeled water for ∼5 s or 60 min and tested for foliar uptake of the label. KEY RESULTS: In both the plot-level and the species-level experiments, some (2)H-labeled water was found in leaves and stems. Additionally, excised individual plant shoots exposed to labeled water for 60 min took up significantly more (2)H-label than shoots exposed ∼5 s. CONCLUSIONS: Evergreen tundra plants take up water under snow cover, some via roots, but also likely by foliar uptake. The ability to take up water in the subnivean environment allows evergreen tundra plants to take advantage of mild spring conditions under the snow and replenish carbon lost by winter respiration.

First direct landscape-scale measurement of tropical rain forest Leaf Area Index, a key driver of global primary productivity.

Leaf Area Index (leaf area per unit ground area, LAI) is a key driver of forest productivity but has never previously been measured directly at the landscape scale in tropical rain forest (TRF). We used a modular tower and stratified random sampling to harvest all foliage from forest floor to canopy top in 55 vertical transects (4.6 m(2)) across 500 ha of old growth in Costa Rica. Landscape LAI was 6.00 +/- 0.32 SEM. Trees, palms and lianas accounted for 89% of the total, and trees and lianas were 95% of the upper canopy. All vertical transects were organized into quantitatively defined strata, partially resolving the long-standing controversy over canopy stratification in TRF. Total LAI was strongly correlated with forest height up to 21 m, while the number of canopy strata increased with forest height across the full height range. These data are a benchmark for understanding the structure and functional composition of TRF canopies at landscape scales, and also provide insights for improving ecosystem models and remote sensing validation.

Explaining variation in tropical plant community composition: influence of environmental and spatial data quality.

The degree to which variation in plant community composition (beta-diversity) is predictable from environmental variation, relative to other spatial processes, is of considerable current interest. We addressed this question in Costa Rican rain forest pteridophytes (1,045 plots, 127 species). We also tested the effect of data quality on the results, which has largely been overlooked in earlier studies. To do so, we compared two alternative spatial models [polynomial vs. principal coordinates of neighbour matrices (PCNM)] and ten alternative environmental models (all available environmental variables vs. four subsets, and including their polynomials vs. not). Of the environmental data types, soil chemistry contributed most to explaining pteridophyte community variation, followed in decreasing order of contribution by topography, soil type and forest structure. Environmentally explained variation increased moderately when polynomials of the environmental variables were included. Spatially explained variation increased substantially when the multi-scale PCNM spatial model was used instead of the traditional, broad-scale polynomial spatial model. The best model combination (PCNM spatial model and full environmental model including polynomials) explained 32% of pteridophyte community variation, after correcting for the number of sampling sites and explanatory variables. Overall evidence for environmental control of beta-diversity was strong, and the main floristic gradients detected were correlated with environmental variation at all scales encompassed by the study (c. 100-2,000 m). Depending on model choice, however, total explained variation differed more than fourfold, and the apparent relative importance of space and environment could be reversed. Therefore, we advocate a broader recognition of the impacts that data quality has on analysis results. A general understanding of the relative contributions of spatial and environmental processes to species distributions and beta-diversity requires that methodological artefacts are separated from real ecological differences.