skip to main content


Title: 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.

 
more » « less
Award ID(s):
0620409 1832229
NSF-PAR ID:
10459245
Author(s) / Creator(s):
 ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
American Journal of Botany
Volume:
107
Issue:
1
ISSN:
0002-9122
Page Range / eLocation ID:
p. 56-65
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    The potential for animals to modify spatial patterns of nutrient limitation for autotrophs and habitat availability for other members of their communities is increasingly recognized. However, net trophic effects of consumers acting as ecosystem engineers remain poorly known. The American AlligatorAlligator mississippiensisis an abundant predator capable of dramatic modifications of physical habitat through the creation and maintenance of pond‐like basins, but its role in influencing community structure and nutrient dynamics is less appreciated.

    We investigated if alligators engineer differences in nutrient availability and changes to community structure by their creation of ‘alligator ponds’ compared to the surrounding phosphorus (P)‐limited oligotrophic marsh.

    We used a halo sampling design of three distinct habitats extending outward from 10 active alligator ponds across a hydrological gradient in the Everglades, USA. We performed nutrient analysis on basal food‐web resources and quantitative community analyses, and stoichiometric analyses on plants and animals.

    Our findings demonstrate that alligators act as ecosystem engineers and enhance food‐web heterogeneity by increasing nutrient availability, manipulating physical structure and altering algal, plant and animal communities. Flocculent detritus, an unconsolidated layer of particulate organic matter and soil, showed strong patterns of P enrichment in ponds. Higher P availability in alligator ponds also resulted in bottom‐up trophic transfer of nutrients as evidenced by higher growth rates (lower N:P) for plants and aquatic consumers. Edge habitats surrounding alligator ponds contained the most diverse communities of invertebrates and plants, but low total abundance of fishes, likely driven by high densities of emergent macrophytes. Pond communities exhibited higher abundance of fish compared to edge habitat and were dominated by compositions of small invertebrates that track high nutrient availability in the water column. Marshes contained high numbers of animals that are closely tied to periphyton mats, which were absent from other habitats.

    Alligator‐engineered habitats are ecologically important by providing nutrient‐enriched ‘hotspots’ in an oligotrophic system, habitat heterogeneity to marshes, and refuges for other fauna during seasonal disturbances. This work adds to growing evidence that efforts to model community dynamics should routinely consider animal‐mediated bottom‐up processes like ecosystem engineering.

     
    more » « less
  2. Dissolved organic matter (DOM) drives biogeochemical processes in aquatic ecosystems. Yet, how hydrologic restoration in nutrient‐enriched ecosystems changes DOM and the consequences of those changes for the carbon cycle remain unclear. To predict the consequences of hydrologic restoration on carbon cycling in restored wetlands, we need to understand how local environmental factors influence production, processing, and transport of DOM. We collected surface water samples along transects in restored peat (organic‐rich, macrophyte‐dominated) and marl (carbonate, periphyton‐dominated) freshwater marshes in the Everglades (Florida, U.S.A.) that varied in environmental factors (water depth, phosphorus [P] concentrations [water, macrophytes, periphyton, and soil], and primary producer biomass) to understand drivers of dissolved organic carbon (DOC) concentrations and DOM composition. Higher water depths led to a “greening” of DOM, due to increasing algal contributions, with decreasing concentrations of DOC in peat wetlands, and a “browning” of DOM, due to increasing humic contributions, with increasing DOC concentrations in marl wetlands. Soil total P was positively correlated with DOC concentrations and microbial contributions to DOM in peat wetlands, and periphyton total P was positively correlated with algal contributions to DOM in marl wetlands. Despite large variations in both vegetation biomass and periphyton biovolume across transects and sites, neither were predictors of DOC concentrations or DOM composition. Hydrologic restoration differentially alters DOM in peat and marl marshes and interacts with nutrient enrichment to shift proportions of green and brown contributions to surface water chemistry, which has the potential to modify wetland food webs, as well as the processing of carbon by micro‐organisms.

     
    more » « less
  3. Excessive phosphorus (P) applications to croplands can contribute to eutrophication of surface waters through surface runoff and subsurface (leaching) losses. We analyzed leaching losses of total dissolved P (TDP) from no-till corn, hybrid poplar (Populus nigra X P. maximowiczii), switchgrass (Panicum virgatum), miscanthus (Miscanthus giganteus), native grasses, and restored prairie, all planted in 2008 on former cropland in Michigan, USA. All crops except corn (13 kg P ha−1 year−1) were grown without P fertilization. Biomass was harvested at the end of each growing season except for poplar. Soil water at 1.2 m depth was sampled weekly to biweekly for TDP determination during March–November 2009–2016 using tension lysimeters. Soil test P (0–25 cm depth) was measured every autumn. Soil water TDP concentrations were usually below levels where eutrophication of surface waters is frequently observed (> 0.02 mg L−1) but often higher than in deep groundwater or nearby streams and lakes. Rates of P leaching, estimated from measured concentrations and modeled drainage, did not differ statistically among cropping systems across years; 7-year cropping system means ranged from 0.035 to 0.072 kg P ha−1 year−1 with large interannual variation. Leached P was positively related to STP, which decreased over the 7 years in all systems. These results indicate that both P-fertilized and unfertilized cropping systems may leach legacy P from past cropland management. Experimental details The Biofuel Cropping System Experiment (BCSE) is located at the W.K. Kellogg Biological Station (KBS) (42.3956° N, 85.3749° W; elevation 288 m asl) in southwestern Michigan, USA. This site is a part of the Great Lakes Bioenergy Research Center (www.glbrc.org) and is a Long-term Ecological Research site (www.lter.kbs.msu.edu). Soils are mesic Typic Hapludalfs developed on glacial outwash54 with high sand content (76% in the upper 150 cm) intermixed with silt-rich loess in the upper 50 cm55. The water table lies approximately 12–14 m below the surface. The climate is humid temperate with a mean annual air temperature of 9.1 °C and annual precipitation of 1005 mm, 511 mm of which falls between May and September (1981–2010)56,57. The BCSE was established as a randomized complete block design in 2008 on preexisting farmland. Prior to BCSE establishment, the field was used for grain crop and alfalfa (Medicago sativa L.) production for several decades. Between 2003 and 2007, the field received a total of ~ 300 kg P ha−1 as manure, and the southern half, which contains one of four replicate plots, received an additional 206 kg P ha−1 as inorganic fertilizer. The experimental design consists of five randomized blocks each containing one replicate plot (28 by 40 m) of 10 cropping systems (treatments) (Supplementary Fig. S1; also see Sanford et al.58). Block 5 is not included in the present study. Details on experimental design and site history are provided in Robertson and Hamilton57 and Gelfand et al.59. Leaching of P is analyzed in six of the cropping systems: (i) continuous no-till corn, (ii) switchgrass, (iii) miscanthus, (iv) a mixture of five species of native grasses, (v) a restored native prairie containing 18 plant species (Supplementary Table S1), and (vi) hybrid poplar. Agronomic management Phenological cameras and field observations indicated that the perennial herbaceous crops emerged each year between mid-April and mid-May. Corn was planted each year in early May. Herbaceous crops were harvested at the end of each growing season with the timing depending on weather: between October and November for corn and between November and December for herbaceous perennial crops. Corn stover was harvested shortly after corn grain, leaving approximately 10 cm height of stubble above the ground. The poplar was harvested only once, as the culmination of a 6-year rotation, in the winter of 2013–2014. Leaf emergence and senescence based on daily phenological images indicated the beginning and end of the poplar growing season, respectively, in each year. Application of inorganic fertilizers to the different crops followed a management approach typical for the region (Table 1). Corn was fertilized with 13 kg P ha−1 year−1 as starter fertilizer (N-P-K of 19-17-0) at the time of planting and an additional 33 kg P ha−1 year−1 was added as superphosphate in spring 2015. Corn also received N fertilizer around the time of planting and in mid-June at typical rates for the region (Table 1). No P fertilizer was applied to the perennial grassland or poplar systems (Table 1). All perennial grasses (except restored prairie) were provided 56 kg N ha−1 year−1 of N fertilizer in early summer between 2010 and 2016; an additional 77 kg N ha−1 was applied to miscanthus in 2009. Poplar was fertilized once with 157 kg N ha−1 in 2010 after the canopy had closed. Sampling of subsurface soil water and soil for P determination Subsurface soil water samples were collected beneath the root zone (1.2 m depth) using samplers installed at approximately 20 cm into the unconsolidated sand of 2Bt2 and 2E/Bt horizons (soils at the site are described in Crum and Collins54). Soil water was collected from two kinds of samplers: Prenart samplers constructed of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) in replicate blocks 1 and 2 and Eijkelkamp ceramic samplers (http://www.eijkelkamp.com) in blocks 3 and 4 (Supplementary Fig. S1). The samplers were installed in 2008 at an angle using a hydraulic corer, with the sampling tubes buried underground within the plots and the sampler located about 9 m from the plot edge. There were no consistent differences in TDP concentrations between the two sampler types. Beginning in the 2009 growing season, subsurface soil water was sampled at weekly to biweekly intervals during non-frozen periods (April–November) by applying 50 kPa of vacuum to each sampler for 24 h, during which the extracted water was collected in glass bottles. Samples were filtered using different filter types (all 0.45 µm pore size) depending on the volume of leachate collected: 33-mm dia. cellulose acetate membrane filters when volumes were less than 50 mL; and 47-mm dia. Supor 450 polyethersulfone membrane filters for larger volumes. Total dissolved phosphorus (TDP) in water samples was analyzed by persulfate digestion of filtered samples to convert all phosphorus forms to soluble reactive phosphorus, followed by colorimetric analysis by long-pathlength spectrophotometry (UV-1800 Shimadzu, Japan) using the molybdate blue method60, for which the method detection limit was ~ 0.005 mg P L−1. Between 2009 and 2016, soil samples (0–25 cm depth) were collected each autumn from all plots for determination of soil test P (STP) by the Bray-1 method61, using as an extractant a dilute hydrochloric acid and ammonium fluoride solution, as is recommended for neutral to slightly acidic soils. The measured STP concentration in mg P kg−1 was converted to kg P ha−1 based on soil sampling depth and soil bulk density (mean, 1.5 g cm−3). Sampling of water samples from lakes, streams and wells for P determination In addition to chemistry of soil and subsurface soil water in the BCSE, waters from lakes, streams, and residential water supply wells were also sampled during 2009–2016 for TDP analysis using Supor 450 membrane filters and the same analytical method as for soil water. These water bodies are within 15 km of the study site, within a landscape mosaic of row crops, grasslands, deciduous forest, and wetlands, with some residential development (Supplementary Fig. S2, Supplementary Table S2). Details of land use and cover change in the vicinity of KBS are given in Hamilton et al.48, and patterns in nutrient concentrations in local surface waters are further discussed in Hamilton62. Leaching estimates, modeled drainage, and data analysis Leaching was estimated at daily time steps and summarized as total leaching on a crop-year basis, defined from the date of planting or leaf emergence in a given year to the day prior to planting or emergence in the following year. TDP concentrations (mg L−1) of subsurface soil water were linearly interpolated between sampling dates during non-freezing periods (April–November) and over non-sampling periods (December–March) based on the preceding November and subsequent April samples. Daily rates of TDP leaching (kg ha−1) were calculated by multiplying concentration (mg L−1) by drainage rates (m3 ha−1 day−1) modeled by the Systems Approach for Land Use Sustainability (SALUS) model, a crop growth model that is well calibrated for KBS soil and environmental conditions. SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, N fertilizer application, and tillage), and genetics63. The SALUS water balance sub-model simulates surface runoff, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons63. The SALUS model has been used in studies of evapotranspiration48,51,64 and nutrient leaching20,65,66,67 from KBS soils, and its predictions of growing-season evapotranspiration are consistent with independent measurements based on growing-season soil water drawdown53 and evapotranspiration measured by eddy covariance68. Phosphorus leaching was assumed insignificant on days when SALUS predicted no drainage. Volume-weighted mean TDP concentrations in leachate for each crop-year and for the entire 7-year study period were calculated as the total dissolved P leaching flux (kg ha−1) divided by the total drainage (m3 ha−1). One-way ANOVA with time (crop-year) as the fixed factor was conducted to compare total annual drainage rates, P leaching rates, volume-weighted mean TDP concentrations, and maximum aboveground biomass among the cropping systems over all seven crop-years as well as with TDP concentrations from local lakes, streams, and groundwater wells. When a significant (α = 0.05) difference was detected among the groups, we used the Tukey honest significant difference (HSD) post-hoc test to make pairwise comparisons among the groups. In the case of maximum aboveground biomass, we used the Tukey–Kramer method to make pairwise comparisons among the groups because the absence of poplar data after the 2013 harvest resulted in unequal sample sizes. We also used the Tukey–Kramer method to compare the frequency distributions of TDP concentrations in all of the soil leachate samples with concentrations in lakes, streams, and groundwater wells, since each sample category had very different numbers of measurements. Individual spreadsheets in “data table_leaching_dissolved organic carbon and nitrogen.xls” 1.    annual precip_drainage 2.    biomass_corn, perennial grasses 3.    biomass_poplar 4.    annual N leaching _vol-wtd conc 5.    Summary_N leached 6.    annual DOC leachin_vol-wtd conc 7.    growing season length 8.    correlation_nh4 VS no3 9.    correlations_don VS no3_doc VS don Each spreadsheet is described below along with an explanation of variates. Note that ‘nan’ indicate data are missing or not available. First row indicates header; second row indicates units 1. Spreadsheet: annual precip_drainage Description: Precipitation measured from nearby Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Weather station, over 2009-2016 study period. Data shown in Figure 1; original data source for precipitation (https://lter.kbs.msu.edu/datatables/7). Drainage estimated from SALUS crop model. Note that drainage is percolation out of the root zone (0-125 cm). Annual precipitation and drainage values shown here are calculated for growing and non-growing crop periods. Variate    Description year    year of the observation crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” precip_G    precipitation during growing period (milliMeter) precip_NG    precipitation during non-growing period (milliMeter) drainage_G    drainage during growing period (milliMeter) drainage_NG    drainage during non-growing period (milliMeter)      2. Spreadsheet: biomass_corn, perennial grasses Description: Maximum aboveground biomass measurements from corn, switchgrass, miscanthus, native grass and restored prairie plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2.   Variate    Description year    year of the observation date    day of the observation (mm/dd/yyyy) crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” replicate    each crop has four replicated plots, R1, R2, R3 and R4 station    stations (S1, S2 and S3) of samplings within the plot. For more details, refer to link (https://data.sustainability.glbrc.org/protocols/156) species    plant species that are rooted within the quadrat during the time of maximum biomass harvest. See protocol for more information, refer to link (http://lter.kbs.msu.edu/datatables/36) For maize biomass, grain and whole biomass reported in the paper (weed biomass or surface litter are excluded). Surface litter biomass not included in any crops; weed biomass not included in switchgrass and miscanthus, but included in grass mixture and prairie. fraction    Fraction of biomass biomass_plot    biomass per plot on dry-weight basis (Grams_Per_SquareMeter) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying column biomass per plot with 0.01 3. Spreadsheet: biomass_poplar Description: Maximum aboveground biomass measurements from poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Data shown in Figure 2. Note that poplar biomass was estimated from crop growth curves until the poplar was harvested in the winter of 2013-14. Variate    Description year    year of the observation method    methods of poplar biomass sampling date    day of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 diameter_at_ground    poplar diameter (milliMeter) at the ground diameter_at_15cm    poplar diameter (milliMeter) at 15 cm height biomass_tree    biomass per plot (Grams_Per_Tree) biomass_ha    biomass (megaGrams_Per_Hectare) by multiplying biomass per tree with 0.01 4. Spreadsheet: annual N leaching_vol-wtd conc Description: Annual leaching rate (kiloGrams_N_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_N_Per_Liter) of nitrate (no3) and dissolved organic nitrogen (don) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen leached and volume-wtd mean N concentration shown in Figure 3a and Figure 3b, respectively. Note that ammonium (nh4) concentration were much lower and often undetectable (<0.07 milliGrams_N_Per_Liter). Also note that in 2009 and 2010 crop-years, data from some replicates are missing.    Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) vol-wtd no3 conc.    Volume-weighted mean no3 concentration (milliGrams_N_Per_Liter) vol-wtd don conc.    Volume-weighted mean don concentration (milliGrams_N_Per_Liter) 5. Spreadsheet: summary_N leached Description: Summary of total amount and forms of N leached (kiloGrams_N_Per_Hectare) and the percent of applied N lost to leaching over the seven years for corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for nitrogen amount leached shown in Figure 4a and percent of applied N lost shown in Figure 4b. Note the fraction of unleached N includes in harvest, accumulation in root biomass, soil organic matter or gaseous N emissions were not measured in the study. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” no3 leached    annual leaching rates of nitrate (kiloGrams_N_Per_Hectare) don leached    annual leaching rates of don (kiloGrams_N_Per_Hectare) N unleached    N unleached (kiloGrams_N_Per_Hectare) in other sources are not studied % of N applied N lost to leaching    % of N applied N lost to leaching 6. Spreadsheet: annual DOC leachin_vol-wtd conc Description: Annual leaching rate (kiloGrams_Per_Hectare) and volume-weighted mean N concentrations (milliGrams_Per_Liter) of dissolved organic carbon (DOC) in the leachate samples collected from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2016. Data for DOC leached and volume-wtd mean DOC concentration shown in Figure 5a and Figure 5b, respectively. Note that in 2009 and 2010 crop-years, water samples were not available for DOC measurements.     Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” crop-year    year of the observation replicate    each crop has four replicated plots, R1, R2, R3 and R4 doc leached    annual leaching rates of nitrate (kiloGrams_Per_Hectare) vol-wtd doc conc.    volume-weighted mean doc concentration (milliGrams_Per_Liter) 7. Spreadsheet: growing season length Description: Growing season length (days) of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in the Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2009-2015. Date shown in Figure S2. Note that growing season is from the date of planting or emergence to the date of harvest (or leaf senescence in case of poplar).   Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation growing season length    growing season length (days) 8. Spreadsheet: correlation_nh4 VS no3 Description: Correlation of ammonium (nh4+) and nitrate (no3-) concentrations (milliGrams_N_Per_Liter) in the leachate samples from corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data shown in Figure S3. Note that nh4+ concentration in the leachates was very low compared to no3- and don concentration and often undetectable in three crop-years (2013-2015) when measurements are available. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” date    date of the observation (mm/dd/yyyy) replicate    each crop has four replicated plots, R1, R2, R3 and R4 nh4 conc    nh4 concentration (milliGrams_N_Per_Liter) no3 conc    no3 concentration (milliGrams_N_Per_Liter)   9. Spreadsheet: correlations_don VS no3_doc VS don Description: Correlations of don and nitrate concentrations (milliGrams_N_Per_Liter); and doc (milliGrams_Per_Liter) and don concentrations (milliGrams_N_Per_Liter) in the leachate samples of corn, switchgrass, miscanthus, native grass, restored prairie and poplar plots in Great Lakes Bioenergy Research Center (GLBRC) Biomass Cropping System Experiment (BCSE) during 2013-2015. Data of correlation of don and nitrate concentrations shown in Figure S4 a and doc and don concentrations shown in Figure S4 b. Variate    Description crop    “corn” “switchgrass” “miscanthus” “nativegrass” “restored prairie” “poplar” year    year of the observation don    don concentration (milliGrams_N_Per_Liter) no3     no3 concentration (milliGrams_N_Per_Liter) doc    doc concentration (milliGrams_Per_Liter) 
    more » « less
  4. Net ecosystem carbon balance is a comprehensive assessment of ecosystem function that can test restoration effectiveness. Coastal peatlands are globally important carbon sinks that are vulnerable to carbon loss with saltwater intrusion. It is uncertain how wetland carbon stocks and fluxes change during freshwater restoration following exposure to saltwater and elevated nutrients. We restored freshwater to sawgrass (Cladium jamaicense) peat monoliths from freshwater marshes of the Everglades (Florida, U.S.A.) that had previously been exposed to elevated salinity (approximately9 ppt) and phosphorus (P) loading (1 g P m−2year−1) in wetland mesocosms. We quantified changes in water and soil physicochemistry, plant and soil carbon and nutrient standing stocks, and net ecosystem productivity during restoration. Added freshwater immediately reduced porewater salinity from >8 to approximately 2 ppt, but elevated porewater dissolved organic carbon persisted. Above‐ and belowground biomass, leaf P concentrations, and instantaneous rates of gross ecosystem productivity (GEP) and ecosystem respiration (ER) remained elevated from prior added P. Modeled monthly GEP and ER were higher in marshes with saltwater and P legacies, resulting in negative net ecosystem productivities that were up to 12× lower than controls. Leaf litter breakdown rates and litter P concentrations were 2× higher in marshes with legacies of added saltwater and P. Legacies of saltwater and P on carbon loss persisted despite freshwater restoration, but recovery was greatest for freshwater marshes exposed to saltwater alone. Our results suggest that restoration in nutrient‐limited freshwater wetlands exposed to saltwater intrusion and nutrient enrichment is a slow process.

     
    more » « less
  5. Abstract Aim and Questions

    Sea‐level rise has been responsible for extensive vegetation changes in coastal areas worldwide. The intent of our study was to analyze vegetation dynamics of a South Florida coastal watershed within an explicit spatiotemporal framework that might aid in projecting the landscape's future response to restoration efforts. We also asked whether recent transgression by mangroves and other halophytes has resulted in reduced plant diversity at local or subregional scales.

    Location

    Florida’'s Southeast Saline Everglades, USA.

    Methods

    We selected 26 locations, representing a transition zone between sawgrass marsh and mangrove swamp, that was last sampled floristically in 1995. Within this transition zone, leading‐ and trailing‐edge subzones were defined based on plant composition in 1995. Fifty‐two site × time combinations were classified and then ordinated to examine vegetation–environment relationships using 2016 environmental data. We calculated alpha‐diversity using Hill numbers or Shannon–Weiner index species equivalents and compared these across the two surveys. We used a multiplicative diversity partition to determine beta‐diversity from landscape‐scale (gamma) diversity in the entire dataset or in each subzone.

    Results

    Mangrove and mangrove associates became more important in both subzones: through colonization and establishment in the leading edge, and through population growth combined with the decline of freshwater species in the trailing edge. Alpha‐diversity increased significantly in the leading edge and decreased nominally in the trailing edge, while beta‐diversity declined slightly in both subzones as well as across the study area.

    Conclusions

    Recent halophyte encroachment in the Southeast Saline Everglades continues a trend evident for almost a century. While salinity is an important environmental driver, species’ responses suggest that restoration efforts based on supplementing freshwater delivery will not reverse a trend that depends on multiple interacting factors. Sea‐level‐rise‐driven taxonomic homogenization in coastal wetland communities develops slowly, lagging niche‐based changes in community structure and composition.

     
    more » « less