Absence of dissolved oxygen (anoxia) in the hypolimnion of lakes can eliminate habitat for sensitive species and may induce the release of sediment‐bound phosphorus. Lake anoxia generally results from decomposition of organic matter, which is exacerbated by high nutrient loads. Total phosphorus (TP) in lakes is regulated by static aspects of the lake’s watershed, but lake TP can be readily increased by human activities. In some low‐nutrient lakes, basin morphometry may induce naturally occurring anoxia. The occurrence of natural anoxia is especially important to consider in lake water quality assessments that compare observed conditions to expected reference conditions. To investigate the occurrence of natural vs. anthropogenically influenced anoxia, we constructed a logistic regression model to calculate the probability of low‐nutrient lakes (TP < 15 µg/L) developing aerial anoxic extent ≥10% by testing the predictive potential of variables related to basin morphometry, depths of lake thermal strata, epilimnetic TP, and dissolved organic carbon (DOC). Maximum lake depth and the proportion of lake area under the top of the metalimnion were the most important variables to predict the likelihood of hypolimnetic anoxia, which correctly predicted anoxic condition in 84% of lakes (Model 1). Adding TP as a third variable to Model 1 produced a significantly improved model (Model 2) but the prediction success rate was comparable (86%). We also present a model for lakes with limited bathymetric data, which predicts anoxia with 81% accuracy based on maximum lake depth and mean thermocline depth at peak stratification. DOC was relatively low (4.3 ± 1.5 mg/L [mean ± SD]) in the study lakes and its inclusion did not improve model performance. In Model 1, lakes with an anoxic extent ≥10% of lake area had significantly higher epilimnetic TP than lakes with oxic hypolimnia, regardless of prediction category or success. Our results indicate that including TP as a variable helps refine models based on morphometry alone, but lake morphometry and stratification dynamics are the most important factors in the development of anoxic extent in low‐nutrient temperate lakes. Our approach informs studies concerned with identifying key factors that influence regime shifts in a variety of ecosystems.
Response of Green Lake, Wisconsin, to Changes in Phosphorus Loading, With Special Emphasis on Near-Surface Total Phosphorus Concentrations and Metalimnetic Dissolved Oxygen Minima
Green Lake is the deepest natural inland lake in Wisconsin, with a maximum depth of about 72 meters. In the early 1900s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity) with low dissolved oxygen (DO) concentrations occurring in only the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake have increased; these changes have led to increased algal production and low DO concentrations not only in the deepest areas but also in the middle of the water column (metalimnion). The U.S. Geological Survey has routinely monitored the lake since 2004 and its tributaries since 1988. Results from this monitoring led the Wisconsin Department of Natural Resources (WDNR) to list the lake as impaired because of low DO concentrations in the metalimnion, and they identified elevated TP concentrations as the cause of impairment.
As part of this study by the U.S. Geological Survey, in cooperation with the Green Lake Sanitary District, the lake and its tributaries were comprehensively sampled in 2017–18 to augment ongoing monitoring that would further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water-quality models were then used to determine the causes of the low DO concentrations and the magnitudes of P-load reductions needed to improve the water quality of the lake enough to meet multiple water-quality goals, including the WDNR’s criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900s but, since the late 1970s, have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–18 (the baseline period for this study), the near-surface geometric mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were above the 0.015-mg/L WDNR criterion for the lake), and the metalimnetic DO minimum concentrations (MOMs) measured in August ranged from 1.0 to 4.7 mg/L. The degradation in water quality was assumed to have been caused by excessive P inputs to the lake; therefore, the TP inputs to the lake were estimated. The mean annual external P load during 2014–18 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. During fall turnover, internal sediment recycling contributed an additional 7,040 kilograms that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there was a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, quantified by Secchi disk depths, had a greater response to decreases in P loading than to increases in P loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration in the east side of the lake, with higher TP concentrations than in the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr is equivalent to a 46-percent reduction in the potentially controllable external P sources (all external sources except for precipitation, atmospheric deposition, and waterfowl) from those measured during water years 2014–18. The total external P loading would need to decrease to 7,680 kg/yr (a 17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to decrease to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June–September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM–AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that MOMs are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM–AED results indicated that settling of organic material during summer might be slowed by the colder, denser, and more viscous water in the metalimnion and thus increase DO consumption. Based on empirical evidence from a comparison of MOMs with various meteorological, hydrologic, water quality, and in-lake physical factors, MOMs were lower during summers, when metalimnetic water temperatures were warmer, near-surface Chl-a and TP concentrations were higher, and Secchi depths were lower. GLM–AED results indicated that the external P load would need to be reduced to about 4,060 kg/yr, a 57-percent reduction from that measured in 2014–18, to eliminate the occurrence of MOMs less than 5 mg/L during more than 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake; however, it may take several years for the full effects of the external-load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
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- Award ID(s):
- 1759865
- NSF-PAR ID:
- 10338057
- Date Published:
- Journal Name:
- Scientific investigations report
- ISSN:
- 2328-0328
- Page Range / eLocation ID:
- 77
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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
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Abstract The concentration of dissolved oxygen (DO) is an important attribute of aquatic ecosystems, influencing habitat, drinking water quality, biodiversity, nutrient biogeochemistry, and greenhouse gas emissions. While average summer DO concentrations are declining in lakes across the temperate zone, much remains unknown about seasonal factors contributing to deepwater DO losses. It is unclear whether declines are related to increasing rates of seasonal DO depletion or changes in seasonal stratification that limit re‐oxygenation of deep waters. Furthermore, despite the presence of important biological and ecological DO thresholds, there has been no large‐scale assessment of changes in the amount of habitat crossing these thresholds, limiting the ability to understand the consequences of observed DO losses. We used a dataset from >400 widely distributed lakes to identify the drivers of DO losses and quantify the frequency and volume of lake water crossing biologically and ecologically important threshold concentrations ranging from 5 to 0.5 mg/L. Our results show that while there were no consistent changes over time in seasonal DO depletion rates, over three‐quarters of lakes exhibited an increase in the duration of stratification, providing more time for seasonal deepwater DO depletion to occur. As a result, most lakes have experienced summertime increases in the amount of water below all examined thresholds in deepwater DO concentration, with increases in the proportion of the water column below thresholds ranging between 0.9% and 1.7% per decade. In the 30‐day period preceding the end of stratification, increases were greater at >2.2% per decade and >70% of analyzed lakes experienced increases in the amount of oxygen‐depleted water. These results indicate ongoing climate‐induced increases in the duration of stratification have already contributed to reduction of habitat for many species, likely increased internal nutrient loading, and otherwise altered lake chemistry. Future warming is likely to exacerbate these trends.
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Hurricanes are recurring high-energy disturbances in coastal regions that change community structure and function of mangrove wetlands. However, most of the studies assessing hurricane impacts on mangroves have focused on negative effects without considering the positive influence of hurricane-induced sediment deposition and associated nutrient fertilization on mangrove productivity and resilience. Here, we quantified how Hurricane Irma influenced soil nutrient pools, vertical accretion, and plant phosphorus (P) uptake after its passage across the Florida Coastal Everglades in September 2017. Vertical accretion from Irma’s deposits was 6.7 to 14.4 times greater than the long-term (100 y) annual accretion rate (0.27 ± 0.04 cm y−1). Storm deposits extended up to 10-km inland from the Gulf of Mexico. Total P (TP) inputs were highest at the mouth of estuaries, with P concentration double that of underlying surface (top 10 cm) soils (0.19 ± 0.02 mg cm−3). This P deposition contributed 49 to 98% to the soil nutrient pool. As a result, all mangrove species showed a significant increase in litter foliar TP and soil porewater inorganic P concentrations in early 2018, 3 mo after Irma’s impact, thus underscoring the interspecies differences in nutrient uptake. Mean TP loading rates were five times greater in southwestern (94 ± 13 kg ha−1d−1) mangrove-dominated estuaries compared to the southeastern region, highlighting the positive role of hurricanes as a natural fertilization mechanism influencing forest productivity. P-rich, mineral sediments deposited by hurricanes create legacies that facilitate rapid forest recovery, stimulation of peat soil development, and resilience to sea-level rise.
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Abstract Effects of climate change‐driven disturbance on lake ecosystems can be subtle; indirect effects include increased nutrient loading that could impact ecosystem function. We designed a low‐level fertilization experiment to mimic persistent, climate change‐driven disturbances (deeper thaw, greater weathering, or thermokarst failure) delivering nutrients to arctic lakes. We measured responses of pelagic trophic levels over 12 yr in a fertilized deep lake with fish and a shallow fishless lake, compared to paired reference lakes, and monitored recovery for 6 yr. Relative to prefertilization in the deep lake, we observed a maximum pelagic response in chl
a (+201%), dissolved oxygen (DO, −43%), and zooplankton biomass (+88%) during the fertilization period (2001–2012). Other responses to fertilization, such as water transparency and fish relative abundance, were delayed, but both ultimately declined. Phyto‐ and zooplankton biomass and community composition shifted with fertilization. The effects of fertilization were less pronounced in the paired shallow lakes, because of a natural thermokarst failure likely impacting the reference lake. In the deep lake there was (a) moderate resistance to change in ecosystem functions at all trophic levels, (b) eventual responses were often nonlinear, and (c) postfertilization recovery (return) times were most rapid at the base of the food web (2–4 yr) while higher trophic levels failed to recover after 6 yr. The timing and magnitude of responses to fertilization in these arctic lakes were similar to responses in other lakes, suggesting indirect effects of climate change that modify nutrient inputs may affect many lakes in the future.
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3133/sir20225003
