Relationship of chlorophyll to phosphorus and nitrogen in nutrient-rich lakes

<p>Nitrogen (N) and phosphorus (P) commonly co-limit primary productivity in lakes, and chlorophyll <i>a</i> (Chl-<i>a</i>) is predicted to be greatest under high N, high P regimes. Because land use practices can alter N and P biogeochemical cycles in watersheds, it is unclear whether previously documented phytoplankton–nutrient relationships apply where landscapes are highly disturbed. Here, we analyzed a lake water quality database from an agricultural region to explore relationships among Chl-<i>a</i>, total N (TN), and total P (TP) under extreme nutrient concentrations. Chl-<i>a</i> was weakly related to TN when TP was ≤100 μg L⁻¹ but displayed a stronger response to TN at higher TP. When TP exceeded 100 μg L⁻¹, Chl-<i>a</i> increased with increasing TN until reaching a TN threshold of ~3 mg L⁻¹ and decreased thereafter, resulting in a high nutrient, low Chl-<i>a</i> region that did not coincide with shifts in nutrient limitation, light availability, cellular Chl-<i>a</i> content, phytoplankton composition, or zooplankton grazing pressure. Beyond the threshold, nitrate comprised most of TN and occurred with reduced dissolved organic matter (DOM). These observations suggest that photolysis of nitrate may produce reactive oxygen species that damage DOM and phytoplankton. Reduction in N loading at high P could therefore increase Chl-<i>a</i> and decrease water clarity, resulting in an apparent worsening of water quality. Our data suggest that monitoring Chl-<i>a</i> or Secchi depth may fail to indicate water quality degradation by extreme nutrient concentrations. These findings highlight how extreme nutrient regimes in lakes can produce novel relationships between phytoplankton and nutrients.</p

Full Archive

Full Archiv

Watershed Sediment Losses to Lakes Accelerating Despite Agricultural Soil Conservation Efforts

<div><p>Agricultural soil loss and deposition in aquatic ecosystems is a problem that impairs water quality worldwide and is costly to agriculture and food supplies. In the US, for example, billions of dollars have subsidized soil and water conservation practices in agricultural landscapes over the past decades. We used paleolimnological methods to reconstruct trends in sedimentation related to human-induced landscape change in 32 lakes in the intensively agricultural region of the Midwestern United States. Despite erosion control efforts, we found accelerating increases in sediment deposition from erosion; median erosion loss since 1800 has been 15.4 tons ha⁻¹. Sediment deposition from erosion increased >6-fold, from 149 g m⁻² yr⁻¹ in 1850 to 986 g m⁻² yr⁻¹ by 2010. Average time to accumulate one mm of sediment decreased from 631 days before European settlement (ca. 1850) to 59 days mm⁻¹ at present. Most of this sediment was deposited in the last 50 years and is related to agricultural intensification rather than land clearance or predominance of agricultural lands. In the face of these intensive agricultural practices, traditional soil conservation programs have not decelerated downstream losses. Despite large erosion control subsidies, erosion and declining water quality continue, thus new approaches are needed to mitigate erosion and water degradation.</p> </div

Changing agricultural practices and regional lake sedimentation rates since European settlement, shown as decadal averages across all 32 lakes.

<p>(A) Percent land in farms (brown), percent of wetlands drained (light blue), maize yield (t ha⁻¹) (yellow), and cumulative USDA financial assistance (inflation adjusted) for soil and water conservation programs in the USA (green). (B) Average regional lake mass accumulation rates for erosional (black) and in-lake (fueled by nutrient enrichment; yellow) derived sediment (g m⁻² yr⁻¹). The time for lakes to add one mm of sediment is also shown (dark blue; days). Error bars represent ±1 standard error. Agricultural data were summarized from the United States Department of Agriculture’s Census of Agriculture (1850–2007) and the National Agricultural Statistics Service. Annual maize yield data were fitted to a LOWESS model.</p

Watershed erosion (tons ha⁻¹ yr⁻¹) versus time for the 32 lakes in this study since 1800.

<p>Red line represents average rate of erosion (LOWESS smoothed fit to the data) from the watershed across all 32 lakes in this study, surrounded by the 95% confidence region. Blue lines represent LOWESS smoothed fits for each of the individual lakes and are included to visualize variability among lakes.</p

Distribution of lake-specific overall average Secchi depths ( from Eq. 1; A), long-term trends ( from Eq. 1; B), and probability of lake specific long-term trend (probability of estimated in Eq. 1 of being either <0 or >0; C) based on a hierarchical Bayesian analysis.

<p>Long-term trend (B) positive values indicate long-term increases in water clarity (i.e., increased water clarity) and negative values indicate long-term declines in water clarity (i.e., decreased water clarity) for individual lakes. Probability of lake specific long-term trends (C) left of the dotted red line indicate the number of lakes with long-term annual declines in water clarity (Secchi depth becoming shallower) and associated probability of those long-term declines; values to the right indicate the number of lakes with long-term increases in annual water clarity (Secchi depth becoming deeper) and associated probability of those long-term increases.</p

Monument to Pietro Angelo Secchi in the Villa Borghese Park in Rome.

<p>Pietro Secchi, who lived in Italy during the 1800s, is recognized as the founder of the method of using a Secchi transparency disk to measure water clarity (Photo by K.E. Webster).</p

Upper Midwest states in the US and Secchi record lengths (years) for all citizen monitored lakes included in the study.

<p>Size and color of symbol designate record lengths. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095769#pone-0095769-t001" target="_blank">Table 1</a> for information on citizen monitoring networks by state.</p

Overview of the data sources and records available from the citizen monitoring networks included in this study.

<p>Numbers represent totals through 2012.</p>1<p>Number of lakes determined from 2007 National Hydrography Dataset (NHD) with surface area equal to or greater than 4 ha.</p>2<p>Includes all citizen Secchi observations available from designated monitoring network.</p>3<p><a href="http://www.epa.state.il.us/water/vlmp" target="_blank">http://www.epa.state.il.us/water/vlmp</a>.</p>4<p><a href="http://www.indiana.edu/~clp" target="_blank">http://www.indiana.edu/~clp</a>.</p>5<p><a href="http://www.Secchidipin.org" target="_blank">http://www.Secchidipin.org</a>.</p>6<p><a href="http://www.micorps.net/" target="_blank">http://www.micorps.net/</a>.</p>7<p><a href="http://www.lmvp.org" target="_blank">http://www.lmvp.org</a>.</p>8<p><a href="http://www.pca.state.mn.us/index.php/water/water-types-and-programs/surface-water/lakes/citizen-lake-monitoring-program/index.html" target="_blank">http://www.pca.state.mn.us/index.php/water/water-types-and-programs/surface-water/lakes/citizen-lake-monitoring-program/index.html</a>.</p>9<p><a href="http://www.olms.org/citizen-lake-awareness-and-monitoring" target="_blank">http://www.olms.org/citizen-lake-awareness-and-monitoring</a>.</p>10<p><a href="http://dnr.wi.gov/lakes/CLMN" target="_blank">http://dnr.wi.gov/lakes/CLMN</a>.</p