Lake-wide phytoplankton production and abundance in the Upper Great Lakes: 2010–2013

Fahnenstiel, Gary L.; Sayers, Michael J.; Shuchman, Robert A.; Yousef, Foad; Pothoven, Steven A.

doi:10.1016/j.jglr.2016.02.004

Cited by 48 publications

(78 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While in situ experiments provided accurate estimates of primary production in small volumes of water, they may not be easily extrapolated to lake-wide estimates [7][8][9]. Moreover, these experiments provide an integrated measure of production that is dependent on many variables (e.g., phytoplankton biomass, light, temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data

Deng

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:We investigated the long-term variations in primary production in Lake Taihu using Moderate Resolution Imaging Spectroradiometer (MODIS) data, based on the Vertically Generalized Production Model (VGPM). We firstly test the applicability of VGPM in Lake Taihu by comparing the results between the model-derived and the in situ results, and the results showed that a strong significant correlation (R 2 = 0.753, p < 0.001, n = 63). Then, VGPM was used to map temporal-spatial distributions of primary production in Lake Taihu. The annual mean daily primary production of Lake Taihu from 2003 to 2013 was 1094.06 ± 720.74 mg·C·m −2 ·d −1 . Long-term primary production maps estimated from the MODIS data demonstrated marked temporal and spatial variations. Spatially, the primary production in bays, especially in Zhushan Bay and Meiliang Bay, was consistently higher than that in the open area of Lake Taihu, which was caused by chlorophyll-a concentrations resulting from high nutrient concentrations. Temporally, the seasonal variation of primary production from 2003 to 2013 was: summer > autumn > spring > winter, with significantly higher primary production found in summer and autumn than in winter (p < 0.005, t-test), primarily caused by seasonal variations in water temperature. On a monthly scale, the primary production exerts a clear character of bimodality, increasing from January to May, decreasing in June or July, and finally reaching its highest value during August or September. Wind is another important factor that could affect the spatial variations of primary production in the large, eutrophic and shallow Lake Taihu.

show abstract

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data

Deng

Zhang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Thus, reductions in NPP, phytoplankton, and zooplankton biomass are substantially greater in the nearshore compared to the offshore, producing a sign reversal in the nearshore‐offshore gradient between the prequagga and postquagga lake (i.e., greatest productivity located nearshore in prequagga lake but offshore in postquagga lake). This result has also been observed using satellite remote sensing data [ Fahnenstiel et al ., ].…”

Section: Discussionmentioning

confidence: 99%

“…These values are lower than the 2008 value of 9.5 TgC/year estimated by Warner and Lesht [] but close to the 5.0–7.2 TgC/year estimated by Fahnenstiel et al . [] for 2010–2013. Using a 1.4 NPP:GPP conversion factor [ Pilcher et al ., ; Fahnenstiel and Scavia , ] yields a model GPP estimate of 6.6 TgC/year, which falls within the range of Fahnenstiel et al .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modeled sensitivity of Lake Michigan productivity and zooplankton to changing nutrient concentrations and quagga mussels

et al. 2017

View full text Add to dashboard Cite

The recent decline in Lake Michigan productivity is often attributed to filter feeding by invasive quagga mussels, but some studies also implicate reductions in lakewide nutrient concentrations. We use a 3‐D coupled hydrodynamic‐biogeochemical model to evaluate the effect of changing nutrient concentrations and quagga mussel filtering on phytoplankton production and phytoplankton and zooplankton biomass. Sensitivity experiments are used to assess the net effect of each change separately and in unison. Quagga mussels are found to have the greatest impact during periods of isothermal mixing, while nutrients have the greatest impact during thermal stratification. Quagga mussels also act to enhance spatial heterogeneity, particularly between nearshore‐offshore regions. This effect produces a reversal in the gradient of nearshore‐offshore productivity: from relatively greater nearshore productivity in the prequagga lake to relatively lesser nearshore productivity after quaggas. The combined impact of both processes drives substantial reductions in phytoplankton and zooplankton biomass, as well as significant modifications to the seasonality of surface water pCO2, particularly in nearshore regions where mussel grazing continues year‐round. These results support growing concern that considerable losses of phytoplankton and zooplankton will yield concurrent losses at higher trophic levels. Comparisons to observed productivity suggest that both quagga mussel filtration and lower lakewide total phosphorus are necessary to accurately simulate recent changes in primary productivity in Lake Michigan.

show abstract

“…Many studies that use remote sensing to estimate productivity or chlorophyll in large freshwater lentic systems focus on whole lake trends (Fahnenstiel et al, 2016;Feng et al, 2015;Shuchman et al, 2013;Warner and Lesht, 2015;Yousef et al, 2014). However, nearshore zones in many large lentic systems, such as the Great Lakes, may have heterogeneous habitats that differ in primary production due to spatial differences in tributary input, bathymetry, substrate, and other environmental conditions.…”

Section: Geographic Information Systemmentioning

confidence: 99%

Spatially heterogeneous trends in nearshore and offshore chlorophyll a concentrations in lakes Michigan and Huron (1998–2013)

et al. 2019

View full text Add to dashboard Cite

1. Nutrient abatement programmes have been successfully implemented around the globe to reduce nutrient loading into aquatic ecosystems. Concurrently, the worldwide spread of invasive filter feeders, such as dreissenid mussels, may alter nutrient dynamics in invaded systems by sequestering nutrients away from pelagic zones and reducing primary production in offshore areas. Such is the case in the Laurentian Great Lakes of North America, where decades of nutrient abatement and the establishment of dreissenid mussels have seemingly resulted in more oligotrophic conditions and altered spatial patterns of nutrient availability and primary production.2. Recent studies have focused on whole lake trends in primary production despite spatial differences in tributary inputs, bathymetry, and other environmental conditions that can affect primary production in nearshore areas. Thus, we hypothesised that trends in nearshore chlorophyll concentrations in different areas may diverge in a manner consistent with spatial differences in nutrient input. To evaluate these differences in surface chlorophyll responses, we assessed temporal trends in four different areas of Lake Michigan and three different areas of Lake Huron.3. We hypothesised that in lakes Huron and Michigan, nearshore zones have experienced slower declines of chlorophyll concentrations relative to offshore zones.To assess this hypothesis, we estimated temporal trends of surface water chlorophyll concentrations (a proxy for primary production) from satellite imagery from 1998 to 2013. We calculated average surface chlorophyll concentrations for 10-m depth intervals ranging from the shore (0-10 m) to offshore (>90 m) during representative months of May, July, and September. We then analysed these data to determine if long-term trends in surface chlorophyll varied by season, proximity to the shoreline, and water depth.4. The rates of annual change in chlorophyll concentrations in nearshore areas were markedly different to offshore trends in both lakes. Chlorophyll concentrations declined overtime in offshore areas, but nearshore chlorophyll concentrations were either stable (in May) or increased (in July and September) throughout the time series. | 367 STADIG eT Al.

show abstract

Lake-wide phytoplankton production and abundance in the Upper Great Lakes: 2010–2013

Cited by 48 publications

References 66 publications

Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data

Temporal and Spatial Dynamics of Phytoplankton Primary Production in Lake Taihu Derived from MODIS Data

Modeled sensitivity of Lake Michigan productivity and zooplankton to changing nutrient concentrations and quagga mussels

Spatially heterogeneous trends in nearshore and offshore chlorophyll a concentrations in lakes Michigan and Huron (1998–2013)

Contact Info

Product

Resources

About