Abstract:We describe the underwater light field of the Strait of Georgia in spring and summer, using apparent optical properties (reflectance, attenuation coefficient of downwelling irradiance, the average cosine of downwelling irradiance, and the attenuation of scalar irradiance). Both the attenuation and reflectance of photosynthetically available radiation (PAR; 400-700 nm) are highest in the turbid waters of the Fraser River plume, due to scattering by mainly inorganic particles and absorption by coloured dissolved… Show more
“…We calculate the PAR 2m extinction coefficient k z , using a dataset of in situ down-welling irradiance and salinity from the study area (Loos et al, 2017). From this data, we derive a relationship between salinity and k z , which was then used to estimate k z based on salinity measurements from the ferry.…”
Section: Defining Underwater Parmentioning
confidence: 99%
“…From this data, we derive a relationship between salinity and k z , which was then used to estimate k z based on salinity measurements from the ferry. PAR 2m was calculated according to Kirk (2011): In this region, light attenuation within the relatively clearer and more saline oceanic waters is primarily driven by phytoplankton, and it is generally lower than in the more turbid less saline plume waters, where attenuation is driven by CDOM and TSM (Johannessen et al, 2006;Loos and Costa, 2010;Loos et al, 2017).…”
Section: Defining Underwater Parmentioning
confidence: 99%
“…As such, a relationship between salinity and k z was defined using in situ data measured by Loos et al (2017) in the SoG during the summer, matching oceanographic and irradiance conditions in this study. The authors measured PAR down-welling irradiance and salinity at different depths, allowing the calculation of k z according to Kirk (2011):…”
Section: Defining Underwater Parmentioning
confidence: 99%
“…Plume and oceanic waters are distinguished based on a variable salinity threshold described in Halverson and Pawlowicz (2011) developed for the SoG. These two classes reflect differences in turbidity (Loos et al, 2017) that may in turn affect the magnitude of NPQ (Halverson and Pawlowicz, 2013). Salinity thresholds (S threshold ) were calculated for each ferry transect, and plume waters were defined where salinity was less than S threshold and oceanic waters were defined where salinity was greater than S threshold .…”
Section: Classifying Plume and Oceanic Watersmentioning
confidence: 99%
“…FIGURE 3 | Salinity and modeled kz values based on 19 CTD profiles acquired byLoos et al (2017). The solid line shows the regression fit, dashed red lines show the 95% confidence interval for the regression, and the dotted black lines show the 95% prediction interval for the data.…”
The in vivo fluorescence of chlorophyll-a is commonly used as a proxy for phytoplankton biomass. Measurement of in vivo fluorescence in the field is attractive because it can be made at high spatial temporal, and vertical resolution relative to discrete sampling and pigment extraction. Fluorometers installed on ships of opportunity provide a cost-effective alternative to many of the traditional sampling methods. However, fluorescence-based estimates of chlorophyll-a can be impacted by sensor calibration and biofouling, variations in phytoplankton taxonomy and physiology (such as non-photochemical quenching) and the influence of other fluorescing matters in the water. Several methods have been proposed to address these issues separately, but few studies have addressed the interaction of multiple sources of error in the in vivo Chl-a fluorescence signal. Here, we demonstrate a method to improve the accuracy of chlorophyll-a concentration retrieved from a coastal ferry system, operating in a dynamic estuarine system. First, we used HPLC chlorophyll-a measurements acquired in low-light conditions to correct sensor level bias. Next, we tested three methods to correct the effect of non-photochemical quenching and evaluated the accuracy of each method using HPLC. As our study area is in highly dynamic coastal waters, we also evaluated the accuracy of our correction procedure across a range of irradiance and biogeochemical conditions. We found that sensor bias accounted for a significant portion of error in the fluorescence signal. The NPQ correction developed by Davis et al. (2008) best improved correspondence between in vivo Chl-a fluorescence and HPLC-based measurement of extracted Chl-a. We suggest the use of this correction for in vivo Chl-a measurements along with pre-processing steps to correct potential sensor biofouling and bias.
“…We calculate the PAR 2m extinction coefficient k z , using a dataset of in situ down-welling irradiance and salinity from the study area (Loos et al, 2017). From this data, we derive a relationship between salinity and k z , which was then used to estimate k z based on salinity measurements from the ferry.…”
Section: Defining Underwater Parmentioning
confidence: 99%
“…From this data, we derive a relationship between salinity and k z , which was then used to estimate k z based on salinity measurements from the ferry. PAR 2m was calculated according to Kirk (2011): In this region, light attenuation within the relatively clearer and more saline oceanic waters is primarily driven by phytoplankton, and it is generally lower than in the more turbid less saline plume waters, where attenuation is driven by CDOM and TSM (Johannessen et al, 2006;Loos and Costa, 2010;Loos et al, 2017).…”
Section: Defining Underwater Parmentioning
confidence: 99%
“…As such, a relationship between salinity and k z was defined using in situ data measured by Loos et al (2017) in the SoG during the summer, matching oceanographic and irradiance conditions in this study. The authors measured PAR down-welling irradiance and salinity at different depths, allowing the calculation of k z according to Kirk (2011):…”
Section: Defining Underwater Parmentioning
confidence: 99%
“…Plume and oceanic waters are distinguished based on a variable salinity threshold described in Halverson and Pawlowicz (2011) developed for the SoG. These two classes reflect differences in turbidity (Loos et al, 2017) that may in turn affect the magnitude of NPQ (Halverson and Pawlowicz, 2013). Salinity thresholds (S threshold ) were calculated for each ferry transect, and plume waters were defined where salinity was less than S threshold and oceanic waters were defined where salinity was greater than S threshold .…”
Section: Classifying Plume and Oceanic Watersmentioning
confidence: 99%
“…FIGURE 3 | Salinity and modeled kz values based on 19 CTD profiles acquired byLoos et al (2017). The solid line shows the regression fit, dashed red lines show the 95% confidence interval for the regression, and the dotted black lines show the 95% prediction interval for the data.…”
The in vivo fluorescence of chlorophyll-a is commonly used as a proxy for phytoplankton biomass. Measurement of in vivo fluorescence in the field is attractive because it can be made at high spatial temporal, and vertical resolution relative to discrete sampling and pigment extraction. Fluorometers installed on ships of opportunity provide a cost-effective alternative to many of the traditional sampling methods. However, fluorescence-based estimates of chlorophyll-a can be impacted by sensor calibration and biofouling, variations in phytoplankton taxonomy and physiology (such as non-photochemical quenching) and the influence of other fluorescing matters in the water. Several methods have been proposed to address these issues separately, but few studies have addressed the interaction of multiple sources of error in the in vivo Chl-a fluorescence signal. Here, we demonstrate a method to improve the accuracy of chlorophyll-a concentration retrieved from a coastal ferry system, operating in a dynamic estuarine system. First, we used HPLC chlorophyll-a measurements acquired in low-light conditions to correct sensor level bias. Next, we tested three methods to correct the effect of non-photochemical quenching and evaluated the accuracy of each method using HPLC. As our study area is in highly dynamic coastal waters, we also evaluated the accuracy of our correction procedure across a range of irradiance and biogeochemical conditions. We found that sensor bias accounted for a significant portion of error in the fluorescence signal. The NPQ correction developed by Davis et al. (2008) best improved correspondence between in vivo Chl-a fluorescence and HPLC-based measurement of extracted Chl-a. We suggest the use of this correction for in vivo Chl-a measurements along with pre-processing steps to correct potential sensor biofouling and bias.
Kelps act as ecosystem engineers on many polar rocky shore coastlines. The underwater light climate and temperature are the main drivers for their vertical and latitudinal distribution. With temperatures rising globally, an Arctic expansion of temperate kelp species and an accelerating glacial melt is predicted. It was our aim to investigate the effects of retreating glaciers and rising temperatures on the potential habitat of kelps in Arctic fjords. We analyzed the underwater light climate of areas being influenced by different stages of glacial retreat (sea-terminating glacier, land-terminating glacier, coastal water) in Arctic Kongsfjorden. We observed reduced light intensities and a changed spectral composition in glacial meltwater plumes, potentially resulting in an upward shift of the lower depth limit of kelp, counteracting the predicted biomass increase in the Arctic. Furthermore, we studied temperature-related changes in light-use characteristics in two kelp species (Alaria esculenta, Saccharina latissima) at 3 C, 7 C, and 11 C. Rising temperatures lead to a significant increase of the compensation irradiance of A. esculenta. The dark respiration of S. latissima increased significantly, correlating with a decreasing carbon content. We detected no differences in photosynthetic rates, although the chlorophyll a concentration of A. esculenta was $ 78% higher compared to S. latissima. Ultimately, temperatureinduced changes in kelps light-use characteristics might lead to a changed species composition, as we found A. esculenta better adapted to polar conditions. We conclude that the deterioration of the underwater light climate and the temperature increase may drive substantial changes of the future Arctic kelp forest structure.
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