Copper and light shape coastal picophytoplankton communities via their combined effects on growth limitation and toxicity
Data files
Sep 10, 2025 version files 67.83 KB
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Chlorophyll.xlsx
14.33 KB
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Flow_Cytometry.xlsx
19.93 KB
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README.md
4.56 KB
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Sample_ID.xlsx
13.88 KB
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Trace_Metals.xlsx
15.12 KB
Abstract
Copper (Cu) and light are two resources that can limit phytoplankton growth at very low (deficiency) or very high (toxicity) levels. In this study, Cu and light interactive effects on picophytoplankton growth and community composition southern California were assessed during four bottle incubation experiments using a 7x7 matrix of overlapping Cu and light gradients. Consistent with prior knowledge, sensitivity to Cu in the two September experiments was greatest in Prochlorococcus, followed by Synechococcus, and then picoeukaryotes. Prochlorococcus abundance declined gradually with Cu additions >6 nM, whereas Synechococcus showed a sharp toxicity threshold at >10 nM added Cu. Synergistic effects between Cu and light exacerbated toxicity in both taxa, suggesting that shared stress response pathways become saturated and less effective when Cu and light are both high. An unexpected increase in ambient seawater Cu concentration prior to the two October experiments brought Prochlorococcus and Synechococcus close to their toxicity thresholds, potentiating their apparent sensitivity to light and leading to steeper population declines in the experiments. Addition of 10 mM nitrate at the start of one of the experiments did not rescue the populations from toxicity, suggesting that relief of N limitation did not allow for greater acclimation via de novo stress response enzyme synthesis. Across all experiments, picoeukaryotes were more resilient to high light and Cu, allowing them to persist or increase under conditions that limited Prochlorococcus and Synechococcus. This robustness combined with relief from competition for other resources (possibly phosphate) upon decline of the other two taxa, ultimately led picoeukaryotes to dominate the communities despite having very low baseline relative abundances. Selection for different Synechococcus clades and picoeukaryote species likely permitted each of these populations to thrive over a broader range of Cu and light combinations than would be possible for less biodiverse populations.
Dataset DOI: 10.5061/dryad.3r2280gv8
Description of the data and file structure
- The experiments in this study were designed to probe the responses picophytoplankton to overlapping Cu and light levels in the dynamic California Current ecosystem where trace metals are likely to play a role in shaping community structure and abundance. We targeted the late summer picophytoplankton community because of its relatively simple structure, comprising three groups that have well-characterized adaptations for trace metal use and tolerance. Our goal was to determine the extent to which the combined effects of Cu and light influence competition and acclimation potential among the three groups. We hypothesized that differences in Cu nutritional requirements and toxicity thresholds would be the main factor to influence community composition, and that the effects of Cu would be enhanced synergistically by light. We further hypothesized that acclimation to high Cu and light, such as via de novo synthesis of enzymes for oxidative stress, would be stimulated following relief of N limitation.
- This dataset contains biological and chemical data from bottle manipulation experiments in the Southern California Bight off of Newport Beach (33.5649oN, -117.9567oE) in 2020.
- Four incubation experiments were conducted Experiment A used surface seawater collected on September 1, 2020, B used water from 30m collected on September 1, 2020, C used surface seawater collected on October 22, 2020, and D used surface seawater collected on October 22, 2020with 10 micomolar added Na NO3.
- For each experiment, forty-nine bottles were prepared in a 7x7 matrix of copper sulphate with equimolar sodium EDTA (0, 0.6, 1, 6, 10, and 60nM Cu’ above background) and light (14, 16, 26, 37, 62, and 84% of the ambient natural sunlight levels). Bottles were incubated for two days.
- Four baseline samples were also collected for each experiment. These had IDs A50, A51, A52, A53, B150, B151, B152, B153, C50, C51, C52, C53, D150, D151, D152, and D153.
- Multiple linear regression was used to analyze the datasets, and showed that the combined effects of copper and light influenced the community composition of picophytoplankton. Prochlorococcus was the most sensitive to copper, followed by Synechococcus, then picoeukaryotes. Synergistic effects with light were observed in two of the four experiments.
Files and variables
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Sample_ID.xlsx The column headers in the file are:
- ID
- Added copper concentration (nM)
- Light treatment (% of ambient)
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Chlorophyll.xlsx The column headers in the file are:
- ID
- Chlorophyll a concentration (micrograms L-1)
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Flow_Cytometry.xlsx The column headers in the file are:
- Sample ID
- Picoeukaryote Concentration (cell mL-1)
- Synechococcus Concentration (cell mL-1)
- Prochlorococcus Concentration (cell mL-1)
- Flag
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Trace_Metals.xlsx The column headers in the file are:
- Sample IDs
- Cd (nM)
- Pb (nM)
- Fe (nM)
- Ni (nM)
- Cu (nM)
- Zn (nM)
- Mn (nM)
- Co (nM)
- Al (nM)
- Ti (nM)
- V (nM)
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The file “Sample IDs.xlxs” shows the treatment identification codes for the experiment. These IDs are used in the remaining files.
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Samples below the limit of quantitation are annotated with the flag “LOQ” for all of the files.
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For some of the flow cytometry samples, Prochlorococcus counts were low due to reduced autofluorescence from stress caused by the experimental treatments (as expected in a toxicity experiment). Typically, >200 cell or particle count is required for statistical accuracy, but given that toxicity was expected, these low values are included in the dataset. Samples falling into this category are noted with an asterisk (*) in the “Flag” column. Outliers >2x standard deviation are flagged with two asterisk (**) in the “Flag” column and were omitted from further analysis.
Code/software
No specialized software is needed to view the dataset. All files are in .xlsx format.
Access information
Data was derived from the following sources:
- This study used CMEMS Level 4, 0.125 degree SLA and geostrophic velocity gridded global ocean dataset, version 008_047 (https://doi.org/10.48670/moi-00148). The OceanParcels v. 3.0.4 Python package was used to conduct Lagrangian particle simulations44 (Delandmeter and Ven Sebille, 2019).
Trace metal concentrations (including Al, Cd, Co, Cu, Fe, Mn, Ni, Pb, V, and Zn) were determined on 0.22 micrometer filtered samples by inductively coupled plasma mass spectrometer (ICP-MS). The method’s LOQs were the following: dAl = 0.30 nM, dCd= 0.03 nM, dCo= 0.12 nM, dCu= 0.82 nM, dFe= 3.35 nM, dMn= 0.08 nM, dNi=0.23 nM, dPb= 0.03 nM, dV= 0.34 nM, dZn=1.39 nM.
Picophytoplankton were enumerated by flow cytometry at the Center for Aquatic Cytometry, Bigelow Laboratory for Ocean Sciences on a Bio-Rad ZE5 with 405 nm, 488 nm, and 640 nm lasers activated following standard methods (Poulton and Martin. 2010. Imaging flow cytometry for quantitative phytoplankton analysis — FlowCAM. In: Intergovernmental Oceanographic Commission of ©UNESCO. Karlson, Cusack, and Bresnan (editors). Microscopic and molecular methods for quantitative phytoplankton analysis. UNESCO. (IOC Manuals and Guides, no. 55.), 110 pages.)
Chlorophyll a was determined from cells filtered from 100mL of sample water and extracted in 5mL 90% acetone in the dark for 24h at -20oC. Extracted chlorophyll was measured fluorometrically using the Turner Trilogy non-acidified chlorophyll filter module (excitation 436/10nm, emission 685/10nm).