Contributors | Affiliation | Role |
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Anderson, Stephanie I. | University of Rhode Island (URI-GSO) | Principal Investigator, Contact |
Franzè, Gayantonia | Institute of Marine Research (Bergen, Norway) (IMR) | Co-Principal Investigator |
Hutchins, David A. | University of Southern California (USC) | Co-Principal Investigator |
Kling, Joshua D. | University of California-Berkeley (UC Berkeley) | Co-Principal Investigator |
Kremer, Colin T. | University of California-Los Angeles (UCLA) | Co-Principal Investigator |
Litchman, Elena | Michigan State University (MSU) | Co-Principal Investigator |
Menden-Deuer, Susanne | University of Rhode Island (URI-GSO) | Co-Principal Investigator |
Rynearson, Tatiana A. | University of Rhode Island (URI-GSO) | Co-Principal Investigator |
Wilburn, Paul | Michigan State University (MSU) | Co-Principal Investigator |
Heyl, Taylor | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Multivariate mesocosm experiments were conducted with a natural phytoplankton community from Narragansett Bay, RI. Water was incubated in triplicate at -0.5ºC, 2.6ºC, and 6ºC for 10 days. At each temperature, treatments included both nutrient amendments (N, P, Si addition) and controls (no macronutrients added).
At the onset and conclusion of the incubation experiments, the microplankton community was identified and quantified to discern treatment effects on community composition. Aliquots from each incubation were fixed in triplicate with 2% acid Lugol’s solution for microscopy, final concentration. Cell enumeration was conducted by performing cell counts ~>10 micrometer (µm) on a 1 milliliter (ml) Sedgewick cell-counting chamber (Structure Probe Inc.) using an Eclipse E800 microscope (Nikon).
These data were assessed as part of “The Interactive Effects of Temperature and Nutrients on a Spring Phytoplankton Community” (Anderson et al, in prep).
BCO-DMO processing description:
- Adjusted field/parameter names to comply with database requirements
- Missing data identifier ‘NA’ and ‘N/A’ replaced with 'nd' (BCO-DMO's default missing data identifier)
- Added a conventional header with dataset name, PI names, version date
File |
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microscopy_cell_counts.csv (Comma Separated Values (.csv), 5.69 KB) MD5:93c4d6bf74809c9dad3b258ccb8d7864 Primary data file for dataset ID 848977 |
Parameter | Description | Units |
Treatment | Treatment identification. Letter corresponds to temperature treatment, L=low (-5_C), M=medium (2.6_C), H=high (6_C); and symbol describes whether macronutrients were added (+) or not (-). T0 is initial community. | days |
Replicate | Biological replicate identification | unitless |
Temperature | Nutrient treatment in which incubation was conducted | degrees celcius (¡C) |
Nutrient | Nutrient treatment in which incubation was conducted | unitless |
Total_abundance | Total cell abundance | cells per liter (cells/L) |
Alexandrium_sp | Species abundance | cells per liter (cells/L) |
Asterionellopsis_glacialis | Species abundance | cells per liter (cells/L) |
Bacteriastrum_sp | Species abundance | cells per liter (cells/L) |
Cerataulina_pelagica | Species abundance | cells per liter (cells/L) |
Chaetoceros_affinis | Species abundance | cells per liter (cells/L) |
Chaetoceros_atlanticus | Species abundance | cells per liter (cells/L) |
Chaetoceros_compressus | Species abundance | cells per liter (cells/L) |
Chaetoceros_costatus | Species abundance | cells per liter (cells/L) |
Chaetoceros_danicus | Species abundance | cells per liter (cells/L) |
Chaetoceros_debilis | Species abundance | cells per liter (cells/L) |
Chaetoceros_decipiens | Species abundance | cells per liter (cells/L) |
Chaetoceros_diadema | Species abundance | cells per liter (cells/L) |
Chaetoceros_didymus | Species abundance | cells per liter (cells/L) |
Chaetoceros_laciniosus | Species abundance | cells per liter (cells/L) |
Chaetoceros_mitra | Species abundance | cells per liter (cells/L) |
Chaetoceros_similis | Species abundance | cells per liter (cells/L) |
Chaetoceros_spp | Species abundance | cells per liter (cells/L) |
Chaetoceros_subtilis | Species abundance | cells per liter (cells/L) |
Chaetoceros_winghamii | Species abundance | cells per liter (cells/L) |
Coscinodiscus_spp | Species abundance | cells per liter (cells/L) |
Cylindrotheca_closterium | Species abundance | cells per liter (cells/L) |
Dactyliosolen_blavyanus | Species abundance | cells per liter (cells/L) |
Dactyliosolen_fragilissimus | Species abundance | cells per liter (cells/L) |
Diatom_unknown | Species abundance | cells per liter (cells/L) |
Dictyocha_speculum | Species abundance | cells per liter (cells/L) |
Dinoflagellates_unknown | Species abundance | cells per liter (cells/L) |
Ditylum_brightwellii | Species abundance | cells per liter (cells/L) |
Flagellate_unknown | Species abundance | cells per liter (cells/L) |
Guinardia_delicatula | Species abundance | cells per liter (cells/L) |
Gymnodinium_spp | Species abundance | cells per liter (cells/L) |
Gyrodinium_spp | Species abundance | cells per liter (cells/L) |
Heterocapsa_rotundata | Species abundance | cells per liter (cells/L) |
Heterocapsa_triquetra | Species abundance | cells per liter (cells/L) |
Leptocylindrus_danicus | Species abundance | cells per liter (cells/L) |
Leptocylindrus_minimus | Species abundance | cells per liter (cells/L) |
Licmophora_spp | Species abundance | cells per liter (cells/L) |
Melosira_spp | Species abundance | cells per liter (cells/L) |
Odontella_spp | Species abundance | cells per liter (cells/L) |
Paralia_sulcata | Species abundance | cells per liter (cells/L) |
Pennate_unknown | Species abundance | cells per liter (cells/L) |
Phaeocystis_spp | Species abundance | cells per liter (cells/L) |
Pleurosigma_spp | Species abundance | cells per liter (cells/L) |
Prorocentrum_spp | Species abundance | cells per liter (cells/L) |
Protoperidinium_spp | Species abundance | cells per liter (cells/L) |
Pseudonitzschia_spp | Species abundance | cells per liter (cells/L) |
Rhabdonema_adriaticum | Species abundance | cells per liter (cells/L) |
Rhizosolenia_pungens | Species abundance | cells per liter (cells/L) |
Rhizosolenia_setigera | Species abundance | cells per liter (cells/L) |
Rhizosolenia_spp | Species abundance | cells per liter (cells/L) |
Rhizosolenia_styliformis_imbricata | Species abundance | cells per liter (cells/L) |
Skeletonema_spp | Species abundance | cells per liter (cells/L) |
Thalassionema_nitzschioides | Species abundance | cells per liter (cells/L) |
Thalassiosira_nordenskioeldii | Species abundance | cells per liter (cells/L) |
Thalassiosira_rotula | Species abundance | cells per liter (cells/L) |
Thalassiosira_spp | Species abundance | cells per liter (cells/L) |
Dataset-specific Instrument Name | Eclipse E800 microscope (Nikon) |
Generic Instrument Name | Microscope - Optical |
Generic Instrument Description | Instruments that generate enlarged images of samples using the phenomena of reflection and absorption of visible light. Includes conventional and inverted instruments. Also called a "light microscope". |
NSF Award Abstract:
Photosynthetic marine microbes, phytoplankton, contribute half of global primary production, form the base of most aquatic food webs and are major players in global biogeochemical cycles. Understanding their community composition is important because it affects higher trophic levels, the cycling of energy and elements and is sensitive to global environmental change. This project will investigate how phytoplankton communities respond to two major global change stressors in aquatic systems: warming and changes in nutrient availability. The researchers will work in two marine systems with a long history of environmental monitoring, the temperate Narragansett Bay estuary in Rhode Island and a subtropical North Atlantic site near Bermuda. They will use field sampling and laboratory experiments with multiple species and varieties of phytoplankton to assess the diversity in their responses to different temperatures under high and low nutrient concentrations. If the diversity of responses is high within species, then that species may have a better chance to adapt to rising temperatures and persist in the future. Some species may already be able to grow at high temperatures; consequently, they may become more abundant as the ocean warms. The researchers will incorporate this response information in mathematical models to predict how phytoplankton assemblages would reorganize under future climate scenarios. Graduate students and postdoctoral associates will be trained in diverse scientific approaches and techniques such as shipboard sampling, laboratory experiments, genomic analyses and mathematical modeling. The results of the project will be incorporated into K-12 teaching, including an advanced placement environmental science class for underrepresented minorities in Los Angeles, data exercises for rural schools in Michigan and disseminated to the public through an environmental journalism institute based in Rhode Island.
Predicting how ecological communities will respond to a changing environment requires knowledge of genetic, phylogenetic and functional diversity within and across species. This project will investigate how the interaction of phylogenetic, genetic and functional diversity in thermal traits within and across a broad range of species determines the responses of marine phytoplankton communities to rising temperature and changing nutrient regimes. High genetic and functional diversity within a species may allow evolutionary adaptation of that species to warming. If the phylogenetic and functional diversity is higher across species, species sorting and ecological community reorganization is likely. Different marine sites may have a different balance of genetic and functional diversity within and across species and, thus, different contribution of evolutionary and ecological responses to changing climate. The research will be conducted at two long-term time series sites in the Atlantic Ocean, the Narragansett Bay Long-Term Plankton Time Series and the Bermuda Atlantic Time Series (BATS) station. The goal is to assess intra- and inter-specific genetic and functional diversity in thermal responses at contrasting nutrient concentrations for a representative range of species in communities at the two sites in different seasons, and use this information to parameterize eco-evolutionary models embedded into biogeochemical ocean models to predict responses of phytoplankton communities to projected rising temperatures under realistic nutrient conditions. Model predictions will be informed by and tested with field data, including the long-term data series available for both sites and in community temperature manipulation experiments. This project will provide novel information on existing intraspecific genetic and functional thermal diversity for many ecologically and biogeochemically important phytoplankton species, estimate generation of new genetic and functional diversity in evolution experiments, and develop and parameterize novel eco-evolutionary models interfaced with ocean biogeochemical models to predict future phytoplankton community structure. The project will also characterize the interaction of two major global change stressors, warming and changing nutrient concentrations, as they affect phytoplankton diversity at functional, genetic, and phylogenetic levels. In addition, the project will develop novel modeling methodology that will be broadly applicable to understanding how other types of complex ecological communities may adapt to a rapidly warming world.
(adapted from the NSF Synopsis of Program)
Dimensions of Biodiversity is a program solicitation from the NSF Directorate for Biological Sciences. FY 2010 was year one of the program. [MORE from NSF]
The NSF Dimensions of Biodiversity program seeks to characterize biodiversity on Earth by using integrative, innovative approaches to fill rapidly the most substantial gaps in our understanding. The program will take a broad view of biodiversity, and in its initial phase will focus on the integration of genetic, taxonomic, and functional dimensions of biodiversity. Project investigators are encouraged to integrate these three dimensions to understand the interactions and feedbacks among them. While this focus complements several core NSF programs, it differs by requiring that multiple dimensions of biodiversity be addressed simultaneously, to understand the roles of biodiversity in critical ecological and evolutionary processes.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |