1b:I[42426,["/_next/static/immutable/chunks/1xctgzqre-wsy.js","/_next/static/immutable/chunks/06_-wz6pdtp2b.js","/_next/static/immutable/chunks/30e52g49hg40p.js","/_next/static/immutable/chunks/02yq6tul2z-oh.js","/_next/static/immutable/chunks/10bx_ec2vj7n_.js","/_next/static/immutable/chunks/33bfn03fo8xnt.js","/_next/static/immutable/chunks/0tfjf78scu61b.js","/_next/static/immutable/chunks/31xnvej4n1bi9.js"],"default"] 1c:Teca,

Organism and Culturing

Tetraselmis suecica (UTEX LB 2286) was cultured in 250 mL Erlenmeyer flasks containing sterilized natural seawater. We used natural seawater as artificial seawater such as Instant Ocean has been shown to sometimes affect experimental outcomes (Pechenik et al. 2019). Seawater was sterilized using vacuum filtration to remove particulate matter and sediment, and subsequently autoclaved at 121ºC for 60 minutes. Non-experimental lineages were created at least every 10 days, and Micro Algae Grow nutrient media (Florida Aqua Farms Inc., Dade City, FL) was added every day beginning on day 0 to prevent nutrient limitation. These cultures were used to seed the experiments. Stock cultures were maintained in Darwin environmental chambers (Darwin Chambers Company, St. Louis, MO) at a constant temperature of 20ºC under continuous light provided by LED light strips. Stock cultures were gently swirled for 10 seconds daily to ensure homogeneity and proper aeration.

Experimental Setup

The experimental design involved acclimating cultures to eight different base temperatures (10ºC, 15ºC, 18ºC, 20ºC, 25ºC, 28ºC, 30ºC, 35ºC) over the course of 5 days in a Darwin environmental chamber. At the beginning of the experiment, cultures were placed on Peltier devices (Adafruit, New York, NY) housed in plastic egg-crate cages to prevent spillage. White LED strip lights were fastened inside the cages. Light was measured using a PAR sensor to verify that each cage was receiving at least 150 µmol m^-2s^-2 (saturating irradiance (Bernhardt et al., 2018), and under 200 µmol m^-2s^-2to maintain consistent light conditions between cages.

Temperature Control and Manipulation

To control temperature during the experiment, we used TEC (thermoelectric cooling) Controllers (Meerstetter Engineering, Rubigen, Switzerland) to control the Peltier devices. Cultures were exposed to one of 5 fluctuating temperature treatments +/- 5ºC around the base temperature, or the constant temperature (n=18 per base temperature, n=3 per treatment within each base temperature). The five fluctuating temperature treatments for each mean temperature were characterized by the following frequencies: (i) frequency of 1 or period of 144 hours, (ii) frequency of 3 or period of 48 hours, (iii) frequency of 6 or period of 24 hours, (iv) frequency of 18 or period of 8 hours, and (v) frequency of 24 or period of 6 hours.

In order to ensure that cultures were in the exponential growth phase for the duration of the experiment, we used cultures at a known concentration of between 1,000,000 and 2,000,000 cells/mL on Day 1 of the experiment. To achieve this, we inoculated cultures the previous week at a lower concentration (i.e. 25,000 cells/mL) such that they would reach 1,000,000 by the end of their pre-experimental acclimation period. This lower concentration depended on the temperature to which they were being acclimated, as cultures further away from their T_OPT would grow more slowly.

Cells were counted daily for each of the five days using a Beckman Coulter Z2 Particle Counter (Beckman Coulter, Indianapolis, IN) set to count particles between 5–16 µm. This size range was experimentally determined by photographing cells on a Hemacytometer and analyzing size with ImageJ (version 1.54). Samples were diluted in ISOTON II Diluent (Beckman Coulter, Indianapolis, IN), with a dilution factor such that cell counts ranged from 10,000 to 200,000 before factoring in a dilution factor. Three samples were taken per culture, and three counts were run on each sample to ensure consistency.

1d:T907,NSF Award Abstract: Although it is well established that temperature extremes associated with heatwaves or cold snaps can have strong and immediate impacts on biological populations, much less is known about the longer-term effects of these short-term events. The investigators are filling this critical knowledge gap by conducting a series of laboratory experiments and developing a new set of mathematical models to identify how temperature fluctuations influence population growth and size. Additionally, the mathematical models are being extended to generate a baseline understanding of how changes in temperature anticipated under global climate change will likely affect populations around the globe over the course of the 21st century. This project addresses important societal needs by cross-training graduate students in biology, statistics, mathematical modeling, and computer programming. The results of this research are being integrated into undergraduate courses in biostatistics, mathematical modeling, and environmental science in order to demonstrate the importance of quantitative and interdisciplinary STEM training for addressing important questions in biology. Finally, multiple interactive web modules are being created to disseminate the results of this research beyond academic circles, including Northeastern University’s K-12 outreach programs. Current empirical and theoretical approaches do not account for the cryptic population structure that emerges when organisms are exposed to variable temperature regimes. The investigators are addressing this limitation by using marine algae as a model system to (i) establish an empirical protocol for properly evaluating the effects of temperature variability on population growth, (ii) develop a novel mathematical modeling framework for accurately predicting the dynamics of populations exposed to temperature fluctuations, and (iii) determine species extinction risk under future ocean temperatures. Overall, this project is expected to yield a new set of empirical and theoretical tools to better forecast the biological effects of environmental change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.6:["$","$L1b",null,{"dataset":{"_id":"953492","description":"","problem":"","acquisition":"$1c","processing":"

The raw data are provided (no processing performed).

\n","bcodmoProcessing":"- Imported \"experimental_data.csv\" into BCO-DMO system\r\n- Converted datetime to ISO format (added 'T' between the date and time) and converted to UTC\r\n- Renamed \"full_date\" to \"measurement_datetime\"\r\n- Exported file as \"953492_v1_tsuecica_exp.csv\"","title":"Time series of T. suecica densities under fluctuating temperatures experiment from May 2023 to Aug 2023","nickname":"Lab experimental time series of T. suecica densities under fluctuating temperatures","dataUrl":"","currentState":"Final no updates expected","currentStateId":"7","validated":true,"restricted":false,"version":"1","version_date":"2025-02-21T05:00:00.000Z","abstract":"This dataset includes the densities (count/mL) of the alga Tetraselmis suecica (LB 2286) cultures grown from a UTEX sample. The counts were obtained using a Beckman Coulter Z2 Particle Counter in the lab over a five-day period under different experimental temperature regimes. The experiment took place between 2023-05-23 and 2023-08-18. The goal of the experiment was to determine how mean temperature and temperature fluctuation frequency affected the growth rate of T. suecica. By comparing the growth rate of T. suecica under constant vs. variable temperatures, one can determine the historical or legacy effects of past temperature variation on subsequent organismal performance. These experiments were conducted by members of the Brian Helmuth Lab at Northeastern University.","citation":"Gouhier, T. C., Helmuth, B. (2025) Time series of T. suecica densities under fluctuating temperatures experiment from May 2023 to Aug 2023. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-02-21 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.953492.1 [access date]","citationParts":{"identifier":"doi:10.26008/1912/bco-dmo.953492.1","versionDate":"2025-02-21","versionId":"1","year":"2025","title":"Time series of T. suecica densities under fluctuating temperatures experiment from May 2023 to Aug 2023","authors":"Gouhier, T. C., Helmuth, B.","publisher":"Biological and Chemical Oceanography Data Management Office (BCO-DMO)."},"doi":"10.26008/1912/bco-dmo.953492.1","release_date":null,"field_dataset_location":"The original strain of T. suecica was collected from La Spezia, Italy","temporal_extent":{"start":"2023-05-23T04:00:00.000Z","end":"2023-08-18T04:00:00.000Z"},"temporal":{"resolution":"time:unitDay","type":"time:Interval","start":{"value":"1684814400","label":"2023-05-23"},"end":{"value":"1692331200","label":"2023-08-18"}},"spatial_extent":{"shape":"","center":{"latitude":0,"longitude":0},"wkt":"","north":0,"east":0,"south":0,"west":0},"dataset_type":["experimental"],"keywords":["biota","oceans"],"data_files":[{"id":"954123","title":"953492_v1_tsuecica_exp.csv","url":"https://datadocs.bco-dmo.org/dataset/953492/file/l89MVLzukpK7jq/953492_v1_tsuecica_exp.csv","showTableUrl":"https://www.bco-dmo.org/dataset/953492/data/view/954123","description":"Primary data file for dataset ID 953492, version 1","filename":"953492_v1_tsuecica_exp.csv","bytesize":83015,"checksum":{"value":"9a87b566acde4eebf1a7a503bdb223e9","Algorithm":{"_id":"190","name":"MD5"}},"mimetype":"text/csv","fileType":{"_id":"192","name":"Comma Separated Values (.csv)"},"isErddap":"bcodmo_dataset_953492_v1","date":"","isPublicDuringEmbargo":false,"primaryDataFile":true,"showTable":true,"restricted":false}],"supplemental_files":[],"roles":[{"role":{"_id":"138","name":"Principal Investigator"},"performedBy":{"_id":"541016","name":"Tarik C. Gouhier"},"performedAtOrganization":{"_id":"490462","name":"Northeastern University"}},{"role":{"_id":"140","name":"Co-Principal Investigator"},"performedBy":{"_id":"537382","name":"Brian Helmuth"},"performedAtOrganization":{"_id":"490462","name":"Northeastern University"}},{"role":{"_id":"145","name":"BCO-DMO Data Manager"},"performedBy":{"_id":"924467","name":"Audrey Mickle"},"performedAtOrganization":{"_id":"191","name":"Woods Hole Oceanographic Institution"}}],"deployments":[],"funding":[{"_id":"856336","name":"OCE-2048894","fundingSource":{"_id":"355","name":"NSF Division of Ocean Sciences"},"url":"https://www.nsf.gov/awardsearch/show-award?AWD_ID=2048894"}],"instruments":[{"_id":"953868","name":"Darwin environmental chambers","description":"Stock cultures were maintained in Darwin environmental chambers (Darwin Chambers Company, St. Louis, MO) at a constant temperature of 20ºC under continuous light provided by LED light strips.","type_id":"839936","type_name":"Algal Growth Chamber","type_desc":"A chamber specifically designed for the growth of algae in flasks. The chamber typically provides controlled temperature, humidity, and light conditions.","type_acronym":""},{"_id":"953872","name":"Beckman Coulter Z2 Particle Counter","description":"Cells were counted daily for each of the five days using a Beckman Coulter Z2 Particle Counter (Beckman Coulter, Indianapolis, IN) set to count particles between 5µm - 16 µm. ","type_id":"668847","type_name":"Coulter Counter","type_desc":"An apparatus for counting and sizing particles suspended in electrolytes. It is used for cells, bacteria, prokaryotic cells and virus particles. A typical Coulter counter has one or more microchannels that separate two chambers containing electrolyte solutions.\r\n\r\nfrom https://en.wikipedia.org/wiki/Coulter_counter","type_acronym":""},{"_id":"953877","name":"Hemacytometer","description":"This size range was experimentally determined by photographing cells on a Hemacytometer and analyzing size with ImageJ. ","type_id":"704","type_name":"Hemocytometer","type_desc":"A hemocytometer is a small glass chamber, resembling a thick microscope slide, used for determining the number of cells per unit volume of a suspension. Originally used for performing blood cell counts, a hemocytometer can be used to count a variety of cell types in the laboratory. Also spelled as \"haemocytometer\". Description from:\r\nhttp://hlsweb.dmu.ac.uk/ahs/elearning/RITA/Haem1/Haem1.html.","type_acronym":"Hemocytometer"},{"_id":"953869","name":"PAR sensor","description":"Light was measured using a PAR sensor to verify that each cage was receiving at least 150 µmol m-2 s-2 (saturating irradiance (Bernhardt et al, 2018), and under 200 µmol m-2 s-2 to maintain consistent light conditions between cages. ","type_id":"439","type_name":"Photosynthetically Available Radiation Sensor","type_desc":"A PAR sensor measures photosynthetically available (or active) radiation. The sensor measures photon flux density (photons per second per square meter) within the visible wavelength range (typically 400 to 700 nanometers). PAR gives an indication of the total energy available to plants for photosynthesis. This instrument name is used when specific type, make and model are not known.","type_acronym":"PAR sensor"},{"_id":"953742","name":"vacuum filtration","description":"Seawater was sterilized using vacuum filtration to remove particulate matter and sediment, and subsequently autoclaved at 121ºC for 60 minutes.","type_id":"936239","type_name":"water filtration device","type_desc":"A manufactured device which is used to remove contaminants from water impeding the flow of particles or solutes. ","type_acronym":""}],"parameters":[{"_id":"953910","name":"full_ID","format":"","frictionlessType":"string","description":"

ID of each row based on mean temperature, treatment, replicate ID, and technical replicate number

","units":{"_id":"","name":"unitless"},"type":{"_id":"1759","name":"exp_id","description":"

Experiment Id

"}},{"_id":"953911","name":"measurement_datetime","format":"%Y-%m-%dT%H:%M:%S","frictionlessType":"datetime","description":"

The date and time of the density measurements

","units":{"_id":"","name":"unitless"},"type":{"_id":"1908","name":"ISO_DateTime_UTC","description":"

Date/Time (UTC) ISO formatted
\nThis standard is based on ISO 8601:2004(E) and takes on any of the following forms:
\n2009-08-30T09:05:00[.xx] (local time)
\n2009-08-30T14:05:00[.xx]Z (UTC time)
\n2009-08-30T14:05:00[.xx]-05:00
\nThe dashes and the colons can be dropped.
\nThe T can also be dropped \"by mutual agreement\", but one needs the trailing Z if the time is UTC.

Sample ISO_datetime_utc:
\n2009-08-30T14:05:00[.xx]Z (UTC time)

Format: YYYY-mm-ddTHH:MM:SS[.xx]Z (UTC time)

"}},{"_id":"953912","name":"days_since_start","format":"","frictionlessType":"number","description":"

Number of days since the start of each growth experiment

","units":{"_id":"","name":"days"},"type":{"_id":"1838","name":"time_elapsed","description":"

Elapsed time. Units can be seconds, minutes, etc.

"}},{"_id":"953913","name":"temperature","format":"","frictionlessType":"integer","description":"

Mean temperature

","units":{"_id":"","name":"degrees Celsius"},"type":{"_id":"541490","name":"temp_incub","description":"

The temperature of the medium in which specimens or organisms are being cultivated/incubated. Typically relates to the temperature of the medium in the container used in controlled experiments. Not the ambient water temperature.

"}},{"_id":"953914","name":"treatment","format":"","frictionlessType":"string","description":"

Frequency of fluctuations (F1 = frequency of 1 or period of 144 hours; F3 = frequency of 3 or period of 48 hours; F6 = frequency of 6 or period of 24 hours; F18 = frequency of 18 or period of 8 hours; F24 = frequency of 24 or period of 6 hours; CON = constant temperature)

","units":{"_id":"","name":"unitless"},"type":{"_id":"489066","name":"treatment","description":"

Experimental conditions applied to experimental units. In comparative experiments, members of the complementary group, the control group, receive either no treatment or a standard treatment.

"}},{"_id":"953915","name":"replicate","format":"","frictionlessType":"string","description":"

Experimental replicate ID (A, B, or C)

","units":{"_id":"","name":"unitless"},"type":{"_id":"953","name":"replicate","description":"

an identifier used to distinguish between one or more replicate samples

"}},{"_id":"953916","name":"count_replicate","format":"","frictionlessType":"string","description":"

(Technical) replicate count for each unique experimental replicate (i, ii, or iii)

","units":{"_id":"","name":"unitless"},"type":{"_id":"953","name":"replicate","description":"

an identifier used to distinguish between one or more replicate samples

"}},{"_id":"953917","name":"final_count","format":"","frictionlessType":"number","description":"

Number of T. suecica cells per mL

","units":{"_id":"","name":"cells/mL"},"type":{"_id":"514901","name":"cell_concentration","description":"

Concentration of cells; often determined by spectrophotometry, flow cytometry, or using a microscope. May be referred to as \"cell count\" (cells per volume unit).

"}}],"related_publications":[{"id":"953496","citation":"Bernhardt, J. R., Sunday, J. M., Thompson, P. L., & O’Connor, M. I. (2018). Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment. Proceedings of the Royal Society B: Biological Sciences, 285(1886), 20181076. https://doi.org/10.1098/rspb.2018.1076\r\n","relation_type":["Methods"],"relation_desc":""},{"id":"953495","citation":"Pechenik, J. A., Levy, M., & Allen, J. D. (2019). Instant Ocean Versus Natural Seawater: Impacts on Aspects of Reproduction and Development in Three Marine Invertebrates. The Biological Bulletin, 237(1), 16–25. https://doi.org/10.1086/705134\r\n","relation_type":["Methods"],"relation_desc":""}],"related_datasets":[],"services":[{"grouping":"","items":[{"name":"View Data","description":"An in-browser view of the primary tabular data file","url":"https://www.bco-dmo.org/dataset/953492/data/view","contentType":"text/html","actionType":"view"}]},{"grouping":"ERDDAP","items":[{"name":"SUBSET DATA","description":"","url":"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_953492_v1.html","contentType":"text/html","actionType":"tool"},{"name":"VIEW TABLE","description":"","url":"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_953492_v1.htmlTable","contentType":"text/html","actionType":"view"},{"name":"CSV","description":"","url":"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_953492_v1.csv","contentType":"text/csv","actionType":"download"},{"name":"TSV","description":"","url":"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_953492_v1.tsv","contentType":"text/tsv","actionType":"download"},{"name":"MATLAB","description":"","url":"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_953492_v1.mat","contentType":"text/x-matlab","actionType":"download"},{"name":"netCDF","description":"","url":"https://erddap.bco-dmo.org/erddap/tabledap/bcodmo_dataset_953492_v1.nc","contentType":"application/x-netcdf","actionType":"download"}]},{"grouping":"Metadata","items":[{"name":"HTML","description":"A simple HTML page of the dataset metadata","url":"https://www.bco-dmo.org/dataset/953492/description","contentType":"text/html","actionType":"view"},{"name":"PDF","description":"A PDF of the HTML dataset metadata","url":"https://www.bco-dmo.org/dataset/953492/Dataset_description.pdf","contentType":"application/pdf","actionType":"download"},{"name":"ISO 19115-2 (NOAA Profile)","description":"","url":"https://www.bco-dmo.org/dataset/953492/iso","contentType":"application/xml","actionType":"download"}]}],"programs":[],"projects":[{"_id":"856335","name":"A novel time-structured framework to account for the cryptic effects of temperature fluctuations on population dynamics","acronym":"Time Structured Modeling","description":"$1d"}],"archives":[{"id_value":"10.26008/1912/bco-dmo.953492.1","url":"https://doi.org/10.26008/1912/bco-dmo.953492.1","date_assigned":"2025-02-24","version_date":"2025-02-21","archiver":"Marine Biological Laboratory/Woods Hole Oceanographic Institution Library (MBLWHOI DLA)","archiver_id":"272"}],"license":{"name":"Creative Commons Attribution 4.0","id":"CC-BY-4.0","uri":"https://creativecommons.org/licenses/by/4.0/","spdxUri":"https://spdx.org/licenses/CC-BY-4.0"},"metrics":{"views":{"count":70},"downloads":{"count":104}}},"fileId":"954123"}]