http://lod.bco-dmo.org/id/dataset/544801
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2015-01-14
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Laboratory results on phytoplankton temperature traits and data sources collected Michigan State University in 2012
2014-12-22
publication
2014-12-22
revision
BCO-DMO Linked Data URI
2014-12-22
creation
http://lod.bco-dmo.org/id/dataset/544801
Elena Litchman
Michigan State University
principalInvestigator
Christopher Klausmeier
Michigan State University
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Litchman, E., Klausmeier, C. (2014) Laboratory results on phytoplankton temperature traits and data sources collected Michigan State University in 2012. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 2014-12-22) Version Date 2014-12-22 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/544801 [access date]
Phytoplankton temperature traits and data sources Dataset Description: <p>This dataset gives phytoplankton temperature optima and the literature sources for the studies.&nbsp;The data are estimates from statistical fits to a compilation of measurements made in laboratories around the world.</p> Methods and Sampling: <p>Text here is modified from the supplementary information of Thomas et al. (2012):</p>
<p><strong>Statistical analysis of thermal tolerance curves </strong></p>
<p>Temperature dependent specific growth rates can be described by the following equation:</p>
<p><em>f</em>(T) = <em>ae<sup>bT</sup></em>[1-{(<em>T</em>-<em>z</em>)/(<em>w</em>/2)}<sup>2</sup>]&nbsp;&nbsp;&nbsp; - (S.1)</p>
<p>Here specific growth rate <em>f </em>is an explicit function of temperature, <em>T</em>. The shape of the thermal tolerance curve is controlled by two important species traits, <em>z </em>and <em>w</em>. The range of temperatures over which growth rate is positive, or the thermal niche width, is given by <em>w</em>. Species trait <em>z </em>determines the location of the maximum of the quadratic portion of this function. In the case where parameter <em>b </em>= 0, this value is identical to the temperature at which a species achieves its maximum growth rate. However, when <em>b </em>is non-zero, the maximum value of S.1 falls above (or potentially below, <em>b &lt;</em>0) the value of <em>z</em>, and can be found through numerical optimization.</p>
<p>We allowed <em>a </em>and <em>b </em>to be free parameters, fit simultaneously with <em>z </em>and <em>w</em>. To describe the growth data for each isolate, we used a maximum likelihood approach, such that the mean growth rate at a given temperature followed equation (S.1),</p>
<p><em>µ</em> = <em>f</em>(T) + <em>N</em>(0, σ<sup>2</sup>)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -(S.2)</p>
<p>Here observational error was described by a normal distribution with a mean of zero and variance of σ<sup>2</sup>.</p>
<p><strong>Determining physiological parameter uncertainty </strong></p>
<p>While confidence intervals for the point estimates of the parameters of (S.1) were easy to obtain, it was not straightforward to determine uncertainty for implicit properties such as the temperature at which growth rate is maximized (or, the ‘optimum temperature’). Yet, this was the property that we were mainly interested in, leading us to adopt a parametric bootstrapping approach.</p>
<p>We used a Monte Carlo approach such that for each thermal tolerance curve having <em>n </em>data points, we simulated <em>n </em>new data points, drawn from a normal distribution such that:</p>
<p>1) The mean of the distribution corresponds to the value of (S.1) at each of the original experimental temperatures, given the coefficients previously estimated for the original curve.</p>
<p>2) The standard deviation of the distribution, σ, was obtained by adjusting the original maximum likelihood estimate, to account for uncertainty in its estimation.</p>
<p>Equation (S.1) was then fit to the simulated data using maximum likelihood estimation, and the new parameter values, as well as the numerically estimated optimum temperature, were retained. Repeating this process a total of 10,000 times (for each isolate), yielded bootstrapped distributions of all parameter estimates. From these distributions we calculated the 95% confidence intervals as the range between the 2.5th and 97.5th quantiles.&nbsp;</p>
<p><strong>Related Reference:</strong></p>
<p>Thomas, M. K., Kremer, C. T., Klausmeier, C. A., &amp; Litchman, E. (2012). A &nbsp;global pattern of thermal adaptation in marine phytoplankton. Science, 338(6110), &nbsp;1085-1088. &nbsp;DOI:&nbsp;10.1126/science.1224836.</p>
<p>Supplemental materials:&nbsp;http://www.sciencemag.org/content/suppl/2012/10/25/science.1224836.DC1/Thomas.SM.pdf&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-0928819 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=0928819
completed
Elena Litchman
Michigan State University
269-671-2338
3700 E Gull Lake Dr.
Hickory Corners
MI
49060
USA
litchman@msu.edu
pointOfContact
Christopher Klausmeier
Michigan State University
W. K. Kellogg Biological Station
Hickory Corners
MI
49060
USA
klausme1@msu.edu
pointOfContact
asNeeded
Dataset Version: 2014-12-22
Unknown
sample
taxon
study
environment
temp_opt
opt_CI_low
opt_CI_up
temp_niche
niche_CI_low
niche_CI_up
theme
None, User defined
sample identification
taxon
No BCO-DMO term
water temperature at measurement depth
featureType
BCO-DMO Standard Parameters
Litchman_2012
service
Deployment Activity
Michigan State University
place
Locations
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Phytoplankton Traits, Functional Groups and Community Organization: A Synthesis
https://osprey.bco-dmo.org/project/543188
Phytoplankton Traits, Functional Groups and Community Organization: A Synthesis
<p><em>Description from NSF award abstract:</em><br />
Phytoplankton account for half of global primary productivity and their biomass and community composition significantly impact global carbon and other biogeochemical cycles and ecosystem functioning. Explaining patterns of global distributions of phytoplankton groups and predicting how phytoplankton communities will re-organize under anthropogenic environmental change requires knowledge of diverse eco-physiological traits defining ecological niches of phytoplankton species. In this project, the investigators will assemble a query-based database of diverse phytoplankton traits such as cell/colony size, growth rates, resource acquisition and predator avoidance traits, among others. Data for all available species and strains will be included. They will use the database to answer fundamental questions in phytoplankton ecology such as:<br />
1) what traits exhibit trade-offs (pairwise and beyond) and what shapes are they?<br />
2) What traits scale allometrically with cell/body size? Can scaling exponents from first principles be predicted? What are potential limits to allometric scaling as a way of simplifying the complex trait space that characterizes real organisms?<br />
3) What are trait differences among major functional/taxonomic groups of phytoplankton and how much does taxonomy/phylogeny constrain particular functional traits?<br />
4) Are there differences in trait distributions between marine and freshwater groups?</p>
<p>The investigators will also use the database to parameterize novel models of phytoplankton community organization and evolution based on adaptive dynamics approaches. They will use the models to explore how community structure emerges under different environmental scenarios, given physiological constraints and ecological interactions. Changes in elemental stoichiometry, size structure and functional group distributions at different spatial and temporal scales will also be examined.</p>
Phytoplankton Traits
largerWorkCitation
project
eng; USA
biota
oceans
Michigan State University
2014-12-22
0
BCO-DMO catalogue of parameters from Laboratory results on phytoplankton temperature traits and data sources collected Michigan State University in 2012
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/544836.rdf
Name: sample
Units: unitless
Description: <p>Index number for each strain in the dataset.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544841.rdf
Name: taxon
Units: unitless
Description: <p>Species name, including strain/clone name where available. In some cases, taxa were not identified to a species level; in this case the available identifying information was provided.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544842.rdf
Name: study
Units: unitless
Description: <p>Reference to the published paper from which the data were collected. The complete reference may be found in the supplementary information of Thomas et al. (2012). Thomas, M. K., Kremer, C. T., Klausmeier, C. A., & Litchman, E. (2012). A global pattern of thermal adaptation in marine phytoplankton. Science, 338(6110), 1085-1088. Specifically, pages 27-36 at <a href="http://www.sciencemag.org/content/suppl/2012/10/25/science.1224836.DC1/Thomas.SM.pdf" target="_blank">http://www.sciencemag.org/content/suppl/2012/10/25/science.1224836.DC1/Thomas.SM.pdf</a></p>
http://lod.bco-dmo.org/id/dataset-parameter/544843.rdf
Name: environment
Units: unitless
Description: <p>Whether these strains were isolated from marine or estuarine environments.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544844.rdf
Name: temp_opt
Units: degrees Celsius
Description: <p>Estimated optimum temperature for growth (in degrees C) of a phytoplankton strain based on statistical fits of a thermal reaction norm equation to measurements of population growth rates across a range of temperatures.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544845.rdf
Name: opt_CI_low
Units: degrees Celsius
Description: <p>Lower bound of 95% confidence interval (i.e. 2.5th quantile) of the optimum temperature for growth (in degrees C) estimated through a bootstapping procedure.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544846.rdf
Name: opt_CI_up
Units: degrees Celsius
Description: <p>Upper bound of 95% confidence interval (i.e. 97.5th quantile) of the optimum temperature for growth (in degrees C) estimated through a bootstapping procedure.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544847.rdf
Name: temp_niche
Units: degrees Celsius
Description: <p>Estimated range of temperatures over which a strain's population growth rate is positive (in degrees C).</p>
http://lod.bco-dmo.org/id/dataset-parameter/544848.rdf
Name: niche_CI_low
Units: degrees Celsius
Description: <p>Lower bound of 95% confidence interval (i.e. 2.5th quantile) of the temperature niche width (in degrees C) estimated through a bootstapping procedure.</p>
http://lod.bco-dmo.org/id/dataset-parameter/544849.rdf
Name: niche_CI_up
Units: degrees Celsius
Description: <p>Upper bound of 95% confidence interval (i.e. 97.5th quantile of the temperature niche width (in degrees C) estimated through a bootstapping procedure.</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
18750
https://datadocs.bco-dmo.org/file/M77XqRBfxlvlO9/temp_traits.csv
temp_traits.csv
Primary data file for dataset ID 544801
download
https://osprey.bco-dmo.org/dataset/544801/data/download
download
onLine
dataset
<p>Text here is modified from the supplementary information of Thomas et al. (2012):</p>
<p><strong>Statistical analysis of thermal tolerance curves </strong></p>
<p>Temperature dependent specific growth rates can be described by the following equation:</p>
<p><em>f</em>(T) = <em>ae<sup>bT</sup></em>[1-{(<em>T</em>-<em>z</em>)/(<em>w</em>/2)}<sup>2</sup>]&nbsp;&nbsp;&nbsp; - (S.1)</p>
<p>Here specific growth rate <em>f </em>is an explicit function of temperature, <em>T</em>. The shape of the thermal tolerance curve is controlled by two important species traits, <em>z </em>and <em>w</em>. The range of temperatures over which growth rate is positive, or the thermal niche width, is given by <em>w</em>. Species trait <em>z </em>determines the location of the maximum of the quadratic portion of this function. In the case where parameter <em>b </em>= 0, this value is identical to the temperature at which a species achieves its maximum growth rate. However, when <em>b </em>is non-zero, the maximum value of S.1 falls above (or potentially below, <em>b &lt;</em>0) the value of <em>z</em>, and can be found through numerical optimization.</p>
<p>We allowed <em>a </em>and <em>b </em>to be free parameters, fit simultaneously with <em>z </em>and <em>w</em>. To describe the growth data for each isolate, we used a maximum likelihood approach, such that the mean growth rate at a given temperature followed equation (S.1),</p>
<p><em>µ</em> = <em>f</em>(T) + <em>N</em>(0, σ<sup>2</sup>)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; -(S.2)</p>
<p>Here observational error was described by a normal distribution with a mean of zero and variance of σ<sup>2</sup>.</p>
<p><strong>Determining physiological parameter uncertainty </strong></p>
<p>While confidence intervals for the point estimates of the parameters of (S.1) were easy to obtain, it was not straightforward to determine uncertainty for implicit properties such as the temperature at which growth rate is maximized (or, the ‘optimum temperature’). Yet, this was the property that we were mainly interested in, leading us to adopt a parametric bootstrapping approach.</p>
<p>We used a Monte Carlo approach such that for each thermal tolerance curve having <em>n </em>data points, we simulated <em>n </em>new data points, drawn from a normal distribution such that:</p>
<p>1) The mean of the distribution corresponds to the value of (S.1) at each of the original experimental temperatures, given the coefficients previously estimated for the original curve.</p>
<p>2) The standard deviation of the distribution, σ, was obtained by adjusting the original maximum likelihood estimate, to account for uncertainty in its estimation.</p>
<p>Equation (S.1) was then fit to the simulated data using maximum likelihood estimation, and the new parameter values, as well as the numerically estimated optimum temperature, were retained. Repeating this process a total of 10,000 times (for each isolate), yielded bootstrapped distributions of all parameter estimates. From these distributions we calculated the 95% confidence intervals as the range between the 2.5th and 97.5th quantiles.&nbsp;</p>
<p><strong>Related Reference:</strong></p>
<p>Thomas, M. K., Kremer, C. T., Klausmeier, C. A., &amp; Litchman, E. (2012). A &nbsp;global pattern of thermal adaptation in marine phytoplankton. Science, 338(6110), &nbsp;1085-1088. &nbsp;DOI:&nbsp;10.1126/science.1224836.</p>
<p>Supplemental materials:&nbsp;http://www.sciencemag.org/content/suppl/2012/10/25/science.1224836.DC1/Thomas.SM.pdf&nbsp;</p>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
Deployment: Litchman_2012
Litchman_2012
lab Litchman
laboratory
Litchman_2012
Elena Litchman
Michigan State University
lab Litchman
laboratory