That's Funny... » sea ice http://staff.washington.edu/rec3141 the website of Eric Collins, grad student Wed, 11 Nov 2009 18:25:46 +0000 http://wordpress.org/?v=2.8.6 en hourly 1 Enrichment of microorganisms into sea ice brine http://staff.washington.edu/rec3141/wordpress/archives/578 http://staff.washington.edu/rec3141/wordpress/archives/578#comments Tue, 21 Jul 2009 01:52:06 +0000 eric http://staff.washington.edu/rec3141/?p=578 As seawater freezes into sea ice, all of the dissolved constituents of the water become concentrated within the solid ice matrix that forms. Because it is more dense than seawater due to the high salt content, a lot of this ‘brine’ will drain from the ice by gravity. However, some brine remains in the ice down to −55°C, the eutectic point of seawater, at which point the ice transitions to a complete solid with no liquid fraction. Between the freezing point of seawater (about −2°C) and the eutectic, there will be brine with a salinity dependent on the temperature of the ice, up to about 8 times that salinity of seawater. But it’s not just salts that are concentrated, but also nutrients, particles, and microorganisms living within the seawater, including viruses and bacteria. A former student in the lab, Dr. Llyd Wells, discussed the consequences of this concentration effect in detail in a 2006 paper in Environmental Microbiology: “Modelled and measured dynamics of viruses in Arctic winter sea-ice brines“. In this paper he used a mathematical model to predict contact rates between bacteria and viruses as a function of temperature in sea ice brine, showing that “virus-bacteria contact rates in underlying −1°C seawater were … up to 600 times lower than those in ice brines at or below −24°C.” Two contrasting factors affected the relative contact rates. First, the brine concentrating effect described above, which increases contact rates by increasing the concentrations of viruses and bacteria in the ice. Second, the diffusivity decreases as a factor of increasing viscosity at lower temperatures, which decreases the contact rates. In the figure shown below, Llyd shows that the result of these contrasting effects is overall a positive one, with very high potential contact rates occurring in the upper, colder sea ice.

sea ice diffusivity, wells and deming 2006

The equations used were as follows:

J = 2\pi dD_vVB

where J = contact rate, “d is the spherical diameter of the average cell (cm), Dv the viral diffusivity (cm2 s-1), and V and B the [in situ] concentrations of viruses and bacteria respectively (ml-1 [brine or seawater]).”

D_v = \dfrac{kT}{3\pi \mu dv}

“where k is Boltzmann’s constant, T the temperature (Kelvin), \mu the viscosity (g cm-1 s-1), and dv the spherical diameter of the average virus (cm)”. Dv can be estimated with the following equation (determined empirically from Figure 1) where t is temperature (°C).

D_v = 40.5882 \times 10^{-9} \times 10^{0.0325t}

The authors provide the following values for constants:

\begin{tabular}{ccc} constant & value & units\\\hline k & 1.38 \times 10^{-16}& g cm^2 K^{-1} s^{-2}\\ d & 0.5 \times 10^{-4}& cm\\ dv & 60 \times 10^{-7} & cm\\ \end{tabular}

but they don’t provide for the calculation of \mu, the viscosity in the ice, referring to a 1975 paper by George Cox (which references a 1960 paper by Dale Kaufmann [which itself references a 1929 paper by Stakelbeck and Plank]).

The following multiple linear equation can be used to estimate the viscosity (in centipoise = 0.01 * g cm-1 s-1) as a function of temperature (T) and brine salinty (S) in the ice, but it is not very good:
\mu = -0.0835419T + 0.0066835S+1.7724989

[The raw data and R script to calculate the multiple linear regression are available here]

A better empirical equation was determined using ZunZun.com, an amazingly useful site for curve fitting. I used the Function Finder, which identified a Reciprocal Polynomial as the best available curve. The simplified equation for that curve is (mu in centipoise= 0.01 * g cm-1 s-1):
\mu = \dfrac{1}{0.62 + 0.020T + 0.00014T^2 -0.0012S -0.000030ST}

T-S-viscosity

Finally, to calculate the relative contact rates between seawater and sea ice, given concentrations of bacteria and viruses (per volume brine or seawater):
\dfrac{J_i}{J_w} = \dfrac{D_{vi}}{D_{vw}} \times \dfrac{B_i}{B_w} \times \dfrac{V_i}{V_w}

which can be generalized to:
\dfrac{J_i}{J_w} = \dfrac{D_{vi}}{D_{vw}} \times \dfrac{f_B}{V_{br}} \times \dfrac{f_V}{V_{br}}

where Vbr is the brine volume fraction (calculator available here), “the subscripts i and w indicate sea ice and water column respectively. The terms fB and fV represent the fraction of bacteria and viruses retained in the brine and serve as a correction to account for possible partitioning within the solid phase … as well as for two major mechanisms of loss: destruction due to impinging ice crystals or osmotic stress and release with rejected brine.

If passive entrainment into the ice (proportional to salts) is expected for both viruses and bacteria, then f_B = f_V = \dfrac{S_i}{S_w}, where S is the bulk salinity of the ice or water.

If active entrainment into the ice is expected (complete/active concentration of bacteria and viruses into ice), then f_B = f_V = 1

If the concentration of bacteria (or viruses) in the ice is 0 then fB = 0.

Another enrichment index has been used by others, including Riedel (2006), originally from Gradinger and Ikalvko (1998). Their index (Is) is 0 when the concentration in ice is 0 (Is = 0 when Ci = 0) and is 1 when the concentration in ice is proportional to the salt retained in the ice (Is = 0 when Ci/Cw =Si/Sw).

I_s = \dfrac{C_i}{C_w} \dfrac{S_w}{S_i}

A third index can be created such that at a value of 0 indicates passive enrichment and a value of 1 indicates complete/active enrichment. A value less than zero indicates loss or mortality in the ice (-1 indicates an in situ concentration of 0). Any value greater than 1 indicates production or growth within the ice.

E = \dfrac{\dfrac{C_i}{C_w}-\dfrac{S_i}{S_w}}{1-\dfrac{S_i}{S_w}}

temperature bulk salinity brine concentrating factor

temperature bulk salinity brine concentrating factor

]]> http://staff.washington.edu/rec3141/wordpress/archives/578/feed 1 Predicting the height of a saturated peak on an electropherogram http://staff.washington.edu/rec3141/wordpress/archives/527 http://staff.washington.edu/rec3141/wordpress/archives/527#comments Thu, 07 May 2009 01:12:08 +0000 eric http://staff.washington.edu/rec3141/?p=527 One way to assess the microbial community structure in an environment is to use a ‘fingerprinting’ technique, like T-RFLP or ARISA, to interrogate the ’species’ living there as determined from their 16S rRNA genes or some functional gene like amoA. Here’s an example of a T-RFLP electropherogram from sea ice:
examplecsv0

You can see that most of the signal in this sample is contained within a few peaks. Sometimes those peaks saturate (max-out, overblow) the detector, which is bad if I am interested in comparing the heights of the peaks (a controversial subject, I should note I am only doing bulk, not individual, comparisons). Of course, I could just add less DNA and run it again, except that then I would be liable to lose some of the smaller peaks (also, it’s not practical for me to re-run these specific samples). So I’ve written a script in the open-source statistical package R to estimate the heights of the saturated peaks by fitting a Gaussian function of the form

f(x) = y_0+\dfrac{b\sqrt{2/\pi}}{d}*e^{-2\left(\dfrac{x-x_0}{d}\right)^2}

where ‘y_0′ is the y-minimum, ‘x_0′ is the center of the peak, ‘b’ is a scaling factor, and ‘d’ is related to the standard deviation of the distribution.

You can download the script here: gaussfit.r

The figures below show (A) a fitted regular-sized peak, and (B) a fitted saturated peak. In my case, the fitted function has a maximum that is 1.6 ± 2.5% of the observed maximum for regular-sized peaks.

examplecsv1examplecsv2

]]> http://staff.washington.edu/rec3141/wordpress/archives/527/feed 0 Reaching the Arctic sea ice annual minimum http://staff.washington.edu/rec3141/wordpress/archives/251 http://staff.washington.edu/rec3141/wordpress/archives/251#comments Thu, 18 Sep 2008 20:53:37 +0000 eric http://staff.washington.edu/rec3141/?p=251 It looks like we’ve hit bottom for the year, and it’s not exactly good news. Although we didn’t break the record set in 2007 for Lowest Minimum Arctic Sea Ice Extent In Recorded Human History, we got close. This year’s minimum, about 4.5 million square kilometers, is much nearer to last year’s low than the long term average minimum: 6.75 million square kilometers.

2008 Arctic Sea Ice Minimum

Here’s a news article, in the ‘Local’ section of the Seattle P-I, discussing the issue with some UW scientists: North Pole ever closer to having no ice. Ignatius Rigor says “It’s hard to see how the ice might come back, unless we are able to curb the greenhouse gases.” But even if we stopped emitting tomorrow, the greenhouse gases we’ve already put into the atmosphere have imparted a kind of ‘chemical inertia’ that will take Nature many human generations to be rid of.

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