ULink 2022 Genotype x OA Growth Analysis
research 03 Aug 2022Overview
Here I review the growth we have observed in our experiment. The total growth was less than we anticipated, but we still produced enough new skeleton with significant differences in growth rates and sensitivities to proceed forward with most of our tests.
Treatment Conditions
Labview Data
The peaks in the standard deviation are almost certainly caused by aquarium cleaning days when corals are removed into a separate bath and the sensors are capped causing logging errors. CO2 injection is turned off during these times so the aquariums themselves are not experiencing the conditions that the logged data are suggesting. The following graph filters out these spiked values which were programmatically identified by occurring during scheduled cleaning times and affecting multiple aquariums at once since cleaning occurred at the same time for all aquariums.
Variability is still present, but the extreme spikes caused by cleaning have been removed. Thus, any variability that remains is due to durafet error or experimental variability that affected the corals. For example, the durafet for T15 had much higher variability than the other aquariums. However, I believe this to be negligible.
Carbonate Chemistry Data
500mL water samples were collected every Tuesday for analysis of the complete carbonate chemistry suite.
Time of Day
The bottles were not taken at exactly the same time of day, and thus the programmed variability will be apart of the variability of each sample along with sampling error, durafet error altering amount of CO2 injected into aquaria (shown above in the LabView data), etc.
Sampling time had a mean of around 10a. 3 sampling times were taken after 12p with one sampling time around 4:20p
Carb Parameters
tank  sal  temp  TA  DIC  pCO2  omega 

T13  34.45 ± 0.65  27.05 ± 0.02  2301.01 ± 21.14  2126.64 ± 22.78  835.70 ± 36.22  2.19 ± 0.07 
T14  34.38 ± 0.66  27.05 ± 0.02  2300.12 ± 21.34  2000.13 ± 21.73  426.93 ± 14.74  3.46 ± 0.08 
T15  34.44 ± 0.65  27.04 ± 0.02  2303.84 ± 21.56  2119.24 ± 24.84  848.20 ± 99.62  2.31 ± 0.18 
T16  34.40 ± 0.67  27.07 ± 0.02  2297.52 ± 21.47  2004.24 ± 23.57  442.12 ± 17.83  3.38 ± 0.08 
Statistics
parameter  term  df  sumsq  meansq  statistic  p.value  significance 

sal  treatment  1  0.040  0.040  0.007  0.933  NS 
sal  treatment:tank  2  0.003  0.002  0.000  1.000  NS 
sal  Residuals  48  269.036  5.605  NA  NA  NS 
temp  treatment  1  0.003  0.003  1.007  0.321  NS 
temp  treatment:tank  2  0.004  0.002  0.636  0.534  NS 
temp  Residuals  48  0.167  0.003  NA  NA  NS 
TA  treatment  1  169.020  169.020  0.028  0.867  NS 
TA  treatment:tank  2  96.270  48.135  0.008  0.992  NS 
TA  Residuals  48  285247.509  5942.656  NA  NA  NS 
DIC  treatment  1  189577.501  189577.501  26.960  0.000  xxx 
DIC  treatment:tank  2  465.459  232.730  0.033  0.967  NS 
DIC  Residuals  48  337526.642  7031.805  NA  NA  NS 
pCO2  treatment  1  2157966.567  2157966.567  56.404  0.000  xxx 
pCO2  treatment:tank  2  2513.434  1256.717  0.033  0.968  NS 
pCO2  Residuals  48  1836441.545  38259.199  NA  NA  NS 
omega  treatment  1  17.735  17.735  109.739  0.000  xxx 
omega  treatment:tank  2  0.135  0.068  0.418  0.661  NS 
omega  Residuals  48  7.757  0.162  NA  NA  NS 
Salinity, temperature, and total alkalinity were not significantly different between treatments or within treatments (p>>0.05). DIC, pCO2, and Ω_{A**r} (p<0.001) were significantly different between treatment, but not between aquariums (p>>0.05). In other words, our system reproducibly altered the carbonate chemistry parameters with high precision.
Calcification Analysis
Following April 15 (Weeky 5), the declining health of the corals stabilized and began to split amongst treatment groups.
There is some obvious genetspecific responses.

CheetosB calcification rate was nearly identical across HCO2 and LCO2 groups. This genet also had high initial mortality and the worst survivorship rate throughout the experiment. It is entirely possible that this genotype did not do well in the aquariums and its diminished calcification rate is an effect of overall health and not treatment.

SIA and AC2 had the highest average calcification rates and there was no significant difference between these two genotypes. However, when you look at the effect of treatment within these genotypes (sensitivity), there is significant differences between them.

When looking at just controls, the only significant different genotype is CheetosB. Thus, there is a difference in sensitivity to OA but no observable differences in ambient conditions.

The relative rankings extracted from the ol’ AcDC are SIA ~ MBC > AC2 > CheetosB. The data collected here fits within that framework, yet reveals intraspecifc differences in sensitivities similar to Enochs et al. (2018). Genet identities are unknown for that paper though.
treatment  n  mean  sd 

HCO2  46  0.316  0.139 
LCO2  43  0.569  0.237 
.y.  group1  group2  n1  n2  statistic  df  p  p.signif 

dailyG  HCO2  LCO2  46  43  6.1944  87  0  \*\*\*\* 
.y.  group1  group2  effsize  n1  n2  magnitude 

dailyG  HCO2  LCO2  1.314  46  43  large 
term  contrast  adj.p.value  significance 

treatment  LCO2HCO2  0.0000  xxx 
genotype  SIACheetosB  0.0000  xxx 
treatment:genotype  LCO2:AC2HCO2:AC2  0.0000  xxx 
genotype  CheetosBAC2  0.0004  xxx 
treatment:genotype  LCO2:MBCHCO2:MBC  0.0004  xxx 
genotype  SIAMBC  0.0039  xx 
genotype  MBCCheetosB  0.1103  NS 
treatment:genotype  LCO2:SIAHCO2:SIA  0.3339  NS 
genotype  SIAAC2  0.5634  NS 
treatment:genotype  LCO2:CheetosBHCO2:CheetosB  0.9741  NS 
treatment  genotype  significance  

3  HCO2  CheetosB  a 
5  HCO2  MBC  a 
4  LCO2  CheetosB  ab 
1  HCO2  AC2  ab 
7  HCO2  SIA  bc 
6  LCO2  MBC  cd 
8  LCO2  SIA  cd 
2  LCO2  AC2  d 
The mean calcification rate in the HCO2 group was mean 0.316 (SD = 0.139) mg/g/day, whereas the mean in the LCO2 group was 0.569 (SD = 0.237). A Student twosamples ttest showed that the difference was statistically significant, t(87) = 6.194, p < 0.0001, d = 1.314. Thus, the ocean acidification group saw on average a 44% reduction in calcification rates.
Tank Effects
We saw above that tank conditions were significantly different among treatment groups, but not individual aquariums within treatment. We also saw that calcification rates were significantly different among treatment groups. Here I am analyzing tank effects on the calcification rate and investigating if calcification rates were significantly different between aquariums within the same treatment group.
Tank Effects Statistics
treatment  .y.  group1  group2  n1  n2  statistic  df  p  p.adj  p.adj.signif 

HCO2  dailyG  13  15  24  22  0.482  42.209  0.632  0.632  ns 
LCO2  dailyG  14  16  22  21  1.456  40.856  0.153  0.153  ns 
No observable differences of mean calcification rate when comparing within treatment groups.
Mixed Effects Model
Here I created a mixed effects model model to account for the lack of independence brought upon by having multiple corals grown in the same tank and the possible tankspecific effects that may have affected calcification rates. Including this random effect decreased the AIC from 66 to 31 as compared to a fixedeffects only model, and therefore the random tank effect should be included for analysis.
As a reminder, here is the fixed effects model as shown above:
modFixed < totalGrowth %>%
aov(dailyG ~ genotype*treatment, data=.)
modFixed %>%
summary()
## Df Sum Sq Mean Sq F value Pr(>F)
## genotype 3 0.9975 0.3325 13.298 3.86e07 ***
## treatment 1 1.2904 1.2904 51.605 2.97e10 ***
## genotype:treatment 3 0.3311 0.1104 4.413 0.00631 **
## Residuals 81 2.0254 0.0250
## 
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Here is the mixed effects model with the tank identity set as a random factor to give each tank its own, random intercept. Notice, including the random effects decreases the absolute value of the AIC. Therefore, this new model better describes the data.
modRandom < totalGrowth %>%
lmerTest::lmer(dailyG ~ genotype * treatment + (1tank),
data=.)
modRandom %>%
anova()
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## genotype 0.96092 0.32031 3 79.039 13.2105 4.486e07 ***
## treatment 0.49517 0.49517 1 2.061 20.4225 0.043159 *
## genotype:treatment 0.32747 0.10916 3 79.039 4.5019 0.005729 **
## 
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
AIC(modFixed, modRandom)
## df AIC
## modFixed 9 66.10356
## modRandom 10 30.71757
Tukey posthoc analysis of the mixed effects model:
pairwise  estimate  std.error  statistic  adj.p.value 

(AC2 HCO2)  (AC2 LCO2)  0.3942  0.0741  5.3202  0.0066 
(CheetosB HCO2)  (CheetosB LCO2)  0.0820  0.0903  0.9082  0.9815 
(MBC HCO2)  (MBC LCO2)  0.3045  0.0751  4.0544  0.0333 
(SIA HCO2)  (SIA LCO2)  0.1365  0.0708  1.9281  0.5701 
Pattern is the same as above with the fixed effects. Significance has decreased (p values increased) for AC2 and MBC, suggesting that there was some variability between tanks in the same treatment (T13 v T15 and T14 v T16) but that this within treatment variability was not significant enough to change our conclusions. Thus, we fail to reject our null hypothesis that there are significant differences between individual genotype’s susceptibility to OA.
Powder Available
The amount of new aragonite precipated is visualized above. Horizontal lines denote thresholds for tests: >500mg = green (complete suite including XRD), >120 mg = orange (complete suite sans XRD), >50mg = red (TGA and isotope only).
Linear Extension
treatment  n  mean  sd 

HCO2  45  0.005  0.004 
LCO2  39  0.006  0.004 
.y.  group1  group2  n1  n2  statistic  df  p  p.signif 

prod  HCO2  LCO2  45  39  1.5559  82  0.124  ns 
.y.  group1  group2  effsize  n1  n2  magnitude 

prod  HCO2  LCO2  0.3404  45  39  small 
term  contrast  adj.p.value  significance 

treatment:genotype  LCO2:SIAHCO2:SIA  0.0012  xx 
treatment  LCO2HCO2  0.0943  NS 
genotype  MBCCheetosB  0.3472  NS 
genotype  SIAMBC  0.4989  NS 
genotype  CheetosBAC2  0.5543  NS 
genotype  SIAAC2  0.7510  NS 
treatment:genotype  LCO2:AC2HCO2:AC2  0.9477  NS 
genotype  SIACheetosB  0.9671  NS 
treatment:genotype  LCO2:CheetosBHCO2:CheetosB  1.0000  NS 
treatment:genotype  LCO2:MBCHCO2:MBC  1.0000  NS 
treatment  genotype  significance  

7  HCO2  SIA  a 
4  LCO2  CheetosB  ab 
3  HCO2  CheetosB  ab 
2  LCO2  AC2  ab 
6  LCO2  MBC  ab 
5  HCO2  MBC  ab 
1  HCO2  AC2  b 
8  LCO2  SIA  b 
The mean linear extension rate in the HCO2 group was mean 0.005 (SD = 0.004) mm/cm/day, whereas the mean in the LCO2 group was 0.006 (SD = 0.004). A Student twosamples ttest showed that the difference was not statistically significant, t(82) = 1.556, p =0.124, d = 0.34.
Tank Effects
treatment  .y.  group1  group2  n1  n2  statistic  df  p  p.adj  p.adj.signif 

HCO2  prod  13  15  23  22  0.911  38.599  0.368  0.368  ns 
LCO2  prod  14  16  21  18  1.227  34.931  0.228  0.228  ns 
## # A tibble: 1 x 4
## term contrast adj.p.value significance
## <chr> <chr> <dbl> <chr>
## 1 treatment:genotype LCO2:SIAHCO2:SIA 0.0012 xx
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## genotype 4.3893e05 1.4631e05 3 74.015 1.1832 0.32200
## treatment 1.7696e05 1.7696e05 1 2.059 1.4311 0.35119
## genotype:treatment 2.1560e04 7.1866e05 3 74.015 5.8119 0.00127 **
## 
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pairwise  estimate  std.error  statistic  adj.p.value 

(AC2 HCO2)  (AC2 LCO2)  0.0017  0.0015  1.1114  0.9453 
(CheetosB HCO2)  (CheetosB LCO2)  0.0004  0.0019  0.1980  1.0000 
(MBC HCO2)  (MBC LCO2)  0.0000  0.0015  0.0210  1.0000 
(SIA HCO2)  (SIA LCO2)  0.0062  0.0015  4.1839  0.0117 
## df AIC
## modFixed 9 700.8051
## modRandom 10 604.1854
AIC tells us the mixed effects model better describes the data. Posthoc testing further tells us that indeed SIA’s linear extension rates were significantly different between treatments (p<0.05), yet all other genotype’s were not.
Takeaways and Next Steps
Overall growth was less than hoped for. However, there is enough new skeleton for nearly all the powder tests that we want to conduct: FTIR, TGA, boron isotopes, and Raman. There is enough powder to run XRD on some samples from genotypes AC2 (OA=2, control=8) and SIA (control=5) and a single sample of CheetosB. The lack of even replication though may preclude analysis of XRD. For nanoindentation tests, we should have enough new growth to run on a majority of samples.
Linear extension was measured by maximum vertical height as measured with calipers. We additionally have initial 3d scans of all corals and post 3d scans of a subset of 48 corals (n=3 per genotype per tank). From this data, we can extract surface area to volume ratios and see how this changed among genotypes and treatments. This analysis still needs to be done. We can also more accurately measure total linear extension of the corals which may have curving or branching morphologies.
Plating of Tissue
Among control corals, I visually noticed significant plating of tissue and a veneer of aragonite above the acrylic tags. This was almost completely absent in OA corals. I can take photos of each coral and calculate surface area of plating.
CT Scanning
The microct scan is currently out of service. We can use the large ctscanner to determine bulk density. The scanner has a resolution of 0.1mm/scan so we can measure the density of the new growth. This growth is isolated to the highly variable apical tips which may cause some problems. See this post which discusses the ctscanning analysis of apical tips done on Langdon’s OA corals.