10K Seed project final survival assay results

manchester-hardening
experimental design
Final lab survival assay results for 10K seed project from Baywater outplants
Author

Ariana Huffmyer

Published

October 5, 2026

Overview

Over the last few weeks, students lab conducted final sampling and survival assays of outplanted oysters that were sampled from the field at Baywater.

This post details the results from these assays.

All data will be stored on GitHub here for the 10K project and the script for today is located here.

Data overview

Data are recorded for individual oysters with a “status” column indicating mortality. Each oyster is recorded as either a “0” (alive) or a “1” (dead) at each assessed time point. While the exact hours for survival checks vary for each of the 2 rounds of trials for this project, in general oysters were checked twice per day for about 50-60 hours of total heat exposure.

Trials were conducted in 2 rounds with n=15 per treatment group (n=5 groups) in each round. The treatment groups are from original hardening treatments conducted in 2024 followed by outplanting through summer 2026:

  • Control
  • Fresh water
  • Fresh water at 35°C
  • 35°C seawater
  • PolyIC immersion

These treatments were originally conducted weekly for ~6 weeks prior to outplant.

For all analyses, we are interested in the effect hardening treatment.

Method overview

In the script linked above, I performed Kaplan-Meier survivorship curves with Cox Proportional Hazards survival models to initially explore the data. This revealed that rounds were significantly different from each other, as expected.

I therefore proceeded with binomial logistic regressions to allow me to account for round and oyster length as random effects while testing for the main effects of hardening treatment.

In this analysis that I describe below, I present the statistical results from the logistic regression and post hoc tests for each group I also show model-projected survivorship curves with calculations of LT50 (time to 50% mortality) to quantify the differences between hardening treatments.

Results

Temperatures from each round

I first plotted temperatures from each round, which were recorded in n=5 cups at each time point.

Both rounds were similar in temperature profiles.

Survival model

I used a binomial logistic regression using the following code.

surv_model<-glmer(status ~ time * group + (1|round) + (1|length.mm), family = binomial(link = "logit"), data=data)

summary(surv_model)
Anova(surv_model)

The ANOVA results are:

Analysis of Deviance Table (Type II Wald chisquare tests)

Response: status
              Chisq Df Pr(>Chisq)    
time       208.6421  1    < 2e-16 ***
group       12.7821  4    0.01239 *  
time:group   0.5093  4    0.97258

This suggests that survival is affected by hardening treatment.

Random effects demonstrate no trend of length or round strongly affecting mortality.

# Plot the random effects
ranef(surv_model)

library(sjPlot)

plot_model(surv_model, type = "re")

library(performance)
r2(surv_model)
$round
       (Intercept)
round1  0.06904816
round2 -0.06765700

I generated post hoc comparisons shown below.

Survival curve plotting

I then generated model predicted mortality probabilities and plotted them for each treatment group.

# Generate predicted probabilities
data$predicted_mortality <- predict(surv_model, type = "response")

# Plot
plot2<-ggplot(data, aes(x = time, y = predicted_mortality, color = group, fill = group, group=group)) +
  geom_point(aes(y = status), alpha = 0.6, position = position_jitter(height = 0.03)) +
  geom_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE) +
  #scale_color_manual(values=c("cyan3", "coral"))+
  labs(
    title = "",
    y = "Probability of Mortality",
  ) +
  theme_classic();plot2

ggsave(plot2, filename="figures/final-lab-survival/survival_group.png", width=8, height=6)

I performed post hoc tests using emmeans.

emm<-emmeans(surv_model, ~group)
pairs(emm)

 contrast                 estimate    SE  df z.ratio p.value
 10K Control - 10K 35C       0.978 0.444 Inf   2.201  0.1791
 10K Control - 10K FW        0.393 0.424 Inf   0.926  0.8868
 10K Control - 10K FW 35C    0.055 0.403 Inf   0.136  0.9999
 10K Control - 10K PolyIC    1.119 0.437 Inf   2.561  0.0776
 10K 35C - 10K FW           -0.586 0.450 Inf  -1.301  0.6908
 10K 35C - 10K FW 35C       -0.923 0.441 Inf  -2.092  0.2236
 10K 35C - 10K PolyIC        0.141 0.465 Inf   0.303  0.9982
 10K FW - 10K FW 35C        -0.338 0.420 Inf  -0.804  0.9296
 10K FW - 10K PolyIC         0.726 0.459 Inf   1.581  0.5095
 10K FW 35C - 10K PolyIC     1.064 0.436 Inf   2.439  0.1051

Even though the main effect of group is significant, individual post hoc comparisons only show a trend for a difference in the Control and PolyIC groups.

I then plotted these groups individually.

This shows that there is a trend for the PolyIC group to have higher survival than the controls. Because this difference is small, it is likely why the post hoc comparison is not P<0.05 although the main effect is significant.

LT50 calculations

I then calculated LT50’s for each family-treatment group. The LT50 is the time to estimated 50% mortality for each group. If this value is higher that indicates a longer time to reach 50% mortality and therefore more tolerance.

| LT50_control| LT50_FW| LT50_PolyIC| LT50_35C| LT50_FW35C|
|------------:|-------:|-----------:|--------:|----------:|
|           31|    33.7|        39.2|       37|       31.7|

Indeed, the PolyIC group has the highest LT50 at 39.2 hours while the control is the lowest at 31 hours.

Conclusions

Overall, this survival test suggests:

  • There may be a slight positive effect of PolyIC hardening, which aligns with some of our other work in the lab
  • There is no strong difference due to other hardening efforts