1 Background

On July 5th, 6th, and 7th 2024 we conducted an ecotox assay on the development of Montipora capitata coral embryo development under exposure to PVC leachate at nominal concentrations of 0 mg/L (0.22um FSW, control), 0.01 mg/L (low), 0.1 mg/L (mid), and 1 mg/L (high). We repeated our assay across three nights of spawning. In the assay, embryos were incubated in 20mL scintillation vials in a climate-controlled lab. Here we detail the conditions of the incubation period.

We left a HOBO logger out on the lab bench, in the lab hood, and in an ambient flow through holding tank where adult coral colonies were being kept. We will compare the data here.

1.1 Inputs

HOBO data logs

1.2 Outputs

Visualizations comparing the lab bench conditions to the ambient seawater at HIMB around the July Montipora capitata spawning event in 2024.

2 Setup

2.1 Install packages

if ("tidyverse" %in% rownames(installed.packages()) == 'FALSE') install.packages('tidyverse')
if ("reshape2" %in% rownames(installed.packages()) == 'FALSE') install.packages('reshape2')
if ("plotly" %in% rownames(installed.packages()) == 'FALSE') install.packages('plotly')
if ("car" %in% rownames(installed.packages()) == 'FALSE') install.packages('car')
if ("agricolae" %in% rownames(installed.packages()) == 'FALSE') install.packages('agricolae')
if ("hms" %in% rownames(installed.packages()) == 'FALSE') install.packages('hms')

2.2 Load packages

library(tidyverse)
library(reshape2)
library(plotly)
library(car) # for Levene's test 
library(agricolae) # for post-hoc tests
library(hms)

2.3 Load HOBO logs

bench <- read.csv("embryo-inc-bench 2024-07-08 13_34_32 HST (Data HST).csv")
hood <- read.csv("embryo-inc-hood 2024-07-08 13_35_01 HST (Data HST).csv")
tank1 <- read.csv("Holding tank  2024-07-03 15_30_47 HST (Data HST).csv")
tank2 <- read.csv("Holding tank  2024-07-08 08_32_55 HST (Data HST).csv")
tank3 <- read.csv("AmbientTemp2 2024-07-08 08_32_20 HST (Data HST).csv")

3 Format HOBO logs

bench <- bench %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>%
  mutate(location = "bench") %>% 
  select(datetime, temp, lux, location)

hood <- hood %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>% 
  mutate(location = "hood") %>% 
  select(datetime, temp, lux, location)

tank1 <- tank1 %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>% 
  mutate(location = "tank1") %>% 
  select(datetime, temp, lux, location)

tank2 <- tank2 %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>% 
  mutate(location = "tank2") %>% 
  select(datetime, temp, lux, location)

tank3 <- tank3 %>% 
  rename(datetime = Date.Time..HST.,
         temp = Temperature.....C.,
         lux = Light....lux.) %>% 
  mutate(location = "tank3") %>% 
  select(datetime, temp, lux, location)

all <-  rbind(bench, hood, tank1, tank2, tank3)

all$datetime <- as.POSIXct(all$datetime, format = "%m/%d/%Y %H:%M:%S")

4 Plot overlapping logs

long <- melt(all, id.vars=c("datetime", "location"), measure.vars=c("temp", "lux"))

The incubation windows were:

1- July 5th 21:20 - July 6th 13:30

2 - July 6th 21:20 - July 7th 13:30

3- July 7th 21:20 - July 8th 13:30

incubation_windows <- data.frame(
  start = as.POSIXct(c("07/05/2024 21:20:00", 
                       "07/06/2024 21:20:00", 
                       "07/07/2024 21:20:00"), 
                     format = "%m/%d/%Y %H:%M:%S"),
  end = as.POSIXct(c("07/06/2024 13:30:00", 
                     "07/07/2024 13:30:00", 
                     "07/08/2024 13:30:00"), 
                   format = "%m/%d/%Y %H:%M:%S"))

# Calculate scaling factor for the secondary (lux) axis
lux_scale <- max(all$temp, na.rm = TRUE) / max(all$lux, na.rm = TRUE)

ggplot(all, aes(x = datetime)) +
  # Highlight windows
  geom_rect(
    data = incubation_windows,
    aes(xmin = start, xmax = end),
    ymin = -Inf, ymax = Inf, fill = "yellow", alpha = 0.2, inherit.aes = FALSE
  ) +
  # Plot temp
  geom_line(aes(y = temp, color = location), size = 1, alpha = 0.5) +
  # Plot scaled lux
  geom_line(aes(y = lux * lux_scale, color = location), linetype = "dashed") +
  # Dual y-axis
  scale_y_continuous(
    name = "Temperature",
    sec.axis = sec_axis(~ . / lux_scale, name = "Lux")
  ) +
  theme_minimal() +
  labs(color = "Location", title = "Temperature and Lux by Datetime with Incubation Highlights")

Caution

tank2 and tank3 look like they had heaters in them that kept the min temp up near 26F…

TO DO:

go back and find HOBO log or sea surface temp from July 5th - 8th 2024.
find calibration to convert lux to PAR

5 Averages

What were the average bench, hood, and tank temps & light measurements across the incubation periods?

Subset to the incubation windows

all_incubation <- all %>% 
    filter(
    sapply(datetime, function(x) any(x >= incubation_windows$start & x <= incubation_windows$end))
  )

Display a table of averages and standard deviations

avg_sd <- all_incubation %>%
  filter(location %in% c("bench", "hood" ,"tank2", "tank3")) %>%
  group_by(location) %>%
  summarize(
    avg_temp = mean(temp, na.rm = TRUE),
    sd_temp = sd(temp, na.rm = TRUE), 
    avg_lux = mean(lux, na.rm = TRUE),
    sd_lux = sd(lux, na.rm = TRUE))

avg_sd

location	avg_temp	sd_temp	avg_lux	sd_lux
bench	26.88575	0.3975264	243.58796	495.4855
hood	26.85602	0.3655191	91.64201	140.3716
tank2	26.37685	0.3216703	2498.80348	5168.1143
tank3	26.33780	0.3106606	2300.97458	5338.2473

Plot average temps

ggplot(avg_sd, aes(x = location, y = avg_temp, color = location)) +
  geom_point(size = 3) +                                           # mean temps as points
  geom_errorbar(aes(ymin = avg_temp - sd_temp, ymax = avg_temp + sd_temp), width = 0.2) +  # std dev error bars
  labs(x = "Location", y = "Temperature (°C)", title = "Average Temperature with Standard Deviation") +
  theme_minimal()

Plot average lux

ggplot(avg_sd, aes(x = location, y = avg_lux, color = location)) +
  geom_point(size = 3) +                                           # mean temps as points
  geom_errorbar(aes(ymin = avg_lux - sd_lux, ymax = avg_lux + sd_lux), width = 0.2) +  # std dev error bars
  labs(x = "Location", y = "Lux", title = "Average lux with Standard Deviation") +
  theme_minimal()

all_incubation <- all_incubation %>% 
  mutate(
    space = case_when(
      location %in% c("bench", "hood") ~ "lab",
      location %in% c("tank2", "tank3") ~ "ambient",
      TRUE ~ NA_character_  # for any other location values
    )
  )

avg_sd <- all_incubation %>%
  filter(space %in% c("lab", "ambient")) %>%
  group_by(space) %>%
  summarize(
    avg_temp = mean(temp, na.rm = TRUE),
    sd_temp = sd(temp, na.rm = TRUE),
    min_temp = min(temp, na.rm = TRUE),
    max_temp = max(temp, na.rm = TRUE),
    avg_lux = mean(lux, na.rm = TRUE),
    sd_lux = sd(lux, na.rm = TRUE),
    min_lux = min (lux, na.rm=TRUE),
    max_lux = max(lux, na.rm=TRUE))

avg_sd

space	avg_temp	sd_temp	min_temp	max_temp	avg_lux	sd_lux	min_lux	max_lux
ambient	26.34765	0.3134719	25.95	27.37	2350.852	5289.2776	0	31733.76
lab	26.87088	0.3818228	25.91	27.84	167.615	371.6999	0	8069.12

Caution

The incubation temperature conditions were:

ambient 26.34 +/- 0.31 C (min 25.95, max 27.37)
lab 26.87 +/- 0.38 C (min 25.91, max 27.84)

The incubation lux conditions were:

ambient 2350.85 +/- 5289.27 lux (min 0, max 31733.76)
lab 167.61 +/- 371.69 lux (min 0, max 8069.12)

6 Hyopthesis testing

6.1 Is temp different across location?

# Check homogeneity of variance
leveneTest(temp ~ location, data = all_incubation)

	Df	F value	Pr(>F)
group	3	4.863347	0.0023137
	937	NA	NA

Caution

Our data has unequal variances (Levene’s test pvalue = 0.002314) so we use Welch’s t-test for ANOVA

# Welch's ANOVA comparing temperature by stage
welch_anova <- oneway.test(temp ~ location, data = all_incubation, var.equal = FALSE)
print(welch_anova)


    One-way analysis of means (not assuming equal variances)

data:  temp and location
F = 173.52, num df = 3.00, denom df = 351.78, p-value < 2.2e-16

Follow up the Welch’s t-test with a post-hoc test

pairwise_results <- pairwise.t.test(
  x = all_incubation$temp,
  g = all_incubation$location,
  p.adjust.method = "bonferroni",
  pool.sd = FALSE
)
print(pairwise_results)


    Pairwise comparisons using t tests with non-pooled SD 

data:  all_incubation$temp and all_incubation$location 

      bench  hood   tank2
hood  1      -      -    
tank2 <2e-16 <2e-16 -    
tank3 <2e-16 <2e-16 1    

P value adjustment method: bonferroni

Caution

Pairwise comparisons of temperature across locations using Welch’s t-tests with Bonferroni-adjusted p-values revealed no significant difference between bench and hood (p = 1) or between tank2 and tank3 (p = 1). There is a significant difference (p<0.001) between the lab spaces (the bench and the hood) and the ambient spaces (The lab spaces are significantly different from the ambient spaces (tank2 and tank3).

6.2 Is temp different across space?

# Check homogeneity of variance
leveneTest(temp ~ space, data = all_incubation)

	Df	F value	Pr(>F)
group	1	13.12683	0.0003067
	939	NA	NA

Caution

Our data has unequal variances so we use Welch’s t-test for ANOVA

# Welch's ANOVA comparing temperature by stage
welch_anova <- oneway.test(temp ~ space, data = all_incubation, var.equal = FALSE)
print(welch_anova)


    One-way analysis of means (not assuming equal variances)

data:  temp and space
F = 520.18, num df = 1.00, denom df = 852.66, p-value < 2.2e-16

Follow up the Welch’s t-test with a post-hoc test

pairwise_results <- pairwise.t.test(
  x = all_incubation$temp,
  g = all_incubation$space,
  p.adjust.method = "bonferroni",
  pool.sd = FALSE
)
print(pairwise_results)


    Pairwise comparisons using t tests with non-pooled SD 

data:  all_incubation$temp and all_incubation$space 

    ambient
lab <2e-16 

P value adjustment method: bonferroni

6.3 Is lab temp different across stage?

Add a column that indicates the duration of exposure by stage

all_incubation <- all_incubation %>%
  mutate(
    time_only = format(datetime, "%H:%M:%S"),      # extract time as string
    time_only = hms::as_hms(time_only),            # convert to hms object for easier comparison
    stage = case_when(
      (time_only >= as_hms("21:20:00") | time_only <= as_hms("03:30:00")) ~ "cleavage",                         # crosses midnight
      (time_only > as_hms("03:30:00") & time_only <= as_hms("08:30:00")) ~ "prawnchip",                          # normal range
      (time_only > as_hms("08:30:00") & time_only <= as_hms("13:30:00")) ~ "early gastrula",                     # normal range
      TRUE ~ NA_character_
    )
  )

bench_by_stage <- all_incubation %>%
  filter(!is.na(stage)) %>% # remove rows where stage is missing
  filter(space == "lab") %>% 
  group_by(stage) %>%
  summarize(
    avg_temp = mean(temp, na.rm = TRUE),
    sd_temp = sd(temp, na.rm = TRUE),
    min_temp = min(temp, na.rm = TRUE),
    max_temp = max(temp, na.rm = TRUE),
    avg_lux = mean(lux, na.rm = TRUE),
    sd_lux = sd(lux, na.rm = TRUE),
    min_lux = min(lux, na.rm = TRUE),
    max_lux = max(lux, na.rm = TRUE)
  )

bench_by_stage

stage	avg_temp	sd_temp	min_temp	max_temp	avg_lux	sd_lux	min_lux	max_lux
cleavage	26.87434	0.2328657	26.08	27.24	151.7127	161.3920	0.00	409.28
early gastrula	26.94533	0.5139978	25.91	27.71	201.0156	187.5445	0.81	613.60
prawnchip	26.79206	0.3650893	26.17	27.84	154.3572	619.0712	0.00	8069.12

Caution

The lab incubation temperature conditions were:

cleavage 26.87 +/- 0.23 C (min 26.08, max 27.24)
prawnchip 26.79 +/- 0.36 C (min 26.17, max 27.84)
early gastrula 26.94 +/- 0.51 C (min 25.91, max 27.71)

The lab incubation lux conditions were:

cleavage 151.7127 +/- 161.39 lux (min 0, max 409.28)
prawnchip 154.35 +/- 619.07 lux (min 0, max 8069.12)
early gastrula 201.01 +/- 187.54 (min 0.81, max 613.60)

lab_incubation <- all_incubation %>%
  filter(!is.na(stage)) %>% # remove rows where stage is missing
  filter(space == "lab")

# Check homogeneity of variance
leveneTest(temp ~ stage, data = lab_incubation)

	Df	F value	Pr(>F)
group	2	73.8332	0
	585	NA	NA

Caution

Our data has unequal variances so we use Welch’s t-test for ANOVA

# Welch's ANOVA comparing temperature by stage
welch_anova <- oneway.test(temp ~ stage, data = lab_incubation, var.equal = FALSE)
print(welch_anova)


    One-way analysis of means (not assuming equal variances)

data:  temp and stage
F = 5.9606, num df = 2.00, denom df = 320.16, p-value = 0.002873

Follow up the Welch’s t-test with a post-hoc test

pairwise_results <- pairwise.t.test(
  x = lab_incubation$temp,
  g = lab_incubation$stage,
  p.adjust.method = "bonferroni",
  pool.sd = FALSE
)
print(pairwise_results)


    Pairwise comparisons using t tests with non-pooled SD 

data:  lab_incubation$temp and lab_incubation$stage 

               cleavage early gastrula
early gastrula 0.2608   -             
prawnchip      0.0269   0.0037        

P value adjustment method: bonferroni

Caution

Pairwise comparisons of temperature across stage using Welch’s t-tests with Bonferroni-adjusted p-values revealed the temperatures experienced during the latest embryonic stage (early gastrula) are significantly different from the prawnchip stage (p<0.05). HOWEVER, the actual temperature range experienced across embryonic development (26.79-26.94) is within the ambient seawater range and is ecologically parallel to the natural sea surface temp fluctuations experienced in a diurnal cycle.

6.4 Is lab temp different across spawn night?

Add a column that indicates the spawn night (1, 2, or 3)

lab_incubation <- lab_incubation %>%
  mutate(
    spawn = case_when(
      datetime >= incubation_windows$start[1] & datetime <= incubation_windows$end[1] ~ 1,
      datetime >= incubation_windows$start[2] & datetime <= incubation_windows$end[2] ~ 2,
      datetime >= incubation_windows$start[3] & datetime <= incubation_windows$end[3] ~ 3,
      TRUE ~ NA_integer_
    )
  )

Print a table of temps and lux by spawn night

by_spawn <- lab_incubation %>%
  group_by(spawn) %>%
  summarize(
    avg_temp = mean(temp, na.rm = TRUE),
    sd_temp = sd(temp, na.rm = TRUE),
    min_temp = min(temp, na.rm = TRUE),
    max_temp = max(temp, na.rm = TRUE),
    avg_lux = mean(lux, na.rm = TRUE),
    sd_lux = sd(lux, na.rm = TRUE),
    min_lux = min(lux, na.rm = TRUE),
    max_lux = max(lux, na.rm = TRUE)
  )

by_spawn

spawn	avg_temp	sd_temp	min_temp	max_temp	avg_lux	sd_lux	max_lux
1	27.23163	0.2814402	26.60	27.84	213.6418	594.5645	8069.12
2	26.85444	0.2351807	26.21	27.24	149.9682	167.1667	472.48
3	26.52658	0.2326195	25.91	27.24	139.2350	176.6241	613.60

lab_incubation <- lab_incubation %>% 
  mutate(spawn = factor(spawn, levels = c(1, 2, 3), 
                        labels = c("1", "2", "3")))  # convert spawn to factor with labels
# Check homogeneity of variance
leveneTest(temp ~ spawn, data = lab_incubation)

	Df	F value	Pr(>F)
group	2	10.11787	4.79e-05
	585	NA	NA

Caution

Our data has unequal variances so we use Welch’s t-test for ANOVA

# Welch's ANOVA comparing temperature by stage
welch_anova <- oneway.test(temp ~ spawn, data = lab_incubation, var.equal = FALSE)
print(welch_anova)


    One-way analysis of means (not assuming equal variances)

data:  temp and spawn
F = 366.72, num df = 2.00, denom df = 387.43, p-value < 2.2e-16

Follow up the Welch’s t-test with a post-hoc test

pairwise_results <- pairwise.t.test(
  x = lab_incubation$temp,
  g = lab_incubation$spawn,
  p.adjust.method = "bonferroni",
  pool.sd = FALSE
)
print(pairwise_results)


    Pairwise comparisons using t tests with non-pooled SD 

data:  lab_incubation$temp and lab_incubation$spawn 

  1      2     
2 <2e-16 -     
3 <2e-16 <2e-16

P value adjustment method: bonferroni

Caution

Pairwise comparisons of temperature across spawn night using Welch’s t-tests with Bonferroni-adjusted p-values revealed that each night is significantly different (p<2.2e-16). HOWEVER the actual range of temps across all three spawn nights is 26.5 to 27.2 (less than 1C), and is an ecological parallel to actual fluctuations in sea surface temp that occur across nights of spawning.

7 Summary

Temperature Although statistically significant differences in temperature were detected across both spawn nights (with temperatures generally decreasing over the three nights) and developmental stage (with the lowest temperatures observed just before dawn during the prawnchip stage, but no significant difference between the cleavage and early gastrula stages), the magnitude of these differences was small and remained within the normal range of ambient seawater temperature fluctuations observed during a spawning event. Therefore, while temperature varied somewhat across the experimental assay, this variation is minor and unlikely to confound our results, as embryos in nature develop under similar thermal conditions.

Light

--- title: "Bench Incubation" subtitle: "Lab bench records and environmental logs for the development of *Montipora capitata* embryos exposed to PVC leachate" date: 07/09/2024 categories: [protocols, records] --- # Background On July 5th, 6th, and 7th 2024 we conducted an ecotox assay on the development of *Montipora capitata* coral embryo development under exposure to PVC leachate at nominal concentrations of 0 mg/L (0.22um FSW, control), 0.01 mg/L (low), 0.1 mg/L (mid), and 1 mg/L (high). We repeated our assay across three nights of spawning. In the assay, embryos were incubated in 20mL scintillation vials in a climate-controlled lab. Here we detail the conditions of the incubation period. We left a HOBO logger out on the lab bench, in the lab hood, and in an ambient flow through holding tank where adult coral colonies were being kept. We will compare the data here. ## Inputs - HOBO data logs - average Temp calibration factors - average PAR calibration factors ## Outputs - Visualizations comparing the lab bench conditions to the ambient seawater at HIMB around the July *Montipora capitata* spawning event in 2024. - Hypothesis testing results comparing lab conditions across spawn night and stage - Average conditions for manuscript methods section - Tables of conditions for manuscript supplemental materials # Setup ```{r setup, include=FALSE} knitr::opts_chunk$set( fig.width = 12, fig.height = 8, fig.align = "center", dpi = 600, echo = TRUE, # Display code chunks eval = TRUE, # Evaluate code chunks warning = FALSE, # Hide warnings message = FALSE, # Hide messages comment = "" # Prevents appending '##' to beginning of lines in code output ) ``` ## Install packages ```{r, eval=FALSE} if ("tidyverse" %in% rownames(installed.packages()) == 'FALSE') install.packages('tidyverse') if ("reshape2" %in% rownames(installed.packages()) == 'FALSE') install.packages('reshape2') if ("plotly" %in% rownames(installed.packages()) == 'FALSE') install.packages('plotly') if ("car" %in% rownames(installed.packages()) == 'FALSE') install.packages('car') if ("agricolae" %in% rownames(installed.packages()) == 'FALSE') install.packages('agricolae') if ("hms" %in% rownames(installed.packages()) == 'FALSE') install.packages('hms') ``` ## Load packages ```{r} library(tidyverse) library(reshape2) library(plotly) library(car) # for Levene's test library(agricolae) # for post-hoc tests library(hms) ``` ## Load HOBO logs ```{r} bench <- read.csv("embryo-inc-bench 2024-07-08 13_34_32 HST (Data HST).csv") hood <- read.csv("embryo-inc-hood 2024-07-08 13_35_01 HST (Data HST).csv") tank1 <- read.csv("Holding tank 2024-07-03 15_30_47 HST (Data HST).csv") tank2 <- read.csv("Holding tank 2024-07-08 08_32_55 HST (Data HST).csv") tank3 <- read.csv("AmbientTemp2 2024-07-08 08_32_20 HST (Data HST).csv") ``` ## Load HOBO calibration factors ```{r} par_cal <- read.csv("avg_par.csv") temp_cal <- read.csv("avg_temp.csv") ``` # Format HOBO logs ```{r} bench <- bench %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(location = "bench") %>% select(datetime, temp, lux, location) hood <- hood %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(location = "hood") %>% select(datetime, temp, lux, location) tank1 <- tank1 %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(location = "tank1") %>% select(datetime, temp, lux, location) tank2 <- tank2 %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(location = "tank2") %>% select(datetime, temp, lux, location) tank3 <- tank3 %>% rename(datetime = Date.Time..HST., temp = Temperature.....C., lux = Light....lux.) %>% mutate(location = "tank3") %>% select(datetime, temp, lux, location) ``` ```{r} all <- rbind(bench, hood, tank1, tank2, tank3) all$datetime <- as.POSIXct(all$datetime, format = "%m/%d/%Y %H:%M:%S") ``` ## Apply calibrations to HOBO logs To apply the intercept and slope from a linear regression calibration to raw data and generate calibrated values, we can use the formula: $$ \text{Calibrated value} = (\text{slope} \times \text{raw value}) + \text{intercept} $$ The calibration slopes and intercepts for `temp` and `lux ➡️PAR` are: ```{r} print(par_cal) print(temp_cal) ``` ```{r} all <- all %>% mutate(raw_temp = temp) %>% mutate( cal_temp = (temp_cal$avg_temp_slope * raw_temp) + temp_cal$avg_temp_intercept, par = (par_cal$avg_par_slope * lux) + par_cal$avg_par_intercept, par_adj = par + 1500) ``` # Plot overlapping logs The incubation windows were: 1- July 5th 21:20 - July 6th 13:30 2 - July 6th 21:20 - July 7th 13:30 3- July 7th 21:20 - July 8th 13:30 ```{r} incubation_windows <- data.frame( start = as.POSIXct(c("07/05/2024 21:20:00", "07/06/2024 21:20:00", "07/07/2024 21:20:00"), format = "%m/%d/%Y %H:%M:%S"), end = as.POSIXct(c("07/06/2024 13:30:00", "07/07/2024 13:30:00", "07/08/2024 13:30:00"), format = "%m/%d/%Y %H:%M:%S")) ``` Clip plot to just experimental incubations (07/05/2024 21:20:00 - 07/08/2024 13:30:00) ```{r} # Define start and end of all three spawning nights start <- as.POSIXct("07/05/2024 21:20:00", format = "%m/%d/%Y %H:%M:%S") end <- as.POSIXct("07/08/2024 13:30:00", format = "%m/%d/%Y %H:%M:%S") # Filter rows where datetime is within the window all_inc <- all %>% filter(datetime >= start & datetime <= end) ``` ```{r} ggplot(all_inc, aes(x = datetime)) + geom_rect( data = incubation_windows, aes(xmin = start, xmax = end), ymin = -Inf, ymax = Inf, fill = "yellow", alpha = 0.2, inherit.aes = FALSE ) + geom_line(aes(y = cal_temp, color = location), size = 1, alpha = 0.5)+ scale_y_continuous( name = "Temperature (°C)", limits = c(26, 28)) theme_minimal() + labs( color = "Location", title = "Temperature by Datetime with Incubation Windows Highlighted" ) ``` ```{r} ggplot(all_inc, aes(x = datetime)) + geom_rect( data = incubation_windows, aes(xmin = start, xmax = end), ymin = -Inf, ymax = Inf, fill = "yellow", alpha = 0.2, inherit.aes = FALSE ) + geom_line(aes(y = par_adj, color = location), size = 1, alpha = 0.5)+ scale_y_continuous( name = "PAR", limits = c(-100, 2000)) theme_minimal() + labs( color = "Location", title = "PAR by Datetime with Incubation Windows Highlighted" ) ``` ::: callout-caution tank2 and tank3 look like they had heaters in them that kept the min temp up near 26F... TO DO: - go back and find HOBO log or sea surface temp from July 5th - 8th 2024. - find calibration to convert lux to PAR ::: # Averages What were the average bench, hood, and tank temps & light measurements across the incubation periods? Subset to the incubation windows ```{r} all_incubation <- all %>% filter( sapply(datetime, function(x) any(x >= incubation_windows$start & x <= incubation_windows$end)) ) ``` Display a table of averages and standard deviations ```{r} avg_sd <- all_incubation %>% filter(location %in% c("bench", "hood" ,"tank2", "tank3")) %>% group_by(location) %>% summarize( avg_temp = mean(cal_temp, na.rm = TRUE), sd_temp = sd(cal_temp, na.rm = TRUE), avg_par = mean(par, na.rm = TRUE), sd_par = sd(par, na.rm = TRUE)) avg_sd ``` Plot average temps ```{r} ggplot(avg_sd, aes(x = location, y = avg_temp, color = location)) + geom_point(size = 3) + # mean temps as points geom_errorbar(aes(ymin = avg_temp - sd_temp, ymax = avg_temp + sd_temp), width = 0.2) + # std dev error bars labs(x = "Location", y = "Temperature (°C)", title = "Average Temperature with Standard Deviation") + theme_minimal() ``` Plot average par ```{r} ggplot(avg_sd, aes(x = location, y = avg_par, color = location)) + geom_point(size = 3) + # mean temps as points geom_errorbar(aes(ymin = avg_par - sd_par, ymax = avg_par + sd_par), width = 0.2) + # std dev error bars labs(x = "Location", y = "PAR", title = "Average PAR with Standard Deviation") + theme_minimal() ``` ```{r} all_incubation <- all_incubation %>% mutate( space = case_when( location %in% c("bench", "hood") ~ "lab", location %in% c("tank2", "tank3") ~ "ambient", TRUE ~ NA_character_ # for any other location values ) ) ``` ```{r} avg_sd <- all_incubation %>% filter(space %in% c("lab", "ambient")) %>% group_by(space) %>% summarize( avg_temp = mean(cal_temp, na.rm = TRUE), sd_temp = sd(cal_temp, na.rm = TRUE), min_temp = min(cal_temp, na.rm = TRUE), max_temp = max(cal_temp, na.rm = TRUE), avg_par = mean(par, na.rm = TRUE), sd_par = sd(par, na.rm = TRUE), min_par = min (par, na.rm=TRUE), max_par = max(par, na.rm=TRUE)) avg_sd ``` ::: callout-caution The incubation temperature conditions were: - ambient 26.34 +/- 0.31 C (min 25.95, max 27.37) - lab 26.87 +/- 0.38 C (min 25.91, max 27.84) The incubation lux conditions were: - ambient 2350.85 +/- 5289.27 lux (min 0, max 31733.76) - lab 167.61 +/- 371.69 lux (min 0, max 8069.12) ::: # Hyopthesis testing ## Is temp different across location? ```{r} # Check homogeneity of variance leveneTest(cal_temp ~ location, data = all_incubation) ``` ::: callout-caution Our data has unequal variances (Levene's test pvalue = 0.002314) so we use Welch's t-test for ANOVA ::: ```{r} # Welch's ANOVA comparing temperature by stage welch_anova <- oneway.test(cal_temp ~ location, data = all_incubation, var.equal = FALSE) print(welch_anova) ``` Follow up the Welch's t-test with a post-hoc test ```{r} pairwise_results <- pairwise.t.test( x = all_incubation$cal_temp, g = all_incubation$location, p.adjust.method = "bonferroni", pool.sd = FALSE ) print(pairwise_results) ``` ::: callout-caution Pairwise comparisons of temperature across locations using Welch’s t-tests with Bonferroni-adjusted p-values revealed no significant difference between bench and hood (p = 1) or between tank2 and tank3 (p = 1). There is a significant difference (p\<0.001) between the lab spaces (the bench and the hood) and the ambient spaces (The lab spaces are significantly different from the ambient spaces (tank2 and tank3). ::: ## Is temp different across space? ```{r} # Check homogeneity of variance leveneTest(cal_temp ~ space, data = all_incubation) ``` ::: callout-caution Our data has unequal variances so we use Welch's t-test for ANOVA ::: ```{r} # Welch's ANOVA comparing temperature by stage welch_anova <- oneway.test(cal_temp ~ space, data = all_incubation, var.equal = FALSE) print(welch_anova) ``` Follow up the Welch's t-test with a post-hoc test ```{r} pairwise_results <- pairwise.t.test( x = all_incubation$cal_temp, g = all_incubation$space, p.adjust.method = "bonferroni", pool.sd = FALSE ) print(pairwise_results) ``` ## Is lab temp different across stage? Add a column that indicates the duration of exposure by stage ```{r} all_incubation <- all_incubation %>% mutate( time_only = format(datetime, "%H:%M:%S"), # extract time as string time_only = hms::as_hms(time_only), # convert to hms object for easier comparison stage = case_when( (time_only >= as_hms("21:20:00") | time_only <= as_hms("03:30:00")) ~ "cleavage", # crosses midnight (time_only > as_hms("03:30:00") & time_only <= as_hms("08:30:00")) ~ "prawnchip", # normal range (time_only > as_hms("08:30:00") & time_only <= as_hms("13:30:00")) ~ "early gastrula", # normal range TRUE ~ NA_character_ ) ) ``` ```{r} bench_by_stage <- all_incubation %>% filter(!is.na(stage)) %>% # remove rows where stage is missing filter(space == "lab") %>% group_by(stage) %>% summarize( avg_temp = mean(cal_temp, na.rm = TRUE), sd_temp = sd(cal_temp, na.rm = TRUE), min_temp = min(cal_temp, na.rm = TRUE), max_temp = max(cal_temp, na.rm = TRUE), avg_par = mean(par, na.rm = TRUE), sd_par = sd(par, na.rm = TRUE), min_par = min(par, na.rm = TRUE), max_par = max(par, na.rm = TRUE) ) bench_by_stage ``` ::: callout-caution The lab incubation temperature conditions were: - cleavage 26.87 +/- 0.23 C (min 26.08, max 27.24) - prawnchip 26.79 +/- 0.36 C (min 26.17, max 27.84) - early gastrula 26.94 +/- 0.51 C (min 25.91, max 27.71) The lab incubation lux conditions were: - cleavage 151.7127 +/- 161.39 lux (min 0, max 409.28) - prawnchip 154.35 +/- 619.07 lux (min 0, max 8069.12) - early gastrula 201.01 +/- 187.54 (min 0.81, max 613.60) ::: ```{r} lab_incubation <- all_incubation %>% filter(!is.na(stage)) %>% # remove rows where stage is missing filter(space == "lab") # Check homogeneity of variance leveneTest(temp ~ stage, data = lab_incubation) ``` ::: callout-caution Our data has unequal variances so we use Welch's t-test for ANOVA ::: ```{r} # Welch's ANOVA comparing temperature by stage welch_anova <- oneway.test(temp ~ stage, data = lab_incubation, var.equal = FALSE) print(welch_anova) ``` Follow up the Welch's t-test with a post-hoc test ```{r} pairwise_results <- pairwise.t.test( x = lab_incubation$temp, g = lab_incubation$stage, p.adjust.method = "bonferroni", pool.sd = FALSE ) print(pairwise_results) ``` ::: callout-caution Pairwise comparisons of temperature across stage using Welch’s t-tests with Bonferroni-adjusted p-values revealed the temperatures experienced during the latest embryonic stage (early gastrula) are significantly different from the prawnchip stage (p\<0.05). **HOWEVER, the actual temperature range experienced across embryonic development (26.79-26.94) is within the ambient seawater range and is ecologically parallel to the natural sea surface temp fluctuations experienced in a diurnal cycle.** ::: ## Is lab temp different across spawn night? Add a column that indicates the spawn night (1, 2, or 3) ```{r} lab_incubation <- lab_incubation %>% mutate( spawn = case_when( datetime >= incubation_windows$start[1] & datetime <= incubation_windows$end[1] ~ 1, datetime >= incubation_windows$start[2] & datetime <= incubation_windows$end[2] ~ 2, datetime >= incubation_windows$start[3] & datetime <= incubation_windows$end[3] ~ 3, TRUE ~ NA_integer_ ) ) ``` Print a table of temps and lux by spawn night ```{r} by_spawn <- lab_incubation %>% group_by(spawn) %>% summarize( avg_temp = mean(temp, na.rm = TRUE), sd_temp = sd(temp, na.rm = TRUE), min_temp = min(temp, na.rm = TRUE), max_temp = max(temp, na.rm = TRUE), avg_lux = mean(lux, na.rm = TRUE), sd_lux = sd(lux, na.rm = TRUE), min_lux = min(lux, na.rm = TRUE), max_lux = max(lux, na.rm = TRUE) ) by_spawn ``` ```{r} lab_incubation <- lab_incubation %>% mutate(spawn = factor(spawn, levels = c(1, 2, 3), labels = c("1", "2", "3"))) # convert spawn to factor with labels # Check homogeneity of variance leveneTest(temp ~ spawn, data = lab_incubation) ``` ::: callout-caution Our data has unequal variances so we use Welch's t-test for ANOVA ::: ```{r} # Welch's ANOVA comparing temperature by stage welch_anova <- oneway.test(temp ~ spawn, data = lab_incubation, var.equal = FALSE) print(welch_anova) ``` Follow up the Welch's t-test with a post-hoc test ```{r} pairwise_results <- pairwise.t.test( x = lab_incubation$temp, g = lab_incubation$spawn, p.adjust.method = "bonferroni", pool.sd = FALSE ) print(pairwise_results) ``` ::: callout-caution Pairwise comparisons of temperature across spawn night using Welch’s t-tests with Bonferroni-adjusted p-values revealed that each night is significantly different (p\<2.2e-16). **HOWEVER the actual range of temps across all three spawn nights is 26.5 to 27.2 (less than 1C), and is an ecological parallel to actual fluctuations in sea surface temp that occur across nights of spawning.** ::: # Summary **Temperature** Although statistically significant differences in temperature were detected across both spawn nights (with temperatures generally decreasing over the three nights) and developmental stage (with the lowest temperatures observed just before dawn during the prawnchip stage, but no significant difference between the cleavage and early gastrula stages), the magnitude of these differences was small and remained within the normal range of ambient seawater temperature fluctuations observed during a spawning event. Therefore, while temperature varied somewhat across the experimental assay, this variation is minor and unlikely to confound our results, as embryos in nature develop under similar thermal conditions. **Light** PAR inside the lab conditions was essentially zero (no light for photosynthesis) across the entirety of each incubation. This is important for us to know to be able to discuss and interpret the gene ontology results from our differential expression analysis (Which genes responded to leachate treatments, and what they do, in the context that photosynthesis of the *Symbiodiniaceae* cells already in the *M. cap* embryos were not photosynthesizing and therefore likey not transferring photosynthates to embryos for metabolic processing.