2019 Drift Energy Density Results

Website last updated 2020-10-27 16:18:29 by Benjamin Meyer (bemeyer@alaska.edu)

Introduction

This document will summarize and plot results from drift invertebrate samples collected in 2019 by South Fork Research.

Import data

# import and prep for examination
drift_dat <- read.csv(paste0(user,"Dropbox/Chena_Data_2020/UAF_Data/2020_Analysis_And_Results/2019 Results from JN/Total Drift by Sample with Correlates.csv")) %>%
  transform(DateTime = mdy_hm(DateTime)) %>%
  mutate(day = yday(DateTime))

# define mainstem sites
ms_sites <- c("Nordale","First Bridge","Third Bridge")

Select Variables vs Time By Site

# plot all sites
drift_dat %>%
  ggplot(aes(day,PreyToDebrisRatio)) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("Prey to Debris Ratio 2019 - All Sites") +
  xaxis

# plot 3 sites
drift_dat %>%
  filter(Site %in% ms_sites) %>%
  ggplot(aes(day,PreyToDebrisRatio)) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("Prey to Debris Ratio 2019 - Mainstem Sites") +
  xaxis

# plot
drift_dat %>%
  ggplot(aes(day,Concentration)) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("Concentrations by Site 2019 - All Sites") +
  xaxis

# plot mainstem sites
drift_dat %>%
  filter(Site %in% ms_sites) %>%
  ggplot(aes(day,Concentration)) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("Concentrations by Site 2019 - Mainstem Sites") +
  xaxis

# plot
drift_dat %>%
  ggplot(aes(day,(Energy/FlowNormalized))) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("Energy Density vs Time by Site -- All Sites") +
  xaxis

# plot
drift_dat %>%
  filter(Site %in% ms_sites) %>%
  ggplot(aes(day,(Energy/FlowNormalized))) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("Energy Density vs Time by Site -- Mainstem Sites") +
  xaxis

# plot
drift_dat %>%
  ggplot(aes(day,LogConcentration)) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("LogConcentration by Site 2019 -- All Sites") +
  xaxis

# plot
drift_dat %>%
  filter(Site %in% ms_sites) %>%
  ggplot(aes(day,LogConcentration)) +
  geom_point() +
  facet_wrap(.~ Site) +
  ggtitle("LogConcentration by Site 2019 -- Mainstem Sites") +
  xaxis

---

Improved graphic of time vs. LogConcentration

# plot
drift_dat %>%
  filter(Site %in% ms_sites) %>%
  ggplot(aes(day,LogConcentration)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(.~ Site) +
  formula_labels +
  ggtitle("LogConcentration by Site 2019") +
  xaxis +
  xlab("")

---

Pairs Plots

# enviro correlates
drift_dat %>%
  select("Temperature",
         "Turbidity",
         "Windspeed",
         "MaxWindspeed",
         "Rainfall",
         "Light",
         "BoatWakes",
         "day") %>%
  ggpairs() +
  ggtitle("Environmental Correlates")

# drift density correlates
drift_dat %>%
  select("Turbidity",
         "Concentration",
         "Energy",
         "FlowNormalized",
         "LogConcentration",
         "PreyToDebrisRatio", 
         "day") %>%
  ggpairs() +
  ggtitle("Drift Density Correlates")