wickham-thesis.Rmd

---
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title: Frequency and Intensity
subtitle: Cyanobacterial Blooms in Nebraska Freshwater Bodies
author: 
- name: Victoria Wickham
  affiliation: Batchelor of Science in Water Science from the College of Agricultural Sciences and Natural Resources, University of Nebraska-Lincoln
- name: Dr. Megan L. Larsen
  affiliation: Water Sciences Laboratory, University of Nebraska-Lincoln
abstract: "Some really good stuff here with 1-2 sentences from the INTRO, RESULTS, and CONCLUSIONS. This section should be no longer than 300 words."
keywords: "harmful algal blooms, frequency, intensity, Nebraska lakes"
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bibliography: C:/Users/Victoria/GitHub/wickham-thesis/doc-setup/wickham-thesis-bib.bib
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---

<Information in these brackets are used for annotating the RMarkdown file. They will not appear in the final version of the PDF document>

<Setup the global options for the R chunks in your document>
```{r setup, include=FALSE}

#setwd("C:/Users/meglarse/Desktop/wickham-thesis/")

# Install packages for the document

## install.packages("ggplot2")
require(ggplot2)

# Package options

```

<Setup the front matter for your document>
\clearpage
\tableofcontents <creates a table of contents for your document>
\newpage
\listoftables <creates a list of all tables>
\newpage
\listoffigures <creates a list of all figures>
\newpage

<Add the text for each of your sections. Check out the [RMarkdown cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf) and [Markdown cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet#lists) for more information about text formatting.>

#Introduction
Cyanobacteria (also known as blue-green algae) are ancient photosynthetic microbes found across diverse habitats. 
These organisms are an important part of the aquatic food chain, but some species produce potent compounds, known as cyanotoxins, that pose a risk to humans, fish, and invertebrates.
Events where the amount of cyanobacteria, and their subsequent cyanotoxins, are so great that they pose a threat to humans, or cause ecological or economic harm, is called a harmful algal bloom (HAB).
Cyanobacterial toxin poisonings are a large component of the concerns surrounding harmful algal blooms, and have been the major driver behind the increase in attention being put on them in the last decade.

Cyanobacterial HABs can cause harm in many different ways: By causing sickness and death in humans, animals, fish, and invertebrates, by causing harm to an ecosystem by decreasing the quality and ecosystem health of a water body, and by causing economic harm by impairing water bodies to such a degree that they cannot be used for drinking, irrigation, fishing, recreation, or aquaculture.

Harm to living organisms comes from the side-effects of ingesting cyanotoxins.
Cyanobacterial toxin poisonings (CTP) in humans are a rare occurrence, primarily because humans avoid water bodies with high toxic cell concentrations.
Cyanotoxins can be present in finished water supplies, however, as the removal of cyanotoxins in this way is not always efficient [@paerl2012a].
The implication of this is that they can be a potential danger to both humans and animals [@blaha2009].
When oral poisonings have occurred, such as through improperly treated drinking water supplies, reported symptoms include abdominal pain, nausea, vomiting, diarrhea, sore throat, dry cough, headache, blistering of the mouth, atypical pneumonia, and elevated liver enzymes [@chorus1999].
In most of these cases, as with most instances of human poisonings due to cyanotoxins, the levels of toxin have not been identified.
In other cases, human poisonings have been suspected but were not confirmed due to a lack of information [@carmichael2001a].
The most common exposure route for humans is through recreational waters, consumption of drinking water, and algal health food tablets.
There have been no known fatalities through these oral and dermal routes.
The only cause of human fatalities due to cyanotoxins was through intravenous exposure from a dialysis clinic in Caruaru, Brazil in 1996.
Symptoms of the exposure, now referred to as Caruaru Syndrome, included painful enlargement of the liver, jaundice, and an increased tendency to suffer from metrorrhagia, nose bleeds, and hemorrhagic spots [@carmichael2001b].

The presence of cyanobacterial HABs can negatively impact the health of a water body and its ecosystem.
The blooms themselves alter the physical and chemical makeup of the surrounding water.
They do this by deoxygenating the hypolimnion, which in turn releases nutrients from the sediment, releasing hydrogen sulfide and methane, and by producing cyanotoxins.
These changes can cause a reduction or even an elimination of benthic flora and fauna, as well as a decrease in the amount of desirable plant species, including algae [@paerl1988].
In fact, cyanobacterial blooms may drive cascading trophic impacts due to their toxicity and palatability which may then drive food webs in affected lakes to switch from planktonic to benthic or detrital in response [@paerl1988].
Cyanobacterial blooms also increase turbidity in the water body, which decreases light penetration to lower levels.
This, naturally, decreases the ability for macrophytes and benthic microalgae to establish, which ultimately affects underwater habitat for other species  [@jeppesen2007][@scheffer1997][@scheffer2004].

Economic harm from cyanobacterial HABs comes from the impairment of water bodies.
These water bodies can be affected to such a degree that they can no longer be used for their intended purposes, such as recreation, drinking, irrigation, fishing, and aquaculture.
Estimating the cost of ecosystem services or damage is always incredibly difficult, and relies on making certain assumptions, which may or may not be true in real-world application.
Regardless, cost estimations have been made.
@hoagland2002 estimated that harmful algal blooms cost the United States approximately 46 million dollars a year.
This estimate takes costs associated with shellfish poisonings, ciguatera fish poisonings, commercial fishery damage, untapped fishery resources, losses associated with recreation and tourism, and the costs of monitoring and management into account [@hoagland2002].
@anderson2000 had a much higher estimate at 449 million dollars per year.
This estimate summed the costs associated with public health, commercial fisheries, recreation and tourism, and monitoring and management [@anderson2000].
Both studies found that the highest costs were associated with public health.
This highlights the need to understand bloom mechanisms and trends.

Throughout the literature, there has thought to have been an increase in the number, distribution, and intensity of harmful algal blooms.
The evidence presented above illustrates why it is very important to quantify if this is true or not, and this question has been approached from several different angles in a number of papers.

The original paper that set this idea forward mainly looked at the global distribution of shellfish poisonings [@hallegraff1993].
The interesting thing to note here is that this paper doesn't really go into the intensity or frequency of these blooms themselves, but simply looks at the instances of reported shellfish poisonings that are caused by cyanotoxins.
The author himself acknowledges that the apparent global increase that he sets forward may not be a real increase, but may simply be the result of increased detection and awareness.
He is not the only author to frame an increase in cyanobacterial blooms in this way.
@chung2013 argues that there is an increase in HABs worldwide, and that this is reflected by the fact that there has also been an increasing number of reports and studies on cyanobacterial blooms in the last decade [@cheung2013].
Again, these authors acknowledge that this trend could be due to increased detection and awareness.
Other papers on the subject use the phrase "apparent increase", but few actually tackle the question head-on.

Another paper by Loftin et al, looked at the concentration and distribution of cyanotoxins across the United States using data from the EPA National Lakes Assessment 2007 [@loftin2016].
Although informative, this paper is simply a nationwide snapshot of distribution and concentration, and does not address increasing frequency over time.
@taranu2015 analyzed paleolimnological pigment sediment core records in order to determine how cyanotoxins have increased since the 1800s.
They found that cyanobacteria have increased significantly since the 1800s, have increased disproportionately relative to other phytoplankton, and have increased more rapidly since 1945 [@taranu2015].
These findings are a step in the right direction, but the amount of actual toxin could not be measured using this method.
Therefore, conclusions about intensity could not be made.
A study comparing the findings presented here with actual toxin amounts in grab samples from the past decade, could be a valuable addition to these findings.

Reasons given for this apparent increase in frequency and intensity are eutrophication due to increased nutrient loading, increasing water temperatures due to climate change, increased vigilance, advances in monitoring efforts and analytical techniques, and anthropogenic activities [@scholz2017][@o'neil2012][@paerl2012b][@brooks2016][@taranu2012][@beaver2014][@heisler2008].

We can create citations like this: 

  - To suppress the author's name: @smith2017a had some really great things to say.
  - Or to include the full citation: One of his other articles completely contradicted the first [@smith2017b]
  
<To create a formatted citation, check out this website: http://truben.no/latex/bibtex/)>

# Methods
## Study system and data collection
Samples were collected weekly by the NDEQ from May through September at 51 lakes and reservoirs.
Current NDEQ sampling protocol is a single mid-beach grab sample, which is used to represent the condition of an entire beach area.
Samples were processed using Abraxis LLC Microcystins Enzyme-Linked Immunosorbent Assay (ELISA) laboratory test kits.
This test analyzes the combined total of 71 different variants of the microcystin toxin.
Water samples are collected and delivered on Monday and Tuesday, processed using freeze-thaw methods on Wednesday, and analyzed on Thursday.

We have defined frequency as the percent of samples in a season that contain microcystin.
We have defined intensity as the amount of microcystin found in a sample.
The threshold of microcystin concentration that we have defined as warranting a beach closure is 4 ug/L, which is based on guidelines given by the World Health Organization (WHO).

We used R, specifically RStudio, to generate all relevant calculations and figures.
The coding language used was RMarkdown, which allowed us to use R as a text editor as well as a computational program.

## Data sources
The data that we analyzed was publically avaliable data from the Water Quality Portal, which is a service provided by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC).
This data was combined from the USGS National Water Information System (NWIS), the EPA STOrage and RETrieval (STORET) Data Warehouse, and the USDA ARS Sustaining The Earth's Watersheds - Agricultural Research Database System (STEWARDS).

## Analyses and calculations
### Frequency
We defined frequency as the percent of samples in a season that contained microcystin.

### Intensity
We defined intensity as the amount of microcystin found in a sample.
The threshold of microcystin concentration that we have defined as warranting a beach closure is 4 ug/L, which is beased on guidelines given by the World Health Organization (WHO).

## Statistics

# Results
<Data cleanup>
```{r}
```

<Calculations>
```{r}
```

<Data Reduction>

## Summarize result 1 in a single sentence.
The frequency of algal blooms across the state of Nebraska increased by X\% between 2005 - 2015 (\autoref{fig1}).

<Figure 1>
```{r fig1, fig.cap="A descriptive title about the frequency. \\label{fig1}", echo = FALSE}
dat <- data.frame(seq(1,10,0.5), seq(1,10,0.5))
ggplot(data = dat, aes(x = dat[,1], y = dat[,2])) +
  geom_point(size = 2, color = "red") +
  xlab("Axis 1") +
  ylab("Axis 2") +
  theme_bw()

```

## Summarize result 2 in a single sentence.
Some great text about this!

<Figure 2>

```{r fig2, fig.cap="A descriptive title about the intensity."}
```

# Conclusions

# References