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metadatabase.Rmd
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metadatabase.Rmd
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---
title: "Meta-database"
author: "Robert Schlegel"
date: '`r format(Sys.Date(), "%d %B %Y")`'
site: workflowr::wflow_site
output:
workflowr::wflow_html:
toc: false
# editor_options:
# chunk_output_type: console
csl: frontiers.csl
bibliography: FACE-IT.bib
---
```{r global_options, include = FALSE}
knitr::opts_chunk$set(fig.width = 8, fig.align = 'center',
echo = TRUE, warning = FALSE, message = FALSE,
eval = TRUE, tidy = FALSE)
```
```{r setup, echo=FALSE}
# Necessary libraries
library(tidyverse)
library(DT)
# Function for smoother meta-data creation
make_meta_data <- function(file_name, data_type, URL, reference, other = NA){
# Create full file name
# determine the file type and load
if(str_detect(file_name, ".tsv")){
dat <- read_tsv(file_name, guess_max = 100000)
} else {
stop("File type not currently accounted for.")
}
# Find longitude range
lon_col <- c(colnames(dat)[str_detect(colnames(dat), "lon")],
colnames(dat)[str_detect(colnames(dat), "Lon")])
lon_min <- min(dat[,lon_col], na.rm = T)
lon_max <- max(dat[,lon_col], na.rm = T)
# Find latitude range
lat_col <- c(colnames(dat)[str_detect(colnames(dat), "lat")],
colnames(dat)[str_detect(colnames(dat), "Lat")])
lat_min <- round(min(dat[,lat_col], na.rm = T), 2)
lat_max <- round(max(dat[,lat_col], na.rm = T), 2)
# Find depth range
depth_col <- c(colnames(dat)[str_detect(colnames(dat), "depth")],
colnames(dat)[str_detect(colnames(dat), "Depth")])
depth_col <- depth_col[!str_detect(depth_col, "bottom")]
depth_col <- depth_col[!str_detect(depth_col, "Bottom")]
depth_min <- round(min(dat[,depth_col], na.rm = T), 2)
depth_max <- round(max(dat[,depth_col], na.rm = T), 2)
# Find depth range
date_col <- c(colnames(dat)[str_detect(colnames(dat), "date")],
colnames(dat)[str_detect(colnames(dat), "Date")])
date_col <- dat[,date_col] %>%
`colnames<-`("t")
date_min <- as.Date(min(date_col$t))
date_max <- as.Date(max(date_col$t))
# Determine ecoregions
# Find point in MEOW polygons
res <- data.frame(data_type, lon_min, lon_max, lat_min, lat_max, depth_min, depth_max,
date_min, date_max, file_name, URL, reference)
return(res)
}
```
## Introduction
This document is designed to satisfy both D1.1 and D1.2 for the Horizon2020 project "FACE-IT". The text in this document is a report on the key drivers of changes in Arctic biodiversity (D1.1). The tables contain the meta-database for the data identified as key drivers of change (D1.2). The text and meta-data from this document will be used for the completion of a review article on past and future changes of key drivers in and around the FACE-IT study sites (D1.3). We begin with a review of known drivers of change in the Arctic before focussing in on each individual FACE-IT study site to discuss any differences from the broader Arctic. Within each section a table is given that shows the meta-data for the drivers of change for the topic of that section.
## Drivers of change in the European Arctic
Many physical processes are known to drive biodiversity in the Arctic. Unsurprisingly, the presence of sea ice is one of these controlling factors (@Pavlova2019). There are however many more, such as photosynthetically available radiation (PAR), ultraviolet radiation (UVR), and turbidity (@Hop2019). In addition to knowing what it is that may cause changes, it is necessary to identify the fonts of biodiversity that may be affected by these drivers. There are many taxa/species etc. that have been identified as important for monitoring throughout the Arctic.
```{r EU-arctic-meta-data, echo=FALSE}
EU_zooplankton <- make_meta_data(file_name = "~/pCloudDrive/FACE-IT_data/EU_arctic/1995-2008-zooplankton-biodiversity.tsv",
data_type = "Zooplankton biodiversity",
URL = "https://data.npolar.no/dataset/9167dae8-cab2-45b3-9cea-ad69541b0448",
reference = "Norwegian Polar Institute (2020). Marine zooplankton and icefauna biodiversity [Data set].
Norwegian Polar Institute. https://doi.org/10.21334/npolar.2020.9167dae8")
metadata_EU_arctic <- rbind(EU_zooplankton, EU_zooplankton)
```
```{r EU-arctic-meta-data-table}
DT::datatable(metadata_EU_arctic, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
```
<!-- Rather have the table be clickable, and the clicked entry pops-up with the larger bits of info. This will save on the width of the table. -->
## Svalbard
While not a study site itself, there are a lot of studies and data products that focus on Svalbard broadly, rather than individual study sites within this region. Therefore this geographical region
## Kongsfjorden
Much of the solar magnetic radiation going towards earth is focussed around Ny Alesund. This can have a knock on effect to air temperature when there are strong auroras. Up to 4°C. Models currently capture this effect very poorly. Land terminating glaciers have been retreating linearly. Water terminating glaciers are less linear. Occasionally water terminating glaciers may surge forward, making it look like the terminating edge has improved. But this is generally due to a destabilisation of the glacier and an overall decrease in the glacier mass balance. 2020 was a record breaking warm year that has led to record breaking melts. Smaller glaciers are more susceptible to the increased in melt driven by climate change as they have smaller areas to accumulate ice etc. during the cold period of the year.
Up until 2007 the fjord tended to be frozen 100% until February. Since 2007 the fjord has been having less and less surface freezing. This is in part due to Atlantic water encroachment. These waters are higher in aragonite and pH. These are also less turbid, allowing for more light for use with plankton etc. The plankton do respond to this with Atlantic species favoured in warmer years.
As to the influence of light, this has been increasing both due to increased melt and less ice cover throughout the year, in addition to increases in tourism.
## References