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key_drivers.Rmd
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---
title: "Key drivers of change in Arctic fjord ecosystems"
author: "Robert Schlegel"
date: '`r format(Sys.Date(), "%d %B %Y")`'
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
css: acid.css
csl: frontiers.csl
bibliography: FACE-IT.bib
---
```{r global_options, include = FALSE}
knitr::opts_chunk$set(fig.width = 8, fig.align = 'center',
echo = FALSE, warning = FALSE, message = FALSE,
eval = TRUE, tidy = FALSE)
# Libraries
library(tidyverse) # The tidy dialect of R
library(kableExtra) # For formatting static tables
```
<center>
{ width=70% }
</center>
# Overview
This document is designed to satisfy D1.1 for the Horizon2020 project [FACE-IT](https://www.face-it-project.eu/).
It is well known that the Arctic is undergoing rapid systemic changes, and that these changes are likely to increase in both rate and extent. The FACE-IT project is primarily interested in how these changes will be impacting the socio-ecological fjord and adjacent coastal systems at seven study sites in the Arctic. There is a steadily growing body of data from these sites that can be used to document and investigate the changes that would be of scientific and societal interest. However, there is too much information/data to document and download. It is therefore necessary, at the outset of the FACE-IT project, to identify the key drivers of change in Arctic biodiversity and to identify the availability of data relating to these drivers.
A Google Sheet was created to allow the experts at FACE-IT to fill-in which drivers were relevant for their work/study sites. The key drivers identified have been distributed into several clusters presented below.
# Cryosphere
The FACE-IT project is focussed on fjord and adjacent coastal socio-ecological systems, which means it is concerned primarily with marine data, but changes in the terrestrial cryosphere have a direct impact on coastal systems and so are key variables when considering drivers of change. These terrestrial variables include any measure of glaciers, such as mass balance and changes to their terminating edges. Also of interest are changes to permafrost and snow cover thickness. All of these terrestrial changes are important to fjords as they have direct impacts on the rates of runoff and river discharge into the coast, which have in turn very large impacts on water quality variables such as salinity, nutrients concentration and turbidity. The importance of changes to water quality are discussed again in the __physical__ and __chemistry__ section.
One of the aspects of the cryosphere that most directly impacts fjord and adjacent coastal systems is sea-ice. This is because the presence of ice can have a dramatic effect on what may be considered the three most fundamental important __physical__ variables of sea water: temperature, salinity, and light. There are many ways of measuring sea-ice including: concentration, extent, thickness, and age. Whereas only one of these variables can be used when necessary, it is almost always preferable to have as many different measures of sea-ice as possible. It has been documented that there is a reliable correlation between fast-ice (ice attached to the coast) and local air and sea temperatures. This means that it may be possible to use air and sea temperatures as a proxy for sea-ice when there is a paucity of these data.
__Key Cryosphere Drivers__
> __Glacier__: land mass balance, terminating edge; __Permafrost__: temperature, thickness; __Snow cover__: thickness; __Coastal ice__: formation/break up date, thickness; __Fast ice__; __Sea ice__: concentration, extent, freeboard, thickness
# Physical
One could consider the __cryosphere__ drivers to be physical drivers, but they are considered separately due to their importance in the Arctic. The physical aspects of fjord and adjacent coastal systems are generally well documented as part of global gridded datasets. The resolution of gridded data is often too coarse to capture well the local scale variability of the FACE-IT study sites. It is therefore preferable to identify local sources of these key drivers wherever possible. Bathymetry being a primary example of this issue.
As mentioned in the __cryosphere__ section, potentially the three most important variables are water temperature, salinity, and light. This is because these three variables have systemic effects on the __biology__ of the foundational species and ecosystems from which the other socio-ecological functioning of the fjords and adjacent coastlines are based. There are a number of global products that provide these data as well as a host of locally collected in situ data. While these three variables alone would generally suffice for the physical characterisation of drivers of change, there are many other variables that are of interest to FACE-IT. For example, water temperatures do not change uninvited. They are acted on by air-sea heat flux and so it is also desirable to know what changes may be occurring in: net heat flux, long+shortwave radiation, latent+sensible heat flux. As part of the balance of flux across the air-sea border, one would also want to know about: the mixed layer depth, sea level pressure, precipitation, evaporation, and wind speed+direction. All of these are important variables in their own right as they tell a more specific part of the story of a changing climate.
In addition to changes in the air-sea fluxes, it is also of great importance to understand what changes to water quality may be occurring. Many of these drivers have been grouped into the __chemistry__ section below, so in this section we focus on river discharge and the more physical aspects of water quality: sedimentation, suspended matter, suspended solids, suspended organics. These drivers are important because they may have a very large impact on the ability of light to penetrate at depth, which can change the competitiveness of different biological groups and have disruptive influences on local ecosystems. For example, changes in the timing and intensity of river runoff can unduly promote the early growth of phytoplankton blooms, which will then deplete the nutrients from the water earlier than expected, making it more difficult for kelp forests to grow, thereby limiting primary production, which in turn may cascade up the trophic food-web.
__Key Physical Drivers__
> __Bathymetry__; __Evaporation__; __Heatflux__: net, latent/sensible, long/shortwave radiation; __Light extinction coefficient__; __Mixed layer depth__; __Precipitation__; __River discharge__; __Salinity__; __Sea level pressure__; __Sea temperature__: surface, mid, bottom; __Sedimentation__; __Suspended__: matter, solids, organics; __Wind__: direction, max/mean speed
# Chemistry
The first of the two primary areas of interest for the chemistry drivers is the amount of inorganic carbon and related minerals such as aragonite and calcite as changes to these minerals are related to shifts in the pH of seawater. This is of concern because the acidification of the ocean has a wide range of well documented effects on sea life. Primarily on calcifying organisms such as coralline algae, coccolithophores, molluscs and sea urchins, which are a foundational species in some pelagic and benthic ecosystems. Parameters of the carbonate system which are of interest are pCO~2~, pH, total alkalinity, and the concentration of dissolved inorganic carbon (DIC).
The other primary area of interest for this category is the changes to the nutrient load of seawater. The main nutrients of interest are: nitrate, nitrite, ammonium, phosphate, and silicate. These are important to monitor as dramatic short-term, or significant long-term changes to the nutrient profiles in fjord and adjacent coastal waters can cause bottom up forcing of ecological structures. Potentially fundamentally restructuring the species composition/biodiversity of a study site. Also of interest for water quality measurements are dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and dissolved O~2~. These variables are important for similar reasons to the nutrients.
__Key Chemistry Drivers__
> __pCO~2~__; __pH__; __Total alkalinity__; __DIC__; __DOC__; __DON__; __DO~2~__; __Nutrients__: nitrate, nitrite, ammonium, phosphate, silicate
# Biology
This category envelops individual species and the broader ecosystems that they populate. Foremost amongst the drivers for these categories is the very broad concept of biodiversity. One of the FACE-IT work packages focuses on this driver primarily oriented around their target species (e.g. kelps and seabirds). To this end the biology category could contain a near limitless list of drivers if every aspect of biological change for the myriad of potential target species found within fjord and adjacent coastal systems were to be considered. As the FACE-IT researchers are experts in their field of study, the specifics of a given target species are left to them to track. In most cases this is of no concern as they will have been the one who generated the original data.
Variables that have a broader interest across focal species and ecosystems are however considered as key drivers of change. These include biogeochemical variables such as photosynthesis, respiration, primary production, calcification and nitrogen fixation. These variables are controlled by a lot of chemical, physical, and biological drivers and their change is itself a driver of changes, for example on upper trophic levels of relevance for humans, including emblematic and species of commercial interest.
Another requirement for biology data is to be able to perform species distribution modelling (SDM) in order to project where the target species may currently be present, and where they may be with future projections. In order to perform SDM a researcher needs access to as much data describing the focal species as possible. Traditionally this is species presence/absence and abundance/biomass for a given site or lon/lat coordinate system.
__Key Biology Drivers__
> __Photosynthesis__; __Respiration__; __Primary production__; __Calcification__; __Nitrogen fixation__; __Species__: presence/absence, abundance/biomass
# Social
Putting the ‘socio’ in socio-ecological fjord systems, the social category contains the most relevant drivers that involve or directly affect human activities in the fjords and adjacent coastlines. Of most direct impact on fjord systems for this category is fish landings, both commercial and recreational. This is because the biomass removed from the study sites have a significant impact on the ecosystem. Also, species migration could create opportunities. Hunting (i.e. game landings) are also of importance as the removal of larger terrestrial organisms from coastal ecosystems can have a trickle down effect on the pressure between trophic levels. To this end it is necessary to document the local and national management regulations strategies for these livelihood activities, and how these are linked to science and scientific advice. It is relevant to understand how and on what basis hunting and fish catch quotas are set. This will provide us insights into how the extent to which fish and marine mammals are managed in a multispecies and ecosystems frame. Resource management is therefore another driver which will impact the species composition and abundance of commercially important fish and marine mammal species and their prey.
An indirect impact on the fjord and adjacent coastal systems comes from human activities and presence, such as the traffic from tourism and recreational vessels, and other activities in the fjords and along the coasts. Indirect impacts of human activities are harder to measure and estimate when compared to intentional direct impacts on the fjord ecosystems such as regulated fishing and hunting. Human activities create waste and pollution to such a degree that it can have disturbing effects in the natural state of the study sites. The drivers that may be tracked to determine changes to anthropogenic perturbations at a study site are: the volume of tourist arrivals per month and their nationality, tourist vessel counts and mileage, seasonal patterns of activities and the national statistics of demography, income, coastal livelihoods, and unemployment.
__Key Social Drivers__
> __Fish landings__: commercial, recreational, quotas, seasonality; __Game landings__: quotas, seasonality; __Local and national resource management__; __Tourist arrivals__: per month, nationality; __Tourist vessels__: count, mileage; __National statistics__: demography, income, unemployment
# Data availability
The data for the drivers outlined above are available from multiple established data sources. Large gridded data sets for most physical drivers at the Arctic scale can be accessed via: [Copernicus](https://marine.copernicus.eu/), [EMODnet](https://www.emodnet.eu/en), and [NOAA](https://www.ncei.noaa.gov/). Site specific datasets can be accessed via: [NPDC](https://data.npolar.no/home/), [NSF](https://arcticdata.io/), [openpolar](https://openpolar.no/), [PANGAEA](https://pangaea.de/), and [SIOS](https://sios-svalbard.org/metadata_search). Data for drivers pertaining more to policy may be found at: [INTAROS](https://intaros.nersc.no/) and [SAON](https://www.arcticobserving.org/). The data portals for national statistics and other social drivers are: [Grønlands statistik](https://stat.gl/dialog/topmain.asp?lang=da&subject=Fiskeri%20og%20fangst&sc=FI), [kystverket](https://ais-public.kystverket.no/ais-download/), [SSB](https://www.ssb.no/), and [statistikknett](https://statistikknett.no/). The specific data of relevance to the key drivers that may be found within each of these sources are given in greater detail in [D1.2: Meta-database](https://FACE-IT-project.github.io/FACE-IT/metadatabase.html).
# Summary
```{r key-drivers-table, eval=TRUE}
# Easiest to show each variable as an smol individual data.frame
# Convenience wrapper for drivers
make_driver <- function(category, driver, why, sites, reference){
res <- data.frame(Category = category, Driver = driver, Why = why, Sites = sites, Reference = reference)
}
# The order of the info:
## Category, Driver, Why, Sites, Reference
## Cryosphere
cryo_coastal_ice <- make_driver("Cryosphere", "Coastal ice: formation/break up date, thickness", "", "", "")
cryo_fast_ice <- make_driver("Cryosphere", "Fast ice", "Decreasing (fast) ice cover can have a range of ecological effect.", "", "Muckenhuber et al. (2016)")
cryo_glacier <- make_driver("Cryosphere", "Glacier: land mass balance, terminating edge", "An indicator of the change to other drivers.", "", "")
cryo_permafrost <- make_driver("Cryosphere", "Permafrost: temperature, thickness", "", "", "")
cryo_sea_ice <- make_driver("Cryosphere", "Sea ice: concentration, extent, freeboard, thickness",
"Affects pelagic and benthic production.", "EU Arctic", "Stroeve and Notz (2018)")
cryo_snow_cover <- make_driver("Cryosphere", "Snow cover: thickness", "", "EU Arctic", "")
cryo_ALL <- rbind(cryo_coastal_ice, cryo_fast_ice, cryo_glacier, cryo_permafrost, cryo_sea_ice, cryo_snow_cover)
# nrow(cryo_ALL)
## Physical
phys_bathy <- make_driver("Physical", "Bathymetry", "", "", "")
phys_evap <- make_driver("Physical", "Evaporation", "", "", "")
phys_heatflux <- make_driver("Physical", "Heatflux: net, latent/sensible, long/shortwave radiation",
"Responsible for the changing of seawater temperature in various important ways.", "EU Arctic", "")
phys_extinct <- make_driver("Physical", "Light extinction coefficient", "Indicates the amount of light to benthic producers.", "EU Arctic", "")
phys_mld <- make_driver("Physical", "Mixed layer depth", "", "EU Arctic", "Popova et al. (2014)")
phys_precip <- make_driver("Physical", "Precipitation", "Affects the amount of fresh water runoff into coasts.", "EU Arctic", "")
phys_river_discharge <- make_driver("Physical", "River discharge",
"Delivers carbon and nutrient rich freshwater to coast.", "EU Arctic", "Delpech et al. (2021)")
phys_salinity <- make_driver("Physical", "Salinity", "", "EU Arctic", "")
phys_mslp <- make_driver("Physical", "Sea level pressure", "", "", "")
phys_sst <- make_driver("Physical", "Sea temperature: surface, mid, bottom", "", "EU Arctic", "Popova et al. (2014)")
phys_sedimentation <- make_driver("Physical", "Sedimentation", "", "", "")
phys_suspended <- make_driver("Physical", "Suspended: matter, solids, organics", "", "", "")
phys_wind <- make_driver("Physical", "Wind: direction, max/mean speed", "", "", "")
phys_ALL <- rbind(phys_bathy, phys_evap, phys_heatflux, phys_extinct, phys_mld, phys_precip, phys_river_discharge,
phys_salinity, phys_mslp, phys_sst, phys_sedimentation, phys_suspended, phys_wind)
# nrow(phys_ALL)
## Chemistry
chem_DIC <- make_driver("Chemistry", "DIC", "", "EU Arctic", "Popova et al. (2014)")
chem_DOC <- make_driver("Chemistry", "DOC", "", "", "")
chem_DON <- make_driver("Chemistry", "DON", "", "", "")
chem_DO2 <- make_driver("Chemistry", "Dissolved 02", "", "", "")
chem_nutrients <- make_driver("Chemistry", "Nutrient: nitrate, nitrite, ammonium, phosphate, silicate", "", "", "")
chem_pCO2 <- make_driver("Chemistry", "pCO2", "", "EU Arctic", "Popova et al. (2014)")
chem_pH <- make_driver("Chemistry", "pH", "", "EU Arctic", "")
chem_alk <- make_driver("Chemistry", "Total alkalinity", "", "EU Arctic", "")
chem_ALL <- rbind(chem_DIC, chem_DOC, chem_DON, chem_DO2, chem_nutrients, chem_pCO2, chem_pH, chem_alk)
# nrow(chem_ALL)
## Biology
bio_calc <- make_driver("Biology", "Calcification", "", "", "")
bio_N_fix <- make_driver("Biology", "Nitrogen fixation", "", "", "")
bio_photo <- make_driver("Biology", "Photosynthesis", "", "", "")
bio_prim_prod <- make_driver("Biology", "Primary production", "", "", "")
bio_resp <- make_driver("Biology", "Respiration", "", "", "")
bio_species <- make_driver("Biology", "Species: presence/absence, abundance/biomass", "", "", "")
bio_ALL <- rbind(bio_calc, bio_N_fix, bio_photo, bio_prim_prod, bio_prim_prod, bio_resp, bio_species)
# nrow(bio_ALL)
## Social
soc_fish_landings <- make_driver("Social", "Fish landings: commercial, recreational, quotas, seasonality",
"", "Porsangerfjorden + Isfjorden + Disko Bay", "")
soc_game_landings <- make_driver("Social", "Game landings: quotas, seasonality", "", "Porsangerfjorden + Isfjorden + Disko Bay", "")
soc_local_management <- make_driver("Social", "Local and national resource management", "", "", "")
soc_national_statistics <- make_driver("Social", "National statistics: demography, income, unemployment", "", "EU Arctic", "")
soc_tourist_arrivals <- make_driver("Social", "Tourist arrivals: per month, nationality", "", "Isfjorden + Disko Bay", "")
soc_tourist_vessels <- make_driver("Social", "Tourist vessels: count, mileage",
"Indicative of direct anthropogenic disturbance.", "Isfjorden + Disko Bay", "")
soc_ALL <- rbind(soc_fish_landings, soc_game_landings, soc_local_management,
soc_national_statistics, soc_tourist_arrivals, soc_tourist_vessels)
# nrow(soc_ALL)
# Combine and print
knitr::kable(rbind(cryo_ALL, phys_ALL, chem_ALL, bio_ALL, soc_ALL),
caption = "Table 1: The key drivers of changes on Arctic fjord and adjacent coastal systems as identified by the experts in FACE-IT for the relevant fields. The drivers have been separated into the following categories for convenience: Cryosphere, Physical, Chemistry, Biology, Social. The 'Crysophere' category contains any drivers related to ice, glaciers, and/or snow. This category contains both marine and terrestrial drivers that impact the marine environements of Arctic fjords and adjacent coastlines. The 'Physical' category contains atmospheric and oceanic drivers. The 'Chemistry' category contains primarily water quality variables that are no longer physical, but not yet biological. This includes inorganic as well as organic nutrients. The 'Biology' category contains drivers related to biology and ecology. The final category 'Social' contains drivers that are primarily related to anthropogenic impacts on the natural world, as well as drivers that directly affect the well-being of local stakeholders. This category therefore contains all drivers relating to fisheries.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>%
row_spec(1:6, background = "mintcream") %>% # Cryosphere
row_spec(7:19, color = "black", background = "skyblue") %>% # Physical
row_spec(20:27, background = "#F6EA7C") %>% # Chemistry
row_spec(28:34, background = "#A2ED84") %>% # Biology
row_spec(35:40, background = "#F48080") # Social
```