mpatools

Tools for working with Marine Protected Areas to compute species diversity metrics using OBIS data.

Requirements

Installation

remotes::install_github("BigelowLab/mpatools")

Usage

Polygons for MPAs

We use the wdpar R package to fetch MPAs by country (or ‘global’ for the complete dataset.) By default, we store the files you download in the path returned by rappdirs::user_data_dir("wdpar") in GeoPackage format. Once you have downloaded a dataset you can simply read the file from disk using read_mpa("name"). Below, we use the list_mpa() function to determine if we already have the Cuba dataset.

library(rappdirs)
library(sf)
library(mpatools)
if ("Cuba" %in% list_mpa()){
  mpa <- read_mpa(name = "Cuba")
} else {
  mpa <- fetch_mpa(name = 'Cuba')
}
# 
plot(sf::st_geometry(mpa))

OBIS occurrence data

Once you have MPA polygons in hand, it is easy to request OBIS records that fall within the polygons. We use the robis R package to fetch occurrence data. By default we store files you downlaod in the path returned by rappdirs::user_data_dir("robis") in GeoPackage. Once you have downloaded format you can simply read the file from disk using read_obis().

if ("Cuba" %in% list_obis()){
  obis <- read_obis(name = "Cuba", form = "sf")
} else {
  obis <- fetch_obis_country(name = 'Cuba')
}
head(obis)

## Simple feature collection with 6 features and 164 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -77.45 ymin: 19.867 xmax: -75.37 ymax: 19.96056
## Geodetic CRS:  WGS 84
## # A tibble: 6 × 165
##   date_year scientificNameID      scientificName   superfamilyid individualCount
##       <int> <chr>                 <chr>                    <int>           <dbl>
## 1      1963 urn:lsid:marinespeci… Balaenoptera br…        148724               1
## 2      1957 urn:lsid:marinespeci… Parapagurion im…        155727               2
## 3      1930 urn:lsid:marinespeci… Tectarius anton…         14766               1
## 4      1930 urn:lsid:marinespeci… Nodilittorina            14766               2
## 5      1974 urn:lsid:marinespeci… Ziphius caviros…        148723               1
## 6      2018 urn:lsid:marinespeci… Reinhardorhynch…            NA              NA
## # … with 160 more variables: associatedReferences <chr>, dropped <lgl>,
## #   aphiaID <int>, subclassid <int>, type <chr>, taxonRemarks <chr>,
## #   phylumid <int>, familyid <int>, catalogNumber <chr>,
## #   occurrenceStatus <chr>, basisOfRecord <chr>, terrestrial <lgl>,
## #   superclass <chr>, modified <chr>, id <chr>, order <chr>,
## #   recordNumber <chr>, georeferencedDate <chr>, superclassid <int>,
## #   verbatimEventDate <chr>, infraorderid <int>, dataset_id <chr>, …

We actually find occurrences that fall within the convex hull of a set of polygons, but you can enforce a stricter policy using the policy argument for fetch_obis(). See ?fetch_obis.

plot(sf::st_geometry(mpa))
plot(sf::st_geometry(obis), col = 'orange', pch = 16, add = TRUE)

Where are MPAs and OBIS data stored?

We use rappsdir R package to manage the stirage location. We store data here rappdirs::user_data_dir("wdpar") and here rappdirs::user_data_dir("robis"). This is a very effective way to store data per user. But if you want to store data at a system-wide location you can do that using the ubiquitous path argument.

BigelowLab / mpatools

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