Public data from the Alberta Biodiversity Monitoring Institute
Install the package:
remotes::install_github("ABbiodiversity/abmidata")The ABMI public data API pulls information from one of ABMI’s main databases.
Load the R package:
library(abmidata)
#> abmidata 0.0.1 2021-02-11List names of tables available via the API:
n <- ad_get_table_names()
data.frame(Description = head(n))
#> Description
#> T01A T01A Site Physical Characteristics
#> CT01A CT01A Site Physical Characteristics
#> T01B T01B Site Suitability
#> CT01C CT01C Site Capability
#> CT01B CT01B Site Suitability
#> T01C T01C Site CapabilityGet a table by its ID, possibly filtered by site ID or by year:
x <- ad_get_table("T01A", year=2010)
str(x)
#> 'data.frame': 1026 obs. of 14 variables:
#> $ Rotation : chr "Rotation 1" "Rotation 1" "Rotation 1" "Rotation 1" ...
#> $ ABMI Site : chr "149" "149" "149" "149" ...
#> $ Year : int 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
#> $ Field Date : chr "27-May-10" "27-May-10" "27-May-10" "27-May-10" ...
#> $ Field Crew Member(s) : chr "TGR" "TGR" "TGR" "TGR" ...
#> $ Nearest Town : chr "DNC" "DNC" "DNC" "DNC" ...
#> $ Public Latitude : num 58.9 58.9 58.9 58.9 58.9 ...
#> $ Public Longitude : num -112 -112 -112 -112 -112 ...
#> $ Collection Methodology: int 1 1 1 1 1 1 1 1 1 1 ...
#> $ Point Count Station : int 1 2 3 4 5 6 7 8 9 1 ...
#> $ Subpoint : chr "P" "P" "P" "P" ...
#> $ Elevation (metres) : int 213 213 213 213 213 213 213 213 213 212 ...
#> $ Slope (degrees) : chr "0" "0" "0" "0" ...
#> $ Aspect (degrees) : chr "VNA" "VNA" "VNA" "VNA" ...ABMI data has specific placeholders for missing data indicating the reason for the missingness. This poses challenges in R when the information is numeric. Here is how you can handle these missing value indicators:
y <- x[["Aspect (degrees)"]][1:100]
y
#> [1] "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA"
#> [13] "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "100" "260" "VNA" "VNA" "80"
#> [25] "VNA" "220" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA"
#> [37] "DNC" "DNC" "DNC" "DNC" "VNA" "DNC" "VNA" "VNA" "DNC" "VNA" "VNA" "VNA"
#> [49] "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA"
#> [61] "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA"
#> [73] "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA"
#> [85] "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "VNA" "87" "VNA" "9" "VNA" "VNA"
#> [97] "VNA" "VNA" "VNA" "VNA"
ad_convert_na(y)
#> [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#> [19] NA 100 260 NA NA 80 NA 220 NA NA NA NA NA NA NA NA NA NA
#> [37] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#> [55] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#> [73] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#> [91] NA 87 NA 9 NA NA NA NA NA NA
summary(ad_process_na(y))
#> VNA DNC PNA SNI value
#> Min. :0.00 Min. :0.00 Min. :0 Min. :0 Min. : 9.00
#> 1st Qu.:1.00 1st Qu.:0.00 1st Qu.:0 1st Qu.:0 1st Qu.: 81.75
#> Median :1.00 Median :0.00 Median :0 Median :0 Median : 93.50
#> Mean :0.88 Mean :0.06 Mean :0 Mean :0 Mean :126.00
#> 3rd Qu.:1.00 3rd Qu.:0.00 3rd Qu.:0 3rd Qu.:0 3rd Qu.:190.00
#> Max. :1.00 Max. :1.00 Max. :0 Max. :0 Max. :260.00
#> NA's :94Once tables are loaded using the ABMI public data API, the data usually needs to be processed further. The tables are in ‘long format’ which means that e.g. sites and species are all in in their own columns:
| Site | Species | Count |
|---|---|---|
| Site 1 | A | 1 |
| Site 1 | B | 2 |
| Site 1 | C | 3 |
| Site 2 | B | 1 |
| Site 2 | D | 2 |
| Site 3 | E | 1 |
There are other R packages that can be used to turn the ‘long format’ data into a ‘wide format’ where e.g. sites represent rows and species represent columns:
| Site | A | B | C | D | E |
|---|---|---|---|---|---|
| 1 | 1 | 2 | 3 | 0 | 0 |
| 2 | 0 | 1 | 0 | 2 | 0 |
| 3 | 0 | 0 | 0 | 0 | 1 |
Check out examples of how to do this for vascular plants, mites, mosses, lichens, and birds in this article about common data processing tasks.
Once the monitoring data is in the desired format, one can match the site locations (and possibly years) with location/year specific predictor variables. One can use the field data about site condition to explain the distribution, abundance, or other measures of species and habitat elements at the ABMI sites.
One can also use geospatial information extracted at the site coordinates. This, however, poses some challenges because exact site coordinates are not publicly available. However, restrictions around site confidentiality have been substantially relaxed and coordinates for most sites will soon be available for research purposes upon request. We will provide relevant information here as soon as it is available. In the meantime, please reach out to ABMI for more information.
See the package website for documentation. A list of tables and associated metadata can be found here.
See also:
- Terrestrial Field Data Collection Protocols
- Wetland Field Data Collection Protocols
- Terrestrial ABMI Autonomous Recording Unit (ARU) and Remote Camera Trap Protocols
https://github.com/ABbiodiversity/abmidata/issues
Solymos P, Fang J, ABMI (2021). abmidata: Accessing Public Data from the ABMI. R package version 0.0.1, <URL: https://github.com/abbiodiversity/abmidata>.
MIT © 2020 Peter Solymos, Joan Fang, ABMI