abmidata

Public data from the Alberta Biodiversity Monitoring Institute

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Setup

Install the package:

remotes::install_github("ABbiodiversity/abmidata")

Usage

Pulling monitoring data

The ABMI public data API pulls information from one of ABMI’s main databases.

Load the R package:

library(abmidata)
#> abmidata 0.0.1    2021-02-11

List 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 Capability

Get 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   :94

Processing data

Once 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.

Matching monitoring data with predictors

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.

Citation

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>.

License

MIT © 2020 Peter Solymos, Joan Fang, ABMI