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Custom Reporting for Intactness and Sector Effects

Linux build status

The R package is a decision support tool that provides an interface to enable custom reporting for intactness and sector effects based on estimates and predictions created by the Alberta Biodiversity Monitoring Institute (ABMI) in collaboration with the Boreal Avian Modelling (BAM) Project.


The estimates, predictions, and related documentation are © ABMI and BAM (2014–2018) under a CC BY-SA 4.0 license.

The R package itself is licensed under MIT license © 2018 Peter Solymos, Brandon Allen, Ermias T. Azeria, Shannon R. White, ABMI & BAM.

Getting help or reporting an issue

To report bugs/issues/feature requests, please file an issue.

How to contribute

If you would like to contribute to the package, please see our CONTRIBUTING guidelines.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


Only GitHub version available now:


If it fails for some reason, you can try:


The NEWS file lists user visible changes in the different versions.


Load the package and the common data set:

## loading common data
##         version yr_first yr_last        method     hf  veg          model
## mammals    2018     2001    2013 snow_tracking 2016v3 v6.1 binomial_logit
## birds      2018     1997    2017   point_count 2016v3 v6.1    poisson_log
## mites      2018     2007    2017     soil_core 2016v3 v6.1 binomial_logit
## mosses     2018     2003    2016   centre_plot 2016v3 v6.1 binomial_logit
## lichens    2018     2003    2017   centre_plot 2016v3 v6.1 binomial_logit
## vplants    2018     2003    2017   centre_plot 2016v3 v6.1 binomial_logit
##         species
## mammals      12
## birds       126
## mites       114
## mosses      125
## lichens     155
## vplants     442

Note: it is possible to download the data the package is using to your hard drive using the dowload_data function.

Workflow with 1 species

id is a vector of Row_Col type IDs of 1 km2 pixels, species is a vector of species IDs:

## define spatial and species IDs (subsets)
Spp <- "Ovenbird"
ID <- c("182_362", "182_363", "182_364", "182_365", "182_366", "182_367",
    "182_368", "182_369", "182_370", "182_371", "182_372")

subset_common_data(id=ID, species=Spp)
## check subsets

## load species data
y <- load_species_data(Spp)

## calculate results and flatten to a 1-liner
x <- calculate_results(y)

Spatial subset specifications

All the possible spatial IDs can be inspected as:

plot(xy <- get_id_locations(), pch=".")

Spatial IDs can be specified as planning/management regions:

## Natural Regions
ID <- get_all_id(nr=c("Boreal", "Foothills"))
## Natural Subregions
ID <- get_all_id(nsr="Lower Boreal Highlands")
## Land Use Framework regions
ID <- get_all_id(luf="North Saskatchewan")

Alternatively, id can refer to quarter sections using the MER-RGE-TWP-SEC-QS format:

Spp <- "Ovenbird"
QSID <- c("4-12-1-2-SE", "4-12-1-2-SW", "4-12-1-3-SE", "4-12-1-3-SW")
qs2km(QSID) # corresponding Row_Col IDs

The subset_common_data function recognizes MER-RGE-TWP-SEC-QS type spatial IDs and onvert those to the Row_Col format using the nearest 1 km2 pixels.

Workflow with multiple species

id and species can be defined using text files:

Spp <- read.table(system.file("extdata/species.txt", package="cure4insect"))
ID <- read.table(system.file("extdata/pixels.txt", package="cure4insect"))
subset_common_data(id=ID, species=Spp)
xx <- report_all()
str(xx), lapply(xx, flatten))

Species subset specifications

Here is how to inspect all possible species IDs


Select one or more taxonomic groups (mammals, birds, mites, mosses, lichens, vpalnst), and fiter for habitat and status:

## birds and mammals
str(get_all_species(taxon=c("birds", "mammals")))
## all upland species
str(get_all_species(taxon="all", habitat="upland"))
## nonnative vascular plants
str(get_all_species(taxon="vplants", status="nonnative"))

Wrapper functions

  • species="all" runs all species
  • species="mites" runs all mite species
  • sender="" will send an email with the results attached
  • increase cores to allow parallel processing
z <- custom_report(id=ID,
    species=c("AlderFlycatcher", "Achillea.millefolium"),
    address=NULL, cores=1)

Working with a local copy of the results is much faster set path via function arguments or the options:

## making of the file raw_all.rda
opar <- set_options(path = "w:/reports")
## see how these compare
system.time(res <- report_all(cores=1))
#system.time(res <- report_all(cores=2))
#system.time(res <- report_all(cores=4))
## this is for testing only
#system.time(res <- .report_all_by1())
(set_options(opar)) # reset options

A few more words about options:

## options
## change configs in this file to make it permanent for a given installation

Sector effects and intactness plots

## *res*ults from calculate_results, all province, all species
fn <- paste0("",
    getOption("cure4insect")$version, "/misc/raw_all.rda")
con <- url(fn)

plot_sector(res[["CanadaWarbler"]], "unit")
plot_sector(res[["CanadaWarbler"]], "regional")
plot_sector(res[["CanadaWarbler"]], "underhf")

z <-, lapply(res, flatten))
class(z) <- c("c4idf", class(z))
plot_sector(z, "unit") # all species
plot_sector(z[1:100,], "regional") # use a subset
plot_sector(z, "underhf", method="hist") # binned version

plot_intactness(z, "SI")
plot_intactness(z, "SI2", method="hist")

Determining spatial IDs based on spatial polygons

id can also be a SpatialPolygons object based on GeoJSON for example:

dsn <- system.file("extdata/polygon.geojson", package="cure4insect")
cat(readLines(dsn), sep="\n")
ply <- readOGR(dsn=dsn)
subset_common_data(id=ply, species=Spp)
xx2 <- report_all()

Spatial IDs of the 1 km2 spatial pixel units are to be used for the custom summaries. The Row_Col field defines the IDs and links the raster cells to the geodatabase or CSV (with latitude/longitude in NAD_1983_10TM_AEP_Forest projection).

For the web application, use your favourite GIS software, or in R use this to get the spatial IDs written into a text file:

dsn <- system.file("extdata/OSA_bound.geojson", package="cure4insect")
ply <- readOGR(dsn=dsn)
ID <- overlay_polygon(ply)
## write IDs into a text file
write.table(data.frame(SpatialID=ID), row.names=FALSE, file="SpatialID.txt")

## spatial pixels: selection in red
xy <- get_id_locations()
plot(xy, col="grey", pch=".")
plot(xy[ID,], col="red", pch=".", add=TRUE)

## compare with the polygons
AB <- readOGR(dsn=system.file("extdata/AB_bound.geojson",
plot(AB, col="grey")
plot(ply, col="red", add=TRUE)

Use the make_subset_map() function to get a raster map of the spatial selection.

Raster objects and maps

The result is a raster stack object with the following layers:

  • NC, NR: current and reference abundance,
  • SI, SI2: one- and two-sided intactness,
  • SE, CV: bootstrap based standard error and coefficient of variation estimates for current abundance.
y <- load_species_data("Ovenbird")
r <- rasterize_results(y)
plot(r, "NC") # current abundance map
col <- colorRampPalette(c("darkgreen","yellow","red"))(250)
plot(r, "SE", col=col) # standadr errors for current abundance

It is possible to make multi-species maps as well: average intactness and expected number of species.

r1 <- make_multispecies_map("richness")
r2 <- make_multispecies_map("intactness")

Spatially explicit (polygon level) predictions

The 1 km2 level predictions provide mean abundance per pixel. Sometimes we need finer detail, e.g. when making predictions as part of spatially explicit simulations.

First we load the spatial/climate related component of the predictions (which is a raster object):

species <- "Achillea.millefolium"
object <- load_spclim_data(species)

The spatial component is then combined with the land cover component describing vegetation/disturbance/soil classes as a factor.

## original levels
levels(veg <- as.factor(get_levels()$veg))
levels(soil <- as.factor(get_levels()$soil))

Sometimes it is best to create a crosswalk table and reclassify using e.g. the mefa4::reclass function:

(rc <- data.frame(In=c("pine5", "decid15", "urban", "industrial"),
    Out=c("Pine0", "Deciduous10", "UrbInd", "UrbInd")))
mefa4::reclass(c("pine5", "pine5", "decid15", "urban", "industrial"), rc)

We need to have spatial locations for each land cover value (same value can be repeated, but but avoid duplicate rownames). We use the sp package to make a SpatialPoints object:

XY <- get_id_locations()
coords <- coordinates(XY)[10^5,,drop=FALSE]
rownames(coords) <- NULL
xy <- data.frame(coords[rep(1, length(veg)),])
coordinates(xy) <- ~ POINT_X + POINT_Y
proj4string(xy) <- proj4string(XY)

Now we are ready to make the predictions:

pred <- predict(object, xy=xy, veg=veg)

The predict function returns a data frame with columns veg, soil, and comb (combines veg and soil based on aspen probability of occurrence using combine_veg_soil as a weighted average based on probability of aspen occurrence).

For some species, either the veg or soil based estimates are unavailable: predict returns NA for these and the combined results will be NA as well.

The next line is a more succinct version that loads the species data as well, but we can’t reuse the species data after:

pred <- custom_predict(species, xy=xy, veg=veg)

Another was of making predictions is to define a spatial grid, and quantify land cover as proportion of the land cover types in each grid cell. This is how we can use multivariate input data in a spatial grid (totally unrealistic data set just for illustration, but user has to make sure the numbers are meaningful):

xy <- xy[1:10,]
mveg <- matrix(0, 10, 8)
colnames(mveg) <- veg[c(1:8 * 10)]
mveg[] <- rpois(80, 10) * rbinom(80, 1, 0.2)
mveg[rowSums(mveg)==0,1] <- 1 # avoid 0 row sum

msoil <- matrix(0, 10, 6)
colnames(msoil) <- get_levels()$soil[1:6]
msoil[] <- rpois(60, 10) * rbinom(60, 1, 0.4)
msoil[rowSums(msoil)==0,1] <- 1 # avoid 0 row sum

Because we used areas (not proportions) we get the output as two matrices containing abundances (density times area) corresdonding to the vegetation and soil matrices:

(prmat1 <- predict_mat(object, xy, mveg, msoil))

Row sums give the total abundance at each location, column sums give the total abundance in a land cover type over all locations:


Using proportions in the input matrices gives mean abundance per spatial unit as output:

(prmat2 <- predict_mat(object, xy, mveg/rowSums(mveg), msoil/rowSums(msoil)))

Combining vegetation and soil based predictions returns a vector, i.e. the aspen probability weighted average of the vegetation and soil based total abundances:

combine_veg_soil(xy, rowSums(prmat2$veg), rowSums(prmat2$soil))

Visualize land cover associations

See the following R markdown file for a worked example of visualizations available in the package:"doc/example-species-report.Rmd", package="cure4insect"))

It is possible to render the R markdown file with a species ID argument, thus programmatically producing reports for multiple species:

    params = list(species = "Ovenbird"))

Habitat associations as shown on the website:

plot_abundance("Achillea.millefolium", "veg_coef")
plot_abundance("Achillea.millefolium", "soil_coef", paspen=1)
plot_abundance("Achillea.millefolium", "veg_lin")
plot_abundance("Achillea.millefolium", "soil_lin")


The web app sits here. To get more control over the results, use the API.

Make a request using the custom_report function:

curl \
-H "Content-Type: application/json" -d \
'{"id":["182_362", "182_363"], "species":["AlderFlycatcher", "Achillea.millefolium"]}'

Access spatially explicit and land cover specific prediction for a species using the custom_predict function:

curl \
-H "Content-Type: application/json" -d \
'{"species":"AlderFlycatcher", "xy":[[-114.4493,58.4651]], "veg":"Mixedwood80"}'

Explore single and multi-species results

To get similar output to this, run script from this file:"doc/custom-report.R", package="cure4insect"))