# An example workflow for building rangeModelMetadata objects

#### 2019-04-30

library(rangeModelMetadata)
library(rgbif)
library(raster)
library(dismo)
library(ENMeval)
library(dplyr)
library(sp)
library(spThin)
library(jsonlite)

# Building a range model

## Get data

# search GBIF for occurrence data
# restrict table to coordinates
occs <- dplyr::select(bv$data, decimalLongitude, decimalLatitude) # remove duplicate values occs <- occs[!duplicated(occs),] # get environmental rasters from dismo package folder files <- list.files(path=paste(system.file(package='dismo'), '/ex', sep=''), pattern='grd', full.names=TRUE) # make a stack of the rasters envs <- raster::stack(files) ## Prepare data # thin points to remove spatial autocorrelation occs.thinned <- spThin::thin.algorithm(data.frame(occs), thin.par=20, reps=1) # make a spatial points object for the occurrences occs.sp <- sp::SpatialPoints(occs.thinned[[1]]) # get the bounding box bb <- sp::bbox(occs.sp) # make extent object and buffer by 5 degrees bb.buf <- raster::extent(bb[1]-5, bb[3]+5, bb[2]-5, bb[4]+5) # crop environmental rasters by the extent to make the background extent bg.ext <- raster::crop(envs, bb.buf) # generate up to 10,000 random points within the background extent (we'll sample from the "biome" raster as it has more holes) bg <- dismo::randomPoints(bg.ext[[9]], n=10000)  ## Model Fitting # run ENMeval e <- ENMeval::ENMevaluate(occs, bg.ext, bg, method='block', RMvalues=1:3, fc=c('L','LQ'), algorithm='maxent.jar') ## Model evaluation # find the index number of the model with the highest mean test AUC: this will be the optimal model i <- which(e@results$avg.test.AUC  == max(e@results$avg.test.AUC )) # extract the optimal model object m <- e@models[[i]] Note that the selected model uses linear and quadratic features with a regularization multiplier of 1. kable(e@results[i,]) ## Predictions # the prediction rasters produced by ENMeval are "raw" values, # which correspond to the relative occurrence rate (ROR) per cell -- # see Merow et al. 2013 (Ecography) p <- e@predictions[[i]] plot(p) ## Transfer model # specify extent further south for model transfer (this region # should have different climatic conditions than the region # used to train the model). This could be useful if no observations # are available in this region, but you're interested in determining # if there is any potentially suitable habitat there. pt.bb <- raster::extent(-80,-40,-60,-30) # crop environmental predictors by transfer extent pt.ext <- raster::crop(envs, pt.bb) pt <- predict(m, pt.ext, args=c("outputformat=raw")) # Build an rmm object Begin by choosing a few families of fields relevant for this analysis and making a template. This will generate all the recommended fields, although the only fields that you must fill in are in base. # generate an empty range model metadata template rmm=rmmTemplate(family=c('base','dataPrep','maxent','prediction', 'transferEnv1','binaryClassification')) Note that to see a little bit less detail, it’s useful to use str(rmm,1) or str(rmm,2) ## Authorship We start by filling in some key authorship information.  # in the form Author_Year_Taxa_Model_fw (where the last 2 characters are random) rmm$authorship$rmmName='MerowMaitnerOwensKassEnquistGuralnick_2018_BradypusVariegatus_Maxent_b3' # names are distinct from citations (below) in case a citation does not exist rmm$authorship$names='Merow, Cory and Maitner, Brian and Owens, Hannah and Kass, Jamie and Enquist, Brian and Guralnick, Rob' rmm$authorship$contact='cory.merow@gmail.com' rmm$authorship$relatedReferences='@article{, title={RMMS: Species’ Range Model Metadata Standards }, author={Merow, Cory and Maitner, Brian S. and Owens, Hannah L. and Kass, Jamie M. and Enquist, Brian and Guralnick, Robert}, journal={Global Ecology and Biogeography}, year={2018}}' rmm$authorship$license='CC' rmm$authorship$miscNotes='Funding from NSF grant DBI-1661510 and DBI-1913673.' ## Study Objective rmm$studyObjective$purpose='transfer' rmm$studyObjective$rangeType='potential' rmm$studyObjective$transfer='detect unoccupied suitable habitat' Note that the first two entities used standardized terms ('projection') but the last one didn’t. That’s ok; the rules are meant to be flexible! ## Autofill when possible To make it easier to fill some rmm fields, we provide autofill functions that extract relevant information from common R objects used in the # occurrence data rmm <- rmmAutofillspocc(rmm, bv$gbif)
# autofill info for environmental rasters used for model training and transfer
rmm <- rmmAutofillEnvironment(rmm, bg.ext, transfer=0)
rmm <- rmmAutofillEnvironment(rmm, pt.ext, transfer=1)
# autofill for ENMeval
rmm <- rmmAutofillENMeval(rmm, e,
selectionCriteria="highest mean test AUC",
optimalModelIndex=i)
# R packages used
rmm <- rmmAutofillPackageCitation(rmm=rmm,
packages=
c('rgbif','sp','raster','dismo','ENMeval'))

## Data

Now we need to fill in some fields manually.

rmm$data$occurrence$sources=lapply(rgbif::gbif_citation(bv),function(x) x$citation$citation) rmm$data$occurrence$presenceSampleSize=length(occs.sp)
rmm$data$occurrence$backgroundSampleSize=nrow(bg) rmm$data$occurrence$yearMin=1970
rmm$data$occurrence$yearMax=2000 Note that in the use of gbif_citation above, we use a journal formatted style for references, instead of bibtex, as used below. We chose this because the journal formatting is returned by gbif_citation and bibtex it not available. Rather than request that authors spend an inordinate amount of time manually reformatting perfectly good citations (which is error prone) we suggest simply using this default format. rmm$data$environment$yearMin=1970
rmm$data$environment$yearMin=2000 rmm$data$environment$sources='@ARTICLE{Fick2017-qs,
title     = "{WorldClim} 2: new 1‐km spatial resolution climate surfaces for
global land areas",
author    = "Fick, S E and Hijmans, R J",
journal   = "Int. J. Climatol.",
publisher = "Wiley Online Library",
year      =  2017}'
rmm$data$dataNotes='WorldClim data accessed through dismo v1.1-4'

To fill in the min and max values of each data layer, it’s easiest to create a data.frame and convert that to JSON.

mm=data.frame(rbind(apply(values(envs),2,min,na.rm=T),
apply(values(envs),2,max,na.rm=T)))
rmm$data$environment$minVal=toJSON(mm[1,]) (rmm$data$environment$maxVal=toJSON(mm[2,])) # printed to show format

To fill in the extent of the layers, you can either do it manually or programatically. I do it programatically because I think it’s easier to make it JSON format.

(ex=extent(envs))
# make an extent object a data.frame, because that's easy to use with toJSON
tmp=as.data.frame(lapply(slotNames(ex), function(i) slot(ex, i) ))
names(tmp)=slotNames(ex)
rmm$data$environment$extentSet=toJSON(tmp) Have I missed anything? rmmSuggest('$data') # note the use of quotes!

Oh yeah, we haven’t dealt with the environment to which the model is transferred. In the modeling above we transferred to an adjacent region. So this follows analogous information provided for the fitting layers.

rmm$data$transfer$environment1$yearMin=1970
rmm$data$transfer$environment1$yearMin=2000
rmm$data$transfer$environment1$resolution=paste0(res(pt.ext)[1],' degrees')
rmm$data$transfer$environment1$sources='@ARTICLE{Fick2017-qs,
title     = "{WorldClim} 2: new 1‐km spatial resolution climate surfaces for
global land areas",
author    = "Fick, S E and Hijmans, R J",
journal   = "Int. J. Climatol.",
publisher = "Wiley Online Library",
year      =  2017}'

mm=data.frame(rbind(apply(values(pt.ext),2,min,na.rm=T),
apply(values(pt.ext),2,max,na.rm=T)))
rmm$data$transfer$environment1$minVal=toJSON(mm[1,])
(rmm$data$transfer$environment1$maxVal=toJSON(mm[2,])) # printed to show format

(ex=extent(pt.ext))
# make an extent object a data.frame, because that's easy to use with toJSON
tmp=as.data.frame(lapply(slotNames(ex), function(i) slot(ex, i) ))
names(tmp)=slotNames(ex)
rmm$data$transfer$environment1$extentSet=toJSON(tmp)

## Data Preparation (cleaning)

# duplicated observations removed within the grid cells defined by the environmental layers
rmm$dataPrep$biological$duplicateRemoval$rule='one observation per cell'
rmm$dataPrep$geographic$spatialThin$rule= "20km used as minimum distance between points"

## Model Fitting

rmm$modelFit$partition$partitionRule='block cross validation: partitions occurrence localities by finding the latitude and longitude that divide the occurrence localities into four groups of (insofar as possible) equal numbers' rmm$modelFit$maxent$featureSet='LQ'
rmm$modelFit$maxent$regularizationMultiplierSet=1 rmm$modelFit$maxent$samplingBiasRule='ignored'
rmm$modelFit$maxent$notes='ENMeval was used to compare models with L and LQ features, each using regularization multipliers of 1,2,3. The best model was selected based on AUC evaluated under cross validation.' rmm$modelFit$maxent$numberParameters=e@results[i,]$parameters Note that we took the value of rmm$modelFit$partition$partitionRule straight from the helpfile of ENMeval::get.block; this can be useful for standardizing text. Also note that we demonstrate how to add a field which is not included in the standards metadata dictionary ’rmm$$modelFit$$maxent$numberParameters to describe the number of parameters retained in the selected model. ## Prediction # fill in remaining fields for model prediction in background extent rmm$prediction$continuous$units <- "relative occurrence rate"
rmm$prediction$continuous$minVal <- cellStats(p, min) rmm$prediction$continuous$maxVal <- cellStats(p, max)
# clamping is the default for predict()
rmm$prediction$extrapolation <- "clamping"

Analogous fields for transfer

rmm$prediction$transfer$environment1$units <- "relative occurrence rate"
rmm$prediction$transfer$environment1$minVal <- cellStats(pt, min)
rmm$prediction$transfer$environment1$maxVal <- cellStats(pt, max)
# clamping is the default for predict()
rmm$prediction$extrapolation <- "clamping"

## Evaluation

rmm$evaluation$trainingDataStats$AUC=e@results[i,]$trainAUC
rmm$evaluation$testingDataStats$AUC=e@results[i,]$avg.test.AUC

## Code

rmm$code$software='@Manual{
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
year = {2017},
url = {https://www.R-project.org/},}'
rmm$code$vignetteCodeLink='https://github.com/cmerow/rangeModelMetadata/blob/master/vignettes/rmm_workflowWithExampleRangeModel.Rmd'

## Check your rmm object

Let’s see how we’re doing so far by printing all the filled fields.

#print just the non-NULL fields
rmmCleanNULLs(rmm)

Check the final object by printing all the filled fields.

#print just the non-NULL fields
rmmCleanNULLs(rmm)
# you can also use this function to omit the NULLs at the end of your workflow using, so if you're happy with the above...
rmm <- rmmCleanNULLs(rmm)

Now run all availables checks to be sure you’re ready to go.

rmmCheckFinalize(rmm)`