enmSdm

Tools for modeling niches and distributions of species

Please note: enmSdm has been replaced by enmSdmX (available on CRAN).

I am no longer actively developing enmSdm because it relies heavily on the soon-to-be-defunct sp and (maybe) raster packages, which in turn rely on the soon-to-be-defunct PROJ4 geodetic/coordinate reference system libraries. Its replacement, enmSdmX works with the new terra and sf packages, and--most importantly--PROJ6!

This package is a complement to the popular dismo package for R by Robert Hijmans. Its contains a suite of efficiency functions for preparing data, training and evaluating species distribution models and ecological niche models, and comparing ecological niches.

You can install this package in R using these commands:

remotes::install_github('adamlilith/enmSdm', dependencies=TRUE)

Note: If for some reason these commands don't work, you can install the package(s) by downloading the latest zip/tar file from the zipTarFiles directory and installing the package(s) manually. If you do this, you will also have to install the legendary package, which is on GitHub under my account (adamlilith), as well as the omnibus and statisfactory packages on CRAN.

Data preparation

elimCellDups: Eliminate duplicate points in each cell of a raster
geoFold: Generate geographically distinct k-folds
geoThin and geoThinApprox: Geographically thin points

Model training

trainByCrossValid: Wrapper for implementing some trainXYZ function across cross-validation folds (see also summaryByCrossValid).
trainBrt: Boosted regression trees (BRTs)
trainCrf: Conditional regression trees (CRFs)
trainGam: Generalized additive models (GAMs)
trainGlm: Generalized linear models (GLMs)
trainGlmDredge: Generalized linear models (GLMs)
trainMaxEnt and trainMaxNet: Maxent models
trainNs: Natural splines (NSs)
trainRf: Random forests (RFs)

Model prediction

predictEnmSdm Predict most model types using default settings
predictMaxNet Predict MaxNet (MaxEnt) model

Model evaluation

aucWeighted: AUC (with/out site weights)
aucMultiWeighted: Multivariate version of AUC (with/out site weight)
contBoyce: Continuous Boyce Index (with/out site weights)
contBoyce2x: "2X coverage" version of the Continuous Boyce Index (with/out site weights)
fpb: Fpb measure of model discrimination capacity (with/out site weights)
thresholdWeighted: Thresholds to convert continuous predictions to binary predictions (with/out site weights)
thresholdStats: Model performance statistics based on thresholded predictions (with/out site weights)
tjursR2Weighted: Tjur's R2 (with/out site weights)
tssWeighted: True Skill Statistic (TSS) (with/out site weights)
modelSize: Number of response values in a model object

Niche overlap

compareNiches: Niche overlap metrics
compareResponse: Compare niche model responses to a single variable
mop: Calculate mobility-oriented parity, a measure of multivariate distance as per Saupe et al. 2012.
nicheOverlap: Calculate niche overlap as per Broennimann et al. Global Ecology and Biogeography 21:481-497
randPointsRespectingSelf: Randomize geographic points while approximately respecting observed spatial autocorrelation structure between points
randPointsRespectingSelfOther2: Randomize two sets of geographic points while approximately respecting observed spatial autocorrelation structure between and within sets
randPointsBatch: Call randPointsRespectingSelf or randPointsRespectingSelfOther2 multiple times
randPointsBatchExtract: Extract environment from a set of rasters for sets of randomized points generated using randPointsBatch
randPointsBatchSampled: Collate all sets of randomized points generated using randPointsBatch
randPointsBatchNicheOverlap: Calculate niche overlap between sets of randomized points that were generated using randPointsBatch

Spatial autocorrelation

spatialCorrForPoints: Calculate pairwise distance-based measure of global spatial autocorrelation between geographic points
spatialCorrForPointsSummary: Characteristic cluster size of spatial points (distance of global autocorrelation)
spatialCorrForPointsPlot: Plot observed and null distributions of pairwise distance-based measure of global spatial autocorrelation
spatialCorrForPointsWeight: Assign weights to points based on pairwise distance-based measure of global spatial autocorrelation

Functions for rasters

bioticVelocity: Velocity of movement across a series of rasters
getCores: Get number of processor cores
extractLarge: Like extract(), but for large-memory spatial objects
interpolateRasters: Interpolate a stack of rasters
longLatRasters: Generate rasters with values of longitude/latitude for cell values
rastWithSquareCells: Create a raster with square cells from an object with an extent
sampleRast and sampleRastStrat: Sample raster with/out replacement and possibly in a stratified manner
squareRastCells: Resample a raster so cells are square

Range area based on minimum convex polygons

mcpFromPolys: Minimum convex polygon from a set of polygons and points

Geographic utility functions

convertTropicosCoords: Convert coordinates from the TROPICOS database
coordPrecision: Calculate maximum possible coordinate precision
createPlotPoly: Create a SpatialPolygon the same size as a plot region
decimalToDms: Convert decimal coordinate to degrees-minutes-seconds
dmsToDecimal: Convert degrees-minutes-seconds coordinate to decimal
getCRS: Return a proj4string (coordinate reference system string) using a nickname
getEPSG: Return a EPSG code (coordinate reference system codeg) using a nickname
pointDist: Geographic distance between set(s) of points
svToSpatial: Convert SpatVector object to a Spatial* object
xToCoords: Extract geographic coordinates from a data frame, matrix, or SpatialPoints* object

Data

lemurs: Lemur occurrences
mad0: Madagascar spatial object

Citation

As of October 2020 there is no package-specific publication for enmSdm, but the package was first used and cited in:

Morelli*, T.L., Smith*, A.B., Mancini, A.N., Balko, E. A., Borgenson, C., Dolch, R., Farris, Z., Federman, S., Golden, C.D., Holmes, S., Irwin, M., Jacobs, R.L., Johnson, S., King, T., Lehman, S., Louis, E.E. Jr., Murphy, A., Randriahaingo, H.N.T., Lucien, Randriannarimanana, H.L.L., Ratsimbazafy, J., Razafindratsima, O.H., and Baden, A.L. 2020. The fate of Madagascar’s rainforest habitat. Nature Climate Change 10:89-96. * Equal contribution https://doi.org/10.1038/s41558-019-0647-x

Abstract. Madagascar has experienced extensive deforestation and overharvesting, and anthropogenic climate change will compound these pressures. Anticipating these threats to endangered species and their ecosystems requires considering both climate change and habitat loss effects. The genus Varecia (ruffed lemurs), which is composed of two Critically Endangered forest-obligate species, can serve as a status indicator of the biodiverse eastern rainforest of Madagascar. Here, we combined decades of research to show that the suitable habitat for ruffed lemurs could be reduced by 29–59% from deforestation, 14–75% from climate change (representative concentration pathway 8.5) or 38–93% from both by 2070. If current protected areas avoid further deforestation, climate change will still reduce the suitable habitat by 62% (range: 38–83%). If ongoing deforestation continues, the suitable habitat will decline by 81% (range: 66–93%). Maintaining and enhancing the integrity of protected areas, where rates of forest loss are lower, will be essential for ensuring persistence of the diversity of the rapidly diminishing Malagasy rainforests.

License

adamlilith/enmSdm

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