ENMnulls {ENMeval}

Generate null ecological niche models (ENMs) and compare null with empirical performance metrics

Description

ENMnulls() iteratively builds null ENMs for a single set of user-specified model settings based on an input ENMevaluation object, from which all other analysis settings are extracted. Summary statistics of the performance metrics for the null ENMs are taken (averages and standard deviations) and effect sizes and p-values are calculated by comparing these summary statistics to the empirical values of the performance metrics (i.e., from the model built with the empirical data). See the references below for more details on this method.

Usage

ENMnulls(
  e,
  mod.settings,
  no.iter,
  eval.stats = c("auc.val", "auc.diff", "cbi.val", "or.mtp", "or.10p"),
  user.enm = NULL,
  user.eval.type = NULL,
  userStats.signs = NULL,
  removeMxTemp = TRUE,
  parallel = FALSE,
  numCores = NULL,
  parallelType = "doSNOW",
  quiet = FALSE
)

Arguments

e

ENMevaluation object

mod.settings

named list: one set of model settings with which to build null ENMs

no.iter

numeric: number of null model iterations

eval.stats

character vector: the performance metrics that will be used to calculate null model statistics

user.enm

ENMdetails object: if implementing a user-specified model

user.eval.type

character: if implementing a user-specified model, specify here which evaluation type to use – either "knonspatial", "kspatial", "testing", or "none"

userStats.signs

named list: user-defined evaluation statistics attributed with either 1 or -1 to designate whether the expected difference between empirical and null models is positive or negative; this is used to calculate the p-value of the z-score

removeMxTemp

boolean: if TRUE, delete all temporary data generated when using maxent.jar for modeling

parallel

boolean: if TRUE, use parallel processing

numCores

numeric: number of cores to use for parallel processing; if NULL, all available cores will be used

parallelType

character:: either "doParallel" or "doSNOW" (default: "doSNOW")

quiet

boolean: if TRUE, silence all function messages (but not errors)

Details

This null ENM technique is based on the implementation in Bohl et al. (2019), which follows the original methodology of Raes & ter Steege (2007) but makes an important modification: instead of evaluating each null model on random validation data, here we evaluate the null models on the same withheld validation data used to evaluate the empirical model. Bohl et al. (2019) demonstrates this approach using a single defined withheld partition group, but Kass et al. (2020) extended it to use spatial partitions by drawing null occurrences from the area of the predictor raster data defining each partition. Please see the vignette for a brief example: <

This function avoids using raster data to speed up each iteration, and instead samples null occurrences from the partitioned background records. Thus, you should avoid running this when your background records are not well sampled across the study extent, as this limits the extent that null occurrences can be sampled from.

Value

An ENMnull object with slots containing evaluation summary statistics for the null models and their cross-validation results, as well as differences in results between the empirical and null models. This comparison table includes z-scores of these differences and their associated p-values (under a normal distribution). See ?ENMnull for more details.

References

Bohl, C. L., Kass, J. M., & Anderson, R. P. (2019). A new null model approach to quantify performance and significance for ecological niche models of species distributions. Journal of Biogeography, 46: 1101-1111. doi:10.1111/jbi.13573

Kass, J. M., Anderson, R. P., Espinosa-Lucas, A., Juárez-Jaimes, V., Martínez-Salas, E., Botello, F., Tavera, G., Flores-Martínez, J. J., & Sánchez-Cordero, V. (2020). Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico. Ecography, 43: 341-352. doi:10.1111/ecog.04886

Raes, N., & ter Steege, H. (2007). A null-model for significance testing of presence-only species distribution models. Ecography, 30: 727-736. doi:10.1111/j.2007.0906-7590.05041.x