# Generate null ecological niche models (ENMs) and compare null with empirical performance metrics `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. ```r ENMnulls( e, mod.settings, no.iter, eval.stats = c("auc.val", "auc.diff", "cbi.val", "or.mtp", "or.10p"), user.enm = NULL, user.eval = NULL, user.eval.type = NULL, userStats.signs = NULL, removeMxTemp = TRUE, parallel = FALSE, numCores = NULL, 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`: function: custom function for specifying performance metrics not included in `ENMeval`. The function must first be defined and then input as the argument `user.eval`. - `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. For example, for AUC, the difference should be positive (the empirical model should have a higher score), whereas for omission rate it should be negative (the empirical model should have a lower 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. - `quiet`: boolean: if TRUE, silence all function messages (but not errors). ## Returns 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. ## 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. ## Examples ```r ## Not run: library(ENMeval) # first, let's tune some models occs <- read.csv(file.path(system.file(package="predicts"), "/ex/bradypus.csv"))[,2:3] envs <- rast(list.files(path=paste(system.file(package="predicts"), "/ex", sep=""), pattern="tif$", full.names=TRUE)) bg <- as.data.frame(predicts::backgroundSample(envs, n = 10000)) names(bg) <- names(occs) ps <- list(orientation = "lat_lat") # as an example, let's use two user-specified evaluation metrics conf.and.cons <- function(vars) { observations <- c( rep(x = 1, times = length(vars$occs.train.pred)), rep(x = 0, times = length(vars$bg.train.pred)), rep(x = 1, times = length(vars$occs.val.pred)), rep(x = 0, times = length(vars$bg.val.pred)) ) predictions <- c(vars$occs.train.pred, vars$bg.train.pred, vars$occs.val.pred, vars$bg.val.pred) evaluation_mask <- c( rep(x = FALSE, times = length(vars$occs.train.pred) + length(vars$bg.train.pred)), rep(x = TRUE, times = length(vars$occs.val.pred) + length(vars$bg.val.pred)) ) measures <- confcons::measures(observations = observations, predictions = predictions, evaluation_mask = evaluation_mask, df = TRUE) measures.metrics <- measures[, c("CPP_eval", "DCPP")] colnames(measures.metrics) <- c("confidence", "consistency") return(measures.metrics) } e <- ENMevaluate(occs, envs, bg, tune.args = list(fc = c("L","LQ","LQH"), rm = 2:4), partitions = "block", partition.settings = ps, algorithm = "maxnet", categoricals = "biome", user.eval = conf.and.cons, parallel = TRUE) d <- eval.results(e) # here, we will choose an optimal model based on validation CBI, but you can # choose yourself what evaluation statistics to use opt <- d |> filter(cbi.val.avg == max(cbi.val.avg)) # now we can run our null models, and we can specify to include estimates for # our user-specified variables too, but we need to make sure we note what # sign we expect these statistics to be # NOTE: you should use at least 100 iterations in practice -- this is just an # example nulls <- ENMnulls(e, mod.settings = list(fc = opt$fc, rm = opt$rm), no.iter = 10, user.eval = conf.and.cons, eval.stats = c("cbi.val", "confidence", "consistency"), userStats.signs = c("confidence" = 1, "consistency" = 1)) # here are the results of all the null iterations null.results(nulls) # and here are the comparisons between the null and empirical values for # the evaluation statistics, including the z-score and p-value # for more details, see Bohl et al. 2019 null.emp.results(nulls) ## End(Not run) ``` ## 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. tools:::Rd_expr_doi("10.1111/jbi.13573") Kass, J. M., Anderson, R. P., Espinosa-Lucas, A., Juarez-Jaimes, V., Martinez-Salas, E., Botello, F., Tavera, G., Flores-Martinez, J. J., & Sanchez-Cordero, V. (2020). Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico. **Ecography**, 43 : 341-352. tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1111/j.2007.0906-7590.05041.x")

ENMnulls function