Native range estimates for red-listed vascular plants

Besides being central for understanding both global biodiversity patterns and associated anthropogenic impacts, species range maps are currently only available for a small subset of global biodiversity. Here, we provide a set of assembled spatial data for terrestrial vascular plants listed at the global IUCN red list. The dataset consists of pre-defined native regions for 47,675 species, density of available native occurrence records for 30,906 species, and standardized, large-scale Maxent predictions for 27,208 species, highlighting environmentally suitable areas within species’ native regions. The data was generated in an automated approach consisting of data scraping and filtering, variable selection, model calibration and model selection. Generated Maxent predictions were validated by comparing a subset to available expert-drawn range maps from IUCN (n = 4,257), as well as by qualitatively inspecting predictions for randomly selected species. We expect this data to serve as a substitute whenever expert-drawn species range maps are not available for conducting large-scale analyses on biodiversity patterns and associated anthropogenic impacts.


Methods
Taxonomic scope. A species list containing all terrestrial vascular plants (n = 52,372) of the global IUCN red list was retrieved from IUCN in April 2021, IUCN version 2021-1 16 . We retrieved each species' accepted name from Plants of the World Online (POWO) 42 to facilitate communication to various data portals using the package taxize 43 in R 44 . Plant family, order and class were retrieved from the Integrated Taxonomic Information System 45 using the package taxize 43 in R. Only species outside the IUCN threat categories "Extinct" and "Extinct in the Wild" were kept, and all species considered as subspecies or varieties according to POWO removed. We attempted to assemble spatial data for each of the remaining 48,144 species. Native regions. Species-specific native regions ( Fig. 1) were retrieved from POWO using a customized web-scraper function (see section Code Availability) and the packages taxize 43 and rvest 46 in R. The data follows the World Geographical Scheme for Recording Plant Distributions (WGSRPD) 23 and includes a continental,  24 and filtered into three different occurrence data types: raw data (blue), presence cells (grey) and thinned data (yellow). Bottom: The different occurrence data types were used in Maxent models to predict relative environmental suitability indices within native regions (i.e. range estimates). Differences between Model 0 and Model 1 to 3. Model 0 was trained to support variable selection using raw data in k-fold cross validated Maxent models (one model for each combination of feature classes, i.e. linear (L), quadratic (Q), hinge (H), product (P) and threshold (T)). The selected variables and each of the three occurrence data types were used to train a set of separate k-fold cross validated Maxent models (one model for each possible combination of feature classes, regularization multipliers and occurrence data type). The overall best performing model was selected for each species based on performance metrics. country and regional level. Retrieved WGSRPD-regions were matched to its corresponding shapefile at level 4, available from the Biodiversity Information Standards GitHub repository 47 and rasterized at 30 arc minutes spatial resolution (approximately 56 km at the equator).
Occurrence records. For species with given native extents in POWO, the maximum number of most recent occurrence points (i.e. 100,000) per native WGSRPD-country was retrieved from the GBIF application programming interface (API) using the package rgbif 48 in R (the equivalent full dataset 49 is available at https:// doi.org/10.15468/dl.uvd56q). The considered environmental variables have changed tremendously in the past decades 50,51 and only cover a limited period of time, i.e. the years 1979-2013 and 2015 respectively (see section Environmental data). Therefore, only records between the years 2000 and 2020 were considered to temporally align occurrence data to both sets of environmental variables as best as possible. If less than 25 records were available for a given species after the year 2000, no temporal filter was set to maximize data retrieval. GBIF records without specified coordinates and with flagged geospatial issues 48  www.nature.com/scientificdata www.nature.com/scientificdata/ inaccurate coordinate notations as well as records of specimens preserved in museums or other biodiversity facilities to be typically detected. Only points inside reported native WGSRPD-regions were kept and duplicated records were removed (hereafter: raw data). The number of raw data records was counted per cell (30 arc min.) using the package raster 52 in R.

Maxent predictions.
We generated spatial predictions within species' native WGSRPD-regions at 30 arc min. resolution (approximately 56 km at the equator) using maximum entropy modelling (Maxent) 19,26,27 , for all species with at least 5 raw data records 53,54 that were distributed across at least 3 cells, and a native region extent of at least 9 cells. Although an arbitrary threshold, we attempted to allocate computational resources to more meaningful predictions, modelled across larger extents. Maxent is a probability density estimation approach widely used for predicting species distributions based on presence-only data 55 . Background information, required to fit response curves 56 , was collected from each cell within each species' native regions 57 . For generating models we utilized a high-performance computing infrastructure 58 allowing for parallel computations using the Maxent software 25 via R packages dismo 59 and ENMeval 60 .
Environmental data. We downloaded all CHELSA bioclimatic variables 61,62 (n = 19, see Table 1 for full list) in 30 arc seconds resolution and aggregated, for computational efficiency, to the chosen modelling resolution (30 arc min.) by averaging. CHELSA bioclimatic variables are a set of modelled, biologically relevant, climatic variables based on data collected during the years 1979-2013 61 . In addition, fractions for different natural land cover types, including different types and mosaics of forest, shrubland, grassland and sparse vegetation, (n = 17, see Table 1 for full list) were calculated based on the European Space Agency's land cover product for the year 2015 in 300 m resolution 63 . Each land cover class was transformed into a binary raster depicting presence (=1) and absence (=0) of the land cover type. The binary raster was then aggregated to modelling resolution by averaging, resulting in one raster for each land cover class, representing the proportion of land covered by that class per pixel.
Occurrence data types. For some species, several raw data records can be in the same cell at the given spatial resolution (30 arc min.). Although pseudo-replication can inflate model performance (here: during model calibration) and, hence, increases the risk of overfitting, we argue that these occurrence points still contain valid information if they are discrete observations and therefore kept this data. However, we henceforth applied two filters to counteract potential spatial biases, as well as pseudo-replication (Fig. 1). We removed all cell-duplicates from the raw data (hereafter: presence cells), and we applied spatial thinning with a minimum distance of two cells on the presence cells (hereafter: thinned data). Occurrence data was spatially filtered using the R package spThin 64 .
Model training. A set of Maxent models was fitted for each species using the differently treated occurrence data types. All models were calibrated using k-fold cross validation. The employed occurrence data was partitioned into training and testing bins. For species with only few data points (n < 25), we used k -1 Jackknife partitioning (k = n) 54 . For species with more data points (n ≥ 25) we used block partitioning (k = 4) to account for spatial autocorrelation of occurrence points in larger datasets 32 . This partitioning splits the occurrence data at a longitudinal and latitudinal line, resulting in approximately equally sized bins 60 .
An initial model ( Fig. 1; Model 0) was trained to support the selection of uncorrelated environmental variables using the raw data and all environmental variables (n = 36) for each species. Separate models, one for each possible combination out of all included feature classes (i.e. environmental variables and transformations thereof), were trained. We included linear (l), quadratic (q), product (p), hinge (h) and threshold (t) transformations, resulting in 6 possible combinations (i.e. l, lq, h, lqh, lqhp, and lqhpt). The best performing model was selected based on the corrected Akaike information criterion (AICc) [65][66][67] . However, if no model performed best in terms of AICc, or if this metric was unavailable for 50% of fitted models, the average testing area under the receiver operating characteristics curve (AUC; see section Technical Validation) during model calibration was used instead. Permutation importance was retrieved for all variables in Model 0. Correlated variables were identified using Spearman's rank correlation coefficient (ρ) and defined as ρ ≥ | ± 0.7|. In any set of correlated variables, only the variable with the greatest permutation importance was kept.
The selected environmental variables were used to train separate models for each of the three differently treated occurrence data types: raw data (Model 1), presence cells (Model 2), and thinned data (Model 3). Model 1 was trained if at least 5 raw data records were available, distributed across at least 3 cells (see above). Model 2 and Model 3 were trained if at least 3 records of the corresponding data type were available to avoid computational failure. Although a smaller sample size, we argue that if those models performed better than Model 1, the threshold of 5 records becomes arbitrary and the assessed performance indicators (see section Technical Validation) more valuable. The same model architecture as in Model 0 was utilized, including model calibration and selection of the best performing model. However, this time, we added five different regularization multipliers (RM; i.e. 1, 2, 3, 5 and 10; based on previous studies [68][69][70] ) to counteract overfitting 20,56 and for building simpler, ecologically more relevant, models 60 . Hence, separate models for each possible combination out of feature classes and RMs were trained ( Fig. 1; Model 1-3), resulting in 30 trained models for each data type and up to 90 models per species.
Metadata. Metadata was assembled for all data and includes general information about species (taxonomy and red list status), provided data type (native regions, occurrence records or Maxent prediction), bounding box of native regions, and if relevant, information about the occurrence data (number of raw data records, Moran's Index 71 , calculated as a measure of spatial autocorrelation and based on the number of raw occurrence points obtained per cell), and Maxent metadata: training data (filter treatment, number of training data points), thresholds for converting the prediction into binary range maps 59 , model settings (features, parameters, transformations, regularization multiplier, variables) and out of the box 60 model performance, including degree of overfit (DOO) quantified as the difference between calibration and testing AUC during k-fold cross validation 70 , as well as self-assessed model performance metrics as described in the section Technical Validation.

Data records
Dataset. The presented dataset is stored in a stable Dryad Digital Repository 72 and can be explored at https:// plant-ranges.indecol.no. The dataset includes spatial information for 47,675 species at different levels of detail. In total, range estimates (i.e. relative environmental suitability within native regions) have been predicted for 27,208 species using Maxent, for 30,906 species native occurrence records are provided, and for 47,675 species the spatial extent of its native WGSRPD-regions is provided.
All gathered and generated data are stored in netCDF files and can be called by specifying a varname. Spatial predictions are provided in Maxent's raw as well as default output (i.e. complementary log-log (cloglog) transformed, but see section Usage Notes) 27,59,60 . The suggested data is stored in folder basic. These netCDF files (default output and raw output) assemble the best performing Maxent prediction (varname: Maxent prediction) for each species selected based on the highest harmonic mean between AUC and AUC PR (see Technical Validation), along with number of occurrence records per cell (varname: Presence cells) and rasterized native WGSRPD-regions (varname: Native region). www.nature.com/scientificdata www.nature.com/scientificdata/ The netCDF files in folder advanced contain one Maxent prediction for each occurrence data type (varname: Model 1, Model 2 or Model 3), instead of best performing Maxent prediction (i.e. varname Maxent prediction is not applicable). Number of occurrence records per cell (varname: Presence cells) and rasterized native WGSRPD-regions (varname: Native region) are identical in all netCDF files.
Each band in the netCDF files assembles the mentioned variables for one species. The corresponding bands can be looked up in the metadata (i.e. speciesID). Furthermore, the metadata can be used to select appropriate cut-off thresholds for generating binary range maps, filter models based on species, performance, or desired datatypes, and to lookup the relevant study extent for masking individual predictions (see Usage Notes).

technical Validation
Maxent predictions. We calculated performance metrics for model 1 to 3 for each species using its corresponding presence cells to validate the Maxent predictions. Receiver operating characteristic curves and the corresponding area under the curve for recall (i.e. true positive rate, sensitivity) versus false positive rate (AUC) as well as precision versus recall (AUC PR ) were generated using the packages ROCR 73 and PRROC 74 in R. Recall was calculated as the fraction of correctly predicted presence cells compared to all presence cells of the reference (Eq. 1), the false positive rate as the fraction of falsely assigned presence cells compared to all true absence cells (Eq. 2), and precision as the fraction of correctly assigned presence cells compared to all predicted presence cells (Eq. 3). In addition, F 1 -scores (Eq. 4) were calculated as harmonic mean between recall and precision at all possible cut-off thresholds to transform the Maxent prediction into a binary range map. The maximum obtained F 1 -score indicates how well a potential binary range map performs at equal importance of recall and precision.

False positive rate False Presence False Presence True Absence
(2) = +

Precision True Presence True Presence False Presence
1 AUC and AUC PR are threshold-independent performance measures for binary classifiers. An AUC value of 1 indicates a perfect model, an acceptable AUC value (>0.7) 75 indicates the ability to predict many true presences at a low false positive rate, and an AUC value of 0.5 indicates the model performing as good as a random guess. The average AUC obtained across the suggested dataset was 0.95 when comparing predictions to its corresponding presence cells (Table 2), indicating well-performing models for the majority of species. For 26,977 species (99%), at least one Maxent prediction had an AUC value above 0.7 75 .
AUC PR is not affected by true negatives (i.e. true absence) which often dominated our dataset. A higher AUC PR value indicates a relatively higher ability to correctly predict a high proportion of presumably true range while maintaining a high precision compared to a lower AUC PR . However, the AUC and AUC PR values, as well as max. F 1 -score, described here were calculated based on presence-background data and are highly influenced by class balances. Strictly speaking, both false presences and true absences cannot be determined  Table 2. Performance of Maxent predictions in the suggested dataset. Mean and median values of area under the receiver operating characteristics curve for true vs. false positive rate (AUC) and recall vs. precision (AUC PR ) for all species and across different IUCN threat categories (i.e. data-deficient (DD), least concern (LC), nearthreatened (NT), vulnerable (VU), endangered (EN) and critically endangered (CR)). Calculations are based on presence-background data (n = 27,208) and on comparison to expert-based range maps retrieved from IUCN (i.e. reference range, n = 4,257).
www.nature.com/scientificdata www.nature.com/scientificdata/ with presence-only data. Hence, the performance metrics described here can only be used to compare different models for a given species, but not across different species 76,77 .
Therefore, we evaluated the Maxent predictions by comparison to available expert-based range maps, as an additional evaluation dataset 32 . Expert-based range maps were retrieved from IUCN, if available (hereafter: reference ranges). Only reference ranges that were labelled as "native" and "extant (resident)" or "probably extant (resident)" were considered. For 4,257 species of our Maxent predictions, range maps were available at IUCN. These species were unevenly distributed in space (Fig. 3a), across IUCN red list categories (Fig. 3d) as well as the plant classes dicots (Magnoliopsida, n = 3,480), monocots (Liliopsida, n = 731), ferns (Polypodiopsida, n = 27), conifers (Pinopsida, n = 17), and lycopods (Lycopodiopsida, n = 2). Reference ranges were used to calculate the above described performance measures (i.e. max. F 1 -score, AUC and AUC PR ). However, this time we dealt, presumably, with actual presences and absences of the given species, making the performance metrics comparable across species 76 . Maxent predictions for species classified as "data-deficient" (DD) obtained the lowest, and predictions for species classified as "near-threatened" (NT), "vulnerable" (VU) and "endangered" (EN) the highest AUC values (Fig. 3d). However, these differences were marginal and all average values consistently high across different IUCN categories (mean AUC: 0.9; Table 2) and across the globe (Fig. 3b). Although AUC is a strong indication of model performance 75 , the predictions seem to rarely accommodate both a high recall and a high precision (represented in either max. F1-score or AUC PR value) when compared to reference ranges. However, we found a large variation and no clear trend in AUC PR values for species across different threat-level categories (Fig. 3d), and although the average AUC PR was lowest for species native to parts of central Africa, India and south-eastern Asia (Fig. 3c), we expect these values to be of little explanatory power due to the limited sample sizes in these regions (Fig. 3a). Moreover, AUC PR seems to increase with increasing data availability (Fig. 3d). We assume that low data coverage in sparsely populated areas influenced modelling performance for some, primarily widespread, species, highlighting that sometimes more spatially distributed occurrence data is required for making expert-alike range maps 78 .
Furthermore, based on a qualitative assessment of predictions for twelve randomly selected species, we expect uncertainties due to differences in data availability across administrative borders as well as for highly naturalized species. For instance, the clustered occurrence records for Cedrus libani in Lebanon (Fig. 2b)  www.nature.com/scientificdata www.nature.com/scientificdata/ resulted in less precise data than elsewhere available for this species 79 , while the prediction for Laburnum anagyroides (Fig. 2c) was affected by naturalized occurrence records outside its native origin 80 but still within its native WGSRPD-regions. However, this will be most problematic for abundant, widespread, and naturalized species, and hence only relevant for the smallest fraction of global biodiversity 33 . In addition, the predictions for more vulnerable species, presumably small-ranged or endemic, seem to perform better than species in the lowest red list category (i.e. least concern (LC)) in terms of AUC when compared to reference ranges (Fig. 3d).
In fact, the remaining randomly selected predictions were either consistent with point data (e.g. Terminalia macrostachya 81 ), reflected the current knowledge of elsewhere available data, although at a coarser spatial resolution and less detailed (e.g. Mammillaria grahamii 82 ), or offered an improvement compared to previously unavailable spatial data (e.g. Eucalyptus elliptica 83 , Megistostegium nodulosum 84 (Fig. 2d), Memecylon elegantulum 85 , Psidium salutare 86,87 , Siparuna conica 88,89 , Trisetaria dufourei 90 ). However, the prediction of Pyracantha angustifolia was difficult to evaluate due to poorly understood range dynamics 91 , highlighting the need for more data for vascular plant species.
We want to stress that our predictions indicate environmentally suitable conditions even if isolated from known species occurrence locations. For instance, Amomum pterocarpum seems to be restricted to southern India and Sri Lanka 92,93 while our prediction indicates environmentally suitable conditions in north-eastern India (Fig. 2a), which in fact, supports a possible observation nearby 94 . We further detected several expert-based range maps with a substantial mismatch to our data, confirming that some of the expert-based data may be too conservative 95 (e.g. Magnolia pugana) 96 . However, we also found expert-based ranges being smaller (e.g. Vallesia glabra or Tetraclinis articulata) 97,98 than predicted environmental suitability indicates, or being incorrectly georeferenced (e.g. Corylus cornuta) 99 . Hence, besides highlighting mismatches to expert-based range maps, we expect this dataset to be of sufficient quality to serve as time-and cost-efficient range map substitutes and pre-assessed range estimates for currently unmapped species.
External data. The retrieved native WGSRPD-regions are provided by POWO under a CC BY 3.0 license (https://creativecommons.org/licenses/by/3.0/) and have been checked for consistency to assure proper workflow of data retrieval from POWO and feature matching to the WGSRPD level 4 shapefile. However, the data provider, POWO, cannot warrant the quality or accuracy of the WGSRPD data 42 . In addition, other data (e.g. ecoregions 100 ) may ecologically be more relevant than administrative boundaries. However, WGSRPD offers the most detailed data on species' native origins available on a large-scale, to the best of our knowledge. An attempt in matching native WGSRPD-regions to ecoregions was discontinued after loss of information due to incompatible geographical boundaries. Hence, we consider the utilized WGSRPD-regions, currently, as the best compromise between level of detail and availability of data on species' native origins. Furthermore, spatial inaccuracies and biases in the occurrence data retrieved from GBIF were counteracted by the implemented filtering steps, the coarse spatial resolution, by avoiding non-native occurrence records and the model calibration techniques. However, any unforeseen misclassified or misreported records may flaw predictions for individual species. In addition, data retrieval via GBIF's API was limited to 100,000 occurrence records per request. We extended this limit by sending one request per native country for each species, and hence, expect this issue to be irrelevant for our study. We further want to stress that most of the generated predictions have not been validated individually, and that some predictions may be erroneous either due to data limitations or simply because digitally stored data can contain minor but crucial blunders. For instance, in terms of nomenclature, the red-listed species Cotoneaster cambricus is endemic to Wales 101 , but also seems to be a synonym for a widespread species according to POWO 42 . Consequently, either our spatial prediction or the expert-based range for this species is incorrect.

Usage Notes
All data handling, modelling and visualization was done using R version 4.0.3 44 in RStudio version 1.4.1103 102 . Handling of all spatial data was done using the R packages raster, rgdal, maptools, rgeos and sp 52,103-106 . A showcase for opening the different data types for individual species, is available at https://github.com/jannebor/plant_ range_estimates. Although functionality of the code may be given at newer, or older, versions, we expect the best user-experience using the versions specified in this descriptor.
Maxent predictions are given as raw and cloglog transformed output. These outputs are related monotonically, meaning that the performance metrics described in this study, as well as a potential binary range map (excluding prevalence dependent thresholds), will be identical for both raw and cloglog output 56 . For users mostly interested in qualitative analyses, both predictions can simply be interpreted as indices of environmental suitability 20 . However, due to rescaling, the exact interpretation and appearance of each output differs. In general, Maxent's output interpretation depends on the underlying data, and differs, in our case between Model 1 (raw data including pseudo-replicates = abundance) compared to Model 2 and 3 (presence), but gives an estimate of the abundance, or presence, of the species in relation to the true modelled quantity (either abundance or presence). Maxent's raw output reflects the exponential Maxent model itself, and can be interpreted as a relative occurrence (or presence) rate summing up to 1 20 . The raw output does not rely on any assumptions 20 , however, it may not perform well in visualizing actual differences in suitability 107 . Being rescaled on a more common range from 0 to 1, the cloglog transformation compresses extreme values, and hence facilities visualization and comparison amongst predictions 27 . It can, arguably, be interpreted as a relative probability of presence under certain assumptions 27 . However, as these assumptions are rarely met, we strongly discourage users from this interpretation and suggest interpreting the cloglog output values as an estimate of relative environmental suitability 20 instead.
We further suggest using Maxent predictions with an AUC below 0.7 only in exceptions, and in large-scale studies. In general, our predictions may overestimate true range extents of endemic species and underestimate www.nature.com/scientificdata www.nature.com/scientificdata/ ranges of widespread species. However, in worst case, the entire native WGSPRD-regions are outlined as being environmentally suitable, which may be acceptable in some cases, but not in others.
In addition, Model 1 has been fitted with the suggested minimum number of records for generating meaningful distributions models 53,54 , but Model 2 and 3 were in some cases trained with less records. Whether this low sample size as well as its implied uncertainty is acceptable or not will differ between users and applications and needs to be considered.
The full data, including Maxent predictions (cloglog transformed), underlying occurrence records, native regions and corresponding metadata, can be explored at https://plant-ranges.indecol.no. Here, the predictions based on individual models (Model 1 to 3) as well as a suggested (i.e. best performing) prediction highlight environmentally suitable conditions, if available for the selected species. Predictions can potentially be transformed into a map indicating where the species is most certainly found, as required for local management and conservation actions 95 , or into a conservative range map, best suited for analysing global patterns 108 and highlighting where a species is certainly absent 109 . However, the choice of an appropriate cut-off threshold is highly application specific. We outlined "potential range maps" in the data explorer for illustrational purposes only and based on the best performing prediction. We applied different cut-off thresholds to represent different levels of confidence using the R package dismo 59 . The threshold at which there was no omission (possibly suitable), the threshold at which the F 1 -score is highest (probably suitable) and presence cells (presence).

Code availability
All data and code is available without restrictions under the terms of a Creative Commons Zero (CC0) waiver (https://creativecommons.org/share-your-work/public-domain/cc0/). R code for retrieving and filtering data from POWO and GBIF, and for generating and evaluating Maxent models is available on GitHub (https://github. com/jannebor/plant_range_estimates). Any further requests can be directed to the corresponding author.