Range outliers and data curation shape our understanding of plant bioclimatic niches

Range outliers and data curation shape our understanding of plant bioclimatic niches | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 July 2025 V1 Latest version Share on Range outliers and data curation shape our understanding of plant bioclimatic niches Authors : Cristina Ronquillo 0000-0001-5945-5147 [email protected] , Juliana Stropp , and Joaquin Hortal 0000-0002-8370-8877 Authors Info & Affiliations https://doi.org/10.22541/au.175146844.45501947/v1 307 views 150 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The limitations, biases and uncertainties associated with digitally available information often limit the utility of occurrence records to properly describe the species’ realised bioclimatic niche. Here we assess changes in the perception of the realised niche of terrestrial vascular plant species after applying filters based on the geographical location of records according to their biogeographical status (native or introduced range), and range outliers documented by Plants of the World Online. We used Principal Component Analyses to describe the realised bioclimatic niches of 156,500 species. Our results show that restricting the available occurrence records to the delimited geographical distributions results in a distortion of the representation of these niches. The bioclimatic niche can be reduced by more than half of the environmental volume occupied when we filter out species records based on expert map distributions. Between 20-40% of the species in each order presented decreases in their observed realised niche after discarding records outside of their recognised range of distribution. Strikingly, this percentage raised up to 68% in Osmundales and Pinales or 83% in Equisetales. Most of the species evaluated (96%) only presented data in native ranges, but c. 90% of the species with introduced ranges expanded their realised niche in the invaded areas, evidencing that biological invasions often occur outside of the native climatic conditions. This calls for caution when selecting occurrence records from public repositories to conduct massive analyses using all available information on terrestrial plants. Validating discrepancies in niche estimations derived from the curation process will allow drawing robust conclusions about species’ responses to environmental changes. INTRODUCTION The availability of large volumes of biodiversity data with spatial information allows integrating a massive number of species occurrences from across the globe to address key questions in biogeography and global change. These macroecological analyses strongly depend on the available data, which often have limited geographical coverage (Hortal et al., 2007; Meyer et al., 2015, 2016; Stropp et al., 2016). One of the core sources of occurrence records is the Global Biodiversity Information Facility (GBIF), constructed from a vast amount of heterogeneous data sources, which lead to the potential inclusion of geographical gaps, errors or mismatches in the information across different data dimensions such as geographical, temporal or taxonomical (Boakes et al., 2010; Feeley & Silman, 2011; Meyer et al., 2016; Serra-Diaz et al., 2017). One of this mismatches occurs between species’ occurrence records and their known geographical distribution ranges (Arlé et al., 2021; Ronquillo et al., 2024). Species range maps such as those provided by the Plants of the World Online (POWO, 2024) or the Red List of Threatened Species database (IUCN, 2022) use expert knowledge to depict the range of distribution and also the native and introduced status (Svenning & Skov, 2004). Biodiversity databases often contain occurrence records located outside of these defined range maps, hereafter ‘range outliers’, which may even be geographically isolated from the main cluster of information (Maldonado et al., 2015; Bosci et al., 2016; Meyer et al., 2016; Zizka et al., 2019). The underlying reasons for this issue can be due to incorrect or outdated distributional maps (including changes in the names of administrative units over time), misidentifications or taxonomical mismatches, misapplication of location information, delays in updating natural and human-assisted changes in the geographic distributions, non-self-sustaining populations, or even ornamental species (Sax et al. 2013; Bosci et al., 2016). Besides this uncertainty, occurrence data typically lack information on their biogeographical status (i.e. if occurrence record is native or alien), which prevents users from differentiating between records located in or out of their native (Serra-Diaz et al., 2017; Arlé et al., 2021). Evaluating the climatic suitability of species using records with such uncertainty or even potential errors could compromise the reliability of macroecological and biogeographical assessments (Hortal et al., 2008, 2012; Feeley & Silman 2010; Lobo et al., 2010; Maldonado et al., 2015; Bocsi et al., 2016; Hughes et al., 2021b). Due to the direct relationship between climate and geographic space (i.e. Hutchinson’s duality, Colwell & Rangel, 2009), any geographic information can be mapped in the bioclimatic space establishing the geographic extent of a climatic condition (bioclimatic area, Coelho et al., 2023). Thus, ecogeographical analyses of the realised species niche can be derived from the available information provided by species occurrences records (Bocsi et al., 2016; Soberón et al., 2017). However, niche inferences using misidentified or incorrectly located records may lead to an incorrect definition of the climatic suitability of the species, and consequently poor modelling and predictions of future climate scenarios or conservation or management actions (Arlé et al., 2021). Whereas niche inferences using properly identified records located out of their range would alter the known climatic limitations of species if they occupy non-overlapping environmental conditions between ranges (Broennimann et al., 2012). Distinguishing uncertain from inaccurate records is critical to assess the dynamics of geographic ranges, as both species’ niches and distributions may shift through time, expanding beyond their native ranges (Araujo 2005; Bocsi et al., 2016). This is further complicated by the fact that some apparent expansions may be due to non-reported invasions, sink populations, and ornamental or cultivated specimens (Bocsi et al., 2016; Meyer et al., 2016). The main objective of this work is to analyse and quantify the importance of species range outliers in the representativeness of their realised bioclimatic niches for vascular plants, taking into account both their geographical distributions, and their biogeographical status in the territories where occurrence records are located. Specifically, we categorise different configurations of the realised bioclimatic niche of each species based on how occurrence records are placed within their published distribution ranges at the botanical country level. In addition, we assess whether and to what extent range outliers (i.e. records placed out of the known distribution) modify the observed niches and the main climate types occupied by each species. MATERIALS & METHODS Records downloading and pre-processing We downloaded all records of terrestrial Tracheophyta occurrences from GBIF via rgbif (Chamberlain, 2017). We included presence records with information of coordinates and no geospatial issues labelled, and discarded ‘machine observations’, ‘fossil’ and ‘living’ specimens (the latter to avoid including records from botanical gardens). This process was done separately for each taxonomic order due to computational restrictions (see Supplemental Information; GBIF.org, 2023). For each dataset, we validated and filtered data following some of the steps included in a guide for curating species occurrence records (Ronquillo et al., 2024). The geographical check discarded records with latitude and/or longitude equal to zero or the exact same value in both fields. We kept only records whose coordinates presented at least one decimal digit. We discarded records with coordinates coinciding with capital cities and country centroids, records located near research institutions, and those located in the ocean. Then, we kept only records placed in the exact same country assigned by collectors. For the taxonomic validation, we kept records identified at species and subspecies level or varieties. The information included in the ‘scientificName’ field was then harmonised following the World Checklist of Vascular Plants (WCVP, Govaerts et al., 2021) using rWCVP R package (Brown et al., 2023). This non-supervised method led us to establish a strict threshold keeping only those records whose species names are ‘Accepted’ by WCVP (version 10) and a match type ‘Exact (with author)’ or ‘Fuzzy (phonetic)’ with match similarity higher than 0.95. No temporal filtering was applied due to the limited data available for many species that could lead us without large reductions of information. We finally discarded duplicate information based on the coordinate values and WCVP accepted species name (see Supplemental Information for occurrence numbers before and after the pre-processing). Each validated and non-duplicated record was classified as native or introduced according to the geographical match between its location and the expert-based distribution maps from Plants of the World Online (POWO, 2024) at botanical countries level 3 ( rWCVP, Brown et al., 2023). POWO defines a ‘native’ botanical country as those countries or country-like territories (for large countries such as Brazil, Canada, China, Russia or the United States of America) where the plants have been present since before the last ice age or arrived by natural colonisation afterwards. ‘Introduced’ countries are defined as those where the plant species were accidentally or deliberately introduced by humans, including hybrids but not the cultivated/planted ones with no self-reproduction. We classified records that were located outside of both native and introduced territories in POWO species distribution maps as ‘range outliers’. Species with less than 10 non-duplicated points (less than five of them in their native or introduced range) were not considered for the niche analyses (‘not Evaluated’). We also identified the predominant type of climate occupied by each species based on the five main classes of the Köppen-Geiger classification (arid, tropical, temperate, cold and polar). We overlapped all selected records with the global map of current climate types at 1 km resolution (Beck et al., 2018, 2023). Then, we repeated the overlap using only the subset of the records placed in their native area of distribution. Species with more than 40% of their records in two different climate classes were assigned to both categories. Representativeness of the realised bioclimatic niche For this study, we calculated the realised bioclimatic niches of species by using ordination techniques to recreate the multidimensional space derived from the occupied geographic space (Broennimann et al., 2012; Varela et al., 2014; Sobral-Souza et al., 2021; Coelho et al., 2023). We used Principal Components Analysis (PCA) to summarise the multivariate bioclimatic space for all terrestrial areas of the world defined by the 19 bioclimatic variables and the aridity index variable from CHELSA (version 2.1; Karger et al., 2018; Brun et al., 2022) at 50 x 50 km grid cell resolution (geographic coordinate system). By reducing this information, we selected the first two axes of the PCA representing 0.74 of the cumulative variability. Then, the bioclimatic space defined by these two PCA axes was binned by dividing these values into equal-area cells within the PCA space. These bins correspond to different ‘climate domains’ that provide a classification of the entire bioclimatic space available (Guisan et al., 2014). We conducted this binning at two different scales. One divided the two axes into 0.2 x 0.2 cells (n = 613) as a fine resolution that can capture more differences between the bioclimatic domains used by each species, and the other divided the two axes into 0.5 x 0.5 cells (n = 138) to provide a coarser description of species’ bioclimatic niches. Each cell represents a unique set of climate conditions (climate type) observed in one or several geographic locations (Colwell & Rangel, 2009, Coelho et al., 2023). Thus, we assigned each species occurrence record to its corresponding cell of the multivariate bioclimatic space based on the values of PCA1 and PCA2 in its geographic location (see Ronquillo et al., 2020 and Sobral-Souza et al., 2021 for similar approaches). We defined which cells are occupied by each species, differing between records within the native and introduced range, as well as identifying range outliers. Then, we established four hypothetical assemblages based on the spatial combination of native and introduced niche cells (that may or may not include environmental outliers). These hypothetical assemblages follows the terms in Guisan et al. (2014), as species (a) whose records occupy native niche or are environmental outliers (Fig. 1a); (b) whose native niche does not overlap with the introduced niche (i.e. niche expansion) (Fig. 1b); (c) whose records are located in the introduced range occupying climatic conditions also available in the native niche (i.e. niche stability) (Fig. 1c); and (d) whose native and introduced records partially overlap of the same available environment (i.e. niche expansion) (Fig. 1d). Besides, there is another case in which the data available in GBIF only allow to define the introduced niche of the species, due to the insufficient number of records placed in the native range (< 10 records). All analyses were performed in R Studio 2024.09.0+375 ”Cranberry Hibiscus” (R Core Team, 2021). RESULTS We downloaded 351,360,932 records from GBIF. After data curation and filtering, the number of validated records decreased to 164,441,270 (see Supplemental Information). These records accounted for the occurrence of 276,090 accepted Tracheophyta species from 84 accepted orders. Our data included all the orders of Lycopodiopsida (Isoetales, Lycopodiales and Selaginalles in PPG I, 2016), vascular ferns (Cyathales, Equisetales, Gleicheniales, Hymenophyllales, Marattiales, Ophioglossales, Osmundales, Polypodiales, Psilotales, Salviniales and Schizaeales in Nitta et al., 2022), as well as Gymnosperm orders (see Yang et al., 2022) being Araucariales and Cupressales included in Pinales. For the angiosperm orders, we included all accepted orders included in The Angiosperm Phylogeny Group (2016). Figure 1. Conceptual framework followed to define the four hypothetical configurations of species’ realised niche based on the biogeographical status of the occurrence records. (a) Native niche; (b) native niche does not overlap the introduced niche; (c) introduced niche is included into the native niche; (d) introduced niche expand the native niche (partial or totally). Note that environmental outliers are represented in all configurations and may 1) not overlap, 2) overlap partially or 3) overlap completely with native/introduced maps in the environmental space. The classification of each species in their corresponding climate space assemblage led us to discard 119,590 species (43.32%), which were not evaluated because they presented less than 10 different valid and unique occurrence records. More than half of the species were not assessed for Arecales, Asparagales, Canellales, Cucurbitales, Gunnerales, Isoetales, Marattiales, Pandanales, Piperales and Zingiberales (Fig. 2). Up to 54% of the species considered (150,200) only occupied native ranges (see example in Fig. 3a), being also the majority for each order except for the species-poor Gingkoales (1 spp) and Acorales (2 spp) orders (Fig. 2). There were 408 species that presented non-overlapping niches for their native and introduced ranges (see example in Fig. 3b). The number of species that had their introduced niche represented within the native niche was 708 (see example in Fig. 3c), while 5,024 species expanded their introduced bioclimatic niche beyond the bioclimatic native niche (see example in Fig. 3d). Finally, for 185 species, not enough data in the native range were available in GBIF, so their bioclimatic niche was defined only using records from their introduced ranges (see Supplemental Information). In addition, no species show a configuration with exactly the same bioclimatic niche occupied by native and introduced cells. Figure 2. Proportion of species by order in each niche configuration. (a) Native (red), (b) native niche does not overlap the introduced niche (yellow), (c) the introduced niche is included into the native niche (orange), (d) both native and introduced niches partially overlap (purple). Species with less than 10 records were not classified (grey). Figure 3. Examples of each hypothesised species’ niche configurations at 0.2 x 0.2 resolution and maps (Mollwide projection) based on the presence of species occurrences in their native (yellow) or introduced (blue) botanical countries; the grey area corresponds to the available environment. (a) Native ( Sorbaria grandiflora ); (b) native niche does not overlap with the introduced niche ( Stauntonia hexaphylla ); (c) introduced niche is included into the native niche ( Cordia dentata) ; (d) both native and introduced niches partially overlap ( Linaria vulgaris ). The use of different grid resolution also led us to obtain different bioclimatic niches when evaluating species individually (Supplemental Information), particularly for those species with records distributed along introduced ranges that occupy part of the niche near the native one. However, the finest resolution gave us more detail of potential differences in the occupied climate. By order, the proportion of the available environment occupied by cells was similar at both resolutions (see Supplemental Information), yet, 0.5 x 0.5 cells cover a bigger proportion. The largest changes between resolutions were observed in Gunnerales, Pandanales and Paracryphiales, occupying respectively 13.7%, 12.8% and 15.7% more of the available environment than at 0.2 x 0.2 resolution. The proportion of cells discarded of the species’ bioclimatic niche was significantly greater after excluding both the environmental outliers and introduced niche cells than when excluding only the introduced cells (Wilcoxon signed-rank test p-values < 0.05, see Supplemental Information). This occurred at both resolutions for most orders (see Fig. 4 for 0.2 x 0.2 cells resolution and Supplemental Information for 0.5 x 0.5 cells). However, it was not significant ( p-values > 0.05) for Acorales, Amborellales, Berberidopsidales, Ceratophyllales, Ginkgoales, Petrosaviales, Trochodendrales, Vahliales and Welwitschiales because all these orders have small numbers of species (between 1 and 6 each; Supplemental Information). Figure 4. Distribution per order of the proportion of 0.2 x 0.2 cells of the bioclimatic space selected after discarding records located in the introduced range (grey), and after discarding both records in the introduced range and range outliers (red). Red lines indicate quartiles of the distribution (note that in the grey area, the three quartiles are placed in 1.00 for all orders). In general, the proportion of species with environmental outliers (cells in the available environment defined only by range outliers) presented high variability by order (Supplemental Information). Nevertheless, it is noteworthy that they represented 83% of the species from Equisetales and 68% from Osmundales and Pinales. Indeed, while less than 3% of the species had more than half of their realised niche defined by range outliers (see examples in Fig. 5), for Pinales and Cycadales this value increased to 7% and 10% of their species, respectively (Supplemental Information). Figure 5. Examples of the niche representation of species occurrence records (Left panel Cycas revoluta; Right panel Osmunda regalis ) if only those located in their native (yellow) and introduced range (blue) were selected or if the range outliers were included (white cells). Graphs at 0.2 x 0.2 resolution and maps use Mollwide projection. Most of the species occupied regions of temperate climate, a trend also observed across the configurations of species’ bioclimatic niche (Fig. 6 and Supplemental Information). However, this was not the case for the 119,590 species ‘non-evaluated’ for analyses (<10 records), which were mostly located in tropical areas, albeit closely followed by the temperate climate class (Supplemental Information). We observed that between c. 90% of species were distributed in the same climate class of Köppen-Geiger after the application of a filter to keep only records within the native range (Fig. 6a and Supplemental Information). The same pattern was observed in species whose introduced niche is included within the native one (Fig. 6c). However, for species whose introduced niche does not overlap or expanded the native one, the inclusion of records out of the native range may produce more than 10% of changes from arid, cold or tropical climate towards temperate (Fig. 6b, d and Supplemental Information). This pattern was also detected for species which information were mainly assessed using records from the introduced ranges and range outliers. By using only one to nine records located in the native range, a high proportion of species were transferred from their native climate classes to all directions (Fig. S1c and Supplemental Information). Even so, most of these species had no native information available in GBIF and thus were not classified in any Köppen-Geiger climate class. Figure 6. Changes in the major Köppen–Geiger climate class occupied by species in each hypothesised species’ niche configuration (a) native niche; (b) native niche does not overlap the introduced niche; (c) introduced niche is included into the native niche; (d) both native and introduced niches partially overlap. Left panels consider only the records placed in their native ranges while right panel considers all records available for a species in GBIF (native range, introduced range and geographical outliers). Note that some species may appear in multiple climate classes. DISCUSSION Our results show that applying geographical filters to occurrence records based on the biogeographical status of species affects the representativeness of their realised bioclimatic niche. The main factor driving change in the size and shape of the bioclimatic niche occupied by each species was the inclusion of records placed outside the known hypothesised species geographical distribution range described in POWO (2024). We showed that filtering range outliers from GBIF data leads to important decreases in the representativeness of bioclimatic niches for most orders. For some species excluding these records that may even correspond to more than half of the cells included. This affected up to 10% of the species of Cycadales or 7% of Pinales, albeit for most orders that proportion was only 1-3% of the species evaluated (see Supplementary Information). Discrepancies between data from GBIF and expert-based maps have been reported previously (Hughes et al., 2021b) and in this study, we corroborate the importance of evaluating the application of filters to records placed outside the known distributions. It is possible that these occurrences correspond to taxonomic misidentifications, ornamental cultivars or sink populations, rather than viable wild populations that provide a fairer representation of the species’ geographical distribution and the occupied environment due to niche tolerance in the range edges (see Sax et al., 2013). However, applying this filter by default could lead to omission errors if those range outliers are already well located in the region but the global maps to overlap the information are too imprecise, not updated, or not properly defining the actual distribution of the species in edge countries (or even countries placed far from the delimited distribution published). In this scenario we would be limiting our correct definition of species niches (Bush et al., 2018, Sax et al. 2013). Rather, our results prove that there is an important proportion of records along Tracheophyta in GBIF that advocates a careful examination of the quality and uncertainty associated with them, especially with the use of alternative and more precise maps at regional level. Here note that without knowing the actual biogeographical status of these range outliers, it is not possible to assess whether the increase in the realised bioclimatic niche is due to previously undetected native areas, or naturalised areas (Soberón, 2007). However, the bioclimatic niches described by range outliers may largely correspond to missing portions of their realised niches included in the species’ fundamental niche. It is therefore necessary to integrate all field observations into the geographical range of species using expert knowledge (Hughes et al., 2021a). By doing so, we can derive consistent biodiversity patterns from reliable species diversity maps (Hughes et al., 2021b). To control this problem, researchers may consider labelling the biogeographical status of occurrence records for specific sets of data. They can filter records placed within either native or non-native distributions, improving data accuracy or even detecting invasion processes to obtain a better definition of niche changes (Meyer et al., 2016; Arlé et al., 2021; Ronquillo et al., 2024). Using only the native range occurrences can be a poor proxy for climatic tolerance (Bocsi et al., 2016). Yet, it might be argued that without this distinction of biogeographical status, the results of macroecological analyses and conservation assessments can be biased by the inclusion of non-natives within native species ranges (Meyer et al., 2016). The majority of species of all orders were only distributed through native ranges while only a limited number of species expanded their bioclimatic niche if we consider their introduced ranges (5,617; 3.6% of all species evaluated). However, our study shows that c. 90% of invasive vascular plants (5,617 out of 6,325 species evaluated) exhibit expanded bioclimatic niches due to the inclusion of records from their introduced ranges. Alterations in the bioclimatic niche to naturalised and invaded areas have been documented for many different groups before (e.g. Silva et al., 2016; Zhu et al., 2017; Pili et al., 2020), but have been considered rare for terrestrial plants (Petitpierre et al., 2012). This confronts any uncertainty assessment associated with invaded ranges, which are climatically distinct from the native ones. An accurate definition of species’ realised niche is key for forecasting invasions under climate change scenarios (Qiao et al., 2017). By adding the introduced ranges, we are including areas without the biotic constraints of the native range (Broennimann & Guisan, 2008). Thus, users should be aware to carefully choose occurrence records that capture species requirements and consequently define the realised niche metrics; niche stability vs. niche drift or expansion (Guisan et al., 2014; Zhu et al., 2017; Pili et al., 2020). Our results may be limited by the use of coarse sources of information on species ranges provided by global checklists such as POWO. However, these type of maps provides a reasonable approach from expert sources to conclude about large-scale biodiversity patterns as they synthesise extensive amounts of information in the presence or absence of species in botanical (or zoological) countries (e.g. Svenning & Skov, 2004; König et al., 2019; Coelho et al., 2023; Divieso et al., 2024). Nevertheless, using botanical countries helps to account for the large variation in climates of big countries such as the USA or China (Serra-Diaz et al., 2017). The use of alternative expert-maps (such as the IUCN) that represent ranges more accurately would be even stricter when discarding range outliers from repositories. For a significant proportion of the species we analyse, many records fall outside the recognised botanical countries, evidencing that these maps provide perhaps too conservative hypotheses about their distribution or no up-to-date information. Indeed, the designation of biogeographical status may be influenced by political boundaries and may miss records that are located near assigned regions (Lemoine & Svenning, 2022). These records represent the limitations and mismatches between up-to-date distributional maps; the impact of administrative boundaries on species ranges, or biases associated with overoptimistic representations of species distributions attributed to the whole territory (Hughes et al., 2021b). One of the main limitations of this study was that a large proportion of species could not be evaluated, as over 43% of the Tracheophyta did not meet the threshold of more than 10 occurrences per species. This percentage was roughly similar for all the orders considered. As highlighted previously, many species still lack sufficient information available to provide a fair representation of their distributions (Lomolino, 2004; Whittaker et al., 2005; Meyer et al., 2016; Cornwell et al., 2019). The limited effort devoted to plant inventories in vast areas of the globe hampers the correct definition of the distributions and bioclimatic niches of many species (Meyer et al., 2015; Ronquillo et al., 2023). Interestingly, our results show that most of the data available in GBIF provides information about regions of temperate climate. This may reflect the historically pervasive recorders’ bias towards high-income countries from Europe and North America (Hughes et al., 2021a). It is therefore not surprising that most species with limited information available (i.e. ‘not evaluated’) were located in the tropical climate. These biomes correspond to the under-sampled and less-known areas of the world (Feeley & Silman, 2010; Hughes et al., 2021a). Filtering records within the native range changed the main climate classification of species that presented part of their bioclimatic niche only defined by data within the introduced range of distribution. These patterns hold true for the two resolutions we used to describe the realised bioclimatic niches. We strongly suggest that future efforts of surveying take place in these areas to improve the definition of species ranges, their realised niche and potential niche shifts. CONCLUSIONS The results of this study advise that labelling geographical range outliers and evaluating changes in the bioclimatic niche occupied by the so-obtained sets of occurrence records can be an important step in assessing the reliability of estimates species’ realised niches based on the incomplete and biased data currently available. We demonstrated that there is a potential source of distortion when defining the realised bioclimatic niche of the species, which may be considered together with other ecological assumptions in broad-scale analyses. A substantial proportion of public records not only falls outside their range maps, but affect the representation of plants’ bioclimatic niches. Identifying these range outliers can also help detect processes of dispersion of alien species, taxonomic misidentifications or undetected geographical areas (Vandepitte et al., 2015). Such information may be key for filtering species occurrence records to diminish data-driven uncertainty, particularly if the analyses will derive climatic responses of the species (e.g. SDMs, ENMs). Only with a cautious assessment of the representativeness of the biodiversity information available, we will provide fair representations of ecological processes, and robust models and projections. Data archiving statement All R codes used for processing, analysing and creating the figures of the study are anonymised (for the review process) in Figshare (https://figshare.com/s/a1650c15e0842a648ff9). 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Keywords biogeographical status digitally accessible information range outliers realised bioclimatic niche species occurrence records vascular plants Authors Affiliations Cristina Ronquillo 0000-0001-5945-5147 [email protected] Museo Nacional de Ciencias Naturales (MNCN-CSIC) View all articles by this author Juliana Stropp University of Trier View all articles by this author Joaquin Hortal 0000-0002-8370-8877 Museo Nacional de Ciencias Naturales (CSIC) View all articles by this author Metrics & Citations Metrics Article Usage 307 views 150 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Cristina Ronquillo, Juliana Stropp, Joaquin Hortal. Range outliers and data curation shape our understanding of plant bioclimatic niches. Authorea . 02 July 2025. DOI: https://doi.org/10.22541/au.175146844.45501947/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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