Deriving inventories of non-native plant species from iNaturalist: Insights from urban centres of the Western Cape, South Africa

27 Deriving inventories of non-native plant species from iNaturalist: Insights from urban centres of the Western Cape, South Africa Christiaan P. Gildenhuys1, Luke J. Potgieter1,2 , Cang Hui3,4,5 , David M. Richardson1 1 Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Stellenbosch, South Africa 2 Department of Biological Sciences, University of Toronto, Scarborough, 1265 Military Trail, Toronto, Canada 3 Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa 4 Biodiversity Informatics Unit, African Institute for Mathematical Sciences, Cape Town, South Africa 5 National Institute for Theoretical and Computational Sciences, Stellenbosch, South Africa Corresponding author: Christiaan P. Gildenhuys ([email protected]) Copyright: © Christiaan P. Gildenhuys et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Methods Abstract Accurate, up-to-date inventories of non-native species are important to document and improve our understanding of biological invasions globally and inform management decisions. Traditional methods for the collation of inventories are timeand resource intensive, and lists become outdated if not regularly updated. The community science platform iNaturalist can contribute to the collation of regularly updatable (“living”) inventories of non-native species. However, robust and transparent workflows are needed to optimise data quality to take full advantage of iNaturalist. We present the semi-Automated Non-Native Inventory Compilation (sANNIC) workflow for the collation and completeness assessment of non-native vascular plant inventories from iNaturalist. The workflow is informed by the World Checklist of Vascular Plants (WCVP) and is used to compare native ranges to a reference area. The utility of the workflow is demonstrated by compiling non-native species inventories of 100 urban centres in the Western Cape province, South Africa. A total of 947 taxa of wild-growing, i.e. casual, naturalised and invasive plants were observed in these urban centres which showed varying levels of sample completeness. Most small towns had too few records for a completeness assessment. Larger urban centres and those near the coast were typically better sampled. This work highlights the potential for iNaturalist to construct non-native species inventories given sufficient coverage and thorough curation. Key words: Alien plant species, biological invasions, citizen science, community science, inventory, plant invasions, urban ecology Introduction Biological invasions are a major threat to biodiversity and human well-being globally and their impacts are projected to increase (Roy et al. 2024). The distribution of non-native species, however, remains poorly understood in many parts of the world (Seebens et al. 2025). Knowledge of which species are present and the non-native species richness of different areas are key indicators for the monitoring and reporting of biological invasions which can allow for informed management (Wilson et al. 2018). Local inventories are important resources to build knowledge Academic editor: Anibal Pauchard Received: 14 April 2025 Accepted: 21 October 2025 Published: 13 November 2025 Citation: Gildenhuys CP, Potgieter LJ, Hui C, Richardson DM (2025) Deriving inventories of non-native plant species from iNaturalist: Insights from urban centres of the Western Cape, South Africa. NeoBiota 104: 27–58. https:// doi.org/10.3897/neobiota.104.155832 NeoBiota 104: 27–58 (2025) DOI: 10.3897/neobiota.104.155832 Advancing research on alien species and biological invasions A peer-reviewed open-access journal NeoBiota This article is part of: Developing lists of alien taxa in the Global South: workflows, protocols, processes, and experiences Edited by John Wilson, Michele Dechoum, Katelyn Faulkner, Barbara Langdon, Shyama Pagad, Aníbal Pauchard, Hanno Seebens, Tsungai Zengeya, Silvía Ziller 28 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist of the distribution of non-native species globally (Van Kleunen et al. 2019), to inform managers and other stakeholders, and to ensure more effective management (Potgieter et al. 2024a). Non-native species inventories are also important for generating and testing hypotheses, answering biogeographical and ecological questions, and building on theories to improve our understanding of biological invasions (Cadotte et al. 2006). Despite the utility and importance of inventories and species lists, their collation and presentation are often haphazard. A wide range of methods are employed to compile inventories, including systematic surveys, literature searches, and expert opinion (Guézou et al. 2010; Inderjit et al. 2018; Aymerich and Sáez 2019; Nelufule et al. 2023). Differences in methodology complicate meaningful comparisons of inventories and reduce their usefulness in research and management. The estimated completeness of inventories and associated uncertainties are rarely reported. A recent review of non-native species reporting in regional species lists revealed that a large proportion of expert-compiled regional inventories excluded non-native species, and when they were included, they were often not adequately separated from native species (Castro et al. 2023). This review also highlighted that human-dominated areas such as urban centres are under-represented, and that most lists were published as unstructured data (e.g. as a table in a paper) and rarely in machine readable formats or in open databases such as the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/) or other data repositories. Moreover, inventories of non-native species need to be updated regularly to reflect current and emerging invasions, but this is rarely and inconsistently done. The community science (also known as citizen science) platform iNaturalist (www.inaturalist.org) can help to produce more consistent, easily updatable (or “living”) inventories, and allow for the quantification of uncertainty and sampling effort. iNaturalist is an unstructured community science initiative which allows voluntary participants (observers) to add occurrence records of any taxon by uploading one or more images (or sound files) of a single species with the associated time, location, and other information onto the online platform (Mesaglio 2024). These records are later verified by the iNaturalist community (identifiers). The process of species identification is facilitated by an artificial intelligence (AI) image recognition tool which is updated monthly. As of 25 February 2025 this AI tool recognises over 100 000 taxa globally with a reported accuracy rate of close to 90% depending on the taxonomic group and continent (https://www.inaturalist.org/blog/107012-new-computer-vision-model-with-over-100k-taxa; accessed: 10 March 2025). iNaturalist data are uploaded to GBIF monthly, provided the data meet specific licensing criteria (CC0, CC BY, or CC-BY NC) and attain “Research Grade” quality. This quality designation is achievable for wild-growing (i.e. non-cultivated) records that have reached a community consensus, species-level identification and have complete and accurate metadata. iNaturalist has been widely applied in many parts of the world to study and monitor non-native invasive species. For example, Grattarola et al. (2024) reported on the status of the invasion of Carpobrotus edulis in Uruguay using 15 years of iNaturalist records. Potgieter et al. (2024b) demonstrated the utility of iNaturalist in monitoring of urban invasive species by developing a monitoring approach for the invasive polyphagous shot hole borer (Euwallacea fornicatus) 29 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist in the urban centres of Cape Town and Stellenbosch, South Africa. Young et al. (2021) used iNaturalist to develop automated watch lists for invasive plants in managed areas in the United States. The database of records exported to GBIF has been cited over 6000 times (https://doi.org/10.15468/ab3s5x; accessed 17 March 2025). iNaturalist is increasingly recognised as an indispensable tool for monitoring biodiversity and biological invasions. Although community science projects provide useful data, limitations can cause misleading results if biases and inaccuracies are not properly addressed (Dobson et al. 2020). Data quality can vary geographically and between taxa (Hochmair et al. 2020; Mesaglio et al. 2023; Alfeus et al. 2024) and can be affected by records of captive/cultivated species if these are not properly flagged as such (Botella et al. 2018; Potgieter et al. 2024a). Much of this variability in data quality can be attributed to variances in identifiers’ taxonomic and geographic specialisation and experience in using the platform (Campbell et al. 2023) and to various observer biases (Di Cecco et al. 2021). Thorough data verification is essential for maintaining quality but can be time-consuming. Workflows that can be regularly and cost-effectively repeated offer the opportunity to streamline the collation of up-to-date inventories, and can standardise and improve reproducibility, transparency, and data quality. For example, Seebens and Kaplan (2022) developed a workflow to Downscale Alien Species Checklists using Occurrence records (DASCO). They integrate the GloNAF database (Global Naturalized Alien Flora; Van Kleunen et al. 2019) with occurrence records from GBIF and the Ocean Biodiversity Information System to create local-scale inventories. However, inventories produced through this and other similar workflows (Young et al. 2021; Saffer et al. 2024) rely on predefined checklists or inventories (such as GloNAF); they therefore have the same limitations related to the collation of inventories as discussed above. Several recent studies have also highlighted shortcomings of databases like GloNAF (Gildenhuys et al. 2024; Potgieter et al. 2024a). The quality of the workflow output also depends on the quality of the input occurrence records (e.g. from GBIF), and many workflows fail to include independent verification of record accuracy. One way of improving quality is to employ truncation, i.e. removing species with observations below a certain threshold. However, this approach discards valuable data for assessing inventory completeness that requires accurate estimates on the numbers of singletons and doubletons (the number of species only observed once or twice in the assemblage respectively) (Chao et al. 2020) and potentially also discards data for taxa at the early stage of invasion which are critical for early detection and monitoring. Alternative workflows that address such limitations by incorporating careful checking of records are therefore required. Given the inconsistent sampling effort of opportunistic data, reporting on the likely completeness of inventories generated from iNaturalist is vital. Tools for assessing sample completeness and diversity estimation of incomplete samples could address this need (Hsieh et al. 2016; Chao et al. 2020). These tools have already been applied in several studies using opportunistic data from iNaturalist and other community science platforms (Callaghan et al. 2022a; Gorta et al. 2023; Shen et al. 2023; Li et al. 2024; Richardson and Potgieter 2024). However, care must be taken to apply such techniques appropriately, acknowledging the opportunistic nature of the data collection. Nonetheless, some measure of completeness needs to be incorporated into workflows. 30 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Urban centres provide a good focus for the development of such a workflow. Although urban centres are not the primary focus for many iNaturalist users (Thompson et al. 2023), many records are nonetheless collected in urban habitats, near where most observers live (Beninde et al. 2023; Dimson et al. 2023). Focus on invasions in urban centres has increased in the last decade (Salomon Cavin and Kull 2017). Many studies have highlighted the facilitative role of human-aided pathways, cultivation, and novel habitats associated with urban centres in the introduction and naturalisation of non-native species (Donaldson et al. 2014; Faulkner et al. 2016; Van Kleunen et al. 2018; Potgieter and Cadotte 2020; Potgieter et al. 2020, 2024c; Palit et al. 2024). In South Africa, iNaturalist has experienced a large uptake, with over 2.7 million records of plants as of 12 October 2024, and most (1.7 million) of these records are concentrated in the Western Cape province, a disproportionate number of which originate from urban centres (https://www.inaturalist.org/projects/vascular-plants-of-western-cape-urban-areas; accessed 3 March 2025). To date, protocols for effective management of invasive species in urban centres in South Africa are lacking and more information on the distribution of non-native species across urban centres is needed to guide management efforts (Potgieter et al. 2020). Western Cape urban centres therefore make for a good case study of the proposed workflow. We aim to develop a semi-automated workflow, the “semi-Automated Non-Native Inventory Compilation” (sANNIC), for deriving non-native plant inventories and assessing their completeness from iNaturalist data. To this end we: a) develop an automated R-based script “Automated Non-Native Inventory Compilation” (ANNIC) to automatically build preliminary non-native species lists; b) integrate these lists into iNaturalist projects to thoroughly curate each species and ensure the highest possible data quality and optimal use of iNaturalist; and c) propose a protocol for the evaluation of species list completeness using principles of Chao completeness assessment. We then apply this workflow in 100 urban centres in the Western Cape province, South Africa, to produce an openly available and constantly updating living inventory of non-native species in urban centres. Finally, we discuss the implications, limitations and transferability of the proposed workflow. Methods Here we describe the proposed semi-Automated Non-Native Inventory Compilation (sANNIC) workflow (Fig. 1). It consists of four steps: 1) acquiring data from iNaturalist; 2) compiling a non-native inventory from the data informed by the World Checklist of Vascular Plants (WCVP) using the ANNIC R script; 3) creating and curating an iNaturalist project of the non-native inventory; and 4) conducting a completeness assessment of the inventory produced. The sequence and execution of steps are informed by decision points (diamonds in Fig. 1). For example, if the data are deemed of sufficient quality after the second step, the workflow proceeds to the fourth step; if not, it proceeds to the third step and then back to the first and so forth. The goal of the sANNIC workflow is to facilitate the production of non-native plant inventories and encourage manual curation of the data in an iterative manner. We also discuss possible alternative approaches at each step. 31 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Figure 1. Representation of the semi-Automated Non-Native Inventory Compilation (sANNIC) workflow for the derivation of inventories of non-native plant taxa from iNaturalist. Steps outlined in a black square represent those conducted in R. Steps highlighted in light orange represent those steps that require working in iNaturalist. Diamond shapes represent decision points. Rounded shapes represent the outcome of the workflow. Asterisks (*) indicate optional steps. Step 1: Acquire data The area and taxon of interest first need to be defined on iNaturalist. The simplest option is to create a project to capture the relevant information. Areas of interest can be uploaded (if not already present) as ‘places’ which can be used to refine a search query or used as a geographic filter in a project. The taxonomic group of interest can be specified using a search query in the project settings (see Mesaglio 2024 for more details). ‘Places’ can be uploaded onto iNaturalist in a KML file format, however, certain requirements must first be met: the user creating the place must have made more than 50 observations on iNaturalist; the place cannot contain more than 100 000 observations before creation; the place area cannot exceed 32 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist ~ 700 000 km2; the file size cannot exceed 1MB; and no more than three places can be added by a single user in a day (Mesaglio 2024). As many of these requirements can be quite limiting, we recommend using existing places where possible. There are several options for exporting data from iNaturalist depending on the amount of data needed. Data can be accessed using the export function on the iNaturalist website which allows for up to 200 000 records per query. Various Application Programming Interfaces (APIs) are available, for example R packages such as ‘rinat’, which can export up to 10 000 observations per day (Barve et al. 2022; https://api.inaturalist.org/v1/docs/). Many places, including all ‘standard places’, in iNaturalist are ‘checklist’ enabled, which allows for the export of a list of all Research Grade taxa recorded within the place boundaries (Mesaglio 2024). This is useful if the number of records of interest far exceeds the iNaturalist export limits. Alternatively, data can be sourced from second parties such as GBIF, however, GBIF only exports Research Grade records and those with particular licencing, representing only a subset of the total number of records on iNaturalist. The data are also only sent to GBIF once a month. Although our workflow is tailored to work with observations exported directly through the iNaturalist website, it can be modified to function with different data sources. For our workflow the following columns need to be included in the iNaturalist export: id (a unique identifier for each observation), observedon (the standardised date the observation was made), user_id (a unique identifier for each user), quality_grade (including “Research Grade” and “needs id”), captive_cultivated (whether the observation is flagged as captive or cultivated), longitude and latitude (the coordinate location associated with each observation), taxon_id (a unique taxonomic identifier), taxon_family_name (family name of the taxon as recorded on iNaturalist), taxon_subfamily_name (subfamily name of the taxon as recorded on iNaturalist), taxon_genus_name (genus name of the taxon), taxon_species_name (species name of the taxon if identified to species level), taxon_hybrid_name (hybrid name of the taxon if identified as a hybrid), taxon_subspecies_name (subspecies name of the taxon if identified as a subspecies). Additional columns like the url (a link to the web address of each observation), positional_accuracy (precision of the coordinates) and others are not required but can be useful to include in any export. Any additional columns can be added to the input as needed. Step 2: Automated Non-Native Inventory Compilation (ANNIC) The Automated Non-Native Inventory Compilation (ANNIC) R script automatically classifies species as native or non-native based on the World Checklist of Vascular Plants (WCVP) by using iNaturalist occurrence records as input and assisting users in manually reviewing species that cannot be assigned automatically. Written in R version 4.4.3 (R Core Team 2025), it is available as an R script on Zenodo (https://zenodo.org/records/15210704) and the latest version is available on GitHub (https://github.com/christiaan-g/sANNIC_workflow). It requires the ‘tidyverse’ packages to conduct data manipulation (Wickham et al. 2019), ‘rWCVPdata’ to access the latest snapshot of the WCVP (Govaerts 2024), and ‘rWCVP’ to provide several functionalities to deal with the WCVP data (Brown et al. 2023). 33 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist 2a: Problematic taxa (optional) Some taxa, regardless of image quality of the record, cannot be consistently identified to species level from iNaturalist records. This is mainly because of unclear distinctions between species within a genus or other taxonomic group, difficulty in distinguishing similar species, absence of sufficiently high-resolution images showing key traits required for species-level identification, hybridisation, difficulty in identifying taxa at the juvenile stage, and a lack of available taxonomic knowledge to identify records to species level. Such taxa are considered “problematic taxa”; the R script allows for these taxa to be collapsed to a higher taxonomic rank, including genus, family, subfamily, or any manual specification. These taxa will be included if present in the dataset regardless of their quality grade; special attention is therefore needed during the curation process (see section 2d. “Manual checking”). This step is optional but when working with iNaturalist data we recommend assessing the presence of problematic taxa. All problematic taxa in this workflow are assumed to be non-native. 2b: Standardise names (matching with the WCVP) The World Checklist of Vascular Plants (WCVP) is a continuously updated database of described vascular plant species curated by the Royal Botanic Gardens, Kew (Govaerts et al. 2021). The database represents a global consensus of vascular plant taxonomy and contains information on species authorship, distribution, lifeform, and climate descriptions. As our proposed workflow focuses on vascular plants, we propose using this database to standardise nomenclature and access native ranges (see section 2c. “Obtaining native ranges”). We recommend collapsing subspecies into species as many subspecies are not well documented in the WCVP. If some subspecies are deemed necessary to include in a given study these can be specified as problematic taxa (see section 2a. “Problematic taxa”). Names are matched to the WCVP to ensure congruity between the different data sources, using exact and fuzzy matching algorithms in the ‘rWCVP’ package (Brown et al. 2023). The matching algorithms return multiple matches for taxa that have multiple entries in the WCVP. In this case, matches are resolved algorithmically if they are the only accepted taxon or accepted synonym among multiple matches. The matching algorithms also return poor matches, namely phonetic fuzzy matches using the “metaphone” algorithm (Howard 2020), edit distance fuzzy matches using the highest Levenshtein edit distance (Levenshtein 1966), and no matches (see Brown et al. 2023 for more details). Phonetic matches are retained if their edit similarity exceeds 0.9. All other poor matches, taxa for which no match was kept, or taxa for which multiple matches were kept, are flagged for subsequent manual checking. All poor matches are visualised in Fig. 2a, b. 2c: Obtain native ranges The WCVP stores distribution data according to the World Geographic Scheme for Recording Plant Distributions (WGSRPD; Brummitt 2001). The finest scale available through the WCVP is the ‘botanical country’ (tdwg3 in Darwin Core) which usually aligns with political boundaries of countries or provinces/states. The choice of region (or regions) of interest is flexible and can be specified in the workflow using the tdwg3 34 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist or ‘botanical country’ code. These codes can be found using the "get_wgsrpd3_codes" function in the ‘rWCVP’ package. Taxa recorded in the WCVP as native within the botanical country (or countries) of interest are classified as “native” and taxa recorded as having native ranges entirely outside this area or which have introduced ranges in the region(s) of interest are classified as “nonnative”. If a taxon is classified as both native and introduced across multiple selected regions of interest, the native status takes precedence. Some taxa listed in the WCVP have no native or introduced range specifications; in these cases taxa will be flagged for manual checking (Fig. 2a, b). 2d: Manual checking (optional) All taxa previously flagged for manual checking because of unresolved matching with the WCVP or missing native range information in the WCVP, can be manually assessed. While this step is optional, omitting it will result in the exclusion of certain taxa (Fig. 2a, b). The workflow generates a spreadsheet of taxa requiring manual verification, which can be edited using any spreadsheet software. The user is prompted to check whether each suggested match to the WCVP should be accepted for those taxa which have poor matches and tag species as native or non-native. This can be done by filling in three columns: 1) “match_correct” which requires a Boolean TRUE if the suggested match to the WCVP is correct or FALSE if it is not correct, 2) “nativeness” which requires the user to specify either “native” or “nonnative” for each taxon, and 3) “checked” which requires the user to enter “checked” if the relevant taxon has been reviewed and should be reincorporated into the workflow, otherwise the taxon will be removed. Upon completion, the spreadsheet will be read back into R and integrated with the remaining data. 2e: Apply manual native and non-native input (optional) Some species listed in the WCVP have erroneous distribution data and can therefore be falsely flagged as native or non-native (Fig. 2e, f). To account for this, the R script allows the user to manually input taxa which, if present, will be tagged as native or non-native (as specified by the user) regardless of their distributions as shown in the WCVP. These incorrect classifications can be identified during the curation process in Step 3, or by using an a priori list of known native or non-native taxa. Although optional, this step is highly recommended to minimise any errors that might arise in the course of the workflow. Step 3: Load and curate the data on an iNaturalist project After a non-native species list is produced, a new project can be created on iNaturalist and set to collate all taxa in the list. This is done in the project settings by manually entering each species name. Due to limitations on the number of taxa that can be added to an iNaturalist project, some taxa can be listed at genus level where all species in that genus are non-native to the reference area. For example, in South Africa, we included the genera Acacia, Eucalyptus, and Pinus for which all species are non-native. If the number of taxa is still too large, one solution is to create multiple co-occurring projects each targeting a number of species in the allowed limit. For example, these can be grouped by alphabetical names of families. 35 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Figure 2. Venn diagram visualising the output of the ANNIC-R script. Proportions are not to scale. The large green circle represents taxa listed in the World Checklist of Vascular Plants (WCVP) and for which native distribution information is available. The orange circle represents taxa listed in our region of interest in iNaturalist. Species above the black line are native, those below the line are non-native. The small dashed green circle represents species which were listed in the WCVP with incorrect native distribution information. a , b. Represent native and non-native species for which good matches were not found in the WCVP or for which distribution information is not available; c, b. Represent species listed in our project for which good matches and distribution information was available in the WCVP; e, f. Represent taxa for which the distribution information in the WCVP was incorrect. Once an iNaturalist project has been created, records need to be systematically reviewed to ensure, and improve, data quality. This should be done within the iNaturalist platform because improving the records will ultimately contribute to improving the overall data quality on the platform. We also recommend engaging with users on the platform to aid in the collection and identification of relevant records. Many users on iNaturalist may also be relevant stakeholders in the region of interest whose activities, livelihoods, or well-being could be directly or indirectly affected by biological invasions. The workload of this manual curation step varies depending on the region and the taxon of interest. Some taxa are already well curated on iNaturalist (Mesaglio et al. 2023). To determine whether certain taxa are well curated, the proportion of records identified to Research Grade level is a good general guide (Mesaglio et al. 2023). Records can be identified as accurately as possible by communicating with taxonomic experts through iNaturalist and using species descriptions and keys in both peer-reviewed and grey literature where available. See similar steps described by Richardson and Potgieter (2024). We recommend investing effort in evaluating whether iNaturalist records represent captive/cultivated records. This workflow is intended for wild-growing plants, but iNaturalist records are often not correctly tagged as captive/cultivated. For plants, records can be tagged as cultivated based on considerations including, signs of cultivation or care (including trimming, weeding, support, planting pattern, etc), life stage (e.g., seedlings are more likely to be wild-growing/self-sown than large adults which may have been planted long ago), and environmental setting (e.g. plants in a garden are more likely to be cultivated than plants in a riparian zone or semi-natural vegetation). Other information such as prior knowledge of the species, location, observer notes, and communication with observers can be useful in separating wild-growing from cultivated plants. For discussion of considerations, see Gildenhuys et al. (2024), Potgieter et al. (2024a), and Richardson and Potgieter (2024). When cultivation status is unclear and cannot be ascertained for an observation, it is generally advised to assume it is wild-growing, following a precautionary principle. Special care should be taken to ensure that rare species, 42 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Taxon Prevalence index Number of urban centres found Total number of observations Gaudium laevigatum* 3.860 19 235 Eucalyptus* 3.851 23 699 Erodium moschatum 3.751 25 500 Oenothera lindheimeri 3.743 28 237 Catharanthus roseus 3.731 24 363 Cenchrus setaceus 3.717 22 467 Cirsium vulgare 3.703 26 381 Lysimachia loeflingii 3.695 29 262 Cortaderia selloana 3.621 17 384 Anredera cordifolia 3.560 21 264 Trifolium angustifolium 3.426 18 498 Stenotaphrum secundatum 3.424 20 426 Helminthotheca echioides 3.416 25 489 Opuntia ficus-indica* 3.315 26 170 Acacia melanoxylon* 3.297 18 346 Verbena bonariensis 3.256 22 322 Avena 3.154 26 497 Hedera 2.966 19 668 Arundo donax 2.959 27 321 Lotus subbiflorus 2.940 21 269 Solanum nigrum 2.882 27 378 Fumaria muralis 2.839 23 436 Coleus barbatus 2.799 22 426 Thunbergia alata 2.789 19 389 Euphorbia peplus 2.763 20 381 Limonium sinuatum 2.742 17 56 Taraxacum officinale 2.727 22 438 Cyrtomium falcatum 2.671 16 123 Melilotus indicus 2.659 23 201 Lysimachia arvensis 2.629 20 145 Oxalis corniculata 2.602 21 407 Chenopodiastrum murale 2.592 23 222 Datura stramonium 2.587 22 197 Atriplex semibaccata 2.516 23 81 Melaleuca viminalis 2.414 17 445 Myoporum montanum 2.409 23 133 Paspalum urvillei 2.377 19 248 Tecoma stans 2.367 19 78 Brassica tournefortii 2.306 20 187 Euphorbia terracina 2.292 15 334 Opuntia monacantha 2.288 16 126 Erigeron karvinskianus 2.267 16 282 Phytolacca octandra 2.258 20 164 Bidens pilosa 2.231 16 173 Hibiscus trionum 2.196 16 135 43 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist perennial (Suppl. material 1: figs S4–S6). Of the 947 taxa that were recorded only 404 (43%) were represented as naturalised or invasive in GloNAF in the Western Cape, 588 (62%) were represented in GloNAF in South Africa, and a further 267 (28%) were recorded as naturalised or invasive elsewhere in the world. Only 91 taxa recorded were not represented in GloNAF at all (Suppl. material 1: fig S7). The distribution of records across the urban centres was highly uneven, with Cape Town having the highest number of records (31 034), followed by Stellenbosch (13 689), Somerset West (3 156), George (2 721), and Mossel Bay (1191). The observed species richness was also highest in Cape Town (686), followed by Stellenbosch (410), Somerset West (383), George (376), and Hermanus (253). According to sample coverage q = 1, the best sampled urban centres were Cape Town (0.994), followed by Stellenbosch (0.991), Somerset West (0.963), George (0.949), and Mossel Bay (0.941). Completeness estimates, number of records and species richness for all urban centres are shown in Table 2. See Appendix 3 for the completeness profile for all urban centres for which completeness assessment was possible. Of the 100 urban centres included in this study, 85 had two Taxon Prevalence index Number of urban centres found Total number of observations Vinca major 2.182 20 351 Pittosporum undulatum 2.177 17 192 Cestrum laevigatum 2.176 17 246 Ipomoea indica 2.065 18 411 Acacia longifolia 2.055 15 464 Canna × hybrida 2.040 20 153 Verbascum virgatum 2.024 20 172 Erigeron bonariensis 2.016 19 412 Vicia benghalensis 1.984 15 202 Hakea sericea* 1.956 16 62 Paraserianthes lophantha 1.928 15 406 Raphanus raphanistrum 1.925 19 374 Cerastium glomeratum 1.920 19 172 Rumex crispus 1.907 18 191 Foeniculum vulgare 1.899 19 264 Limonium perezii 1.799 14 145 Kalanchoe delagoensis 1.795 15 50 Vicia sativa 1.792 18 182 Sesbania punicea 1.788 20 282 Myoporum laetum 1.786 18 103 Erigeron sumatrensis 1.784 17 250 Nothoscordum gracile 1.769 17 269 Amaranthus 1.766 21 278 Acacia podalyriifolia 1.756 16 87 Geranium molle 1.744 17 201 Portulaca oleracea 1.692 17 241 Nasturtium officinale 1.679 10 194 Solanum lycopersicum 1.652 15 121 Diplotaxis muralis 1.636 14 67 44 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist or more records (the theoretical minimum number of records required for species estimation). Fifty-seven towns had 20 or more records and only 25 had more than 100 records (Table 2). Thirteen towns had a sample coverage (q = 1) estimate of greater than 0.8, and 37 towns had an abundant species sensitive sample completeness (q = 2) greater than 0.8. This indicates that though most towns (87%) were not sampled sufficiently to have a high degree of completeness, many towns did have a large proportion of highly abundant species sampled (Fig. 4). These species are likely to correspond to the most invasive species in the region. Table 2. One hundred urban centres in the Western Cape, South Africa, included in the study to derive an inventory of non-native plant taxa from iNaturalist using a semi-automated workflow. SC.LCL is the lower confidence limit (95%) and SC.UCL is the upper confidence limit of the sample coverage (sample completeness for q = 1). Nr Urban centre Area (km2)Number records Number species Sample Coverage SC.LCL SC.UCL 1 Abbotsdale 2.32 0 0 NA NA NA 2 Albertinia 3.48 3 3 NA NA NA 3 Amalienstein & Zoar 3.31 1 1 NA NA NA 4 Ashton 4.93 7 5 NA NA NA 5 Atlantis 16.44 20 15 NA NA NA 6 Barrydale 2.91 37 29 0.281 0.139 0.423 7Beaufort West 14.56 45 18 NA NA NA 8Bella Vista 2.55 0 0 NA NA NA 9 Betty’s Bay 8.48 753 161 0.900 0.881 0.918 10 Bonnievale 7.24 0 0 NA NA NA 11 Bot River 1.74 47 30 0.374 0.175 0.573 12 Brakrivier 24.96 781 195 0.868 0.845 0.891 13 Bredasdorp 7.50 46 36 0.323 0.154 0.492 14 Brenton 2.51 175 84 0.743 0.688 0.797 15 Caledon 6.23 55 31 0.569 0.456 0.682 16 Calitzdorp 3.86 25 17 NA NA NA 17 Cape Town 846.78 31034 686 0.994 0.994 0.995 18 Ceres 6.65 8 8 NA NA NA 19 Chatsworth 2.95 0 0 NA NA NA 20 Citrusdal 4.20 0 0 NA NA NA 21 Clanwilliam 4.79 14 7 0.601 0.353 0.850 22 Dana Bay 4.84 213 81 0.746 0.703 0.788 23 Darling 4.16 37 19 0.731 0.585 0.878 24 De Doorns 9.05 0 0 NA NA NA 25 Dysseldorp 3.01 1 1 NA NA NA 26 Fisantekraal 6.39 2 1 NA NA NA 27 Fisherhaven 1.94 564 75 0.926 0.908 0.945 28 Franschhoek 4.28 35 25 0.479 0.338 0.621 29 Gansbaai 12.14 106 53 0.684 0.594 0.774 30 Genadendal 2.35 10 8 NA NA NA 31 George 74.79 2721 376 0.949 0.941 0.957 32 Grabouw 9.92 73 53 0.425 0.298 0.553 33 Greyton 2.67 175 102 0.581 0.502 0.659 34 Haarlem 2.58 0 0 NA NA NA 35 Hawston 2.41 30 15 NA NA NA 36 Heidelberg 3.80 7 6 NA NA NA 45 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Nr Urban centre Area (km2)Number records Number species Sample Coverage SC.LCL SC.UCL 37 Hermanus 23.32 1110 253 0.904 0.888 0.919 38 Herolds Bay 3.63 54 33 0.461 0.355 0.568 39 Hoekwil 3.07 97 59 0.529 0.409 0.650 40 Hopefield 5.86 10 8 NA NA NA 41 Klapmuts 6.81 11 10 NA NA NA 42 Klawer 2.54 4 4 NA NA NA 43 Kleinmond 5.16 763 215 0.837 0.819 0.855 44 Knysna 35.30 575 202 0.818 0.787 0.849 45 Kranshoek 8.62 88 43 0.635 0.519 0.750 46 Ladismith 3.98 18 16 NA NA NA 47 Laingsburg 2.77 2 2 NA NA NA 48 Lambert’s Bay 3.52 0 0 NA NA NA 49 Langebaan 19.68 54 39 NA NA NA 50 Leeu Gamka 1.98 3 2 NA NA NA 51 Lutzville 4.19 1 1 NA NA NA 52 Malmesbury 12.19 30 25 0.246 0.017 0.475 53 Mamre 2.15 6 6 NA NA NA 54 Melkbosstrand 8.17 68 33 0.661 0.537 0.785 55 Montagu 6.34 35 27 0.358 0.167 0.550 56 Moorreesburg 6.08 0 0 NA NA NA 57 Mossel Bay 39.68 1192 182 0.941 0.930 0.952 58 Murraysburg 2.05 1 1 NA NA NA 59 Napier 2.92 111 48 0.749 0.666 0.833 60 Outshoorn 23.63 64 37 NA NA NA 61 Paarl & Wellington 96.54 451 172 0.789 0.754 0.823 62 Paternoster 2.15 51 30 0.558 0.426 0.689 63 Pearly Beach 2.35 133 29 0.595 0.448 0.742 64 Piketberg 4.67 19 9 0.563 0.313 0.812 65 Plettenberg Bay 24.59 627 136 0.840 0.812 0.868 66 Pniel & Kylemore 4.01 151 60 0.660 0.581 0.739 67 Porterville 3.70 16 9 0.293 0.000 0.692 68 Prince Albert 4.33 19 13 0.457 0.180 0.734 69 Prince Alfred Hamlet 2.93 3 3 NA NA NA 70 Pringle Bay 2.93 127 62 0.673 0.591 0.734 71 Riebeek West 2.00 42 31 0.406 0.230 0.583 72 Riebeek-Kasteel 2.45 67 39 0.606 0.454 0.758 73 Riversdale 7.63 61 39 0.564 0.436 0.692 74 Riviersonderend 2.21 14 12 NA NA NA 75 Robertson 10.04 7 7 NA NA NA 76 Saldanha Bay 16.47 12 9 NA NA NA 77 Saron 2.52 0 0 NA NA NA 78 Sedgefield 6.66 153 73 0.718 0.656 0.779 79 Slangrivier 2.31 1 1 NA NA NA 80 Somerset West 78.96 3156 383 0.963 0.958 0.969 81 St Helena Bay 13.46 20 15 NA NA NA 82 Stanford 2.76 63 49 NA NA NA 83 Stellenbosch 33.57 13689 410 0.991 0.990 0.993 84 Still Bay 8.77 244 110 0.776 0.730 0.824 85 Struisbaai & L’Agulhas 7.23 28 16 0.689 0.526 0.852 86 Swellendam 10.48 165 86 0.689 0.631 0.747 46 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Discussion Main strengths of the workflow Our study confirms the utility of iNaturalist data for compiling species inventories. The inventory generated from the workflow ultimately contributes to the iNaturalist platform by encouraging species identification and data curation which improves the data for future use (e.g., for monitoring and detection). The project derived from the workflow (https://www.inaturalist.org/projects/non-nativeplants-of-western-cape-urban-areas) is freely accessible. As additional observations are posted in our region of interest, the project will automatically be updated. All data and R code used in this paper are freely available on Zenodo (https://zenodo. org/records/15210704) and GitHub (https://github.com/christiaan-g/sANNIC_ workflow). The sample completeness estimation flags both areas that are adequately sampled and those that are under-sampled and can guide the allocation of resources for adaptive biodiversity monitoring (Henrys et al. 2024). The Prevalence Index provides a unique way of ranking species based on their local abundance and broader extent within the study area considering sample completeness. Our workflow further quantifies some of the uncertainty inherent in the compilation of non-native species inventories. We used a sample completeness profile to assess the inventory completeness of urban centres (Fig. 4) which is based on the biodiversity sampling measures recommended by Chao et al. (2020). When the completeness profile increases and the measures of order 1 and 2 are similar and high, it suggests that the area is well-sampled. Alternatively, if there is high uncertainty in the different measures or if the profile decreases, it can indicate insufficient or unrepresentative sampling. Species diversity can be compared between areas which have a similar sample coverage. This can be done using sample rarefaction and extrapolation or asymptotic estimation (Hsieh et al. 2016). Although our data were sourced from iNaturalist and our code is designed for this platform, the workflow can easily be adapted to incorporate data from other sources. For example, data can be sourced from other popular community science platforms such as Pl@ntNet (https://plantnet.org/en/) and eBird (https:// ebird.org/home) or from traditional databases of herbarium and museum specimens. We recommend using the proposed workflow alongside other sources of Nr Urban centre Area (km2)Number records Number species Sample Coverage SC.LCL SC.UCL 87 Touws River 3.22 3 3 NA NA NA 88 Tulbagh 3.72 20 18 NA NA NA 89 Uniondale 3.09 24 16 0.463 0.210 0.716 90 Vanrhynsdorp 3.69 17 13 0.426 0.143 0.709 91 Velddrif 8.56 37 25 0.448 0.226 0.671 92 Villiersdorp 3.25 5 5 NA NA NA 93 Vredenburg 11.44 11 9 NA NA NA 94 Vredendal 10.85 6 6 NA NA NA 95 Wilderness 8.16 332 110 0.804 0.766 0.842 96 Windmeul 2.98 16 13 NA NA NA 97 Witsand 2.22 43 22 0.675 0.545 0.804 98 Wolseley 4.86 14 10 0.521 0.223 0.819 99 Worcester 34.95 45 32 0.422 0.262 0.582 100 Yzerfontein 3.46 46 29 0.527 0.379 0.674 47 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist data to improve the comprehensiveness of the inventory. The data from many of these sources are already available on GBIF. Each data source will have their own data quality requirements which should be carefully evaluated and used to guide appropriate adaptations of the workflow. We used the WCVP to extract native range information for each species because it is the most extensive database available for vascular plants. Other sources of information (e.g. IUCN native range shapefiles) could be used for other taxonomic groups such as birds and mammals. However, for many taxonomic groups, no compiled databases are available, and knowledge remains limited. The proposed sANNIC workflow functions fundamentally differently from workflows such as DASCO (Seebens and Kaplan 2022). While other workflows often attempt to downscale or synthesise already existing inventories focussed on species that are already advanced on the naturalisation-invasion continuum (Richardson et al. 2000), sANNIC is aimed at building an inventory from scratch using iNaturalist data, focussing on species at all stages along the naturalisation-invasion continuum. As a result, it generates a more comprehensive inventory and is less likely to overlook non-native species recorded on iNaturalist compared to other workflows. A limitation to the workflow is that it requires intensive curation and knowledge of the taxa of interest and can be time consuming. Our workflow can potentially be used together with other workflows and applications such as infinitylists which allow users to rapidly generate species checklists for a particular area from GBIF data (Mesaglio et al. 2025). We stress the need for data curation to ensure the robustness of generated inventories. Figure 4. Completeness profiles for four urban centres in the Western Cape ranging from most complete (top left) to least complete (bottom right). Panels (a–d) represent urban centres with the following profiles. a. Highly complete sample coverage (q = 1, > 0.8); b. Lower completeness, but with a high proportion of highly abundant species sampled (q = 2, > 0.8); c. Poor sampling, but a rising completeness profile indicates that sample completeness can at least be estimated; d. Poor sampling and a falling completeness profile indicates that estimates of sample completeness may be unreliable. 48 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Limitations and practical recommendations The level of curation required for the application of the sANNIC workflow in different regions is likely to vary, given the differences in identification and observation efforts across regions (Campbell et al. 2023). The drawbacks of using iNaturalist include dealing with difficult taxonomic groups for which many species cannot be identified given the typical image quality on iNaturalist (Ackland et al. 2024); potential crypticity and hybridisation with native species pose additional challenges. Moreover, the workflow does not differentiate the status of species along the naturalisation-invasion continuum but can be used as a foundation for further refinement. The prevalence index can be used as a proxy of advancement along the naturalisation-invasion continuum, as abundant and widespread species are more likely to be invasive than rarer “casual” species. However, this may not necessarily be the case, and the introduction status, e.g. categorisation according to the Blackburn et al. (2011) framework, needs to be determined separately from the workflow. We observed a highly uneven sampling effort across the urban centres of the Western Cape as most towns had insufficient records for a completeness assessment. Sampling effort is skewed toward larger urban centres closer to the coast, though there were some exceptions. This spatial skewness is typical for iNaturalist and other unstructured community science platforms as users are more likely to reside in these areas or visit them for recreational purposes. The skewness provides a challenge for species monitoring across the area of interest, as many areas remain under sampled. We accounted for the uneven sampling by using the prevalence index which considers the rank of a species at each urban centre where it was sampled, weighed by the Sample Coverage of that centre. Using this approach, we produced a list of the most prevalent species in urban centres of the Western Cape while minimising potential biases introduced by spatial heterogeneity. The prevalence index can also be adapted for use in a gridor observer-based framework to account for spatial heterogeneity or potential observer bias. However, this index may still be biased by general observer preferences and/or the inherent recognisability of species (Mesaglio et al. 2023). The number of records in some areas could be increased by City Nature Challenges (Di Cecco et al. 2021); these events challenge users to collect as many records as possible over several days. Although records should be collected consistently throughout the year, seasonal heterogeneity can be mitigated by assessing whether observations are distributed year-round, promoting data collection during off-seasons, or applying data coarsening where sufficient data are available. Furthermore, the capacity for boosting research grade records, increasing the number of high-quality records, is limited by the number and quality/experience of identifiers. We recommend engaging with existing identifiers on the platform and experts not yet engaged with iNaturalist to overcome the shortage of identifiers (Callaghan et al. 2022b). Although estimating sample completeness is essential for interpreting the data, several limitations need to be considered. Uneven spatial or seasonal sampling within urban centres can affect sample coverage and influence how accurately the number of species can be estimated. For example, if observations are strongly concentrated during a particular time of year, the sample coverage will be overestimated since only species visible during that time of year will be observed. Strong spatial autocorrelation of records may result in an overestimation of sample coverage and underestimation of species richness if species occurrence is highly heterogeneous. We encourage caution when using sample completeness measures, especially in areas with few records and 49 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist strongly encourage the reporting of uncertainty of sample completeness measures using bootstrapping, as is available in the ‘iNEXT’ packages (Chao et al. 2020). These tools, however, have their limitations and comparing diversity of areas with differing levels of sample completeness is less robust and therefore not recommended. Even when sample completeness is low for a given area, the resulting species list remains valid and can be valuable for some ecological questions or management purposes. Western Cape inventory case study The list of species generated in this case study has many potential uses and implications for the regional understanding of plant invasions in urban areas of the Western Cape of South Africa. Many taxa in our list are among the most common urban plant invaders globally (Li et al. 2024) but there are many exceptions. Many species in our list are largely invaders of natural or semi-natural vegetation embedded within or adjoining the urban areas, e.g. Acacia and Pinus species (Richardson et al. 2020). Invaders of natural habitats may benefit strongly from the movement of propagules (intentional or unintentional) through urban centres. Other species owe their presence to widespread plantings, mainly as ornamentals, and subsequently self-seed into urban green spaces (Donaldson et al. 2014; Gildenhuys et al. 2024; Louw et al. 2024; Milton and Dean 2025). Urban centres are often donors of non-native species to surrounding areas (McLean et al. 2018). Novel urban habitats and disturbances created by urbanisation may aid in the establishment of many species within urban centres. Potgieter et al. (2024c) highlight the different ways in which urban habitats can shape biological invasions. The best represented families, (Fabaceae, Asteraceae, and Poaceae) in our case study are not surprising; these are generally the best represented plant families in non-native floras globally (Suppl. material 1: figs S1–S3). However, there were a few families such as Myrtaceae which are disproportionally represented in the non-native flora of this region. This may partly be because of the long history of introduction of many species in this family to South Africa (Poynton 2009). Plant life forms were varied (Suppl. material 1: figs S4–S6) with the most common life form being annual and/or perennial herbs followed by shrubs and/or trees. The high representation of herbaceous life forms is to be expected in highly modified urban environments where a ruderal strategy may be more advantageous (Frazee et al. 2019). Further work is needed to elucidate the patterns and drivers of invasion in the study area. Of the 947 taxa, 856 (90%) were represented in GloNAF globally, but only 404 (43%) were listed as being naturalised in the Western Cape in GloNAF. Another 185 (20%) were listed as naturalised in South Africa in GloNAF, but not in the Western Cape, and 91 taxa were not represented in GloNAF at all (Suppl. material 1: fig S7). This highlights some of the gaps in the GloNAF database especially at the sub-national level. The absence of some of the taxa in GloNAF but present in our inventory may be attributable to differences in categorising species as “casual” rather than “naturalised” or “invasive” (sensu Richardson et al. 2000), but others have simply been overlooked in previous regional assessments. Future directions We believe our workflow provides the foundation for further research into the non-native species of any region with sufficient iNaturalist coverage. If an inventory with a high coverage for a particular area is available, research can focus on clarifying the introduction status of each species, introduction pathways, possible impacts, 50 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist and patterns of establishment and invasion within the region of interest. We highlight the necessity of thorough curation of the records directly on iNaturalist and believe that our workflow could be used alongside other sources of data such as systematic surveys to produce high quality inventories of non-native species. Acknowledgements This research was supported by the Centre for Invasion Biology and the Department of Botany and Zoology, Stellenbosch University. We are deeply grateful to everyone who contributed observations and identifications to the iNaturalist project. These include but are not limited to Clare Archer (@sedgesrock), Brian du Preez (@mr_fab), Sandra Falanga (@sandraf), Bianke Fouche (@biankefouche), Jeremy Gilmore (@jeremygilmore), David Hoare (@dhoare), Suzaan Kritzinger-Klopper (@zaniekk), Caroline Mashau (@carolinemashau), Phil McLean (@fynbosphil), Erick Munro (@erickmunro), Tony Rebelo (@tonyrebelo), Norbert Sauberer (@brothernorbert), Charles Stirton (@charles_stirton), Diana Studer (@dianastuder), Hedi Stummer (@venturefoth), Shaun Swanepoel (@shauns), Pieter Winter (@pieterwinter), Mark van Dalsen (@vandalsen), @linkie, and many more. A full list of identifiers and observers that contributed to the project can be found at https://www.inaturalist.org/ projects/non-native-plants-of-western-cape-urban-areas. Additional information Conflict of interest The authors have declared that no competing interests exist. Ethical statement No ethical statement was reported. Use of AI No use of AI was reported. Funding CPG is supported by a postgraduate scholarship from the National Research Foundation (PMDS230601112319). LJP acknowledges support from the Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, and from the Natural Sciences and Engineering Research Council of Canada (grant 386151). CH is supported by the NRF (grant 89967) and the European Union’s Horizon Europe Research and Innovation Programme (B3 – Biodiversity Building Blocks for policy, ID 101059592). Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the EU nor the EC can be held responsible for them. Author contributions Data cleaning was performed by DMR, CPG, and LJP. Supervision was performed by DMR, CH, and LJP. Writing of associated software was performed by CPG. All authors contributed to the conceptualisation and writing of the manuscript. Author ORCIDs Christiaan P. Gildenhuys https://orcid.org/0009-0006-5226-3250 51 NeoBiota 104: 27–58 (2025), DOI: 10.3897/neobiota.104.155832 Christiaan P. Gildenhuys et al.: Deriving inventories of non-native plant species from iNaturalist Luke J. Potgieter https://orcid.org/0000-0001-7790-2721 Cang Hui https://orcid.org/0000-0002-3660-8160 David M. Richardson https://orcid.org/0000-0001-9574-8297 Data availability All code and data used in this study are available in Zenodo (10.5281/zenodo.15210704) and the latest versions will be made available in GitHub (https://github.com/christiaan-g/sANNIC_workflow). All additional figures referred to are available in the Suppl. material 1. References Ackland SJ, Richardson DM, Robinson TB (2024) A method for conveying confidence in iNaturalist observations: A case study using non‐native marine species. 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See Fig. 4 for selected examples. Supplementary material 1 The most abundant families in the inventory Authors: Christiaan P. Gildenhuys, Luke J. Potgieter, Cang Hui, David M. Richardson Data type: docx Copyright notice: This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.3897/neobiota.104.155832.suppl1