Assessing the effectiveness of genetic observatory networks in detecting and monitoring marine non-indigenous species.

doi:10.21203/rs.3.rs-4804152/v1

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Assessing the effectiveness of genetic observatory networks in detecting and monitoring marine non-indigenous species.

https://doi.org/10.21203/rs.3.rs-4804152/v1

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The increasing prevalence of non-indigenous species (NIS) in marine ecosystems poses significant challenges for biodiversity conservation and ecosystem management. Advances in molecular techniques enable early detection and long-term monitoring of such taxa, especially when coupled with spatio-temporally wide-scaled sampling by networks such as the European ARMS Marine Biodiversity Observation Network (ARMS-MBON). This initiative performs standardised sampling campaigns using autonomous reef monitoring structures (ARMS) along European coasts and adjacent regions, providing open-access DNA metabarcoding data sets. We tested the efficacy of genetic observatory networks to detect and monitor marine NIS by analysing all publicly available ARMS-MBON cytochrome c oxidase subunit I (COI) and 18S rRNA amplicon sequencing data as of February 2024 using a customised bioinformatic pipeline. Screening against the World Register of Introduced Marine Species (WRiMS) and applying manual curation, we identified 63 marine taxa considered non-indigenous at one or more locations. This included widespread taxa and potential new introductions, such as Eucheilota menoni in the Adriatic Sea. However, we found no significantly higher number of NIS in samples from locations particularly impacted by maritime traffic compared to other areas. Our results suggest that the genetic observatory network approach is effective for detecting and monitoring marine NIS, including mapping of potential range shifts, detection of NIS hot spots, and as an early warning system. We recommend key improvements including more spatially intense sampling across diverse environments, and enhancement of NIS reference lists and genetic databases to ensure accurate identification of both known and unknown NIS across Europe.

alien species

invasive species

Autonomous Reef Monitoring Structures (ARMS)

COI

18S rRNA

amplicon sequencing

Marine ecosystems play crucial environmental and socio-economic roles, yet they face significant threats such as biodiversity loss and degradation (Luypaert et al., 2020; Mazaris et al., 2019; O’hara et al., 2021), often exacerbated by non-indigenous species (NIS). These species, usually introduced through global maritime traffic, pose significant risks to native marine communities (De Poorter et al., 2008; Katsanevakis et al., 2023; Pyšek et al., 2020; Tsiamis et al., 2020). Rapid identification and management of NIS are crucial, especially when introduced populations are still localised and manageable (Pyšek et al., 2020; Trebitz et al., 2017).

In the scientific literature, various terms are used - often interchangeably - to refer to introduced taxa in marine systems, oftentimes describing different stages of establishment or levels of impact. These terms include invasive species, alien species, introduced species, non-native species, or neobiota (Kuhlenkamp and Kind, 2018). We here use the term NIS throughout this study to refer to taxa introduced to marine regions outside of their known native range likely through anthropogenic activities. This term does not necessarily imply a potential harmful status of a taxon (often termed invasive in this context), which may yet to be determined for various NIS.

Historically, marine NIS have been recorded through visual surveys based on morphological identifications, which requires taxonomic expertise and tends to overlook small and cryptic organisms (Leray and Knowlton, 2016). Recently, DNA-based methods have emerged as an innovative tool for the detection and identification of NIS (Ammon et al., 2018; Coster et al., 2021; Darling and Mahon, 2011; Fonseca et al., 2023; Holman et al., 2019; Othman et al., 2023; Pearman et al., 2020; Wu et al., 2023). Combining DNA metabarcoding with high-throughput sequencing (HTS) represents a non-disruptive approach utilising unique genetic signatures present in environmental or bulk community samples to determine taxonomic composition of biotic communities and screen rapidly for the presence of potentially harmful organisms (Hablützel et al., 2023). While in this context metabarcoding often refers to the analysis of environmental DNA (eDNA) present in soil, water, or air, it can also be applied to other genetic material, such as whole organism community DNA (wocDNA) (Creedy et al., 2022) or DNA from gut contents (Trujillo-González et al., 2022) and faecal samples (McInnes et al., 2017). Molecular techniques also tackle the challenges posed by morphological identification of species at early life stages, such as the egg and larval stage (Gallage et al., 2023; Hoffman et al., 2021), and through their ability to detect rare and elusive species (Goldberg et al., 2016). In fact, molecular methods are extremely sensitive, able to detect DNA of NIS and other rare species making up only a small fraction of total biomass in a sample (Hatzenbuhler et al., 2017).

To this date, despite the immense potential of DNA metabarcoding for marine invasion ecology, studies applying this method across large spatial scales remain relatively scarce. Previous works have analysed eDNA to assess marine metazoan diversity in a regional context (Fraija-Fernández et al., 2020; West et al., 2021). Recently, the Tara Oceans initiative - through its global sampling campaigns and provision of resulting metabarcoding data sets - has enabled groundbreaking research on marine plankton communities on a planetary scale (Bork et al., 2015).Brown et al. (2016) were the first to apply metabarcoding on marine and freshwater samples to screen for NIS on a (semi)continental scale but this approach has not yet been integrated into long term ecological research. However, in the context of global change and resulting species migrations, large-scale studies of marine biodiversity and invasion patterns are essential to understand global ecosystem dynamics (Lacoursière-Roussel et al., 2018).

Previous research on NIS in aquatic environments typically employed molecular techniques aimed at detecting specific species like the American bullfrog (Lithobates catesbeianus) (Dejean et al., 2012), Asian carps (Hypophthalmichthys nobilis and Hypophthalmichthys molitrix) (Jerde et al., 2013; Mahon et al., 2013), the bluegill sunfish (Lepomis macrochirus) (Takahara et al., 2013), the New Zealand mudsnail (Potamopyrgus antipodarum) (Goldberg et al., 2013), and the red swamp crayfish (Procambarus clarkii) (Tréguier et al., 2014). Although these methods are efficient for tracking well-known invaders, they fail to account for other potentially significant NIS (Lawson Handley, 2015). A more beneficial approach to NIS management could involve passive surveillance based on the description of entire communities through metabarcoding.

A number of studies have used metabarcoding to detect NIS among whole marine communities, however they have primarily focused on limited geographic areas (Borrell et al., 2017; Günther et al., 2018; Westfall et al., 2020; Zarcero et al., 2024) or used customised reference libraries tailored to known NIS (Couton et al., 2019), thereby limiting the detection to already known species in an area. In such spatially and taxonomically more limited contexts, it is common to compile a preliminary list of target taxa to screen for, which can be more challenging for studies on a larger geographical scale, however. Global, curated databases such as the World Register of Introduced Marine Species (WRiMS) (Costello et al., 2021) containing entries of NIS and their recognized regions of introduction are indispensable for large-scale, comprehensive screening. Previous studies have relied on such databases to screen for NIS on a national scale (Holman et al., 2019), while Grey et al. (2018) for example, used previous records from databases such as the World Register of Marine Species (WoRMS) (Ahyong et al., 2024a) and the IUCN Red List to assess if a putative NIS truly was introduced in a specific area.

Various methodologies have been used to sample DNA in terrestrial and aquatic environments (Thomsen and Willerslev, 2015). Autonomous reef monitoring structures (ARMS) are standardised, easy-to-produce devices used for non-destructive sampling of marine benthic communities. These structures were originally designed to mimic natural coral reef habitats to facilitate the collection of genetic material from cryptobenthic reef communities (Knowlton et al., 2010; Leray and Knowlton, 2015; Plaisance et al., 2011), but are now applied in a variety of marine ecosystems (Obst et al., 2020; Thomasdotter et al., 2023). These sampling units offer a unique opportunity to study marine biodiversity on hard-bottom substrates and to detect NIS in a standardised and replicable manner by obtaining bulk samples of the communities that inhabit these structures.

In this study, we aim to investigate the effectiveness of a standardised sampling approach with DNA metabarcoding for detecting marine NIS on hard-bottom substrates and studying their potential range expansion or new introduction across broad geographic scales using amplicon sequencing raw data generated by the European ARMS Marine Biodiversity Observation Network (ARMS-MBON) (Obst et al., 2020). This network continuously deploys ARMS in the vicinity of marine stations, ports, marinas, marine protected areas (MPAs), and long-term ecological research (LTER) sites distributed along the shores of Europe as well as in additional subtropical and polar regions. To this end, we harvested all publicly available genetic data (as of February 2024) of two universal marker genes from this network, which encompasses the entirety of ARMS-MBON sampling campaigns conducted from its initiation in 2018 up to the year 2021. To the best of our knowledge, our study represents i) the largest set of ARMS metabarcoding data analysed to date; ii) the first work using ARMS MBON metabarcoding data solely to comprehensively screen for NIS; and iii) a NIS scan across one of the largest geographical areas to date. By analysing data of two universal molecular markers (i.e., one mitochondrial and one nuclear gene) and developing a custom pipeline for efficient data processing and manual taxonomic occurrence curation, we evaluate the potential of the genetic observatory network approach for the early detection and continuous monitoring of marine NIS, including the study of their range expansion and the identification of potential hotspots. We further describe the limitations encountered, suggest points for improvement, and give recommendations for future applications.

Spatial and temporal coverage

The data set used for this study was generated by the European ARMS-MBON programme (Obst et al., 2020). It encompasses samples collected by 19 observatories distributed across 14 countries, which deployed (and continuously deploy) ARMS units at one to seven sites each, in the vicinity of marinas, ports, MPAs, and LTER sites - from Svalbard in the north to the Red Sea in the south (Fig. 1). This comprises 112 ARMS units in total, which remained deployed for varying periods between April 2018 and November 2021. The duration of sampling events (i.e., deployment duration of individual ARMS units) ranged from 37 to 649 days. Observatories processed all ARMS units and for each unit preserved homogenised samples of the following three fractions: i) one sessile fraction (all 40-µm-filtered sessile material attached to ARMS plates, hereafter referred to as “sessile fraction”); and ii) two motile fractions representing all biological material not attached to ARMS plates, i.e., one fraction of material with a size of 100 to 500 µm (hereafter “motile fraction 100 µm”) and one fraction of material larger than 500 µm (hereafter “motile fraction 500 µm”). Details on observatories and deployments can be found in Supplementary File 1. The descriptions of observatory design and fieldwork procedures have been expanded upon since Obst et al. (2020). ARMS units were deployed, retrieved, photographed, and processed by individual partners in the ARMS-MBON network following the ARMS-MBON protocols (accessible through the ARMS-MBON GitHub repository at https://github.com/arms-mbon). See Supplementary Table S1 for links to ARMS-MBON webpages and GitHub repositories.

Description of the amplicon sequencing data set

Molecular laboratory procedures (i.e., DNA extraction, PCR amplification and amplicon sequencing) for samples from ARMS units processed by each network partner (see above) were carried out centrally at the facilities of an ARMS-MBON network partner, the Hellenic Centre for Marine Research (HCMR), Crete, Greece. Protocols and documentation on this are accessible via the ARMS-MBON GitHub repository (https://github.com/arms-mbon), see Supplementary Table S1 for links. We obtained all publicly available (as of February 17th, 2024) amplicon sequencing raw data of the mitochondrial cytochrome c oxidase subunit I (COI) and nuclear 18S rRNA (18S) marker genes from ARMS-MBON. These markers amplify the 313-bp-long “Leray fragment” of COI (Geller et al., 2013; Leray et al., 2013) and a fragment of the hypervariable V9 region of 18S (Hardy et al., 2010). Contrary to the descriptions by (Hardy et al., 2010), we observed that most amplicons of the ARMS-MBON 18S data set were shorter than 200 bp.

In total, the data set encompasses 349 sequencing samples for COI (plus nine negative control samples) and 354 sequencing samples for 18S (plus eight negative control samples). Raw sequences of all samples are available on the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena/browser/home) (Yuan et al., 2024) and accession numbers can be found in Supplementary File 2. We refer to the computational protocol described below (Daraghmeh, 2024) for details on how raw data was obtained for this study.

Procedure for NIS detection

A computational protocol with detailed descriptions of bioinformatics processing of sequencing data and subsequent NIS identification has been published (Daraghmeh, 2024) (Private link for reviewers: https://www.protocols.io/private/0EB7453E33BB11EFA81B0A58A9FEAC02 to be removed before publication). We briefly describe the procedure below and refer to the published protocol for all code and Supplementary Text S1 for details on software used and parameter settings applied.

Sequence denoising, clustering into molecular operational taxonomic units and taxonomic classification

We processed COI and 18S raw reads separately in R v4.3.1 (R Core Team, 2021). We used cutadapt v4.5 (Martin, 2011) to remove primers from sequences still containing them (some of the demultiplexed sequence reads provided by ARMS-MBON were already devoid of primers). Quality assessment and read trimming were conducted using DADA2 v1.28.0 (Callahan et al., 2016) prior to constructing error models and inferring amplicon sequence variants (ASVs). We subsequently merged paired-end reads and removed chimeric sequences via DADA2's removeBimeraDenovo function. Read numbers remaining in each sample were tracked through each step of the pipeline (see Supplementary File 3).

Taxonomic classification for 18S ASVs was performed within DADA2 via the RDP classifier (Wang et al., 2007), using the SILVA v132 reference set (Quast et al., 2013) formatted for use in DADA2 (Callahan, 2018) and a subset of its eukaryotic sequences clustered at 99% similarity (Morien and Parfrey, 2018); as well as the PR2 v5.0.0 (Guillou et al., 2013) database. An ensemble taxonomy was generated for 18S ASVs using parts of the ensembleTax v1.2.2 (Catlett et al., 2023) package, merging the taxonomy tables of the three different reference assignments.

Putative nuclear mitochondrial pseudogenes (NUMTs) were identified using MACSE v2.05 (Ranwez et al., 2018) in R v4.1.0 and removed from the COI data set. We applied a negative-control correction to remove all ASVs for which the read count within negative control samples amounted to 10% or more of their total read count. Subsequently, clustering of ASVs into molecular operational taxonomic units (mOTUs) was performed using swarm v3.0.0 (Mahé et al., 2015), setting the parameter d to 13 and 1 for COI and 18S, respectively. As described by Mahé et al. (2014), the use of high d-values may be considered when working with relatively fast evolving marker genes such as COI. A comparatively high d-value of 13 has been identified as most suitable for the “Leray fragment” (Antich et al., 2021) and has now been used widely in studies applying COI amplicon sequencing (Antich et al., 2021; Atienza et al., 2020; Bakker et al., 2019; Siegenthaler et al., 2019; Wangensteen et al., 2018). The resulting mOTUs were further curated using the R package LULU v0.1.0 (Frøslev et al., 2017) to remove putative erroneous mOTUs after generating a match list using BLAST + v2.11.0 (Camacho et al., 2009).

Taxonomy was assigned to COI mOTUs using two reference databases via two classification tools. We queried against the Barcode of Life Data System (BOLD) (Ratnasingham and Hebert, 2007) database using BOLDigger-commandline v2.2.1 (Buchner and Leese, 2020). In addition, we assigned taxonomy to our COI dataset using MIDORI2 (Leray et al., 2022) via the associated web server tool MIDORI server (Leray et al., 2018). The latter was based on GenBank release 257 (Benson et al., 2012) at the time of access (February 2024). In R, we then compared classification based on MIDORI2 and BOLD and created a final taxonomy table using packages from the tidyverse v1.3.1 collection (Wickham et al., 2019). Ultimately, both COI and 18S mOTUs with the same species classification were merged and the corresponding read abundances summed. Additional R packages used during all previously described steps include ggplot2 v3.4.2 and v3.4.3 (Wickham, 2016), Biostrings v2.68.1 (Pagès et al., 2020), ShortRead v1.58.0 (Morgan et al., 2009), hiReadsProcessor v1.29.1 and v1.36.0 (Malani, 2021), dplyr v1.0.9 and v1.1.3 (Wickham, François, et al., 2023), tidyr v1.3.0 (Wickham, et al., 2023), seqinr v4.2.30 (Charif and Lobry, 2007), argparse v2.2.2 (Davis, 2023), stringr v1.5.0 (Wickham, 2022), devtools v2.4.3 (Wickham et al., 2022), and remotes v2.4.2 (Csárdi et al., 2024).

Screening for non-indigenous species

We queried against the WRiMS database (Costello et al., 2024) to obtain a list of putative NIS to screen for. Here, we downloaded lists of species registered in WRiMS for the regions corresponding to the observatory locations (accessed on February 26th, 2024). For details on how the standardised search was performed, see Daraghmeh, (2024). All individual lists were then merged in R using packages mentioned above and the xlsx v0.6.5 package (Dragulescu and Arendt, 2020) to obtain a list of taxa registered in WRiMS for regions corresponding to the ARMS-MBON sampling locations.

We then filtered the COI and 18S data sets in R using the packages phyloseq v1.36.0(McMurdie and Holmes, 2013) and data.table v1.14.2(Dowle and Srinivasan, 2021) to extract mOTUs classified at the species level. Species-level assignments were matched against the World Register of Marine Species (WoRMS) database(Ahyong et al., 2024b) to obtain the respective accepted names for each species using the web services of LifeWatch Belgium (https://www.lifewatch.be/data-services/). All mOTUs assigned to a species found in the compiled WRiMS list were subset with their respective occurrence information. We then tracked all ASVs throughout the pipeline to obtain information on which of these putative NIS mOTUs they ended up in. This was done to manually check every NIS occurrence (see below). Occurrences of ASVs with less than 5 reads were set to zero. All size fraction samples for each ARMS sampling event (i.e., an individual ARMS unit deployed for a given period) were merged and corresponding read abundances were summed.

Manual curation of NIS occurrences

Taxonomic classification of sequences from DNA metabarcoding remains challenging, as the markers applied may lack sufficient species-level resolution and/or the amplicons may be too short to obtain species-level classification with a high confidence (Porter and Hajibabaei, 2020). We meticulously curated potential NIS occurrences. For the COI marker, ASVs were again manually queried against BOLD (or NCBI's GenBank in some cases). For 18S, ASVs were manually queried against GenBank (or PR2 in some cases). Where ASVs did not have a sufficient match at the species level or also matched native species they were removed. If ASVs only occurred at locations where the respective taxon was considered native or where literature indicated questionable NIS status, they were also excluded (see Supplementary Text S1 and Supplementary File 5 for details). Ultimately, curated data for COI and 18S markers were combined to generate a comprehensive data set detailing NIS distribution across sampling events.

Analysis of NIS observations across the network

We initially assessed a data set including all occurrences of taxa considered as putative NIS at at least one of our study locations. This allowed us to obtain information on the pan-European distribution of these taxa across their range of introduction as well as across their native range (for cases where the latter included one or more of our surveyed locations) (see Supplementary File 6). Given varying taxonomic rank specifications in reference databases, we manually corrected the taxonomy of putative NIS mOTUs to obtain correct phylum-level assignments. We then assessed phylum-wise relative abundances of the NIS in our data set, as well as the number of observatories each NIS occurred at. To quantify and visualise unique and shared occurrences of NIS across observatories, an UpSet plot was generated. For this, we considered only the occurrences representing non-indigenous status at a given location (i.e., removing occurrences of a putative NIS at locations where it was considered native). Exploratory analyses were performed using R v4.3.1 and the packages dplyr v1.1.3 (Wickham, François, et al., 2023), ggplot2 v3.5.1 (Wickham, 2016), scales v1.3.0 (Wickham et al., 2023), forcats v1.0.0 (Wickham, 2023), tidyr v1.3.0 (Wickham et al., 2024), RColorBrewer v1.1.3 (Neuwirth, 2014), ggpubr v0.6.0 (Kassambara, 2023), UpSetR v1.4.0 (Conway et al., 2017), and phyloseq v1.44.0. All code utilised for analyses detailed below can be found at https://github.com/JustinePa/studyNIS_ARMS.

We then compared the occurrences of all detected NIS with their currently known range according to the information obtained during the manual curation, to identify potential range shifts (for details see Supplementary Text S2). For NIS that were found in previously unknown locations, we used the R package rgbif v3.8.0 (Chamberlain et al., 2024) to retrieve and plot their occurrences data from the Global Biodiversity Information Facility (GBIF) (GBIF, 2024). We used GBIF occurrences as a proxy for the known range of each species (including both native and introduced ranges) and mapped occurrences deposited in GBIF and occurrences from our data set using the R package rnaturalearth v1.0.1 (Massicotte and South, 2024) to visualise potential range shifts. See Supplementary File 7 for information on retrieved GBIF occurrences for each investigated NIS.

Standardisation of data sets and comparative analysis

The data we used for the above steps was the most unfiltered data set, as our aim was to perform a comprehensive assessment with all available data. However, to be able to compare NIS prevalence between sites and types of monitoring areas in a robust manner, similar sampling and sequencing efforts needed to be achieved within data sets. See Supplementary Text S3 for details.

Briefly, we kept the initial data of both marker genes separate, removed certain replicate samples and rarefied samples to achieve even sequencing depth. We then again only considered the occurrences representing non-indigenous status at a given location (i.e., removing occurrences of a putative NIS at locations where it was considered native). First, we tested whether samples of monitoring areas particularly impacted by maritime traffic (i.e., marinas, harbours, and industrial ports) showed higher NIS prevalence than samples from other types of areas using the non-parametric Kruskal-Wallis rank sum test in R.

Then, observatories were re-classified into so-called “sites”, i.e., standardised groups of field replicates, using the R package geosphere v1.5.18 (Hijmans, 2022) to achieve equal sampling effort. In fact, within ARMS-MBON, observatories deploy ARMS units at one or multiple locations and therefore are not suitable as standardised entities to be compared among each other. Additionally, as the number of size fraction samples remaining for each ARMS unit after bioinformatic processing varied, we generated five separate “fraction” data sets for each marker gene, comprising ARMS sampling events were the samples of i) all three fractions, ii) both motile fractions, ii) the sessile fraction, iv) the motile fraction 100 µm, and v) the motile fraction 500 µm remained after data curation. This allowed us to compare the number of NIS per site and fraction data set for entities of equal sampling and sequencing effort. Using QGIS v3.34, we then visualised the number of detected NIS per fraction data set for each site for both the COI and 18S marker (input files used to generate these maps are provided in Supplementary File 8).

General description of the sequencing data set

After sequence filtering, denoising and clustering, the COI data set used for comprehensive NIS screening comprised 305 remaining samples with a total of 7,711,978 reads in 10,646 mOTUs. The 18S data set comprised 343 remaining samples containing a total of 14,722,467 reads in 13,771 mOTUs. Only around 27% of COI mOTUs could be classified at the phylum level, but these accounted for ~ 78% of the total read number. In the case of 18S, ~ 47% of mOTUs representing ~ 85% of all reads could be classified at the phylum level. For COI, 807 mOTUs (~ 8%) making up ~ 58% of all reads could be assigned a species name. Classification to species level could be obtained for 1,076 18S mOTUs (~ 8%) which amounted to ~ 34% of the total read number.

Curation of putative NIS identification

We found 824 species registered in WRiMS for the larger marine regions covered in our study. Screening against this list, we obtained species matches for 78 COI mOTUs (equalling 78 species) containing 447 ASVs and for 35 mOTUs of 18S rRNA (equalling 35 species) comprising 154 ASVs (see Supplementary File 4).

We excluded 93 ASVs from the COI data set due to ambiguous classification or insufficient taxonomic resolution, and four ASVs that also matched native species. The exclusion of these 97 ASVs led to the removal of nine COI mOTUs. Additionally, two mOTUs matched with reference sequences for both Watersipora subtorquata and W. subatra and these were separately kept as Watersipora sp. as both species are considered NIS at their location of occurrence in our data set.

For 18S, 71 ASVs displayed ambiguous taxonomic assignments and it could not be clearly determined if they represented a NIS, hence, these were discarded. The exclusion of these ASVs led to the elimination of ten mOTUs. Five mOTUs were manually assigned to a higher taxonomic rank as they were assigned to more than one taxon considered as NIS at the location of occurrence, and it could not be determined which species they belonged to.

In addition, we reviewed the likely origin and introduction status for each putative NIS based on available literature. As a result, 16 mOTUs (containing a total of 43 ASVs) were excluded from the COI data set and 5 mOTUs (containing a total of 52 ASVs) were excluded from the 18S data set as there was no clear evidence concerning their non-indigenous status at any of their locations of occurrence. Details on manual curation and associated references which were consulted to determine NIS status can be found in Supplementary File 5.

Overall description of the curated NIS dataset

After validation of taxonomic classification and putative NIS status, 52 species (i.e., 52 mOTUs with 307 ASVs) and 20 species (i.e., 20 mOTUs with 31 ASVs) remained as putative NIS in the COI and 18S data sets, respectively (see Supplementary Figure S1). These represented 63 unique taxa which were putatively non-indigenous at one or more locations of their occurrence. Of these, 43 NIS were detected with the COI marker only, eleven with the 18S marker only, and nine were detected by both genes.

The majority of NIS detected were arthropods (n = 16), followed by molluscs (n = 9), chordates (n = 8) and bryozoans (n = 8) (Fig. 2a). We initially assessed the data set including all occurrences of taxa considered as putative NIS at at least one of our study locations. This allowed us to assess changes in the known distribution range of these taxa. The most widespread NIS (based on the number of observatories they occurred at, and therefore not implying non-indigenous status at all observatories they were detected at) were Amphibalanus improvisus (14 observatories), Bonnemaisonia hamifera (12 observatories), Corella sp. (8 observatories), Bugula neritina (7 observatories), Balanus trigonus (7 observatories), Acartia (Acanthacartia) tonsa (7 observatories) and Hydroides elegans (7 observatories) (Fig. 2b). See Supplementary File 6 for details on pan-European occurrences of all 63 NIS.

Additionally, of the 12 observatories featuring multiple sampling events at the same location, eleven of these showed cases where some NIS were detected multiple times (Supplementary File 6). The Koster observatory in Sweden serves as a notable example where five NIS were persistently identified from 2018 through 2020 in at least one of its three deployment sites: Acartia (Acanthacartia) tonsa, Bonnemaisonia hamifera, Corella sp., Dasysiphonia japonica and Fenestrulina delicia. Similarly, in Limfjord, Denmark, five species were consistently detected between June 2019 and November 2021: Caprella mutica, Dasysiphonia japonica, Fenestrulina delicia, Fibrocapsa japonica, and Polydora cornuta. These examples of consistent detection across the years show continuous presence of these NIS within the monitored ecosystems.

Prevalence of NIS across observatories

Each of the 19 investigated ARMS-MBON observatories displayed occurrences of taxa considered non-indigenous at the respective location or at any of the other study locations (Supplementary Figure S2) and all showed occurrences of at least one taxon that was considered as NIS in the respective area (Supplementary Figure S3).

To quantify and visualise unique and shared occurrences of NIS across observatories, we employed an UpSet plot (Fig. 3). For this, we considered only the occurrences representing non-indigenous status at a given location. As shown in Fig. 3, 40 NIS were detected at more than one observatory while 23 NIS were detected at one observatory only. A similar plot displaying all occurrences of all NIS (i.e., occurrences across native ranges and locations of putative introduction) can be found in Supplementary Figure S4.

The largest sets of NIS were found in Plymouth, United Kingdom, and in the Gulf of Piran, Slovenia, with 17 non-indigenous taxa detected at both locations, suggesting significant hubs for NIS. Four taxa were exclusively found in Plymouth: Boccardia proboscidea, Botrylloides violaceus, Schizoporella japonica and Watersipora sp._3 (one of the three MOTUs classified as Watersipora sp.). Two NIS were only found in Piran: Asparagopsis taxiformis and Polycerella emertoni, both previously known NIS in the Mediterranean.

RavennaM, Italy, showed a high number of detected NIS (n = 14) and five of these were only detected at this observatory: Cutleria multifida, Eucheilota menoni, Paracerceis sculpta, Haloa japonica, and Paranthura japonica. All of them were previously known in the area, except E. menoni. Despite being in close proximity to this observatory, RavennaH displayed a lower number of detected NIS (n = 8) and two were solely detected there: Mawia benovici and Xenostrobus securis.

The two observatories located in the Baltic Sea, Gdynia, Poland, and TZS, Finland, showed a lower number of detected NIS (n = 6 and n = 8, respectively) including a number of taxa exclusively detected there. Two taxa were only detected at the TZS observatory: Dreisseninae sp. and Marenzellaria sp. In the first case, taxonomic classification indicated that this mOTU belonged to either Dreissena polymorpha or Mytilopsis leucophaeata, both known NIS in the area. In the second case, the mOTU was initially assigned to Marenzellaria arctia, but manual re-classification indicated a potential match with Marenzellaria neglecta. Both species are known as NIS in the Baltic Sea. One NIS was solely detected in Gdynia, Pleurosira leavis.

Detection and mapping of potential new introductions and range shifts

We detected a number of potential new introductions or range shifts, i.e. species in our curated NIS list that had occurrences in places where they had not been recorded previously according to GBIF and our literature search. For all these cases, occurrences were represented by a relatively low number of sequences reads.

Eucheilota menoni was detected on the north coast of the Adriatic Sea, at the RavennaM observatory in Italy, with 54 reads in the COI dataset. At the observatory located in the Red Sea (Eilat, Israel), Bugula neritina was detected with 15 reads. Fenestrulina delicia, known on the Atlantic coasts of Europe, showed 8 reads in Crete, Greece, in the COI dataset. Additionally, a few species originating from regions with warmer water temperatures occurred with a low number of reads in observatories at higher latitudes. In Koster, Sweden, Ostreopsis ovata was found with 8 reads in the 18S dataset. In the COI dataset, Apionsoma (Apionsoma) misakianum, originating in the Indo-Pacific, was detected with 273 reads in Eilat, Israel, 10 reads in Getxo, Spain, 7 reads in Piran, Slovenia, and 7 reads in Roscoff, France. Herdmania momus, a known invader in the Mediterranean Sea from the Indo-Pacific ocean, occurred with 6 reads in Limfjord, Denmark.

Mapping these novel detections against known occurrences from GBIF, we observed that they fell outside of the respective species‘ current known range (see examples in Fig. 4 and Supplementary Figures S5 to S9). Some were located further north compared to the current known distribution range (e.g., Ostreopsis ovata or Herdmania momus), some were found in marine regions they had not been observed in before (e.g. Fenestrulina delicia found in the Mediterranean, but previously only known in the Atlantic).

Comparative analyses across observatories

Spatial distribution of non-indigenous species

Maps of NIS abundance (Fig. 5) revealed locations with high introduction pressure. The north of the Adriatic Sea appeared as the main hotspot: Ravenna had 10.2 NIS per dataset on average (Fig. 5a) and the Gulf of Piran had NIS per dataset on average (Fig. 5b). Figure 5a highlighted another hotspot in Plymouth with an average of 5.4 NIS per dataset.

Detection of non-indigenous species in marinas and harbours

We tested for statistically significant differences in the number of taxa that could be considered as NIS at their location of occurrence between types of deployment sites. Here, we compared the number of NIS (i.e., the number of taxa considered as NIS at a specific location) detected in individual samples from locations particularly impacted by maritime traffic to the number of NIS in samples from all other locations. For the COI data set, after manual curation, on average 2.50 ± 2.08 NIS were detected in samples from ARMS deployed in marinas, harbours, and ports. Samples from all other locations displayed a mean number of 2.00 ± 1.51 detected NIS. Given that a lower number of putative NIS was recovered using 18S compared to COI data, the mean number of detected NIS per 18S sample was substantially lower. On average, 0.55 ± 0.83 NIS were detected in samples from ARMS deployed in marinas, harbours, and ports, while samples from all other locations recovered 0.50 ± 0.79 NIS. For both genes, differences between the two sample types were not statistically significant (COI: Kruskal-Wallis χ² = 1.273, p = 0.259; 18S: Kruskal-Wallis χ² = 0.144, p = 0.705). See Supplementary Table S2 for details on test results.

Broad monitoring of NIS across regions, habitats, and taxa

In recent years, genetic methods have gained attention in the field of marine conservation and management (Darling and Blum, 2007; Westfall et al., 2020) and DNA metabarcoding has been promoted as a tool to monitor biological invasions (Comtet et al., 2015). While earlier surveillance efforts for aquatic NIS have primarily monitored one or a few species and have been limited geographically (Ficetola et al., 2008; Mahon et al., 2013; Tréguier et al., 2014), the methods used in this study allow for a broad and continuous screening of NIS across the pan-European regional seas, with sample locations in various habitats, and detecting a wide range of eukaryotic taxa.

During this study, 63 non-indigenous taxa were detected from 1828 identified species across 112 ARMS-MBON sampling events in 19 observatories across 14 countries. Most of these NIS were previously known in the areas where they have been detected.

The most widespread NIS throughout the dataset were Amphibalanus improvisus, Bonnemaisonia hamifera, Bugula neritina, Balanus trigonus, Acartia tonsa and Hydroides elegans. This was an expected result as all these species are well-known marine NIS and successful invaders on European coasts. Information about their respective known ranges and impacts can be found in Supplementary Text S4. The presence of these species across our dataset shows that the chosen approach in this study allowed for the clear detection of well-known invaders of European waters and therefore can be used to monitor their potential expansion in the future. Also, this study showed that a genetic monitoring network such as ARMS-MBON, when performing continuous deployments over long periods of time, does detect well-established NIS in an area.

However, some potential NIS detected in the samples did not appear in our curated data sets. The reason for this is that some taxa, despite being known NIS in Europe, are not registered in WRiMS for the respective study area. As an example, Juxtacribrilina mutabilis (which appeared with its current unaccepted name Cribrilina mutabilis in our data set), a recently described bryozoan from Japan (Ito et al., 2015), is not registered in WRiMS for any European marine region but only for the Pacific and Atlantic coasts of the US. However, it was sampled in the observatories Koster and Swedish West Coast (Sweden), Laeso (Denmark), Svalbard (Norway), Vigo (Spain) and Plymouth (United Kingdom) during this study. This species has shown an unusually fast range expansion within the last two decades (Martaeng et al., 2023), probably because, like many other invaders, this species can attach to different substrate (including ships’ hulls) and be spread across long distances (Dick et al., 2020).

Bougainvillia mucus is a similar case: it is a potential new invader in the Red Sea, where we detected it with 25 reads. It did not appear in our curated NIS data set because it is not listed in WRiMS for any of the selected areas either, as it originates from Europe and has not been previously recorded in the Red Sea. Only manual checking of the overall species list alongside literature search allowed for the identification of its introduction status in the Red Sea which underlines the drawbacks when relying on databases such as WRiMS.

Early warning potential

The methods used to detect NIS in our dataset relied on WRiMS and were designed to minimise false positives and negatives. While this increased the certainty of our results, this also limited the possibilities to detect newly introduced species mainly because the screening was limited to 824 species registered in WRiMS for the larger marine regions our study sites were located in. Despite these limitations, and since we targeted a large geographic area, we encountered a few cases of potential new introductions.

Eucheilota menoni was detected on the north coast of the Adriatic Sea, in Ravenna (Supplementary Figure S9). The respective mOTU was composed of one ASV that was matched to E. menoni with 100% similarity on BOLD (GenBank: 100% query cover and 100% percentage identity). The following match was with Lovenella assimilis, with a 99.68% similarity on BOLD (GenBank: 100% query cover and 99.68% percentage identity). Brylinski et al., (2016) summarised the debate about whether these two species could represent a single species, noting that the criteria for morphological differentiation are still under discussion. Both species, originating from the Indo-Pacific, have never been found in the Adriatic before. We only detected E. menoni in our study as it is registered in WRiMS for both the Bay of Biscay and the Belgian coast where some of our study locations were located at. However, we found no occurrences of this species on ARMS from observatories in these two regions. It is also interesting to note that no occurrences of this taxon were recorded in GBIF for the Belgian coast. The first record in Europe for E. menoni dates from 2009, in the Bay of Biscay (Altuna, 2009). In 2010, L. assimilis was detected in the same area (Altuna and De Okendo, 2010), which was a first record of this species outside of the Indo-Pacific Ocean. In 2016, specimens resembling L. assimilis have been reported for the first time in the eastern English Channel and the southern bight of the North Sea. Morphological analyses showed differences between the two species while genetic analyses using 18S, ITS and mitochondrial COI and 16S rDNA (16S), showed high genetic similarities to available sequences in DNA repositories attributed to E. menoni (Brylinski et al., 2016). For COI, the only reliable L. assimilis COI partial sequence was almost identical to the E. menoni COI sequence (only 1 bp difference). For 16S, the sequences the authors obtained from their specimens were 99% identical to E. menoni sequences. For 18S, their sequences were 100% identical to the only E. menoni sequences available on GenBank. Although this could indicate that these two species are in fact one, the authors highlight that these data should be carefully approached, as some sequences on GenBank do not have a valid morphological description of the specimen used. It is possible that some sequences available in GenBank of E. menoni are based on misidentified specimens of L. assimilis. Therefore, the mOTU found in our study is considered to be E. menoni, but this result should be further investigated. Considering the fouling behaviour of this species, and its ability to easily reproduce asexually, it was expected to be found in the Mediterranean at some point after its discovery on the Iberian Peninsula (Altuna, 2009). In this study, the relatively low read number of this occurrence could point to a recent introduction and a currently low abundance of this species in the area.

Potential new invaders were also detected in the Red Sea. Bugula neritina, a colonial bryozoan originating from the Mediterranean Sea, and which is widespread in Europe (Ryland et al., 2011), is one example. No previous records of this species in the Red Sea were found in GBIF (Supplementary Figure S6) or in the Ocean Biodiversity Information System, OBIS (Intergovernmental Oceanographic Commission of UNESCO, 2024), nor in WoRMS or in other scientific literature. However, it appeared in our data set because Bugula neritina is recorded in WRiMS for four of our selected areas (“Spanish part of the Bay of Biscay”, “Spanish part of the North Atlantic Ocean”, “Swedish part of the Skagerrak'' and “North Sea”). It was detected with 15 reads during one sampling event in the Red Sea, which could suggest a recent introduction. Similarly, Bougainvillia muscus, which we detected manually and not based on WRiMS records (see above), is also a potential new invader in the Red Sea. This species is a hydrozoan native and commonly reported in the Mediterranean Sea, the North Sea and English Channel (Lee II and Reusser, 2012), which was detected with a total of 25 reads in Eilat, Israel, in the COI dataset (Supplementary Figure S10). The presence of B. neritina and B. muscus in the Red Sea might be due to a potential reverse Lessepsian migration through the Suez Canal, so-called anti-Lessepsian migration (Bos et al., 2020). Few Mediterranean species can migrate southwards via this path due to the prevailing south-to-north flow of the canal, but the Suez Canal hosts heavy ship traffic that has been identified as a vector for biological invasions in the Mediterranean (Bereza et al., 2020) and that can be responsible for these anti-Lessepsian movements. Additionally, the Red Sea is characterised by higher salinity, fewer nutrients, and a notably richer diversity of life forms compared to the eastern Mediterranean, which can impede the arrival of these potential invaders (Elsaeed et al., 2021). Therefore, potential anti-Lessepsian migrants should be closely monitored and researched to better understand their migration patterns, ecological impact, and adaptation strategies in new environments, particularly in the context of changing marine conditions and human-induced environmental shifts.

Apionsoma (Apionsoma) misakianum, originating in the Indo-Pacific, was detected in Eilat, Red Sea (part of its native range), in Getxo, Spain, in the Gulf of Piran, Slovenia, and in Roscoff, France. It had previously been detected and considered non-indigenous in the Aegean Sea in 2007(Açik, 2008) and in other Mediterranean areas according to SeaLifeBase (Palomares and Pauly, 2024), but in GBIF no record of this species can be found further north than the Red Sea (Supplementary Figure S5). Our results suggest a potential range extension of this species outside of the Mediterranean Sea.

Fenestrulina delicia, a fouling bryozoan, showed 8 reads in Crete, Greece, in the COI dataset. This species is known on the European Atlantic coasts from France to Norway (Supplementary Figure S7) and could have been recently introduced in the Mediterranean (De Blauwe et al., 2014).

Ostreopsis ovata, an epiphytic, toxic dinoflagellate, coming from tropical regions was found with 8 reads in the 18S dataset in the observatory located in Koster in Sweden (Supplementary Figure S8). (Granéli et al., 2011) concluded that current global warming can influence the geographical expansion to higher latitudes and biomass accumulation by blooms of O. ovata. It was previously known in the Mediterranean, on the Atlantic coasts of France and the Iberian Peninsula (Accoroni et al., 2024; Chomérat et al., 2022; David et al., 2013; Fabri-Ruiz et al., 2024).

Herdmania momus, a known invader in the Mediterranean Sea from the Indo-Pacific Ocean and a known case of Lessepsian migration (Evans et al., 2013; Gewing et al., 2014; Shenkar and Loya, 2008), occurred with 6 reads in Limfjord, Denmark (Fig. 4). This species appears to be adapted to tropical climates; however, according to Gewing et al., (2019), the invasive population in the Mediterranean demonstrated significantly greater survivability under cold conditions than the native population from the Red Sea.

It is important to highlight that these results should be carefully interpreted as the read numbers these species were detected with in the respective areas are relatively low. They might point towards early introductions, spontaneous detections (as opposed to an established and growing population), but they might also be artefacts of sequencing or bioinformatic processing. Future investigations of ARMS-MBON data will increase the confidence of these observations.

Mapping potential range shifts

By comparing the occurrences from our dataset with the extent of those recorded in GBIF, among the latter being many records of species already found outside their native range, we identified discrepancies that may indicate new range expansions and recent introduction events. The geographic location of new detections and distance to known occurrences could potentially indicate the cause of the range shift e.g., global warming (i.e., species detected at moderate distance further north than their known occurrence) or shipping (i.e.,. species from Europe found in distant locations such as the Red Sea).

Scaling up the integration of recent sightings with established extent of occurrence data from databases like GBIF can be a useful tool to identify dynamics of range shifts, vectors of introduction, and factors influencing habitat suitability for NIS. This can also enable predictions of species' responses to various environmental conditions. As ARMS-MBON continues to generate more data, and as ecological data from around the world become increasingly shared (Tenopir et al., 2020) and Findable, Accessible, Interoperable, and Reusable (FAIR) (Tanhua et al., 2019), such methods could provide a robust framework for studying shifts in species distribution both in Europe and globally. Additionally, coupling these methods with species distribution models and/or artificial intelligence algorithms to efficiently identify migrations patterns can facilitate the detection of new NIS in an area or region.

Identification of potential NIS hotspots

We did not detect a significantly higher number of NIS in samples from marinas and harbours compared to samples from other locations (e.g., marine protected areas, areas with low human influence, etc.). Marinas and harbours are often described as NIS hotspots as they are subject to increased anthropogenic impacts such as ballast water discharge (Bailey, 2015) and “transport” of fouling biota (Bailey et al., 2020; Chan et al., 2022; Georgiades et al., 2021), which are common vectors for the introduction of NIS. Marinas and harbours are known to serve as gateways for international and local vessels, which inadvertently transport organisms across biogeographical boundaries. The frequent movement and docking of boats create a dynamic environment that facilitates the establishment and spread of NIS (Ashton et al., 2022). Moreover, the infrastructure of marinas and harbours often provides suitable habitats for NIS to thrive in, including sheltered waters and hard substrates for attachment. However, NIS can also migrate due to climate-change driven vectors such as changes in water temperature and salinity (Floerl et al., 2013; Occhipinti-Ambrogi, 2021). Hence, locations impacted by maritime traffic may not be the only places with high prevalence of NIS which could explain our results.

Additionally, investigating data sets with standardised sampling and sequencing effort allowed for a robust detection of areas with relatively high NIS counts (i.e., potential hotspots): the north of the Adriatic Sea (Ravenna and Gulf of Piran) and the English Channel appeared to be the locations with the most NIS detected across the comparable data sets. These observatories are located near or in marinas/ports, which could indicate a tendency of higher NIS prevalence in these types of monitoring sites. Overall, these results show that large-scale observatory networks such as ARMS-MBON can facilitate the identification of NIS hotspots. As more data continues to be collected through ARMS-MBON, this enables further investigations in this direction.

Methodological challenges

This study demonstrates the potential of our chosen methods for monitoring NIS across extensive geographical areas like Europe. We advocate for the increased use of DNA metabarcoding to detect and track NIS, but also for the application of this method at a large geographic scale, to have a better understanding of species movements beyond the boundaries of countries or single marine regions.

However, we also acknowledge various technical and practical challenges associated with this approach. They can concern different aspects:

the importance of standardisation when working with a larger monitoring network,
the accuracy of taxonomic identification through DNA metabarcoding,
the curation and updating of databases recording introduced species,
the sensitivity of species detection.

We provide specific points of attention and suggestions for addressing these topics in future research in Supplementary Text S5.

This study tested the efficacy of a genetic observatory network to detect and monitor marine non-indigenous species and advance our understanding of such taxa, which is crucial to preserve biodiversity and maintain healthy ecosystems. By identifying 63 non-indigenous species, we showed that genetic observatory networks can monitor distant phylogenetic groups across a vast geographical region and detect potential new introductions and range shifts at the earliest possible stage. We also showed the potential of such a study to identify hotspots in areas and habitats which are potentially particularly susceptible to the establishment of non-indigenous species.

DATA AVAILABILITY

All raw ARMS MBON data and metadata supporting this work are accessible through the ARMS MBON GitHub repository at https://github.com/arms-mbon. Standard operating procedures and protocols are available on the dedicated ARMS-MBON GitHub repository (https://github.com/armsmbon/documentation). All metadata and access to all image data generated by ARMS-MBON to date can be found on GitHub (https://github.com/arms-mbon/data_workspace). All genetic raw data generated by ARMS-MBON to date can be accessed on the European Nucleotide Archive (ENA) through the accession numbers provided via the GitHub repository (https://github.com/arms-mbon/data_workspace) and under the umbrella study PRJEB72316 (https://www.ebi.ac.uk/ena/browser/view/PRJEB72316). The code for the bioinformatic pipeline and the initial NIS scan is published in Daraghmeh (2024). The code used for data exploration and visualisation can be found at https://github.com/JustinePa/studyNIS_ARMS.

Funding

The ARMS-MBON network was established under the infrastructure program ASSEMBLE Plus (grant no. 730984) and is currently financed by the European Marine Biological Resource Centre (EMBRC) under the program European Marine Omics Biodiversity Observation Network (EMO BON). Data publication and analysis is funded by the projects DTO-bioflow (grant agreement no. 101112823) and MARCO BOLO (grant agreement no. 101082021). Funding for individual ARMS observatories was provided by the INTERREG project GEANS (North Sea Program of the European Regional Development Fund of the European Union), the Swedish Agency for Marine and Water Management (grant no. 3181-2019), the Flanders LifeWatch contribution (Research Foundation Flanders grant I000819N), and the Aquanis 2.0 project (FONDATION Total). Data management and computational resources were provided by the Swedish Biodiversity Data Infrastructure (grant no. 2019-00242) and by IMBBC (Institute of Marine Biology, Biotechnology and Aquaculture) of the HCMR (Hellenic Centre of Marine Research). Funding for establishing the IMBBC HPC has been received by the MARBIGEN (EU Regpot) project, LifeWatchGreece RI and the CMBR (Center for the study and sustainable exploitation of Marine Biological Resources) RI.

Competing Interest

All authors certify that they have no affiliations with or involvement in any organisation or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Author Contributions

All authors contributed to the study conception and design. Material preparation and data collection were executed by JP and ND, with ND handling the processing of sequencing data and JP performing the manual curation of the initial NIS dataset. Data analysis was performed jointly by JP and ND. The first draft of the manuscript was written by JP, with substantial input and feedback from ND. MO provided critical revisions and feedback on all versions of the manuscript. MO initiated and is leading ARMS-MBON, from which the data was obtained, and provided resources and supervision. All authors read and approved the final manuscript.

ACKNOWLEDGMENTS

We thank the numerous researchers, field workers and technical staff across the ARMS MBON network for their dedicated efforts in data collection and logistical support. We appreciate the reviewers for their contributions to the peer review process. We thank Katrina Exter and Christina Pavloudi for their support with managing and accessing the data. We acknowledge the support of the various funding organisations and projects: ASSEMBLE Plus (grant no. 730984), the European Marine Biological Resource Centre (EMBRC) via the European Marine Omics Biodiversity Observation Network (EMO BON), DTO-bioflow (grant no. 101112823) and MARCO BOLO (grant no. 101082021), the INTERREG GEANS project, the Swedish Agency for Marine and Water Management (grant no. 3181-2019), the Flanders LifeWatch contribution (Research Foundation Flanders grant I000819N), and the Aquanis 2.0 project (FONDATION Total). We also thank the Swedish Biodiversity Data Infrastructure (grant no. 2019-00242), the IMBBC of the HCMR, MARBIGEN, LifeWatchGreece RI, and the CMBR RI for data management and computational resources.

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Assessing the effectiveness of genetic observatory networks in detecting and monitoring marine non-indigenous species.

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Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

Spatial and temporal coverage

Description of the amplicon sequencing data set

Procedure for NIS detection

Sequence denoising, clustering into molecular operational taxonomic units and taxonomic classification

Screening for non-indigenous species

Manual curation of NIS occurrences

Analysis of NIS observations across the network

Standardisation of data sets and comparative analysis

RESULTS

General description of the sequencing data set

Curation of putative NIS identification

Overall description of the curated NIS dataset

Prevalence of NIS across observatories

Detection and mapping of potential new introductions and range shifts

Comparative analyses across observatories

Spatial distribution of non-indigenous species

Detection of non-indigenous species in marinas and harbours

DISCUSSION

Broad monitoring of NIS across regions, habitats, and taxa

Early warning potential

Mapping potential range shifts

Identification of potential NIS hotspots

Methodological challenges

CONCLUSION

DATA AVAILABILITY

Declarations

Funding

Competing Interest

Author Contributions

ACKNOWLEDGMENTS

References

Supplementary Files

Status:

Version 1