Scientific document summarising the results of the Species distribution modelling and mapping of the alien species impacts on the European coastal and marine habitats

Deliverable 5.5 Scientific document summarising the results of the Species distribution modelling and mapping of the alien species impacts on the European coastal and marine habitats Version 1.0 Date of delivery 2025-06-30 Authors names Matthias Obst1 Affiliations 1 Department of Marine Sciences, University of Gothenburg, Box 461, 405 30 Gothenburg, Sweden; PUBLIC DOCUMENT Ref. Ares(2025)7308469 - 05/09/2025 Document Information Grant Agreement 101082021 Project Acronym MARCO-BOLO Project Title MARine COastal BiOdiversity Long-term Observations Deliverable Number D5.5 Work Package Number WP5 Deliverable Title Scientific document summarising the results of the Species distribution modelling and mapping of the alien species impacts on the European coastal and marine habitats Lead Beneficiary University of Gothenburg, Partner Number 9 Author(s) Matthias Obst (University of Gothenburg) Due Date 30.05.2025 Submission Date 30.06.2025 Dissemination Level PU Type of Deliverable R Version 0.5 05.05.2025, Matthias Obst Version 0.6 25.06.2025, Matthias Obst, Carlota Muñiz Version 1.0 30.06.2025, Matthias Obst Report Edit 3 Executive Summary This deliverable summarizes the scientific outputs generated by task T5.4.“Mapping the impacts of Non-Indigenous species (NIS) on European coastal and marine habitats, based on multi-disciplinary approaches”. In this task we modelled 82 species identified by marine genetic monitoring programs in 2020-2024 as occurring outside their native range. Models produced predictions of suitable habitat in current and future climate scenarios for these non-indigenous species (NIS) and can be used to identify regions with elevated risk for establishment and spread of alien species. Report Edit 4 Contents Document Information ............................................................................................................................ 2 Executive Summary ................................................................................................................................. 3 1. Objective .......................................................................................................................................... 5 2. Background ...................................................................................................................................... 5 3. Methodology ................................................................................................................................... 5 3.1. Modelling approach ...................................................................................................................... 5 4. Results and Discussion ..................................................................................................................... 7 4.1. Modelling trial ............................................................................................................................... 7 4.2. Shortfalls of the current approach ............................................................................................... 8 4.3. Future work................................................................................................................................... 8 4.4. Conclusion ..................................................................................................................................... 8 5. Research outputs ............................................................................................................................. 9 5.1. Summary table .............................................................................................................................. 9 5.2. Conference contributions ............................................................................................................. 9 6. Appendix ........................................................................................................................................10 6.1. Table 1.........................................................................................................................................10 7. References .....................................................................................................................................12 Report Edit 5 1. Objective The objective of this task was to run a new analysis with the invasive alien species (IAS) hotspot model for a number of non-indigenous species (NIS) recently detected by genetic monitoring campaigns and produce maps showing areas of potential invasion and ecological impact for these NIS in European coastal waters. 2. Background Marine ecosystems are affected by human activities in the sea such as fishing, aquaculture and shipping. These processes often cause invasions of alien species (also called non-indigenous species or NIS), which alter native communities and lead to the global decline of biodiversity. Common approaches to prevent and mitigate marine invasions include risk assessments and early warning programs for alien species. Recently, several risk assessment tools for alien species have been developed based on species distribution modelling methods (SDMs). Here statistical models are used to analyse the potential habitat suitability of alien species and thereby predict potential range extensions and areas of potential impact. Here we model the potential distribution of 80 nonindigenous species detected by the genetic monitoring programmes, ARMS-MBON and the Swedish national port monitoring programme (Pagnier et al 2025; Sundberg et al 2024) and their ability to forecast potential range expansions of these species in European coastal waters. 3. Methodology 3.1. Modelling approach Modelling of hotspots was carried out using Species Distribution Models (SDM). To this end we developed a workflow to identify potential high-risk areas for the spread and establishment of alien species in European coastal waters. The SDM workflow is based on a modelling approach developed by the University of Gothenburg (Karlsson et al 2019; Leidenberger et al 2015) and was further adapted to deal with properties of alien species dynamics, for example identify optimal alien species monitoring sites (Bergkvist et al 2020) and predict transitions between freshwater and brackish and marine habitats (Obst & Andersson 2023). The modelling results not only show potential distribution areas for individual alien species but can also be used to map regions where suitable habitats for known alien species overlap. Such regions with a high overall invasion risk, i.e. high risk of introduction and establishment, can be considered invasive hotspots. The models are based on a Random Forest machine learning algorithm. A detailed explanation of all analytical steps included in the modeling can be found in Leidenberger et al (2015) and Karlsson et al (2019). Species-specific models were created based on each species' maximum distribution within the study area and the environmental variables listed below. Random Forest models were run with 10.000 background points (including input points) drawn from the study area. All models were set to produce a probability rather than binary projection in raster format. The models were tested using confusion matrices and ROC curves, and then projected with the same environmental variables back into the Report Edit 6 study area. The results were visualized as maps of potential distribution areas, i.e. geographical projections of suitable habitats. Geographical scope The study area, i.e. the geographic area where models were trained for, are coastal waters of Europe including the Black Sea, the Mediterranean Sea, the North-East Atlantic and the Baltic Sea. Predictions of the models were generated for the same region. Documentation The model's source code, as well as inputs and results with species-specific and group-based projections, are available on GitHub https://github.com/biomobst/IAS_hotspot_model/tree/master/Modelling_trial_2025. Biological data The list of target species (Table 1) was obtained from genetic monitoring programs ARMS-MBON (Pagnier et al 2025) program and the Swedish national port monitoring campaign (Sundberg et al 2024). Environmental data Gridded environmental data available as global marine data layers through the Bio-Oracle website (http://www.bio-oracle.ugent.be/) with a resolution of 5 arc-min (Tyberghein et al 2012) were used to generate environmental variables for the SDMs. These data layers are generated from monthly satellite data (Aqua-MODIS and SeaWiFS website https://oceancolor.gsfc.nasa.gov/) as well as in-situ measured oceanographic data from the World Ocean Database 2009 (Boyer et al 2009). The following data layers were used for current and future climate scenario SP119: Data layer Description nos_mean_depthsurf Mean surface nitrate in mmol per m3 chl_mean_depthsurf Mean surface chlorophyll in mg per m3 02_max_depthsurf Maximum surface dissolved molecular oxygen in mmol per m3 po4_mean_depthsurf Mean surface phosphate in mmol per m3 thetao_min_depthsurf Minimum surface sea water temperature in ºC par_mean_mean_depthsurf Mean surface photosynthetic available radiation in Einstein per m2 and day 02_mean_depthsurf Mean surface dissolved molecular oxygen in mmol per m3 phyc_mean_depthsurf Mean phytoplankton concentration at the surface thetao_mean_depthsurf Mean surface sea water temperature in ºC thetao_max_depthsurf Maximum surface sea water temperature in ºC ph_mean_depthsurf Mean surface PH 02_mindepthsurf Minimum surface dissolved molecular oxygen in mmol per m3 si_mean_depthsurf Mean surface silicate in mmol per m3 02_max_depthmax Maximum benthic dissolved molecular oxygen in mmol per m3 02_mean_depthmax Mean benthic dissolved molecular oxygen in mmol per m3 02_min_depthmax Minimum benthic dissolved molecular oxygen in mmol per m3 phyc_mean_depthmean Mean phytoplankton concentration at mean depth. thetao_mean_depthmean Mean midwater sea water temperature in ºC so_mean_depthmean Mean salinity at mean depth 02_mean_depthmean Mean benthic dissolved molecular oxygen in mmol per m3 po4_mean_depthmean Mean benthic phosphate in mmol per m3 Report Edit 7 so_mean_depthsurf Mean salinity at the surface sws_mean_depthsurf Mean sea water speed at the surface siconc_mean_depthsurf Mean benthic sea ice cover in fraction sithick_ Sea ice thickness in m siconc_max_depthsurf Maximum benthic sea ice cover in fraction 4. Results and Discussion 4.1. Modelling trial Summary of results The results of the modelling trial are documented on the GitHub project page https://github.com/biomobst/IAS_hotspot_model/tree/master/Modelling_trial_2025 in the folder “results” https://github.com/biomobst/IAS_hotspot_model/tree/master/Modelling_trial_2025/results. Individual projections Species specific projections can be used to identify areas of suitable habitat where species have not yet been observed or areas where species may migrate to in the near future, for example, the Baltic coast for Bugula neritina (Fig. 1). The maps of projected suitable habitat for all the species can be found in the “Plots_species” folder: https://github.com/biomobst/IAS_hotspot_model/tree/master/Modelling_trial_2025/results/Plots_ species. Figure 1. Example of model predictions for Bugula neritina. Left: currently known distribution indicated by red dots, with pseudo-absence points indicated by blue dots. Colour scale is linear from 0.00 (unsuitable habitat) to 1.00 (highly suitable habitat). Right: Difference in suitable habitat between SSP119 scenario prediction for 2100 and current climate scenario. SSP119 refers to a climate scenario within the Shared Socioeconomic Report Edit 8 Pathways (SSPs) framework, specifically SSP1-RCP1.9, which is a low-emission scenario. Colour scale is linear from -1.00 (maximum loss of suitable habitat) to +1.00 (maximum gain of suitable habitat). 4.2. Shortfalls of the current approach The models are sensitive to many of parameters, which means that the outputs presented from this trial have to be interpreted with caution. When applied in decision making, these models should be built from a consensus of several algorithms (e.g., not only Random forest) and climate scenarios (e.g., not only SPP119) to be confident about the predictions of suitable habitats and migration trends. Visualisation of results is challenging as there are many model outputs that need to be inspected simultaneously for interpretation, such as e.g., test statistics, parameter settings, maps in various resolutions (high, low), formats (png, geotiff), and projection settings (present, future scenarios). A visualisation interface (e.g., R-shiny) may be appropriate to solve this problem. But such applications should be developed only after the workflow is consolidated and end users of the models have provided detailed specifications for how and when they would use such an application. The analytical workflow applied here should be better documented to allow reproducibility and repeatability and thereby enable swift and ad-hoc predictions for relevant species as soon as these are detected in the genetic monitoring programs. This work is currently scheduled for the remainder of the project. 4.3. Future work The modelling trials need to be better documented and interpreted across different climate scenarios. Also, the individual projections will have to be combined to produce updated maps of the hotspots. These are planned to be used in decision making by Swedish authorities, e.g., when granting exemptions from ballast water treatment in Swedish ports. A workshop is planned for September 2025 to specify the end user demands for output maps. Furthermore, results from the modelling trial will be presented at the 12th International Conference on Marine Bioinvasions in October 2025. We currently plan one more scientific manuscript which will focus on the publication of the SDM workflow an results. Finally, the SDM analysis tool will be added to the EDITO platform (https://www.edito.eu) during spring 2026 and thereby become an analytical tool in the Digital Twin of the Ocean (DTO). 4.4. Conclusion The genetic monitoring programs that donated the alien species lists for this experiment do not yet provide enough species occurrence data to influence the outcome of the model predictions, which means that GBIF and OBIS still provide most of the occurrence data for the modelling. However, the Report Edit 9 genetic monitoring programs do provide the list of species detected to be “on the move”. For these species the models provide valuable knowledge on potential alien species impact as they are able to align the presently known distribution with the potential distribution of these species in the future. 5. Research outputs 5.1. Summary table Output description Reference Documentation Species distribution model (SDM) workflow to predict potential high-risk areas for introduction, establishment and spread of invasive species Bergkvist et al (2020); Obst & Andersson (2023) https://github.com/biomobst/IAS_hotspot_m odel/ Script-based workflow to identify known alien species from genetic observation data sets using WRiMS Daraghmeh et al (2024) A long-term ecological research data set from the marine genetic monitoring programme ARMS-MBON 2018-2020. Mol Ecol Res. In Review. https://github.com/vliz-be-opsci/lw-ijiinvasive-checker Data paper presenting the first batch of genetic observatory data from EMO BON Daraghmeh et al (2024) A long-term ecological research data set from the marine genetic monitoring programme ARMS-MBON 2018-2020. Mol Ecol Res. In Review. Molecular Ecology Resources 25 (4), e14073, https://doi.org/10.1111/1755-0998.14073 Data paper presenting the nonindigenous species detected by the ARMS MBON Pagnier et al (2025) Using the long-term genetic monitoring network ARMS-MBON to detect marine non-indigenous species along the European coasts. Biological Invasions 27, 77 (2025), https://doi.org/10.1007/s10530-024-03503-2 Conference talk at 12th ICMB, Madeira Island, Portugal Pagnier et al (2025) Assessing the effectiveness of genetic observatory networks in detecting and monitoring marine non-indigenous species https://marinebioinvasions.info/program Conference talk at 12th ICMB, Madeira Island, Portugal Obst et al (2025) A science-policy interface for non-indigenous species monitoring and management. https://marinebioinvasions.info/program 5.2. Conference contributions 12th International Conference on Marine Bioinvasions, Madeira Island, Portugal; Dates: 7-9 October 2025