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opencc-by-4.0Feb 2024View details →

Perfect-microsatellite distribution in the genomes of 36 reptile species

Microsatellite DNA sequences in the genomes of 36 reptiles were identified and localized using the Krait software.  This dataset was for the distribution of perfect-microsatellites.

opencc-zeroMar 2024View details →

Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression

Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers' decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

opencc-zeroMar 2024View details →

Incorporating plant phenological responses into species distribution models (SDMs) reduces estimates of future species loss and turnover

Anthropogenetic climate change has caused distribution shifts of many species, and species distribution models (SDMs) are central for documenting this relationship. However, most SDMs rarely consider the evolution of climate-sensitive functional traits, such as phenology, which strongly affect species fitness. Using >120,000 herbarium specimens representing 360 plant species across the eastern United States, we developed a novel "phenology-informed" SDM that integrates dynamic phenological responses to changing climates. Compared to standard SDMs, our phenology-informed SDMs forecast lower species habitat loss and less species turnover under climate change. These results suggest that phenotypic plasticity or local adaptation in phenology may help species adjust their ecological niches and persist in their habitats under rapid environmental change. Our findings reveal how phenology variation mediates species distributions and affects regional biodiversity patterns. Our newly developed model also circumvents the need for mechanistic models, facilitating the deployment of trait-based SDMs across unprecedented spatial and taxonomic scales.

opencc-by-4.0Mar 2024View details →

Fig. 22 in Four new species and five new distribution records of the jumping spider genus Stenaelurillus Simon, 1886 (Salticidae: Aelurillines) from India

Fig. 22. New distributional records of species of Stenaelurillus Simon, 1886 studied in this work.

opencc-by-4.0Mar 2024View details →

Fig. 16 in Four new species and five new distribution records of the jumping spider genus Stenaelurillus Simon, 1886 (Salticidae: Aelurillines) from India

Fig. 16. Type localities of new species of Stenaelurillus Simon, 1886 studied in this work.

opencc-by-4.0Mar 2021View details →

Figure 4 in Cladocera (Crustacea, Branchiopoda) species of Bahia State, Brazil: a critical update on species descriptions, distributions, and new records

Figure 4. Rank of Cladoceran richness based on states' checklists.

Figure 3 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 3. Points of the studied samples for Ace-2 marker.

Figure 2 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 2. Distribution of Culex species in sample collection areas.

Figure 1 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 1. The study area (study areas are red-lined areas).

Figure 6 in Distribution and molecular differentiation of Culex pipiens complex species in the Middle and Eastern Black Sea Regions of Turkey

Figure 6. Points of the studied samples for CQ11 marker.

opencc-by-4.0Nov 2023View details →

Figure 72 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean

Figure 72: Distribution map of Alexandrium species from this study in the Mexican Pacific.

opencc-by-4.0Nov 2024View details →

Data from: "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied"

<h2>Abstract</h2> This data contains occurrence observations (presence-absence) of 11 aquatic macrophytes from Bothnian Sea and Lake Puruvesi, and environmental covariates used to build species distribution models (SDMs) in  paper "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied" in Ecological Modelling. In addition to data files, also R code for fitting the SDMs is supplied, as is the R code to replicate the analysis conducted in the paper. The data is stored in rdata format (point data), without coordinate information due to data policy restrictions. The species in the data are Isoëtes lacustris, Isoëtes echinospora, Ranunculus reptans, Ranunculus schmalhausenii, Potamogeton berchtoldii, Potamogeton perfoliatus, Potamogeton gramineus, Myriophyllum alterniflorum, Equisetum fluviatile, Eleocharis acicularis and Elodea canadensis. The environmental covariates are bottom water salinity, turbidity, sandy substrate occurrence, colored dissolved organic matter (CDOM), surface fetch, sampling depth, total nitrogen and total phosphorus, and distance to closest Phragmites australis reed bed.  <h2>Objective of the study </h2> The modelling objective of the paper was species distribution model (SDM) transferability assesment. Transferability was assessed using models built in marine areas in projecting the distributions of the target species in Lake Puruvesi, Saimaa, Eastern Finland. Macrophyte mapping data from Lake Puruvesi was used as independent test data, against which transferability of the models was assessed. <h2>Location</h2> The species data was collected from two geographic areas: Bothnian Bay (Baltic Sea) and Lake Puruvesi (Eastern Finland). The marine observations from Bothnian Bay were split into three overlapping areas (areas 1-3), to test the effect of input data gradient length to SDM transferability. The largest marine sampling area (Area 3) ranged from 62.95, 65.91 latitude and 19.14, 27.99 longitude. Area 2 ranged from 63.95, 65.91 latitude and 21.53, 27.99 longitude. Area 1 ranged from 64.91, 65.91 latitude and 23.82, 27.99 longitude. The Hummonselkä subbasin of Lake Puruvesi, where the macrophyte test data was collected, is located at 61.89, 62.05 latitude and 29.58, 29.78 longitude. <h2>Species data </h2> The species observations were collected using diving transects placed in the floor of the sea or lake, and species observations were recorded in 2 m22 grid cells separated by 10 meters along the transect or 1 meters depth, depending which criteria was met first. The species data was collected in 2010 - 2020 from marine area, and 2017 from Lake Puruvesi. All macrophytes in 2 x 1 m frames were identified to species level by the diver, and the data contained information on species presence or absence in each grid cell. The diving transects were conducted using systematic survey protocol used in the underwater inventories of the Finnish Underwater Biodiversity Survey Program (VELMU) (Frosblom & Virtanen et al. 2024). The locations of the diving transects were not randomly distributed, but were placed using expert judgement. As all our study species are macroscopic and relatively easily identifiable in the field (with the exception of possibility of mixing I. echinospora and I. lacustris), we consider the absences in our observation data to indicate true absences. That said, as the observation area is rather small (2 m2), it is possible that a species may be found in the site of investigation (e.g. a small lagoon) but be located outside the vegetation sampling grid. <h2>Environmental data</h2> <h3>Bottom water salinity</h3> The seasonal mean bottom water salinity was modeled using a generalized additive model with mean salinity as response, with log-link and gamma distribution for the errors. This was necessary to keep the resulting predictions positive. Bottom depth, CDOM, river influence and spatial location were used as predictors. Data from 448 locations were used and each location had a minimum of three observations. The model was validated using 30 % of the data left outside of the model fitting. The explained deviance of the model was 0.94 and the correlation between raw data and predicted values was 0.95 with few outliers. <h3>Turbidity</h3> Maps of turbidity (in FNU, Formazin Nephelometric Unit) were generated from Sentinel-2 Multi-Spectral Imager (MSI) observations using the Case-2 Regional Coast Colour (C2RCC) bio-optical inversion model, containing separate atmospheric correction and water quality parts. Before computing C2RCC, the original 10-meter input data was downsampled to 60 meters. The output variable of the C2RCC processor correlative to turbidity is the backscattering of total suspended sediments at 443 nm, which was further calibrated into turbidity (FNU) values using SYKE's empirical equations for coastal waters and clear lakes (for a similar approach, see Attila et al. (2013) and Sagerman, Hansen, and Wikström (2020)). Monthly observations of turbidity were aggregated into median composites to reduce the effects of cloud cover and other disturbances. Due to low solar elevation and ice cover in winter, the turbidity distribution maps are generated only for the summer months (May to September). The current processing covers years 2017 to 2021. An average raster layer was created from monthly observations as input for SDM building. <h3>Probability of sandy substrate </h3> Random forest model was used to classify sandy bottoms from Sentinel 2 MSI satellite images in shallow water areas. Identifying sandy substrate is based on the higher reflectance compared to other substrates. The model was trained and validated using diver recorded field observations in the Baltic area, and diver recorded and echo sounding observations in the freshwater area. For full coverage including areas beyond the shallow water, the satellite image classification was combined with boosted regression tree modelling result in the Baltic, and echo sounding based product in the freshwater region. The resulting layers were probabilities of sandy substrate with 10-meter cell resolution. <h3>CDOM</h3> We used different methods to estimate the CDOM levels in Bothnian Bay and Lake Puruvesi, based on biogeochemical model data and satellite images. For Lake Puruvesi, we applied the Finnish Environment Institute's (Syke) in-house CDOM algorithm to the Sentinel-2 MSI images processed by the C2RCC bio-optical processor (Brockmann et al. 2016). The observations in 10 m resolution were aggregated as monthly averages for each month of the summer season (May to October) from 2017 to 2021. For Bothnian Bay, we used Syke's in-house Sentinel-2 MSI CDOM layers (resolution: 60 m) aggregated as seasonal averages (1 Jul to 7 Sep). CDOM values are given as absorption coefficient of CDOM at 400 nm [m⁻¹]. <h3>Surface fetch</h3> A surface fetch raster was produced to the Puruvesi and Bothnian Bay. The analysis required a feature layer of shorelines from Puruvesi and Baltic Sea. First, we created polyline from north to south spanning over the whole area of interest with a gap of 20 meters which is also the resolution of the output raster. These lines were then cut each time they hit the shoreline and the part of the line that was overlapping land was removed. The distance of the remaining lines was then calculated and a point with the distance value was created every 20 meters. Each time the line was cut when hitting an island for example and starting again from the other side of the island, the distance calculation started from 0. This created a point dataset with a distance value in each point. We repeated the procedure for 15 times for different compass directions with 22.5 degree intervals and calculated average fetch for each point location on 20 meters grid from these 15 point layers. <h3>Depth</h3> Depth was measured by a diver using a dive computer while surveying each vegetation grid cell, and measured depth was used when projecting model results to Puruvesi (transferability performance). In addition, a depth model for the freshwater region was created from Sentinel 2 MSI satellite image using the logarithmic band ratio model of blue and red band. The model was calibrated using diver recorded field observations and validated against echo sounding measurements. For more complete coverage and to include deep areas, echo soundings from multiple sources were combined with the satellite derived bathymetry. The cell resolution of the resulting depth layer was 10 meters. <h3>Total nitrogen and phosphorus</h3> Mean total nitrogen and phosphorus layers for marine area were produced using ArcGIS "splines with barriers" tool for the EEZ of Finland with 20 meters spatial resolution (Virtanen et al. 2018). Summer (July - September) nutrient measurements from 0 to 10 meters depth between 2010 and 2020, obtained from the VESLA database, were used as input data for the interpolation. Nitrogen and phosphorus measurements in Puruvesi between 2010 and 2020 was gathered from the VESLA database. Data from July to September was selected to represent the growing season. A mean value of NTOT and PTOT was then calculated for each location. Spline with Barriers (SwB) tool was used to interpolate the values (Arcmap 10.7.1). The tool uses a feature layer as barrier to create the raster representing only the area of interest. For the barrier and the extent of the interpolated raster we used a shapefile representing Lake Puruvesi shoreline. The resolution was set to 5x5 meters. SwB tool created an "extent box" around the area of interest which was removed with Extract by Mask tool using the shoreline feature layer. After the interpolation we noticed that either one of the locations was situated on land or the polygon used as barrier was "leaking". SwB doesn´t interpolate areas that doesn't have locations with values or aren´t connected to the main body of water. To fix this, the raster was extended outwards based on the values of nearby cells and after that the raster was masked again to remove any cells on land. The phosphorus interpolation provided negative values in southern parts on Enanlahti in Kontiolahti and Muholanlahti. These negative values were caused by considerably larger phosphorus values in Enanlahti Lamminniemi (9m) Enanlahti Lamminniemi (4m) locations when compared with the nearby Puruvesi Enanlahti location. The interpolation apparently continued to decrease the values according to the trend set by the difference between these locations and caused it to reach negative values. The southern parts of the bay, about 750 meters, was removed and new values were calculated based on the surrounding cells with Focal Statistics tool. The interpolations were validated by removing 20 % of the locations and reproducing the interpolation. The removed locations and their values were then compared to the interpolated raster. R&lowast;2&lowast;2 value from phosphorus interpolation model was 0.91 after removing two outliers and R22 value from nitrogen interpolation model was 0.715 after removing one outlier. <h3>Distance to closest reed </h3> The aquatic vegetation (Phragmites australis reeds) presence/absence maps were also generated from Sentinel-2 MSI data. The processing included extracting one month of data (July 2019) from green and near-infra-red bands from Sentinel-2 Global Mosaic (S2GM) service and transforming those to normalized-difference vegetation indices (NDVIs). After that, Bayesian statistics were used to predict the posterior probability of vegetation occurrence when distance from shore and NDVI were used as predictor variables. The posterior variable was thresholded and the resulting vegetation presence areas were sieved so that both too small vegetation areas (fewer than 5 pixels) or areas that were not directly attached to shoreline were removed. The resulting map has 10 m pixel size and tentatively represents the locations of reed belts or other shoreline-attached vegetation. This EO-based layer could also be referred to as helophytes or helophytic macrophytes, as it denotes a specific zone of vegetation with emergent aquatic plants containing leaf-green, particularly those that grow densely and have horizontally oriented leaves. In some lakes, this layer can represent, for example, thick stands of Equisetum fluviatile, although in most cases, it is associated with common reed belts. The approach is described in more detail in Koponen et al. (2022). <h2>Data partitioning </h2> Data was partitioned with 70/30 splitting into training and test (interpolation accuracy) data. The splitting was repeated 100 times for each species by randomly selecting 70 % of observations which were used to build each of the SDMs (GLM, GAM, BRT and BART). The partitioning was repeated for each of the three input data areas and 11 species. The input data indexes for replicating the split are supplied in the data files.  <h2>R code </h2> Code files contain scripts for fitting the SDM models described in the paper using the data. Also code for calculating calidation statistics (modelling results) and code for statistical analyses for making inferences in the paper, are supplied.  <h2>Additional info</h2> More details on modelling protocol may be found in the original paper in Ecological Modelling, and in the Supplementary Information file of the original paper, which follows the model reporting template "ODMAP" by Zurell et al. (2020). <h2>References </h2> Attila, J., et al. 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. - Remote Sensing of Environment 128: 138-149. Brockmann, C., et al. 2016. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. - In: Living Planet Symposium. p. 54. Forsblom, L., et al. 2024. Finnish inventory data of underwater marine biodiversity   -Scientic data Koponen, S., et al. 2022. Blue Carbon Habitats: – a comprehensive mapping of Nordic salt marshes for estimating Blue Carbon storage potential. - Nordisk Ministerråd. Sagerman, J., et al. 2020. Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. - Ambio 49: 517-530. Zurell, D., et al. 2020. A standard protocol for reporting species distribution models. - Ecography 43: 1261-1277.

opencc-by-4.0Jul 2021View details →

Figure 1 – Distribution map for L. eketi, L. seyboui, L in Taxonomic notes on Liptena eketi Bethune-Baker, 1926 and related species (Papilionoidea: Lycaenidae: Poritiinae)

Figure 1 – Distribution map for L. eketi, L. seyboui, L. kiellandi

opencc-by-4.0Dec 2024View details →

Checklist and distribution of Pitcairnia species in the Brazilian Amazon

We present here the checklist of Pitcairnia (Bromeliaceae) species in the Brazilian Amazon. It contains 24 species that occur in the Amazon basin and 211 distribution points. These two files contain information on the taxonomy, collection and geographic references of these taxa.

opencc-zeroNov 2021View details →

Directional selection shifts trait distributions of planted species in dryland restoration

1. The match between species trait values and local abiotic filters can restrict community membership. An often-implicit assumption of this relationship is that abiotic filters select for a single locally optimal strategy, though difficulty in isolating effects of the abiotic environment from those of dispersal limitation and biotic interactions has resulted in few empirical tests of this assumption. Similar constraints have made it difficult to assess whether the type and intensity of abiotic filters shift along gradients of environmental harshness, as predicted by the stress dominance hypothesis. 2. We planted 9,216 plants of perennial grass and forb species that had a range of functional trait values and were assigned to a warm, intermediate, or cool temperature tolerance pools across eight sites on the Colorado Plateau. We compared the distributions of traits of surviving individuals to null distributions to evaluate whether there were shifts in trait means and variation. Borrowing from phenotypic selection concepts in evolutionary biology, we assessed support for stabilizing, directional, and disruptive abiotic filtering of trait distributions and whether these types of filtering varied with initial species pool. 3. Functional composition was significantly different from null distributions for nearly all traits at all sites, with trait variation more restricted in harsher abiotic conditions, supporting the stress-dominance hypothesis. Contrary to expectations, we primarily found evidence for directional selection, which increased in frequency in warm species pools while disruptive selection was found more often in cool and intermediate species pools. 4. Synthesis: This study provides a controlled experimental approach to test the effect of the abiotic environment on plant trait filtering. We found that opportunistic strategies allowing for rapid water acquisition during favorable periods improved survival at warmer sites. Species with these strategies may be expected to benefit from increasing aridity and may be selected for active management efforts. More generally, the prevalence of directional selection may have important implications for dynamic vegetation models that rely on trait distributions for translating environmental variation into ecosystem processes.

opencc-by-4.0Nov 2021View details →

Fish species reported to occur in the lake but not recorded in Projet Lac in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report

Fish species reported to occur in the lake but not recorded in Projet Lac

opencc-zeroDec 2021View details →

Codes for simulation and data for: The relationship between local and regional extinction rates depends on species distribution patterns

The rapid loss of biodiversity poses a great threat to ecosystem functions and services. Credible estimation of species extinction rates is essential for understanding the magnitude of biodiversity loss and for informing conservation, but this has been a challenge because estimated extinctions are unverifiable due to the lack of data. In this study, we investigated the relationship between local and regional extinctions and assessed the effects of range size, spatial segregation, and patchiness of species distribution on this local-regional extinction relationship. We found that regional extinction rates had a convex relationship with local extinction rates, that is, the regional extinction rate was most likely to be lower than the average local rate. The regional rates deviated from local rates as the sampling area decreased. The difference between local and regional extinction rates (local-regional extinction difference) became larger if a higher number of species had larger range sizes and patchiness. We also detected that there were interactive effects among these factors. Species segregation had a weak positive relationship with the local-regional extinction difference if more species had relatively large range sizes. As the sampling areas increased, the range size showed smaller positive effects on local-regional differences, but patchiness showed larger positive effects. The local-regional extinction relationship of this study provides insights into the spatial scaling of biodiversity loss and offers some important cues for estimating regional extinctions from local data in future studies.

opencc-zeroFeb 2022View details →

Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

Species distribution models (SDMs) are a popular tool in theoretical and quantitative ecology, and constitute the most widely used modelling framework in global change science and biodiversity conservation. As main data sources, SDMs require georeferenced biodiversity observations as a response or dependent variable (e.g. species occurrence, species richness, etc) and geographic layers of environmental information as predictors or independent variables (e.g. climate, land cover, vegetation indices derived from remote sensing, etc). However, although SDMs have become one of the most important quantitative tools for addressing regular and timely biodiversity assessments worldwide, these techniques are still subject to different sources of uncertainty that have been unequally assessed. Thus, despite uncertainty related to niche-based or distribution-based models has been addressed at different stages in the modelling process, an analysis of the effect of uncertainty coming from alternative data sources on the predictive ability of SDMs is still limited. Citizen-collected species occurrence data (e.g. eBird) are often used for fitting SDMs when data from standardized and expert-supported surveys (e.g. Atlases) are unavailable. On the other hand, macroclimate variables are much more commonly used as predictors in SDMs than other sources of information coming from remote sensing data. We assessed the effects of using different data sources (in both response and predictor variables) on SDM performance across a wide range of bird species with contrasting distributional ranges in the Iberian Peninsula (Portugal and Spain). To do that, a SDM ensemble-forecasting approach was implemented by using bird data from two different data sources: the semi-structured eBird project and standardized Atlases. We fitted SDMs with three predictor types: macroclimate, remotely sensed ecosystem functional attributes (EFAs) from vegetation indices, and their combination. Species were grouped in four range size classes. We also used different evaluation metrics to better assess the uncertainty of model predictions. We then applied generalized linear mixed-effects models to test the effect on model performance of input data source across distributional range sizes while accounting for different accuracy metrics. Pairwise comparisons between range projections were used to assess their spatial similarity. Our models demonstrated the usefulness and complementarity of different input data sources when modelling species distribution across different distributional ranges. Citizen science and remote sensing data contribute to update the knowledge of the distribution of the most threatened bird species by increasing the model accuracy. These findings highlight the need to integrate different data sources to improve the model predictions at regional scale. Our framework also underlines that model uncertainty should be examined more exhaustively at early stages of the modelling process. To perfom and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable; ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four *.csv files named as: 1) The_Iberian_bird_species_occurrences_dataset_10km.csv 2) CHELSA_bioclimate_variables_IP10km.csv 3) MODIS_EVI-based_EFAs_IP10km.csv 4) Combined_bioclimate_EFA_dataset_IP10km.csv For a more detailed description of the main dataset and each of these subdatasets, please refer to the attached README file. Keywords: bird atlas, eBird data, ecosystem functional attributes (EFAs), Iberian Peninsula, IUCN categories, Model accuracy, MODIS EVI, narrow-ranged species, remote sensing, species distribution models (SDMs), widespread species

opencc-by-4.0Feb 2022View details →

The distribution and impact of an invasive plant species (Senecio inaequidens) on a dune building engineer (Calamagrostis arenaria)

These data sets are used to run the analyses in the paper 'The distribution and impact of an invasive plant species (Senecio inaequidens) on a dune building engineer (Calamagrostis arenaria)' by Van De Walle et al., 2022, Neobiota (in progress). The presence/absence data (PA) of Senecio inaequidens in European coastal dunes can be found in 'Senecio_PA.xlsx', in the tab 'senecio_PA', together with the country and location where the occurrences were mapped. All coordinates of the samples are available in the tab 'coordinates samples'. 'Marram_growth_experiment.xlsx' contains the data gathered during the growth experiment. The origin of the sand is subdivided in 3 columns: 'Location' represents the location along the Belgian coast where sand was gathered, 'senecio' represents whether sand was gathered from underneath a senecio plant or not  and 'biota' represents whether biota could affect marram grass growth (biota = 0 thus means that the sand was sterilized).   Abstract Disturbance is thought to enhance the probability of invasive species establishment, a prerequisite for naturalization. Coastal dunes are characterized by disturbance in the form of sand dynamics. We studied the effect of this disturbance on the establishment and spread of an invasive plant species (Senecio inaequidens) in European coastal dunes. Local sand dynamics dictate the spatial configuration of marram grass (Calamagrostis arenaria). Therefore, marram grass configuration was used as a reliable proxy for disturbance. As marram grass plays a crucial role in natural dune formation, we evaluated the possible effects S. inaequidens could have on this process, if it would be able to naturalize in European coastal dunes.  We expected the highest probability of S. inaequidens establishment at intermediate marram grass cover because too low cover would increase sand burial, whereas high cover would increase competition. However, our results indicate that S. inaequidens is quite capable of handling higher levels of sand burial. Thus, probability of S. inaequidens establishment was high under low marram cover but slightly lowered when marram cover was high, hinting at the importance of competition. We expected a negative impact of Senecio-altered soils on marram grass growth mediated by soil biota. However, marram grass grew better in sand gathered underneath Senecio plants due to abiotic soil modifications. This enhanced growth may be caused by Senecio leaf litter elevating nutrient concentrations in an otherwise nutrient-poor substrate. If  such increased plant growth is a general phenomenon, further expansion of S. inaequidens could accelerate natural succession in European coastal dunes.