Research Article |
Corresponding author: Antoni Vivó-Pons ( avipo@aqua.dtu.dk ) Academic editor: Anthony Ricciardi
© 2024 Antoni Vivó-Pons, Pieter Daniël van Denderen, Louise Flensborg, Cornelia Jaspers, Martin Lindegren.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Vivó-Pons A, van Denderen PD, Flensborg L, Jaspers C, Lindegren M (2024) Disentangling the effects of abiotic and biotic processes on non-indigenous species dominance. NeoBiota 94: 159-177. https://doi.org/10.3897/neobiota.94.128736
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Relatively little attention has been paid to the underlying mechanisms determining the dominance of non-indigenous species (NIS) once established, despite being regarded as a proxy of invasion success and potential impacts in recipient communities. To bridge this knowledge gap, here we evaluate the potential direct and indirect effects of community filters on the dominance of two widespread NIS in the Baltic Sea: Marenzelleria spp. and the round goby (Neogobius melanostomus) within their corresponding communities. We applied a structural equation modelling approach to assess the direct and indirect effects amongst multiple abiotic and biotic variables on the relative biomass (as proxy of dominance) of NIS. The biotic variables represented the taxonomic- and functional diversity of the recipient communities, as well as the trait similarity between NIS and native species. We observed a comparable influence of abiotic and biotic drivers on the dominance of both NIS, with biotic variables having a somewhat stronger overall direct effect. Specifically, the dominance of both NIS was similarly affected negatively by the richness and positively by the evenness of the native communities. However, we also detected that both NIS might need different ecological strategies to become dominant in their recipient communities, which underwent similar assembly processes. Such strategies were partly highlighted by the different degrees of trait similarity between each NIS and their respective co-occurring native species. A better understanding of the underlying processes affecting NIS dominance is of high relevance to mitigate potential impacts of NIS once established. Furthermore, the provided approach could be further applied to unveil the potential strategies that NIS might follow in other regions and ecosystem types.
Benthos, biological invasions, coastal fish, community assembly rules, dominance, functional distinctiveness, functional ecology, SEM, traits
The spread of non-indigenous species (NIS) pose a major threat to biodiversity and the integrity of ecosystems worldwide (
The establishment of NIS has been shown to be influenced by the same community assembly processes structuring the composition of native communities (
Here, we aim to bridge this knowledge gap by providing one of the first comprehensive assessments of the main drivers of NIS dominance in recipient communities by integrating existing community assembly theory (
The first report of the Marenzelleria species complex in the Baltic Sea was in 1985, probably introduced through ballast water from North America (
To evaluate the potential direct and indirect effects of both abiotic and biotic community filters on the dominance of both NIS, we applied a structural equation modelling (SEM) approach (
Conceptual figure summarising the key assembly processes acting on community composition and NIS dominance along with the three main questions and selected species.
Community assembly theory predicts that the environment is more important in shaping communities across larger spatial scales, while biotic interactions gain relevance at more local scales (
We collected available monitoring data on Marenzelleria and round goby, as well as the co-occurring native benthic invertebrates and fish species throughout the study area. For Marenzelleria and the native benthic invertebrates, wet weight was obtained from the Swedish Ocean Archive (https://sharkweb.smhi.se), containing a total of 3534 unique sampling events from 1993 to 2020 covering the Baltic Sea from the Bothnian Bay in the north-east, to the south-western Baltic Sea (Fig.
Mean relative biomass of Marenzelleria (A) and round goby (B) at each sampling location. The pie plots illustrate the percentage of total biomass corresponding to NIS (coloured) or native species (grey) in each region.
In addition to the monitoring data, we collected available trait information for all species representing the fundamental ecological processes of feeding, growth, reproduction, survival and behaviour following existing trait-based descriptions of marine organisms (
On the basis of the data, the relative biomass of NIS compared to the native species at each sample unit was used to represent NIS dominance. Furthermore, to examine potential biotic factors affecting dominance of NIS, we computed several community metrics representing the taxonomic and functional richness and evenness in each sampled community. For species evenness, we used Pielou’s Index (J), based on the specific measure of biomass of species at each unique sampling event. Functional richness (FRic) was measured as the minimum amount of functional space (convex hull) filled by all the species in a community (
To assess the potential individual strategies of NIS in their recipient communities, we further included the functional distinctiveness metric as a predictor. This metric reflects the degree of niche differentiation between species given by their traits, measured as the mean functional distances from one species to all the others within the same community (
Spatial differences in salinity and bottom oxygen concentrations, as well as temperature and depth have been shown to influence the structure and composition of benthic invertebrates and fish communities in the Baltic Sea (
Additionally, we computed the standard deviation for the set of environmental predictors by year (Marenzelleria), month (round goby) and location to represent the variability and seasonality of environmental conditions. To test for potential multi-collinearity amongst predictor variables, we performed a variance inflation factor (VIF) analysis. Based on the VIF results, we removed bottom temperature variation from Marenzelleria (VIF > 5; Suppl. material
To assess multiple relationships between NIS dominance and the set of environmental and biotic variables, we used a structural equation modelling (SEM) framework, based on linear mixed models. We first developed a SEM with links considered only between NIS dominance and biotic variables and between biotic variables and environmental drivers separately. After evaluating model fits (Suppl. material
NIS relative biomass = a + β1 (NIS functional distinctiveness) + β2 (Richness) + β3 (Evenness) + β4 (Functional richness) + β5 (Functional evenness) + β6 (Environmental predictor 1) + … + βN (Environmental predictor N) + d (Year) + e…n (Random effects) + ε
where α and β reflect the intercept and regression coefficients for each predictor (N) on NIS relative biomass (as a response) and ε the residual error term. We also tested for non-linear relationships by adding a second term for each predictor variable x that reflected the quadratic effect: (x – mean (x)) 2 (
After model fit and validation, we compared the strength and relative importance of environmental and biotic predictors by summarising the standardised coefficients of all the significant direct and indirect effects on NIS dominance. We estimated indirect effects of environmental variables by multiplying the path coefficients from any environmental variable by the path coefficient of any biotic variable that showed a significant link with NIS dominance. We also estimated the overall effect of environment and biotic variables on NIS dominance by obtaining the absolute sum of all the direct effects within each group.
Overall, both SEMs demonstrate pronounced direct links between the environmental and biotic variables, including dominance (Fig.
SEM structures for Marenzelleria (A) and round goby (B) dominance showing the direct and indirect links between abiotic and biotic variables. Blue boxes indicate a significant quadratic effect of the corresponding predictor. The values next to the arrows show the standardised coefficients. Non-causal correlations are expressed as light blue arrows. Fisher’s C test parameters and corresponding p-value (i.e. goodness-of-fit) of each SEM structure are shown in the dashed box. The coefficient of determination (R2) is shown for each biotic variable and NIS dominance. The direct links between abiotic and biotic variables are shown in Fig.
Direct significant effects of the environment on biotic variables and relative biomass on Marenzelleria (A) and round goby (B). Direct (darker colour) and indirect effects (lighter colour) from environmental variables on the relative biomass of Marenzelleria (C) and round goby (D). Cumulative absolute direct effects from both environmental and biotic variables on NIS relative biomass (E).
NIS dominance in both SEMs was positively related to species evenness (Fig.
Amongst the set of environmental predictors, the dominance of Marenzelleria showed strong positive and negative (non-linear) links with depth and bottom salinity, respectively (Figs
Partial effects plot from all significant variables (A–I) illustrating their effects on the relative biomass of Marenzelleria (orange) and round goby (blue). Panel D included the non-significant relationship between distinctiveness and NIS dominance for Marenzelleria to illustrate the opposite direction of both trends. The y-axis in each plot represents the change of NIS relative biomass values in function of each variable, with its entire range of values represented in the x-axis.
Marenzelleria’s dominance was reasonably well explained by the environmental and biotic variables (r2 = 46%) (Fig.
Our findings indicate a comparable direct influence of environmental conditions and biotic factors on the dominance of NIS, with biotic variables exerting a slightly stronger overall effect. These outcomes emphasise the importance of biotic drivers (i.e. potential biotic interactions) as small-scale community assembly processes, although biotic interactions are also relevant beyond local extents (
The biotic attributes from the host community showed a similar influence on NIS dominance in both SEMs, in line with our second hypothesis. More specifically, the observed negative relationship with richness suggests that a higher number of native species or functional groups, present at a given sampled location, may provide some sort of biotic resistance towards both NIS (
Amongst the environmental variables considered in the SEMs, only depth and bottom temperature had strong and similar effects on richness and evenness in both communities. This corroborates previous studies on the role of both depth and temperature as primary factors structuring marine communities in the Baltic Sea (
While no significant effect of distinctiveness was found for Marenzelleria, the observed negative effect of species richness may provide additional insight. It has been observed that Marenzelleria has the potential to displace or strongly compete with other native species (
In conclusion, our results show that local-scale biotic drivers together with the environment constitute key determinants of both NIS dominance in recipient communities. These findings highlight that biotic interactions may play a fundamental role in community assembly at small spatial scales (
We wish to thank the Swedish Agency for Marine and Water Management and OKG Aktiebolag for funding the regional and national monitoring programmes together with all the people involved who sorted, identified and measured the included species in the database, ensuring the access and availability to high-quality.
The authors have declared that no competing interests exist.
No ethical statement was reported.
A.V.P and M.L. acknowledge financial support from the European Union’s Horizon 2020 projects “Mission Atlantic” (ID: 862428), “B-USEFUL” (ID: 101059823) and “ACTNOW” (ID: 101060072). P.D.V.D. was funded by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 101024886. C.J was funded by Villum Fonden (agreement No. 25512).
A.V.P. and M.L. conceived the ideas and designed methodology; A.V.P., D.vD. and M.L. conducted the main research with contributions of C.J. and L.F.; A.V.P. analysed the data with contributions of M.L., D.vD. and L.F.; A.V.P. and M.L. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
Antoni Vivó-Pons https://orcid.org/0000-0002-6952-6372
Pieter Daniël van Denderen https://orcid.org/0000-0001-6351-0241
Louise Flensborg https://orcid.org/0000-0002-1677-3222
Cornelia Jaspers https://orcid.org/0000-0003-2850-4131
Martin Lindegren https://orcid.org/0000-0002-9185-951X
The data and R code used in this study are publicly available in Dryad (https://doi.org/10.5061/dryad.4f4qrfjkr) and GitHub (https://github.com/ToniVP/NIS_dominance).
Supplementary information
Data type: docx