The invasive spionid polychaetes of the genus Marenzelleria spp., consisting of the three sibling species M. neglecta, M. viridis, and M. arctia, has been found in the Baltic Sea since the 1980s. Because of difficulties in species identification, little is known about species-dependent sediment reworking and solute transport. The closely related species are apparently similar in feeding and sediment-dwelling behavior, but size and burrowing depth indicate differences in bioturbation and the biogeochemical consequences thereof. To investigate these potential differences, a tracer experiment with artificial particles (luminophores) and solute tracer (bromide) was conducted. Polychaetes were identified to species level using a molecular genetic key. Modeled results show that all three species display markedly low particle reworking rates with biodiffusion coefficients (Db) of 1.76 cm 2 yr 21 (M. viridis) and 0.07 cm 2 yr 21 (M. neglecta) at an abundance of 1273 individuals m 22 and 0.44 cm 2 yr 21 (M. arctia) at twice that abundance. Nonlocal transport coefficients are negligible in all cases. Solute transport by M. neglecta and M. viridis are more similar to one another than to M. arctia, whose solute transport mode is much more diffusive in character (10.9-fold enhanced diffusivity with nonlocal irrigation coefficients [a] of 55.3 yr 21 ) than that of the two other species, which affect tracer distributions in the sediment predominantly through a nonlocal, advective transport mode (a of 108.9 yr 21 M. viridis and 130.9 yr 21 M. neglecta). Thus, a functional grouping of the sibling species in terms of bioirrigation is not recommended.

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The spread of the sibling polychaete species Marenzelleria neglecta, M. viridis, and M. arctia in the Baltic since the early 1980s has been accompanied by various effects on the biogeochemistry of bioturbated sediments. Their influence on benthic nutrient fluxes (NH 4 + , NO 3 − PO 4 3−) and oxygen uptake was examined in a laboratory experiment. Confirming a previous experiment, M. neglecta and M. viridis are more similar to each other with respect to their direct and indirect ecosystem function than to M. arctia. In contrast to the predominantly non-local transport mode of the deep-burrowing species M. viridis (solute transport coefficient α = 284.1 yr −1 ) and M. neglecta (α = 277.1 yr −1 ) in the sediment, the solute transport mode of M. arctia is more diffusive in character (11.8-fold enhanced diffusivity and α = 4.3 yr −1 ). While the release of ammonium and phosphate, as well as the total oxygen uptake (TOU) of the sediment, was stimulated by the presence of all polychaetes, the fluxes (NH 4 + , PO 4 3− , TOU) in cores colonized with M. viridis and M. neglecta were substantially higher than those of M. arctia. The presence of M. arctia had only slight stimulatory effects on nitrification, whereas M. neglecta and M. viridis enhanced nitrification. This suggests negligible stimulation of denitrification for M. arctia and leaves the source of ammonium in M. neglecta and M. viridis unresolved. Consequently, M. viridis and M. neglecta may affect eutrophication-related benthic fluxes considerably more than M. arctia, and a functional grouping of all 3 sibling species in terms of bioirrigation is not reasonable.

Benthic community bioirrigation potential (BIPc), an index developed to quantify the anticipated capacity of macrofauna to influence the solute exchange at the sediment–water interface, was calculated for the south-western Baltic Sea. This index can be regarded as an effect trait that is useful for predicting ecosystem processes impacted by animal burrow ventilation. The special feature, and presumably an advantage, of BIPc, compared to alternative recently developed benthic macrofauna-based bioirrigation indices, lies in its ability to distinguish the taxa-specific score values between diffusion- and advection-dominated sediment systems. The usefulness of the BIPc index was compared against the estimates of the well-established community bioturbation potential index (BPc). The BIPc index displayed a moderately but significantly stronger correlation with estimates of irrigation rates derived from tracer experiments. Using a random forest machine learning approach and a number of available relevant environmental predictor layers, we have modelled and mapped the spatial differences in this ecosystem functioning expression. The key species contributing to bioirrigation potential in the study area were identified. The interannual variation in BIPc was assessed on a small exemplary dataset. The scores required to calculate the index, that were assigned to 120 taxa dominating abundance and biomass in the region, are provided for reuse. The utility, temporal variability and uncertainty of the distribution estimate are discussed.

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