Establishing Functional Relationships between Abiotic Environment, Macrophyte Coverage, Resource Gradients and the Distribution of Mytilus trossulus in a Brackish Non-Tidal Environment

Abstract

Benthic suspension feeding mussels are an important functional guild in coastal and estuarine ecosystems. To date we lack information on how various environmental gradients and biotic interactions separately and interactively shape the distribution patterns of mussels in non-tidal environments. Opposing to tidal environments, mussels inhabit solely subtidal zone in non-tidal waterbodies and, thereby, driving factors for mussel populations are expected to differ from the tidal areas. In the present study, we used the boosted regression tree modelling (BRT), an ensemble method for statistical techniques and machine learning, in order to explain the distribution and biomass of the suspension feeding mussel Mytilus trossulus in the non-tidal Baltic Sea. BRT models suggested that (1) distribution patterns of M. trossulus are largely driven by separate effects of direct environmental gradients and partly by interactive effects of resource gradients with direct environmental gradients. (2) Within its suitable habitat range, however, resource gradients had an important role in shaping the biomass distribution of M. trossulus. (3) Contrary to tidal areas, mussels were not competitively superior over macrophytes with patterns indicating either facilitative interactions between mussels and macrophytes or co-variance due to common stressor. To conclude, direct environmental gradients seem to define the distribution pattern of M. trossulus, and within the favourable distribution range, resource gradients in interaction with direct environmental gradients are expected to set the biomass level of mussels.

Population Dynamics at the Range Margin: Implications of Climate Change on Sublittoral Blue Mussels (Mytilus trossulus)

“…Here we significantly extend previous snapshot assessments of spatial distribution patterns of Baltic Sea blue mussels along environmental gradients (e.g., Westerbom et al, 2002;Nyström Sandman et al, 2013;Kotta et al, 2015) by adding time into the spatial analysis. While not sampling the entire range, our areas that were spaced along a salinity gradient, showed responses that fit the central-marginal prediction (e.g., Hengeveld and Haeck, 1982).…”

“…Blue mussel beds are mainly found in the depth range 3-20 m, although mussels dominate the animal community down to at least 30 m. Traditionally, populations have been considered stable, living near the carrying capacity with regard to food and space (Kautsky, 1982). Toward the margins, the demography and dynamics are different (Westerbom et al, 2002) -with lower biomasses, shorter life span and higher turnover of populations -and mainly driven by environmental factors, foremost by salinity (Westerbom et al, 2002;Nyström Sandman et al, 2013;Kotta et al, 2015).…”

The importance of benthic–pelagic coupling for marine ecosystem functioning in a changing world

“…This results from the decreasing diversity and biomass of suspension feeders with decreasing salinity in both soft‐bottom (Bonsdorff & Pearson, ) and hard‐bottom areas (blue mussels, Westerbom et al ., ). Projected changes in nutrients and salinity could have negative effects on the distribution and productivity of mussels (Kotta et al ., ) and diminish their role in benthic–pelagic exchange (Fig. b; Table ).…”

“…Climate change is expected to influence waterbird phenology and distribution (Guillemain et al ., ; Lehikoinen et al ., ), increasing the duration and intensity of benthic predation in the northeastern Baltic Sea while decreasing their presence in the southern and western part of the area (Table ). Decreasing salinity is likely to shift the occurrence, size, and densities of mussel beds (as discussed above, Kotta et al ., ; Fig. b) in turn affecting the availability and quality of benthic prey and bird consumers.…”

Effects of abiotic factors on ecosystem health of Taihu Lake, China based on eco-exergy theory

“…Meanwhile, the method can also illustrate the marginal effect of the selected independent variable on the response variable while all other covariates were kept constant at their observations using a partial dependence plot 23 . This kind of modeling has been successfully applied into ecology and biology for simulating non-linear interactions between response and predictor variables 24 25 26 27 . E.g., Randall and Van Woesik 28 investigated the effect of eight sea surface temperature metrics on white-band disease of reef-building corals in the Caribbean, indicating that the disease was associated with climate change, which resulted in the regional population decline.…”