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

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.

Predicting species cover of marine macrophyte and invertebrate species combining hyperspectral remote sensing, machine learning and regression techniques.

In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.

Realized niche width of a brackish water submerged aquatic vegetation under current environmental conditions and projected influences of climate change.

Little is known about how organisms might respond to multiple climate stressors and this lack of knowledge limits our ability to manage coastal ecosystems under contemporary climate change. Ecological models provide managers and decision makers with greater certainty that the systems affected by their decisions are accurately represented. In this study Boosted Regression Trees modelling was used to relate the cover of submerged aquatic vegetation to the abiotic environment in the brackish Baltic Sea. The analyses showed that the majority of the studied submerged aquatic species are most sensitive to changes in water temperature, current velocity and winter ice scour. Surprisingly, water salinity, turbidity and eutrophication have little impact on the distributional pattern of the studied biota. Both small and large scale environmental variability contributes to the variability of submerged aquatic vegetation. When modelling species distribution under the projected influences of climate change, all of the studied submerged aquatic species appear to be very resilient to a broad range of environmental perturbation and biomass gains are expected when seawater temperature increases. This is mainly because vegetation develops faster in spring and has a longer growing season under the projected climate change scenario.