Academic journal article Estonian Journal of Ecology

Linking Nutrient Loading, Local Abiotic Variables, Richness and Biomasses of Macrophytes, and Associated Invertebrate Species in the Northeastern Baltic Sea

Academic journal article Estonian Journal of Ecology

Linking Nutrient Loading, Local Abiotic Variables, Richness and Biomasses of Macrophytes, and Associated Invertebrate Species in the Northeastern Baltic Sea

Article excerpt

INTRODUCTION

In order to establish standards for ecological conditions in groundwater, streams, lakes, and coastal areas most European countries have to implement the EU Water Framework Directive (WFD) by 2015. The aims of the WFD are to protect, enhance, and restore all water bodies and to obtain good water quality (European Union, 2000). For implementing the WFD it is necessary to establish efficient methods to link the ecological status with catchment loading.

Nutrient loading is considered to be the main process that causes changes in the structure and functioning of many coastal sea ecosystems (Elmgren, 1989; Duarte, 1995). Similarly, an elevated nutrient loading is a key risk for the Baltic Sea ecosystem shown by significant shifts in community structure associated to a dramatic increase in nutrient concentrations in recent decades (Kotta et al., 2000; Elmgren, 2001; Ronnberg, 2001; Bonsdorff et al., 2002; Grall and Chauvaud, 2002; Kotta and Witman, 2009).

Phytobenthos is regarded as a good early-warning indicator of the environmental status because they are stationary and visible by the naked eye. It is known that increasing availability of inorganic nutrients stimulates the abundance of ephemeral macroalgae and causes the dominance shift from perennial to annual macroalgae in shallow coastal waters (Sand-Jensen and Borum, 1991; Duarte, 1995; Valiela et al., 1997). It is therefore expected that ephemeral species are especially good indicators of water quality. However, other environmental variables such as substrate type and salinity may modulate the relationships between nutrient loading and ephemeral macroalgae (Eriksson and Bergstrom, 2005; Kotta et al., 2009).

Benthic invertebrate communities represent an intermediate trophic level and nutrient additions affect them in many ways. Increasing nutrient loads enhance the production of benthic and/or pelagic microalgae (Graneli and Sundback 1985; Howarth, 1988) and, hence, increase the amount of available food for benthic grazers, suspension feeders, and deposit feeders and ultimately for carnivores. As a consequence, abundance and growth responses of invertebrates are observed at moderate levels of nutrients (Posey et al., 1999; Kotta and Olafsson, 2003; Lauringson and Kotta, 2006; Lauringson et al., 2007). Further addition of nutrients leads to hypoxia, appearance of ammonia and hydrogen sulphide, and consequently to the disappearance of benthic invertebrates (Gray et al., 2002; Kotta et al., 2007).

To date, we still lack scientific evidence for the nutrient-macrophyte-invertebrate relationship in order to adequately assess the water quality in the Baltic Sea region. Such a relationship can be found, however, when the correlation structure between environmental variables and biotic patterns is analysed in the multivariate space and non-linearities are taken into account. Multivariate analysis enables us to distinguish separate and interactive effects of nutrient loading and to estimate the relative contribution of nutrient loading to the overall variability of macrophyte communities. Besides, novel machine learning techniques such as Boosted Regression Trees (BRT) modelling also enables fitting complex nonlinear relationships. Avoiding overfitting the data, BRT provide very robust estimates. What is most important in the ecological perspective it automatically handles interaction effects between predictors. Consequently, such analysis can inform us about the relative contribution of different environmental variables to the variability of biotic patterns as well as to compute the functional form relationship between the environment (e.g. nutrient load) and biota, independent of other environmental variables. Due to its strong predictive performance, BRT is increasingly used in ecology (Elith et al., 2008).

We analysed links between nutrient loading and phytobenthic community structure and sought whether and how local abiotic variables modulated the nutrient-phytobenthos relationship in the north-eastern Baltic Sea. …

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