Empirical relationships linking distribution and abundance of marine vegetation to eutrophication

In order to decide on measures to preserve and restore seagrasses and macroalgae, there is a need for identifying quantitative links between eutrophication pressure and vegetation response. This study compiles existing empirical relationships between eutrophication-related variables and responses measured in terms of distribution and abundance of seagrasses and macroalgae and analyses similarities and differences between responses in different ecosystems. The compilation includes 73 relationships originating from 38 publications from the period 1982 to 2007 and covering a wide range of ecosystems. Of the 73 relationships, 38 link vegetation responses significantly to eutrophication pressure as expressed by nutrient richness or water transparency, 18 link the responses to combinations of eutrophication pressure and ecosystem characteristics and 9 link the responses to ecosystem characteristics alone. The remaining relationships are either non-significant (3) or include no information on significance levels (5). The compilation demonstrates that seagrasses and macroalgae generally respond quantitatively to changes in eutrophication pressure by growing deeper, being more abundant and more widely distributed in clear waters with low nutrient concentration as compared to more turbid and nutrient-rich ecosystems. Vegetation in deeper waters shows the strongest response because it is most markedly affected by shading effects of eutrophication. This similarity in the patterns of response indicates a wide robustness and generality of the findings. However, the sensitivity of the vegetation to shading effects of eutrophication varies widely across ecosystems. We attribute this variability to additional eutrophication effects such as anoxic events, and ecosystem characteristics such as water residence time, sediment characteristics, or presence of grazers that may modify the response of the vegetation to a given eutrophication pressure. We encourage taking into account and quantifying such effects in order to improve the predictive power of future empirical relationships.