Seasonal patterns of tidepool macroalgal assemblages in the North of Portugal. Consistence between species and functional group approaches

Macroalgae are useful organisms to monitor the environmental quality and to detect impacts due to anthropogenic activities. However, it is very important to identify the scales of variation in natural assemblages, particularly for the detection of environmental impacts. Otherwise, changes due to anthropogenic impacts may be confused with differences due to natural temporal variability. Another important task is to determine the appropriate level of taxonomical effort needed to detect changes in the assemblage structure. Many taxonomical surrogates, at higher taxonomic levels than that of species, have been proposed but, the consistence in space and time of the results produced by surrogates with those obtained at specific level should be tested. The objectives of this study are to identify the seasonal patterns of tidepool macroalgal communities using objective procedures and to test the consistence between the patterns obtained considering data at the species level and functional groups. Results showed that the seasonal pattern obtained using functional groups and species was consistent. Tidepool macroalgal assemblages showed a seasonal pattern with significant differences between spring–summer and autumn–winter. This pattern can be explained by changes in environmental variables and the seasonal development of the dominating species. Ulva spp. and the non-indigenous species Grateloupia turuturu were the species responsible for this pattern due to their high seasonality in terms of biomass. Finally, the abundance and species diversity within the corticated functional group was proposed as indicator of environmental impacts due to its relatively constant abundance and its sensitivity to environmental impacts.

► We explore seasonal patterns of tidepool macroalgal assemblages.
► Species and functional group approaches were used.
► Results will be useful to understand and design future experiments.
► This study provides valuable information to future monitoring programmes.