Macrofauna impact on Ulva rigida C. Ag. production and relationship with environmental variables in the lagoon of Venice

The grazing pressure of the macrofaunal invertebrates associated with the b iomass of Ulva rigida in the Venice lagoon, their species composition and r elationship with environmental variables have been studied. Fifteen samplin g campaigns were carried out during different seasons both in the central b asin of the lagoon, dominated by macroalgae (especially U. rigida C. Ag.) a nd in the southern basin, prevalently populated by seagrasses (especially Z ostera marina L.). Replicate experiments were conducted in the field by exp osing Ulva fronds in net cages of 10 and 1 mm (control) mesh-sizes to allow or prevent grazer entrance. The grazing pressure was determined as Ulva gr owth rate difference in the cages. In the absence of invertebrate herbivore s, Ulva exhibited per cent relative growth rates (%RGRs) ranging from 1.5 t o 9.5% day(-1), whereas in their presence the %RGRs were significantly lowe r (from -2.5 to 3.4% day(-1)) and frequently negative, especially in the st ation dominated by macroalgae. In this area, peak grazing rates and macrofa una biomasses of up to 8.6% day(-1) and 1480 g m(-1) fwt (84.4 ash-free dry weight), respectively, were found. On the whole, during in field experimen ts in the Ulva-dominated station, herbivores removed an amount of biomass w hose percentage ranged from 59 to 165% (mean: 103%) of the biomass yield (g razers excluded) found in the cages. These results suggest the possibility that grazers could act as an important factor affecting Ulva production in the Venice lagoon. Macrofauna populations were analysed by means of multiva riate techniques applied to biological variables only and biological and en vironmental variables together. Data of individual abundance, after a log(x +1) transformation and the calculation of the Bray-Curtis matrix, were clas sified using the Cluster Analysis and ordinated by means of the Non-Metric Multidimensional Scaling (MDS) technique, in accordance with the strategies used in the study of multispecies distributions. Finally, biological and e nvironmental variables were analysed together by means of correlation matri ces and the Principal Component Analysis. (C) 2001 Elsevier Science Ltd. Al l rights reserved.