Influence of sampling effort on ecological descriptors and indicators in perturbed and unperturbed conditions: a study case using benthic macroinvertebrates in Mediterranean transitional waters

The effectiveness and accuracy of biomonitoring programs, based on benthic macroinvertebrates, is strictly related to the sampling design and effort, whereas the feasibility depends on the economic sustainability of sample collection and processing methodologies. In the last decade, how to improve the Rapid Bioassessment Protocols (RBPs) maintaining the accuracy of the results has been a topic recurrently debated among researchers. It is well know that the sample unit size (i.e., surface of the sampled area; SUS) and the sieve mesh size (SMS), selected to collect and to retain benthic macroinvertebrates from soft-bottom samples, may affect the evaluation of the aquatic ecosystem ecological status; however, studies analyzing the combined influence of SUS and SMS on assessment tools are lacking, in particular for transitional water ecosystems. Even if the Water Framework Directive (WFD) suggests rapid and cost-effectiveness sampling effort and procedures, the identification of optimal SUS and SMS is a basic step to improve the RBPs and to meet WFD suggestions. Therefore, this research analyzes the effects of four soft-bottom sample unit sizes (0.0225 m2, 0.0450 m2, 0.0675 m2, 0.0900 m2), and three sieve mesh sizes (4 mm2, 1 mm2, 0.25 mm2) on the selection of benthic macroinvertebrates and, thus, on assessment tools, in a Mediterranean lagoon. A sampling survey was performed in September 2009 at a perturbed and an unperturbed study site in the Lesina lagoon (SE Italian coastline); three replicates were taken for each SUS and SMS using an Ekman-Birge grab (15 cm x 15 cm). The samples were sieved on a column of three sieves, with decreasing mesh size. Benthic macroinvertebrates were sorted, identified, measured, weighted and included in twelve datasets (4 SUS x 3 SMS). Sampling effort (SE) was calculated for each SUS and SMS combination as: SE = [SUS m2 x (1/SMS mm2)] x 100. Four simple community descriptors (numerical density, taxonomic richness, biomass density, individual body-size) and four ecological indicators (AMBI, BENTIX, BITS, M-AMBI) were compared for each combination of SUS and SMS in both study sites. Simple community descriptors and ecological indicators varied significantly between perturbed and unperturbed study site. The results showed that SMS had significant effects on simple community descriptors and ecological indicators, except for BITS index. Conversely, no significant differences were observed for different SUS analyzing simple community descriptors and ecological indicators, except for taxonomic richness and M-AMBI index. The response of the ecological indicators was only slightly affected by the SMS, whereas SUS choice did not influence the ecological status assessment. Anyway, using the larger SMS (4 mm2), all ecological indicators showed either the same ecological quality status as the 1 mm2 and 0.25 mm2 SMS or, in some cases, one class higher, except for the AMBI index