Sampling completeness in seed dispersal networks: When enough is enough

Abstract

Ecological networks are an increasingly popular tool to explore community assembly rules and frame practical conservation issues. However, most described networks vary largely in sampling effort, hampering the distinction of true biological patterns from artefacts caused by poor sampling. Identifying entire seeds in the droppings of mist-netted birds is generally considered a preferred sampling method for building unbiased, quantitative seed dispersal networks. We retrieved seeds from the droppings of 936 mist-netted birds captured during five days in seven sites in Portugal and estimated sampling completeness as the diversity of seed species, disperser species, and links detected with respect to those predicted by Chao 2 estimator. In one of those sites, sampling effort was extended to 25 days to evaluate the sensitivity of ten network structure descriptors to increasing sampling effort. After five sampling days we detected 93% of the seed species, 97% of the disperser species, and 79% of the links predicted by Chao 2, however sampling for 25 days resulted in the detection of more seeds, dispersers, and links than those estimated at day 5. Most network descriptors only began to stabilize around day 8, except for Connectance and Weighted Connectance that stabilized earlier. Similarly, only after 8 days most networks descriptors significantly departed from the confidence interval estimated by null models exclusively constrained by species abundances, thus reflecting independent ecological patterns. Nestedness was the only exception, as it never departed from the null models. We suggest that Chao 2 may slightly underestimate the real diversity and that in our case at least eight sampling days were needed to build a sound seed dispersal network as 67% of the seeds, 88% of the dispersers, and 71% of the links were detected. Our results have important implications for the interpretation of seed dispersal networks because under-sampled networks may produce biased descriptors that do not suitably characterize the focal communities.