|1. Virtually all empirical ecological interaction networks to some extent suffer from
undersampling. However, how limitations imposed by sampling incompleteness
affect our understanding of ecological networks is still poorly explored, which
may hinder further advances in the field.
2. Here, we use a plant-hummingbird network with unprecedented sampling effort
(2,716 hours of focal observations) from the Atlantic Rainforest in Brazil, to
investigate how sampling effort affects the description of network structure (i.e.
widely used network metrics) and the relative importance of distinct processes
(i.e. species abundances vs traits) in determining the frequency of pairwise
3. By dividing the network into time slices representing a gradient of sampling
effort, we show that quantitative metrics, such as interaction evenness,
specialization (H2'), weighted nestedness (wNODF) and modularity (Q;
QuanBiMo algorithm), were less biased by sampling incompleteness than binary
metrics. Furthermore, the significance of some network metrics changed along the
sampling effort gradient. Nevertheless, the higher importance of traits in
structuring the network was apparent even with small sampling effort.
4. Our results (i) warn against using very poorly sampled networks as this may bias
our understanding of networks, both their patterns and structuring processes, (ii)
encourage the use of quantitative metrics little influenced by sampling when
performing spatio-temporal comparisons, and (iii) indicate that in networks
strongly constrained by species traits, such as plant-hummingbird networks, even
small sampling is sufficient to detect their relative importance for the structure of|