Separating sampling bias from abundance shows that different methods catch different wild bees

Ecological community sampling methods have taxonomic biases, producing
samples where relative abundances of taxa may differ from the underlying
sampled community. Evaluating sampling methods’ relative biases is
therefore necessary for accurately interpreting community data. Wild bees
(Hymenoptera: Apoidea) have been the focus of intensive community sampling
and many studies have compared the properties of samples collected by
different methods. However, comparative studies have often conflated
differences in sampling bias with differences in effort and absolute
abundance between methods, potentially obscuring methods’ true biases.
Here, we compare wild bee communities in the northeastern United States as
sampled by pan traps, vane traps, and hand netting. Using a dataset of
simultaneous sampling by different methods, we compare sample richness and
composition between pairs of methods while accounting for differences in
the overall number of bees sampled by each. For a given number of
individuals sampled, hand netting captured more bee species than pan
traps, which captured more species than vane traps. Pan traps sampled a
different pool of species than either of the other two methods. Of 21 bee
genera analyzed, eight were overrepresented in pan trap samples relative
to hand netting, while seven were relatively underrepresented in pan
traps. When compared against vane traps, four genera of 20 were relatively
overrepresented in pan traps while six were relatively underrepresented.
Pan traps poorly represented very large-bodied genera as compared with the
other methods. We find pervasive biases in bee community sampling methods,
with most genera showing significant differences in relative abundance in
at least one methodological comparison. At times, genera were relatively
underrepresented even by methods that collected them in higher absolute
abundance. Since bias is unavoidable in community sampling, studies must
measure taxon-specific biases in the context of their system and evaluate
the robustness of analytical results.