DOI: 10.5061/dryad.r2280gbr8
title: Separating sampling bias from abundance shows that different methods catch different wild bees

description: 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.
description: # Separating sampling bias from abundance shows that different methods
 catch different wild bees Dataset DOI:
 [10.5061/dryad.r2280gbr8](https://doi.org/10.5061/dryad.r2280gbr8) ##
 Description of the data and file structure ### Files and variables ####
 File: methods--final_EE_010926.R **Description:** See Code/software below.
 #### File: raupcrick_Chase.R **Description:** See Code/software below.
 #### File: IT_species.csv **Description:**  Species-level average
 intertegular distances (ITD) from a selection of specimens in the Winfree
 Lab collection. Intertegular (IT) distance is measured as the distance
 across the thorax of a bee specimen, between the bases of the wings
 (tegulae). ##### Variables * genus: genus-level identification of bees
 that measurements pertain to * species: species-level identification of
 bees that measurements pertain to * femaleIT: average intertegular
 distance (measured in millimeters) of female specimens measured for a
 given species * maleIT: average intertegular distance (measured in
 millimeters) of female specimens measured for a given species #### File:
 pan_net_all.csv **Description:** Collection information and taxonomic
 identifications for bee specimens collected during sampling events with
 simultaneous use of hand-netting and pan traps. ##### Variables *
 uniqueID: unique identifying code for each individual specimen * genus:
 genus-level taxonomic specimen identification * species: species- or
 species-group-level taxonomic specimen identification * collector:
 individual or individuals who collected a given specimen * method:
 sampling method by which a given specimen was collected - either hand net
 ("net") or pan trap ("pan") * date: date
 (month/day/year) on which a given specimen was collected in the field *
 round: order of an individual sampling event relative to other sampling
 events conducted at the same site and year (e.g., first sampling event of
 the year at a site is round 1, second is round 2, etc.) * site: unique
 identifier for field site/sampling location * study: unique identifier for
 each individual research study * latitude: decimal latitude of geographic
 location of field site/sampling location * longitude: decimal longitude of
 geographic location of field site/sampling location #### File:
 pan_vane_all.csv **Description:** Collection information and taxonomic
 identifications for bee specimens collected during sampling events with
 simultaneous use of pan traps and blue vane traps. ##### Variables *
 uniqueID: unique identifying code for each individual specimen * genus:
 genus-level taxonomic specimen identification * species: species- or
 species-group-level taxonomic specimen identification * method: sampling
 method by which a given specimen was collected - either vane trap
 ("vane") or pan trap ("pan") * date: date
 (month/day/year) on which a given specimen was collected in the field *
 site: unique identifier for field site/sampling location * study: unique
 identifier for each individual research study * latitude: decimal latitude
 of geographic location of field site/sampling location *
 longitude: decimal longitude of geographic location of field site/sampling
 location 
 
 ## Code/software methods--final_EE_010926.R: R script needed to run all
 main analyses in our paper entitled "Separating sampling bias from
 abundance shows that different methods catch different wild bees".
 This script compares bee community samples as collected by pan traps
 against simultaneously collected samples from 1) hand netting and 2) blue
 vane traps. Specifically, we compare richness of samples via rarefaction
 and species composition/identities via multivariate methods (Raup-Crick
 dissimilarity, as calculated using code made available by Chase et al.
 (2011)). Finally, we measure methods' relative taxonomic biases with
 respect to individual bee genera using generalized linear mixed models
 (GLMMs) and test whether genus-level differences in bias may be
 attributable to differences in body size. raupcrick_Chase.R: *This file is
 not our original code - all of the code within was provided by Chase et
 al. (2011) for calculating the Raup-Crick dissimilarity metric. Our main
 analysis R script (above) sources the function "raup_crick" from
 this script to calculate Raup-Crick dissimilarity between bee community
 samples. See citation for this file below. Citation: M. Chase, Jonathan;
 J. B. Kraft, Nathan; G. Smith, Kevin; Vellend, Mark; Inouye, Brian D
 (2016). Using null models to disentangle variation in community
 dissimilarity from variation in α-diversity. Wiley. Collection.
 [https://doi.org/10.6084/m9.figshare.c.3308220.v1](https://doi.org/10.6084/m9.figshare.c.3308220.v1) ## Access information Other publicly accessible locations of the data: * [https://doi.org/10.1086/675716](https://doi.org/10.1086/675716) * [https://doi.org/10.1111/gcb.13921](https://doi.org/10.1111/gcb.13921) * [https://doi.org/10.1016/j.biocon.2021.109202](https://doi.org/10.1016/j.biocon.2021.109202 "Persistent link using digital object identifier") Data was derived from the following sources: *