DOI: 10.5061/dryad.hdr7sqvqm
title: Community science enhances modelled bee distributions in a tropical Asian city

description: Bees and the ecosystem services they provide are vital to urban
 ecosystems, but little is understood about their distributions,
 particularly in the Asian tropics. This is largely due to taxonomic
 impediments and limited inventorying, monitoring, and digitization of
 occurrence records. While expert collections (EC) are demonstrably
 insufficient by themselves as a data source to model and understand bee
 distributions, the boom of community science (CS) in urban areas provides
 an untapped opportunity to learn about bee distributions within our
 cities. We used CS observations in combination with EC observations to
 model the distribution of bees in Singapore, a small tropical city-state
 in Southeast Asia. To address the restricted spatial context, we performed
 multiple bias corrections and show that species distribution models
 performed well when estimating the distribution of habitat specialists
 with distinct range limits detectable within Singapore. We successfully
 modelled 37 bee species, where model statistics improved for 23 species
 upon the incorporation of CS observations. Nine species had insufficient
 EC observations to obtain acceptable models, but could be modelled with
 the incorporation of CS observations. This is the first study to combine
 both EC and CS observations to map and model the occurrences of tropical
 Asian bee species for a highly urbanised region at such fine resolution.
 Our results suggest that urban landscapes with impervious surfaces and
 higher temperatures are less suitable for bee species, and such findings
 can be used to advise the management of urban landscapes to optimise the
 diversity of bee pollinators and other organisms.
description: Observations were obtained from both CS and EC databases (Table
 1). EC observations were defined as those collected by individuals
 possessing specialised knowledge and experience in the study of bees,
 including formall-trained students in entomology and researchers
 associated with the National University of Singapore Insect Diversity
 Laboratory (PI Ascher), using targeted and standardised sampling methods
 (Ascher et al., 2019) supplemented by opportunistic sampling. This dataset
 was the primary basis for recent conservation assessments (Ascher et al.,
 2022). CS sources were defined as observations collected in large part by
 non-experts or non-formally trained individuals through open access
 repositories, such as iNaturalist (Robinson et al., 2020) and social media
 - specifically from the Facebook Group “The Bees and Wasps of Singapore”
 (https://www.facebook.com/groups/1450495321695805). Observations can be
 submitted by anyone to iNaturalist, which are then publicly available for
 other users to identify. By crowdsourcing identifications, with validation
 by two identifiers required to achieve Research Grade status, iNaturalist
 promotes knowledge-building and information sharing to the benefit of
 various stakeholders – public, scientists and resource managers. Although
 there may be misidentifications by non-experts, validation by one or more
 expert users making comprehensive checks across taxa and areas, including
 of already Research Grade observations, can help to mitigate this. To
 assemble our dataset, CS images from Singapore submitted by more than 500
 observers were validated by multiple experts on Asian bees including the
 same experts (especially JSA and ZWWS with help from colleagues; see
 above) who also collected many of and identified nearly all of the recent
 EC specimens. The latter were consulted directly to confirm the most
 difficult identifications made from images. Observations were curated to
 include only data from 2007 until 2022 to match the land use data from Gaw
 et al. (2019), and observations were inspected to remove data points with
 clearly misrepresented localities. With the CS and EC datasets, we
 combined them to create a third dataset (hereafter referred to as
 ‘combined’) to examine the feasibility of combining the CS and EC datasets
 for greater observation coverage and sampling effort. Blanks in cells
 represent the lack of data at the time of collection. 
description: # Community science enhances modelled bee distributions in a tropical
 Asian city ## Description of the data and file structure Observations were
 obtained from both community science(CS) and expert-collected(EC)
 databases (Table 1). EC observations were defined as those collected by
 individuals possessing specialised knowledge and experience in the study
 of bees, including formally-trained students in entomology and researchers
 associated with the National University of Singapore Insect Diversity
 Laboratory (PI Ascher), using targeted and standardised sampling methods
 (Ascher et al., 2019) supplemented by opportunistic sampling. This dataset
 was the primary basis for recent conservation assessments (Ascher et al.,
 2022). CS sources were defined as observations collected in large part by
 non-experts or non-formally trained individuals through open access
 repositories, such as iNaturalist (Robinson et al., 2020) and social media
 - specifically from the Facebook Group “The Bees and Wasps of Singapore”
 (). Observations
 can be submitted by anyone to iNaturalist, which are then publicly
 available for other users to identify. By crowdsourcing identifications,
 with validation by two identifiers required to achieve Research Grade
 status, iNaturalist promotes knowledge-building and information sharing to
 the benefit of various stakeholders – public, scientists and resource
 managers. Although there may be misidentifications by non-experts,
 validation by one or more expert users making comprehensive checks across
 taxa and areas, including of already Research Grade observations, can help
 to mitigate this. To assemble our dataset, CS images from Singapore
 submitted by more than 500 observers were validated by multiple experts on
 Asian bees including the same experts (especially JSA and ZWWS with help
 from colleagues; see above) who also collected many of and identified
 nearly all of the recent EC specimens. The latter were consulted directly
 to confirm the most difficult identifications made from images.
 Observations were curated to include only data from 2007 until 2022 to
 match the land use data from Gaw et al. (2019), and observations were
 inspected to remove data points with clearly misrepresented localities.
 'NA's in cells represent the lack of data at the time of
 collection. So as not to adversely impact conservation efforts or
 introduce unintended risk to the threatened/vulnerable species associated
 with our dataset, the precision of geographic coordinates are generalized
 by reducing the number of decimal places included in the coordinates to
 0.1 decimal degrees.