Inferring trends in pollinator distributions across the Neotropics from publicly available data remains challenging despite mobilization efforts

Robin J. Boyd, Marcelo A. Aizen, Rodrigo M. Barahona-Segovia, Luis Flores-Prado, Francisco E. Fontúrbel, Tiago M. Francoy, Manuel Lopez-Aliste, Lican Martinez, Carolina L. Morales, Jeff Ollerton, Oliver L. Pescott, Gary D. Powney, Antonio Mauro Saraiva, Reto Schmucki, Eduardo E. Zattara, Claire Carvell

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Aim: Aggregated species occurrence data are increasingly accessible through public databases for the analysis of temporal trends in the geographic distributions of species. However, biases in these data present challenges for statistical inference. We assessed potential biases in data available through GBIF on the occurrences of four flower-visiting taxa: bees (Anthophila), hoverflies (Syrphidae), leaf-nosed bats (Phyllostomidae) and hummingbirds (Trochilidae). We also assessed whether and to what extent data mobilization efforts improved our ability to estimate trends in species' distributions. Location: The Neotropics. Methods: We used five data-driven heuristics to screen the data for potential geographic, temporal and taxonomic biases. We began with a continental-scale assessment of the data for all four taxa. We then identified two recent data mobilization efforts (2021) that drastically increased the quantity of records of bees collected in Chile available through GBIF. We compared the dataset before and after the addition of these new records in terms of their biases and estimated trends in species' distributions. Results: We found evidence of potential sampling biases for all taxa. The addition of newly-mobilized records of bees in Chile decreased some biases but introduced others. Despite increasing the quantity of data for bees in Chile sixfold, estimates of trends in species' distributions derived using the postmobilization dataset were broadly similar to what would have been estimated before their introduction, albeit more precise. Main conclusions: Our results highlight the challenges associated with drawing robust inferences about trends in species' distributions using publicly available data. Mobilizing historic records will not always enable trend estimation because more data do not necessarily equal less bias. Analysts should carefully assess their data before conducting analyses: this might enable the estimation of more robust trends and help to identify strategies for effective data mobilization. Our study also reinforces the need for targeted monitoring of pollinators worldwide.

Original languageEnglish
Pages (from-to)1404-1415
Number of pages12
JournalDiversity and Distributions
Issue number7
StatePublished - Jul 2022


  • GBIF
  • bees
  • hoverflies
  • hummingbirds
  • leaf-nosed bats
  • pollinators
  • sampling bias
  • species occurrence data


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