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

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Rob James Boyd, Marcelo Aizen, Rodrigo Barahon-Segovia, Luis Flores, Francisco Fontúrbel, Tiago Francoy, Manuel Lope-Aliste, Lican Martinez, Carolina Morales, Jeff Ollerton


Aim: Aggregated species occurrence data are increasingly accessible through public databases for the analysis of temporal trends in species’ distributions. However, biases in these data present challenges for robust 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 mobilisation 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 mobilisation 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 their impact on estimated trends in species’ distributions.
Results: We found evidence of potential sampling biases for all taxa. The addition of newly-mobilised records of bees in Chile decreased some biases but introduced others. Despite increasing the quantity of data for bees in Chile sixfold, estimates of temporal trends in species’ distributions derived using the post-mobilisation dataset were broadly similar to what would have been estimated before their introduction.
Main conclusions: Our results highlight the challenges associated with drawing statistically robust inferences about trends in species’ distributions using publicly available data. Mobilising historic records will not always enable trend estimation because more data does 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 mobilisation. Our study also reinforces the need for well-designed, standardized monitoring of pollinators worldwide.



Bioinformatics, Ecology and Evolutionary Biology, Life Sciences, Other Ecology and Evolutionary Biology


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


Published: 2022-01-25 03:47

Last Updated: 2022-01-25 04:30

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Data and Code Availability Statement:
The GBIF data can be accessed using the DOIs given in the reference list. All code needed to fully reproduce our analyses can be found here