Tracking trends in Neotropical pollinators: how good is our understanding and is more data always better?

In my recent book Pollinators & Pollination: Nature and Society I discussed the current state of our knowledge of how populations of pollinators have changed over time. Although we have some quite detailed data for particular, often charismatic, species or for certain geographic localities or regions, for most species we know almost nothing. As I wrote in the chapter “The shifting fates of pollinators”:

“For most pollinators we are ‘data deficient’, in other words, we don’t know how their populations are performing. They could be doing well, but they may not be”

This is particularly true for those regions for the world that hold the greatest terrestrial biodiversity: the tropics. For the vast majority of species in the tropics we know precious little about trends in their populations and how their distributions have changed over time in the face of wide-scale land transformation and recent climatic shifts. Filling in some of the gaps in our knowledge of Neotropical pollinator distributions is one of its aims of SURPASS2, a collaboration between South American and UK ecologists, and one of several research and outreach projects with which I’m involved.

In a new study that’s come out of that work, led by Rob Boyd from the UK Centre for Ecology and Hydrology, we’ve used the GBIF database to look at the changing distributions of four important groups of pollinators: bees, hoverflies, leaf-nosed bats and hummingbirds. In particular we were interested in understanding the kinds of biases that come with such publicly available data, and whether recent efforts to add data to GBIF has improved our understanding of trends.

Our overall conclusion is that there are significant limitations and biases inherent in all of these data sets even for groups like hummingbirds which one would imagine are well documented by scientists and bird-watching naturalists. In addition, having more data does not necessarily help matters: it can introduce its own biases.

The paper is open access and feely available; here’s the reference with a link:

Boyd, R. J., Aizen, M.A., Barahona-Segovia, R.M., Flores-Prado, L., Fontúrbel, F.E., Francoy, T.M., Lopez-Aliste, M., Martinez, L., Morales, C.L., Ollerton, J., Pescott, O.L., Powney, G.D., Saraiva, A.M., Schmucki, R., Zattara, E.E., & Carvell, C. (2022) Inferring trends in pollinator distributions across the Neotropics from publicly available data remains challenging despite mobilization efforts. Diversity and Distributions (in press)

Here’s the abstract:

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.

The Neotropics.

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.

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.


SURPASS2 has been a hugely productive project as you’ll see if you look at the Publications page of the website. There’s much more to come and I’ll report on those research papers as they appear.

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