Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases

Adriana De Palma, Stefan Abrahamczyk, Marcelo A. Aizen, Matthias Albrecht, Yves Basset, Adam Bates, Robin J. Blake, Céline Boutin, Rob Bugter, Stuart Connop, Leopoldo Cruz-López, Saul A. Cunningham, Ben Darvill, Tim Diekötter, Silvia Dorn, Nicola Downing, Martin H. Entling, Nina Farwig, Antonio Felicioli, Steven J. Fonte, Robert Fowler, Markus Franzén, Dave Goulson, Ingo Grass, Mick E. Hanley, Stephen D. Hendrix, Farina Herrmann, Felix Herzog, Andrea Holzschuh, Birgit Jauker, Michael Kessler, M. E. Knight, Andreas Kruess, Patrick Lavelle, Violette Le Féon, Pia Lentini, Louise A. Malone, Jon Marshall, Eliana Martínez Pachón, Quinn S. McFrederick, Carolina L. Morales, Sonja Mudri-Stojnic, Guiomar Nates-Parra, Sven G. Nilsson, Erik Öckinger, Lynne Osgathorpe, Alejandro Parra-H, Carlos A. Peres, Anna S. Persson, Theodora Petanidou, Katja Poveda, Eileen F. Power, Marino Quaranta, Carolina Quintero, Romina Rader, Miriam H. Richards, T’ai Roulston, Laurent Rousseau, Jonathan P. Sadler, Ulrika Samnegård, Nancy A. Schellhorn, Christof Schüepp, Oliver Schweiger, Allan H. Smith-Pardo, Ingolf Steffan-Dewenter, Jane C. Stout, Rebecca K. Tonietto, Teja Tscharntke, Jason M. Tylianakis, Hans A. F. Verboven, Carlos H. Vergara, Jort Verhulst, Catrin Westphal, Hyung Joo Yoon, Andy Purvis
  • Scientific Reports, August 2016, Nature Publishing Group
  • DOI: 10.1038/srep31153

The dangers of data bias: a study on bees

What is it about?

After three and a bit years, the PREDICTS project (www.predicts.org.uk), with the help of its many data contributors, have amassed a large, taxonomically and geographically representative database of biodiversity facing different land-use pressures. This is a remarkable achievement; for most fields in ecology, we’re still well off the mark. In pollination ecology in particular, unrepresentative data has been recognised as an issue; ecological data on bees are often most readily available in North America and Western Europe, a geographic bias that also leads to a taxonomic one, as bumblebees are common in these areas. This is potentially a big problem; if different regions and taxa show different responses to land use, then biases in the underlying data can lead to misleading predictions when we try to generalize models to future impacts or to other regions and taxa. However, until recently, it wasn’t clear whether these biases in data really affected our inferences. In this paper, we explored this question. As suspected, we found that bee communities respond differently to land-use impacts depending on the region. This is in part because species in different regions may vary in how sensitive they are to human impacts (with bumblebees often responding differently to other bees) and also potentially because the nature of threats vary regionally. These results suggest that global extrapolation of models based on geographically and taxonomically restricted data may underestimate the true uncertainty, increasing the risk of ecological surprises.

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http://dx.doi.org/10.1038/srep31153

The following have contributed to this page: Professor Andy Purvis, Professor Carlos A. Peres, and Adriana De Palma