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

Overview of attention for article published in Scientific Reports, August 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
38 X users
facebook
2 Facebook pages

Citations

dimensions_citation
97 Dimensions

Readers on

mendeley
452 Mendeley
citeulike
1 CiteULike
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Title
Predicting bee community responses to land-use changes: Effects of geographic and taxonomic biases
Published in
Scientific Reports, August 2016
DOI 10.1038/srep31153
Pubmed ID
Authors

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

Abstract

Land-use change and intensification threaten bee populations worldwide, imperilling pollination services. Global models are needed to better characterise, project, and mitigate bees' responses to these human impacts. The available data are, however, geographically and taxonomically unrepresentative; most data are from North America and Western Europe, overrepresenting bumblebees and raising concerns that model results may not be generalizable to other regions and taxa. To assess whether the geographic and taxonomic biases of data could undermine effectiveness of models for conservation policy, we have collated from the published literature a global dataset of bee diversity at sites facing land-use change and intensification, and assess whether bee responses to these pressures vary across 11 regions (Western, Northern, Eastern and Southern Europe; North, Central and South America; Australia and New Zealand; South East Asia; Middle and Southern Africa) and between bumblebees and other bees. Our analyses highlight strong regionally-based responses of total abundance, species richness and Simpson's diversity to land use, caused by variation in the sensitivity of species and potentially in the nature of threats. 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 38 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 452 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 2 <1%
France 2 <1%
Poland 2 <1%
United Kingdom 2 <1%
Switzerland 1 <1%
Netherlands 1 <1%
India 1 <1%
Australia 1 <1%
Portugal 1 <1%
Other 4 <1%
Unknown 435 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 98 22%
Researcher 84 19%
Student > Master 62 14%
Student > Bachelor 47 10%
Other 18 4%
Other 68 15%
Unknown 75 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 218 48%
Environmental Science 81 18%
Biochemistry, Genetics and Molecular Biology 7 2%
Medicine and Dentistry 5 1%
Engineering 5 1%
Other 33 7%
Unknown 103 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 30. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 November 2016.
All research outputs
#1,322,200
of 25,837,817 outputs
Outputs from Scientific Reports
#12,973
of 142,708 outputs
Outputs of similar age
#24,378
of 372,729 outputs
Outputs of similar age from Scientific Reports
#376
of 3,727 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 142,708 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.8. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 372,729 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 3,727 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.