Cover Picture and Issue Information. Issue 5 (4th May 2021)

• Record Type: Journal Article
• Is Part Of: Methods in ecology and evolution. Volume 12:Issue 5(2021)
• Page Start: 767
• Page End: 769
• Publication Date: 2021-05-04
• Subjects: Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13413
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract : This month's cover image shows a random sample of pollen grains from a large library of fuchsine stained pollen, produced by Olsson et al. for their article 'Efficient, automated and robust pollen analysis using deep learning'. Each image represents a pollen grain of a different species, scanned at 0.25 μm, and varying in size from <10 to >100 μm. With training, humans can learn to identify pollen based on variation in shape, features, and texture. However, some species are similar and there is also variation within species, so it is not possible to identify all species with certainty. Convoluted neural networks (CNN) can learn to identify pollen, but Olsson et al. show that they are approximately on par with humans, struggling to separate the same groups as us. As usual, though, the computers outperform us in speed, and once trained, a CNN can locate, classify and count a sample with up to 10, 000 pollen in less than a minute. Thus, the method described by Olsson et al. can increase efficiency of pollen analysis dramatically, allowing large‐scale pollen analysis in situations where it was previously too expensive. Image credit: ©Ola Olsson