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Opportunities and challenges for digital morphology

DOI: 10.1186/1745-6150-5-45

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This article was reviewed by Marc D. Sutton (nominated by Stephan Beck), Gonzalo Giribet (nominated by Lutz Walter), and Lennart Olsson (nominated by Purificación López-García).The digital era, which has so successfully transformed scientific work in various disciplines, has also begun to be implemented in biology [1], but has not yet found satisfactory acceptance in morphology. Although this discipline is clearly at the heart of biological understanding, its digital implementation today is still in its infancy. Almost ten years ago, C. Godfray [2] already identified a number of problems associated with comparative morphology, in particular the virtual absence of web-based taxonomic information. A further problem was emphasized by E. Pennisi [3], who pointed out that by using traditional morphological techniques "a complete tree of life is centuries away". However, it is now foreseeable that non-invasive imaging techniques, in particular because they offer the opportunity to conduct high-throughput studies and the suitability to analyze whole specimens in a non-destructive manner, together with the broader availability of the respective instruments, will change the way by which morphological data will be acquired and analyzed in the future. Based on these technical advances, many morphologists do in fact believe that a golden age of morphology has begun [4-6]. As we show here, the advent of non-invasive, digital imaging techniques offers a multitude of opportunities, but presents also numerous challenges which need to be discussed within the life sciences and bioinformatics communities.Within the last ten years, resolution and suitability of modern imaging techniques, among them confocal laser scanning microscopy (CLSM), synchrotron radiation micro-computed tomography (SRμCT), micro-computed tomography (μCT), and magnetic resonance imaging (MRI), have led to a considerable increase in morphological studies that generate entirely digital raw data [e.g. [7-10]]. Conve

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