To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 10 we selected ‘An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks’ by Grames et al. (2019), an Application article introducing the R package litsearchr.

In this post Thomas White and Laura Graham, two of our Associate Editors with expertise in Application articles, highlight their favourite of our papers about R packages.

Thomas White University of Sydney, Australia

Much of evolutionary ecology is at some level interested in the links between form and function.  The development of geometric morphometrics in the latter 20th century massively advanced this program by specifying a set of analytical and graphical tools for quantifying variation in shape. The methods are somewhat complex and involved, however, which can prove daunting to people looking to apply these tools to address pressing problems in their own fields.

The geomorph package (Adams & Otarola-Castillo 2013) fills precisely this gap and does so with aplomb. It provides a cohesive framework for all aspects of 2D and 3D shape analysis—from image manipulation and landmark specification, through to statistical shape analysis—which has seen it become the de facto standard for morphometric work. Its reach hasn’t been limited to ecology, however, as a stroll through the substantial list of citing works reveals its fingerprints on anthropology and palaeontology, through to medicine and engineering.

Of course the comparative analyses enabled by geomorph demand data on species’ relatedness, which is why rotl (Michonneau et al. 2016) regularly sits atop my scripts too. It’s a straightforward but invaluable piece of software which acts as the corridor between the incredible Open Tree of Life, and the massive ecosystem of complementary R packages. And by allowing us to retrieve this essential data from within R the documented history of an analysis can remain self-contained, which is a boon for reproducibility.

The proven impact of these packages speaks to the importance of free, user-friendly, and open-source software for the advancement of science more generally. Few of us have the resources to invest in expensive proprietary software, or the time to develop our own solutions from the ground-up, which is why resources like geomorph and rotl are so valuable. They level the playing field, in a sense, and in doing so open the door to the exploration of questions which might otherwise remain unasked.

Laura Graham University of Birmingham, UK

One package in particular comes to mind – Sciaini et al. (2018). Simulated landscapes make it possible for ecologists to conduct experiments with multiple replicate landscapes around the ecological effects of changing landscape composition and configuration – something that is impossible to do in reality and reach sufficient replication. Sciaini et al (2018) provide a near comprehensive library of landscape simulation algorithms which a user can parameterise to fit their question. Through the NLMR and associated landscapetools R package, there is now a way of generating and manipulating simulated landscapes in a reproducible way, and in a language familiar to most ecologists. Special mention should also go to the nlmpy package and associated paper (Etherington et al. 2014), which provides similar functionality in the Python environment. Both packages have proven invaluable in my own work.

Oh, another one – Auffret et al. (2017). In order to understand how land-use change affects ecological communities, it’s key to understand not only the current landscape, but also its history. Auffret et al. (2017) provide an R package – HistMapR – which allows for fast and reproducible digitisation of historical maps. This means we can now digitise historical maps beyond just small extents, which are feasible to do by hand, and characterise and quantify landscape change over broader extents.

To find out more about the litsearchr package selected as our Volume 10 highlight, read the authors’ reflections on their article:

An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks by Grames et al. (2019).

You can also read about the other articles selected for our 10th anniversary blog series here.