The BES Quantitative Ecology SIG: Who We Are, What We Do and What to Look Out for at #EAB2017

Post provided by Susan Jarvis and Laura Graham

Ecologists are increasingly in need of quantitative skills and the British Ecological Society Quantitative Ecology Special Interest Group (QE SIG) aims to support skills development, sharing of good practice and highlighting novel methods development within quantitative ecology. We run events throughout the year, as well as contributing to the Annual Meeting and providing a mailing list to share events, jobs and quantitative news.

Ecology Hackathon

The run up to the Ecology Across Borders joint Annual Meeting in Ghent this month is an exciting time for the SIG as we look forward to catching up with existing members as well as hopefully meeting some new recruits! Several of our SIG committee members will be in attendance and if you’ve been lucky enough to get a place at the Hackathon on the Monday you’ll meet most of us there. The Hackathon has been jointly developed by us and two of our allied groups; the GfÖ Computational Ecology Working Group and the NecoV Ecological Informatics SIG and is being sponsored by Methods in Ecology and Evolution. We’ll be challenging participants to work together to produce R packages suggested by the ecological community. You can see the list of package suggestions here. If you weren’t able to book a place at the Hackathon, but are interested in writing your own packages, you may be interested in the new Guide to Reproducible Code from the BES. Continue reading

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Making YOUR Code Reproducible: Tips and Tricks

When we were putting together the British Ecological Society’s Guide to Reproducible Code we asked the community to send us their advice on how to make code reproducible. We got a lot of excellent responses and we tried to fit as many as we could into the Guide. Unfortunately, we ran out of space and there were a few that we couldn’t include.

Luckily, we have a blog where we can post all of those tips and tricks so that you don’t miss out. A massive thanks to everyone who contributed their tips and tricks for making code reproducible – we really appreciate it. Without further ado, here’s the advice that we were sent about making code reproducible that we couldn’t squeeze into the Guide:

Organising Code

©Leejiah Dorward

“Don’t overwrite data files. If data files change, create a new file. At the top of an analysis file define paths to all data files (even if they are not read in until later in the script).” – Tim Lucas, University of Oxford

“Keep one copy of all code files, and keep this copy under revision management.” – April Wright, Iowa State University

“Learn how to write simple functions – they save your ctrl c & v keys from getting worn out.” – Bob O’Hara, NTNU

For complex figures, it can make sense to pre-compute the items to be plotted as its own intermediate output data structure. The code to do the calculation then only needs to be adjusted if an analysis changes, while the things to be plotted can be reused any number of times while you tweak how the figure looks.” – Hao Ye, UC San Diego Continue reading

A Guide to Reproducible Code in Ecology and Evolution

Post provided by Natalie Cooper and Pen-Yuan Hsing

Cover image by David J. Bird

The way we do science is changing — data are getting bigger, analyses are getting more complex, and governments, funding agencies and the scientific method itself demand more transparency and accountability in research. One way to deal with these changes is to make our research more reproducible, especially our code.

Although most of us now write code to perform our analyses, it’s often not very reproducible. We’ve all come back to a piece of work we haven’t looked at for a while and had no idea what our code was doing or which of the many “final_analysis” scripts truly was the final analysis! Unfortunately, the number of tools for reproducibility and all the jargon can leave new users feeling overwhelmed, with no idea how to start making their code more reproducible. So, we’ve put together the Guide to Reproducible Code in Ecology and Evolution to help. Continue reading

Solving YOUR Ecology Challenges with R: Ecology Hackathon in Ghent

©2016 The R Foundation

Scientific software is an increasingly important part of scientific research, and ecologists have been at the forefront of developing open source tools for ecological research. Much of this software is distributed via R packages – there are over 200 R packages for ecology and evolution on CRAN alone. Methods regularly publishes Application articles introducing R packages (and other software) that enable ecological research, and we’re always looking for new ways to enable even more and better ecological software.

This December, we will be teaming up with rOpenSci and special interest groups from BES, GfÖ and NecoV to hold our first Ecology Hackathon at the Ecology Across Borders conference in Ghent. The hackathon will be held as a one-day pre-conference workshop on Monday 11th December. Together, the attendees will identify some challenges for ecological research, and team up to build R packages that help solve them.

We’ve started compiling potential topics for new R packages in a collaborative document, but we need more. Are you having any difficulties in your research that could be solved with an R package? Is there a package that you wish existed but have never been able to find? If so, WE WANT TO HEAR FROM YOU!

Please take a look at our current list of challenges and add your suggestions!

Editor Recommendation – HistMapR: Rapid Digitization of Historical Land-Use Maps in R

Post provided by Sarah Goslee

For an ecologist interested in long-term dynamics, one of the most thrilling experiences is discovering a legacy dataset stashed away somewhere.

For an ecologist interested in long-term dynamics, one of the most daunting experiences is figuring how to turn that box full of paper into usable data.

The new tool HistMapR, described in ’HistMapR: Rapid digitization of historical land-use maps in R’ by Alistair Auffret and colleagues, makes one part of that task much easier.

Examples of input (©Lantmäteriet) and output maps from (a–b) the District Economic map and (c–d) the Economic map.

Examples of input (©Lantmäteriet) and output maps from (a–b) the District Economic map and (c–d) the Economic map.

Historical maps with coloured areas denoting different land cover or use are a valuable record, but difficult to analyse. This R package automates much of the time-consuming and tedious process of turning paper maps into classified categorical raster maps.

A map is scanned, imported into R, and the software is trained by clicking in different areas of each category. It then automatically classifies pixels based on which colour they are most similar to. The resulting classification is assessed manually. The process can be repeated with slightly different parameters until a good fit is achieved.

The authors found 80-90% agreement between HistMapR classification and manual digitisation (sources of error included clarity of original maps and scan quality). Using HistMapR reduced the time needed for digitising a series of historical land cover maps from two months to two days. Ecologists interested in long-term dynamics should be cheering!

The HistMapR package is available on GitHub and you can find example scripts on Figshare, so you can get right to work.

HistMapR: Rapid digitization of historical land-use maps in R‘ by Auffret et al. is a freely available Applications article (no subscription required).

Building Universal PCR Primers for Aquatic Ecosystem Assessments

Post provided by Vasco Elbrecht

Many things can negatively affect stream ecosystems – water abstraction, eutrophication and fine sediment influx are just a few. However, only intact freshwater ecosystems can sustainably deliver the ecosystem services – such as particle filtration, food biomass production and the supply of drinking water – that we rely on. Because of this, stream management and restoration has often been in the focus of environmental legislation world-wide. Macrozoobenthic communities are often key biological components of stream ecosystems. As many taxa within these communities are sensitive to negative stressors introduced by humans, they’re ideal for assessing the quality of water.

Unfortunately, most macrozoobenthic taxa – such as stone-, may-, and caddisflies as well as most other invertebrates – are often found in juvenile larval life stages in these ecosystems, so they’re often difficult to identify based on morphology. With the DNA based metabarcoding method though, almost all taxa in a stream can be reliably identified up to species level using a standardised gene fragment. One key component of this strategy is the development of universal markers, which allow detection of the diverse macrozoobenthic groups.

Our new R package PrimerMiner provides a framework for obtaining sequence data from available reference databases and identifying suitable primer binding sites for marker amplification. The package makes this process quicker and easier. In the following pictures, we summarise the key steps of DNA metabarcoding.

To find out more about PrimerMiner, read our Methods in Ecology and Evolution article ‘PrimerMiner: an r package for development and in silico validation of DNA metabarcoding primers’. Like all Applications articles, this paper is freely available to everyone.

Digitizing Historical Land-use Maps with HistMapR

Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision.

Historical land-use maps are important for documenting how habitat cover has changed over time, but digitizing these maps is a time consuming process. HistMapR is an R package designed to speed up the digitization process, and in this video we take an example map to show you how the method works.

Digitization is fast, and agreement with manually digitized maps of around 80–90% meets common targets for image classification. We hope that the ability to quickly classify large areas of historical land use will promote the inclusion of land-use change into analyses of biodiversity, species distributions and ecosystem services.

This video is based on the Applications article ‘HistMapR: Rapid digitization of historical land-use maps in R‘ by Auffret et al. This article is freely available to anyone (no subscription required).

The package is hosted on GitHub and example scripts can be downloaded from Figshare.

piecewiseSEM: Exploring Nature’s Complexity through Statistics

Post provided by Jonathan S. Lefcheck

Nature is complicated. As a scientist, you might say, “Well, duh,” but as students of nature, this complexity is probably the single greatest challenge we must face in trying to dissect the hows and whys of the natural world.

History is a Set of Lies Agreed Upon: Moving beyond ANOVA

For a long time, we tried to strip this complexity away by conducting very controlled experiments adhering to rigid designs. The ‘two-way fully-crossed analysis of variance’ will be familiar to anyone who has taken even the most basic stats class, because, for many decades, it was the gold standard for any experiment.

It might be tough to manipulate this whole reef.

The problem is: the real world doesn’t adhere to an ANOVA design. By this, I mean that by their very nature, manipulative experiments are artificial. It’s hard—if not impossible—to manipulate an entire forest or a coral reef, and as such, we retreat to more tractable, smaller investigations. There is certainly a lot of value in determining whether the phenomenon can occur, but these tightly regulated designs say nothing about whether they are likely to occur, particularly at the scales most relevant to humanity.

To get at the latter point, we must leave the safety of the greenhouse. However, our trusty ANOVA toolbox isn’t very useful anymore, because real-world data often violate the most basic statistical assumptions, not to mention the presence of numerous additional influences that may drive spurious relationships. Continue reading

Biogeography Virtual Issue

Photo © An-Yi Cheng

© An-Yi Cheng

To coincide with the International Biogeography Society’s 2017 conference in Tuscon, Arizona, we have compiled a Virtual Issue that shows off new Methods in Ecology and Evolution articles in the field from a diverse array of authors.

To truly understand how species’ distributions vary through space and time, biogeographers often have to make use of analytical techniques from a wide array of disciplines. As such, these papers cover advances in fields such as evolutionary analysis, biodiversity definitions, species distribution modelling, remote sensing and more. They also reflect the growing understanding that biogeography can include experiments and highlight the increasing number of software packages focused towards biogeography.

This Virtual Issue was compiled by Methods in Ecology and Evolution Associate Editors Pedro Peres-Neto and Will Pearse (both of whom are involved in the conference). All of the articles in this Virtual Issue are free for a limited time and we have a little bit more information about each of the papers included here: Continue reading

moveHMM: An Interview with Théo Michelot

David Warton (University of New South Wales) interviews Théo Michelot (University of Sheffield) about an article on his recent R package moveHMM in Methods in Ecology and Evolution. David and Théo also discuss the case study in the paper – on the understudied wild haggis – and what advances could be made to the package in future.

Continue reading