The number of studies published every year in ecology and evolutionary biology has increased rapidly over the past few decades. Each new study contributes more to what we know about a topic, adding nuance and complexity that helps improve our understanding of the natural world. To make sense of this wealth of evidence and get closer to a complete picture of the world, researchers are increasingly turning to systematic review methods as a way to synthesise this information.
What is a Systematic Review?
Systematic reviews, first developed in public health fields, take an experimental design approach to reviewing the literature. They treat the search for primary studies as a transparent and reproducible data gathering process. The rigorous methods used in systematic reviews make them a trusted form of evidence synthesis. Researchers use them to summarise the state of knowledge on a topic and make policy and practice recommendations. Continue reading →
Researchers at Washington State University and Smith-Root recently invented an environmental DNA (eDNA) filter housing that automatically preserves captured eDNA by desiccation. This eliminates the need for filter handling in the field and/or liquid DNA preservatives. The new material is also biodegradable, helping to reduce long-lasting plastic waste associated with eDNA sampling.
This video explains their new innovation in the field of eDNA sampling technology:
It’s more important than ever for us to have accurate information to help marine conservation efforts. Jordan Goetze and his colleagues have provided the first comprehensive guide for researchers using diver operated stereo-video methods (or stereo-DOVs) to survey fish assemblages and their associated habitat.
But what is Stereo DOV? What makes it a better method than the traditional UVC (Underwater Visual Census) method? And when should you use it? Find out in this video:
At a time when data is everywhere, and data science is being talked about as the future in different fields, a method that produces huge amounts of multimedia data is camera-trapping. We need ways to manage these kinds of media data efficiently. ViXeN is an attempt to do just that.
Camera traps have been a game-changer for ecological studies, especially those involving mammals in the wild. This has resulted in an increasing amount of camera trap datasets. However, the tools to manage camera trap data tend to be very specific and customised for images. They typically come with stringent data organisation requirements. There’s a growing amount of multimedia datasets and a lack of tools that can manage several types of media data.
In ‘ViXeN: An open‐source package for managing multimedia data’ we try to fix this visible gap. Camera trap management is a very specific a use-case. We thought that the field was missing general-purpose tools, capable of handling a variety of media data and formats, that were also free and open source. ViXeN was born from this idea. It stands for View eXtract aNnotate (media data). The name is also an ode to the canids I was studying at the time which included two species of foxes.
In our recent publication (Rabosky et al. 2018) we assembled a huge phylogeny of ray-finned fishes: the most comprehensive to date! While all of our data are accessible via Dryad, we felt like we could go the extra mile to make it easy to repurpose and reuse our work. I’m pleased to report that this effort has resulted in two resources for the community: the Fish Tree of Life website, and the fishtree R package. The package is available on CRAN now, and you can install it with:
The source is on GitHub in the repository jonchang/fishtree. The manuscript describing these resources has been published in Methods in Ecology and Evolution (Chang et al. 2019).
Some things are just irresistible to a community manager – PhD student Hugo Gruson’s recent tweets definitely fall into that category.
I was surprised and intrigued to see an example of our software peer review guidelines being used in a manuscript review, independent of our formal collaboration with the journal Methods in Ecology and Evolution (MEE). This is exactly the kind of thing rOpenSci is working to enable by developing a good set of practices that broadly apply to research software.
But who was this reviewer and what was their motivation? What role did the editors handling the manuscript play? I contacted the authors and then the journal and, in less than a week we had everyone on board to talk about their perspectives on the process. Continue reading →
Quantifying animal movement is central to research spanning a variety of topics. It’s an important area of study for behavioural ecologists, evolutionary biologists, ecotoxicologists and many more. There are a lot of ways to track animals, but they’re often difficult, especially for people who don’t have a strong background in programming.
Vivek Hari Sridhar, Dominique G. Roche and Simon Gingins have developed a new, simple software to help with this though: Tracktor. This package provides researchers with a free, efficient, markerless video-based tracking solution to analyse animal movement of single individuals and groups.
Vivek and Simon explain the features and strengths of Tracktor in this new video:
Modelling species distributions involves relating a set of species occurrences to relevant environmental variables. An important step in this process is assessing how good your model is at figuring out where your target species is. We generally do this by evaluating the predictions made for a set of locations that aren’t included in the model fitting process (the ‘testing points’).
Random splitting of the species occurrence data into training and testing points
The normal, practical advice people give about this suggests that, for reliable validation, the testing points should be independent of the points used to train the model. But, truly independent data are often not available. Instead, modellers usually split their data into a training set (for model fitting) and a testing set (for model validation), and this can be done to produce multiple splits (e.g. for cross-validation). The splitting is typically done randomly. So testing points sometimes end up located close to training points. You can see this in the figure to the right: the testing points are in red and training points are in blue. But, could this cause any problem? Continue reading →
مدلسازی توزیع گونهها به تخمین و برآورد ارتباط بین مجموعهای از نقاط حضور گونه با متغیرهای زیستمحیطی مرتبط می پردازد. یکی از مراحل اساسی این فرایند، ارزیابی قدرت مدل برای پیشبینی مکانهایی است که احتمال حضورگونه در آنجا وجود دارد. این کار اغلب با ارزیابی پیشبینی انجام شده در مجموعهای ازنقاط که در فرآیند مدلسازی مورد استفاده قرار نگرفته اند (نقاط آزمایشی) صورت میگیرد.
تقسیم تصادفی دادههای حضور گونه به نقاط آزمایشی و آموزشی
مطالعات پیشین بر این نکته تاکید دارند که به منظور ارزیابی معتبر، نقاط آزمایشی باید مستقل از نقاط آموزشی باشند، این درحالیست که داده مستقل واقعی به ندرت در دسترس می باشد. به همین دلیل، در فرایند مدلسازی معمولا دادههای موجود را به دو قسمت دادههای آموزشی (برای کالیبره کردن مدل) و داده های آزمایشی (برای ارزیابی دقت مدل) تقسیم میکنند، این استراتژی میتواند چند قسمتی هم باشد (برای مثال اعتبارسنجی متقاطع یا cross-validation). از آنجاییکه این تقسیم بندی معمولا بصورت تصادفی انجام میشود، بنابراین گاهی اوقات نقاط آزمایشی در فواصل نزدیک به نقاط آموزشی قرار میگیرند. شکل زیر این مساله را به خوبی نشان می دهد که در آن نقاط آزمایشی به رنگ قرمز و نقاط آموزشی آبی هستند. اما آیا این مساله میتواند مشکلی ایجاد کند؟ Continue reading →
‘babette‘ is an R package that works with the popular phylogenetic tool BEAST2. BEAST2 uses one or more alignments and a model setup to create a Bayesian posterior of jointly estimated model parameters and phylogenies.
babette lets you call BEAST2 from an R script. This makes it easier to explore models and/or alignments than using the graphical user interface programs that BEAST2 provides. It will also help you to improve the reproducibility of your work with BEAST2.
babette Tutorial Videos
If you’re new to phylogentic analyses, the video ‘babette demo‘ demonstrates the package. It has all of the information that you need to be able to start using the package