Issue 10.4: Bayesian Models, Isoscapes, Camera Traps and More

The April issue of Methods is now online!

This month we’re thinking about hierarchical Bayesian models and Approximate Bayesian Computation, improving ecological niche models, and learning how to make our own Environmental Microcontroller Units (more on that below). We’ve got articles on Phylogenetics, Space (not outer space), Camera Traps and much more. Plus, there are six papers that are completely free to everybody, no subscription required!

Find out a little more about the new issue of Methods in Ecology and Evolution (including details about the bobcat on this month’s cover) below. Continue reading

Using Experimental Methodology to Determine Grassland Response to Climate Change

Post provided by Heather Hager

©Hajnal Kovacs

In the second chapter of Grasslands and Climate ChangeMethodology I: Detecting and predicting grassland changeJonathan Newman and I take an in-depth look at the experimental methodology that has been used to determine how grassland ecosystems will respond to climate change. When we set out, we were interested in knowing, for example, the magnitudes and types of treatments applied, plot sizes, replication, study durations, and types of response variables that were measured and by how many studies. For simplicity(!), we focused on three treatment types: changes in atmospheric carbon dioxide levels, changes in temperature (mean, minimum, maximum), and changes in precipitation (increases, decreases, timing).

Using the methods of a formal systematic review, we identified 841 relevant studies, for which we extracted information on study location and experimental methodology. There were some surprises, both good and bad. For instance, mean and median plot sizes were actually larger than we had expected. On the other hand, numbers of true experimental replicates were low. Although many of the study methods were well reported, some areas lacked critical detail such as descriptions of (at least) the dominant plant species in the study area.

Continue reading

Phylogenetic Tip Rates: How Well Can We Estimate Diversification?

Post provided by Pascal Title and Dan Rabosky

Analyzing diversification rate heterogeneity across phylogenies allows us to explore all manner of questions, including why Australia has such an incredible diversity of lizards and snakes.

Analyzing diversification rate heterogeneity across phylogenies allows us to explore all manner of questions, including why Australia has such an incredible diversity of lizards and snakes.

Within the tree of life there are differences in speciation and extinction rates over time and across lineages. Biologists have long been interested in how speciation rates change as a function of ecological opportunity or whether key innovations lead to increases in the rate of speciation. Exploring this rate variation and examining how clades differ in terms of their diversification dynamics can help us to understand why species diversity varies so dramatically in time and space. Learning more about the relationship between traits and diversification rates is especially important because it has the potential to reveal the causes of pervasive variation in species richness among clades and across geographic regions.

Several different classes of methods are available for studying the effects of species traits on lineage diversification rates. These include state-dependent diversification models (e.g., BiSSE, QuaSSE, HiSSE) and several non-model-based approaches. In our article – ‘Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates?’ – we assessed the accuracy of a number of model-free metrics (the DR statistic, node density metric, inverse of terminal branch lengths) and model-based approaches (Bayesian Analysis of Macroevolutionary Mixtures, BAMM) to determine how they perform under a variety of different types of rate heterogeneity. The “tip rates” using these approaches have become widely used for a few reasons, including ease of computation and how easy it is to pair them with other types of data. Continue reading

New Technologies Could Help Conservationists Keep Better Track of Serengeti Wildebeest Herds

Below is a press release about the Methods in Ecology and Evolution article ‘A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images‘ taken from the University of Glasgow.

A wildebeest herd in the Serengeti. ©Daniel Rosengren

A wildebeest herd in the Serengeti. ©Daniel Rosengren

Mathematicians and conservationists from the UK, Africa and the United States have used machine-learning and citizen science techniques to accurately count wildebeest in the Serengeti National Park in Tanzania more rapidly than is possible using traditional methods.

Evaluating wildebeest abundance is currently extremely costly and time-intensive, requiring manual counts of animals in thousands of aerial photographs of their habitats. From those counts, which can take months to complete, wildlife researchers use statistical estimates to determine the size of the population. Detecting changes in the population helps wildlife managers make more informed decisions about how best to keep herds healthy and sustainable. Continue reading

R-Ladies: For More Balance in the R Community!

The theme for this year’s International Women’s Day is #BalanceForBetter. So, we decided that we’d like to take this opportunity to promote an organisation that supports and empowers women and gender minorities in STEM fields that still suffer from underrepresentation. As a journal, we publish a lot of articles on software and code that are used in the study of different fields in ecology and evolutionary biology. We have a wide audience of R coders and R users who follow us on social media and read our blog. With that in mind, R-Ladies seemed like a fairly obvious group for us to promote…

Post provided by MAËLLE SALMON and HANNAH FRICK, two members of the R-LADIES GLOBAL TEAM.

What is R-Ladies?

R-Ladies is a global grassroots organisation whose aim is to promote gender diversity in the R community. The R community suffers from an underrepresentation of gender minorities (including but not limited to cis/trans women, trans men, non-binary, genderqueer, agender). This can be seen in every role and area of participation: leaders, package developers, conference speakers, conference participants, educators, users (see recent stats). What a waste of talent!

As a diversity initiative, the mission of R-Ladies is to achieve proportionate representation by encouraging, inspiring, and empowering people of genders currently underrepresented in the R community. So our primary focus is on supporting minority gender R enthusiasts to achieve their programming potential. We’re doing this by building a collaborative global network of R leaders, mentors, learners, and developers to help and encourage individual and collective progress worldwide. Continue reading

Quantifying Animal Movement from Videos

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 SridharDominique 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:

Read the full Methods in Ecology and Evolution article ‘Tracktor: Image‐based automated tracking of animal movement and behaviour
(No Subscription Required).

Download and start using Tracktor via GitHub.

Citizen Science Projects Have a Surprising New Partner – The Computer

Below is a press release about the Methods in Ecology and Evolution article ‘Identifying animal species in camera trap images using deep learning and citizen science‘ taken from the University of Minnesota-Twin Cities.

The computer’s accuracy rates for identifying specific species, like this warthog, are between 88.7 percent and 92.7 percent. Image credit: ©Panthera

The computer’s accuracy rates for identifying specific species, like this warthog, are between 88.7 percent and 92.7 percent. ©Panthera

For more than a decade, citizen science projects have helped researchers use the power of thousands of volunteers who help sort through datasets that are too large for a small research team. Previously, this data generally couldn’t be processed by computers because the work required skills that only humans could accomplish.

Now, computer machine learning techniques that teach the computer specific image recognition skills can be used in crowdsourcing projects to deal with massively increasing amounts of data—making computers a surprising new partner in citizen science projects.

Continue reading

Field Work on a Shoestring: Using Consumer Technology as an Early Career Researcher

Post provided by CARLOS A. DE LA ROSA

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Champagne Tastes on a Beer Budget

Freshly outfitted with a VACAMS camera and GPS unit, #1691 heads off into the forest with her calf. ©Carlos A. de la Rosa

Freshly outfitted with a VACAMS camera and GPS unit, #1691 heads off into the forest with her calf. ©Carlos A. de la Rosa

There’s a frustrating yin and yang to biological research: motivated by curiosity and imagination, we often find ourselves instead defined by limitations. Some of these are fundamental human conditions. The spectrum of light detectable by human eyes, for example, means we can never see a flower the way a bee sees it. Others limitations, like funding and time, are realities of modern-day social and economic systems.

Early career researchers (ECRs) starting new projects and delving into new research systems must be especially creative to overcome the odds. Large grants can be transformative, giving a research group the equipment and resources to complete a study, but they’re tough to get. Inexperienced ECRs are at a disadvantage when competing against battle-hardened investigators with years of grant writing experience. Small grants of up to about $5000 USD, on the other hand, are comparatively easy to find. So, how can ECRs make the most of small, intermittent sources of funding?

I found myself faced with this question in the second year of my PhD field work. Continue reading