Category Archives: Robert May Prize

2019 Robert May Prize Winner: Corneile Minnaar

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The Robert May Prize is awarded annually for the best paper published in Methods in Ecology and Evolution by an Early Career Researcher. We’re delighted to announce that the 2019 winner is Corneile Minnaar, for his article ‘Using quantum dots as pollen labels to track the fates of individual pollen grains‘.

A central component of an organism’s fitness is its ability to successfully reproduce. This includes finding a potential mate and successful mating. For plants, movement of pollen from an anther to a conspecific stigma is essential for successful reproduction, but directly tracking movement of individual pollen grains heretofore has been impossible (with the exception of those species of orchids and milkweeds whose pollen comes in large packages (pollinia)). Knowing how pollen move around, whether or not they successfully fertilize ovules, is also central to understanding the evolution and ecology of flowering plants (angiosperms) and floral traits.

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Anacapa Toolkit: Automating the Cataloguing of Biodiversity

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Post provided by Emily Curd

Imagine that you want to catalogue all of the biodiversity (all of the living organisms) from a particular location; how many trained experts would that require? How many person hours would it take to collect and identify all of the rare, well-disguised, and microscopic organisms? How many of these organisms would have to be removed from the environment and taken back to a lab for taxonomic analysis.

With eDNA, you can survey the presence of this gorgeous opalescent nudibranch without capturing or even touching it.
©Natural History Museum of Los Angeles County — Amanda Bemis & Brittany Cumming

Although there is no substitute for human expertise, we have begun using the traces of DNA that organisms leave behind (e.g. excretions, skin and hair cells) in the environment to catalogue biodiversity. These traces of DNA, referred to as environmental DNA, can persist in the environment for minutes or can persist for centuries depending on where they end up. This field of environmental DNA (eDNA) is rapidly becoming an effective tool to complement surveys of biodiversity, both past and present.

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2019 Robert May Early Career Researcher Prize Shortlist

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Each year Methods in Ecology and Evolution awards the Robert May Prize to the best paper in the journal by an author at the start of their career. Today we present the shortlisted papers for 2019’s award, based on articles published in volume 10 of the journal.

The winner will be chosen by the journal’s Senior Editors in a few weeks. Keep an eye on the blog for the announcement.

This year’s shortlisted candidates are:

Extracting individual trees from lidar point clouds using treeseg – Andrew Burt

A quantitative framework for investigating the reliability of empirical network construction – Alyssa R. Cirtwill

A novel biomechanical approach for animal behaviour recognition using accelerometers – Pritish Chakravarty

Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets – Emily E. Curd

MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks – Tsung‐Yu Lin

Using quantum dots as pollen labels to track the fates of individual pollen grains – Corneile Minnaar

Untangling direct species associations from indirect mediator species effects with graphical models – Gordana C. Popovic

Matrix methods for stochastic dynamic programming in ecology and evolutionary biology – Jody R. Reimer

Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections – Kim Whoriskey

Over the next month or so, we’ll be finding out more about these articles. You’ll be able to keep up to date with all of the Robert May Prize news here.

Exploring Population Responses to Environmental Change When There’s Never Enough Data

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Post provided by Bethan Hindle

Understanding Population Responses to Environmental Change

Rapid climatic change has increased interest about how populations respond to environmental change. This has broad applications, for example in the management of endangered and economically important species, the control of harmful species, and the spread of disease. At the population level changes in abundance are driven by changes in vital rates, such as survival and fecundity. So studies that track individual survival and reproduction over time can provide useful insights into the drivers of such changes. They allow us to make future population level predictions on things like abundance, extinction risk and evolutionary strategies.

Archbold Biological Station - site of numerous long-term demographic studies, including that of Eryngium cuneifolium used in this paper. ©Reed Bowman

Archbold Biological Station – site of numerous long-term demographic studies, including that of Eryngium cuneifolium used in this paper. ©Reed Bowman

Predicting the future isn’t a simple task though. Anyone whose washing has got soaked through after the weather forecast suggested the day would be dry and sunny will know that (though the accuracy of short term weather forecasts has increased dramatically in recent years). Ideally, if we want to predict what will happen to populations as their environment changes, we would identify the drivers of variation in their survival and reproduction. We do this by asking questions like ‘are years of low survival associated with high rainfall?’ But, this is not a simple task; identifying drivers and the time periods over which they act and accurately estimating their effects requires long-term demographic data.   Continue reading →

ViXeN: View eXtract aNnotate Multimedia Data

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Post provided by KadaMbari Devarajan

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.


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Editor Recommendation: Quantitative Evolutionary Patterns in Bipartite Networks

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Post provided by ROB FRECKLETON

The study of interactions and their impacts on communities is a fundamental part of ecology. Much work has been done on measuring the interactions between species and their impacts on relative abundances of species. Progress has been made in understanding of the interactions at the ecological level, but we know that co-evolution is important in shaping the structure of communities in terms of the species that live there and their characteristics. Continue reading →

2018 Robert May Prize Winner: Laura Russo

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The Robert May Prize is awarded annually for the best paper published in Methods in Ecology and Evolution by an Early Career Researcher. We’re delighted to announce that the 2018 winner is Laura Russo, for her article ‘Quantitative evolutionary patterns in bipartite networks: Vicariance, phylogenetic tracking or diffuse co‐evolution?‘.

Plant-pollinator interactions are often considered to be the textbook example of co-evolution. But specialised interactions between plants and pollinators are the exception, not the rule. Plants tend to be visited by many different putative pollinator species, and pollinating insects tend to visit many plant hosts. This means that diffuse co-evolution is a much more likely driver of speciation in these communities. So, the standard phylogenetic methods for evaluating co-evolution aren’t applicable in most plant-pollinator interactions. Also, many plant-pollinator communities involve insect species for which we do not yet have fully resolved phylogenies. Continue reading →

Fourier Methods Gain Wide Appeal for Tropical Phenology Analysis

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Post provided by Emma Bush

Lopé National Park. ©Jeremy Cusack

Lopé National Park. ©Jeremy Cusack

Like all living things, plant species must reproduce to persist. Key stages in successful plant reproduction must be carefully timed to make sure resources are available and conditions are optimal. There will be little success if flowers mature in bad weather conditions for their insect pollinators or if fruits ripen but the seed dispersers have migrated elsewhere.

Because plants rely on the abiotic environment for sunlight, nutrients and water, and in some cases for the dispersal of pollen and seeds, it is not surprising that their life stages are closely linked to environmental cycles. Continue reading →

The Global Pollen Project: An Update for Methods Readers

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Post Provided by Andrew C. Martin

The Global Pollen Project is an online, freely available tool and data source developed to help people identify and disseminate palynological resources. Palynology – the study of pollen grains and other spores – is used across many fields of study including modern and fossil vegetation dynamics, forensic sciences, pollination, and beekeeping. To help make pollen identification quicker and more transparent, we developed the Global Pollen Project (GPP) – an open, peer-reviewed database of global pollen morphology, where content and expertise is crowdsourced from across the world. Our approach to developing this tool was open: open code, open data, open access. It connects to other data services, including the Global Biodiversity Information Facility and Neotoma Palaeoecology Database, to provide occurrence data for each taxon, alongside pollen images and metadata. Continue reading →

How Strong is Natural Selection? Stitching Together Linear and Nonlinear Selection on a Single Scale

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Post provided by Robert May Prize Winner Jonathan Henshaw

Some individuals survive and reproduce better than others. Traits that help them do so may be passed on to the next generation, leading to evolutionary change. Because of this, evolutionary biologists are interested in what differentiates the winners from the losers – how do their traits differ, and by how much? These differences are known as natural selection.

Linear and Nonlinear Selection

Traditionally, natural selection is separated into linear selection (differences in average trait values) and nonlinear selection (any other differences in trait distributions between winners and the rest). For example, successful individuals might be unusually close to average: this is known as stabilizing selection. Alternatively, winners might split into two camps, some with unusually high trait values, and others with unusually low trait values. This is disruptive selection (famously thought to explain the ur-origin of sperm and eggs). Stabilizing and disruptive selection are important types of nonlinear selection. In general, though, the trait distribution of successful individuals can differ from the general population in arbitrarily complicated ways.

When individuals with larger trait values have higher fitness on average (left panel), the trait distribution of successful individuals is shifted towards the right (right panel, orange curve). The difference in mean trait values between the winners and the general population is called linear selection.

When individuals with larger trait values have higher fitness on average (left panel), the trait distribution of successful individuals is shifted towards the right (right panel, orange curve). The difference in mean trait values between the winners and the general population is called linear selection.

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