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.
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.
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.
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.
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.
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 →
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 →
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 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 →
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.
The standard approach to quantifying natural selection, developed by Lande and Arnold, does not allow for comparable metrics between linear (i.e. selection on the mean phenotype) and nonlinear (i.e. selection on all other aspects of the phenotypic distribution, including variance and the number of modes) selection gradients. Jonathan Henshaw’s winning submission provides the first integrated measure of the strength of selection that applies across qualitatively different selection regimes (e.g. directional, stabilizing or disruptive selection). Continue reading →