Archive for September, 2013

Award-Winning Bat Conservation

By Claire Asher, on 16 September 2013

This year’s Vincent Weir Scientific award for bat conservation biology has been awarded to GEE’s Charlotte Walters for her PhD work on the iBatsID tool.

The Vincent Weir Scientific Award is an annual award given to a UK-based student for their outstanding contribution to the conservation biology of Bats. It is awarded by the Bat Conservation Trust (BCT), a national organisation devoted to the conservation of bats and their habitats within the UK. Charlotte Walters, who recently completed her PhD with the Zoological Society of London (ZSL), University College London (UCL), University of Kent and BCT, has been awarded the prize for her contribution to bat conservation and particularly her work for the Indicator Bats Program (iBats).

iBats is a partnership between ZSL and BCT, aiming to monitor global changes in bat biodiversity and provide valuable data for policy makers and conservation groups. They provide training and equipment to projects monitoring bat biodiversity to ensure standardised methodology which will enable global comparisons. They have also developed a number of free tools for iPhone and Android which enable fast, simple and efficient detection and identification of bats, and Charlotte’s iBatsID program is a key part of this.

Myotis bechsteini
Image Credit: Gilles San Martin, used under creative commons licence.

During her PhD, Charlotte developed the iBatsID tool, an automatic tool for acoustic identification of European bat ecolocation calls. The tool is able to identify 34 different species of bat based on their calls alone, and is enabling scientists to achieve consistent monitoring of bat populations across Europe. The tool uses ensembles of artificial neural networks to classify bat echolocation calls and identify which species or group the call belongs to. Dr Karen Haysom (Director of Science, BCT) says “New tools and techniques to assist monitoring help us find out more about these fascinating and vulnerable creatures, [and] Charlotte particularly impressed the judges with the innovation and technical quality of her research”.

Eptesicus nilssonii

Bats are ecologically important, playing a key role as predators and seed dispersers. They are also very sensitive to human activities, and are useful as ‘indicator species’ for monitoring biodiversity patterns in general. In Europe, all 52 species of Bat are protected by law as part of the “Agreement on the Conservation of Populations of European Bats“. However, being nocturnal and generally small, they are difficult to detect visually or by trapping. Recording bat calls can allow researchers to survey difficult habitats and gain a clearer picture of what bat species are present and in what numbers. But a standardised statistical method for identifying the species of bat based upon it’s call was needed. This has previously been difficult to achieve, but the recent publication of a global library of bat calls, EchoBank, enabled this type of large-scale identification project to be attempted.

Bat calls vary between species and have been shaped by natural selection relating to species’ ecology. However, calls also vary between individuals within a species according to sex, age, habitat and geographical location, and social environment. Bats also vary their calls depending on what they’re doing – calls are longer when a bat is searching for prey and become shorter as it narrows in on it’s target. So, identifying a species by it’s call is a little more complex than one might expect. Charlotte developed an artificial neural network which was trained on calls of known species and can then be used to identify new calls recorded in the field.

Example of an Artificial Neural Network
Image by Chrislb, used under creative commons licence.

Artificial neural networks are computer models inspired by the central nervous system of animals. They are represented as an interconnected set of ‘neurons’, each of which makes simple calculations which together generate complex behaviour. Artificial neural networks are ‘trained’ first and this training determines the simple algorithms performed by each neuron. The trained network can then be used on real data. In the case of iBats, this involves training the network using calls for which the bat species is known, and the finished neural network can then be used to estimate which species an unknown recorded call belongs to. ANNs are a form of computer learning, and will improve in their accuracy with training – the network of neurons is able to ‘learn’ from it’s mistakes and refine the algorithm to improve classification. This method proved to be highly accurate; 98% of calls from 34 species can be accurately classified into a ‘call-type’ group, and 84% can be classified to species-level.

The iBatsID tool is freely available online, enabling researchers to utilise a standardised methodology for identifying bat species across Europe. This will facilitate large-scale comparative studies and will be particularly useful for studying European bats that have a large geographical range or are migratory. This data will be important for making conservation decisions for the future, and is therefore crucial for bat conservation but also for biodiversity monitoring in general, as bats can provide an accurate assessment of the health of entire biological communities.

Original Article:

() Journal of Applied Ecology


This research was made possible by funding from the Natural Environment Research Council (NERC) and the Bat Conservation Trust

Maintaining the Status Quo:
Constraints on the Evolution of Gene Regulation

By Claire Asher, on 10 September 2013

Every living cell, whether solitary or part of a larger multicellular organism, is an extremely complex system, involving a multitude of simultaneous chemical reactions regulated by proteins and RNA. Keeping this machine running relies upon a careful balance of gene expression and protein degradation, and cells must be prepared to modulate these processes in response to environmental variation (both internal and external). Homeostatic mechanisms can be an efficient way to regulate gene expression, however one key mechanism – negative autoregulation – is rarely used in organisms like flies and humans. Mathematical modelling by GEE’s Max Reuter and Andrew Pomiankowski shows that evolutionary constraints in the evolution of negative feedback may exist for species who carry multiple copies of each chromosome in their cells.

A Eukaryotic (Animal) Cell and a Prokaryotic (Bacterial) Cell

Homeostasis: From Radiators to Cells
One of the simplest ways to regulate any system is through negative feedback, whereby a particular process is inhibited by the products of that process. This is the system employed by central-heating systems, which use the temperature to regulate the action of a radiator. The radiator pumps out heat, and when the thermometer detects too much heat in the room, it signals the radiator to stop. Negative feedback systems can be highly efficient, stable and responsive.

Negative feedback, or homeostasis, would also be a sensible way to regulate the expression of genes within a cell – a particular gene would continue to be expressed and produce its protein product until there is enough of that product in the cell, at which point expression would stop. This system would be sensitive to the demands of the cell – if the product was being used up quickly, then expression would continue as long as demand was high. In fact, many simple, single-celled organisms, such as E. coli, use this system to respond quickly to changes in their environment.

Around half of all genes in Escherichia coli are regulated by this kind of negative feedback loop, known as negative autoregulation. However, when we look at other organisms such as yeast (Saccharoymyces cerevisiae), fruit flies (Drosophila melanogaster), and humans, we find a very different picture – almost no genes show signs of negative autoregulation (around 2%). If negative autoregulation is such a neat solution to apparently common problem, why aren’t humans and flies using it?

By Ehamberg (Own work)
[CC-BY-SA-3.0 or GFDL],
via Wikimedia Commons

A Matter of Ploidy
One of the key differences between humans and E. coli (although probably not the one that springs to mind!), is that E. coli carry only a single copy of each gene in each cell. They are haploid, with a single circular chromosome in each cell that carries a single copy of each gene in the E. coli genome. By contrast, humans, yeast and fruit flies are all diploid, meaning that they carry two copies of each gene in every cell. Our genes are split up into many, straight chromosomes, and we have two copies of each chromosome. Recent research in the department of GEE has used a mathematical modelling approach to investigate how diploidy (having two sets of chromosomes) might constrain the evolution of negative autoregulation.

In a paper in PLoS Computational Biology in March this year, Dr Alexander Stewart, Professor Rob Seymour, Professor Andrew Pomiankowski and Dr Max Reuter from UCL’s GEE produced a mathematical model of how gene regulation might evolve differently in species with one or two sets of chromosomes. Their model focuses on mutations in the promoters of genes, which alter how other protein molecules interact with and repress the expression of those genes.

In haploids, with a single copy of each gene, negative autoregulation produces very tight regulation of gene expression, giving a very rapid response to changing demand. Likewise, in a diploid species with two identical copies of a particular gene, negative autoregulation tends to be beneficial and achieves very efficient gene regulation.

Constraints in the Evolution of Regulation
The problem arises when the diploid carries two different variants of the same gene. This would be the situation whenever a new mutation arises – new mutations appear in a single copy of a particular gene. For a new mutation to be favoured and spread through the population, it must be able to do well, at least initially, as a single copy. The mutated gene must be able to work well alongside the original version. And this is where the problem arises. Stewart, Seymour, Pomiankowski and Reuter (2013) found that mutations that altered the negative autoregulation of a gene didn’t tend to play well with others. Their model considered mutations that alter the strength of the binding site – essentially how strongly regulated that gene is. An individual carrying one strongly regulated gene and one weakly regulated gene actually did worse than an individual with two weakly regulated genes. These heterozygote individuals responded more slowly to changes in demand, and there was more noise in the system. This situation, known as underdominance, where a genetic variant has a lower fitness in the heterozygote form, could be a major constraint to evolution.


Underdominance in negatively autoregulated systems arises because of the disparity in binding site strength between the two different copies of a gene. As each gene pumps out gene product, the stronger binding site is quickly suppressed by the product produced by both genes. It takes much longer for the weaker binding site to be suppressed, as it requires more product to be activated, and most of this is being used up by the stronger binding site. Compared to a haploid, the strong site shows faster response times but the weak site shows a much slower response time, and this averages out to an overall slower response.

Stewart et al (2013)’s model showed that the extent of underdominance depended on how different the two genetic variants were. Large differences in the strength of their binding sites reduced response time and created more noise than smaller differences. Slower response times and increased noise in heterozygotes mean that the maximum strength of regulation achievable in a diploid may be as much as ten-fold lower than in haploids.

Because very small differences between genetic variants in their binding site strength did not experience such a strong effect of underdominance, they were more likely to lead to the evolution of autoregulation. Evolution in diploids could proceed through many very small changes, however there is also likely to be lower limit on the size of mutations – very small changes cannot be ‘seen’ by natural selection and are unlikely to spread. Likewise, multiple binding sites, each relatively weak but which act together cooperatively, were also more likely to overcome the issue of underdominance in the model. However, in general, diploids had a much harder time evolving negative feedback as a mechanism for gene regulation. This evolutionary constraint might have forced diploids such as fruit flies and humans to develop alternative mechanisms to achieve rapid responses, such as increased rates of protein degradation or alternative regulatory mechanisms.

Original Article:

() PLOS Biology

This research was made possible by funding from the Natural Environment Research Council (NERC), the Engineering and Physical Sciences Research Council (EPSRC), and the McDonnell Foundation