Historically, abundance and distribution data for a range of data species from large spatial areas were collected by surveying large distances along pre-defined tracks, or from visual aerial survey data from manned aircraft by trained volunteers. While this can provide reliable data which is simultaneously geo-referenced by the volunteers, it also provides relatively low spatial coverage over often vast spatial areas. More recently, to efficiently increase spatial coverage, data of this sort is collected using unmanned survey vehicles (or drones) which obtain high resolution images over vast spatial areas at relatively low cost. Drones can be programmed to work over very large spatial areas (outside the 'line of sight') and are becoming a routine alternative for surveying animals over large areas. One consequence of data acquisition in this way is that these high resolution images for very large areas must subsequently be scrutinised (by human eye) to identify the location and species of each animal in each and this processing can be prohibitively time consuming if undertaken by trained individuals. For this reason, an automated approach (or at least automated assistance) which more quickly enables processing these images is necessary if we are to routinely use drones to collect survey data and extract reliably geo-referenced data in a timely fashion. Regardless of the method of data collection used in these cases, very large sets of data which apply to a very large spatial area (>1 million km2) are returned. The subsequent modelling of this geo-referenced data therefore also often presents substantial computational challenges. As a result, compromises are often made between capturing genuine trends in the spatial patterns across the area which vary substantially in nature across the area of interest (e.g. allocating sufficient numbers of parameters to adequately capture realistic spatial patterns) and fitting a single model (or few models) for the area of interest. The former must be considered to ensure any surfaces are realistically complex and the latter is desirable in order to seamlessly predict across large areas. Spatially adaptive modelling is a recently developed approach to model surfaces with variable structure across an area (e.g. some surfaces need to be highly structured in some areas but are relatively flat in others) however choosing the flexibility of these models can be a computationally intensive even for more modest spatial areas. While to address this, the spatial area may (arbitrarily) be partitioned into segments for analysis, the challenge lies in `stitching' these parts together somehow post-analysis and decisions about any partitioning must be made in advance or iteratively as part or analysis. This process would be both time-consuming and ad-hoc. This project will involve two main parts: the development of an automated image processing approach appropriate for high resolution images and a `spatial tiling' approach permitting spatially adaptive modelling over large spatial areas. The data under investigation comprises high resolution multi-species data from the Namib desert which has been processed by human eye and will serve as a comparison for the automated image processing approach developed. These data will also be used to evaluate the effectiveness of the spatial tiling method at capturing spatial patterns in the data.

Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

The aim of this research is to increase our understanding of the processes by which new knowledge and skills is transferred between animals, including humans, through copying ('social learning'). For human beings, imitation and teaching are thought to be the most important processes by which information is transferred from knowledgeable to naive individuals. However, in other animals there is little evidence that teaching occurs but nonetheless inexperienced and young individuals pick up clues from more knowledgeable others concerning, for instance, the identity of predators or how best to process foods. This social learning can generate 'traditions' for performing particular behaviour patterns, for instance, eating specific foods, or taking particular pathways. As a consequence, the behaviour of animals may vary from one population to the next, not just because they possess different genes, or are exposed to different environmental resources, but also because they have learned different habits from more experienced members of their population. A challenge for researchers studying animal behaviour is to identify these 'traditions' and to work out how novel behaviour and skills ('innovations') spread through populations. Currently it is very difficult for researchers to tell if animals are copying each other outside of the laboratory context, or to specify the psychological and social processes that underlie the spread of innovations. This research programme will generate useful data using laboratory populations of starlings and then use mathematics to develop statistical packages that specify when animals acquire their behaviour through social learning, how novel behaviours spread through social learning in animal populations, and what learning rules are deployed. The usefulness of these statistical tools will then be tested in natural or naturalistic populations of New Caledonian crows, meerkats, chimpanzees and capuchin monkeys - animals renowned for their traditional behaviour - working with expert collaborators from Oxford, Cambridge and Durham universities. These statistical tools will be made available as freeware to a variety of researchers (biologists, psychologists, anthropologists, archaeologists, economists) interested in detecting and understanding social learning and predicting the diffusion of innovations, including technological innovations in humans. As there are some parallels between the spread of information and certain transmittable diseases through populations, the project also contains a pilot study to investigate whether the developed statistical tools potentially can be applied to predict disease flow, using a guppy model system. This project involves collaborations with outstanding researchers at Princeton, Oxford, Cambridge, Durham and Exeter Universities and the MPI Leipzig.

LAY SUMMARY I want to catch evolution in the act, and my proposal aims to test what happens to genomes when novel mutations "invade". Such an invasion could occur through spontaneous mutation in a section of DNA that codes for or regulates the activity of a gene, or by migration of genes from another population or species due to hybridization. I am particularly interested in how the ability of an organism to adjust its behaviour depending on the prevailing conditions might compensate for negative effects of such a genomic invasion and facilitate more rapid evolutionary change. The emergence and spread of new mutations with a selective advantage is at the heart of the evolutionary process, but this process is extraordinarily challenging to observe in a natural system for two reasons: the first is that the likelihood of detecting such a genomic invasion is miniscule because they happen so rarely. The second is that evolutionary change has been thought to occur at a very slow pace, much longer than a researcher's lifetime. My proposal capitalises on a textbook example of rapid evolution that is occurring right now in the Oceanic field cricket, Teleogryllus oceanicus. Male crickets usually sing to attract females for mating, but in the Hawaiian archipelago, they also attract a deadly parasitoid fly (Ormia ochracea). Recently, a mutation that feminises male wings by erasing sound-producing structures on male wings arose and spread. It is called flatwing, and it exists in two populations and appears to have different genetic origins. My research will work out what, exactly, has changed in the genome of these different populations to cause this mutant male type, and it will test how the rest of the genome has responded. In particular, I will make use of a time-series of genomic DNA collections from the wild to visualise and test how the genes that lie in close physical proximity to the mutation get "swept" along with it, or become homogenized with the rest of the genome. In other words, when the flatwing mutation invaded the T. oceanicus genome, two things may have happened. The first is that genes nearby got dragged along and are now over-represented in the population, and the second is that genes in other parts of the genome produced phenotypic effects that worked particularly well with the mutation, and therefore are more likely to be found in the mutant variety of males than in normal males. I know from previous work that crickets are sensitive to their social environment, in particular, to the presence or absence of acoustic songs that males produce. Both females and males change their mating behaviour to suit the prevailing social conditions, and I will test the hypothesis that the flatwing mutation was able to spread in response to selection from parasitoids more rapidly because social flexibility enabled crickets to cope with the changed social environment, namely, the silent environment that emerged as flatwings became more numerous. I have devised a cricket tracking setup in the lab, which replicates the wild environment and which I can use to test cricket behaviour and mating success. It involves video and audio recording crickets, and enables me to manipulate the composition of interacting individuals during trials. In this manner, I can vary crickets' social experience, what population they are from, and the relative abundance of the different morphs, to test how social flexibility contributes to the reproductive success of mutant males. Results from these trials stand to illuminate how behaviour interacts with the evolutionary process, and how the rate of evolutionary change can be affected by the social environment and individual organisms' responses to that environment.

The archaea are a group of microbes, often found in extreme environments such as volcanic pools and salt pans. With the advent of DNA sequencing, it was recognised that the archaea are not closely related to bacteria such as the ones that make us sick, but rather are more similar to the eukarya (organisms with a nucleus), such as yeast, worms and humans. This relationship is reflected in similarities between the archaea and eukarya in the machinery that makes copies of the genetic material and transfers information (the information processing pathways). For this reason archaea have been studied as a useful model system, but they are also important and interesting in their own right, as they make up a big proportion of the living world. There is still a lot we don't know about information processing in the archaea. We have studied these pathways in archaea for the last 7 years and have discovered and characterised a lot of new proteins. We have been studying the archaeal nuclease XPF (a protein that cuts DNA strands) that is similar to a human protein important for repair of DNA damage. We know the structure of the protein and we wish to carry out studies of its structure, function, mechanism and interactions.