The environment that most natural populations experience is changing. Not only as a result of global climate change, but also as a consequence of other anthropogenic activities including hunting, habitat modification and the introduction of alien species. Such environmental change has already been demonstrated to impact animal and plant populations and the ecosystems in which these populations are embedded. The speed of environmental change continues to accelerate with potentially catastrophic consequences. Unfortunately biologists do not have a good track record of accurately predicting how environmental change will impact natural populations. This is because populations and the environment they experience are complex. We have identified an approach that is likely to improve upon the predictions that biologists are able to make about how populations will respond to environment change. The types of response we are able to model include both short-term ecological ones and longer-term evolutionary responses. Our approach relies on a powerful, recently developed modelling framework that can be applied to the types of data that biologists often collect. We will apply the approach to construct a detailed model to a well-studied population of Yellowstone wolves. Analysis of this model will allow us to predict how the Yellowstone wolf population may respond to various environmental change scenarios. We will also construct simplified versions of the model for a wide range of animal data sets to identify more general patterns in ways that animal populations are likely to respond to environmental change. Our work has the potential to substantially improve our understanding of how environmental change is likely to impact natural populations in both the short and long term.

The environment that most natural populations experience is changing. Not only as a result of global climate change, but also as a consequence of other anthropogenic activities including hunting, habitat modification and the introduction of alien species. Such environmental change has already been demonstrated to impact animal and plant populations and the ecosystems in which these populations are embedded. The speed of environmental change continues to accelerate with potentially catastrophic consequences. Unfortunately biologists do not have a good track record of accurately predicting how environmental change will impact natural populations. This is because populations and the environment they experience are complex. We have identified an approach that is likely to improve upon the predictions that biologists are able to make about how populations will respond to environment change. The types of response we are able to model include both short-term ecological ones and longer-term evolutionary responses. Our approach relies on a powerful, recently developed modelling framework that can be applied to the types of data that biologists often collect. We will apply the approach to construct a detailed model to a well-studied population of Yellowstone wolves. Analysis of this model will allow us to predict how the Yellowstone wolf population may respond to various environmental change scenarios. We will also construct simplified versions of the model for a wide range of animal data sets to identify more general patterns in ways that animal populations are likely to respond to environmental change. Our work has the potential to substantially improve our understanding of how environmental change is likely to impact natural populations in both the short and long term.

The oceans have been explored for hundreds of years and the activities still continue, but they are always limited by the number of diving experts, technologies and in particular costs. Advanced imaging enables transferable underwater discovery to onshore experts with specific knowledge required, such as geologists, archaeologists and biologists. Three-dimensional (3D) reconstruction from these image sequences enhance understanding of underwater organisms, objects and the seabed. However, current solutions have not yet provided high resolution and definition of underwater 3D representation without months of intense enhancements and processing time. This is mainly because of limitation of data and computational complexity as, obviously, processing the sequences of underwater environments is challenging due to distortion, backscatter of light and turbidity conditions. MyUnderwaterWorld project aims to provide intensive analysis of underwater imagery for Artificial Intelligence (AI)-based development, leading to a novel framework for image quality enhancement and high-resolution 3D scene representation of underwater scene, which contains seabed and objects of interest. We hypothesise that the 3D scene could be modelled accurately and directly from raw underwater data using well-defined prior knowledge. This could be achieved by characterising diverse and reliable underwater datasets. We will combine real-time visual SLAM and sparse radiance fields hierarchically, trained with a novel loss function developed from prior knowledge of underwater. This will improve quality of 3D representation, and offer more efficient and flexible workflows. It will also facilitate more robust feature extraction for subsequent machine-based processing and more efficient compression for delivery.

The Arctic is changing rapidly, and it is predicted that areas which are today tundra will become tree-covered as warming progresses, with, for example, forest spreading northwards to the coast of northern European Russia by 2100. In some parts of the Arctic, such as Alaska, this process, commonly referred to as "greening", has already been observed over the past few decades; woody shrubs are expanding their distribution northwards into tundra. Such vegetation changes influence nutrient cycling in soils, including carbon cycling, but the extent to which they will change the storage or release of carbon at a landscape scale is debated. Nor do we fully understand the role that lakes play in this system although it is known that many lakes in the tundra and northern forests are today releasing carbon dioxide and methane into the atmosphere in significant amounts, and a proportion of this carbon comes into the lake from the vegetation and soils of the surrounding landscape. Lakes form an important part of arctic landscapes: there are many thousands of them in our study areas in Russia and west Greenland, and they act as focal points for carbon cycling within in the wider landscape. It is vital that we understand the interactions between plants, soils, nutrients, and lakes because there are massive carbon stores in the high northern latitudes, particularly in frozen soils, and if this carbon is transferred into the atmosphere (as carbon dioxide (CO2) or methane) it will create a positive feedback, driving further global warming. For this reason, the Arctic represents a critical component of the Earth System, and understanding how it will it respond to global environmental change is crucial. Lakes are a key link in this process. As lakes are tightly coupled with terrestrial carbon cycling, changes in the flows of carbon to a lake are faithfully recorded in lake sediment records, as are changes in the biological processing of that carbon within the lake. We also know that similar vegetation changes to those observed or predicted today occurred in the past when climate was warmer than today, and thus past events can provide an analogue for future changes. This project will examine lake sediment records, using techniques that extract a range of chemical signals and microscopic plant and animal remains, to see how vegetation changes associated with past natural climate warming, such as migration of the tree-line northwards, affected lake functioning in terms of the overall biological productivity, the species composition, and the types of carbon processing that were dominant. Depending upon the balance between different biological processes, which in turn are linked to surrounding vegetation and soils, lakes may have contributed mostly to carbon storage or mostly to carbon emissions ?at a landscape scale. Changes in vegetation type also influence decomposition of plant remains and soil development, and this is linked to nitrogen cycling and availability. Nitrogen is an important control over productivity and hence of carbon fixation and storage, and thus it is important to study the dynamics of nitrogen along with those of carbon. Due to the spatial variability of climate and geology, the pace of vegetation development (and of species immigration) and the types of plants involved have not been uniform around the Arctic. By examining several lakes in each of three regions (Alaska, Greenland, Russia) we will be able to describe a broad range of different vegetation transitions and the associated responses of the lakes. Our results can be used to inform our understanding of the likely pathways of recently initiated and future changes. They can also be up-scaled to the whole Arctic and so contribute to the broader scientific goal of understanding feedbacks to global warming.

There is something hugely emotive about leaving or finding a footprint that provides a direct connection to the past and to past behaviour. White Sands National Park in New Mexico is a dried lake bed with literally thousands of footprints left by humans and extinct Ice Age megafauna such as mammoths. They tell of story of life on this dried lakebed (playa) during the ice age. The footprints are so extensive they allow us to track mammoths, camels, giant ground sloth and dire wolves across the lake bed and reveal their interaction with early human hunters. Like no other site they provide a unique window into the past. It is a truly amazing place for ichno-archaeology or put another way the study of track fossils a discipline called ichnology. In September 2021 we managed to date these footprints at one locality and the age surprised us and challenges conventional wisdom about the peopling of the Americas. They date from the height of the glacial cycle, referred to as the Last Glacial Maximum when ice sheets formed an impenetrable east-west barrier across North America. Traditional models have folk penned up in Alaska having walked from Asia waiting for this ice sheet barrier to melt before migrating south. Our work changes that with people confirmed at White Sands south of the ice sheet barrier over two millennia between 21,000 and 23,000 years ago. Clearly, they must have arrived before the ice barrier, but how much before? What were these people doing in New Mexico? How did they survive? What were their lifeways? The footprints at White Sands can answer these question; we have written the headline but now need to mine the full potential of archaeological information that we can from this site. There is another hugely important dimension to this work. These footprints represent the ancestral footfall of indigenous peoples, yet scientific hegemony emasculates their voice, yet they have a right to tell their stories and for their footprint narratives to be heard alongside those of archaeologists. We propose to develop new ways of collaborative working based on respect and reciprocity which recognises and values multiple truths about the White Sands footprints. There is a need to develop methodologies that embrace indigenous methods since footprint discoveries are occurring around the world with increasing frequency. This research will train and equip a new generation of ichno-archaeologists for this brave new world of discovery while adding vital information to long debated questions around the peopling of the Americas.