Our ability to accurately perceive time changes from one moment to the next. These variations contribute to moment-to-moment fluctuations in our experience of the world and other psychological functions such as coordinating our movements in response to environmental cues. Variations in our perception of time are thought to be caused in part by fluctuations in a specific brain chemical, dopamine. Dopamine is believed to activate brain regions involved in matching time intervals against other intervals we hold in memory and thereby affecting how we perceive the passage of time. When and how dopamine influences our experience of time is poorly understood because until very recently it was impossible to measure dopamine in the human brain over short timescales. A newly-developed method, fast-scan cyclic voltammetry (FSCV), allows us to accurately measure dopamine in humans and thus offers an excellent opportunity to study how dopamine contributes to variations in human time perception for the first time. Another method, electroencephalography (EEG), allows us to record a particular brain rhythm (beta oscillations) that is closely associated with dopamine and can thereby provide a complementary way to study the role dopamine plays in affecting different phases of time perception. The proposed research aims to use FSCV and EEG to investigate whether moment-to-moment variations in time perception can be predicted from participants' brain states. FSCV will be used to measure dopamine concentrations in striatum, a brain region previously implicated in time perception, in Parkinson's patients whilst they complete time perception tasks. This will allow us to determine whether dopamine concentrations can be used to predict how participants perceive time. Further analyses will investigate whether dopamine plays a similar role in time perception when we're storing an interval in memory as when we're trying to remember an interval and whether dopamine plays a similar role in both time perception and other cognitive functions such as attention and working memory. In a second, complementary set of studies, an advanced analysis technique, multivariate pattern analysis, will be applied to EEG data in healthy adults whilst they complete the same time perception tasks. This method will allow us to determine approximately when in time participants' experiences of time can be predicted and the role of specific brain rhythms. This approach will also help to clarify whether similar brain mechanisms support different phases of time perception and both time perception and other cognitive functions. This project has the potential to significantly advance current understanding of how fluctuations in brain states influence our subjective experience of time. This research will help to update contemporary theories of timing including when and how brain states shape our perception of time, how these states contribute to different phases of time perception, and how time perception relates to other basic psychological functions. Our perception of time influences how we perform a variety of actions such as coordinating our movements and predicting the trajectory of a ball so that we are able to catch it. Superior understanding of the brain mechanisms that contribute to variability in time perception may thus help to understand the sources of variability in human performance more generally. Individuals with different disorders such as Parkinson's disease and schizophrenia experience pronounced alterations in their perception. These time distortions are typically characterized by increased variability in the perception of time. By helping to strengthen current understanding of how variability in brain states contributes to fluctuations in our time perception, this project may also help to provide a methodological and theoretical framework for studying these distortions in a more refined manner including how they relate to other clinical symptoms.

This project will advance our ability to quantify the influence of climatic warming on the emission of CO2 from soil by investigating how soil biological and functional diversity (roots and microbes), and soil chemical properties, limit respiration processes in soil. This work will be the first of its kind to address this question over a large elevation gradient and in a tropical region where biodiversity and biogeochemical cycling of carbon are very high. The carbon balance of an ecosystem is strongly dependent on the balance between photosynthesis and respiration. Globally, respiration on land is at present very slightly smaller than photosynthesis, meaning that terrestrial ecosystems are thought to be a 'sink' for atmospheric carbon dioxide, slowing the continual rise in carbon dioxide (CO2) concentration in the atmosphere. The largest fraction of total respiration from land comes from the decomposition of organic matter in soil. This decomposition leads to emissions of CO2 to the atmosphere. The rate of decomposition may increase under climatic warming, possibly accelerating climate change over this century, so we need urgently to understand what the risk of this happening is. Our study site is in the tropical rain forests of Peru, ranging in altitude from 3000 m to 220 m above sea level. The soil carbon stock is large, particularly at high elevation and so represents a risk in the sense that this carbon could be broken down and emitted as CO2 under climatic warming. Our preliminary data suggest that there are large differences in the temperature sensitivity of soil CO2 emissions in these forests, with high sensitivity at high elevations. This project aims to understand these differences in sensitivity by examining controls over the decomposition of organic matter that are exerted by the physical environment and also by roots, and by the decomposing microbes in soil. Our study site is ideally suited to address this question because it spans a natural temperature gradient of 12-26 degrees Celsius. We will use this in two ways: (i) to observe natural differences in CO2 emissions at different elevations and temperatures and (ii) to examine the effects of transplanting soil from one elevation and 're-planting' it at another. We have performed part (ii) for 4 sites across our elevation gradient and now haave an exceptional opportunity to study the effects, and to advance our understanding of short- and long-term climatic warming of soil CO2 emissions. Our approach will be to observe the temperature response characteristics of soil CO2 emission in natural and transplanted soil. We will make high temporal resolution measurements over 2.5 years, further manipulating the soil to see the effects of removing roots and mycorrhizal fungi from the decomposing system. We will measure the physical environment and the chemical complexity of the soil carbon. We will also measure the biological diversity of microbes in the soil using leading edge membrane- and DNA-based techniques. Finally we will use a laboratory experiment to trace the types of carbon compound that different microbes use from different sites along the study transect. Here we will 'feed' the soil with a stable (safe) carbon isotope and trace where that carbon is used and emitted - ie how much labeled CO2 is emitted and which organisms use it in their metabolism. This will give us valuable information to inform our analysis of the data we get from field measurements. In our analysis we will statistically examine what microbes/root functions are most important for constraining the response by soil respiration to climatic change and use our laboratory data to provide mechanistic interpretations of our statistical analysis. Combined we will develop a new understanding of the response by soil respiration to climatic warming and we will test how important biological diversity is for controlling and constraining that response, and its effect on climatic change.

Tropical ecosystems are major sources of the greenhouse gases (GHGs) methane (CH4) and nitrous oxide (N2O), which are 25 and 298 times more effective than carbon dioxide (CO2), respectively, in trapping long-wave radiation in the atmosphere. Increases in CH4 and N2O concentrations since the start of the Industrial Revolution are responsible for over one-third of global warming, and future changes in the atmospheric budgets of these GHGs have implications for the Earth's climate and environmental conditions. N2O emissions, in particular, are projected to rise in the future due to agricultural expansion and enhanced atmospheric nitrogen deposition. Recent studies of the global budgets of CH4 and N2O using satellite imagery, atmospheric measurements, and modelling suggest that significantly more CH4 and N2O are released from the tropics than previously thought due to unaccounted sources of CH4 and N2O. It is critical for us to identify and characterise these 'missing' sources if we wish to understand the current contribution of the tropics to GHG budgets. Knowledge of these 'missing' sources is also necessary for predicting how tropical GHG emissions are likely to respond to future environmental or climatic change. One strong potential candidate for these 'missing' sources of CH4 and N2O are tropical uplands. Tropical uplands have been conspicuously absent from existing atmospheric budgets, because scientific attention has largely focused on CH4 and N2O emissions from lowland forests, savannas, or wetlands. Studies from tropical uplands suggest that they are potentially large sources of CH4 and N2O, with emissions that are equal to or greater than those from lowland environments. Upland rainforests in Puerto Rico, for example, emit more CH4 than lowland forests, with emission rates that are on par with northern wetlands, the largest natural sources of CH4 worldwide. To address these gaps in knowledge, we will conduct a comprehensive study of CH4 and N2O cycling in the Peruvian Andes, using a mixture of field measurements, controlled environment studies, and mathematical modelling. Specifically, we will: 1. Investigate how CH4 and N2O fluxes vary in space and time along an environmental gradient that spans 3000 m of altitude, from lowland rainforest to upper montane rainforest. 2. Explore how key environmental variables (e.g., plant productivity, climate, soil moisture, carbon and nitrogen availability, oxygen) influence CH4 and N2O emissions. 3. Determine if existing mathematical models are able to simulate CH4 and N2O emissions from tropical ecosystems, adapting these models as necessary to better simulate field observations. 4. Determine if GHG emissions from the Andes are able to account for some of the 'missing' tropical sources of CH4 and N2O by extrapolating our field observations to the regional scale using a combination of mathematical modelling, satellite imagery, and land cover databases (i.e., GIS). The proposed research will greatly advance our understanding of CH4 and N2O emissions for an important but understudied region, and will help us determine the relative contribution of Andean ecosystems to the CH4 and N2O budgets for South America. Knowledge of the emission rates and environmental controls on CH4 and N2O fluxes from upland Andean ecosystems will also help us evaluate whether other tropical uplands are likely to be sources of CH4 and N2O, and assess their potential contributions to the global atmospheric budgets of CH4 and N2O. Lastly, the development and adaptation of mathematical models that accurately simulate tropical CH4 and N2O fluxes will allow us to predict the likely response of tropical uplands to future environmental or climatic change.

Tropical ecosystems are major sources of the greenhouse gases (GHGs) methane (CH4) and nitrous oxide (N2O), which are 25 and 298 times more effective than carbon dioxide (CO2), respectively, in trapping long-wave radiation in the atmosphere. Increases in CH4 and N2O concentrations since the start of the Industrial Revolution are responsible for over one-third of global warming, and future changes in the atmospheric budgets of these GHGs have implications for the Earth's climate and environmental conditions. N2O emissions, in particular, are projected to rise in the future due to agricultural expansion and enhanced atmospheric nitrogen deposition. Recent studies of the global budgets of CH4 and N2O using satellite imagery, atmospheric measurements, and modelling suggest that significantly more CH4 and N2O are released from the tropics than previously thought due to unaccounted sources of CH4 and N2O. It is critical for us to identify and characterise these 'missing' sources if we wish to understand the current contribution of the tropics to GHG budgets. Knowledge of these 'missing' sources is also necessary for predicting how tropical GHG emissions are likely to respond to future environmental or climatic change. One strong potential candidate for these 'missing' sources of CH4 and N2O are tropical uplands. Tropical uplands have been conspicuously absent from existing atmospheric budgets, because scientific attention has largely focused on CH4 and N2O emissions from lowland forests, savannas, or wetlands. Studies from tropical uplands suggest that they are potentially large sources of CH4 and N2O, with emissions that are equal to or greater than those from lowland environments. Upland rainforests in Puerto Rico, for example, emit more CH4 than lowland forests, with emission rates that are on par with northern wetlands, the largest natural sources of CH4 worldwide. To address these gaps in knowledge, we will conduct a comprehensive study of CH4 and N2O cycling in the Peruvian Andes, using a mixture of field measurements, controlled environment studies, and mathematical modelling. Specifically, we will: 1. Investigate how CH4 and N2O fluxes vary in space and time along an environmental gradient that spans 3000 m of altitude, from lowland rainforest to upper montane rainforest. 2. Explore how key environmental variables (e.g., plant productivity, climate, soil moisture, carbon and nitrogen availability, oxygen) influence CH4 and N2O emissions. 3. Determine if existing mathematical models are able to simulate CH4 and N2O emissions from tropical ecosystems, adapting these models as necessary to better simulate field observations. 4. Determine if GHG emissions from the Andes are able to account for some of the 'missing' tropical sources of CH4 and N2O by extrapolating our field observations to the regional scale using a combination of mathematical modelling, satellite imagery, and land cover databases (i.e., GIS). The proposed research will greatly advance our understanding of CH4 and N2O emissions for an important but understudied region, and will help us determine the relative contribution of Andean ecosystems to the CH4 and N2O budgets for South America. Knowledge of the emission rates and environmental controls on CH4 and N2O fluxes from upland Andean ecosystems will also help us evaluate whether other tropical uplands are likely to be sources of CH4 and N2O, and assess their potential contributions to the global atmospheric budgets of CH4 and N2O. Lastly, the development and adaptation of mathematical models that accurately simulate tropical CH4 and N2O fluxes will allow us to predict the likely response of tropical uplands to future environmental or climatic change.

What is the relationship between the composition of an ecological community and its ecosystem function? How do changes in community composition affect carbon and nutrient cycling? How does a shift in ecosystem productivity (e.g. through fertilization) feed through to changes in diversity? These are perhaps the most important questions in ecology day, in the context of direct human pressure on ecosystems and indirect pressure through global atmospheric change. Here we propose to collect the data and develop and evaluate a framework to advance these ideas, in the context of tree community composition of tropical forests. We take advantage of three powerful tools that our team of investigators and project partners have developed: (i) an elevation transect of study sites in the Andes-Amazon where tree community composition and dynamics have been described in detail; (ii) airborne hyperspectral and lidar data that have recently been collected over this same transect, that enable determination of forest structure and chemistry in unprecedented detail, and (iii) a theoretical framework that utilises plant traits to propose a mechanistic approach to scale from community composition to ecosystem function. We will add to these datasets by: 1. Conducting an extensive leaf and wood traits collection campaign for seven sites along this transect, and 2. Collecting data on nitrogen and phosphorus cycling. Then we will develop a 3D model of the forest canopy of each plot (based on forest tree census and lidar data) to: 3. Explore the relationship between leaf traits and tree level characteristics (gross primary production, wood production, above-ground net primary production and nutrient cycling) 4. Scale from individual trees to the whole plot ecosystem characteristics (productivity, wood production, nutrient cycling) Having developed this detailed framework for relating individual tree properties to plot-level function, we will try to simplify the system to see if ecosystem level properties can be derived from an understanding of the mean value and distribution of traits in a community. Finally, we will explore how well tree-level characteristics can be described by airborne hyperspectra and lidar, and thus explore whether it is possible to describe landscape level ecosystem functioning at the scale of thousands of hectares. We have assembled a team of leading UK and USA researchers, and have an opportunity to make major advances and novel contributions to these important questions. Ultimately, we seek to acquire a mechanistic understanding of the relationship between forest community assembly and ecosystem level processes. Achievement of this goal would represent a major advance in ecology, in developing a both a theoretical and empirical toolkit with which to reach this goal.