The wind energy sector is an industry of strategic national importance, which can help secure our energy supplies, reduce our emissions and dependence on imported fossil fuels, and protect our environment. It is an industry on which our clean energy future rests. Despite the positive benefits of wind farms however, there is concern and uncertainty over the possible negative effects wind turbines may have on the environment, particularly on birds. For example, uncertainty remains over collision mortality i.e. the number of birds killed directly through collision with wind turbines. These uncertainties are far from trivial for the industry and have real consequences, potentially delaying wind farm projects and inhibiting the ability of the UK to meet its binding 2020 targets. Three projects in Round 2 of wind farm developments in the UK were delayed by over three years due in part to uncertainties over the assessment of impacts. Therefore better quantification of the uncertainty and variability associated with the estimation of impacts is required. During Environmental Impact Assessments of wind farm developments, bird collision mortality is estimated using a mathematical model which describes the interaction of a bird with a wind turbine and predicts the risks of bird collisions with turbines. There are a limited number of collision risk models in use, not only in the UK but globally. However, it is recognised by many, including industry, statutory nature conservation agencies and academics that there is much room for improvement of these models. For example, collision risk models are deterministic and rarely include variation in the input parameters such as bird density, or bird biometrics which are inherently variable, but instead use average values. Additionally, any uncertainty in these values is not expressed. Adopting a single best value for parameters may reduce complexity and increase the accessibility of results for decision-makers however it can be misleading because it ignores the range of consequences that are plausible. This project aims to i) review current models that are used to predict bird collision mortality caused by wind farm developments, ii) determine statistical methods suited to address any shortcomings of current models and then, using this information, iii) develop an updated model which incorporates variability and uncertainty. Reviewing current models and highlighting their strengths and weaknesses, as well as reviewing methods to incorporate variability and uncertainty will aid the development of a product, a collision risk model, which is fit for purpose. Development of the understanding of uncertainty in the outputs of collision risk models will be a key part of this project, and will be of direct benefit to industry, government advisors and regulators in the assessment and licensing processes for wind farm projects. The involvement of these parties will be vital in steering this project because any revision of a collision risk model has to function to better inform planning decisions for wind farm developments. To ensure that all relevant parties are involved, contribute and ultimately buy-in to the development of a new, updated model, there will be a workshop to discuss issues surrounding current practices to which developers, licensing authorities, statutory nature conservation bodies, academics and others will be invited. Also, to ensure the outputs of this project have impact and are used by the industry, the model and any documents produced will be made freely available and accessible through a dedicated webpage. Wind energy has an important role in meeting energy targets, so there is a clear need to ensure that decisions made through the planning processes use the best available information, data and models. Improved understanding of the risks of collision to birds - a key effect considered in ornithological impact assessments of wind farms - is thus vital.

Aonachadh (ún`-ach-A) is gaelic for coming together, for two faces of a mountain that meet to form a uniting ridge. Building on and expanding an existing network of over 280+ organisations, we will bring together a wide range of stakeholders interested in investable biodiversity uplift projects. We will develop methods for creating standardised, accessible, and verifiable data, metrics and tools for voluntary biodiversity markets, and co-create research questions and a programme of work that can lead to a common framework for data gathering and business models and community engagement methods acceptable to supply-side projects as well as demand side investors. Research activity will enable us to come together in workshops and working groups to collaboratively co-create research questions, and then share, discuss and learn from lessons emerging from biodiversity uplift pilot projects engaging with voluntary markets in Scotland. Our research network - of established and emerging projects, financiers and policy makers - will contribute to NERC's Nature Positive Future programme from the unique context of Scotland, which is experiencing unprecedented increases in land values alongside a land reform agenda that seeks to deliver benefits from biodiversity markets for local communities. Scotland's place-based approach to ecosystem market development provides a unique opportunity to understand interactions between biodiversity, finance and society and what this means for environmental and economic resilience. Recent and ongoing work from the core team, and established connections with UK stakeholders and channel partners Ecosystems Knowledge Network and the Green Finance Institute, means we can initiate a quick start for more results and impact.

The UK is committed to quantifying and managing its emissions of greenhouse gases (GHG, i.e. CO2, CH4, N2O) to reduce the threat of dangerous climate change. Sinks and sources of GHGs vary in space and time across the UK because of the landscape's mosaic of managed and semi-natural ecosystems, and the varying temporal sensitivities of each GHG's emissions to meteorology and management. Understanding spatio-temporal patterns of biogenic GHG emissions will lead to improvements in flux estimates, allow creation of inventories with greater sensitivity to management and climate, and advance the modelling of feedbacks between climate, land use and GHG emissions. Addressing Deliverable C of the NERC Greenhouse Gas Emissions and Feedbacks Research Programme, we will use extensive existing UK field data on GHG emissions, supplemented with targeted new measurements at a range of scales, to build accurate GHG inventories and improve the capabilities of two land surface models (LSMs) to estimate GHG emissions. Our measurements will underpin state-of-the-art temporal and spatial upscaling frameworks. The temporal framework will evaluate diurnal, seasonal and inter-annual variation in emissions of CO2, CH4 and N2O over dominant UK land-covers, resolving management interventions such as ploughing, fertilizing and harvesting, and the effects of weather and climate variability. The spatial framework will evaluate landscape heterogeneity at patch (m), field (ha) and landscape (km2) scales, in two campaigns combining chambers, tower and airborne flux measurements in arable croplands of eastern England, and grazing and forest landscapes of northern Britain. For modelling, we will update two LSMs - JULES and CTESSEL- so that each generates estimates of CO2, CH4 and N2O fluxes from managed landscapes. The models will be updated to include the capabilities to represent changes in land use over time, to represent changes in land management over time (crop sowing, fertilizing, harvesting, ploughing etc), and the capacity to simulate forest rotations. With these changes in place, we will determine parameterisations for dominant UK land-covers and management interventions, using our spatio-temporal data. The work is organized in five science work-packages (WP). WP1: Data assembly and preliminary analysis. We will create a database of GHG flux data and ancillary data for major UK landcovers/landuses in order to calibrate and evaluate the LSMs' capabilities, and generate spatial databases of environmental and management drivers for the models. WP2. GHG measurement at multiple scales. We will deploy advanced technology to generate new information on spatial GHG processes from simultaneous measurement from chamber (<1 m) to landscape (40 km) length scales, and on temporal flux variation from minutes to years. WP3. Earth observation (EO) to support upscaling. EO data will provide: i) driving data for LSM upscaling, from flux tower to aircraft campaign scales; and ii) spatial data for testing LSM outputs at these larger scales. WP4 Upscaling GHG processes. Firstly, the two LSMs will be updated to allow the impacts of management activities on GHG emissions to be simulated, with calibration against an array of temporal flux data. Then, we will use the LSMs to model the fluxes of GHGs at larger spatial scales, based on a rigorous understanding of how the nonlinearity of responses and the non-Gaussian distribution of environmental input variables interact, for each GHG, using all available field data at finer scales. WP5 Application at the regional scale. The LSMs will upscale GHG emissions for both campaign regions (E. England, N. Britain) using 1-km2 resolution simulations with a focus on the airborne campaign periods of 4 weeks. We will determine how regional upscaling error can be reduced with intensive spatial soil and land management data.