In recent years, three major innovations have occurred in ecology. (1) The emergence of new statistical methods for analysing community data; (2) the rapid detection of species and whole communities from environmental DNA (eDNA) and bulk-sample DNA; and (3) the wide availability of remotely sensed environmental covariates. The efficiency gains are such that hundreds or even thousands of species can now be detected and, to an extent, quantified in hundreds or even thousands of samples. Collectively, these three innovations have the potential to relieve the problems of data limitation and analysis that environmental management has been struggling with, opening the way to near-real-time tracking of state and change in biodiversity and its functions and services over whole landscapes. The aim of our project is to develop an integrated statistical framework for DNA-based surveys of biodiversity. The framework will allow the estimation of community compositions and the identification of the landscape characteristics that drive them. We will develop a Bayesian hierarchical model accounting for the probabilistic nature of DNA-based data due to observation error and taxonomic uncertainty and for model uncertainty due to the unknown strength and direction of landscape effects on the system. We will build sophisticated and efficient algorithms within a Bayesian framework for identifying the important landscape covariates that predict community structure and provide guidelines on optimal allocation of resources in DNA-based surveys for achieving the required power to infer species distributions and to link them to landscape covariates. The huge potential contribution of DNA-based data to landscape decision-making is demonstrated by how Natural England, Local Planning Authorities, and the NatureSpace Partnership use eDNA to create a biodiversity-offset market ('District Licensing') for the protected Great Crested Newt (GCN). Water samples from 500 ponds across the South Midlands (spanning ~3320 sq km) were tested for GCN and used to create a distribution map, which was then zoned into four 'impact risk' levels. Builders pay a known, sliding-scale fee, and a portion of the fee is used to build and manage new habitat. District Licensing is only feasible with eDNA's greater efficiency. GCN District Licensing expands to at least 16 LPAs in 2020, aiming to go nationwide, which would make it the largest biodiversity-focused, land-use decision scheme in the UK, if not the world. The natural-and highly desirable-extension to the GCN scheme would be to map 'all biodiversity' and to make land-use decisions (e.g. impact risk maps, offset markets, habitat creation) on this broader basis. In fact, samples originally collected for GCN can be repurposed for this larger goal by using 'metabarcoding,' meaning that the eDNA is PCR-amplified for a larger range of taxa. Given the District-Licensing expansion plans, pond eDNA metabarcoding alone could provide an efficient way to map biodiversity across much of the UK. This is far from the only such programme. Ecologists in industry and academia around the world are plunging ahead with large-scale DNA-sampling campaigns, and there is, as yet, no comprehensive set of statistical methods for modelling the individual steps of the new observation processes, quantifying the resulting uncertainty, and assessing how it affects decision-making at the landscape level. Our proposed modelling framework will provide such tools by explicitly capturing measurement bias within biodiversity models as a set of observation processes, and not merely as error. Improving sampling designs and workflows as a result of our proposed models will profoundly increase the efficiency and credibility of inference and therefore reduce the risk of biodiversity loss during the political process of allocating land to different uses.

Land-use and agriculture are responsible for around one quarter of all human greenhouse gas (GHG) emissions. While some of the activities that contribute to these emissions, such as deforestation, are readily observable, others are not. It is now recognised that freshwater ecosystems are active components of the global carbon cycle; rivers and lakes process the organic matter and nutrients they receive from their catchments, emit carbon dioxide (CO2) and methane to the atmosphere, sequester CO2 through aquatic primary production, and bury carbon in their sediments. Human activities such as nutrient and organic matter pollution from agriculture and urban wastewater, modification of drainage networks, and the widespread creation of new water bodies, from farm ponds to hydro-electric and water supply reservoirs, have greatly modified natural aquatic biogeochemical processes. In some inland waters, this has led to large GHG emissions to the atmosphere. However these emissions are highly variable in time and space, occur via a range of pathways, and are consequently exceptionally hard to measure on the temporal and spatial scales required. Advances in technology, including high-frequency monitoring systems, autonomous boat-mounted sensors and novel, low-cost automated systems that can be operated remotely across multiple locations, now offer the potential to capture these important but poorly understood emissions. In the GHG-Aqua project we will establish an integrated, UK-wide system for measuring aquatic GHG emissions, combining a core of highly instrumented 'Sentinel' sites with a distributed, community-run network of low-cost sensor systems deployed across UK inland waters to measure emissions from rivers, lakes, ponds, canals and reservoirs across gradients of human disturbance. A mobile instrument suite will enable detailed campaign-based assessment of vertical and spatial variations in fluxes and underlying processes. This globally unique and highly integrated measurement system will transform our capability to quantify aquatic GHG emissions from inland waters. With the support of a large community of researchers it will help to make the UK a world-leader in the field, and will facilitate future national and international scientific research to understand the role of natural and constructed waterbodies as active zones of carbon cycling, and sources and sinks for GHGs. We will work with government to include these fluxes in the UK's national emissions inventory; with the water industry to support their operational climate change mitigation targets; and with charities, agencies and others engaged in protecting and restoring freshwater environments to ensure that the climate change mitigation benefits of their activities can be captured, reported and sustained through effectively targeted investment.

LANDMARK (LAND MAnagement for flood RisK reduction in lowland catchments) will evaluate the effectiveness of realistic and scalable land-based NFM measures to reduce the risk from flooding from surface runoff, rivers and groundwater in groundwater-fed lowland catchments. We will study measures like crop choice, tillage practices and tree planting, that have been identified by people who own and manage land, to have the greatest realisable potential. NFM measures will be evaluated for their ability to increase infiltration, evaporative losses and/or below-ground water storage, thereby helping to store precipitation to reduce surface runoff and slow down the movement of water to reduce peak levels in groundwater and rivers. However, we need to carefully examine the balance between increased infiltration, soil water storage and evaporative losses under different types of NFM measures, because long-term increases in infiltration could actually increase groundwater and river flood risk if there is less capacity within the ground and in rivers to store excess precipitation from storm events. Also, following a review of the available research to date, other researchers (Dadson et al, 2017) came to the conclusion that land-based NFM measures would only provide effective protection against small flood events in small catchments. As the catchment size and flood events increase, the effectiveness of land-based NFM measures in reducing flood risk would decrease significantly. However, this idea needs to be tested further. Currently, there are many unanswered gaps in knowledge that make it hard to include land-based NFM measures in flood risk mitigation schemes. The Environment Agency tell us that there are no case studies on land-based NFM measures to support decision making, with most focusing on leaky barriers made from trees. Yet, land-based NFM measures have potential to do more than just reduce flood risk, including improving water quality, biodiversity and sustainable food and fibre production. So in LANDMARK, we will carry out research to help to fill this evidence gap, and test the ideas Dadson et al. proposed about land-based NFM using the West Thames River Basin as a case-study area. We will work at three spatial scales (field, catchment and large river basin) and explore modelling scenarios, developed with people who own and manage land and live at risk of flooding, to look at how land-based NFM could affect flooding. Scenarios will include experiences in the recent past in July 2007 and over the winter of 2013-14, and how future land use and management could affect flood risk in 2050 as the climate changes. We will consider how government policy could change after we leave the EU to support land-based NFM. Work will be carried out in five stages: (1) we will bring together available maps, data and local knowledge on current land use and management, and use this to create scenarios for modelling experiments to explore land use and management measures impact on events from the past and in the future; (2) we will make measurements to see how below-ground water storage and infiltration vary between different land-based NFM in fields where innovative land management is being practiced; (3) we will collect data from sensors sitting above the ground, flying on drones and on satellites to see how vegetation and soil moisture vary across large catchment areas; (4) we will use all the data collected from 1-3 to run modelling experiments across a range of scales, linking together models that capture soil and vegetation processes, overland and groundwater flows and catchment hydrology, exploring variation in model outputs; and (5) we will create web applications to display and explore the outputs from the modelling experiments. All this work will be supported by workshops, field visits, reports and resources to support people and their learning about how land-based NFM measures work and could be used to reduce flood risk.

The project aims to unlock two significant but untapped resources for citizen science: the use of environmental DNA to detect species and the capacity of the National Trust (NT) as a major landowning and volunteering organisation. We propose a scoping study and co-designed pilot to bring these together. Environmental DNA (eDNA) are traces of DNA released into the environment by species. Sources of eDNA include secreted faeces, mucous, gametes, shed skin, hair and carcasses and these can be detected in samples of water or soil. The potential for eDNA to transform species recording and open it up to people with few or no traditional species identification skills is huge. So far, the most developed application is for fish and amphibians in aquatic environments where a simple water sample can be analysed to produce a list of species present and even provide some indication of relative proportions of different species. Initial work suggests non-specialists (anglers and schoolchildren) can collect adequate (uncontaminated) water samples that provide robust data of which species are present in a waterbody. There is an opportunity to get interested communities collecting information on what lives in their local river, lakes or pond in a way that was hitherto impossible (due to complicated and expensive survey methods or lack of species identification skills). We believe that being able to collect these data will enthuse people to find out more about the health of their local waters leading to further action either in getting more involved in data collection or in tackling issues to improve freshwaters. The use of an exciting and novel scientific tool could open up a pathway of engagement and direct action. By involving volunteers in the decision-making process and supporting them to identify local need, we believe participants will develop the confidence to share their skills with others. The National Trust is public-facing IRO and charity as well as large landowner (250,000ha) with ambitions to transform our land to be better for wildlife and to provide a wide range benefits of benefits to society. With 5.5million members and more than 60,000 volunteers the potential to reach and engage a large community with this work is considerable. Partnership working, and public engagement are embedded strongly across the NT's overall research programme. One of the NT's challenges is to monitor the effectiveness of the changes we are making across our estate but also to ensure that our monitoring effort plays a part in a wider UK network tracking the health of our landscapes. We strongly believe that citizen science, capitalising on our member, volunteer and visitor assets, could play an important role in this respect. Through this project we will bring together NT researchers, leading UK freshwater and eDNA scientists, citizen science specialists and a group of NT volunteers to explore the state of the art in terms of eDNA monitoring for freshwaters. Through a workshop and co-design process we will: Generate a series of recommendations for the development of a UK-wide citizen science project based on eDNA in freshwaters Develop and implement a co-designed pilot to test citizen science collection of eDNA species data (fish and amphibians) in a catchment where NT are leading a partnership project to improve and restore the freshwater environment (Upper Bure, Norfolk) Review lessons learned from the pilot to inform further development of eDNA based citizen science The project will lead directly to the development of a future funding bid for a national scale eDNA based citizen science research project.

In our changing world there is an increasing urgency to understand interactions among multiple environmental stressors, such as pollution and warming. Much of the concern surrounding multiple stressors is due to their potential to interact, creating more severe impacts than they would do independently. Freshwater ecosystems are particularly vulnerable and freshwater biodiversity is the most threatened across the globe: a recent report estimated average population declines of >80% among freshwater vertebrate species compared to <40% in terrestrial and marine species (since 1970; WWF Living Planet Report, 2018). Although the combined impacts of multiple stressors has started to receive more attention, our knowledge on their interactive effects still remains almost non-existent. In reality, stressors are unlikely to occur in the same space at exactly the same time, yet studies that measure the combined effects of multiple stressors often assume this to be the case. In other words, they lack temporal realism. Most of these studies also lack biological realism by quantifying the effects of stressors on model species at lower levels of organisation (e.g. range shifts, survival, abundance) and ignoring feeding interactions. Here, we will consider how the order, or sequence, of stressor events alters individual-to-ecosystem responses of freshwaters, with a focus on food web interactions. Ecosystems will have multiple responses to the multiple stressors they face, including changes in diversity, abundance, body size and feeding behaviour. Even minor alterations to any of these can shift food web structure, with implications for the effects of future stressors, yet these critically important interactions have been largely ignored to date. This leaves us with little or no predictive ability about the consequences of future change in natural systems. Therefore, here we will use mesocosm experiments to quantify the combined effects of staggered nutrient pollution and warming events (i.e. previous exposure) on freshwater ecosystems, and scale our results up to the catchment level by adapting a suite of dynamic water quality models. Our experimental results will be used to parameterize temperature and nutrient controlled population sizes and growth rates, and to simulate how these changed rates will alter food web structure at the larger river system scale. This interdisciplinary study will generate an unprecedented breadth and depth of data: from individual changes in fitness and population shifts in size structure to food web complexity. We will show how the order of multiple stressor events (i.e. previous exposure) affects community resistance and resilience to change. These unique data sets will allow us to ask numerous novel questions in pure and applied ecology, and to characterise the little known multiple impacts of multiple stressors on freshwater food webs. Such a comprehensive coverage of responses has never been attempted before and this study will address this glaring gap in our knowledge of stressor impacts.