UNR

Ice melting and sea level rise caused by climatic change in turn cause large-scale redistribution of surface water mass. Such large-scale water mass movements alter the Earth's gravity field and deform the Earth's surface, such that the Earth essentially weighs the water load. Measurements of the Earth's gravity field and surface deformation can therefore place constraints on large scale movements of water mass. Conventional hydrological experiments do not have the global coverage to constrain such large-scale water movements, whereas satellite measurements are far better placed in this regard. Despite this, measurement at the largest spatial scales has proved problematic, for example; long term changes in the inter-hemispheric component of this mass motion are as yet unmeasured and present a considerable gap in knowledge. Without this knowledge making accurate measurements of sea level rise will prove difficult. Here we propose to infer large scale water mass movements over the past two decades by developing an integrated observation model using gravity measurements from GRACE and SLR, and measurements of the Earth's shape from GPS. In addition, our results will improve the realisation of the Terrestrial Reference Frame used throughout the Earth Sciences and in particular for altimetric and tide gauge estimates of sea level rise.

With increasing recognition of the importance of insects, there are growing concerns that insect biodiversity has declined globally, with serious consequences for ecosystem function and services. Yet, gaps in knowledge limit progress in understanding the magnitude and direction of change. Information about insect trends is fragmented, and time-series data are restricted and unrepresentative, both taxonomically and spatially. Moreover, causal links between insect trends and anthropogenic pressures are not well-established. It is, therefore, difficult to evaluate stories about "insectageddon", to understand the ecosystem consequences, to devise mitigation strategies, or predict future trends. To address the shortfalls, we will bring together diverse sources of information, such as meta-analyses, correlative relationships and expert judgement. GLiTRS will collate these diverse lines of evidence on how insect biodiversity has changed in response to anthropogenic pressures, how responses vary according to functional traits, over space, and across biodiversity metrics (e.g. species abundance, occupancy, richness and biomass), and how insect trends drive further changes (e.g. mediated by interaction networks). We will integrate these lines of evidence into a Threat-Response model describing trends in insect biodiversity across the globe. The model will be represented in the form of a series of probabilistic statements (a Bayesian belief network) describing relationships between insect biodiversity and anthropogenic pressures. By challenging this "Threat-Response model" to predict trends for taxa and places where high-quality time series data exist, we will identify insect groups and regions for which indirect data sources are a) sufficient for predicting recent trends, b) inadequate, or c) too uncertain. Knowledge about the predictability of threat-response relationships will allow projections - with uncertainty estimates - of how insect biodiversity has changed globally, across all major taxa, functional groups and biomes. This global perspective on recent trends will provide the basis for an exploration of the consequences of insect decline for a range of ecosystem functions and services, as well as how biodiversity and ecosystem properties might be affected by plausible scenarios of future environmental change. GLiTRS is an ambitious and innovative research program: two features are particularly ground-breaking. First, the collation of multiple forms of evidence will permit a truly global perspective on insect declines that is unachievable using conventional approaches. Second, by validating "prior knowledge" (from evidence synthesis) with recent trends, we will assess the degree to which insect declines are predictable, and at what scales.