1. Characterizing changes in trait diversity at large spatial scales provides insight into the impact of human activity on ecosystem structure and function. However, the approach is often based on trait datasets that are incomplete and unrepresentative, with uncertain impacts on trait diversity estimates. 2. To address this knowledge gap, we simulated random and biased removal of data from a near complete avian trait dataset (9579 species) and assessed whether trait diversity metrics were robust to data incompleteness with and without using imputation to fill data gaps. Specifically, we compared two commonly used metrics each calculated with two methods: trait richness (calculated with convex hulls and trait probabilities densities) and trait divergence (calculated with distance-based Rao and trait probability densities). 3. Without imputation, estimates of global avian trait diversity (richness and divergence) were robust when 30-70% of species had missing data for four out of 11 continuous traits, depending on severity of bias and the method used. However, when missing traits were imputed based on present morphological trait data and phylogeny, trait diversity metrics consistently remained representative of the true value, even when 70% of species were missing data for four out of 11 traits and data were not missing at random (biased with respect to body mass). Trait probability densities and distance-based Rao were particularly robust to missingness and bias when combined with imputation, with convex hull-based trait richness being less reliable. 4. Expanding global morphometric datasets to represent more taxa and traits, and to quantify intraspecific variation, remains a priority. In the meantime, our results show that widely used methods can successfully quantify large-scale trait diversity even when data are missing for two-thirds of species, so long as missing traits are estimated using imputation.

Background: Studying dietary trends can help monitor progress towards healthier and more sustainable diets but longitudinal data are often confounded by lack of standardized methods. Two main data sources are used for longitudinal analysis of diets: food balance sheets on food supply (FBS) and household budget surveys on food purchased (HBS). Methods: We used UK longitudinal dietary data on food supply, provided by the Food and Agriculture Organisation (FAO) (FAO-FBS, 1961-2018), and food purchases, provided by the Department for Environment, Food and Rural Affairs (Defra) (Defra-HBS, 1942-2018). We assessed how trends in dietary change per capita compared between FAO-FBS and Defra-HBS for calories, meat and fish, nuts and pulses, and dairy, and how disparities have changed over time. Results: Estimates made by FAO-FBS were significantly higher (p<0.001) than Defra-HBS for calorie intake and all food types, except nuts and pulses which were significantly lower (p<0.001). These differences are partly due to inclusion of retail waste in FAO-FBS data and under-reporting in Defra- HBS data. The disparities between the two datasets increased over time for calories, meat and dairy; did not change for fish; and decreased for nuts and pulses. Between 1961 and 2018, both FAO-FBS and Defra-FBS showed an increase in meat intake (+11.5% and +1.4%, respectively) and a decrease in fish (-3.3% and -3.2%, respectively) and dairy intake (-11.2% and -22.4%). Temporal trends did not agree between the two datasets for calories, and nuts and pulses. Conclusions: Our finding raises questions over the robustness of both data sources for monitoring UK dietary change, especially when used for evidence-based decision making around health, climate change and sustainability.

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