Global change impacts the structure of communities within trophic food webs well before a decline in biodiversity can be measured for instance by species numbers. While land use change is the most important direct driver of terrestrial biodiversity loss, its impact in combination with climate change is less well understood. We investigate the combined effects of climate change (temperature, precipitation) and land-use intensification on terrestrial trophic networks, with a process-based general mechanistic ecosystem model (‘MadingleyR’) that simulates ecosystem dynamics for trait-based functional groups of species (i.e., ectothermic, and endothermic herbivores, carnivores, and omnivores) for different regions such as India and Europe. Resulting patterns are widely consistent across selected regions. The greatest effect of climate change and land use is observed for carnivores. Both, land use intensification and climate change, cause a substantial decrease in the biomass of different functional groups, while some functional groups (generalists, omnivores), partially benefit. Large endotherms are negatively affected by land use intensification, while ectotherms are under pressure from rising temperatures in a changing climate. Arid and tropical regions show a higher response to climate change, with losses of up to -3.4% and -1.9% in total biomass, while areas with low net primary productivity show the most negative response to land use intensification (up to -6.7% in total biomass). Our results suggest that land use intensification (a) has a significant impact on larger organisms and predators, leading to a major restructuring of global food webs, (b) Ectotherms are particularly vulnerable to rising temperatures, and (c) regions in tropical or arid climates experience significant changes in their community composition and will be threatened in their biodiversity.

Meta-analyses often encounter studies with incompletely reported variance measures (e.g. standard deviation values) or sample sizes, both needed to conduct weighted meta-analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta-analyses showing that the majority of meta-analyses encountered incompletely reported studies. We then simulated meta-analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size-based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta-analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best-case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta-analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.