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Invertebrate herbivory is a crucial process contributing to the cycling of nutrients and energy in terrestrial ecosystems. While the function of herbivory can decrease with land‐use intensification, the underlying mechanisms remain unclear. We hypothesize that land‐use intensification impacts invertebrate leaf herbivory rates mainly through changes in characteristics of plants and insect herbivores. We investigated herbivory rates (i.e., damaged leaf area) on the most abundant plant species in forests and grasslands and along land‐use intensity gradients on 297 plots in three regions of Germany. To evaluate the contribution of shifts in plant community composition, we quantified herbivory rates at plant species level and aggregated at plant community level. We analyzed pathways linking land‐use intensity, plant and insect herbivore characteristics, and herbivory rates. Herbivory rates at plant species and community level decreased with increasing land‐use intensity in forests and grasslands. Path analysis revealed strong direct links between land‐use intensity and herbivory rates. Particularly at the plant community level, differences in plant and herbivore composition also contributed to changes in herbivory rates along land‐use intensity gradients. In forests, high land‐use intensity was characterized by a larger proportion of coniferous trees, which was linked to reduced herbivory rates. In grasslands, changes in the proportion of grasses, plant fiber content, as well as the taxonomic composition of herbivore assemblages contributed to reduced herbivory rates. Our study highlights the potential of land‐use intensification to impair ecosystem functioning across ecosystems via shifts in plant and herbivore characteristics. De‐intensifying land use in grasslands and reducing the share of coniferous trees in temperate forests can help to restore ecosystem functionality in these systems. [ABSTRACT FROM AUTHOR]

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%–86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species‐level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave‐one‐species‐out" cross‐validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available. [ABSTRACT FROM AUTHOR]