Studies have reported the important role of soil properties in regulating insect herbivory under controlled conditions or at relatively large scales. However, whether fine-scale variation of soil properties affects insect herbivory under natural conditions in forests is still unclear. We selected a ca. 300 ha Quercus variabilis forest area where the leaf damage was mainly caused by Lampronadata cristata (Lepidoptera: Notodontidae) and set 200 10 x 10 m plots within the area. We examined insect herbivory (percent leaf area damaged) on Q. variabilis and correlated it to soil properties and tree characteristics. Insect herbivory decreased with soil sand percentage and bulk density and increased with DBH and tree height. Effects of soil sand percentage and bulk density on insect herbivory were partly mediated by DBH and tree height. Our results indicated that soil physical properties may have significant effects on insect herbivory by directly influencing insect herbivores that need to complete their life cycle in the soil, or by indirectly affecting insect herbivores through influencing DBH and tree height which reflects the total leaf biomass available to the insect herbivore. This study may help to understand the complex relationship between soil and plant-insect interactions in forest ecosystems.

Forests provide multiple ecosystem services including water and soil protection, biodiversity conservation, carbon sequestration, and recreation, which are crucial in sustaining human health and wellbeing. Global changes represent a serious threat to Mediterranean forests, and among known impacts, there is the spread of invasive pests and pathogens, often boosted by climate change and human pressure. Remote sensing can provide support to forest health monitoring, which is crucial to contrast degradation and adopt mitigation strategies. Here, different multispectral and SAR data are used to detect the incidence of ink disease driven by Phytophthora cinnamomi in forest sites in central Italy, dominated by chestnut and cork oak respectively. Sentinel 1, Sentinel 2, and PlanetScope data, together with ground information, served as input in Random Forests to model healthy and disease classes in the two sites. The results indicate that healthy and symptomatic trees are clearly distinguished, whereas the discrimination among disease classes of different severity (moderate and severe damage) is less accurate. Crown dimension and sampled spectral regions are a critical factors in the selection of the sensor; better results are obtained for the larger chestnut crowns with Sentinel 2 data. In both sites, the red and near infra-red bands from multispectral data resulted well suited to monitor the spread of the ink disease.