Aim Light Detection And Ranging (LiDAR) is a promising remote sensing technique for ecological applications because it can quantify vegetation structure at high resolution over broad spatial extents. Using country‐wide airborne laser scanning (ALS) data, we test to what extent fine‐scale LiDAR metrics capturing low vegetation, medium‐to‐high vegetation and landscape‐scale habitat structures can explain the habitat preferences of threatened butterflies at a national extent. Location The Netherlands. Methods We applied a machine‐learning (random forest) algorithm to build species distribution models (SDMs) for grassland and woodland butterflies in wet and dry habitats using various LiDAR metrics and butterfly presence–absence data collected by a national butterfly monitoring scheme. The LiDAR metrics captured vertical vegetation complexity (e.g., height and vegetation density of different strata) and horizontal heterogeneity (e.g., vegetation roughness, microtopography, vegetation openness and woodland edge extent). We assessed the relative variable importance and interpreted response curves of each LiDAR metric for explaining butterfly occurrences. Results All SDMs showed a good to excellent fit, with woodland butterfly SDMs performing slightly better than those of grassland butterflies. Grassland butterfly occurrences were best explained by landscape‐scale habitat structures (e.g., open patches, microtopography) and vegetation height. Woodland butterfly occurrences were mainly determined by vegetation density of medium‐to‐high vegetation, open patches and woodland edge extent. The importance of metrics generally differed between wet and dry habitats for both grassland and woodland species. Main conclusions Vertical variability and horizontal heterogeneity of vegetation structure are key determinants of butterfly species distributions, even in low‐stature habitats such as grasslands, dunes and heathlands. The information content of low vegetation LiDAR metrics could further be improved with country‐wide leaf‐on ALS data or surveys from drones and terrestrial laser scanners at specific sites. LiDAR thus offers great potential for predictive habitat distribution modelling and other studies on ecological niches and invertebrate–habitat relationships.

Irrigation modulates the water cycle by making water available for plants, increasing transpiration and atmospheric humidity, while decreasing temperatures due to the energy that is needed for evaporation. Irrigation is usually not included in atmospheric reanalysis systems, but moisture can be added to the soil due to data assimilation. This paper compares these soil moisture additions to the irrigation patterns. In the ERA‐interim atmospheric reanalysis, 2 m temperature observations are assimilated. A mismatch between modeled and observed temperatures is corrected by adding or removing moisture from the soil. These corrections show a clear pattern of mean soil moisture additions in many areas. To determine the cause of these increments, the spatial and temporal patterns of these soil moisture increments are compared to irrigation water demand and precipitation bias. In irrigated areas, the annual means and cycles of soil moisture increments correlate well with irrigation, and less with precipitation bias. Therefore, in irrigated areas, the soil moisture increments are more likely caused by irrigation than by the precipitation bias. In nonirrigated areas, a weak statistical relation between soil moisture increments and precipitation bias is present. Irrigation is currently not included in reanalysis systems. However, as irrigation indirectly influences the water balance in atmospheric reanalysis systems, we recommend to include this process in reanalysis models. Moreover, the influence of irrigation on the local and regional atmosphere should be taken into account when interpreting atmospheric data over strongly irrigated areas.

We present an analysis of arthropod diversity patterns in native forest communities along the small elevation gradient (0–1021 m a.s.l.) of Terceira island, Azores (Portugal). We analysed (1) how the alpha diversity of Azorean arthropods responds to increasing elevation and (2) differs between endemic, native non-endemic and introduced (alien) species, and (3) the contributions of species replacement and richness difference to beta diversity. Arthropods were sampled using SLAM traps between 2014 and 2018. We analysed species richness indicators, the Hill series and beta diversity partitioning (species replacement and species richness differences). Selected orders (Araneae, Coleoptera, Hemiptera and Psocoptera) and endemic, native non-endemic and introduced species were analysed separately. Total species richness shows a monotonic decrease with elevation for all species and Coleoptera and Psocoptera, but peaks at mid-high elevation for Araneae and endemic species. Introduced species richness decreases strongly with elevation especially. These patterns are most likely driven by climatic factors but also influenced by human disturbance. Beta diversity is, for most groups, the main component of total (gamma) diversity along the gradient but shows no relation with elevation. It results from a combined effect of richness decrease with elevation and species replacement in groups with many narrow-ranged species.

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