Abstract
Background: Rapid climate changes lead to an increase in forest disturbance, which in turn lead to growing concerns for biodiversity. While saproxylic beetles are relevant indicators for studying different aspects of biodiversity, most are smaller than 2 mm and difficult to sample. This, together with a high number of species and trophic roles, make their study remarkably challenging, time-consuming, and expensive. The Landsat mission provides data since 1984 and represents a powerful tool in this scenario. While we believe that remote sensing data cannot replace on-site sampling and analysis, in this study we aim to prove that the Landsat Time Series (TS) may support the identification of insects’ hotspots and consequently guide the selection of areas where to concentrate field analysis. Methods: With this aim, we constructed a Landsat-derived NDVI TS (1984–2020) and we summarised the NDVI trend over time by calculating eight Temporal Metrics (TMs) among which four resulted particularly successful in predicting the amount of saproxylic insects: (i) the slope of the regression line obtained by linear interpolating the NDVI values over time; (ii) the Root Mean Square Error (RMSE) between the regression line and the NDVI TS; (iii) the median, and the (iv) minimum values of the NDVI TS. The study area consists of four monitoring sectors in a Mediterranean-managed beech forest located in the Apennines (Molise, Italy), where 60 window flight traps for flying beetles were installed. First, the saproxylic beetle's biodiversities of monitoring sectors were quantified in terms of species richness and alpha-diversity. Second, the capability of TMs in predicting the richness of saproxylic beetles family and trophic categories was assessed in terms of Pearson's product-moment correlation. Results: The alpha diversity and species richness analysis indicate dissimilarities across the four monitored sectors (Shannon and Simpson's index ranging between 0.67 to 2.31 and 0.69 to 0.88, respectively), with Landsat TS resulting in effective predictors for estimating saproxylic beetle richness. The strongest correlation was reached between the Monotomidae family and the RMSE temporal metric (R ¼ 0.66). The mean absolute correlation (r) between the NDVI TMs and the saproxylic community was 0.46 for Monotomidae, 0.31 for Cerambycidae, and 0.25 for Curculionidae. Conclusions: Our results suggest that Landsat TS has important implications for studying saproxylic beetle distribution and, by helping the selection of monitoring areas, increasing the amount of information acquired while decreasing the effort required for field analysis