The need for reliable biological indicators to support soil biomonitoring is increasing, particularly in agricultural systems where management practices and environmental pressures interact to influence soil ecological integrity. Among soil fauna-based indicators that measure soil health, the Soil Biological Quality indices based on microarthropods (QBS-ar) and earthworms (QBS-e) provide functional measures of soil condition, however their combined application remains largely unexplored. In this study, for the first time both indices were applied simultaneously to assess soil biological quality in the same agroecosystem. The context was that of contrasting agricultural systems (strip cropping vs. pure stands) in a real-farm experimental set-up. Additional biological variables and soil physico-chemical parameters were also considered. Statistical analyses included Spearman correlations, linear mixed-effect models (LMMs), and redundancy analysis (RDA). QBS-ar and QBS-e showed contrasting responses to management systems, with higher QBS-ar values under strip cropping and higher QBS-e values in pure stands. No significant relationship was observed between the two indices. Multivariate analyses indicated that both indices were significantly influenced by management and environmental variables, although with different patterns of association. QBS-ar appeared more responsive to variation in topsoil conditions, whereas QBS-e was associated with broader soil properties and seasonal community dynamics. These results indicate that QBS-ar and QBS-e capture complementary aspects of soil biological quality and should not be used interchangeably. Their combined use improves the interpretation of soil biological responses to agricultural management and associated environmental pressures. Overall, this study highlights the potential of soil fauna-based indices as practical tools for biomonitoring in agroecosystems and supports further exploration on the comparative responses of biological indexes.

The increasing availability of vegetation-plot data results from decades of field surveys conducted by numerous observers. This highlights observer error not as a marginal issue but as a structural component of uncertainty, with direct consequences for estimates of species turnover and temporal trends. Observer-related sampling bias can be investigated through variation in species richness, species composition and abundance. Specifically, pseudoturnover refers to changes in species composition caused by overlooking or misidentification of taxa between sampling events or among different observers surveying the same plot. Although the causes and implications of observer error have been widely discussed, it remains unclear whether observer-related pseudoturnover decreases within observer groups as a result of training. Using data from training and intercalibration sessions carried out in 2023 and 2025 within two forest monitoring programmes in Italy (the LIFE project ModerNEC and the Italian National Forest Inventory), we assessed whether targeted training and collective briefing reduce observer-induced pseudoturnover. We applied Bayesian multilevel models to estimate changes in inter-observer species richness variability and inter-observer dissimilarity. The former decreased across observers in both years, while the latter declined after training when using Jaccard and Euclidean distances in both years; Bray–Curtis dissimilarity decreased only in 2023 and increased in 2025. Overall, training and intercalibration are likely to reduce observer-induced pseudoturnover related to species presence, while variability in abundance estimation needs further study and remains a key challenge for future vegetation monitoring programmes.

Long-term monitoring is pivotal for the collection of data capable of unravelling spatio-temporal changes in plant diversity. Here we present a dataset including plant presence and abundance data collected using a probabilistic sampling design in the "Montagna di Torricchio" Strict Nature Reserve, central Apennines, Italy. Five surveys were conducted in 35 plots during a period spanning 22 years (2002-2024). This dataset allows for the study of plant diversity changes over space and time across different habitat types by using statistical inference based on solid sampling theory.

Land management and its cessation influence temporal dynamics of plant diversity. In Europe, secondary grasslands and forests are often affected by abandonment, leading to changes in species richness and composition. We monitored beech forests and grassland plant communities in a Strict Nature Reserve after land use abandonment to assess passive rewilding effects. Specifically, we tested whether (1) species richness declines over time, (2) temporal beta diversity increases with habitat-specific contributions of turnover and nestedness, and (3) Ellenberg-type indicator values (EIV’s) indicate decreasing light availability and increasing moisture and nutrient conditions during succession. After 22 years, species richness and temporal beta diversity increased in grasslands and beech forests. Temporal beta diversity was mainly driven by species turnover, with the strongest increase observed in beech forests. EIVs showed habitat-specific trends, suggesting a shift toward more thermophilous, mesic, and nutrient-demanding communities in beech forests and transition communities. Plant communities underwent directional reorganization. In dry grasslands, shallow and eroded soils likely limited competitive exclusion, while in forests, edge effects and colonization by generalist and open-habitat species promoted higher richness and turnover. These findings underscore the value of permanent plots in understanding the long-term dynamics of abandoned ecosystems and in guiding conservation and rewilding strategies, while also providing empirical evidence to assess the effectiveness of passive rewilding in strictly protected areas.

Ecological monitoring recently acquired importance given the current global changes and the human impact on ecosystems. Most of research focus on investigating variation in species diversity with short-term collections of data. Other scholars use long-term data but based on resurveys with two distinct points in time. In this work, we present a different approach dealing with temporal variation of plant communities in term of alpha, beta and gamma diversity. Data belongs to a dataset published on the journal “Data in Brief” as a data article and available on the repository “b2Share” (https://doi.org/10.34730/044c89dc80f648d18d0ce236600b8584). These data represent a long-lasting monitoring of 22 years with 5 monitoring sessions (2002, 2003, 2015, 2020, 2024) at the LTER site “Montagna di Torricchio” (Central Apennines, Italy). Data were collected following a probabilistic sampling scheme based on 35 permanent plots (10 m x 10 m). Preliminary analysis of alpha and gamma diversity (through presence/absence matrix) pointed out that the species richness is stable through years despite climatic fluctuations. Indeed, the vegetative season of 2003 had 47% less precipitation than that of 2002, and 57% less than that of 2015. Grouping the plots according to habitats (i.e., grassland, beech forest, and transition from grassland to forest) revealed that the above-mentioned pattern depends on a greater variability of grassland and transition habitats. On the contrary, beech forests show a high stability of species richness over time, probably depending on the effect of tree canopies on grass species and to buffer macroclimate extremes. In addition, the analysis of beta diversity showed remarkable values of the turnover component over time with surprising results for the beech forest habitat. Changes in composition might evidence a pattern of change in the LTER site of Torricchio. Nevertheless, our results suggest that 22 years of monitoring could be insufficient to make reliable inferences on long-lasting vegetation dynamics in our LTER site. This highlights the need to continue the long-term monitoring with frequent re-surveys over time.