While the importance of conservation mowing for mesic grasslands is generally accepted, its use for fens and fen grasslands interspersed within agricultural land is still controversial. Although fens may persist naturally, ongoing environmental changes increase productivity and accelerate succession. These processes can be mitigated through conservation management with appropriate settings. However, long-term management experiments are challenging and provide only locally valid results. Here, we analysed vegetation data (bryophytes and vascular plants) from seven management experiments (spanning 3-20 years) conducted in Central European poor, moderately-rich, and calcareous spring fens (Czech Republic, Slovakia). Two of these experiments examined the effects of restoration of abandoned fens, while five experiments examined changes in mowing regimes in managed fens (cessation, intensification, delay to autumn, and litter removal). Data were analysed using unidimensional and multidimensional methods separately for the initial, extended, and entire period. Mowing had a statistically significant effect on species composition except for the shortest (3-year) experiment. Litter removal did not compensate for mowing. Mowing twice or delayed mowing significantly affected the species composition of calcareous fens. In all cases, cessation of mowing significantly reduced the richness of species, especially those of conservation importance. In contrast, any mowing of abandoned fens increased species richness. The effects of mowing intensification or cessation on species richness and composition of a restored calcareous fen were evident in the first 2-3 years. Other effects were initially weak or nonsignificant but later became stronger, such as mowing delay and restoration removal of litter, which became significant only after nearly 20 years. We found that cessation or restoration of mowing usually triggers a rapid vegetation change, whereas it can take decades to detect the response caused by changes in mowing timing. Importantly, mowing can stabilise or even restore vegetation of fen ecosystems that have been weakened by their fragmentation in the temperate agricultural landscapes.

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process \"borrows\" from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.