Based on a combination of data from the Czech National Phytosociological Database and expert knowledge, a database of vascular plant species pools for 88 habitats, representative of the diversity of Czech vegetation, was compiled. This database contains 1820 native species, 249 archaeophytes and 278 neophytes, each assigned to one or more habitats. Besides the data on species occurrence in different habitats, the database contains information on a species’ ecological optimum in the habitat or its dominance. The largest pools of native species were found in rather rare habitats of dry and warm herbaceous or woody habitats at low altitudes, some of which contain > 530 species (maximum of 695 species for thermophilous forest fringes). These were followed by common habitats on mesic soils. The smallest pools of native species were in saline, aquatic and bog habitats ( 350 native species always contained > 5 archaeophytes and > 5 neophytes, and often many more. Two hundred and thirty two native species, 18 archaeophytes and 30 neophytes were identified as potential dominants in at least one habitat. However, potentially dominant species made up less than 3% of the species pool for 78 out of 88 habitats. Larger percentages (up to 14.6%) of potential dominants were included in habitats with small species pools and species-poor stands (e.g., aquatic, saline and mire habitats). The number of habitats in which a species occurred was used as a measure of its ecological range. Most ecological generalists were found among the native species, less among the archaeophytes and least among the neophytes. Out of the 36 species that occur as dominants in three or more habitats, 34 were native (many are grasses), onewas an archaeophyte (Cirsium arvense) and one was a neophyte (Impatiens parviflora).
Subtropical regions have clay-rich, weathered soils, and long dry periods followed by intense rainfall that produces large fluctuations in soil water content (SWC) and hydrological behavior. This complicates predictions of spatiotemporal dynamics, as datasets are typically collected at too coarse resolution and observations often represent a duration that is too short to capture temporal stability. The aim of the present study was to gain further insights into the role of temporal sampling scale on the observed temporal stability features of SWC order to aid the design of optimal SWC sampling strategies. This focused on both sampling frequency and total monitoring duration, as previous analyses have not considered both of these sampling aspects simultaneously. We collected relatively high resolution data of SWC (fortnightly over 3.5 years) for various soil depths and under contrasting crops (peanuts and citrus) at the red soil region of southeast China. The dataset was split into a three-year training period and a six-month evaluation period. Altogether 13 sampling frequencies (intervals ranging from 15 to 240 days) and eight monitoring duration periods (between three and 36 months) were derived from the training period to identify temporal stability features and the most time stable location (MTSL). The prediction accuracies of these MTSLs were tested using the independent evaluation data. Results showed that vegetation type did affect the spatio-temporal patterns of SWC, whereby the citrus site exhibited a stronger temporal variation and weaker temporal stability than the peanut site. However, the effects of both sampling frequency and observation duration were more pronounced, irrespective of the role of vegetation type or soil depth. With increasing sampling interval or decreasing monitoring duration, temporal stability of SWC was generally overestimated; by less than 10% when sampling interval increased from every 15 to 240 days and by up to 40% with duration decreasing from 36 to 3 months. Our results suggest that sampling strategies and trade-offs between sampling interval and duration should focus on capturing the main variability in hydro-climatological conditions. For subtropical conditions, we found that sampling once every 45 days over 24 months to be the minimum sampling strategy to ensure errors in SWC temporal stability of less than 10%.