Dataset for 'Invasive <i>Prunus serotina </i>and <i>Robinia pseudoacacia </i>impact on understory vegetation is species-, habitat- ,and season-specific'

<p dir="ltr">We conducted the study in western Poland managed forests, in five forest inspectorates: Babki, Czerniejewo, Jarocin, Konstantynowo, and Łopuchówko (Fig. 2). We located study plots between 51° 59′ 4.08′′ and 52° 40′ 9.36′′ N and 16° 35′ 28.98′′ and 17° 37′ 13.26′′ E, in two geographical regions: the Greater Poland Lakeland (northern part) and Greater Poland Lowland (southern part). The climatic conditions are similar in the study area, with an annual temperature of 8.5 °C and mean annual precipitation of 500-550 mm (BDL 2024). Designing the study we aimed to cover the quantitative gradient of invader abundance, as most of previous studies focused on comparing invaded and non-invaded sites. Assessment of invader quantity using precise measurements would take much time and effort, therefore we decided to select study plots based on invader cover and then, after measurement, quantify the abundance using aboveground biomass, following the approach in our previous study (Bury and Dyderski 2025). During plot selection we search for control plots (zero individuals of studied invaders >1.3 m height), medium (<30% cover) and high (>50%). We decided to cover two habitat-related environmental contexts: nutrient-rich habitats that are typical of studied species in the native range, and nutrient-poor, where these species had been introduced (Starfinger et al. 2003, Cierjacks et al. 2013). Nutrient-poor sites included Leucobryo-Pinetum W. Mat. (1962) 1973 communities or secondary P. sylvestris forests. In our study, nutrient-rich sites include different subtypes of Galio sylvatici-Carpinetum betuli Oberd. 1957 communities or secondary Quercus spp. forests. Some areas had characteristics of poorer communities or slightly more fertile ones, with species characteristic of Potentillo albae-Quercetum Libb. 1933 or Querco-roboris Pinetum Mat. et Polak. 1955 s.l. We also included two management contexts: stands in the middle of rotation age and close to rotation age, as these age classes differ in light conditions beneath stand canopies. In total we established 160 plots (500 m2), including 32 control plots (8 replications × 2 habitat types × 2 stand age classes), 64 plots with R. pseudoacacia (8 replications × 2 invasion levels × 2 habitat types × 2 stand age classes) and 64 plots with P. serotina (same as R. pseudoacacia). The distance between plots of particular variants were higher than 5 km.To better describe the invader quantity and invasion phase in the autumns of 2021, 2022, 2023 we measured all trees >1.3 m height in each plot. We measured the diameter at breast height (DBH) of all the individuals on the 102 plots. On 58 plots, we accurately measured the diameter at the breast height of trees thicker than 5 cm. We counted trees thinner than 5 cm by species. Then, from a database of 102 plots for each species, we calculated the average thickness of individuals thinner than 5 cm. We imputed these mean DBH in the mentioned 58 plots (see Table S1 for mean and SD values). Then, we used published allometric formulas (Table S2-S3) to calculate the aboveground biomass for individual trees and stands (in the file)</p><p><br></p><p dir="ltr">We used the modified nine-scaled Braun-Blanquet method (r ─ 1-2 ind.; + ─ <1%; 1 ─ 1-3%; 2m ─ 3-10%; 2a ─ 10-18%; 2b ─ 18-25%; 3 ─ 25-50%; 4 ─ 50-75%; 5 ─ > 75%) to assess the cover of particular understory species of vascular plants. We assessed both herbaceous and woody plants (up to 0.5 m height) on four randomly distributed squared 25 m2 subplots. We made an understory survey in the spring (May) and summer (July) of 2021, 2022, and 2023. For the analysis purposes, we used following coverages for each Braun-Blanquet class (r = 0.1%; + = 0.5%; 1 = 4%; 2m = 7.5%; 2a = 15%; 2b = 20%; 3 = 37.5%; 4 = 62.5%; 5 = 87.5%).We aggregated the results at the plot level by averaging species cover from four subplots</p>