This dataset contains records for vegetation in 49 plots across 14 fragmented forest sites and 4 continuous forest sites in Sabah, Malaysian Borneo. Living vegetation and deadwood were surveyed in two or three 0.28-ha plots in each of the 18 sites. In addition to vegetation data, the dataset contains topsoil parameters, measurements of forest structure, and metrics of the degree of forest fragmentation in the landscape surrounding the plots. These data were collected in order to conduct studies examining (1) the factors supporting invasion of exotic plant species into fragmented forest areas; and (2) the value of conservation set-asides for carbon storage and associated plant diversity in oil palm plantations. Full details about this dataset can be found at https://doi.org/10.5285/c67b06b7-c3f6-49a3-baf2-9fc3a72cb98a

This dataset details information collected from smallholder oil palm farms in Sabah, Malaysian Borneo. Including: management practices, oil palm fruit yield, understorey vegetation, and soil chemical properties (SOC, total N, total P and available P). We collected data between August to November 2019 from 40 smallholdings (defined as farms < 50 ha) across six governance areas in Sabah. We used responses from face-to-face questionnaires to collect information about their management practices, including Best Management Practices (BMPs), and reported Fresh Fruit Bunch (FFB) yields. We also carried out field surveys on these farms to quantify vegetation cover and soil chemical properties. All smallholder farms had mature fruiting trees i.e. > 8 years since planting. The project received ethical approval from the Biology Ethics Committee, University of York (Ref. SGA201906), and permission from the Sabah Biodiversity Council (Ref. JKM/MBS.1000-2/2 JLD.8), Danum Valley Management Committee (Ref. YS/DVMC/2019/27), and South East Asia Rainforest Research Partnership (project number 18033) for permission to conduct our research in Sabah, Malaysia. This work was funded by the NERC iCASE studentship (NE/R007624/1) and Proforest. Full details about this dataset can be found at https://doi.org/10.5285/38487932-b32a-4b15-9fda-ea812c463466