Data from: Timber and trails: Low-intensity selective logging and elephant trails shape seedling dynamics in an Afrotropical forest

Very low-intensity logging can be a compromise between strict conservation and income-generating land use in tropical forests. Investigating how selective logging influences the understory environment and seedling dynamics as the forest regenerates offers insights into whether logging negatively impacts forest dynamics, influencing the composition and structure of future forests. We explored how very low-intensity logging (<2 trees ha-1) influences understory factors and seedling dynamics across a logging chronosequence (unlogged forest vs. forest actively logged and forest logged four and fourteen years prior). To do this, we assessed (i) how light levels, prevalence of vegetation damage, and elephant trails differ in logged forests at different recovery stages compared to unlogged forest; (ii) how these understory factors influence seedling dynamics; (iii) how seedling dynamics differ across the logging chronosequence; and (iv) how logging impacts liana vs. tree seedlings across the chronosequence. We observed higher light availability and vegetation damage in logged forests up to four years after logging and higher elephant trail prevalence at fourteen years after logging compared to unlogged forests. Seedling survival was lower in plots with vegetation damage, elephant trails, or lower light, while seedling growth rates were higher in vegetation-damaged areas. Selectively logged forests initially had lower seedling survival and recruitment, but higher growth rates compared to unlogged forests. However, 14 years after logging, seedling dynamics were similar to patterns in unlogged forests. Lianas had a slight seedling growth advantage over trees in all logged forests compared to unlogged forests. Results from our study suggest that logging causes temporary shifts in understory dynamics rather than long-term shifts in forest recovery trajectories. These managed areas have potential as land that can contribute to OECM targets – functioning as mixed-use corridors, connecting protected areas across a landscape and contributing to biodiversity and wildlife conservation – especially in high forest cover, low deforestation countries. Data collection methods: Study area and seedling censuses We sampled vegetation communities across eighty 20×20 m plots in the Société Equatoriale d'Exploitation Forestière (SEEF), a logging concession to the east of the Mbé sector of the Monts de Cristal National Park (0°42′41′′ N, 10°17′18′′ E) in northwestern Gabon (Sullivan et al. 2022). We sampled 20 plots within each of four different treatment areas, or logging “blocks” that varied in the year that they were logged (Fig. 1A). The blocks in our study were logged in 2008, 2018, 2020, and one additional block was sampled as an unlogged control. SEEF started systematic logging in this logging concession in 2000 and has a very low timber removal rate (<2 trees per ha, Medjibe et al. 2011, SEEF 2019), which is typical for the Congo basin region (Sist, 2000). 
Each 20×20 m vegetation plot was established between September 2018 and October 2019 (Sullivan et al. 2022) and contained nine 1×1 m seedling plots (Fig. 1B), spaced 10 m apart, for a total of 180 seedling plots in each of the three logging treatments and the unlogged block. In each seedling plot, we identified, tagged, and measured the height of all woody seedlings ≤1m tall. We re-censused the seedling plots between February and September 2022, recording the survival status (dead or alive) and height of each previously tagged seedling, and tagging and measuring new seedlings that had recruited into the seedling plots since the previous census. Any observations that were missing survival or height data were removed before analyses. Tree seedlings were identified to species or morphospecies level, and liana seedlings were identified to the taxonomic level that was possible in the field – any liana that lacked family-level taxonomic information was labeled “liana” in the data (see Sullivan et al. 2022 for more details). Environmental factors and elephant trails To quantify understory light availability, we measured canopy openness using a Spherical Crown Densiometer (Concave Model C, Forestry Suppliers, Jackson, MS, USA) (Lemmon, 1956) at a height of 1m in the center of each 1×seedling plot. In each 1×1m seedling plot, we also recorded the presence/absence of vegetation damage from falling woody debris or skid trails and the presence/absence of elephant trails. All environmental factors and elephant trails were assessed during the 2022 census period. # Data from: Timber and trails: Low-intensity selective logging and elephant trails shape seedling dynamics in an Afrotropical forest ## Description of the data and file structure We studied forest recovery in northwestern Gabon by setting up 80 plots, each 20×20 m, across areas logged in different years and one untouched forest. Inside each plot, we placed smaller seedling plots to track young trees and vines—measuring their growth and survival from 2018–2019 and again in 2022. We also recorded light levels, signs of damage, and elephant trails to understand how these factors affect regeneration. ### Description: recensus_2022.csv contains the vegetation plot information from the 720 seedling plots: * date - date that plots were recensused * plot - vegetation plot subplot - seedling subplot ID * spp - species code * height_cm - height from census1 (2019) in cm * tag_no - tag number on seedling * code - codes indicating broken, liana, etc. from census1 * code_2022 - codes indicating broken, liana, etc. from census2 * height_cm_2022 - height from census2 (2022) in cm * survival - status of seedling in census2 (2022): alive (1), dead (0) plot_info.csv* c*ontains descriptive information about understory factors and other details in each of the 720 seedling plots: * plot - vegetation plot ID * subplot - seedling subplot ID * ele_trail - status of elephant trails in each seedling plot: presence (1) or absence (0) * log_veg - status of damage from logging or falling vegetation (i.e. treefall or branchfall) in each seedling plot: presence (1) or absence (0) * light - canopy openness as measured via densiometer in each seedling subplot * plot_type - ID of each seedling plot in chronosequence * date1 - date each plot was measured from census1 * date2 - date each plot was measured from census2 Any empty cells indicate that there is no data for that entry and NA represents not applicable