Assessment of inbreeding depression on progeny of Afrocarpus gracilior

Human activities such as agriculture, urbanization, and logging have caused widespread destruction of forests, leading to forest fragmentation. Fragmentation has been shown to reduce genetic diversity and increase inbreeding in forest populations, potentially leading to inbreeding depression manifested through decreased reproductive success and progeny vigor. The severity of these impacts, however, varies among species and is largely influenced by their mating systems. This study examines the effects of forest fragmentation on Afrocarpus gracilior, a dioecious, wind-pollinated conifer, by assessing genetic diversity, reproductive success, and early progeny fitness. Our analysis revealed alarmingly low genetic diversity and high genetic drift, especially in small and isolated populations. Consistent with these findings, reduced progeny fitness was observed, with small populations showing 53% lower germination rates, 33% reduced acclimatization, 30% slower diameter growth, 41% reduced height growth, and an 80% increase in leaf scorch. Correlation analysis further confirmed a strong relationship between the genetic diversity and progeny fitness traits. These findings suggest that inbreeding depression severely affects the fitness of progeny from small and isolated populations of A. gracilior, posing a serious threat to their long-term survival. The implications for conservation and restoration efforts are immense, underscoring the need to prioritize genetically diverse populations for conservation and strategically procure seeds to support the survival of this species. The data was collected through field inventory, seed sample collection, seed processing, physical examination, in vitro germination, and early progeny vigor assessment in a lath house. We measured the number of intact seeds, seed weight, in vitro germination, acclimatization or survival in the lath house, seedling diameter and height growth, and the extent of leaf scorch. We entered the data into an MS Excel spreadsheet, saved it in CSV format, and prepared it for analysis. Additionally, we used data on genetic diversity parameters obtained from a related work by the authors. For analysis, we used the R statistical software to conduct analysis of variance (ANOVA), correlation analysis, and plotting for visualization. The scripts used to generate the results in R are also included in this dataset. # Afrocarpus gracilior\_inbreeding depression [doi.org](doi.org) ## Description of the data and file structure Dataset title: Assessment of inbreeding depression on progeny of Afrocarpus gracilior #### Date of data collection 2022-2024 #### Contributors Nigussu B. Abate, Hewan D. Degu, Tesfaye Abebe: School of Plant and Horticultural Sciences, Hawassa University, Hawassa, Ethiopia Marie Kalousová: Department of Crop Sciences and Agroforestry, Czech University of Life Sciences Prague, Czech Republic #### Overview This dataset contains data we collected to assess the effects of inbreeding depression on the reproductive success and early progeny vigor of A. gracilior. It includes the exact dataset we analyzed and used to draw the interpretations and conclusions presented in our manuscript. By uploading it here, we aim to enable verification of the reproducibility of the results presented in our paper. #### Files and variables ### File: A.gracilior\_data\_and\_script.zip **Description:**  #### Spreadsheet files, in CSV format, included in archived (.zip) folder **seedset_data_2.csv**: It contains data on physical seed quality assessment of the A. gracilior populations. Variables: Popn = population codes; Region = the administrative region in Ethiopia where the populations are found; Altitude = the altitude category of the populations (below or above 2000 m. a.s.l.); Age = Estimated age of sampled trees (in years); Size = Size class of the populations; Rep = replication; intact = percentage of intact seeds; empty = percentage of empty seeds; abnormal = percentage of abnormal seeds; weight_1000 = weight of 1000 seeds (in grams). Missing values are entered as "NA" **seedset_data_3.csv**: This is the same dataset as above, except that the "Size" column is recoded to facilitate plotting in R. The recoding is as follows: 1 = Large; 2 = Intermediate; 3 = Small. **invitro_germn_final_2.csv**: This is the in vitro germination data at the end of the experiment.Variables: Popn = population codes; Size = Size class of the populations; Altitude = the altitude category of the populations (below or above 2000 m. a.s.l.); Age = estimated age of sampled trees (in years); GP = germination percentage; gp = germination percentage (as decimal). **invitro_data_plot_2.csv**: This is data we used to plot the in vitro germination trend from start to end of the experiment. Variables: Popn = population codes; Size = size class of the populations; Altitude = the altitude of the populations (m. a.s.l.); Week = weeks (since the start of the experiment) data is taken on; GP = germination percentage **lathouse_acclimatzn_3.csv**: This is data on the acclimatization of in vitro-germinated seedlings after they were transferred to the lathhouse. Variables: Popn = population codes; Age = estimated age of sampled trees (in years); Size = size class of the populations; Altitude = the altitude of the populations (m. a.s.l.); Transferred = number of seedlings transferred to the lathhouse; Survived = number of seedlings that survived in the lathhouse; AccP = acclimatization percentage **lathouse_acclimatzn_4.csv**: This is the same dataset as above, except that the "Size" column is recoded to facilitate plotting in R. The recoding is as follows: 1 = Large; 2 = Intermediate; 3 = Small. **lathouse_growth_3.csv**: This is data on seedling growth traits in the lathhouse. Variables: Popn = population codes; Age = estimated age of sampled trees (in years); Size = size class of the populations; Altitude = the altitude of the populations (m. a.s.l.); Ht = height of the seedlings (cm); Diam = diameter of the seedlings (cm); Scorch = leaf scorching percentage **lathouse_growth_4.csv**: This is the same dataset as above, except that the "Size" column is recoded to facilitate plotting in R. The recoding is as follows: 1 = Large; 2 = Intermediate; 3 = Small. **correlation_data_5.csv**: This is data we used for the correlations analysis. Variables: Popn = population codes; Altitude = the altitude of the populations (m. a.s.l.); Ar = allelic richness; He = expected heterozygosity; Fst = population-specific Fst; INT = percentage of intact seeds; TSW = weight of 1000 seeds; IGP = in vitro germination percentage; ACL = acclimatization percentage; DIM = seedling diameter (cm); HGT = seedling height (cm); SCR = leaf scorch #### R script code included in archived (.zip) folder **Script_afrocarpus.R** This is the R script file that contains all the codes we used to run the data analysis that generated the results presented in our paper. #### Code/software We used R (version 4.3.3) open-source statistical software and RStudio (version 2024.09.1+394) to run our data analysis. All the codes we used and the packages required for each analysis are included in the script file **Script_afrocarpus.R**, which we have included in the archived folder we uploaded. Users can simply run the codes in this script file to reproduce our results, provided they have the latest versions of the software installed and have extracted the data files and saved them in their working directory.