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Genetic tracking of density‐dependent adult recruitment: A case study in a subtropical oak

Citation

Tong, Xin; Nason, John D.; Ding, Yuan‐Yuan; Chen, Xiao-Yong (2021), Genetic tracking of density‐dependent adult recruitment: A case study in a subtropical oak, Dryad, Dataset, https://doi.org/10.5061/dryad.cz8w9gj2w

Abstract

Density-dependent recruitment is fundamental to understanding species diversity and community dynamics in plants. Although there is compelling evidence that seeds and seedlings die from conspecific negative density dependence (CNDD) as predicted by the Janzen–Connell hypothesis, characterizing adult recruitment remains a challenge for long-living trees. Previous studies have used the decrease of fine-scale spatial genetic structure (FSGS) across life stages to indicate CNDD; however, this has not been tested rigorously. We addressed these challenges by integrating dispersal kernels and FSGS. To establish links between density dependence and FSGS, we simulated seedlings based on the estimated dispersal kernels from parentage analyses, and further simulated adults under various seedling-to-adult recruitment scenarios, using an individual-based spatially explicit model. We tested this method in an isolated Cyclobalanopsis glauca population on the Dajinshan Island of China. We detected significant FSGS in the seedlings and a weaker, though also significant, FSGS in the adults. As expected, the empirical FSGS of seedlings was well predicted by the simulated seedlings, with observations falling inside the 95% confidence envelopes over all distance classes. However, the simulation showed that CNDD enhanced the FSGS and positive density dependence dampened it during the seedling-to-adult transition. The adult FSGS of our population was therefore explained by positive rather than negative density-dependent adult recruitment.

Usage Notes

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Funding

National Natural Science Foundation of China, Award: 31800446

National Natural Science Foundation of China, Award: 31630008

National Key Research and Development Program of China, Award: 2016YFC0503102