Successional shifts in tree demographic strategies in wet and dry Neotropical forests

This dataset summarizes demographic rates, abundances and basal area across a succession of ~800 (sub) tropical tree species to explore generalities in demographic trade-offs and successional shifts in demographic strategies across four Neotropical forests that cover a large rainfall gradient. We used repeated forest inventory data from chronosequences in two wet (Costa Rica, Panama) and two dry forests (Yucatán, Oaxaca, both Mexico) to quantify demographic rates of ~800 tree species. For each forest, we explored the main demographic trade-offs and assigned tree species to five demographic groups by performing a weighted Principal Component Analysis (PCA) that accounts for differences in sample size. We aggregated the basal area and abundance across demographic groups to identify successional shifts in demographic strategies over the entire successional gradient from very young (&lt;5 years) to old-growth forests. This dataset provides raw and transformed demographic rates, their weight..., See Rüger et al. in revision. Successional shifts in tree demographic strategies in wet and dry Neotropical forests. Global Ecology and Biogeography The file ‘weightedPCA.R’ contains an implementation of the weighted principal component analysis described in Delchambre, L. 2014. Weighted principal component analysis: a weighted covariance eigendecomposition approach. Mon. Not. R. Astron. Soc. 446(2), 3545-3555.,本数据集汇总了约800种（亚）热带树木的种群统计速率、多度与胸高断面积（basal area），旨在探究覆盖大范围降雨梯度的新热带区四大森林中，种群统计权衡与种群统计策略演替转变的共性规律。本研究依托两处湿林（哥斯达黎加、巴拿马）与两处干林（墨西哥尤卡坦州、瓦哈卡州）的演替序列重复森林清查数据，量化了约800种树木的种群统计速率。针对每片森林，我们通过考虑样本量差异的加权主成分分析（weighted Principal Component Analysis, PCA）探究核心种群统计权衡关系，并将树木划分为5个种群统计类群。我们对各统计类群的胸高断面积与多度进行聚合，以解析从极幼龄林（林龄<5年）到原始林的完整演替梯度上，种群统计策略的演替转变规律。本数据集包含原始与经转换的种群统计速率及其权重……详见待刊论文Rüger等人的《Successional shifts in tree demographic strategies in wet and dry Neotropical forests》，发表于《Global Ecology and Biogeography》（全球生态学与生物地理学）。文件"weightedPCA.R"包含了Delchambre于2014年提出的加权主成分分析实现代码，原文参见：Delchambre, L. 2014. Weighted principal component analysis: a weighted covariance eigendecomposition approach. Mon. Not. R. Astron. Soc. 446(2), 3545-3555.