Growth-mortality relationships for southern Appalachian trees from the Coweeta Hydrologic Laboratory in 1995

Ecologists and foresters have long noted a link between tree growth rate and mortality, and recent work suggests that interspecific differences in low growth tolerance is a key force shaping forest structure. Little information is available, however, on the growth-mortality relationship for most species. We present three methods for estimating growth-mortality functions from readily obtainable field data. All use annual mortality rates and the recent growth rates of living and dead individuals. Annual mortality rates are estimated using both survival analysis and a Bayesian approach. Growth rates are obtained from increment cores. Growth-mortality functions are fitted using two parametric approaches and a non-parametric approach.
The three methods are compared using bootstrapped confidence intervals and likelihood ratio tests. For two example species, Acer rubrum and Cornus florida, growth-mortality functions indicate a substantial difference in the two species abilities to withstand slow growth. Both survival analysis and Bayesian estimates of mortality rates lead to similar growth-mortality functions, with the Bayesian approach providing a means to overcome the absence of long-term census data. In fitting growth-mortality functions, the non-parametric approach reveals that inflexibility in parametric methods can lead to errors in estimating mortality risk at low growth. We thus suggest that non-parametric fits be used as a tool for assessing parametric models.

生态学家与林学家早已注意到树木生长速率与死亡率之间存在关联，近期研究表明，物种间低生长耐受性的差异是塑造森林群落结构的关键驱动力。然而，针对绝大多数树种的生长-死亡率关联的相关数据仍较为匮乏。我们提出了三种可基于易于获取的野外实测数据估算生长-死亡率函数的方法，所有方法均用到了存活个体与死亡个体的年死亡率及近期生长速率。年死亡率可通过生存分析（survival analysis）与贝叶斯方法（Bayesian approach）两种途径估算；生长速率则取自树木生长芯（increment core）。生长-死亡率函数的拟合采用两种参数化方法与一种非参数化方法。
我们采用自助法置信区间（bootstrapped confidence intervals）与似然比检验（likelihood ratio tests）对三种方法进行了对比。以红花槭（Acer rubrum）和多花梾木（Cornus florida）两个树种为例，生长-死亡率函数显示，二者耐受低速生长的能力存在显著差异。无论是生存分析还是贝叶斯死亡率估算方法，得到的生长-死亡率函数结果均较为相似；其中贝叶斯方法可有效弥补长期普查数据缺失的局限。在拟合生长-死亡率函数的过程中，非参数化方法揭示出：参数化方法的灵活性不足可能会导致低速生长条件下死亡率风险估算出现误差。因此我们建议，可将非参数化拟合结果作为评估参数化模型的辅助工具。