Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes

TL;DR

This study analyzes tree survival across different life stages using demographic models that account for missing data and small sample sizes, revealing size-dependent mortality early on and constant mortality later.

Contribution

It introduces a demographic modeling approach that effectively handles missing data and small samples to study tree survival with covariates like growth, size, and light.

Findings

Tree mortality is size-dependent at early stages.

Survival rates are constant between years for most species.

Method allows analysis of small, incomplete datasets with covariates.

Abstract

Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose. We investigate the survival rates of ten tree species in a dataset designed to monitor growth rates. As some individuals were not included in the census at some time points we use capture-mark-recapture methods both to allow us to account for missing individuals, and to estimate relocation probabilities. Growth rates, size, and light availability were included as covariates in the model predicting survival rates. The study demonstrates that tree mortality is best described as constant between years and size-dependent at early life stages and size independent at later life stages for most species of UK hardwood. We have demonstrated that even with a twenty-year dataset it is possible to discern variability both between individuals and between species. Our work illustrates the potential utility of the method applied here for calculating plant population dynamics parameters in time replicated datasets with small sample sizes and missing individuals without any loss of sample size, and including explanatory covariates.