Accurate, unbiased demographic data are essential for constructing population projection matrix models, integral projection models, and other models of plant population dynamics and for evaluating and managing endangered species and invasive species. However limited resources of time and funds, including limitations in training and supervising field crews, constrain both statistical power and the avoidance of statistical bias when collecting plant demographic data. We combined our collective field experiences with computer simulations to explore ways to optimize data quantity and quality under typical field constraints.
Results/Conclusions
Transects sacrifice truly random sampling but can be placed so as to prevent bias in estimates of population size and of landscape-level abundance. Unlike the commonly-used haphazard search path, properly placed transects can yield unbiased estimates of plant numbers, albeit with fewer total plants located. Because plant size distribution usually has a long right tail, estimates of demographic parameters for larger plants can be very poor. Simulations show that under certain conditions stratified size sampling in which most or all large plants but only a subset of small plants are sampled can greatly improve the estimates for larger size classes used in population projection matrix construction. There are also benefits of stratified size sampling for integral projection models but these benefits are harder to predict: simulations show that these benefits depend on how well size-dependent functions fitted mostly to smaller plants match the true functions for larger plants, which in turn depends on the degree and nature of non-linearity in each function. In our experience, the greatest limit to the use of stratified sampling is whether the field crew can use it reliably; simplicity is essential.