Distance‐based methods for estimating density of nonrandomly distributed populations

Abstract

Population density is the most basic ecological parameter for understanding population dynamics and biological conservation. Distance-based methods (or plotless methods) are considered as a more efficient but less robust approach than quadrat-based counting methods in estimating plant population density. The low robustness of distance-based methods mainly arises from the oversimplistic assumption of completely spatially random (CSR) distribution of a population in the conventional distance-based methods for estimating density of non-CSR populations in natural communities. In this study we derived two methods to improve on density estimation for plant populations of non-CSR distribution. The first method modified an existing composite estimator to correct for the long-recognized bias associated with that estimator. The second method was derived from the negative binomial distribution (NBD) that directly deals with aggregation in the distribution of a species. The performance of these estimators was tested and compared against various distance-based estimators by both simulation and empirical data of three large-scale stem-mapped forests. Results showed that the NBD point-to-tree distance estimator has the best and most consistent performance across populations with vastly different spatial distributions. This estimator offers a simple, efficient and robust method for estimating density for empirical populations of plant species.

Surveyor and analyst biases in forest density estimation from United States Public Land Surveys

“…These analyses are constrained by the distances of two or four nearby witness trees at each of widely spaced corners in possibly different forest types. Despite myriad PDEs, the most appropriate and flexible equation known to fit these conditions is the Morisita estimator (Bouldin, 2008; Cogbill et al, 2018; Goring et al, 2016; Hanberry et al, 2011; Levine et al, 2017; Morisita, 1957; Shen et al, 2020): $$$$ {\displaystyle \begin{array}{c}\mathrm{Morisita}\ \mathrm{Plotless}\ \mathrm{Density}\ \mathrm{Estimator}:\\ {}\kern0ex {\uplambda}_{\mathrm{M}}=\left[\frac{\ \left(g\times k-1\right)}{\uppi \times N}\right]\times \left[\sum \limits_{i=1}^N\frac{k}{\left({\sum}_{j=1}^k{r}_{ij}^2\right)}\right],\end{array}} $$$$ where λ is density, g is distance rank order, k is the number of equiangular sectors, N is the number of corners, r ij is the distance from post to the g th nearest tree in the j th sector at the i th corner.…”

“…These analyses are constrained by the distances of two or four nearby witness trees at each of widely spaced corners in possibly different forest types. Despite myriad PDEs, the most appropriate and flexible equation known to fit these conditions is the Morisita estimator (Bouldin, 2008;Cogbill et al, 2018;Goring et al, 2016;Hanberry et al, 2011;Levine et al, 2017;Morisita, 1957;Shen et al, 2020):…”