Estimating competition coefficients in tree communities: a hierarchical Bayesian approach to neighborhood analysis

Abstract

Abstract Quantifying the strength of competition and understanding how it translates into consequences at the community level are among the key aims of plant ecology. Neighborhood analysis based on the neighborhood competition index has been widely used to estimate species‐specific competition coefficients in tree communities. These estimates, however, could not be estimated for rare species with small sample sizes using the conventional species‐by‐species approach. Here, we develop a new modeling framework for neighborhood analysis in which the competition coefficient is assumed to have a hierarchical parameter structure. Using actual tree census data consisting of 38 species, we demonstrate that the hierarchical model enables us to estimate competition coefficients for all species, including rare ones, within a community. The hierarchical models were selected over the models based on the species‐by‐species approach as a result of model selection, in either cases where we assumed the competitive strength is determined by niche difference or competitive ability difference. Our results suggest that the hierarchical approaches can serve as a useful alternative to species‐by‐species approach for estimating competition coefficients in tree communities.

Keywords

• community modeling
• competitive ability
• competitive hierarchy
• hierarchical Bayes
• interspecific interactions
• limiting similarity